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As an emerging financial market, the trading value of carbon emission trading market has definitely increased in recent years. The carbon emission is not only trading in carbon emitters but also has become an important investment target. To determine the mechanism of this growing market, we analyzed the EU allowances (EUA) price series in European Climate Exchange (ECX) that is the leading European emissions futures market. As other financial market, the absolute value of price change (volatility) in carbon emission trading market also shows long-term power-law correlations. Our analysis shows that definite cross correlations exist between EUA and many other markets. These cross correlations exist in wild-range fields, stock market index, futures of crude, sugar, cocoa, etc., suggesting that in this new carbon emission trading market the speculation behavior had already become a main factor that can affect the price change.

Seismic noise includes the information that comes from crust of earth and hypocenter. The study of seismic noise is an essential topic for earthquake predictability and investigation of crustal structure. In this paper we analyze the seismic noise from 46 stations in all around Japan of Full Range Seismograph Network of Japan (F-net). To investigate the influence from earthquake we analyze two types of seismic noise data, one is the data after earthquake shake, which from 2,000 seconds after the max shake of an earthquake, and others are without any earthquake effects. We find that (i) the magnitude of seismic noise values follows a log-normal distribution and (ii) the conditional mean growth rate and the standard deviation of the growth rate of the magnitude value both obey power-law with the magnitude value. Furthermore we also find that the Hurst exponent depends on the max amplitude of earthquake, and shows a linear correlation with logarithmic max amplitude.

Terra and Aqua, two satellites launched by the NASA-centered International Earth Observing System project, house MODIS (moderate resolution imaging spectroradiometer) sensors. Moderate-resolution remote sensing allows the quantifying of land-surface type and extent, which can be used to monitor changes in land cover and land use for extended periods of time. In this article, we propose land-surface classification by applying an ensemble technique based on fault masking among individual classifiers in N-version programming. An N-version programming ensemble of artificial neural networks is created, in which the majority vote result is used to predict land-surface cover from MODIS data. It is shown by experiment that an N-version programming ensemble of neural networks greatly improves the classification error rate of land-cover type.

Eight networks, based on temperature records from four different geographical regions in two pressure levels, are created in resolute snapshots of time for the last 28 years. The links represent the level of robust phase synchronization between places in the interior of each regime. The number of links appears to be a sensitive measure of the El-Nino influence even on regimes far away from the El-Nino basin. A comparison between the statistical information of the phase synchronization network and the similar information obtained previously using cross correlation technique is provided.

The ability to understand speakers’ intentions is examined for typically developing children (TDC), children with autism spectrum disorders (ASD), and children with attention deficit/hyperactivity disorder (ADHD). Four types of spoken phrases, expressing praise, sarcasm, blame, and banter, were presented, and subjects were asked to judge if the speaker praises you or not, or if she blames you or not. The children could correctly judge the speaker's intention for congruent phrases such as praise and blame. TDC younger than 8 years had significantly lower correct percent compared to the TDC older than them for the sarcastic and banter phrases, which have incongruent linguistic and affective valences. The correct percent was significantly lower for ASD aged 10 years compared to the age-matched TDC and ADHD groups.

We analyze the S&P 500 index data for the 13-year period, from January 1, 1984 to December 31, 1996, with one data point every 10 min. For this database, we study the distribution and clustering of volatility return intervals, which are defined as the time intervals between successive volatilities above a certain threshold q. We find that the long memory in the volatility leads to a clustering of above-median as well as below-median return intervals. In addition, it turns out that the short return intervals form larger clusters compared to the long return intervals. When comparing the empirical results to the ARMA-FIGARCH and fBm models for volatility, we find that the fBm model predicts scaling better than the ARMA-FIGARCH model, which is consistent with the argument that both ARMA-FIGARCH and fBm capture the long-term dependence in return intervals to a certain extent, but only fBm accounts for the scaling. We perform the Student's t-test to compare the empirical data with the shuffled records, ARMA-FIGARCH and fBm. We analyze separately the clusters of above-median return intervals and the clusters of below-median return intervals for different thresholds q. We find that the empirical data are statistically different from the shuffled data for all thresholds q. Our results also suggest that the ARMA-FIGARCH model is statistically different from the S&P 500 for intermediate q for both above-median and below-median clusters, while fBm is statistically different from S&P 500 for small and large q for above-median clusters and for small q for below-median clusters. Neither model can fully explain the entire regime of q studied.

