Atis Kapenieks, Bruno Zuga, Janis Kapenieks sen., Aleksandrs Gorbunovs, Kristaps Kapenieks, Merija Jirgensons, Riga Technical University, Distance Education Study Centre, Kronvalda bulvaris 1, LV1010, Riga, Latvia

TELECI project aims to develop an advanced e-learner profile model and build a support system for multi-screen e-learning scenarios.  A set of software and application programming interfaces will be integrated into encompassing e-learning support system that will be interactive and provide visualized information, analysis and interpretation of user behaviour and the systems’ functioning.

The design of the system will incorporate problem-oriented models using adaptive algorithms that are based on machine learning and are represented with graphical interfaces.

The TELECI e-learner profile model will be designed around three questions: first- creation and maintenance of representation of the e-learning domain that translates the existing e-learning reality. The second question will focus on the testing of e-learner reactions and the intervention measures needed for support with the aim to create a learner support management profile concerning the trajectories of e-learner behaviours. The final question will focus on future scenarios and the updating of e-learning domains. 

TELECI has designed a concept complimentary with the FuturICT2.0 ERA-NET project led by Institute of Cognitive Sciences and Technologies (Italy) and implemented by a consortium of ten partners including Riga Technical University.

Giovanni E. Gigante e Stefano Ridolfi, Department of Basic anad Applied Sciences - Sapienza University of Rome

Chemical analysis seems a very distant field for quantitative modelling and simulation methods, although the degree of accuracy achieved by these is significantly improved in recent times. In the analysis, as well as in non-destructive testing, direct comparison with reference samples seems the only method that offers a guarantee of sufficient accuracy. Since the 1980s, probably with the introduction of Fundamental parameter method in the X fluorescence spectrometry (XRF) and gamma spectrometry, have spread calculation methods, which made it possible to greatly, extend the applications of certain techniques, especially in the field of non-destructive analysis and microstructured materials. The reasons for this success are:

1. Reduce the complexity of calibration procedures and extend the number of determinations that you can do with a single measure;

2.Allow the reconstruction of the microstructure (e.g. stratification) of the sample examined.

The conditions for use of this procedure is to have a sufficient detailed logical representation of the process by which it determines the analytical data (such as in the case of XRF x-ray emission mechanisms, excitation process and absorption of radiation within the sample).

In this communication, we will examine the most significant aspects of the methodology of simulation in the emission of characteristic x-rays with excitation by photons (XRF and SRXRF) and charged particles (EDS and PIXE). Finally, we will discuss some applications of interest for archaeometry and conservation of cultural heritage such as the detection of superficial layers of precious metal and evaluation of corrosion processes.

Finally it will be discussed the possibility to use methods of Montecarlo Simulation.

“Handbook of X-Ray Spectrometry: Methods and Techniques,” eds. R.E. van Grieken and A.A. Markowicz, Marcel Dekker, Inc., New York (1993).

. B. Vekemans, K. Janssens*,L. Vincze,F. Adams and P. Van Espen   Analysis of X-ray spectra by iterative least squares (AXIL): New developments †X-RAY  SPECTROMETRY, VOL. 23,   278-285  (1994)  DOI:10.1002/xrs.1300230609

Cesareo R., Bustamante A., Fabian  J., Zambrano S., Alva W., Chero L., Espinoza C., Rodriguez R, Seclen M., Gutierrez V, Lévano E.B., Gonzales A.J.,.Rizzutto M.A., Poli E, Calza C, dos Anjos M, Lopes R.T., Gigante G.E., Ingo G.M., Elera C., Shimada I., Curay V., Castillo M., Lopes F., Holmquist U., Diestra D. “Multilayered artifacts in the pre-Columbian metallurgy from the North of Peru” Applied Physics A: Materials science & Processing,   (2013) 113, 889–903, DOI 10.1007/s00339-013-7738-8

N.P. BarradasK. Arstilac,G. Battistig, M. Bianconi, N. Dytlewskif,C. Jeynesg,E. Kótaih,G. Lullie,M. Mayeri,E. Rauhalaj,E. Szilágyi,M. Thompson International Atomic Energy Agency intercomparison of ion beam analysis software  Nuclear Instruments and Methods B 262, (2007) 281-303

Tamaki Suzuki, Kyoto University Center, Heidelberg Office, Germany

In May 2014, the Kyoto University European Center Heidelberg Office was formally opened in a historic building on the campus of Heidelberg University. The “European Center,” as it is known, was established by integrating the Heidelberg office and former European representative office in London.

Kyoto University’s international strategy was established on the three central pillars of research, education, and international service. Along this strategy, the primary functions of the Kyoto University European Center Heidelberg office are to support the university’s research and education activities in Europe, promote the internationalization of faculty and students, and enhance the university’s international public relations, industry-government-academia collaboration, and international networks.

The URAs of the Kyoto University Research Administration office (KURA) are managing the Heidelberg Office and are involved in promoting joint research with European institutions. The staff have organizing symposia/workshops, supported fund raising, and consulted MoU. The office staff have also participated in Higher education fairs, study fairs for attracting international student from Europe.

With aiming to extend Kyoto University’s current partnerships and explore new opportunities for collaboration, the office is a gateway for any European institutions for research and educational collaboration with Kyoto University. 

Dirk Helbing, ETH Zurich, Switzerland

The world is running into great troubles: the Anthropocene challenges (including climate change, impending resource shortages, demographic change, conflict, financial and economic crises) call for entirely new answers. As a result, we are now seeing the emergence of data-driven societies around the globe. 

What framework should we choose? What would be the implications?

at's this item about? What makes it interesting? 

Ruedi Stoop, Karlis Kanders, Tom Lorimer, Florian Gomez, Institutes of Neuroinformatics ETHZ/UZH and Computational Science UZH, Switzerland

Biological sensors often deal with inputs across many orders of magnitude (expressed by a logarithmic stimulus scale, e.g., decibel or pH scales). They usually have the ability to strongly amplify weak inputs and to compress higher input levels. A very prominent manifestation of these characteristics is the hearing system with its “compressive nonlinearity” [1,2]. Hearing sensors arguably deal with perhaps the most intricate signal among all recognized physical human sensors: a sound event may be any distribution along an excessively large frequency interval, with strengths that cover, in every-day life a dynamic range up to even more than one hundred dB. Sound is not only tied to the three dimensions of space, where it is often of great significance, from where sound arrives. In contrast to vision, sound intrinsically requires taking in space-time a whole temporal measurement chunk; due to its frequency characteristics, sound can also not be captured by making a snapshot in time.
Correspondingly, it has taken much longer to get a coherent understanding of the physical sensor of sound provided by the mammalian (and other) constructions. Already Helmholtz [3] profoundly addressed the question how the nonlinearity of the human hearing sensor, the cochlea, might shape human sound perception. At his time, research was, however, obstructed by the lack of experimental data regarding the amplification properties of the inner ear. In the meantime, accurate measuring methods permit the comparison of models of the hearing sensor with empirical data, leading to a strong revival of the interest into Helmholtz’s original research questions. In our paper, we describe some recent theoretical and modeling advances in the understanding of the nature of human pitch perception [2,4-6] and beyond. We reveal a number of to date unexplained human auditory percept effects to be direct consequences of the nonlinear properties of the mammalian hearing sensor. Our insights also demonstrate, as a by-note, the limitations of the present reverse engineering approach towards cochlear implants. And most importantly, our approach and insights may provide a template or guideline for how to deal with signals of a very rich and intricate nature [7].

