The Computer

Below some quotes and links related to the book‘s coverage:

“At the heart of every computer is one or more hardware units known as processors. A processor controls what the computer does. For example, it will process what you type in on your computer’s keyboard, display results on its screen, fetch web pages from the Internet, and carry out calculations such as adding two numbers together. It does this by ‘executing’ a computer program that details what the computer should do […] Data and programs are stored in two storage areas. The first is known as main memory and has the property that whatever is stored there can be retrieved very quickly. Main memory is used for transient data – for example, the result of a calculation which is an intermediate result in a much bigger calculation – and is also used to store computer programs while they are being executed. Data in main memory is transient – it will disappear when the computer is switched off. Hard disk memory, also known as file storage or backing storage, contains data that are required over a period of time. Typical entities that are stored in this memory include files of numerical data, word-processed documents, and spreadsheet tables. Computer programs are also stored here while they are not being executed. […] There are a number of differences between main memory and hard disk memory. The first is the retrieval time. With main memory, an item of data can be retrieved by the processor in fractions of microseconds. With file-based memory, the retrieval time is much greater: of the order of milliseconds. The reason for this is that main memory is silicon-based […] hard disk memory is usually mechanical and is stored on the metallic surface of a disk, with a mechanical arm retrieving the data. […] main memory is more expensive than file-based memory”.

The Internet is a network of computers – strictly, it is a network that joins up a number of networks. It carries out a number of functions. First, it transfers data from one computer to another computer […] The second function of the Internet is to enforce reliability. That is, to ensure that when errors occur then some form of recovery process happens; for example, if an intermediate computer fails then the software of the Internet will discover this and resend any malfunctioning data via other computers. A major component of the Internet is the World Wide Web […] The web […] uses the data-transmission facilities of the Internet in a specific way: to store and distribute web pages. The web consists of a number of computers known as web servers and a very large number of computers known as clients (your home PC is a client). Web servers are usually computers that are more powerful than the PCs that are normally found in homes or those used as office computers. They will be maintained by some enterprise and will contain individual web pages relevant to that enterprise; for example, an online book store such as Amazon will maintain web pages for each item it sells. The program that allows users to access the web is known as a browser. […] A part of the Internet known as the Domain Name System (usually referred to as DNS) will figure out where the page is held and route the request to the web server holding the page. The web server will then send the page back to your browser which will then display it on your computer. Whenever you want another page you would normally click on a link displayed on that page and the process is repeated. Conceptually, what happens is simple. However, it hides a huge amount of detail involving the web discovering where pages are stored, the pages being located, their being sent, the browser reading the pages and interpreting how they should be displayed, and eventually the browser displaying the pages. […] without one particular hardware advance the Internet would be a shadow of itself: this is broadband. This technology has provided communication speeds that we could not have dreamed of 15 years ago. […] Typical broadband speeds range from one megabit per second to 24 megabits per second, the lower rate being about 20 times faster than dial-up rates.”

“A major idea I hope to convey […] is that regarding the computer as just the box that sits on your desk, or as a chunk of silicon that is embedded within some device such as a microwave, is only a partial view. The Internet – or rather broadband access to the Internet – has created a gigantic computer that has unlimited access to both computer power and storage to the point where even applications that we all thought would never migrate from the personal computer are doing just that. […] the Internet functions as a series of computers – or more accurately computer processors – carrying out some task […]. Conceptually, there is little difference between these computers and [a] supercomputer, the only difference is in the details: for a supercomputer the communication between processors is via some internal electronic circuit, while for a collection of computers working together on the Internet the communication is via external circuits used for that network.”

“A computer will consist of a number of electronic circuits. The most important is the processor: this carries out the instructions that are contained in a computer program. […] There are a number of individual circuit elements that make up the computer. Thousands of these elements are combined together to construct the computer processor and other circuits. One basic element is known as an And gate […]. This is an electrical circuit that has two binary inputs A and B and a single binary output X. The output will be one if both the inputs are one and zero otherwise. […] the And gate is only one example – when some action is required, for example adding two numbers together, [the different circuits] interact with each other to carry out that action. In the case of addition, the two binary numbers are processed bit by bit to carry out the addition. […] Whatever actions are taken by a program […] the cycle is the same; an instruction is read into the processor, the processor decodes the instruction, acts on it, and then brings in the next instruction. So, at the heart of a computer is a series of circuits and storage elements that fetch and execute instructions and store data and programs.”

“In essence, a hard disk unit consists of one or more circular metallic disks which can be magnetized. Each disk has a very large number of magnetizable areas which can either represent zero or one depending on the magnetization. The disks are rotated at speed. The unit also contains an arm or a number of arms that can move laterally and which can sense the magnetic patterns on the disk. […] When a processor requires some data that is stored on a hard disk […] then it issues an instruction to find the file. The operating system – the software that controls the computer – will know where the file starts and ends and will send a message to the hard disk to read the data. The arm will move laterally until it is over the start position of the file and when the revolving disk passes under the arm the magnetic pattern that represents the data held in the file is read by it. Accessing data on a hard disk is a mechanical process and usually takes a small number of milliseconds to carry out. Compared with the electronic speeds of the computer itself – normally measured in fractions of a microsecond – this is incredibly slow. Because disk access is slow, systems designers try to minimize the amount of access required to files. One technique that has been particularly effective is known as caching. It is, for example, used in web servers. Such servers store pages that are sent to browsers for display. […] Caching involves placing the frequently accessed pages in some fast storage medium such as flash memory and keeping the remainder on a hard disk.”

“The first computers had a single hardware processor that executed individual instructions. It was not too long before researchers started thinking about computers that had more than one processor. The simple theory here was that if a computer had n processors then it would be n times faster. […] it is worth debunking this notion. If you look at many classes of problems […], you see that a strictly linear increase in performance is not achieved. If a problem that is solved by a single computer is solved in 20 minutes, then you will find a dual processor computer solving it in perhaps 11 minutes. A 3-processor computer may solve it in 9 minutes, and a 4-processor computer in 8 minutes. There is a law of diminishing returns; often, there comes a point when adding a processor slows down the computation. What happens is that each processor needs to communicate with the others, for example passing on the result of a computation; this communicational overhead becomes bigger and bigger as you add processors to the point when it dominates the amount of useful work that is done. The sort of problems where they are effective is where a problem can be split up into sub-problems that can be solved almost independently by each processor with little communication.”

Symmetric encryption methods are very efficient and can be used to scramble large files or long messages being sent from one computer to another. Unfortunately, symmetric techniques suffer from a major problem: if there are a number of individuals involved in a data transfer or in reading a file, each has to know the same key. This makes it a security nightmare. […] public key cryptography removed a major problem associated with symmetric cryptography: that of a large number of keys in existence some of which may be stored in an insecure way. However, a major problem with asymmetric cryptography is the fact that it is very inefficient (about 10,000 times slower than symmetric cryptography): while it can be used for short messages such as email texts, it is far too inefficient for sending gigabytes of data. However, […] when it is combined with symmetric cryptography, asymmetric cryptography provides very strong security. […] One very popular security scheme is known as the Secure Sockets Layer – normally shortened to SSL. It is based on the concept of a one-time pad. […] SSL uses public key cryptography to communicate the randomly generated key between the sender and receiver of a message. This key is only used once for the data interchange that occurs and, hence, is an electronic analogue of a one-time pad. When each of the parties to the interchange has received the key, they encrypt and decrypt the data employing symmetric cryptography, with the generated key carrying out these processes. […] There is an impression amongst the public that the main threats to security and to privacy arise from technological attack. However, the threat from more mundane sources is equally high. Data thefts, damage to software and hardware, and unauthorized access to computer systems can occur in a variety of non-technical ways: by someone finding computer printouts in a waste bin; by a window cleaner using a mobile phone camera to take a picture of a display containing sensitive information; by an office cleaner stealing documents from a desk; by a visitor to a company noting down a password written on a white board; by a disgruntled employee putting a hammer through the main server and the backup server of a company; or by someone dropping an unencrypted memory stick in the street.”

“The basic architecture of the computer has remained unchanged for six decades since IBM developed the first mainframe computers. It consists of a processor that reads software instructions one by one and executes them. Each instruction will result in data being processed, for example by being added together; and data being stored in the main memory of the computer or being stored on some file-storage medium; or being sent to the Internet or to another computer. This is what is known as the von Neumann architecture; it was named after John von Neumann […]. His key idea, which still holds sway today, is that in a computer the data and the program are both stored in the computer’s memory in the same address space. There have been few challenges to the von Neumann architecture.”

[A] ‘neural network‘ […] consists of an input layer that can sense various signals from some environment […]. In the middle (hidden layer), there are a large number of processing elements (neurones) which are arranged into sub-layers. Finally, there is an output layer which provides a result […]. It is in the middle layer that the work is done in a neural computer. What happens is that the network is trained by giving it examples of the trend or item that is to be recognized. What the training does is to strengthen or weaken the connections between the processing elements in the middle layer until, when combined, they produce a strong signal when a new case is presented to them that matches the previously trained examples and a weak signal when an item that does not match the examples is encountered. Neural networks have been implemented in hardware, but most of the implementations have been via software where the middle layer has been implemented in chunks of code that carry out the learning process. […] although the initial impetus was to use ideas in neurobiology to develop neural architectures based on a consideration of processes in the brain, there is little resemblance between the internal data and software now used in commercial implementations and the human brain.”


Byte. Bit.
Moore’s law.
Computer program.
Programming language. High-level programming language. Low-level programming language.
Zombie (computer science).
Cloud computing.
Instructions per second.
Fetch-execute cycle.
Grace Hopper. Software Bug.
Transistor. Integrated circuit. Very-large-scale integration. Wafer (electronics). Photomask.
Read-only memory (ROM). Read-write memory (RWM). Bus (computing). Address bus. Programmable read-only memory (PROM). Erasable programmable read-only memory (EPROM). Electrically erasable programmable read-only memory (EEPROM). Flash memory. Dynamic random-access memory (DRAM). Static random-access memory (static RAM/SRAM).
Hard disc.
Wireless communication.
Radio-frequency identification (RFID).
NP-hardness. Set partition problem. Bin packing problem.
Cray X-MP. Beowulf cluster.
Vector processor.
Denial-of-service attack. Melissa (computer virus). Malware. Firewall (computing). Logic bomb. Fork bomb/rabbit virus. Cryptography. Caesar cipher. Social engineering (information security).
Application programming interface.
Data mining. Machine translation. Machine learning.
Functional programming.
Quantum computing.


March 19, 2018 Posted by | Books, Computer science, Engineering | Leave a comment

Safety-Critical Systems

Some related links to topics covered in the lecture:

Safety-critical system.
Safety engineering.
Fault tree analysis.
Failure mode and effects analysis.
Value of a statistical life.
ALARP principle.
Hazards and Risk (HSA).
Software system safety.
Aleatoric and epistemic uncertainty.
N-version programming.
An experimental evaluation of the assumption of independence in multiversion programming (Knight & Leveson).
Safety integrity level.
Software for Dependable Systems – Sufficient Evidence? (consensus study report).

March 15, 2018 Posted by | Computer science, Economics, Engineering, Lectures, Statistics | Leave a comment

Some things you need to know about machine learning but didn’t know whom to ask (the grad school version)

Some links to stuff related to the lecture’s coverage:
An overview of gradient descent optimization algorithms.
Rectifier (neural networks) [Relu].
Escaping From Saddle Points – Online Stochastic Gradient for Tensor Decomposition (Ge et al.).
How to Escape Saddle Points Efficiently (closely related to the paper above, presumably one of the ‘recent improvements’ mentioned in the lecture).
Linear classifier.
Concentration inequality.
A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks (Neyshabur et al.).
Off the convex path (the lecturer’s blog).

February 19, 2018 Posted by | Computer science, Lectures, Mathematics | Leave a comment


Complexity theory is a topic I’ve previously been exposed to through various channels; examples include Institute for Advanced Studies comp sci lectures, notes included in a few computer science-related books like Louridas and Dasgupta, and probably also e.g. some of the systems analysis/-science books I’ve read – Konieczny et al.’s text which I recently finished reading is another example of a book which peripherally covers content also covered in this book. Holland’s book pretty much doesn’t cover computational complexity theory at all, but some knowledge of computer science will probably still be useful as e.g. concepts from graph theory are touched upon/applied in the coverage; I am also aware that I derived some benefit while reading this book from having previously spent time on signalling models in microeconomics, as there were conceptual similarities between those models and their properties and some of the stuff Holland includes. I’m not really sure if you need to know ‘anything’ to read the book and get something out of it, but although Holland doesn’t use much mathematical formalism some of the ‘hidden’ formalism lurking in the background will probably not be easy to understand if you e.g. haven’t seen a mathematical equation since the 9th grade, and people who e.g. have seen hierarchical models before will definitely have a greater appreciation of some of the material covered than people who have not. Obviously I’ve read a lot of stuff over time that made the book easier for me to read and understand than it otherwise would have been, but how easy would the book have been for me to read if I hadn’t read those other things? It’s really difficult for me to say. I found the book hard to judge/rate/evaluate, so I decided against rating it on goodreads.

Below I have added some quotes from the book.