自閉症や学習障害，注意欠陥/多動性障害を持つ児童と定型発達児とを対象に，音声から発話意図を理解する能力を調べた。言語的意味としては相手を賞賛する語と非難する語を，語の意味と一致する意図あるいは反対の意図を持って女性話者1 名が発話した音声を刺激とし，発話意図を判断させた。その結果，定型発達児では高い正答率を示したものの，小学1 年生(6 歳児)では言語的意味と発話意図が矛盾する音声に対する正答率が一致する音声に対する正答率より有意に低かった。矛盾する音声に対する自閉症児の正答率は対象群に比べて有意に低かった。また「心の理論検査」の誤信念課題を通過できなかった自閉症児は，できた自閉症児より正答率が有意に低かった。この結果は，自閉症児が音声を介した発話意図理解に障害を持つこと，その障害は誤信念課題による「心の理論検査」で検出される障害と関連していることを示唆する。

We introduce a model of proportional growth to explain the distribution $P(g)$ of business firm growth rates. The model predicts that $P(g)$ is Laplace in the central part and depicts an asymptotic power-law behavior in the tails with an exponent $\zeta=3$. Because of data limitations, previous studies in this field have been focusing exclusively on the Laplace shape of the body of the distribution. We test the model at different levels of aggregation in the economy, from products, to firms, to countries, and we find that the its predictions are in good agreement with empirical evidence on both growth distributions and size-variance relationships., Comment: 8 pages, 4 figures

We discuss recent results concerning statistical regularities in the return intervals of volatility in financial markets. In particular, we show how the analysis of volatility return intervals, defined as the time between two volatilities larger than a given threshold, can help to get a better understanding of the behavior of financial time series. We find scaling in the distribution of return intervals for thresholds ranging over a factor of 25, from 0.6 to 15 standard deviations, and also for various time windows from one minute up to 390 min (an entire trading day). Moreover, these results are universal for different stocks, commodities, interest rates as well as currencies. We also analyze the memory in the return intervals which relates to the memory in the volatility and find two scaling regimes, � � ∗ with α2 =0 .92 ± 0.04; these exponent values are similar to results of Liu et al. for the volatility. As an application, we use the scaling and memory properties of the return intervals to suggest a possibly useful method for estimating risk.

We examine the out-of-equilibrium phase reported by Plerou {\it et. al.} in Nature, {\bf 421}, 130 (2003) using the data of the New York stock market (NYSE) between the years 2001 --2002. We find that the observed two phase phenomenon is an artifact of the definition of the control parameter coupled with the nature of the probability distribution function of the share volume. We reproduce the two phase behavior by a simple simulation demonstrating the absence of any collective phenomenon. We further report some interesting statistical regularities of the demand fluctuation of the financial market., Submitted to Quantitative Finance 1 year back, presently waiting for last 4 months response from Plerou et al

For both stock and currency markets, we study the return intervals τ between the daily volatilities of the price changes that are above a certain threshold q . We find that the distribution function P q (τ) scales with the mean return interval \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} \begin{equation*}{\bar {{\tau}}}\end{equation*}\end{document} as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} \begin{equation*}P_{q}({\tau})={\bar {{\tau}}}^{-1}f({\tau}/{\bar {{\tau}}})\end{equation*}\end{document} . The scaling function f ( x ) is similar in form for all seven stocks and for all seven currency databases analyzed, and f ( x ) is consistent with a power-law form, f ( x ) ∼ x -γ with γ ≈ 2. We also quantify how the conditional distribution P q (τ|τ 0 ) depends on the previous return interval τ 0 and find that small (or large) return intervals are more likely to be followed by small (or large) return intervals. This “clustering” of the volatility return intervals is a previously unrecognized phenomenon that we relate to the long-term correlations known to be present in the volatility.