References:

[1] A. Kern and  R. Stoop, Physical Review Letters 91, 128101 (2003).
[2] F. Gomez and R. Stoop, Nature Physics 10, 530 (2014).
[3] H. von Helmholtz, Die Lehre von den Tonempfindungen (F. Vieweg, Braunschweig, (1862)).
[4] F. Gomez, T. Lorimer, and R. Stoop, Physical Review Letters 116, 108101 (2016).
[5] R. Stoop and F. Gomez, Physical Review Letters, in press. (2016).
[6] K. Kanders, F. Gomez and R. Stoop, in preparation (2016).
[7] R. Stoop, K. Kanders, T. Lorimer, J. Held, C. Albert, Chaos, Solitons & Fractals, DOI:10.1016/j.chaos.2016.02.035 (2016).

Anirban Chakraborti, Kiran Sharma, School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi-110067, India

A brief overview of the models and data analyses of income, wealth, consumption distributions by the physicists, will be presented. Distributions of income and wealth possess fairly robust features, like the bulk of both the income and wealth distributions seem to reasonably fit both the log-normal and Gamma distributions, while the tail of the distribution fits well to a power law (as first observed by Pareto). We recently studied the unit-level expenditure on consumption across multiple countries and multiple years, and found invariant features of consumption distribution. We showed that the bulk is log-normally distributed, followed by a power law tail at the limit. The distributions coincide with each other under normalization by mean expenditure and log scaling even though the data is sampled across multiple dimension including, e.g., time, social structure and locations. Further we have studied using empirical data, the relations of socio-economic factors with ethnic conflicts and human rights violations, along with their spatio-temporal distributions.

References

1.       B.K. Chakrabarti, A. Chakraborti, S.R. Chakravarty and A. Chatterjee, Econophysics of Income and Wealth Distributions (Cambridge University Press, Cambridge, 2013).

2.       “Physicists’ approach to studying socio-economic inequalities”, K. Sharma and A. Chakraborti, Eds. S.K. Dhiman, P. Sharma and P. Sharma, Book Chapter in Physics in New Dimensions (forthcoming, 2016).

Davron Matrasulov, Turin Polytechnic University in Tashkent

Branched structures and networks appear in many physical systems and in complex systems from biology, ecology, epidemic spreading, sociology, economy and finance [1, 2]. Usually transport of energy, information, charge and other important characteristics of complex system is realized in the form of wave transport. Particle and wave dynamics in such systems can be effectively modeled by nonlinear evolution equations on metric graphs. The latter are one dimensional system of bonds which are connected at one or more vertices (branching points). The connection rule is called the topology of the graph. When the bonds can be assigned a length, the graph is called a metric graph. Recently the nonlinear evolution equations on metric graph attracted much attention in the literature [3-5]. Most important problem in the study of wave equations in networks is transmission of waves through the  network branching points (vertices).  Wave may reflect from the vertex or transmit without the reflection. In the first case, i.e. when full of partial reflection is possible the wave transport  will be possible.  In this work by considering different wave equations, such as nonlinear Schrodinger, sine-Gordon and Burgers equation, we study possibility of reflectionless transmission of waves through the network vertices. In particular, conditions under which soliton transport in network can be ballistic are derived for he above mentioned nonlinear evolution equations. Such conditions provide also  integrability of these   equation on networks.  We discuss possible application of the results to ballistic transport of solitons inDNA, branched seismic faults and traffil flow.

References

1. R. Albert, A-L. Barabasi, Rev. Mod.Phys. A, 74, 47 (2002).

2. R. Cohen, S. Havlin, Complex Networks: Structure, Robustness and Function. (Cambridge University Press.2010).

3.  Z. Sobirov, D. Matrasulov, K. Sabirov, S. Sawada, and K. Nakamura, Phys.Rev.E 81, 066602 (2010).

4. R. Adami, C. Cacciapuoti, D. Finco, and D. Noja, Rev.Math. Phys. 23, 409 (2011).

5. C. Cacciapuoti, D. Finco, and D. Noja, Phys. Rev. E 91, 013206 , (2015).

6.   H. Uecker, D. Grieser, Z. Sobirov, D. Babajanov and D. Matrasulov, Phys. Rev. E 91, 023209, (2015).

7.   J.-G Caputo , D. Dutykh, Phys. Rev. E 90, 022912, (2014).

L. Pietronero, University of Rome Sapienza and Institute of Complex Systems, ISC-CNR, Rome, Italy, http://pil.phys.uniroma1.it/twiki/bin/view/Pil/LucianoPietronero

Economic Complexity refers to a new line of research which portrays economic growth as a process of evolution of ecosystems of technologies and industrial capabilities. Complex systems analysis, simulation, systems science methods, and big data capabilities offer new opportunities to empirically map technology and capability ecosystems of countries, industrial sectors and companies, analyse their structure, understand their dynamics and measure economic complexity. This approach provides a new vision of a data driven fundamental economics in a strongly connected, globalised world. 

According to the standard economic theory the specialization of countries towards certain specific products should be optimal. The observed data (COMTRADE) show that this is not the case and that diversification is actually more important. The situation is different for companies or sectors which seem instead to specialize only on few products.

The crucial challenge is then how to turn these qualitative observations into quantitative variables. We have introduced a new metrics for the Fitness of countries and the Complexity of products which is a sort of economic version of the Google Page rank approach. The direct comparison of the Fitness with the country GDP gives an assessment of the non-expressed potential of the country. This can be used as a predictor of GDP evolution or stock index and sectors performances. These results are also useful for risk analysis, planning of industrial development and strategies to exit from the “poverty trap”. Analogously the Complexity of products can be compared with its added value leading also to new information.

The dynamics in the GDP-Fitness plane reveals a heterogeneous structure and certain areas behave in a laminar way (high predictability) while others appear turbulent (low predictability). This situation requires an analysis inspired to the theory of Dynamical Systems and it is not appropriate to study with the usual regressions.

We are considering the extension of these ideas also to the Fitness of Companies which are instead mostly specialized in terms of products. This requires different datasets and a new algorithm. The implication of the present study for the general problem of Big Data Science will be discussed.

Recently there has been an interest from the World Bank to adopt these methods for their strategic analysis. A close collaboration is already active in this direction.

References

(1) M. Cristelli, A. Gabrielli, A. Tacchella, G. Caldarelli and L. Pietronero:

Measuring the Intangibles: A Metrics for the Economic Complexity of Countries and Products, PLOS One Vol. 8, e70726 (2013)

(2) M. Cristelli, A. Tacchella, L. Pietronero: The Heterogeneous Dynamics of Economic Complexity, PLOS One 10(2): e0117174 (2015) and Nature editorial 2015: http://www.nature.com/news/physicists-make-weather-forecasts-for-economies-1.16963

Andrea Rapisarda, University of Catania, Italy

 I will present some financial market models, characterized by self-organized criticality, that are able to generate endogenously a realistic price dynamics and to reproduce well-known stylized facts. In a community of heterogeneous traders, the spreading of information, based on a realistic imitative behavior, drives contagion and causes market fragility. It will be shown how the introduction of a small number of random traders is able to stabilize the market and produce beneficial effects both a micro and macro level.

