complexpapers:

Characteristics of the transmission of autoregressive sub-patterns in financial time series – Scientific Reports | Nature Publishing Group
There are many types of autoregressive patterns in financial time series, and they form a transmission process. Here, we define autoregressive patterns quantitatively through an econometrical regression model. We present a computational algorithm that sets the autoregressive patterns as nodes and transmissions between patterns as edges, and then converts the transmission process of autoregressive patterns in a time series into a network. We utilised daily Shanghai (securities) composite index time series to study the transmission characteristics of autoregressive patterns. We found statistically significant evidence that the financial market is not random and that there are similar characteristics between parts and whole time series. A few types of autoregressive sub-patterns and transmission patterns drive the oscillations of the financial market. A clustering effect on fluctuations appears in the transmission process, and certain non-major autoregressive sub-patterns have high media capabilities in the financial time series. Different stock indexes exhibit similar characteristics in the transmission of fluctuation information. This work not only proposes a distinctive perspective for analysing financial time series but also provides important information for investors.
China’s rise as a major contributor to science and technology In 2012 China spent one trillion yuan ($164 billion) on research & development, just under 2% of its gross domestic product. The same year, the U.S. spent $447 billion, or 2.8% of its GDP. But as China’s economy continues to grow rapidly, so does its R&D spending — and it’s projected to overtake that of the United States by 2022. Scientific advances contributed 51.7% to China’s economic growth in 2011, and the country is betting that technical innovations can help it address many challenges, including the need to upgrade its industrial base, reduce air pollution and address growing inequality. The Chinese government unveiled its “indigenous innovation” campaign in 2006, with a goal of turning the country into a “science powerhouse” by 2020 through an emphasis on human capital. Approximately 7 million Chinese citizens will graduate from college in 2014, up from just 1.1 million in 2001, and the country wants to lure back Chinese-origin scientists working abroad. China’s increasing number of academics have pushed China up in global rankings of published science and engineering papers: The country was 14th in 1995 and by 2007 was second only to the United States. […] [Our] study’s findings include: China’s R&D spending as a percentage of GDP increased from 0.7% in 1991 to 1.8% in 2010, still lower than the U.S. level of 2.8% but growing rapidly. The percentage of engineers in the S/E labor force was higher in China than in the United States. In 2010, China had 2.4 million engineers out of a S/E labor force of 3.2 million (75%), while the United States had 1.4 million engineers out of a S/E labor force of 4.3 million (33%). Engineers in both countries earn approximately 25% more than scientists. Relative to their professional counterparts, Chinese scientists are better paid than those in the United States: Chinese scientists earn 25% more than social scientists, 13% more than medical doctors and 5% more than lawyers. For the United States, the figures are 7% less, 50% less and 34% less, respectively. China had 1.1 million bachelor’s degrees in S/E in 2010, more than quadruple the number in the United States. (Note that China’s population that year, 1.338 billion, was 4.33 times that of the United States, 314 million.) About 44% of college students in China majored in S/E, compared with 16% in the U.S. In 1992 the number of S/E doctoral degrees awarded in China was 10% of the U.S. figure; by 2010, China’s number was 18% higher than that of the United States. The number of Chinese graduate students attended the S/E programs in the United States nearly tripled between 1987 and 2010, growing from 15,000 to 43,000. In 2007, 4,300 Chinese students received doctoral degrees in S/E from American universities, more than students from any other foreign country. From 1990 to 2011, China’s total number of S/E papers increased from 6,104 to 122,672, which was two-thirds of the 2011 U.S. figure, making China the second largest producer of scientific papers. The average citation count of a paper produced in China rose from 8.4 during 1990-1994 to 10.7 during 2000-2004. From 1990 to 2010, the proportion of the average number of citations for Chinese papers compared to those from the U.S. rose from 26% to 55%. However, the China-U.S. ratio fell slightly between 2010 and 2011. From 2001 to 2011, the ratio of Chinese papers in the top 1% of highly cited articles relative to the U.S. increased from 6% to 31%. In 2011, “the China-U.S. ratio of article production was 98% in physical sciences [for every 100 U.S. papers, there are 98 papers in the physical sciences], 77% in engineering, 62% in mathematical sciences, and 34% in biological sciences.” The ratio was “169% in material science and 127% in chemistry” in favor of China, while the United States led China “by large margins in immunology, molecular biology and genetics.” The public is paying more attention to scientific misconduct in China. The counts of pages containing key words “scientific fabrication,” “scholarly corruption” and “plagiarism” in Chinese in 2011 were “respectively 6.8 times, 2.9 times, and 1.3 times the counts in 2005.” [via]