対人コミュニケーションに問題をもつ児の早期発見に役立つ検査手法を開発するため, 小学生, 中学生, 成人, 計339名 (男性173名, 女性166名) を対象に, 話し言葉から相手の心を理解する能力を調べた.言語属性として辞書的意味が肯定的な短文と否定的な短文を, 感情属性として肯定的な感情と否定的な感情をもって, 女性1名が話した短文音声を刺激として, 言語課題では言語属性を, 感情課題では感情属性を判断した.その結果, 言語属性と感情属性とが一致しない皮肉音声やからかい音声に対して, 話者の発話意図つまり心を理解する能力が小学生から中学生にかけて上昇し発達するものの, 中学生になってもなお成人の能力には達しないことがわかった.この結果は, 言語属性と感情属性とを適正に分離・統合して話者の発話意図を理解する能力が, 誤信念課題などによる心の理論テストで予測される能力よりも遅く成熟するものであることを示唆する.

In this work, we show two main results. One is that we were able to extract the macroscopic stochastic process of returns and volumes from the microscopic stochastic process of the traders’ judgments, using a simple case market simulation. The other is that we successfully reproduced ‘stylized facts’ in speculative markets through a simple market simulation. In our model, the origin of long-ranged correlations is traced to the traders’ profit pursuit trades and these correlations have no special time scale.

In the current era of worldwide stock market interdependencies, the global financial village has become increasingly vulnerable to systemic collapse. The recent global financial crisis has highlighted the necessity of understanding and quantifying interdependencies among the world's economies, developing new effective approaches to risk evaluation, and providing mitigating solutions. We present a methodological framework for quantifying interdependencies in the global market and for evaluating risk levels in the world-wide financial network. The resulting information will enable policy and decision makers to better measure, understand, and maintain financial stability. We use the methodology to rank the economic importance of each industry and country according to the global damage that would result from their failure. Our quantitative results shed new light on China's increasing economic dominance over other economies, including that of the USA, to the global economy., 17 pages, 6 figures

「ミニチュア経済in コンピュータ」への小さなアプローチー動的環境下での学習進化システム, 山崎和子［東京情報大学経営情報学部］＊所属は当時のものを記載

It has been observed that the earthquake events possess short-term memory, i.e. that events occurring in a particular location are dependent on the short history of that location. We conduct an analysis to see whether real-time earthquake data also possess long-term memory and, if so, whether such autocorrelations depend on the size of earthquakes within close spatiotemporal proximity. We analyze the seismic waveform database recorded by 64 stations in Japan, including the 2011 "Great East Japan Earthquake", one of the five most powerful earthquakes ever recorded which resulted in a tsunami and devastating nuclear accidents. We explore the question of seismic memory through use of mean conditional intervals and detrended fluctuation analysis (DFA). We find that the waveform sign series show long-range power-law anticorrelations while the interval series show long-range power-law correlations. We find size-dependence in earthquake auto-correlations---as earthquake size increases, both of these correlation behaviors strengthen. We also find that the DFA scaling exponent $\alpha$ has no dependence on earthquake hypocenter depth or epicentral distance., Comment: 19 pages, 6 figures

In a highly interdependent economic world, the nature of relationships between financial entities is becoming an increasingly important area of study. Recently, many studies have shown the usefulness of minimal spanning trees (MST) in extracting interactions between financial entities. Here, we propose a modified MST network whose metric distance is defined in terms of cross-correlation coefficient absolute values, enabling the connections between anticorrelated entities to manifest properly. We investigate 69 daily time series, comprising three types of financial assets: 28 stock market indicators, 21 currency futures, and 20 commodity futures. We show that though the resulting MST network evolves over time, the financial assets of similar type tend to have connections which are stable over time. In addition, we find a characteristic time lag between the volatility time series of the stock market indicators and those of the EU CO2 emission allowance (EUA) and crude oil futures (WTI). This time lag is given by the peak of the cross-correlation function of the volatility time series EUA (or WTI) with that of the stock market indicators, and is markedly different (>20 days) from 0, showing that the volatility of stock market indicators today can predict the volatility of EU emissions allowances and of crude oil in the near future., 4 figures