“[C]omplex systems exhibits a distinctive property called emergence, roughly described by the common phrase ‘the action of the whole is more than the sum of the actions of the parts’. In addition to complex systems, there is a subfield of computer science, called computational complexity, which concerns itself with the difficulty of solving different kinds of problems. […] The object of the computational complexity subfield is to assign levels of difficulty — levels of complexity — to different collections of problems. There are intriguing conjectures about these levels of complexity, but an understanding of the theoretical framework requires a substantial background in theoretical computer science — enough to fill an entire book in this series. For this reason, and because computational complexity does not touch upon emergence, I will confine this book to systems and the ways in which they exhibit emergence. […] emergent behaviour is an essential requirement for calling a system ‘complex’. […] Hierarchical organization is […] closely tied to emergence. Each level of a hierarchy typically is governed by its own set of laws. For example, the laws of the periodic table govern the combination of hydrogen and oxygen to form H2O molecules, while the laws of fluid flow (such as the Navier-Stokes equations) govern the behaviour of water. The laws of a new level must not violate the laws of earlier levels — that is, the laws at lower levels constrain the laws at higher levels. […] Restated for complex systems: emergent properties at any level must be consistent with interactions specified at the lower level(s). […] Much of the motivation for treating a system as complex is to get at questions that would otherwise remain inaccessible. Often the first steps in acquiring a deeper understanding are through comparisons of similar systems. By treating hierarchical organization as sine qua non for complexity we focus on the interactions of emergent properties at various levels. The combination of ‘top–down’ effects (as when the daily market average affects actions of the buyers and sellers in an equities market) and ‘bottom–up’ effects (the interactions of the buyers and sellers determine the market average) is a pervasive feature of complex systems. The present exposition, then, centres on complex systems where emergence, and the reduction(s) involved, offer a key to new kinds of understanding.”

“As the field of complexity studies has developed, it has split into two subfields that examine two different kinds of emergence: the study of complex physical systems (CPS) and the study of complex adaptive systems (CAS): The study of complex physical systems focuses on geometric (often lattice-like) arrays of elements, in which interactions typically depend only on effects propagated from nearest neighbours. […] the study of CPS has a distinctive set of tools and questions centring on elements that have fixed properties – atoms, the squares of the cellular automaton, and the like. […] The tools used for studying CPS come, with rare exceptions, from a well-developed part of mathematics, the theory of partial differential equations […] CAS studies, in contrast to CPS studies, concern themselves with elements that are not fixed. The elements, usually called agents, learn or adapt in response to interactions with other agents. […] It is unusual for CAS agents to converge, even momentarily, to a single ‘optimal’ strategy, or to an equilibrium. As the agents adapt to each other, new agents with new strategies usually emerge. Then each new agent offers opportunities for still further interactions, increasing the overall complexity. […] The complex feedback loops that form make it difficult to analyse, or even describe, CAS. […] Analysis of complex systems almost always turns on finding recurrent patterns in the system’s ever-changing configurations. […] perpetual novelty, produced with a limited number of rules or laws, is a characteristic of most complex systems: DNA consists of strings of the same four nucleotides, yet no two humans are exactly alike; the theorems of Euclidian geometry are based on just five axioms, yet new theorems are still being derived after two millenia; and so it is for the other complex systems.”

“In a typical physical system the whole is (at least approximately) the sum of the parts, making the use of PDEs straightforward for a mathematician, but in a typical generated system the parts are put together in an interconnected, non-additive way. It is possible to write a concise set of partial differential equations to describe the basic elements of a computer, say an interconnected set of binary counters, but the existing theory of PDEs does little to increase our understanding of the circuits so-described. The formal grammar approach, in contrast, has already considerably increased our understanding of computer languages and programs. One of the major tasks of this book is to use a formal grammar to convert common features of complex systems into ‘stylized facts’ that can be examined carefully within the grammar.”

“Many CPS problems (e.g. the flow of electrons in superconductive materials) […] involve flows — flows that are nicely described by networks. Networks provide a detailed snapshot of CPS and complex adaptive systems (CAS) interactions at any given point in their development, but there are few studies of the evolution of networks […]. The distinction between the fast dynamic of flows (change of state) and the slow dynamic of adaptation (change of the network of interactions) often distinguishes CPS studies from CAS studies. […] all well-studied CAS exhibit lever points, points where a small directed action causes large predictable changes in aggregate behaviour, as when a vaccine produces long-term changes in an immune system. At present, lever points are almost always located by trial and error. However, by extracting mechanisms common to different lever points, a relevant CAS theory would provide a principled way of locating and testing lever points. […] activities that are easy to observe in one complex system often suggest ‘where to look’ in other complex systems where the activities are difficult to observe.”

“Observation shows that agents acting in a niche continually undergo ‘improvements’, without ever completely outcompeting other agents in the community. These improvements may come about in either of two ways: (i) an agent may become more of a generalist, processing resources from a wider variety of sources, or (ii) it may become more specialized, becoming more efficient than its competitors at exploiting a particular source of a vital resource. Both changes allow for still more interactions and still greater diversity. […] All CAS that have been examined closely exhibit trends toward increasing numbers of specialists.”

“Emergence is tightly tied to the formation of boundaries. These boundaries can arise from symmetry breaking, […] or they can arise by assembly of component building blocks […]. For CAS, the agent-defining boundaries determine the interactions between agents. […] Adaptation, and the emergence of new kinds of agents, then arises from changes in the relevant boundaries. Typically, a boundary only looks to a small segment of a signal, a tag, to determine whether or not the signal can pass through the boundary. […] an agent can be modelled by a set of conditional IF/THEN rules that represent both the effects of boundaries and internal signal-processing. Because tags are short, a given signal may carry multiple tags, and the rules that process signals can require the presence of more than one tag for the processing to proceed. Agents are parallel processors in the sense that all rules that are satisfied simultaneously in the agent are executed simultaneously. As a result, the interior of an agent will usually be filled with multiple signals […]. The central role of tags in routing signals through this complex interior puts emphasis on the mechanisms for tag modification as a means of adaptation. Recombination of extant conditions and signals […] turns tags into building blocks for specifying new routes. Parallel processing then makes it possible to test new routes so formed without seriously disrupting extant useful routes. Sophisticated agents have another means of adaptation: anticipation (‘lookahead’). If an agent has a set of rules that simulates part of its world, then it can run this internal model to examine the outcomes of different action sequences before those actions are executed.”

“The flow of signals within and between agents can be represented by a directed network, where nodes represent rules, and there is a connection from node x to node y if rule x sends a signal satisfying a condition of rule y. Then, the flow of signals over this network spells out the performance of the agent at a point in time. […] The networks associated with CAS are typically highly tangled, with many loops providing feedback and recirculation […]. An agent adapts by changing its signal-processing rules, with corresponding changes in the structure of the associated network. […] Most machine-learning models, including ‘artificial neural networks’ and ‘Bayesian networks’, lack feedback cycles — they are often called ‘feedforward networks’ (in contrast to networks with substantial feedback). In the terms used in Chapter 4, such networks have no ‘recirculation’ and hence have no autonomous subsystems. Networks with substantial numbers of cycles are difficult to analyse, but a large number of cycles is the essential requirement for the autonomous internal models that make lookahead and planning possible. […] The complexities introduced by loops have so far resisted most attempts at analysis. […] The difficulties of analysing the behaviour of networks with many interior loops has, both historically and currently, encouraged the study of networks without loops called trees. Trees occur naturally in the study of games. […] because trees are easier to analyse, most artificial neural networks constructed for pattern recognition are trees. […] Evolutionary game theory makes use of the tree structure of games to study the ways in which agents can modify their strategies as they interact with other agents playing the same game. […] However, evolutionary game theory does not concern itself with the evolution of the game’s laws.”

“It has been observed that innovation in CAS is mostly a matter of combining well-known components in new ways. […] Recombination abets the formation of new cascades. […] By extracting general mechanisms that modify CAS, such as recombination, we go from examination of particular instances to a unified study of characteristic CAS properties. The mechanisms of interest act mainly on extant substructures, using them as building blocks for more complex substructures […]. Because signals and boundaries are a pervasive feature of CAS, their modification has a central role in this adaptive process.”

February 12, 2018 Posted by | Books, Computer science, Mathematics | Leave a comment

Random stuff

I have almost stopped posting posts like these, which has resulted in the accumulation of a very large number of links and studies which I figured I might like to blog at some point. This post is mainly an attempt to deal with the backlog – I won’t cover the material in too much detail.

i. Do Bullies Have More Sex? The answer seems to be a qualified yes. A few quotes:

“Sexual behavior during adolescence is fairly widespread in Western cultures (Zimmer-Gembeck and Helfland 2008) with nearly two thirds of youth having had sexual intercourse by the age of 19 (Finer and Philbin 2013). […] Bullying behavior may aid in intrasexual competition and intersexual selection as a strategy when competing for mates. In line with this contention, bullying has been linked to having a higher number of dating and sexual partners (Dane et al. 2017; Volk et al. 2015). This may be one reason why adolescence coincides with a peak in antisocial or aggressive behaviors, such as bullying (Volk et al. 2006). However, not all adolescents benefit from bullying. Instead, bullying may only benefit adolescents with certain personality traits who are willing and able to leverage bullying as a strategy for engaging in sexual behavior with opposite-sex peers. Therefore, we used two independent cross-sectional samples of older and younger adolescents to determine which personality traits, if any, are associated with leveraging bullying into opportunities for sexual behavior.”

“…bullying by males signal the ability to provide good genes, material resources, and protect offspring (Buss and Shackelford 1997; Volk et al. 2012) because bullying others is a way of displaying attractive qualities such as strength and dominance (Gallup et al. 2007; Reijntjes et al. 2013). As a result, this makes bullies attractive sexual partners to opposite-sex peers while simultaneously suppressing the sexual success of same-sex rivals (Gallup et al. 2011; Koh and Wong 2015; Zimmer-Gembeck et al. 2001). Females may denigrate other females, targeting their appearance and sexual promiscuity (Leenaars et al. 2008; Vaillancourt 2013), which are two qualities relating to male mate preferences. Consequently, derogating these qualities lowers a rivals’ appeal as a mate and also intimidates or coerces rivals into withdrawing from intrasexual competition (Campbell 2013; Dane et al. 2017; Fisher and Cox 2009; Vaillancourt 2013). Thus, males may use direct forms of bullying (e.g., physical, verbal) to facilitate intersexual selection (i.e., appear attractive to females), while females may use relational bullying to facilitate intrasexual competition, by making rivals appear less attractive to males.”

The study relies on the use of self-report data, which I find very problematic – so I won’t go into the results here. I’m not quite clear on how those studies mentioned in the discussion ‘have found self-report data [to be] valid under conditions of confidentiality’ – and I remain skeptical. You’ll usually want data from independent observers (e.g. teacher or peer observations) when analyzing these kinds of things. Note in the context of the self-report data problem that if there’s a strong stigma associated with being bullied (there often is, or bullying wouldn’t work as well), asking people if they have been bullied is not much better than asking people if they’re bullying others.

ii. Some topical advice that some people might soon regret not having followed, from the wonderful Things I Learn From My Patients thread:

“If you are a teenage boy experimenting with fireworks, do not empty the gunpowder from a dozen fireworks and try to mix it in your mother’s blender. But if you do decide to do that, don’t hold the lid down with your other hand and stand right over it. This will result in the traumatic amputation of several fingers, burned and skinned forearms, glass shrapnel in your face, and a couple of badly scratched corneas as a start. You will spend months in rehab and never be able to use your left hand again.”

iii. I haven’t talked about the AlphaZero-Stockfish match, but I was of course aware of it and did read a bit about that stuff. Here’s a reddit thread where one of the Stockfish programmers answers questions about the match. A few quotes:

“Which of the two is stronger under ideal conditions is, to me, neither particularly interesting (they are so different that it’s kind of like comparing the maximum speeds of a fish and a bird) nor particularly important (since there is only one of them that you and I can download and run anyway). What is super interesting is that we have two such radically different ways to create a computer chess playing entity with superhuman abilities. […] I don’t think there is anything to learn from AlphaZero that is applicable to Stockfish. They are just too different, you can’t transfer ideas from one to the other.”

“Based on the 100 games played, AlphaZero seems to be about 100 Elo points stronger under the conditions they used. The current development version of Stockfish is something like 40 Elo points stronger than the version used in Google’s experiment. There is a version of Stockfish translated to hand-written x86-64 assembly language that’s about 15 Elo points stronger still. This adds up to roughly half the Elo difference between AlphaZero and Stockfish shown in Google’s experiment.”

“It seems that Stockfish was playing with only 1 GB for transposition tables (the area of memory used to store data about the positions previously encountered in the search), which is way too little when running with 64 threads.” [I seem to recall a comp sci guy observing elsewhere that this was less than what was available to his smartphone version of Stockfish, but I didn’t bookmark that comment].

“The time control was a very artificial fixed 1 minute/move. That’s not how chess is traditionally played. Quite a lot of effort has gone into Stockfish’s time management. It’s pretty good at deciding when to move quickly, and when to spend a lot of time on a critical decision. In a fixed time per move game, it will often happen that the engine discovers that there is a problem with the move it wants to play just before the time is out. In a regular time control, it would then spend extra time analysing all alternative moves and trying to find a better one. When you force it to move after exactly one minute, it will play the move it already know is bad. There is no doubt that this will cause it to lose many games it would otherwise have drawn.”

iv. Thrombolytics for Acute Ischemic Stroke – no benefit found.