[1] Modeling financial markets by self-organized criticalityPhys. Rev. E 92, 042814 (2015)

[2] Micro and Macro Benefits of Random Investments in Financial Markets, Contemporary Physics 55 (2014) 318

Ken Kiyono, Osaka University, Japan

We propose a general and unified framework to study the time and frequency domain characteristics of detrending-operation-based random-walk analysis methods, such as detrended fluctuation analysis

(DFA) and detrending moving average (DMA) analysis. This framework is based on the frequency responses of detrending operations, which are calculated analytically using either the time or frequency domain approach. Although the frequency domain approach using conventional linear analysis techniques is only applicable to linear detrending methods, the time domain approach presented here is applicable to both linear and nonlinear detrending methods. Furthermore, by deriving the relationship between the time and frequency domain representations of the frequency responses, the frequency domain characteristics of nonlinear detrending operations can be obtained. Based on the calculated frequency responses, it is possible to establish a direct connection between the root-mean-square deviation of the detrending-operation-based analysis and the power spectrum for linear stochastic processes. By applying our methods to DFA and DMA, including higher-order and multivariate cases, exact frequency responses can be derived. In addition, the application of the detrending-operation-based random-walk analysis to biosignal time series, such as heartbeat, breathing and gait rhythms, is discussed.

Nicola Strisciuglio (1,2), Mario Vento (2), Nicolai Petkov (1), (1) Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, Netherlands

In the past years, the main interest of audio data analysis has been mainly devoted to speech recognition, speaker identification and music processing. In these applications, the sound source is assumed to be very close to the microphone, implying low influence of background noise on the quality of the audio signal. In other applications, such as event detection for intelligent surveillance systems, the sound source of interest can be at any distance from the microphone. This implies that intelligent audio surveillance systems have to deal with sounds that can occur at various, not pre-determined, values of signal to noise ratio (SNR). In such cases, background noise is highly variable both in terms of energy and types of sound.

We focus on the task of detecting abnormal audio events, such as glass breaking, gun shots, screams, car accidents and tire skidding in public environments, where the background sound is generally not predictable. This particular problem recently gained a great interest of the signal processing and pattern recognition communities due to the increasing demand for safety of public places. In order to carry out benchmarking experiments, we publicly released two data sets of audio events that occur in various environments[1].

Starting from the consideration that an audio stream is composed of small, atomic unit of sound, similarly to a text that is composed of a number of words, we propose a system for detection of audio events based on the bag of features principle. Since the events of interest can be mixed with various types of background sounds, we tailored the training phase of the proposed method in order to build a system robust to such variability. We carried out experiments for public/private safety [1] and roads monitoring [2].

Considering that human capabilities of detecting sounds of interest are exceptional even in case of very low SNR, we introduce biology-inspired approaches to audio data analysis and events detection. We particularly take into account the way the outer part of the human auditory system converts sound pressure waves into vibrations of the cochlea membrane. It creates time frequency patterns of the energy content of the audio signal (practically they are represented by Gammatone filters responses), that we use for the classification of different types of event of interest, through an AdaBoost classifier [3]. In a very recent work, we further investigate the properties of  human auditory processing and propose trainable CoPE (Combination of Peaks of Energy) filters that detect particular patterns in the time-frequency energy distribution of the input signal, which correspond to neural stimuli sent to the auditory nerve.

In this research work, we demonstrate that data analytic systems can benefit from biological evidences and inspiration in order to build effective solutions to specific relevant problems.

[1] Pasquale Foggia, Nicolai Petkov, Alessia Saggese, Nicola Strisciuglio, Mario Vento, “Reliable detection of audio events in highly noisy environments,” Pattern Recognition Letters, Volume 65, 2015, Pages 22-28, ISSN 0167-8655, http://dx.doi.org/10.1016/j.patrec.2015.06.026

[2] Pasquale Foggia, Nicolai Petkov, Alessia Saggese, Nicola Strisciuglio, Mario Vento, “Audio Surveillance of Roads: A System for Detecting Anomalous Sounds,” in Intelligent Transportation Systems, IEEE Transactions on, vol.17, no.1, pp.279-288, 2016, doi: 10.1109/TITS.2015.2470216

[3] Pasquale Foggia, Alessia Saggese, Nicola Strisciuglio, Mario Vento, “Cascade classifiers trained on Gammatonegrams for reliably detecting Audio Events”, AVSS 2014

[4] The MIVIA audio events and the MIVIA road events data sets are publicly available at the url http://mivia.unisa.it

Đorđe Stratimirović (1,2), Suzana Blesić (2,3), Darko Sarvan (4), Vladimir Miljković (5) (1) University of Belgrade, Faculty of Dental Medicine, Belgrade, Serbia (2) Institute for Research and Advancement in Complex Systems, Belgrade, Serbia (3) University of Belgrade, Institute for Medical Research, Belgrade, Serbia (4) University of Belgrade, Faculty of Veterinary Medicine, Belgrade, Serbia (5) University of Belgrade, Faculty of Physics, Belgrade, Serbia

 We have analyzed cyclical behavior of the three types of stock market indexes (SMI) time series: data belonging to stock markets of developed economies, emerging economies, and of the underdeveloped or transitional economies. We have used two techniques of data analysis to obtain and verify our findings: the wavelet transformation (WT) spectral analysis to study SMI returns data, and the Hurst exponent formalism to study local behavior around market cycles and trends. We calculated WT power spectra for all our SMI series, and have searched for characteristic peaks (local maxima) that point to existence of cycles in our data. We found cyclical behavior in all SMI data sets that we have analyzed. Moreover, the positions and the boundaries of cyclical intervals that we found seem to be common for all markets in our dataset. We differentiated nine such periods in our SMI data. Our results show that measures like the relative WT energy content and the relative WT amplitude for the peaks in the small scales region could be used to partially differentiate levels of growth and/or maturity of market economies in our dataset. Finally, we propose a way to quantify and compare the level of development of a stock market, based on the Hurst scaling exponent approach. From the local scaling exponents calculated for our nine peak regions we have defined what we named the Development Index (HDI), which proved, at least in the case of our dataset, to be suitable to rank the SMI series that we have analyzed in three distinct groups. Further verification of this method remains open for future research.

References:

[1] Stratimirovic Dj, Sarvan D, Miljkovic V, Blesic S (2015) Analysis of cyclical behavior in time series of stock market returns. arXiv:1507.03378.

[2] Sarvan D, Stratimirovic Dj, Blesic S, Miljkovic V (2014) Scaling analysis of time series of daily prices from stock markets of transitional economies in the Western Balkans. Eur. Phys. J. B 87, 297-303.

[3] Blesic S, Stratimirovic Dj, Milosevic S, Maric J, Kostic V, Ljubisavljevic M (2011) Scaling analysis of the effects of load on hand tremor movements in essential tremor. Physica A 390, 1741-1746.

[4] Stratimirovic Dj, Milosevic S, Blesic S, Ljubisavljevic M (2007) Wavelet transform analysis of time series generated by the stimulated neuronal activity. Physica A 374, 699-706.

Nicolai Petkov, University of Groningen, NL G. Azzopardi, N. Strisciuglio, J. Guo, C. Shi, N. Jansonius, M. Vento

A trainable filter is a filter that is configured by the automatic analysis of a pattern specified by a user. Subsequently, such a filter can detect similar patterns. This approach is illustrated by the design of filters that can detect bifurcations in retinal fundus images. The user presents a vascular bifurcation as a local pattern of interest. The automatic analysis system applies a bank of Gabor filters to this pattern and identifies which of them respond most strongly and in which positions. The response of the composite trainable filter is then computed as a combination (e.g. a geometric mean) of the responses of the selected Gabor filters, shifted by certain off-set vectors determined in the analysis phase. We call this method Combination of Shifted Filter Responses (COSFIRE). An advantage of this approach is its ease of use, as it requires no programming effort – the parameters of a filter are derived automatically from a single training pattern. This approach is further illustrated by the segmentation of blood vessels and the localization and segmentation of the optic nerve head in retinal fundus images.