We construct and analyze a climate network which represents the interdependent structure of the climate in different geographical zones and find that the network responds in a unique way to El-Ni\~{n}o events. Analyzing the dynamics of the climate network shows that when El-Ni\~{n}o events begin, the El-Ni\~{n}o basin partially loses its influence on its surroundings. After typically three months, this influence is restored while the basin loses almost all dependence on its surroundings and becomes \textit{autonomous}. The formation of an autonomous basin is the missing link to understand the seemingly contradicting phenomena of the afore--noticed weakening of the interdependencies in the climate network during El-Ni\~{n}o and the known impact of the anomalies inside the El-Ni\~{n}o basin on the global climate system., Comment: 5 pages,10 figures

Volatility fluctuations are of great importance for the study of financial markets, and the temporal structure is an essential feature of fluctuations. To explore the temporal structure, we employ a new approach based on the return interval, which is defined as the time interval between two successive volatility values that are above a given threshold. We find that the distribution of the return intervals follows a scaling law over a wide range of thresholds, and over a broad range of sampling intervals. Moreover, this scaling law is universal for stocks of different countries, for commodities, for interest rates, and for currencies. However, further and more detailed analysis of the return intervals shows some systematic deviations from the scaling law. We also demonstrate a significant memory effect in the return intervals time organization. We find that the distribution of return intervals is strongly related to the correlations in the volatility.

Financial fluctuations play a key role for financial markets studies. A new approach focusing on properties of return intervals can help to get better understanding of the fluctuations. A return interval is defined as the time between two successive volatilities above a given threshold. We review recent studies and analyze the 1000 most traded stocks in the US stock markets. We find that the distribution of the return intervals has a well approximated scaling over a wide range of thresholds. The scaling is also valid for various time windows from one minute up to one trading day. Moreover, these results are universal for stocks of different countries, commodities, interest rates as well as currencies. Further analysis shows some systematic deviations from a scaling law, which are due to the nonlinear correlations in the volatility sequence. We also examine the memory in return intervals for different time scales, which are related to the long-term correlations in the volatility. Furthermore, we test two popular models, FIGARCH and fractional Brownian motion (fBm). Both models can catch the memory effect but only fBm shows a good scaling in the return interval distribution.

The temperatures in different zones in the world do not show significant changes due to El Niño except when measured in a restricted area in the Pacific Ocean. We find, in contrast, that the dynamics of a climate network based on the same temperature records in various geographical zones in the world is significantly influenced by El Niño. During El Niño many links of the network are broken, and the number of surviving links comprises a specific and sensitive measure for El Niño events. While during non-El Niño periods these links which represent correlations between temperatures in different sites are more stable, fast fluctuations of the correlations observed during El Niño periods cause the links to break.

We study the volatility time series of 1137 most traded stocks in the US stock markets for the two-year period 2001-02 and analyze their return intervals $\tau$, which are time intervals between volatilities above a given threshold $q$. We explore the probability density function of $\tau$, $P_q(\tau)$, assuming a stretched exponential function, $P_q(\tau) \sim e^{-\tau^\gamma}$. We find that the exponent $\gamma$ depends on the threshold in the range between $q=1$ and 6 standard deviations of the volatility. This finding supports the multiscaling nature of the return interval distribution. To better understand the multiscaling origin, we study how $\gamma$ depends on four essential factors, capitalization, risk, number of trades and return. We show that $\gamma$ depends on the capitalization, risk and return but almost does not depend on the number of trades. This suggests that $\gamma$ relates to the portfolio selection but not on the market activity. To further characterize the multiscaling of individual stocks, we fit the moments of $\tau$, $\mu_m \equiv )^m>^{1/m}$, in the range of $10 < \le 100$ by a power-law, $\mu_m \sim ^\delta$. The exponent $\delta$ is found also to depend on the capitalization, risk and return but not on the number of trades, and its tendency is opposite to that of $\gamma$. Moreover, we show that $\delta$ decreases with $\gamma$ approximately by a linear relation. The return intervals demonstrate the temporal structure of volatilities and our findings suggest that their multiscaling features may be helpful for portfolio optimization., Comment: 16 pages, 6 figures