“Thrombolysis has been rigorously studied in >60,000 patients for acute thrombotic myocardial infarction, and is proven to reduce mortality. It is theorized that thrombolysis may similarly benefit ischemic stroke patients, though a much smaller number (8120) has been studied in relevant, large scale, high quality trials thus far. […] There are 12 such trials 1-12. Despite the temptation to pool these data the studies are clinically heterogeneous. […] Data from multiple trials must be clinically and statistically homogenous to be validly pooled.14 Large thrombolytic studies demonstrate wide variations in anatomic stroke regions, small- versus large-vessel occlusion, clinical severity, age, vital sign parameters, stroke scale scores, and times of administration. […] Examining each study individually is therefore, in our opinion, both more valid and more instructive. […] Two of twelve studies suggest a benefit […] In comparison, twice as many studies showed harm and these were stopped early. This early stoppage means that the number of subjects in studies demonstrating harm would have included over 2400 subjects based on originally intended enrollments. Pooled analyses are therefore missing these phantom data, which would have further eroded any aggregate benefits. In their absence, any pooled analysis is biased toward benefit. Despite this, there remain five times as many trials showing harm or no benefit (n=10) as those concluding benefit (n=2), and 6675 subjects in trials demonstrating no benefit compared to 1445 subjects in trials concluding benefit.”

“Thrombolytics for ischemic stroke may be harmful or beneficial. The answer remains elusive. We struggled therefore, debating between a ‘yellow’ or ‘red’ light for our recommendation. However, over 60,000 subjects in trials of thrombolytics for coronary thrombosis suggest a consistent beneficial effect across groups and subgroups, with no studies suggesting harm. This consistency was found despite a very small mortality benefit (2.5%), and a very narrow therapeutic window (1% major bleeding). In comparison, the variation in trial results of thrombolytics for stroke and the daunting but consistent adverse effect rate caused by ICH suggested to us that thrombolytics are dangerous unless further study exonerates their use.”

“There is a Cochrane review that pooled estimates of effect. 17 We do not endorse this choice because of clinical heterogeneity. However, we present the NNT’s from the pooled analysis for the reader’s benefit. The Cochrane review suggested a 6% reduction in disability […] with thrombolytics. This would mean that 17 were treated for every 1 avoiding an unfavorable outcome. The review also noted a 1% increase in mortality (1 in 100 patients die because of thrombolytics) and a 5% increase in nonfatal intracranial hemorrhage (1 in 20), for a total of 6% harmed (1 in 17 suffers death or brain hemorrhage).”

v. Suicide attempts in Asperger Syndrome. An interesting finding: “Over 35% of individuals with AS reported that they had attempted suicide in the past.”

Related: Suicidal ideation and suicide plans or attempts in adults with Asperger’s syndrome attending a specialist diagnostic clinic: a clinical cohort study.

“374 adults (256 men and 118 women) were diagnosed with Asperger’s syndrome in the study period. 243 (66%) of 367 respondents self-reported suicidal ideation, 127 (35%) of 365 respondents self-reported plans or attempts at suicide, and 116 (31%) of 368 respondents self-reported depression. Adults with Asperger’s syndrome were significantly more likely to report lifetime experience of suicidal ideation than were individuals from a general UK population sample (odds ratio 9·6 [95% CI 7·6–11·9], p<0·0001), people with one, two, or more medical illnesses (p<0·0001), or people with psychotic illness (p=0·019). […] Lifetime experience of depression (p=0·787), suicidal ideation (p=0·164), and suicide plans or attempts (p=0·06) did not differ significantly between men and women […] Individuals who reported suicide plans or attempts had significantly higher Autism Spectrum Quotient scores than those who did not […] Empathy Quotient scores and ages did not differ between individuals who did or did not report suicide plans or attempts (table 4). Patients with self-reported depression or suicidal ideation did not have significantly higher Autism Spectrum Quotient scores, Empathy Quotient scores, or age than did those without depression or suicidal ideation”.

The fact that people with Asperger’s are more likely to be depressed and contemplate suicide is consistent with previous observations that they’re also more likely to die from suicide – for example a paper I blogged a while back found that in that particular (large Swedish population-based cohort-) study, people with ASD were more than 7 times as likely to die from suicide than were the comparable controls.

Also related: Suicidal tendencies hard to spot in some people with autism.

This link has some great graphs and tables of suicide data from the US.

Also autism-related: Increased perception of loudness in autism. This is one of the ‘important ones’ for me personally – I am much more sound-sensitive than are most people.

vi. Early versus Delayed Invasive Intervention in Acute Coronary Syndromes.

“Earlier trials have shown that a routine invasive strategy improves outcomes in patients with acute coronary syndromes without ST-segment elevation. However, the optimal timing of such intervention remains uncertain. […] We randomly assigned 3031 patients with acute coronary syndromes to undergo either routine early intervention (coronary angiography ≤24 hours after randomization) or delayed intervention (coronary angiography ≥36 hours after randomization). The primary outcome was a composite of death, myocardial infarction, or stroke at 6 months. A prespecified secondary outcome was death, myocardial infarction, or refractory ischemia at 6 months. […] Early intervention did not differ greatly from delayed intervention in preventing the primary outcome, but it did reduce the rate of the composite secondary outcome of death, myocardial infarction, or refractory ischemia and was superior to delayed intervention in high-risk patients.”

vii. Some wikipedia links:

Behrens–Fisher problem.
Sailing ship tactics (I figured I had to read up on this if I were to get anything out of the Aubrey-Maturin books).
Anatomical terms of muscle.
Phatic expression (“a phatic expression […] is communication which serves a social function such as small talk and social pleasantries that don’t seek or offer any information of value.”)
Three-domain system.
Beringian wolf (featured).
Subdural hygroma.
Cayley graph.
Schur polynomial.
Solar neutrino problem.
Hadamard product (matrices).
True polar wander.
Newton’s cradle.

viii. Determinant versus permanent (mathematics – technical).

ix. Some years ago I wrote a few English-language posts about some of the various statistical/demographic properties of immigrants living in Denmark, based on numbers included in a publication by Statistics Denmark. I did it by translating the observations included in that publication, which was only published in Danish. I was briefly considering doing the same thing again when the 2017 data arrived, but I decided not to do it as I recalled that it took a lot of time to write those posts back then, and it didn’t seem to me to be worth the effort – but Danish readers might be interested to have a look at the data, if they haven’t already – here’s a link to the publication Indvandrere i Danmark 2017.

x. A banter blitz session with grandmaster Peter Svidler, who recently became the first Russian ever to win the Russian Chess Championship 8 times. He’s currently shared-second in the World Rapid Championship after 10 rounds and is now in the top 10 on the live rating list in both classical and rapid – seems like he’s had a very decent year.

xi. I recently discovered Dr. Whitecoat’s blog. The patient encounters are often interesting.

December 28, 2017 Posted by | Astronomy, autism, Biology, Cardiology, Chess, Computer science, History, Mathematics, Medicine, Neurology, Physics, Psychiatry, Psychology, Random stuff, Statistics, Studies, Wikipedia, Zoology | Leave a comment

The mystery of over-parametrization in neural networks

October 6, 2017 Posted by | Computer science, Mathematics | Leave a comment

Interactive Coding with “Optimal” Round and Communication Blowup

The youtube description of this one was rather longer than usual, and I decided to quote it in full below:

“The problem of constructing error-resilient interactive protocols was introduced in the seminal works of Schulman (FOCS 1992, STOC 1993). These works show how to convert any two-party interactive protocol into one that is resilient to constant-fraction of error, while blowing up the communication by only a constant factor. Since these seminal works, there have been many follow-up works which improve the error rate, the communication rate, and the computational efficiency. All these works assume that in the underlying protocol, in each round, each party sends a *single* bit. This assumption is without loss of generality, since one can efficiently convert any protocol into one which sends one bit per round. However, this conversion may cause a substantial increase in *round* complexity, which is what we wish to minimize in this work. Moreover, all previous works assume that the communication complexity of the underlying protocol is *fixed* and a priori known, an assumption that we wish to remove. In this work, we consider protocols whose messages may be of *arbitrary* lengths, and where the length of each message and the length of the protocol may be *adaptive*, and may depend on the private inputs of the parties and on previous communication. We show how to efficiently convert any such protocol into another protocol with comparable efficiency guarantees, that is resilient to constant fraction of adversarial error, while blowing up both the *communication* complexity and the *round* complexity by at most a constant factor. Moreover, as opposed to most previous work, our error model not only allows the adversary to toggle with the corrupted bits, but also allows the adversary to *insert* and *delete* bits. In addition, our transformation preserves the computational efficiency of the protocol. Finally, we try to minimize the blowup parameters, and give evidence that our parameters are nearly optimal. This is joint work with Klim Efremenko and Elad Haramaty.”

A few links to stuff covered/mentioned in the lecture:

Coding for interactive communication correcting insertions and deletions.
Efficiently decodable insertion/deletion codes for high-noise and high-rate regimes.
Common reference string model.
Small-bias probability spaces: Efficient constructions and applications.
Interactive Channel Capacity Revisited.
Collision (computer science).
Chernoff bound.

September 6, 2017 Posted by | Computer science, Cryptography, Lectures, Mathematics | Leave a comment


I gave the book two stars. Some quotes and links below.

“Lenses are ubiquitous in image-forming devices […] Imaging instruments have two components: the lens itself, and a light detector, which converts the light into, typically, an electrical signal. […] In every case the location of the lens with respect to the detector is a key design parameter, as is the focal length of the lens which quantifies its ‘ray-bending’ power. The focal length is set by the curvature of the surfaces of the lens and its thickness. More strongly curved surfaces and thicker materials are used to make lenses with short focal lengths, and these are used usually in instruments where a high magnification is needed, such as a microscope. Because the refractive index of the lens material usually depends on the colour of light, rays of different colours are bent by different amounts at the surface, leading to a focus for each colour occurring in a different position. […] lenses with a big diameter and a short focal length will produce the tiniest images of point-like objects. […] about the best you can do in any lens system you could actually make is an image size of approximately one wavelength. This is the fundamental limit to the pixel size for lenses used in most optical instruments, such as cameras and binoculars. […] Much more sophisticated methods are required to see even smaller things. The reason is that the wave nature of light puts a lower limit on the size of a spot of light. […] At the other extreme, both ground- and space-based telescopes for astronomy are very large instruments with relatively simple optical imaging components […]. The distinctive feature of these imaging systems is their size. The most distant stars are very, very faint. Hardly any of their light makes it to the Earth. It is therefore very important to collect as much of it as possible. This requires a very big lens or mirror”.

“[W]hat sort of wave is light? This was […] answered in the 19th century by James Clerk Maxwell, who showed that it is an oscillation of a new kind of entity: the electromagnetic field. This field is effectively a force that acts on electric charges and magnetic materials. […] In the early 19th century, Michael Faraday had shown the close connections between electric and magnetic fields. Maxwell brought them together, as the electromagnetic force field. […] in the wave model, light can be considered as very high frequency oscillations of the electromagnetic field. One consequence of this idea is that moving electric charges can generate light waves. […] When […] charges accelerate — that is, when they change their speed or their direction of motion — then a simple law of physics is that they emit light. Understanding this was one of the great achievements of the theory of electromagnetism.”

“It was the observation of interference effects in a famous experiment by Thomas Young in 1803 that really put the wave picture of light as the leading candidate as an explanation of the nature of light. […] It is interference of light waves that causes the colours in a thin film of oil floating on water. Interference transforms very small distances, on the order of the wavelength of light, into very big changes in light intensity — from no light to four times as bright as the individual constituent waves. Such changes in intensity are easy to detect or see, and thus interference is a very good way to measure small changes in displacement on the scale of the wavelength of light. Many optical sensors are based on interference effects.”

“[L]ight beams […] gradually diverge as they propagate. This is because a beam of light, which by definition has a limited spatial extent, must be made up of waves that propagate in more than one direction. […] This phenomenon is called diffraction. […] if you want to transmit light over long distances, then diffraction could be a problem. It will cause the energy in the light beam to spread out, so that you would need a bigger and bigger optical system and detector to capture all of it. This is important for telecommunications, since nearly all of the information transmitted over long-distance communications links is encoded on to light beams. […] The means to manage diffraction so that long-distance communication is possible is to use wave guides, such as optical fibres.”