China’s rise as a major contributor to science and technology

In 2012 China spent one trillion yuan ($164 billion) on research & development, just under 2% of its gross domestic product. The same year, the U.S. spent $447 billion, or 2.8% of its GDP. But as China’s economy continues to grow rapidly, so does its R&D spending — and it’s projected to overtake that of the United States by 2022. Scientific advances contributed 51.7% to China’s economic growth in 2011, and the country is betting that technical innovations can help it address many challenges, including the need to upgrade its industrial base, reduce air pollution and address growing inequality.

The Chinese government unveiled its “indigenous innovation” campaign in 2006, with a goal of turning the country into a “science powerhouse” by 2020 through an emphasis on human capital. Approximately 7 million Chinese citizens will graduate from college in 2014, up from just 1.1 million in 2001, and the country wants to lure back Chinese-origin scientists working abroad. China’s increasing number of academics have pushed China up in global rankings of published science and engineering papers: The country was 14th in 1995 and by 2007 was second only to the United States. […] [Our] study’s findings include:

  • China’s R&D spending as a percentage of GDP increased from 0.7% in 1991 to 1.8% in 2010, still lower than the U.S. level of 2.8% but growing rapidly.
  • The percentage of engineers in the S/E labor force was higher in China than in the United States. In 2010, China had 2.4 million engineers out of a S/E labor force of 3.2 million (75%), while the United States had 1.4 million engineers out of a S/E labor force of 4.3 million (33%). Engineers in both countries earn approximately 25% more than scientists.
  • Relative to their professional counterparts, Chinese scientists are better paid than those in the United States: Chinese scientists earn 25% more than social scientists, 13% more than medical doctors and 5% more than lawyers. For the United States, the figures are 7% less, 50% less and 34% less, respectively.
  • China had 1.1 million bachelor’s degrees in S/E in 2010, more than quadruple the number in the United States. (Note that China’s population that year, 1.338 billion, was 4.33 times that of the United States, 314 million.)
  • About 44% of college students in China majored in S/E, compared with 16% in the U.S. In 1992 the number of S/E doctoral degrees awarded in China was 10% of the U.S. figure; by 2010, China’s number was 18% higher than that of the United States.
  • The number of Chinese graduate students attended the S/E programs in the United States nearly tripled between 1987 and 2010, growing from 15,000 to 43,000. In 2007, 4,300 Chinese students received doctoral degrees in S/E from American universities, more than students from any other foreign country.
  • From 1990 to 2011, China’s total number of S/E papers increased from 6,104 to 122,672, which was two-thirds of the 2011 U.S. figure, making China the second largest producer of scientific papers.
  • The average citation count of a paper produced in China rose from 8.4 during 1990-1994 to 10.7 during 2000-2004. From 1990 to 2010, the proportion of the average number of citations for Chinese papers compared to those from the U.S. rose from 26% to 55%. However, the China-U.S. ratio fell slightly between 2010 and 2011.
  • From 2001 to 2011, the ratio of Chinese papers in the top 1% of highly cited articles relative to the U.S. increased from 6% to 31%.
  • In 2011, “the China-U.S. ratio of article production was 98% in physical sciences [for every 100 U.S. papers, there are 98 papers in the physical sciences], 77% in engineering, 62% in mathematical sciences, and 34% in biological sciences.” The ratio was “169% in material science and 127% in chemistry” in favor of China, while the United States led China “by large margins in immunology, molecular biology and genetics.” The public is paying more attention to scientific misconduct in China.
  • The counts of pages containing key words “scientific fabrication,” “scholarly corruption” and “plagiarism” in Chinese in 2011 were “respectively 6.8 times, 2.9 times, and 1.3 times the counts in 2005.”