The distribution of the return intervals $\tau$ between volatilities above a threshold $q$ for financial records has been approximated by a scaling behavior. To explore how accurate is the scaling and therefore understand the underlined non-linear mechanism, we investigate intraday datasets of 500 stocks which consist of the Standard & Poor's 500 index. We show that the cumulative distribution of return intervals has systematic deviations from scaling. We support this finding by studying the m-th moment $\mu_m \equiv )^m>^{1/m}$, which show a certain trend with the mean interval $$. We generate surrogate records using the Schreiber method, and find that their cumulative distributions almost collapse to a single curve and moments are almost constant for most range of $$. Those substantial differences suggest that non-linear correlations in the original volatility sequence account for the deviations from a single scaling law. We also find that the original and surrogate records exhibit slight tendencies for short and long $$, due to the discreteness and finite size effects of the records respectively. To avoid as possible those effects for testing the multiscaling behavior, we investigate the moments in the range $10<\leq100$, and find the exponent $\alpha$ from the power law fitting $\mu_m\sim^\alpha$ has a narrow distribution around $\alpha\neq0$ which depend on m for the 500 stocks. The distribution of $\alpha$ for the surrogate records are very narrow and centered around $\alpha=0$. This suggests that the return interval distribution exhibit multiscaling behavior due to the non-linear correlations in the original volatility., Comment: 19 pages, 6 figures

Terra and Aqua, 2 satellites launched by the NASA-centered international Earth Observing System project, house MODIS (Moderate Resolution Imaging Spectroradiometer) sensors. Moderate resolution remote sensing allows the quantifying of land surface type and extent, which can be used to monitor changes in land cover and land use for extended periods of time. In this paper, we propose applying an ensemble technique, based on fault masking among individual classifier for N-version programming. We create an N-version programming ensemble of artificial neural networks and use the majority voting result to predict land surface cover from MODIS data. We show that an N-version programming ensemble of neural networks greatly improves the classification error rate of land cover type.

We present a preferential attachment growth model to obtain the distribution $P(K)$ of number of units $K$ in the classes which may represent business firms or other socio-economic entities. We found that $P(K)$ is described in its central part by a power law with an exponent $\phi=2+b/(1-b)$ which depends on the probability of entry of new classes, $b$. In a particular problem of city population this distribution is equivalent to the well known Zipf law. In the absence of the new classes entry, the distribution $P(K)$ is exponential. Using analytical form of $P(K)$ and assuming proportional growth for units, we derive $P(g)$, the distribution of business firm growth rates. The model predicts that $P(g)$ has a Laplacian cusp in the central part and asymptotic power-law tails with an exponent $\zeta=3$. We test the analytical expressions derived using heuristic arguments by simulations. The model might also explain the size-variance relationship of the firm growth rates., Comment: 19 pages 6 figures Applications of Physics in Financial Analysis, APFA5