“[O]ptical waves […] guided along a fibre or in a glass ‘chip’ […] underpins the long-distance telecommunications infrastructure that connects people across different continents and powers the Internet. The reason it is so effective is that light-based communications have much more capacity for carrying information than do electrical wires, or even microwave cellular networks. […] In optical communications, […] bits are represented by the intensity of the light beam — typically low intensity is a 0 and higher intensity a 1. The more of these that arrive per second, the faster the communication rate. […] Why is optics so good for communications? There are two reasons. First, light beams don’t easily influence each other, so that a single fibre can support many light pulses (usually of different colours) simultaneously without the messages getting scrambled up. The reason for this is that the glass of which the fibre is made does not absorb light (or only absorbs it in tiny amounts), and so does not heat up and disrupt other pulse trains. […] the ‘crosstalk’ between light beams is very weak in most materials, so that many beams can be present at once without causing a degradation of the signal. This is very different from electrons moving down a copper wire, which is the usual way in which local ‘wired’ communications links function. Electrons tend to heat up the wire, dissipating their energy. This makes the signals harder to receive, and thus the number of different signal channels has to be kept small enough to avoid this problem. Second, light waves oscillate at very high frequencies, and this allows very short pulses to be generated This means that the pulses can be spaced very close together in time, making the transmission of more bits of information per second possible. […] Fibre-based optical networks can also support a very wide range of colours of light.”

“Waves can be defined by their wavelength, amplitude, and phase […]. Particles are defined by their position and direction of travel […], and a collection of particles by their density […] and range of directions. The media in which the light moves are characterized by their refractive indices. This can vary across space. […] Hamilton showed that what was important was how rapidly the refractive index changed in space compared with the length of an optical wave. That is, if the changes in index took place on a scale of close to a wavelength, then the wave character of light was evident. If it varied more smoothly and very slowly in space then the particle picture provided an adequate description. He showed how the simpler ray picture emerges from the more complex wave picture in certain commonly encountered situations. The appearance of wave-like phenomena, such as diffraction and interference, occurs when the size scales of the wavelength of light and the structures in which it propagates are similar. […] Particle-like behaviour — motion along a well-defined trajectory — is sufficient to describe the situation when all objects are much bigger than the wavelength of light, and have no sharp edges.”

“When things are heated up, they change colour. Take a lump of metal. As it gets hotter and hotter it first glows red, then orange, and then white. Why does this happen? This question stumped many of the great scientists [in the 19th century], including Maxwell himself. The problem was that Maxwell’s theory of light, when applied to this problem, indicated that the colour should get bluer and bluer as the temperature increased, without a limit, eventually moving out of the range of human vision into the ultraviolet—beyond blue—region of the spectrum. But this does not happen in practice. […] Max Planck […] came up with an idea to explain the spectrum emitted by hot objects — so-called ‘black bodies’. He conjectured that when light and matter interact, they do so only by exchanging discrete ‘packets’, or quanta, or energy. […] this conjecture was set to radically change physics.”

“What Dirac did was to develop a quantum mechanical version of Maxwell’s theory of electromagnetic fields. […] It set the quantum field up as the fundamental entity on which the universe is built — neither particle nor wave, but both at once; complete wave–particle duality. It is a beautiful reconciliation of all the phenomena that light exhibits, and provides a framework in which to understand all optical effects, both those from the classical world of Newton, Maxwell, and Hamilton and those of the quantum world of Planck, Einstein, and Bohr. […] Light acts as a particle of more or less well-defined energy when it interacts with matter. Yet it retains its ability to exhibit wave-like phenomena at the same time. The resolution [was] a new concept: the quantum field. Light particles — photons — are excitations of this field, which propagates according to quantum versions of Maxwell’s equations for light waves. Quantum fields, of which light is perhaps the simplest example, are now regarded as being the fundamental entities of the universe, underpinning all types of material and non-material things. The only explanation is that the stuff of the world is neither particle nor wave but both. This is the nature of reality.”

Some links:

Coherence (physics).
Electromagnetic spectrum.
Joseph von Fraunhofer.
Transverse wave.
Spatial frequency.
Polarization (waves).
Specular reflection.
Negative-index metamaterial.
Interference (wave propagation).
Young’s interference experiment.
Photoactivated localization microscopy.
Stimulated emission depletion (STED) microscopy.
Fourier’s theorem (I found it hard to find a good source on this one. According to the book, “Fourier’s theorem says in simple terms that the smaller you focus light, the broader the range of wave directions you need to achieve this spot”)
X-ray diffraction.
Brewster’s angle.
Liquid crystal.
Liquid crystal display.
Wave–particle duality.
Fermat’s principle.
Maupertuis’ principle.
Johann Jakob Balmer.
Max Planck.
Photoelectric effect.
Niels Bohr.
Matter wave.
Quantum vacuum.
Lamb shift.
Light-emitting diode.
Fluorescent tube.
Synchrotron radiation.
Quantum state.
Quantum fluctuation.
Spontaneous emission/stimulated emission.
Optical cavity.
X-ray absorption spectroscopy.
Diamond Light Source.
Atomic clock.
Time dilation.
High harmonic generation.
Frequency comb.
Optical tweezers.
Bose–Einstein condensate.
Pump probe spectroscopy.
Vulcan laser.
Plasma (physics).
Nonclassical light.
Photon polarization.
Quantum entanglement.
Bell test experiments.
Quantum key distribution/Quantum cryptography/Quantum computing.

August 31, 2017 Posted by | Books, Chemistry, Computer science, Physics | Leave a comment


This book was ‘okay…ish’, but I must admit I was a bit disappointed; the coverage was much too superficial, and I’m reasonably sure the lack of formalism made the coverage harder for me to follow than it could have been. I gave the book two stars on goodreads.

Some quotes and links below.


“In the 19th century, the principles were established on which the modern electromagnetic world could be built. The electrical turbine is the industrialized embodiment of Faraday’s idea of producing electricity by rotating magnets. The turbine can be driven by the wind or by falling water in hydroelectric power stations; it can be powered by steam which is itself produced by boiling water using the heat produced from nuclear fission or burning coal or gas. Whatever the method, rotating magnets inducing currents feed the appetite of the world’s cities for electricity, lighting our streets, powering our televisions and computers, and providing us with an abundant source of energy. […] rotating magnets are the engine of the modern world. […] Modern society is built on the widespread availability of cheap electrical power, and almost all of it comes from magnets whirling around in turbines, producing electric current by the laws discovered by Oersted, Ampère, and Faraday.”

“Maxwell was the first person to really understand that a beam of light consists of electric and magnetic oscillations propagating together. The electric oscillation is in one plane, at right angles to the magnetic oscillation. Both of them are in directions at right angles to the direction of propagation. […] The oscillations of electricity and magnetism in a beam of light are governed by Maxwell’s four beautiful equations […] Above all, Einstein’s work on relativity was motivated by a desire to preserve the integrity of Maxwell’s equations at all costs. The problem was this: Maxwell had derived a beautiful expression for the speed of light, but the speed of light with respect to whom? […] Einstein deduced that the way to fix this would be to say that all observers will measure the speed of any beam of light to be the same. […] Einstein showed that magnetism is a purely relativistic effect, something that wouldn’t even be there without relativity. Magnetism is an example of relativity in everyday life. […] Magnetic fields are what electric fields look like when you are moving with respect to the charges that ‘cause’ them. […] every time a magnetic field appears in nature, it is because a charge is moving with respect to the observer. Charge flows down a wire to make an electric current and this produces magnetic field. Electrons orbit an atom and this ‘orbital’ motion produces a magnetic field. […] the magnetism of the Earth is due to electrical currents deep inside the planet. Motion is the key in each and every case, and magnetic fields are the evidence that charge is on the move. […] Einstein’s theory of relativity casts magnetism in a new light. Magnetic fields are a relativistic correction which you observe when charges move relative to you.”

“[T]he Bohr–van Leeuwen theorem […] states that if you assume nothing more than classical physics, and then go on to model a material as a system of electrical charges, then you can show that the system can have no net magnetization; in other words, it will not be magnetic. Simply put, there are no lodestones in a purely classical Universe. This should have been a revolutionary and astonishing result, but it wasn’t, principally because it came about 20 years too late to knock everyone’s socks off. By 1921, the initial premise of the Bohr–van Leeuwen theorem, the correctness of classical physics, was known to be wrong […] But when you think about it now, the Bohr–van Leeuwen theorem gives an extraordinary demonstration of the failure of classical physics. Just by sticking a magnet to the door of your refrigerator, you have demonstrated that the Universe is not governed by classical physics.”

“[M]ost real substances are weakly diamagnetic, meaning that when placed in a magnetic field they become weakly magnetic in the opposite direction to the field. Water does this, and since animals are mostly water, it applies to them. This is the basis of Andre Geim’s levitating frog experiment: a live frog is placed in a strong magnetic field and because of its diamagnetism it becomes weakly magnetic. In the experiment, a non-uniformity of the magnetic field induces a force on the frog’s induced magnetism and, hey presto, the frog levitates in mid-air.”

“In a conventional hard disk technology, the disk needs to be spun very fast, around 7,000 revolutions per minute. […] The read head floats on a cushion of air about 15 nanometres […] above the surface of the rotating disk, reading bits off the disk at tens of megabytes per second. This is an extraordinary engineering achievement when you think about it. If you were to scale up a hard disk so that the disk is a few kilometres in diameter rather a few centimetres, then the read head would be around the size of the White House and would be floating over the surface of the disk on a cushion of air one millimetre thick (the diameter of the head of a pin) while the disk rotated below it at a speed of several million miles per hour (fast enough to go round the equator a couple of dozen times in a second). On this scale, the bits would be spaced a few centimetres apart around each track. Hard disk drives are remarkable. […] Although hard disks store an astonishing amount of information and are cheap to manufacture, they are not fast information retrieval systems. To access a particular piece of information involves moving the head and rotating the disk to a particular spot, taking perhaps a few milliseconds. This sounds quite rapid, but with processors buzzing away and performing operations every nanosecond or so, a few milliseconds is glacial in comparison. For this reason, modern computers often use solid state memory to store temporary information, reserving the hard disk for longer-term bulk storage. However, there is a trade-off between cost and performance.”

“In general, there is a strong economic drive to store more and more information in a smaller and smaller space, and hence a need to find a way to make smaller and smaller bits. […] [However] greater miniturization comes at a price. The point is the following: when you try to store a bit of information in a magnetic medium, an important constraint on the usefulness of the technology is how long the information will last for. Almost always the information is being stored at room temperature and so needs to be robust to the ever present random jiggling effects produced by temperature […] It turns out that the crucial parameter controlling this robustness is the ratio of the energy needed to reverse the bit of information (in other words, the energy required to change the magnetization from one direction to the reverse direction) to a characteristic energy associated with room temperature (an energy which is, expressed in electrical units, approximately one-fortieth of a Volt). So if the energy to flip a magnetic bit is very large, the information can persist for thousands of years […] while if it is very small, the information might only last for a small fraction of a second […] This energy is proportional to the volume of the magnetic bit, and so one immediately sees a problem with making bits smaller and smaller: though you can store bits of information at higher density, there is a very real possibility that the information might be very rapidly scrambled by thermal fluctuations. This motivates the search for materials in which it is very hard to flip the magnetization from one state to the other.”

“The change in the Earth’s magnetic field over time is a fairly noticeable phenomenon. Every decade or so, compass needles in Africa are shifting by a degree, and the magnetic field overall on planet Earth is about 10% weaker than it was in the 19th century.”

Below I have added some links to topics and people covered/mentioned in the book. Many of the links below have likely also been included in some of the other posts about books from the A Brief Introduction OUP physics series which I’ve posted this year – the main point of adding these links is to give some idea what kind of stuff’s covered in the book:

William Gilbert/De Magnete.
Alessandro Volta.
Ampère’s circuital law.
Charles-Augustin de Coulomb.
Hans Christian Ørsted.
Leyden jar
/voltaic cell/battery (electricity).
Homopolar motor.
Michael Faraday.
Electromagnetic induction.
Zeeman effect.
Alternating current/Direct current.
Nikola Tesla.
Thomas Edison.
Force field (physics).
Ole Rømer.
Centimetre–gram–second system of units.
James Clerk Maxwell.
Maxwell’s equations.
Permeability (electromagnetism).
Gauss’ law.
Michelson–Morley experiment
Special relativity.
Drift velocity.
Curie’s law.
Curie temperature.
Andre Geim.
Exchange interaction.
Magnetic domain.
Domain wall (magnetism).
Stern–Gerlach experiment.
Dirac equation.
Giant magnetoresistance.
Spin valve.
Racetrack memory.
Perpendicular recording.
Bubble memory (“an example of a brilliant idea which never quite made it”, as the author puts it).
Single-molecule magnet.
Earth’s magnetic field.
Van Allen radiation belt.
South Atlantic Anomaly.
Geomagnetic storm.
Geomagnetic reversal.
ITER (‘International Thermonuclear Experimental Reactor’).
Spin glass.
Quantum spin liquid.
Spin ice.
Magnetic monopole.
Ice rules.