​

 References:

 [1] G. Azzopardi, N. Petkov: Trainable COSFIRE filters for keypoint detection and pattern recognition. IEEE Trans. on Pattern Analysis and Machine Intelligence, 35 (2): 490-503, 2013.

 [2] G. Azzopardi, N. Petkov: Ventral-stream-like shape representation: from pixel intensity values to trainable object-selective COSFIRE models. Frontiers in computational neuroscience, 8, article nr. 80, 9 pages, 2014.

 [3] G. Azzopardi, N. Strisciuglio, M. Vento, N. Petkov: Trainable COSFIRE filters for vessel delineation with application to retinal images. Medical image analysis,  19 (1): 46-57, 2015.

Aki-Hiro Sato, Graduate School of Informatics, Kyoto University (Japan)

Global systems not only consist of enormous numbers of elements located globally but also have connections with one another which create some interactions. For example, financial markets, climate changes, global economy, international trades, logistic systems and international tourisms are classified into one of them. Global Systems Science (GSS) may provide scientific evidence and knowledge to support policy-making, public action and civic society to collectively engage in societal actions [1].

Due to the development of information and communications technology, we are eventually obtaining an ability to monitor the global systems and manage them [2]. To do so more, we need to develop methodologies, computational resources, and collaborations among stakeholders in different fields under some engagements to behave collectively. Hence, we need standards to generate data and use information.

In this talk, I will mention 1-km grid square statistics data in Japan, which are provided as government open data by Bureau of Statistics, Ministry of Internal Affairs and Communications in Japan. I will show three types results of empirical analysis by using grid square statistics data [3,4,5]. Grid square statistics is useful to investigate socioeconomic-technological-environmental systems. There are various types of grid square statistics in both UK and Japan. These countries possess their own industrial standard to define grid squares (OSGB 36 in UK and JIS X0410 in Japan) and have long histories where they have promoted grid square statistics.

 Firstly, I will mention Japanese tourism tendency from grid square statistics data about Japanese Tourism Statistics [3]. The Accommodation Survey in Japanese Tourism Statistics is a quarterly survey conducted by the Japan Tourism Agency of the Ministry of Land, Infrastructure, Transport and Tourism. The aim of the survey is to capture a whole picture of monthly travel and tourism tends in Japan and obtain data to inform for Japanese tourism policies. Data collected include the total number of tourists, the number of foreign tourists with their nationality and the number of Japanese tourists with their residential prefecture. These data are collected from hotels, inns, and other private and public accommodations listed in the establishment frame database defined in Article 27 of the Statistics Act. In this study, the 1-km grid square statistics data were generated by using micro-data from 50,802 accommodations that took place from January 2013 to June 2014 (18 months); use of this data is in accordance with Article 33 of the Statistics Act. These data were analyzed for spatio-temporal patterns of Japanese travel trends. The 1-km grid square statistics data were computed by converting postal addresses into geographical information expressed as latitude and longitude and encoding it into 1-km grid square code standardized in JIS X0410. The relationships between the number of travelers and the number of foreign travelers in each 1-km grid square were clarified. It was confirmed that the power–law relationship between the number of travelers and the number of foreign travelers exists. The power–law exponent is greater than 1 for 15 months of the observation period. This implies that foreign travelers tend to choose accommodations concentrated in several areas, including areas in which Japanese travelers frequently visit and stay. Furthermore, Japanese travelers tend to stay in some areas close to their own residential prefecture.

 Secondly, I will show a relationship between socioeconomic flows (passengers and freight) and social stocks (population, the number of workers and the number of firms) based on government grid square statistics data in the Japanese domestic air transportation system [4]. To determine the relationship, a gravity model, which proposes that a socioeconomic flow between two places is proportional to a power law relationship among social stocks around the places and their geodesic distance, is assumed. Parameters with the relationship for passengers and freight are estimated, and an adequate radius distance to compute social stocks around Japanese airports is determined. This result can be used to infer socioeconomic flows from social stocks.

Thirdly, I will show empirical results for the number of job opportunities collected from a Japanese job searching site (“fromAnavi”) and an international job searching site (“Indeed”) [5]. We confirm that a relationship between the number of job opportunities and socioeconomic quantities (the population, the numbers of firms and workers) in each 1-km grid square in Japan. The number of workers is the best explanatory variable to explain the number of job opportunities in Japan. The regression coefficients can be used as an indicator to grasp Japanese macroeconomic conditions. From a global point of view, we analyze the number of job opportunities in about 16,000 cities all over the world. We confirm the daily number of job opportunities in each city varies in time and show some associations with macroeconomic indicators. We compute a relationship between means of the daily number of job opportunities and their standard deviations and confirm that it follows a scaling relationship with power law exponent alpha = 1. A possible model based on Poisson processes with intensity of which varies in time on the basis of a common noise is proposed to explain the phenomenon empirically observed.

 Consequently, I address several issues which we face when we attempt to analyze socioeconomic-technological-environmental systems and some challenges for our investigation such as real-time grid square statistics data and international standard to share grid square statistics data. These results can be compared based on the same platform, which I develop under my research project funded by Japan Science and Technology Agency (JST).

As future work, we should extend the activity to generate and share grid square statistics into worldwide in the Big Data era. Currently, I proposed a method to extend the definition of grid square coding system (JIS X0410) into the global level [6]. If we generate the grid square statistics under the same standards in different layers and different countries, we may share such grid square statistics data and use them to behave collectively on the global scale. Such activities may contribute to solving various types of problems related to global systems.

Keywords: Population, grid square statistics data, geographical risk assessment, Japanese domestic air transportation system, labor market, job opportunities, applied data-centric social sciences

References

[1] The Global Systems Science portal [ONLINE] http://global-systems-science.eu/ Accessed on 15 May 2016.

[2] Aki-Hiro Sato, “Applied Data-Centric Social Sciences”, Springer, Tokyo (2014).

[3] Aki-Hiro Sato, “Microdata analysis of the accommodation survey in Japanese tourism statistics”, Big Data (Big Data), 2015 IEEE International Conference on, Oct. 29 2015-Nov. 1, 2015, pp. 2700—2708.

[4] Aki-Hiro Sato, Hidefumi Sawai, "Relationship between socioeconomic flows and social stocks: Case study on Japanese Air Transportation", Evolutionary and Institutional Economics Review, Vol. 12(2) (2016) pp. 243—263.

[5] Aki-Hiro Sato, Chihiro Shimizu, Takayuki Mizuno, Takaaki Ohnishi, Tsutomu Watanabe, "Relationship between job opportunities and economic environments measured from data in internet job searching sites", Procedia Computer Science, Volume 60 (2015) pp. 1255—1262.

[6] Research Institute for World Grid Squares (RIWG) [ONLINE] http://www.fttsus.jp/worldmesh

[1] This is based on joint works with Dr. Hidefumi Sawai in National Institute of Information and Communications Technology, Prof. Chihiro Shimizu in National University of Singapore, Associate Prof. Takayuki Mizuno in National Institute of Informatics, Associate Prof. Takaaki Ohnishi in the University of Tokyo, and Prof. Tsutomu Watanabe in the University of Tokyo.