[via]

“The trick that makes Escher’s drawings intriguing is a geometric construction psychologists refer to as an “impossible figure,” a line-form suggesting a three-dimensional object that could never exist in our experience. Psychologists, including a team led by Catya von Károlyi of the University of Wisconsin-Eau Claire, have used such figures to study human cognition. When the team asked people to pick out impossible figure from similarly drawn illustrations that did not violate causality, they were surprised to discover that some people were faster at this than others. And most surprising of all, among those who were the fastest were those with dyslexia. […] In our laboratory at the Harvard-Smithsonian Center for Astrophysics we have carried out studies funded by the National Science Foundation to investigate talents for science among those with dyslexia. The dyslexic scientist Christopher Tonkin described to me his sense of this as a sensitivity to “things out of place.” He’s easily bothered by the weeds among the flowers in his garden, and he felt that this sensitivity for visual anomalies was something he built on in his career as a professional scientist. Such differences in sensitivity for causal perception may explain why people like Carole Greider and Baruj Benacerraf have been able to perform Nobel prize-winning science despite lifelong challenges with dyslexia. In one study, we tested professional astrophysicists with and without dyslexia for their abilities to spot the simulated graphical signature in a spectrum characteristic of a black hole. The scientists with dyslexia…were better at picking out the black holes from the noise, an advantage useful in their careers. Another study in our laboratory compared the abilities of college students with and without dyslexia for memorizing blurry-looking images resembling x-rays. Again, those with dyslexia showed an advantage, [one] that can be useful in science or medicine.”
Foreign Government Contributions to Nine Think Tanks | New York Times

Foreign governments and state-controlled or state-financed entities have paid tens of millions of dollars to dozens of American think tanks in recent years, according to a New York Times investigation. While the think tanks argue that the relationships do not compromise the integrity of their research, foreign officials say the contributions are pivotal in furthering their policy priorities, as many groups produce papers and host forums or briefings that are typically consistent with foreign government interests. Here are examples of contributors to nine major think tanks in recent years.
“The completeness property of [Algorithmic Probability (ALP)] is closely associated with its incomputability. Any complete induction system cannot be computable. Conversely, any computable induction system cannot be complete. For any computable induction system, it is possible to construct data sequences for which that system gives extremely poor probability values. The sum of the squared errors diverges linearly in the sequence length. […] We note that the incomputability of ALP makes such a construction impossible and its probability error always converges to zero for any finitely describable sequence.”
“Here we should emphasize the non-philosophical dimension of econometrics that Joncas sees as a foil to philosophical reasoning: it is not just because econometrics formulates its knowledge quantitatively in mathematically formalized non-discursive mathemes or because it eludes the logicizing, ontologizing, or logocentric language of philosophy that force one down—in an authoritarian manner—discursive garden paths and into philosophical decisions, it is because econometrics ‘in-expresses’ exogenous variables by accounting for them non-decisionally/mathematically without having to build a ‘world’ or subordinate one’s thought to a decisional framework. In other words, it is democratic (even libertarian), i.e., there is no forcing or philosophical coercion into a decisional matrix or a world; as we said before, it simply avoids philosophical politics and manages to account for the Real without making a judgment about it. Econometrics, then, is like a science. For philosophy, however, the only way to deal with exogeneity (the philosophical name for this is ‘the Other’) is to make a ‘philosophical decision.’ Here Joncas inherits the force of thought of the deconstructionist critique of metaphysics and its violence towards the Other. This is also essential to Joncas’ critique of philosophy: “Philosophy has no equivalent of exogeneity as used in economics, so it has to deal with all of these ‘causalities’ by an act of synthesis—usually subordinating them all to an overarching theme, or otherwise ‘overcoding’ them. […] Economics has to be done in numbers, not words, or else it would just be more philosophy” (“There is no economic world.”). This act of overcoding or erasure is arbitrary and violent; it is an unnecessary discursive suture that simply autotelically drives philosophy to its end independently of any intervening Real. However, what this discourse on econometrics admits is that econometric facts are not, strictly speaking, understood. Or rephrased, because it ‘in-expresses’ exogeneity and has the ability to model the inherent randomness and noise in any given economic situation without discursively or dialectically suturing it—all the while adequately representing its truth without making a judgment or a truth claim about it—econometric analysis excludes the moment of comprehension; it is knowledge without a subject.”