August 28, 2017 Posted by | Books, Computer science, Geology, Physics | Leave a comment

Computer Science

I have enjoyed the physics books I’ve recently read in the ‘…A very short introduction’-series by Oxford University Press, so I figured it might make sense to investigate whether the series also has some decent coverage of other areas of research. I must however admit that I didn’t think too much of Dasgupta’s book. I think the author was given a very tough task. Having an author write a decent short book on a reasonably well-defined sub-topic of physics makes sense, whereas having him write the same sort of short and decent book about the entire field of ‘physics’ is a different matter. In some sense something analogous to this was what Dasgupta had been asked to do(/had undertaken to do?). Of course computer science is a relatively new field so arguably the analogy doesn’t completely hold; even if you cover every major topic in computer science there might still be significantly less ground to cover here than there would be, had he been required to cover everything from Newton (…Copernicus? Eudoxus of Cnidus? Gan De?) to modern developments in M-theory, but the main point stands; the field is much too large for a book like this to do more than perhaps very carefully scratch the surfaces of a few relevant subfields, making the author’s entire endeavour exceedingly difficult to pull off successfully. I noted while reading the book that document searches for ‘graph theory’ and ‘discrete mathematics’ yielded zero results, and I assume that many major topics/areas of relevance are simply not mentioned at all, which to be fair is but to be expected considering the format of the book. The book could have been a lot worse, but it wasn’t all that great – I ended up giving it two stars on goodreads.

My coverage of the book here on the blog will be relatively lazy: I’ll only include links in this post, not quotes from the book – I looked up a lot of links to coverage of relevant concepts and topics also covered in the book while reading it, and I have added many of these links below. The links should give you some idea of which sort of topics are covered in the publication.

Church–Turing thesis.
Turing machine.
Automata theory.
Donald Knuth.
Procedural knowledge.
Machine code.
Infix notation.
Polish notation.
Time complexity.
Linear search.
Big O notation.
Computational complexity theory.
P versus NP problem.
Programming language.
Assembly language.
Hardware description language.
Data type (computer science).
Statement (computer science).
Instruction cycle.
Assignment (computer science).
Computer architecture.
Control unit.
Computer memory.
Memory buffer register.
Cache (computing).
Parallel computing (featured article).
Instruction pipelining.
Amdahl’s law.
FCFS algorithm.
Exact algorithm.
Artificial intelligence.
Means-ends analysis.

June 8, 2017 Posted by | Books, Computer science | Leave a comment

The Mathematical Challenge of Large Networks

This is another one of the aforementioned lectures I watched a while ago, but had never got around to blogging:

If I had to watch this one again, I’d probably skip most of the second half; it contains highly technical coverage of topics in graph theory, and it was very difficult for me to follow (but I did watch it to the end, just out of curiosity).

The lecturer has put up a ~500 page publication on these and related topics, which is available here, so if you want to know more that’s an obvious place to go have a look. A few other relevant links to stuff mentioned/covered in the lecture:
Szemerédi regularity lemma.
Turán’s theorem.
Quantum graph.

May 19, 2017 Posted by | Computer science, Lectures, Mathematics, Statistics | Leave a comment

Quantifying tradeoffs between fairness and accuracy in online learning

From a brief skim of this paper, which is coauthored by the guy giving this lecture, it looked to me like it covers many of the topics discussed in the lecture. So if you’re unsure as to whether or not to watch the lecture (…or if you want to know more about this stuff after you’ve watched the lecture) you might want to have a look at that paper. Although the video is long for a single lecture I would note that the lecture itself lasts only approximately one hour; the last 10 minutes are devoted to Q&A.

May 12, 2017 Posted by | Computer science, Economics, Lectures, Mathematics | Leave a comment

Information complexity and applications

I have previously here on the blog posted multiple lectures in my ‘lecture-posts’, or I have combined a lecture with other stuff (e.g. links such as those in the previous ‘random stuff’ post). I think such approaches have made me less likely to post lectures on the blog (if I don’t post a lecture soon after I’ve watched it, my experience tells me that I not infrequently simply never get around to posting it), and combined with this issue is also the issue that I don’t really watch a lot of lectures these days. For these reasons I have decided to start posting single lecture posts here on the blog; when I start thinking about the time expenditure of people reading along here in a way this approach actually also seems justified – although it might take me as much time/work to watch and cover, say, 4 lectures as it would take me to read and cover 100 pages of a textbook, the time expenditure required by a reader of the blog would be very different in those two cases (you’ll usually be able to read a post that took me multiple hours to write in a short amount of time, whereas ‘the time advantage’ of the reader is close to negligible (maybe not completely; search costs are not completely irrelevant) in the case of lectures). By posting multiple lectures in the same post I probably decrease the expected value of the time readers spend watching the content I upload, which seems suboptimal.

Here’s the youtube description of the lecture, which was posted a few days ago on the IAS youtube account:

“Over the past two decades, information theory has reemerged within computational complexity theory as a mathematical tool for obtaining unconditional lower bounds in a number of models, including streaming algorithms, data structures, and communication complexity. Many of these applications can be systematized and extended via the study of information complexity – which treats information revealed or transmitted as the resource to be conserved. In this overview talk we will discuss the two-party information complexity and its properties – and the interactive analogues of classical source coding theorems. We will then discuss applications to exact communication complexity bounds, hardness amplification, and quantum communication complexity.”

He actually decided to skip the quantum communication complexity stuff because of the time constraint. I should note that the lecture was ‘easy enough’ for me to follow most of it, so it is not really that difficult, at least not if you know some basic information theory.

A few links to related stuff (you can take these links as indications of what sort of stuff the lecture is about/discusses, if you’re on the fence about whether or not to watch it):
Computational complexity theory.
Shannon entropy.
Shannon’s source coding theorem.
Communication complexity.
Communications protocol.
Information-based complexity.
Hash function.
From Information to Exact Communication (in the lecture he discusses some aspects covered in this paper).
Unique games conjecture (Two-prover proof systems).
A Counterexample to Strong Parallel Repetition (another paper mentioned/briefly discussed during the lecture).
Pinsker’s inequality.

An interesting aspect I once again noted during this lecture is the sort of loose linkage you sometimes observe between the topics of game theory/microeconomics and computer science. Of course the link is made explicit a few minutes later in the talk when he discusses the unique games conjecture to which I link above, but it’s perhaps worth noting that the link is on display even before that point is reached. Around 38 minutes into the lecture he mentions that one of the relevant proofs ‘involves such things as Lagrange multipliers and optimization’. I was far from surprised, as from a certain point of view the problem he discusses at that point is conceptually very similar to some problems encountered in auction theory, where Lagrange multipliers and optimization problems are frequently encountered… If you are too unfamiliar with that field to realize how the similar problem might appear in an auction theory context, what you have there are instead auction partipants who prefer not to reveal their true willingness to pay; and some auction designs actually work in a very similar manner as does the (pseudo-)protocol described in the lecture, and are thus used to reveal it (for some subset of participants at least)).

March 12, 2017 Posted by | Computer science, Game theory, Lectures, Papers | Leave a comment

Random stuff

i. A very long but entertaining chess stream by Peter Svidler was recently uploaded on the Chess24 youtube account – go watch it here, if you like that kind of stuff. The fact that it’s five hours long is a reason to rejoice, not a reason to think that it’s ‘too long to be watchable’ – watch it in segments…

People interested in chess might also be interested to know that Magnus Carlsen has made an account on the ICC on which he has played, which was a result of his recent participation in the ICC Open 2016 (link). A requirement for participation in the tournament was that people had to know whom they were playing against (so there would be no ultra-strong GMs playing using anonymous accounts in the finals – they could use accounts with strange names, but people had to know whom they were playing), so now we know that Magnus Carlsen has played under the nick ‘stoptryharding’ on the ICC. Carlsen did not win the tournament as he lost to Grischuk in the semi-finals. Some very strong players were incidentally kicked out in the qualifiers, including Nepomniachtchi, the current #5 in the world on the FIDE live blitz ratings.

ii. A lecture:

iii. Below I have added some new words I’ve encountered, most of them in books I’ve read (I have not spent much time on recently). I’m sure if I were to look all of them up on some (many?) of them would not be ‘new’ to me, but that’s not going to stop me from including them here (I included the word ‘inculcate’ below for a reason…). Do take note of the spelling of some of these words – some of them are tricky ones included in Bryson’s Dictionary of Troublesome Words: A Writer’s Guide to Getting It Right, which people often get wrong for one reason or another:

Conurbation, epizootic, equable, circumvallation, contravallation, exiguous, forbear, louche, vituperative, thitherto, congeries, inculcate, obtrude, palter, idiolect, hortatory, enthalpy (see also wiki, or Khan Academy), trove, composograph, indite, mugginess, apodosis, protasis, invidious, inveigle, inflorescence, kith, anatopism, laudation, luxuriant, maleficence, misogamy (I did not know this was a word, and I’ll definitely try to remember it/that it is…), obsolescent, delible, overweening, parlay (this word probably does not mean what you think it means…), perspicacity, perspicuity, temblor, precipitous, quinquennial, razzmatazz, turpitude, vicissitude, vitriform.

iv. Some quotes from this excellent book review, by Razib Khan:

“relatively old-fashioned anti-religious sentiments […] are socially acceptable among American Left-liberals so long as their targets are white Christians (“punching up”) but more “problematic” and perhaps even “Islamophobic” when the invective is hurled at Muslim “people of color” (all Muslims here being tacitly racialized as nonwhite). […] Muslims, as marginalized people, are now considered part of a broader coalition on the progressive Left. […] most Left-liberals who might fall back on the term Islamophobia, don’t actually take Islam, or religion generally, seriously. This explains the rapid and strident recourse toward a racial analogy for Islamic identity, as that is a framework that modern Left-liberals and progressives have internalized and mastered. The problem with this is that Islam is not a racial or ethnic identity, it is a set of beliefs and practices. Being a Muslim is not about being who you are in a passive sense, but it is a proactive expression of a set of ideas about the world and your behavior within the world. This category error renders much of Left-liberal and progressive analysis of Islam superficial, and likely wrong.”

“To get a genuine understanding of a topic as broad and boundless as Islam one needs to both set aside emotional considerations, as Ben Affleck can not, and dig deeply into the richer and more complex empirical texture, which Sam Harris has not.”

“One of the most obnoxious memes in my opinion during the Obama era has been the popularization of the maxim that “The arc of the moral universe is long, but it bends towards justice.” It is smug and self-assured in its presentation. […] too often it becomes an excuse for lazy thinking and shallow prognostication. […] Modern Western liberals have a particular idea of what a religion is, and so naturally know that Islam is in many ways just like United Methodism, except with a hijab and iconoclasm. But a Western liberalism that does not take cultural and religious difference seriously is not serious, and yet all too often it is what we have on offer. […] On both the American Left and Right there is a tendency to not even attempt to understand Islam. Rather, stylized models are preferred which lead to conclusions which are already arrived at.”

“It’s fine to be embarrassed by reality. But you still need to face up to reality. Where Hamid, Harris, and I all start is the fact that the vast majority of the world’s Muslims do not hold views on social issues that are aligned with the Muslim friends of Hollywood actors. […] Before the Green Revolution I told people to expect there to be a Islamic revival, as 86 percent of Egyptians polled agree with the killing of apostates. This is not a comfortable fact for me, as I am technically an apostate.* But it is a fact. Progressives who exhibit a hopefulness about human nature, and confuse majoritarian democracy with liberalism and individual rights, often don’t want to confront these facts. […] Their polar opposites are convinced anti-Muslims who don’t need any survey data, because they know that Muslims have particular views a priori by virtue of them being Muslims. […] There is a glass half-full/half-empty aspect to the Turkish data. 95 percent of Turks do not believe apostates should be killed. This is not surprising, I know many Turkish atheists personally. But, 5 percent is not a reassuring fraction as someone who is personally an apostate. The ideal, and frankly only acceptable, proportion is basically 0 percent.”

“Harris would give a simple explanation for why Islam sanctions the death penalty for apostates. To be reductive and hyperbolic, his perspective seems to be that Islam is a totalitarian cult, and its views are quite explicit in the Quran and the Hadith. Harris is correct here, and the views of the majority of Muslims in Egypt (and many other Muslim nations) has support in Islamic law. The consensus historical tradition is that apostates are subject to the death penalty. […] the very idea of accepting atheists is taboo in most Arab countries”.

“Christianity which Christians hold to be fundamental and constitutive of their religion would have seemed exotic and alien even to St. Paul. Similarly, there is a much smaller body of work which makes the same case for Islam.

A précis of this line of thinking is that non-Muslim sources do not make it clear that there was in fact a coherent new religion which burst forth out of south-central Arabia in the 7th century. Rather, many aspects of Islam’s 7th century were myths which developed over time, initially during the Umayyad period, but which eventually crystallized and matured into orthodoxy under the Abbasids, over a century after the death of Muhammad. This model holds that the Arab conquests were actually Arab conquests, not Muslim ones, and that a predominantly nominally Syrian Christian group of Arab tribes eventually developed a new religion to justify their status within the empire which they built, and to maintain their roles within it. The mawali (convert) revolution under the Abbasids in the latter half of the 8th century transformed a fundamentally Arab ethnic sect, into a universal religion. […] The debate about the historical Jesus only emerged when the public space was secularized enough so that such discussions would not elicit violent hostility from the populace or sanction form the authorities. [T]he fact is that the debate about the historical Muhammad is positively dangerous and thankless. That is not necessarily because there is that much more known about Muhammad than Jesus, it is because post-Christian society allows for an interrogation of Christian beliefs which Islamic society does not allow for in relation to Islam’s founding narratives.”