Marc Höll, Max Planck Institute for the Physics of Complex Systems, Holger Kantz, Max Planck Institute for the Physics of Complex Systems

Long-range correlation is an important phenomenon found in many time series, such as in hydrology, climate, and finance. Since its first discovery by Hurst analyzing Nile river flows in 1951, there has been a fruitful debate on the reliable distinction of long-range correlated noise and external non-stationarities such as trends. In this talk, we investigate analytically the influence of long-range correlations on the estimation of linear trends and their detection with the method of detrended fluctuation analysis, thereby we found new insights on this field of study. First, we provide an analytical study on the estimation of linear trends for time series with long-range correlations and show, that the longer the correlations the higher is the uncertainty of estimated trends. We apply our framework on the long-term daily air temperature record in Potsdam. Then, we focus on the detection of long-range correlations in time series and propose the fluctuation function as an

integral transform of the autocorrelation function with kernel working as filter. This function enables us to estimate the autocorrelation function indirectly via segmentation of the time axis. We show, that this form of the fluctuation function represents the fluctuation function of the well-known method of detrended fluctuation analysis (DFA). Finally, we investigate the DFA analytically. We show, that short-range correlated series causes a crossover behaviour on the scaling of the fluctuation function and we also investigate how DFA works on the intrinsic non-stationary fractional brownian motion.

References:

1) M. Höll and H. Kantz (2015). The fluctuation function of the detrended fluctuation analysis – Investigation on the AR(1) process. The European Physics Journal B, 88 (5): 126

2) M. Höll and H. Kantz (2015). The relationship between the detrended  fluctuation analysis and the autocorrelation function of a signal. The European Physics Journal B, 88 (5): 327

3) Y. Zhou and Y. Leung (2010). Multifractal temporally weighted detrended fluctuation analysis and its application in the analysis of scaling behavior in temperature series. Journal of Statistical Mechanics: Theory and Experiment, 2010(06): P06021

Lukasz Machura(*), Paulina Trybek, Michal Nowakowski

The signals detected from the external anal sphincter muscle with the help of multi-channel surface electromyography are analysed for more than three decades now. Information about localization of the innervation zone, fiber length, muscle fiber conduction velocity or single motor unit (MU) details was obtained from this signals. The electric impulse that propagates along the motoneuron and reaches the neuromuscular junction determines the excitation of the muscle fiber membranes and the generation of propagating action potentials. The summation of the action potentials generated by fibers innervated by a single motoneuron determines the MU action potential (MUAP) as well as provides important information about the anatomical properties of the muscle [1, 2].
Properly analysed signals can be used for the diagnosis of the etiology of the fecal incontinence or in general for the computer assisted classification of patients. To date analysis dealt usually with the analysis of the average rectified value of MUAPs at three depth within the anal canal and corresponding coefficient of variation of MUAP amplitude [3]. Here we propose the in-depth analysis of the raw signals based on two non-linear approaches. We will present the preliminary analysis of the empirical mode decomposition (EMD). This technique allow for breaking down a signal into specific modes without leaving the time domain also for non-stationary and non-linear data. Moreover we discuss the fractal nature of the sEMG signals [4] by means of the multifractal spectrum calculated for two distinct scaling regions which exist in each signal registered from the group of patients exhibiting rectal cancer.

[1] Merletti R, Bottin A, Cescon C, Farina D, Gazzoni M, Martina S, Mesin L, Pozzo M, Rainoldi A, Enck P, Multichannel Surface EMG for the Non-Invasive Assessment of the Anal Sphincter Muscle, Digestion 69, 112 (2004)

[2] Cescon C, Mesin L, Nowakowski M, Merletti R, Geometry assessment of anal sphincter muscle based on monopolar multichannel surface EMG signals, J Electromyogr Kinesiol. 21, 394 (2011)

[3] Nowakowski M, Tomaszewski KA, Herman RM, Salowka J, Romaniszyn M, Rubinkiewicz M, Walocha JA, Developing a new electromyography-based algorithm to diagnose the etiology of fecal incontinence, Int J Colorectal Dis. 29, 747 (2014)

[4] Trybek P, Nowakowski M, Machura L, Evaluation of the training objectives with surface electromyography, Bio-Algorithms and Med-Systems 12, 25 (2016)

Ladislav Kristoufek, Ondrej Filip, Karel Janda, David Zilberman, Institute of Economic Studies, Faculty of Social Sciences, Charles University in Prague, Czech Republic & Institute of Information Theory and Automation, Czech Academy of Sciences, Czech Republic

We examine co-movements between biofuels and a wide range of commodities and assets in the US, Europe, and Brazil. We analyze a unique dataset of 32 commodities and relevant assets (between 2003 and 2015) which is unprecedented in the biofuels literature. We combine the minimum spanning trees correlation filtration to detect the most important connections of the broad analyzed system with continuous wavelet analysis which allows for studying dynamic connections between biofuels and relevant commodities and assets and their frequency characteristics as well. We confirm that for the Brazilian and US ethanol, their respective feedstock commodities lead the prices of biofuels, and not vice versa. This dynamics remains qualitatively unchanged when controlling for the influence of crude oil prices. As opposed to the Brazilian and US ethanol, the European biodiesel exhibits only moderate ties to its production factors. We show that financial factors do not significantly interact with biofuel prices.

References

[1] Kristoufek, L. & Janda, K. & Zilberman, D. (2016): Co-movements of ethanol related prices: Evidence from Brazil and the USA, GCB Bioenergy 8(2), pp. 346-356
[2] Kristoufek, L. & Janda, K. & Zilberman, D. (2014): Price transmission between biofuels, fuels and food commodities, Biofuels Bioproducts & Biorefining 8(3), pp. 362-373
[3] Kristoufek, L. & Janda, K. & Zilberman, D. (2013): Regime-dependent topological properties of biofuels networks, European Physical Journal B 86:40
[4] Kristoufek, L. & Janda, K. & Zilberman, D. (2012): Correlations between biofuels and related commodities before and during the food crisis: A taxonomy perspective, Energy Economics 34(5), pp. 1380-1391

Kiran Sharma , Anirban Chakraborti, Marco Patriarca, Els Heinsalu and Jean Léo Léonard School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, Indiab National Institute of Chemical Physics and Biophysics, Tallinn, Estoniac University of Paris-Sorbonne, STIH & LabEx EFL, France*Presenting author: kiransharma1187@gmail.com

The Mazatec dialects [1], belonging to the family of Oto-Manguean languages (Mexico), represent an emblematic example of linguistic diversity. We carry out an interdisciplinary study of Mazatec dialects by using a complex systems approach. The study consists of two parts, a phenomenological and theoretical one, which are carried out in parallel and eventually matched and combined with each other in a global comprehensive picture of the Mazatec linguistic system.

The phenomenological side of the work is based on the analysis of some available linguistic databases. We carry out a complex network analysis of a database of grammatical taxonomy [2], that we analyze in terms of their mutual Levenshtein distances, and one obtained from a series of mutual dialect intelligibility tests [3]. The results of these analyses provide an overall picture of the relatedness between the various dialects and are able to recover the known sub-families of Mazatec dialects.

Then we construct a reaction-diffusion model of language dynamics [4, 5, 6] which can also take into account the evolution of a language into dialects [7], mimicking the expansion and differentiation of the Mazatec linguistic community. We study the model by performing extended numerical simulations and comparing the results with the constraints from the known data.  It turns out that in order to reproduce the observed distribution and diversity of the dialects, described by the databases, it is essential to take into account both the local physical and economic geography, as well as the road connections and other ecological factors.

References

[1] J.L. Leonard, Emergence forte versus émergence faible en écologie linguistique : le cas du mazatec. 38 Colloque 2014 de l'Institut Universitaire de France, Dijon, 26-28 Mai 2014, "Nature et Culture"

[2] P.L. Kirk, Proto-Mazatec phonology. PhD dissertation, University of Washington (1966).