We study the return interval $\tau$ between price volatilities that are above a certain threshold $q$ for 31 intraday datasets, including the Standard & Poor's 500 index and the 30 stocks that form the Dow Jones Industrial index. For different threshold $q$, the probability density function $P_q(\tau)$ scales with the mean interval $\bar{\tau}$ as $P_q(\tau)={\bar{\tau}}^{-1}f(\tau/\bar{\tau})$, similar to that found in daily volatilities. Since the intraday records have significantly more data points compared to the daily records, we could probe for much higher thresholds $q$ and still obtain good statistics. We find that the scaling function $f(x)$ is consistent for all 31 intraday datasets in various time resolutions, and the function is well approximated by the stretched exponential, $f(x)\sim e^{-a x^\gamma}$, with $\gamma=0.38\pm 0.05$ and $a=3.9\pm 0.5$, which indicates the existence of correlations. We analyze the conditional probability distribution $P_q(\tau|\tau_0)$ for $\tau$ following a certain interval $\tau_0$, and find $P_q(\tau|\tau_0)$ depends on $\tau_0$, which demonstrates memory in intraday return intervals. Also, we find that the mean conditional interval $$ increases with $\tau_0$, consistent with the memory found for $P_q(\tau|\tau_0)$. Moreover, we find that return interval records have long term correlations with correlation exponents similar to that of volatility records., Comment: 19 pages, 8 figures

Computer aided training has become an important method for improving computer education. For this research, we propose a programming training support system which targets understanding program structures which satisfy required program specifications. In our proposed training system, a given source code is broken up into separate puzzle pieces, and the user must layout the pieces in the correct order to reconstruct the program. The proposed system applies genetic algorithm (GA) and allows the system to self-adjust the difficulty of the programming problems matching the trainee's competency. We created a prototype system and applied it in a 1st year university programming course.

We introduce a model of proportional growth to explain the distribution of business firm growth rates. The model predicts that the distribution is exponential in the central part and depicts an asymptotic power-law behavior in the tails with an exponent 3. Because of data limitations, previous studies in this field have been focusing exclusively on the Laplace shape of the body of the distribution. In this article, we test the model at different levels of aggregation in the economy, from products to firms to countries, and we find that the model's predictions agree with empirical growth distributions and size-variance relationships., Comment: 22 pages, 5 Postscript figures, uses revtex4. to be published in Proc. Natl. Acad. Sci. (2005)

Complex systems can be characterized by classes of equivalency of their elements defined according to system specific rules. We propose a generalized preferential attachment model to describe the class size distribution. The model postulates preferential growth of the existing classes and the steady influx of new classes. According to the model, the distribution changes from a pure exponential form for zero influx of new classes to a power law with an exponential cut-off form when the influx of new classes is substantial. Predictions of the model are tested through the analysis of a unique industrial database, which covers both elementary units (products) and classes (markets, firms) in a given industry (pharmaceuticals), covering the entire size distribution. The model's predictions are in good agreement with the data. The paper sheds light on the emergence of the exponent tau approximately 2 observed as a universal feature of many biological, social and economic problems.

We proposed a market simulation model (micro model) which displays nmltifractality and reproduces many important stylized facts of speculative markets. From this model we analytically extracted the MMAR model (IVluitifractal Model of Asset Returns) [3] for the macroscopic limit.

Learning is the adaptation of the individual level in the life of the individual, and evolution is the adaptation of the population level in the creature's history. In this paper, the authors consider the connection of the time scale of changes in the environment, the time scale of learning and the time scale of learning evolution.

We construct a network from climate records of different geographical sites in the North Atlantic. A link between two sites represents the cross-correlations between the records of each site. We find that within the different phases of the North Atlantic Oscillation (NAO) the correlation values of the links are significantly different. By setting an optimize threshold on the correlation values, we find that the number of strong links in the network is increased during times of positive NAO indices, and decreased during times of negative NAO indices. We find a pronounced sensitivity of the network structure to the oscillations which is significantly higher compared to the observed response of spatial average of the records. Our result suggests a new measure that tracks the NAO pattern.

Using measurements of atmospheric temperatures, we create a weighted network in different regions on the globe. The weight of each link is composed of two numbers —the correlations strength between the two places and the time delay between them. A characterization of the different typical links that exist is presented. A surprising outcome of the analysis is a new dynamical quantity of link blinking that seems to be sensitive especially to El Nino even in geographical regimes outside the Pacific Ocean. Copyright c EPLA, 2008