“When it comes to understanding religion you need to start with psychology. In particular, cognitive psychology. This feeds into the field of evolutionary anthropology in relation to the study of religion. Probably the best introduction to this field is Scott Atran’s dense In Gods We Trust: The Evolutionary Landscape of Religion. Another representative work is Theological Incorrectness: Why Religious People Believe What They Shouldn’t. This area of scholarship purports to explain why religion is ubiquitous, and, why as a phenomenon it tends to exhibit a particular distribution of characteristics.

What cognitive psychology suggests is that there is a strong disjunction between the verbal scripts that people give in terms of what they say they believe, and the internal Gestalt mental models which seem to actually be operative in terms of informing how they truly conceptualize the world. […] Muslims may aver that their god is omniscient and omnipresent, but their narrative stories in response to life circumstances seem to imply that their believe god may not see or know all things at all moments.

The deep problem here is understood [by] religious professionals: they’ve made their religion too complex for common people to understand without their intermediation. In fact, I would argue that theologians themselves don’t really understand what they’re talking about. To some extent this is a feature, not a bug. If the God of Abraham is transformed into an almost incomprehensible being, then religious professionals will have perpetual work as interpreters. […] even today most Muslims can not read the Quran. Most Muslims do not speak Arabic. […] The point isn’t to understand, the point is that they are the Word of God, in the abstract. […] The power of the Quran is that the Word of God is presumably potent. Comprehension is secondary to the command.”

“the majority of the book […] is focused on political and social facts in the Islamic world today. […] That is the best thing about Islamic Exceptionalism, it will put more facts in front of people who are fact-starved, and theory rich. That’s good.”

“the term ‘fundamentalist’ in the context of islam isn’t very informative.” (from the comments).

Below I have added some (very) superficially related links of my own, most of them ‘data-related’ (in general I’d say that I usually find ‘raw data’ more interesting than ‘big ideas’):

*My short review of Theological Correctness, one of the books Razib mentions.

*Of almost 163,000 people who applied for asylum in Sweden last year, less than 500 landed a job (news article).

*An analysis of Danish data conducted by the Rockwool Foundation found that for family-reunificated spouses/relatives etc. to fugitives, 22 % were employed after having lived in Denmark for five years (the family-reunificated individuals, that is, not the fugitives themselves). Only one in three of the family-reunificated individuals had managed to find a job after having stayed here for fifteen years. The employment rate of family-reunificated to immigrants is 49 % for people who have been in the country for 5 years, and the number is below 60 % after 15 years. In Denmark, the employment rate of immigrants from non-Western countries was 47,7 % in November 2013, compared to 73,8 % for people of (…’supposedly’, see also my comments and observations here) Danish origin, according to numbers from Statistics Denmark (link). When you look at the economic performance of the people with fugitive status themselves, 34 % are employed after 5 years, but that number is almost unchanged a decade later – only 37 % are employed after they’ve stayed in Denmark for 15 years.
Things of course sometimes look even worse at the local level than these numbers reflect, because those averages are, well, averages; for example of the 244 fugitives and family-reunificated who had arrived in the Danish Elsinore Municipality within the last three years, exactly 5 of them were in full-time employment.

*Rotherham child sexual exploitation scandal (“The report estimated that 1,400 children had been sexually abused in the town between 1997 and 2013, predominantly by gangs of British-Pakistani Muslim men […] Because most of the perpetrators were of Pakistani heritage, several council staff described themselves as being nervous about identifying the ethnic origins of perpetrators for fear of being thought racist […] It was reported in June 2015 that about 300 suspects had been identified.”)

*A memorial service for the terrorist and murderer Omar El-Hussein who went on a shooting rampage in Copenhagen last year (link) gathered 1500 people, and 600-700 people also participated at the funeral (Danish link).

*Pew asked muslims in various large countries whether they thought ‘Suicide Bombing of Civilian Targets to Defend Islam [can] be Justified?’ More than a third of French muslims think that it can, either ‘often/sometimes’ (16 %) or ‘rarely’ (19 %). Roughly a fourth of British muslims think so as well (15 % often/sometimes, 9 % rarely). Of course in countries like Jordan, Nigeria, and Egypt the proportion of people who do not reply ‘never’ is above 50 %. In such contexts people often like to focus on what the majorities think, but I found it interesting to note that in only 2 of 11 countries (Germany – 7 %, & the US – 8 %) queried was it less than 10 % of muslims who thought suicide bombings were not either ‘often’ or ‘sometimes’ justified. Those numbers are some years old. Newer numbers (from non-Western countries only, unfortunately) tell us that e.g. fewer than two out of five Egyptians (38%) and fewer than three out of five (58%) Turks would answer ‘never’ when asked this question just a couple of years ago, in 2014.

*A few non-data related observations here towards the end. I do think Razib is right that cognitive psychology is a good starting point if you want to ‘understand religion’, but a more general point I would make is that there are many different analytical approaches to these sorts of topics which one might employ, and I think it’s important that one does not privilege any single analytical framework over the others (just to be clear, I’m not saying that Razib’s doing this); different approaches may yield different insights, perhaps at different analytical levels, and combining different approaches is likely to be very useful in order to get ‘the bigger picture’, or at least to not overlook important details. ‘History’, broadly defined, may provide one part of the explanatory model, cognitive psychology another part, mathematical anthropology (e.g. stuff like this) probably also has a role to play, etc., etc.. Survey data, economic figures, scientific literatures on a wide variety of topics like trust, norms, migration analysis, and conflict studies, e.g. those dealing with civil wars, may all help elucidate important questions of interest, if not by adding relevant data then by providing additional methodological approaches/scaffoldings which might be fruitfully employed to make sense of the data that is available.

v. Statistical Portrait of Hispanics in the United States.

vi. The Level and Nature of Autistic Intelligence. Autistics may be smarter than people have been led to believe:

“Autistics are presumed to be characterized by cognitive impairment, and their cognitive strengths (e.g., in Block Design performance) are frequently interpreted as low-level by-products of high-level deficits, not as direct manifestations of intelligence. Recent attempts to identify the neuroanatomical and neurofunctional signature of autism have been positioned on this universal, but untested, assumption. We therefore assessed a broad sample of 38 autistic children on the preeminent test of fluid intelligence, Raven’s Progressive Matrices. Their scores were, on average, 30 percentile points, and in some cases more than 70 percentile points, higher than their scores on the Wechsler scales of intelligence. Typically developing control children showed no such discrepancy, and a similar contrast was observed when a sample of autistic adults was compared with a sample of nonautistic adults. We conclude that intelligence has been underestimated in autistics.”

I recall that back when I was diagnosed I was subjected to a battery of different cognitive tests of various kinds, and a few of those tests I recall thinking were very difficult, compared to how difficult they somehow ‘ought to be’ – it was like ‘this should be an easy task for someone who has the mental hardware to solve this type of problem, but I don’t seem to have that piece of hardware; I have no idea how to manipulate these objects in my head so that I might answer that question’. This was an at least somewhat unfamiliar feeling to me in a testing context, and I definitely did not have this experience when doing the Mensa admissions test later on, which was based on Raven’s matrices. Despite the fact that all IQ tests are supposed to measure pretty much the same thing I do not find it hard to believe that there are some details here which may complicate matters a bit in specific contexts, e.g. for people whose brains may not be structured quite the same way ‘ordinary brains’ are (to put it very bluntly). But of course this is just one study and a few personal impressions – more research is needed, etc. (Even though the effect size is huge.)

Slightly related to the above is also this link – I must admit that I find the title question quite interesting. I find it very difficult to picture characters featuring in books I’m reading in my mind, and so usually when I read books I don’t form any sort of coherent mental image of what the character looks like. It doesn’t matter to me, I don’t care. I have no idea if this is how other people read (fiction) books, or if they actually imagine what the characters look like more or less continuously while those characters are described doing the things they might be doing; to me it would be just incredibly taxing to keep even a simplified mental model of the physical attributes of a character in my mind for even a minute. I can recall specific traits like left-handedness and similar without much difficulty if I think the trait might have relevance to the plot, which has helped me while reading e.g. Agatha Christie novels before, but actively imagining what people look like in my mind I just find very difficult. I find it weird to think that some people might do something like that almost automatically, without thinking about it.

vii. Computer Science Resources. I recently shared the link with a friend, but of course she was already aware of the existence of this resource. Some people reading along here may not be, so I’ll include the link here. It has a lot of stuff.

June 8, 2016 Posted by | autism, Books, Chess, Computer science, Data, Demographics, Psychology, Random stuff, Religion | Leave a comment

Random stuff

I find it difficult to find the motivation to finish the half-finished drafts I have lying around, so this will have to do. Some random stuff below.


(15.000 views… In some sense that seems really ‘unfair’ to me, but on the other hand I doubt neither Beethoven nor Gilels care; they’re both long dead, after all…)

ii. New/newish words I’ve encountered in books, on or elsewhere:

Agleyperipeteia, disseverhalidom, replevinsocage, organdie, pouffe, dyarchy, tauricide, temerarious, acharnement, cadger, gravamen, aspersion, marronage, adumbrate, succotash, deuteragonist, declivity, marquetry, machicolation, recusal.

iii. A lecture:

It’s been a long time since I watched it so I don’t have anything intelligent to say about it now, but I figured it might be of interest to one or two of the people who still subscribe to the blog despite the infrequent updates.

iv. A few wikipedia articles (I won’t comment much on the contents or quote extensively from the articles the way I’ve done in previous wikipedia posts – the links shall have to suffice for now):

Duverger’s law.

Far side of the moon.

Preference falsification.

Russian political jokes. Some of those made me laugh (e.g. this one: “A judge walks out of his chambers laughing his head off. A colleague approaches him and asks why he is laughing. “I just heard the funniest joke in the world!” “Well, go ahead, tell me!” says the other judge. “I can’t – I just gave someone ten years for it!”).

Political mutilation in Byzantine culture.

v. World War 2, if you think of it as a movie, has a highly unrealistic and implausible plot, according to this amusing post by Scott Alexander. Having recently read a rather long book about these topics, one aspect I’d have added had I written the piece myself would be that an additional factor making the setting seem even more implausible is how so many presumably quite smart people were so – what at least in retrospect seems – unbelievably stupid when it came to Hitler’s ideas and intentions before the war. Going back to Churchill’s own life I’d also add that if you were to make a movie about Churchill’s life during the war, which you could probably relatively easily do if you were to just base it upon his own copious and widely shared notes, then it could probably be made into a quite decent movie. His own comments, remarks, and observations certainly made for a great book.

May 15, 2016 Posted by | Astronomy, Computer science, History, Language, Lectures, Mathematics, Music, Random stuff, Russia, Wikipedia | Leave a comment

A few lectures

The sound quality of this lecture is not completely optimal – there’s a recurring echo popping up now and then which I found slightly annoying – but this should not keep you from watching the lecture. It’s a quite good lecture, and very accessible – I don’t really think you even need to know anything about genetics to follow most of what he’s talking about here; as far as I can tell it’s a lecture intended for people who don’t really know much about population genetics. He introduces key concepts as they are needed and he does not go much into the technical details which might cause people trouble (this of course also makes the lecture somewhat superficial, but you can’t get everything). If you’re the sort of person who wants details not included in the lecture you’re probably already reading e.g. Razib Khan (who incidentally recently blogged/criticized a not too dissimilar paper from the one discussed in the lecture, dealing with South Asia)…

I must admit that I actually didn’t like this lecture very much, but I figured I might as well include it in this post anyway.

I found some questions included and some aspects of the coverage a bit ‘too basic’ for my taste, but other people interested in chess reading along here may like Anna’s approach better; like Krause’s lecture I think it’s an accessible lecture, despite the fact that it actually covers many lines in quite a bit of detail. It’s a long lecture but I don’t think you necessarily need to watch all of it in one go (…or at all?) – the analysis of the second game, the Kortschnoj-Gheorghiu game, starts around 45 minutes in so that might for example be a good place to include a break, if a break is required.

February 1, 2016 Posted by | Anthropology, Archaeology, Chess, Computer science, Evolutionary biology, Genetics, History, Lectures | Leave a comment

A few lectures

Below are three new lectures from the Institute of Advanced Study. As far as I’ve gathered they’re all from an IAS symposium called ‘Lens of Computation on the Sciences’ – all three lecturers are computer scientists, but you don’t have to be a computer scientist to watch these lectures.

Should computer scientists and economists band together more and try to use the insights from one field to help solve problems in the other field? Roughgarden thinks so, and provides examples of how this might be done/has been done. Applications discussed in the lecture include traffic management and auction design. I’m not sure how much of this lecture is easy to follow for people who don’t know anything about either topic (i.e., computer science and economics), but I found it not too difficult to follow – it probably helped that I’ve actually done work on a few of the things he touches upon in the lecture, such as basic auction theory, the fixed point theorems and related proofs, basic queueing theory and basic discrete maths/graph theory. Either way there are certainly much more technical lectures than this one available at the IAS channel.

I don’t have Facebook and I’m not planning on ever getting a FB account, so I’m not really sure I care about the things this guy is trying to do, but the lecturer does touch upon some interesting topics in network theory. Not a great lecture in my opinion and occasionally I think the lecturer ‘drifts’ a bit, talking without saying very much, but it’s also not a terrible lecture. A few times I was really annoyed that you can’t see where he’s pointing that damn laser pointer, but this issue should not stop you from watching the video, especially not if you have an interest in analytical aspects of how to approach and make sense of ‘Big Data’.