[3] P.L. Kirk, Intelligibility Testing: The Mazatec Study, IJAL, 36 (3) 205-211 (1970).

[4] A. Kandler, Demography and language competition, Hum Biol, 81(2-3), 181-210 (2009).

[5] M. Patriarca, E. Heinsalu, Influence of geography on language competition, Physica A 388, 174 (2009).

[6] M. Patriarca, X. Castelló, J.R. Uriarte, V.M. Eguíluz, M. San Miguel, Modeling two-language competition dynamics, Adv. Comp. Syst. 15 (3&4), 1250048 (2012).

[7] S. Wichmann, The Emerging Field of Language Dynamics, Language and Linguistics Compass 2/3, 442 (2008)

Jinhyuk Yun 1 (Presenting author), Sang Hoon Lee 2, and Hawoong Jeong1,3,4 1 Department of Physics, Korea Advanced Institute of Science and Technology, Daejeon 34141, Korea 2 School of Physics, Korea Institute for Advanced Study, Seoul 02455, Korea 3 Institute for the BioCentury, Korea Advanced Institute of Science and Technology, Daejeon 34141, Korea 4 Asia Pacific Center for Theoretical Physics, Pohang, Gyeongbuk 37673, Korea

Wikipedia is a free open-editing Internet encyclopedia with an enormous amount of content, written by volunteers with various backgrounds in a collective fashion. The open-editing nature may give us prejudice that Wikipedia is an unstable and unreliable source; yet many studies suggest that Wikipedia is even more accurate and self-consistent than traditional encyclopedias. Scholars have attempted to understand such extraordinary credibility, but usually used the number of edits as the unit of time, lacking consideration of real time. In this work [1], we probe the formation of such collective intelligence through a systematic analysis using the complete history of 267 304 095 articles covering entire Wikimedia projects from the onset. Our primary analysis of English Wikipedia articles shows universality of both timewise and lengthwise editing scales, which suggests that it is essential to consider the real-time dynamics. By considering real time, we reveal the existence of clear distinction of growth patterns that are unobserved by utilizing the number of edits as the unit of time. To account for these results, we present a mechanistic model that adopts the article editing dynamics based on both editor-editor and editor-article interactions. The model successfully generates the key properties of real Wikipedia articles such as distinct types of articles for the editing patterns characterized by the interrelationship between the numbers of edits and editors, and the article size. In addition, the model indicates that infrequently referred articles tend to grow faster than frequently referred ones, and articles attracting a high motivation to edit counterintuitively reduce the number of participants. We suggest that this decay of participants eventually brings inequality among the editors, which will become more severe with time. Our analysis extended to entire 863 Wikimedia projects suggests that this entrenchment inequality stem from the nature of such open-editing communal datasets, namely abiogenesis of “super-editors’ cartel.” We present the evidence that these groups created at the early initial stage of these open-editing media and have kept alive until the present, forewarning the pessimistic prospect of such communal databases.

​

References:

[1] Jinhyuk Yun, Sang Hoon Lee, and Hawoong Jeong, Intellectual interchanges in the history of the massive online open-editing encyclopedia, Wikipedia, Phys. Rev. E 93, 120307 (2016).

[2] Jinhyuk Yun, Pan-Jun Kim, and Hawoong Jeong, Anatomy of scientific evolution, PLoS One 10, e0117388 (2015).

[3] Sang Hoon Lee, Robyn Ffrancon, Daniel M. Abrams, Beom Jun Kim, and Mason A. Porter, Matchmaker, matchmaker, make me a match: Migration of populations via marriages in the past, Phys. Rev. X 4, 041009 (2014).

[4] Hawoong Jeong, Bálint Tombor, Réka Albert, Zoltan N Oltvai, Albert-László Barabási, The large-scale organization of metabolic networks, Nature 407, 651-654, 4569 (2000).

Ilias Lymperopoulos, Athens University of Economics and Business, Greece

Modeling online and offline social dynamical processes constitutes a challenging undertaking due to: (a) The complexity of human behavior. (b) The large number of interconnected heterogeneous individuals who interact in parallel by exchanging messages. (c) The non-equilibrium nature of social systems. (d) The interaction between social networks and the external environment.

In this talk I will present a modeling approach to dynamic patterns of online activity meeting the aforementioned challenges, thereby providing an overarching and generalizable framework for social simulations and human dynamics research.  The proposed model lies at the cross-borders of complex systems, neuroscience, artificial intelligence, big data, computer science, mathematics, sociology, and psychology. Individuals are modeled as leaky Integrate-and-Fire neurons driven by endogenous and exogenous stimuli modulating their behavioral state, while firing thresholds and refractoriness regulate their activation pattern. The time-dependent characteristics of the received stimuli render the interconnected individuals a non-autonomous network dynamical system producing time-varying population activity patterns reflecting the temporal structure and strength of the stimuli driving the activation process. By analyzing online activity patterns related to discussions on Twitter, we derive their characteristics and precisely reproduce the temporal and statistical properties of the empirical observations in a qualitative and quantitative way through simulations. The accurate replication of real online activity patterns demonstrates the capacity of the proposed approach to serve as a state-of-the-art platform of social simulations aiming at understanding, explaining, but also predicting the evolution of social dynamical processes.

The proposed method incorporates the dynamics of epidemiological models, of complex contagion, and of open systems. In addition, it features substantial flexibility capable of accommodating heterogeneity in the individuals’ behavioral characteristics, connectivity, and received stimulus. Through various interpretations of the model parameters it is possible to represent the dynamics of diverse socio-economic phenomena, such as opinion formation, emotional contagion, market behavior, adoption of products, viral marketing, and spread of rumors among others. The capacity of the model to accurately fit real activity patterns in conjunction with the availability of social activity data which can be used for the estimation of the model parameters, break new ground in the simulation of social phenomena. As such, the proposed model provides a framework for the study of macro-level activity patterns emerging from the micro-level dynamics of interconnected individuals driven by self-generated, interpersonal and external influences.

References:

[1]. Lymperopoulos, Ilias N. “Modeling the online social transmission through the theory of complex adaptive systems and neural network models based on the brain dynamics”, PhD Thesis, 2015.

[2]. Lymperopoulos, Ilias N., and George D. Ioannou. “Online social contagion modeling through the dynamics of Integrate-and-Fire neurons.” Information Sciences 320 (2015): 26-61.  

[3]. Lymperopoulos, Ilias N., and George D. Ioannou. “Understanding and modeling the complex dynamics of the online social networks: a scalable conceptual approach.” Evolving Systems (2016): 1-26, in press.

[4] Lymperopoulos, Ilias, and George Lekakos. "Analysis of Social Network Dynamics with Models from the Theory of Complex Adaptive Systems."Collaborative, Trusted and Privacy-Aware e/m-Services. Springer Berlin Heidelberg, 2013. 124-140.     

[5] Lymperopoulos, Ilias N. and George D. Ioannou. “Micro-level dynamics of the online information propagation: A user behavior model based on spiking neurons.” Submitted to Neural Networks (2015), under review.  