Spectral analysis is a method by which time series data are converted into the frequency domain for examination. Through the Fourier transform it can be shown that any stationary time series can be decomposed into a summation of sinusoidal waves of different frequencies (or equivalently, different periods). Each of these waves is completely described by its frequency, amplitude, and phase shift. The frequency is simply the fraction of a cycle that is completed in one period. The amplitude is the height of the wave. The phase shift is the fraction of a cycle that the wave is displaced from zero.
[A] time series may be viewed either through the time domain {xt} or through the frequency domain {xf}. That is, {xt} and {xf} are equivalent; they just represent alternative views of the same data. […] The time domain refers to the description of data in terms of their values over time. The frequency domain refers to the same data, but with reference to the collection of sine and cosine waves that would be required to produce those data.

— Cooper - “The Use of Spectral Analytic Techniques in Economics” 3-4
I’m totally geeking out over this. omg
“There is, however, a problem with this Chinese fixation on the UN as a source of authority: namely, that (in part at least) Beijing appears to value the organization precisely because it is not democratic but hierarchical in its structure. China welcomes the constraints the institution could theoretically impose on US power, but it also values the status benefits it derives from permanent membership of the Security Council, and especially the influence that comes with the privilege of the veto. Hence on UN reform Beijing stresses the need to enhance the ‘authority and efficiency of the Council and strengthening its capacity’; and, while acknowledging the need to increase developing-country representation on the Security Council, it implies that this is best done through rotating membership on a regional basis.”
“As they become more affordable and application programming becomes easier with more sophisticated user interfaces, robots are making small-batch production economically more feasible, because line changeovers are much faster. Given that product life cycles are getting shorter and just-in-time manufacturing helps minimize the need for inventory, robotic flexibility and responsiveness are important benefits. And since many of the new robots have multiple arms, they can multitask with ease—and without losing focus. In the Netherlands, Philips uses 128 robots to make razors. The only humans are the nine workers who perform quality checks. Robots can also do without lighting, heat, air conditioning, supervision, food, and bathroom breaks. As a result, ‘lights out’ manufacturing plants that offer significant cost and energy savings are emerging. At some factories, robots are even building other robots, producing about 50 robots per 24-hour shift and operating unsupervised for as long as 30 days at a time.”
Kandinsky – Chess Theory (1937)

Kuhn’s theorem stat[es that] any finite, deterministic, sequential, noncooperative game of perfect information has an optimal strategy.
Consider Chess. The game is finite. It doesn’t involve chance, so it’s deterministic. It’s obviously sequential and noncooperative. Since you know the entire history of the game when you go to make your move, it’s also a game of perfect information. Kuhn’s theorem says there’s a best way to play chess. If the best strategies are used, every game will end up the same way. There are three possibilities:
Chess is a guaranteed win for white; there is nothing that black can do to stop this unless white makes a mistake, since white goes first.
Chess is a guaranteed win for the second player, black; there’s nothing that white can do to stop this unless black makes a mistake.
Chess is a guaranteed draw, like tic-tac-toe; neither player ever wins unless someone makes a mistake.
Now even though there are more possible strategies for chess than there are subatomic particles in the universe, [by] Kuhn’s theorem, chess strategically “equates” to tic-tac-toe. [via]