I’ve noticed that Scott Alexander has said some nice things about Scott Aaronson a few times, but until now I’ve never actually read any of the latter guy’s stuff or watched any lectures by him. I agree with Scott (Alexander) that Scott (Aaronson) is definitely a smart guy. This is an interesting lecture; I won’t pretend I understood all of it, but it has some thought-provoking ideas and important points in the context of quantum computing and it’s actually a quite entertaining lecture; I was close to laughing a couple of times.

January 8, 2016 Posted by | Computer science, Economics, Game theory, Lectures, Mathematics, Physics | Leave a comment

Random stuff/Open Thread

i. A lecture on mathematical proofs:

ii. “In the fall of 1944, only seven percent of all bombs dropped by the Eighth Air Force hit within 1,000 feet of their aim point.”

From wikipedia’s article on Strategic bombing during WW2. The article has a lot of stuff. The ‘RAF estimates of destruction of “built up areas” of major German cities’ numbers in the article made my head spin – they didn’t bomb the Germans back to the stone age, but they sure tried. Here’s another observation from the article:

“After the war, the U.S. Strategic Bombing Survey reviewed the available casualty records in Germany, and concluded that official German statistics of casualties from air attack had been too low. The survey estimated that at a minimum 305,000 were killed in German cities due to bombing and estimated a minimum of 780,000 wounded. Roughly 7,500,000 German civilians were also rendered homeless.” (The German population at the time was roughly 70 million).

iii. Also war-related: Eddie Slovik:

Edward Donald “Eddie” Slovik (February 18, 1920 – January 31, 1945) was a United States Army soldier during World War II and the only American soldier to be court-martialled and executed for desertion since the American Civil War.[1][2]

Although over 21,000 American soldiers were given varying sentences for desertion during World War II, including 49 death sentences, Slovik’s was the only death sentence that was actually carried out.[1][3][4]

During World War II, 1.7 million courts-martial were held, representing one third of all criminal cases tried in the United States during the same period. Most of the cases were minor, as were the sentences.[2] Nevertheless, a clemency board, appointed by the Secretary of War in the summer of 1945, reviewed all general courts-martial where the accused was still in confinement.[2][5] That Board remitted or reduced the sentence in 85 percent of the 27,000 serious cases reviewed.[2] The death penalty was rarely imposed, and those cases typically were for rapes or murders. […] In France during World War I from 1917 to 1918, the United States Army executed 35 of its own soldiers, but all were convicted of rape and/or unprovoked murder of civilians and not for military offenses.[13] During World War II in all theaters of the war, the United States military executed 102 of its own soldiers for rape and/or unprovoked murder of civilians, but only Slovik was executed for the military offense of desertion.[2][14] […] of the 2,864 army personnel tried for desertion for the period January 1942 through June 1948, 49 were convicted and sentenced to death, and 48 of those sentences were voided by higher authority.”

What motivated me to read the article was mostly curiosity about how many people were actually executed for deserting during the war, a question I’d never encountered any answers to previously. The US number turned out to be, well, let’s just say it’s lower than I’d expected it would be. American soldiers who chose to desert during the war seem to have had much, much better chances of surviving the war than had soldiers who did not. Slovik was not a lucky man. On a related note, given numbers like these I’m really surprised desertion rates were not much higher than they were; presumably community norms (”desertion = disgrace’, which would probably rub off on other family members…’) played a key role here.

iv. Chess and infinity. I haven’t posted this link before even though the thread is a few months old, and I figured that given that I just had a conversation on related matters in the comment section of SCC (here’s a link) I might as well repost some of this stuff here. Some key points from the thread (I had to make slight formatting changes to the quotes because wordpress had trouble displaying some of the numbers, but the content is unchanged):

“Shannon has estimated the number of possible legal positions to be about 1043. The number of legal games is quite a bit higher, estimated by Littlewood and Hardy to be around 1010^5 (commonly cited as 1010^50 perhaps due to a misprint). This number is so large that it can’t really be compared with anything that is not combinatorial in nature. It is far larger than the number of subatomic particles in the observable universe, let alone stars in the Milky Way galaxy.

As for your bonus question, a typical chess game today lasts about 40­ to 60 moves (let’s say 50). Let us say that there are 4 reasonable candidate moves in any given position. I suspect this is probably an underestimate if anything, but let’s roll with it. That gives us about 42×50 ≈ 1060 games that might reasonably be played by good human players. If there are 6 candidate moves, we get around 1077, which is in the neighbourhood of the number of particles in the observable universe.”

“To put 1010^5 into perspective:

There are 1080 protons in the Universe. Now imagine inside each proton, we had a whole entire Universe. Now imagine again that inside each proton inside each Universe inside each proton, you had another Universe. If you count up all the protons, you get (1080 )3 = 10240, which is nowhere near the number we’re looking for.

You have to have Universes inside protons all the way down to 1250 steps to get the number of legal chess games that are estimated to exist. […]

Imagine that every single subatomic particle in the entire observable universe was a supercomputer that analysed a possible game in a single Planck unit of time (10-43 seconds, the time it takes light in a vacuum to travel 10-20 times the width of a proton), and that every single subatomic particle computer was running from the beginning of time up until the heat death of the Universe, 101000 years ≈ 1011 × 101000 seconds from now.

Even in these ridiculously favorable conditions, we’d only be able to calculate

1080 × 1043 × 1011 × 101000 = 101134

possible games. Again, this doesn’t even come close to 1010^5 = 10100000 .

Basically, if we ever solve the game of chess, it definitely won’t be through brute force.”

v. An interesting resource which a friend of mine recently shared with me and which I thought I should share here as well: Nature Reviews – Disease Primers.

vi. Here are some words I’ve recently encountered on augury, spangle, imprimatur, apperception, contrition, ensconce, impuissance, acquisitive, emendation, tintinnabulation, abalone, dissemble, pellucid, traduce, objurgation, lummox, exegesis, probity, recondite, impugn, viscid, truculence, appurtenance, declivity, adumbrate, euphony, educe, titivate, cerulean, ardour, vulpine.

May 16, 2015 Posted by | Chess, Computer science, History, Language, Lectures, Mathematics | Leave a comment

Belief-Based Stability in Coalition Formation with Uncertainty…

“In this book we present several novel concepts in cooperative game theory, but from a computer scientist’s point of view. Especially, we will look at a type of games called non-transferable utility games. […] In this book, we extend the classic stability concept of the non-transferable utility core by proposing new belief-based stability criteria under uncertainty, and illustrate how the new concept can be used to analyse the stability of a new type of belief-based coalition formation game. Mechanisms for reaching solutions of the new stable criteria are proposed and some real life application examples are studied. […] In Chapter 1, we first provide an introduction of topics in game theory that are relevant to the concepts discussed in this book. In Chapter 2, we review some relevant works from the literature, especially in cooperative game theory and multi-agent coalition formation problems. In Chapter 3, we discuss the effect of uncertainty in the agent’s beliefs on the stability of the games. A rule-based approach is adopted and the concepts of strong core and weak core are introduced. We also discuss the effect of precision of the beliefs on the stability of the coalitions. In Chapter 4, we introduce private beliefs in non-transferable utility (NTU) games, so that the preferences of the agents are no longer common knowledge. The impact of belief accuracy on stability is also examined. In Chapter 5, we study an application of the proposed belief-based stability concept, namely the buyer coalition problem, and we see how the proposed concept can be used in the evaluation of this multi-agent coalition formation problem. In Chapter 6, we combine the works of earlier chapters and produce a complete picture of the introduced concepts: non-transferable utility games with private beliefs and uncertainty. We conclude this book in Chapter 7.”

The above quote is from the preface of the book, which I finished yesterday. It deals with some issues I was slightly annoyed about not being covered in a previous micro course; my main problem being that it seemed to me back then that the question of belief accuracy and the role of this variable was not properly addressed in the models we looked at (‘people can have mistaken beliefs, and it seems obvious that the ways in which they’re wrong can affect which solutions are eventually reached’). The book makes the point that if you look at coalition formation in a context where it is not reasonable to assume that information is shared among coalition partners (because it is in the interest of the participants to keep their information/preferences/willingness to pay private), then the beliefs of the potential coalition partners may play a major role in determining which coalitions are feasible and which are ruled out. A key point is that in the model context explored by the authors, inaccurate beliefs of agents will expand the number of potential coalitions which are available, although coalition options ruled out by accurate beliefs are less stable than ones which are not. They do not discuss the fact that this feature is unquestionably a result of implicit assumptions made along the way which may not be true, and that inaccurate beliefs may also in some contexts conceivably lead to lower solution support in general (e.g. through variables such as disagreement, or, to think more in terms of concepts specifically included in their model framework, higher general instability of solutions which can feasibly be reached, making agents less likely to explore the option of participating in coalitions in the first place due to the lower payoffs associated with the available coalitions likely to be reached – dynamics such as these are not included in the coverage). I decided early on to not blog the stuff in this book in major detail because it’s not the kind of book where this makes sense to do (in my opinion), but if you’re curious about how they proceed, they talk quite a bit about the (classical) Core and discuss why this is not an appropriate solution concept to apply in the contexts they explore, and they then proceed to come up with new and better solution criteria, developed with the aid of some new variables and definitions along the way, in order to end up with some better solution concepts, their so-called ‘belief-based cores’, which are perhaps best thought of as extensions of the classical core concept. I should perhaps point out, as this may not be completely clear, that the beliefs they talk about deal both with the ‘state of nature’ (which in part of the coverage is assumed to be basically unobservable) and the preferences of agents involved.

If you want a sort of bigger picture idea of what this book is about, I should point out that in general you have two major sub-fields of game theory, dealing with cooperative and non-cooperative games respectively. Within the sub-field of cooperative games, a distinction is made between games and settings where utilities are transferable, and games/settings where they are not. This book belongs in the latter category; it deals with cooperative games in which utilities are non-transferable. The authors in the beginning make a big deal out of the distinction between whether or not utilities are transferable, and claim that the assumption that they’re not is the more plausible one; whereas they do have a point, I however also actually think the non-transferability assumption might in some of the specific examples included in the book be a borderline questionable assumption. To give an example, the non-transferability assumption seems in one context to imply that all potential coalition partners have the same amount of bargaining power. This assumption is plausible in some contexts, but wildly implausible in others (and I’m not sure the authors would agree with me about which contexts would belong to which category).

The professor teaching the most recent course in micro I took had a background in computer science, rather than economics – he was also Asian, but this perhaps goes without saying. This book is supposedly a computer science book, and they argue in the introduction that: “instead of looking at human beings, we study the problem from an intelligent software agent’s perspective.” However I don’t think a single one of the examples included in the book would be an example you could not also have found in a classic micro text, and it’s really hard to tell in many parts of the coverage that the authors aren’t economists with a background in micro – there seems to be quite a bit of field overlap here (this field overlap incidentally extends to areas of economics besides micro, is my impression; one econometrics TA I had, teaching the programming part of the course, was also a CS major). In the book they talk a bit about coalition formation mechanisms and approaches, such as propose-and-evaluate mechanisms and auction approaches, and they also touch briefly upon stuff like mechanism design. They state in the description that: “The book is intended for graduate students, engineers, and researchers in the field of artificial intelligence and computer science.” I think it’s really weird that they don’t include (micro-)economists as well, because this stuff is obviously quite close to/potentially relevant to the kind of work some of these people are working on.

There are a lot of definitions, theorems, and proofs in this book, and as usual when doing work on game theory you need to think very carefully about the stuff they cover to be able to follow it, but I actually found it reasonably accessible – the book is not terribly difficult to read. Though I would probably advise you against reading the book if you have not at least read an intro text on game theory. Although as already mentioned the book deals with an analytical context in which utilities are non-transferable, it should be pointed out that this assumption is sort of implicit in the coverage, in the sense that the authors don’t really deal with utility functions at all; the book only deals with preference relations, not utility functions, so it probably helps to be familiar with this type of analysis (e.g. by having studied (solved some problems) dealing with the kind of stuff included in the coverage in chapter 1 of Mas-Colell).

Part of the reason why I gave the book only two stars is that the authors are Chinese and their English is terrible. Another reason is that as is usually the case in game theory, these guys spend a lot of time and effort being very careful to define their terms and make correct inferences from the assumptions they make – but they don’t really end up saying very much.

February 28, 2015 Posted by | Books, Computer science, Economics | Leave a comment

Wikipedia articles of interest

i. Trade and use of saffron.

Saffron has been a key seasoning, fragrance, dye, and medicine for over three millennia.[1] One of the world’s most expensive spices by weight,[2] saffron consists of stigmas plucked from the vegetatively propagated and sterile Crocus sativus, known popularly as the saffron crocus. The resulting dried “threads”[N 1] are distinguished by their bitter taste, hay-like fragrance, and slight metallic notes. The saffron crocus is unknown in the wild; its most likely precursor, Crocus cartwrightianus, originated in Crete or Central Asia;[3] The saffron crocus is native to Southwest Asia and was first cultivated in what is now Greece.[4][5][6]

From antiquity to modern times the history of saffron is full of applications in food, drink, and traditional herbal medicine: from Africa and Asia to Europe and the Americas the brilliant red threads were—and are—prized in baking, curries, and liquor. It coloured textiles and other items and often helped confer the social standing of political elites and religious adepts. Ancient peoples believed saffron could be used to treat stomach upsets, bubonic plague, and smallpox.