Wei Shen and Francesco Porcelli 1 Institute of Plasma Physics, Chinese Academy of Science, Hefei, Anhui, China

Magnetically confined plasmas of controlled thermonuclear interest provide a wealth of data and information on the nonlinear dynamics of complex many-particle plasma systems. In this study, we investigate the onset of magneto-hydro-dynamic instabilities (MHD) in toroidal plasmas capable of causing a topological rearrangement of the magnetic confinement configuration. This is often diagnosed as periodic, sawtooth-like relaxation oscillations of the temperature and density of the confined plasma.
Due to the high nonlinearities of the MHD system, the study is best carried out on the basis of nonlinear simulation codes benchmarked against known analytic results obtained in specific asymptotic regimes1. One such code is M3D-K, initially2 developed in Princeton and at MIT in the ‘90s and later extended by several authors to include relevant effects such as plasma resistivity, viscosity, diamagnetic and other so-called two-fluid effects of the thermal plasma, as well as the effects of fast ion populations arising either from auxiliary plasma heating or from thermonuclear fusion reactions.
More specifically, we shall present results obtained with the M3D-K code concerning the stability of MHD modes with dominant poloidal m=1 and toroidal n=1 mode numbers, which are experimentally known to lead, under certain circumstances, to the well-known internal kink sawtooth phenomenon in tokamak plasmas4. We will demonstrate that diamagnetic, viscosity and two-fluid effects can completely suppress the sawtooth instability in regimes that are consistent with experimental observations. In addition to its relevance for magnetic fusion, this study may also shed light on violent MHD relaxation phenomena observed in space plasmas, such as solar flares and magnetic storms in planetary magnetospheres.

References.
[1] F. Porcelli, Viscous resistive magnetic reconnection, Phys. Fluids 30, 1734-1742 (1987).
[2] W. Park et al, Plasma simulation studies using multilevel physics models, Phys.
Plasmas 6, 1796 (1999).
[3] Wei Shen et al, M3D-K simulations of sawteeth and energetic particle transport in
tokamak plasmas, Phys. Plasmas 21, 092514 (2014).
[4] F. Porcelli, D. Boucher and M. N. Rosenbluth, Model for the sawtooth period and
amplitude, Plasma Physics and Controlled Fusion 38, 2163-2186 (1996).

Silvana De Lillo, Department of Mathematics and Computer Science,University of Perugia, Diletta Burini and Livio Gibelli,Department of Mathematical Science,Politecnico of Torino,Italy

In the framework of our study of Complex Systems we propose  a systems approach to the theory of perception and learning in populations composed of many living entities. Starting from a phenomenological description of these processes, a mathematical structure is derived which is deemed to incorporate their complexity features. The modeling is based on a generalization of kinetic theory methods where interactions are described by theoretical tools of game theory. As an application, the proposed approach is used to model the learning processes that take place in a classroom.Moreover,we presented  a review and critical analysis on a mathematical theory of learning in populations composed of many interacting individuals. The aim is to provide a foundational mathematical framework which may incorporate the main features of the learning process in view of applications to modeling complex systems, including crowds, swarms, and social systems.
The proposed approach is based on the kinetic theory of active particles which has been specifically developed to deal with living systems. The novelty of our contribution is the focus on collective, rather than individual, learning dynamics. This topic presents a certain analogy with evolutionary game theory, where populations take the place of individual players.

References

1.  D.Burini,S.De Lillo and L.Gibelli  “ Collective learning modeling based on the kinetic theory of active particles”
Physics of Life Reviews 16 (2016) 123–139

2. D.Burini,S.De Lillo and L.Gibelli “ Learning dynamics towards modeling living systems”  Physics of Life Reviews 16 (2016) 158–162 

3. V.Coscia,S.De Lillo and M.L.Prioriello “On the modeling of learning dynamics in large living systems” Comm.Appl.Ind.Math.,5,(2014) 469-480

Anna Franczak (*)(1), Marcin W. Zielinski (2) (1) Polish Academy of Sciences, (2) Warsaw University

Scandal with PRISM global data surveillance program exposed by The Guardian and The Washington Post in 2013 was not a surprise, but rather a confirmation that we live in a surveillance culture. Users of service providers, of such giants as Google, YouTube, Skype, Yahoo, Facebook or Apple realized that everything they did online, at one of these platforms, could potentially be recorded and analysed by the NSA – US National Security Agency. The explosion of Social Media services would not be possible without us – online we share our personal data; we inform others of our taste in movies and music, brands we like, where we travel, our relationship status and background on family connections, we present our opinions and use the internet to communicate with friends as well as look for a job or potentially find our other half. Without our regular updates, posts and demographic information, social media services would not exist. So, what was the effect of Edward Snowden’s revelations? Was this just another media news story or did it have an impact on our behaviour? Is protection of our data important for us? In everyday life, do we think about our data privacy?

The project is governed by a basic assumption that there is a correlation between trust of institutions, democratic values and attitudes towards sharing personal data. In this context we investigated the approach to surveillance and data privacy from a cross national perspective. To estimate public interest - for the purpose of the project - we used Google tools, which allow us to check what people search for. We focused on topics of data privacy and surveillance. We analyzed the data spanning a period of 2 years, from 179 countries and using over 143 000 search terms in local languages. From June 2013 up to the present we followed a number of searches for each of these topics to estimate public interest, and as a result to answer the following questions: when do we search the most, now or right after publications? How this trend is changing over time? Do we search more often about it? What are the differences? In which country is the interest about surveillance and data protection highest and in which lowest? If we add variables like GDP and Democracy Index – are there any interesting correlations?

We have used statistical quantitative methods, mainly linear regression in multilevel modelling to control the impact of 24 units (months) in each of the countries. This analysis was a source of many methodological challenges and surprises. During the conference we will present the result of our study but we will also present a step by step journey of conducting the research, including failures and successes. 

Ahmad Alsahaf, Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, (a.m.j.a.alsahaf@rug.nl), Dr Bart Ducro, Animal Breeding and Genomics Center, Wageningen UR, (bart.ducro@wur.nl), Prof. Nicolai Petkov, Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, (n.petkov@rug.nl), Dr George Azzopardi, Department of Intelligent Computer Systems, The University of Malta, (george.azzopardi@um.edu.mt), Prof. Dr Roel Veerkamp, Anima

Machine learning has made great advances in many scientific and industrial applications. One application area that remains largely unexplored is that of animal breeding. Traditional data analysis methods in animal breeding are model-driven. They rely on genetic-based models to predict phenotypic traits. However, modern animal management systems and sensor technologies provide large amounts of data, throughout the lifespan of an animal, that are not suited for genetic analysis. Therefore, data-driven methods are necessary to explore the full potential of this rapidly accumulating data.

In this work, we use the case study of finisher pigs to investigate the benefits of machine learning for modelling the phenotypic variation throughout the lifespan of a pig. Data-driven models are used to combine genetic and non-genetic (phenotypic, environmental) factors for the prediction of growth patterns in finisher pigs. This novel approach is compared to traditional genotype-to-phenotype modelling, and to statistical curve fitting. Moreover, we asses the value of adding phenotypic and environmental data to genetic data for the prediction accuracy of future phenotypes. In addition to that, we establish a methodological framework suitable for future case studies, such as that of dairy cattle.

Suzana Blesić1,2,Đorđe Stratimirović3, Jelena Ajtić4, Caradee Wright5,6, Martin Allen7 1University of Belgrade, Institute for Medical Research, Belgrade, Serbia; 2Ca'Foscari University of Venice, Department of Environmenlas Sciences, Informatics and Statistics, Venice, Italy; 3University of Belgrade, Faculty of Dental Medicine, Belgrade, Serbia; 4University of Belgrade, Faculty of Veterinary Medicine, Belgrade, Serbia; 5South African Medical Research Council, Environment & Health Unit, Pretoria,

Personal sun exposure measurements provide important information to guide the development of sun awareness and disease prevention campaigns. We have investigated scaling properties of personal solar ultraviolet radiation exposure (pUVR) records using the Wavelet Transform (WT) spectral analysis. Personal UVR recordings were collected by UVR monitors designed to measure erythemal (or sunburning) UVR and calculate daily erythemal pUVR exposures. We have analyzed sun exposure recordings of school children, farmers, marathon runners and outdoor workers in South Africa, and outdoor workers and work site supervisors in New Zealand.

We found scaling behavior in all the analysed pUVR datasets. We found that the observed pUVR scaling changes from uncorrelated to long-range correlated with increasing duration of sun exposure. Peaks in the WT spectra that we found suggest the existence of characteristic times in sun exposure behavior that were to some extent universal across our dataset. Our study also showed that scaling measures enable group classification, as well as distinction between individual UVR exposures, otherwise unattainable by conventional statistical methods. In cases of continuous personal daily exposure, associated with high daily exposure indexes, the scaling analysis proved able to differentiate the high dose UVR exposure behavior from the continuous low risk outdoor behavior.

References:

1. S Blesic, Dj Stratimirovic, J Ajtic, C Wright, M Allen (2016) Novel approach to analysing large datasets of personal sun exposure measurements. Submitted to Journal of Exposure Science and Environmental Epidemiology.

2. V Nurse, CY Wright, M Allen, RL McKenzie (2015) Solar Ultraviolet Radiation Exposure of South African Marathon Runners during Competition Marathon Runs and Training Sessions: A Feasibility Study. Photochem. Photobiol. 91, 971.

3. CY Wright, M Makgabutlane (2015) Real-time measurement of outdoor worker’s exposure to solar ultraviolet radiation in Pretoria, South Africa. South African Journal of Science 5, 111.

4. B Køster, JSøndergaard, JB Nielsen, M Allen, M Bjerregaard, A Olsen, JBentzen (2015) Feasibility of smartphone diaries and personal dosimeters to quantitatively study exposure to ultraviolet radiation in a small national sample. Photoderm.Photoimmun.Photomed. (In Press): 1.

Stefan Pickl (*), Maximilian Moll – Universität der Bundeswehr München

In recent years a very popular approach is modelling complex networks as multiplex networks. These are multi-layered networks with replica of each node in every layer. One of the key practical problems with complex networks is learning their structure. One approach for single layer networks is based on co-occurrences, i.e. the knowledge which nodes occur on paths through the network but not in which order. In this paper we extend the methodology to be applicable to multiplex networks. First results will be presented and embedded in the context of citation and transport networks.

References:

M. Nistor, M. Zsifkovits, S. Meyer-Nieberg, S. Pickl; Passenger Pattern Recognition in Railway Stations using Quantitative Network Analysis, 2016
M.Nistor, M. Dehmer, S. Pickl; Network Exploratory Analysis on Subway Transportation Systems against Complex Threats Including a Human Factors Perspective, 2015
D. Lozovanu, S. Pickl; Determining the optimal strategies for discrete control problems on stochastic networks with discounted costs, 2015

M. Nistor, S. Pickl, M. Raap, M. Zsifkovits; Quantitative Network Analysis of Metro Transportation Systems: Introducing the Flow-Weighted Efficiency Measure, 2015 

Armin Bunde, Institute of Theoretical Physics, University of Giessen, Germany

We study how rare events can be characterized in records with linear long-term memory (examples are temperature data), in records with non-linear memory (examples are the returns in financial records), as well as in literary texts, where the rare events are the rare words. In our approach, we consider the return intervals ri  between events (e.g. temperature data, returns, or word ranks) above a certain threshold Q. The return period of these events is RQ. We are mainly interested in the probability density function PQ(r) of the return intervals, for fixed RQ , and the related exceeding probability SQ(r) (PQ(r)=-dSQ(r)/dr). We are also interested in the Hazard function HQ(dt), which describes the probability that in a given range dt after a rare event above Q at least one more rare event occurs. For Gaussian white noise, SQ=exp(-r/RQ) which leads to HQ=1-exp(-dt/RQ). We show that for linearly correlated records, SQ follows approximately a Weibull function, SQ=exp(-a(beta)(r/RQ)beta ) where beta is identical to the correlation exponent, while for the returns in all financial assets considered, SQ has the same universal q-exponential form. For all literary texts considered, SQ perfectly follows  a Weibull function with beta close to ¾.

We discuss the Hazard functions and show that in all systems considered, the rare events have the tendency to cluster. The tendency is strongest in the linear correlated systems. The talk is based on references [1-4]

[1] A. Bunde, J. Eichner, S. Havlin, and J.W. Kantelhardt, Phys. Rev. Lett. 94, 048701 (2005)

[2] J. Ludescher, C. Tsallis, and A. Bunde, EPL 95, 68002 (2011)

[3] J. Ludescher and A. Bunde Phys. Rev. E 90, 062809 (2014)

[4] K. Tanaka-Ishii and A. Bunde, preprint (2016)

Takaaki Ohnishi(*) (The University of Tokyo), Takayuki Mizuno (National Institute of Informatics), Tsutomu Watanabe (The University of Tokyo)

How different urban properties such as number of hospitals, shops, patents, and crimes depend on city size? It has been demonstrated that most urban properties Y follow the allometric scaling law: Y is proportional to N^b, where N and b are population size of a city [1] and the scaling exponent. Urban infrastructure has been shown to scale sub-linearly (b<1) reflecting large cities don't need large infrastructure, whereas output and income have been shown to scale super-linearly (b>1) reflecting high per capita in large cities. Japan has three levels of government: national, prefectural, and municipal. No consensus exists on how cities should be defined. We define cities as municipalities. The nation is divided into 1913 municipalities. In econophysics, many studies have been made on economic big data such as financial market [2], housing market [3], and buyer-supplier networks [4] to provide insights regarding economic phenomenon. Here we empirically analyze urban scaling observed in Japanese telephone directory (Yellow Pages) data compiled by NTT Telephone Data on a nationwide scale. This data contains comprehensive individual listings of 5,424,047 shops or facilities (nearly all shops, firms, hospitals, schools, parks, etc). Name, address, phone number, and industrial sector of the shop or the facility are also included. We can count the number of stores or facilities in each city. The industrial sector is divided into 25 categories. Each category is further divided into 332 subcategories. This allows us to study and discuss systematically the scaling exponent that are associated with various aspects of industry agglomeration. We show that obtained scaling exponents help to characterize urban properties.

[1] S. Fujimoto, T. Mizuno, T. Ohnishi, C. Shimizu, and T. Watanabe. "Geographic Dependency of Population Distribution." Proceedings of the International Conference on Social Modeling and Simulation, plus Econophysics Colloquium 2014 (2015): 151-162.
[2] Takaaki Ohnishi, Hideki Takayasu, Takatoshi Ito, Yuko Hashimoto, Tsutomu Watanabe, and Misako Takayasu. "On the Nonstationarity of the Exchange Rate Process." International Review of Financial Analysis 23 (2012): 30-34.
[3] Takaaki Ohnishi, Takayuki Mizuno, Chihiro Shimizu, and Tsutomu Watanabe. "Power Laws in Real Estate Prices during Bubble Periods." International Journal of Modern Physics: Conference Series 16 (2012): 61-81.
[4] Mizuno, Takayuki, Takaaki Ohnishi, and Tsutomu Watanabe. "Structure of global buyer-supplier networks and its implications for conflict minerals regulations." EPJ Data Science 5.1 (2016): 1-15.