Saffron crocus cultivation has long centred on a broad belt of Eurasia bounded by the Mediterranean Sea in the southwest to India and China in the northeast. The major producers of antiquity—Iran, Spain, India, and Greece—continue to dominate the world trade. […] Iran has accounted for around 90–93 percent of recent annual world production and thereby dominates the export market on a by-quantity basis. […]

The high cost of saffron is due to the difficulty of manually extracting large numbers of minute stigmas, which are the only part of the crocus with the desired aroma and flavour. An exorbitant number of flowers need to be processed in order to yield marketable amounts of saffron. Obtaining 1 lb (0.45 kg) of dry saffron requires the harvesting of some 50,000 flowers, the equivalent of an association football pitch’s area of cultivation, or roughly 7,140 m2 (0.714 ha).[14] By another estimate some 75,000 flowers are needed to produce one pound of dry saffron. […] Another complication arises in the flowers’ simultaneous and transient blooming. […] Bulk quantities of lower-grade saffron can reach upwards of US$500 per pound; retail costs for small amounts may exceed ten times that rate. In Western countries the average retail price is approximately US$1,000 per pound.[5] Prices vary widely elsewhere, but on average tend to be lower. The high price is somewhat offset by the small quantities needed in kitchens: a few grams at most in medicinal use and a few strands, at most, in culinary applications; there are between 70,000 and 200,000 strands in a pound.”

ii. Scramble for Africa.

“The “Scramble for Africa” (also the Partition of Africa and the Conquest of Africa) was the invasion and occupation, colonization and annexation of African territory by European powers during the period of New Imperialism, between 1881 and 1914. In 1870, 10 percent of Africa was under European control; by 1914 it was 90 percent of the continent, with only Abyssinia (Ethiopia) and Liberia still independent.”

Here’s a really neat illustration from the article:


“Germany became the third largest colonial power in Africa. Nearly all of its overall empire of 2.6 million square kilometres and 14 million colonial subjects in 1914 was found in its African possessions of Southwest Africa, Togoland, the Cameroons, and Tanganyika. Following the 1904 Entente cordiale between France and the British Empire, Germany tried to isolate France in 1905 with the First Moroccan Crisis. This led to the 1905 Algeciras Conference, in which France’s influence on Morocco was compensated by the exchange of other territories, and then to the Agadir Crisis in 1911. Along with the 1898 Fashoda Incident between France and Britain, this succession of international crises reveals the bitterness of the struggle between the various imperialist nations, which ultimately led to World War I. […]

David Livingstone‘s explorations, carried on by Henry Morton Stanley, excited imaginations. But at first, Stanley’s grandiose ideas for colonisation found little support owing to the problems and scale of action required, except from Léopold II of Belgium, who in 1876 had organised the International African Association (the Congo Society). From 1869 to 1874, Stanley was secretly sent by Léopold II to the Congo region, where he made treaties with several African chiefs along the Congo River and by 1882 had sufficient territory to form the basis of the Congo Free State. Léopold II personally owned the colony from 1885 and used it as a source of ivory and rubber.

While Stanley was exploring Congo on behalf of Léopold II of Belgium, the Franco-Italian marine officer Pierre de Brazza travelled into the western Congo basin and raised the French flag over the newly founded Brazzaville in 1881, thus occupying today’s Republic of the Congo. Portugal, which also claimed the area due to old treaties with the native Kongo Empire, made a treaty with Britain on 26 February 1884 to block off the Congo Society’s access to the Atlantic.

By 1890 the Congo Free State had consolidated its control of its territory between Leopoldville and Stanleyville, and was looking to push south down the Lualaba River from Stanleyville. At the same time, the British South Africa Company of Cecil Rhodes was expanding north from the Limpopo River, sending the Pioneer Column (guided by Frederick Selous) through Matabeleland, and starting a colony in Mashonaland.

To the West, in the land where their expansions would meet, was Katanga, site of the Yeke Kingdom of Msiri. Msiri was the most militarily powerful ruler in the area, and traded large quantities of copper, ivory and slaves — and rumours of gold reached European ears. The scramble for Katanga was a prime example of the period. Rhodes and the BSAC sent two expeditions to Msiri in 1890 led by Alfred Sharpe, who was rebuffed, and Joseph Thomson, who failed to reach Katanga. Leopold sent four CFS expeditions. First, the Le Marinel Expedition could only extract a vaguely worded letter. The Delcommune Expedition was rebuffed. The well-armed Stairs Expedition was given orders to take Katanga with or without Msiri’s consent. Msiri refused, was shot, and the expedition cut off his head and stuck it on a pole as a “barbaric lesson” to the people. The Bia Expedition finished the job of establishing an administration of sorts and a “police presence” in Katanga.

Thus, the half million square kilometres of Katanga came into Leopold’s possession and brought his African realm up to 2,300,000 square kilometres (890,000 sq mi), about 75 times larger than Belgium. The Congo Free State imposed such a terror regime on the colonised people, including mass killings and forced labour, that Belgium, under pressure from the Congo Reform Association, ended Leopold II’s rule and annexed it in 1908 as a colony of Belgium, known as the Belgian Congo. […]

“Britain’s administration of Egypt and the Cape Colony contributed to a preoccupation over securing the source of the Nile River. Egypt was overrun by British forces in 1882 (although not formally declared a protectorate until 1914, and never an actual colony); Sudan, Nigeria, Kenya and Uganda were subjugated in the 1890s and early 20th century; and in the south, the Cape Colony (first acquired in 1795) provided a base for the subjugation of neighbouring African states and the Dutch Afrikaner settlers who had left the Cape to avoid the British and then founded their own republics. In 1877, Theophilus Shepstone annexed the South African Republic (or Transvaal – independent from 1857 to 1877) for the British Empire. In 1879, after the Anglo-Zulu War, Britain consolidated its control of most of the territories of South Africa. The Boers protested, and in December 1880 they revolted, leading to the First Boer War (1880–81). British Prime Minister William Gladstone signed a peace treaty on 23 March 1881, giving self-government to the Boers in the Transvaal. […] The Second Boer War, fought between 1899 and 1902, was about control of the gold and diamond industries; the independent Boer republics of the Orange Free State and the South African Republic (or Transvaal) were this time defeated and absorbed into the British Empire.”

There are a lot of unsourced claims in the article and some parts of it actually aren’t very good, but this is a topic about which I did not know much (I had no idea most of colonial Africa was acquired by the European powers as late as was actually the case). This is another good map from the article to have a look at if you just want the big picture.

iii. Cursed soldiers.

“The cursed soldiers (that is, “accursed soldiers” or “damned soldiers”; Polish: Żołnierze wyklęci) is a name applied to a variety of Polish resistance movements formed in the later stages of World War II and afterwards. Created by some members of the Polish Secret State, these clandestine organizations continued their armed struggle against the Stalinist government of Poland well into the 1950s. The guerrilla warfare included an array of military attacks launched against the new communist prisons as well as MBP state security offices, detention facilities for political prisoners, and concentration camps set up across the country. Most of the Polish anti-communist groups ceased to exist in the late 1940s or 1950s, hunted down by MBP security services and NKVD assassination squads.[1] However, the last known ‘cursed soldier’, Józef Franczak, was killed in an ambush as late as 1963, almost 20 years after the Soviet take-over of Poland.[2][3] […] Similar eastern European anti-communists fought on in other countries. […]

Armia Krajowa (or simply AK)-the main Polish resistance movement in World War II-had officially disbanded on 19 January 1945 to prevent a slide into armed conflict with the Red Army, including an increasing threat of civil war over Poland’s sovereignty. However, many units decided to continue on with their struggle under new circumstances, seeing the Soviet forces as new occupiers. Meanwhile, Soviet partisans in Poland had already been ordered by Moscow on June 22, 1943 to engage Polish Leśni partisans in combat.[6] They commonly fought Poles more often than they did the Germans.[4] The main forces of the Red Army (Northern Group of Forces) and the NKVD had begun conducting operations against AK partisans already during and directly after the Polish Operation Tempest, designed by the Poles as a preventive action to assure Polish rather than Soviet control of the cities after the German withdrawal.[5] Soviet premier Joseph Stalin aimed to ensure that an independent Poland would never reemerge in the postwar period.[7] […]

The first Polish communist government, the Polish Committee of National Liberation, was formed in July 1944, but declined jurisdiction over AK soldiers. Consequently, for more than a year, it was Soviet agencies like the NKVD that dealt with the AK. By the end of the war, approximately 60,000 soldiers of the AK had been arrested, and 50,000 of them were deported to the Soviet Union’s gulags and prisons. Most of those soldiers had been captured by the Soviets during or in the aftermath of Operation Tempest, when many AK units tried to cooperate with the Soviets in a nationwide uprising against the Germans. Other veterans were arrested when they decided to approach the government after being promised amnesty. In 1947, an amnesty was passed for most of the partisans; the Communist authorities expected around 12,000 people to give up their arms, but the actual number of people to come out of the forests eventually reached 53,000. Many of them were arrested despite promises of freedom; after repeated broken promises during the first few years of communist control, AK soldiers stopped trusting the government.[5] […]

The persecution of the AK members was only a part of the reign of Stalinist terror in postwar Poland. In the period of 1944–56, approximately 300,000 Polish people had been arrested,[21] or up to two million, by different accounts.[5] There were 6,000 death sentences issued, the majority of them carried out.[21] Possibly, over 20,000 people died in communist prisons including those executed “in the majesty of the law” such as Witold Pilecki, a hero of Auschwitz.[5] A further six million Polish citizens (i.e., one out of every three adult Poles) were classified as suspected members of a ‘reactionary or criminal element’ and subjected to investigation by state agencies.”

iv. Affective neuroscience.

Affective neuroscience is the study of the neural mechanisms of emotion. This interdisciplinary field combines neuroscience with the psychological study of personality, emotion, and mood.[1]

This article is actually related to the Delusion and self-deception book, which covered some of the stuff included in this article, but I decided I might as well include the link in this post. I think some parts of the article are written in a somewhat different manner than most wiki articles – there are specific paragraphs briefly covering the results of specific meta-analyses conducted in this field. I can’t really tell from this article if I actually like this way of writing a wiki article or not.

v. Hamming distance. Not a long article, but this is a useful concept to be familiar with:

“In information theory, the Hamming distance between two strings of equal length is the number of positions at which the corresponding symbols are different. In another way, it measures the minimum number of substitutions required to change one string into the other, or the minimum number of errors that could have transformed one string into the other. […]

The Hamming distance is named after Richard Hamming, who introduced it in his fundamental paper on Hamming codes Error detecting and error correcting codes in 1950.[1] It is used in telecommunication to count the number of flipped bits in a fixed-length binary word as an estimate of error, and therefore is sometimes called the signal distance. Hamming weight analysis of bits is used in several disciplines including information theory, coding theory, and cryptography. However, for comparing strings of different lengths, or strings where not just substitutions but also insertions or deletions have to be expected, a more sophisticated metric like the Levenshtein distance is more appropriate.”

vi. Menstrual synchrony. I came across that one recently in a book, and when I did it was obvious that the author had not read this article, and lacked some knowledge included in this article (the phenomenon was assumed to be real in the coverage, and theory was developed assuming it was real which would not make sense if it was not). I figured if that person didn’t know this stuff, a lot of other people – including people reading along here – probably also do not, so I should cover this topic somewhere. This is an obvious place to do so. Okay, on to the article coverage:

Menstrual synchrony, also called the McClintock effect,[2] is the alleged process whereby women who begin living together in close proximity experience their menstrual cycle onsets (i.e., the onset of menstruation or menses) becoming closer together in time than previously. “For example, the distribution of onsets of seven female lifeguards was scattered at the beginning of the summer, but after 3 months spent together, the onset of all seven cycles fell within a 4-day period.”[3]

Martha McClintock’s 1971 paper, published in Nature, says that menstrual cycle synchronization happens when the menstrual cycle onsets of two women or more women become closer together in time than they were several months earlier.[3] Several mechanisms have been hypothesized to cause synchronization.[4]

After the initial studies, several papers were published reporting methodological flaws in studies reporting menstrual synchrony including McClintock’s study. In addition, other studies were published that failed to find synchrony. The proposed mechanisms have also received scientific criticism. A 2013 review of menstrual synchrony concluded that menstrual synchrony is doubtful.[4] […] in a recent systematic review of menstrual synchrony, Harris and Vitzthum concluded that “In light of the lack of empirical evidence for MS [menstrual synchrony] sensu stricto, it seems there should be more widespread doubt than acceptance of this hypothesis.” […]

The experience of synchrony may be the result of the mathematical fact that menstrual cycles of different frequencies repeatedly converge and diverge over time and not due to a process of synchronization.[12] It may also be due to the high probability of menstruation overlap that occurs by chance.[6]


December 4, 2014 Posted by | Biology, Botany, Computer science, Geography, History, Medicine, Neurology, Psychology, Wikipedia | Leave a comment