Statistical Laws in Urban Mobility from microscopic GPS data in the area of Florence

The application of Statistical Physics to social systems is mainly related to the search for macroscopic laws, that can be derived from experimental data averaged in time or space,assuming the system in a steady state. One of the major goals would be to find a connection between the statistical laws to the microscopic properties: for example to understand the nature of the microscopic interactions or to point out the existence of interaction networks. The probability theory suggests the existence of few classes of stationary distributions in the thermodynamics limit, so that the question is if a statistical physics approach could be able to enroll the complex nature of the social systems. We have analyzed a large GPS data base for single vehicle mobility in the Florence urban area, obtaining statistical laws for path lengths, for activity downtimes and for activity degrees. We show also that simple generic assumptions on the microscopic behavior could explain the existence of stationary macroscopic laws, with an universal function describing the distribution. Our conclusion is that understanding the system complexity requires dynamical data-base for the microscopic evolution, that allow to solve both small space and time scales in order to study the transients.Comment: 17 pages, 14 figures .jpg, use imsart.cl

Biodiversity As An Ecological Safety Condition. The European Dimension

The main purpose of the paper is to indicate the theoretical bases of biodiversity protection from the perspective of the natural and economic sciences, and to describe the diversity of biodiversity protection levels in the EU states. A specific aim is to indicate the forms and instruments of nature conservation involved in biodiversity protection, and to carry out an overview of established nature conservation programmes in selected EU countries. In order to accomplish such a complex aim, this article presents an overview of literature found in the natural, economic and legal sciences and popular magazines presenting scientific research within the field of biodiversity. Then a comparative analysis is presented based on the statistical data coming from various international statistics resources (OECD, EUROSTAT, EEA)

In All Likelihood, Deep Belief Is Not Enough

Statistical models of natural stimuli provide an important tool for researchers in the fields of machine learning and computational neuroscience. A canonical way to quantitatively assess and compare the performance of statistical models is given by the likelihood. One class of statistical models which has recently gained increasing popularity and has been applied to a variety of complex data are deep belief networks. Analyses of these models, however, have been typically limited to qualitative analyses based on samples due to the computationally intractable nature of the model likelihood. Motivated by these circumstances, the present article provides a consistent estimator for the likelihood that is both computationally tractable and simple to apply in practice. Using this estimator, a deep belief network which has been suggested for the modeling of natural image patches is quantitatively investigated and compared to other models of natural image patches. Contrary to earlier claims based on qualitative results, the results presented in this article provide evidence that the model under investigation is not a particularly good model for natural image

Statistical mechanics of complex networks

Complex networks describe a wide range of systems in nature and society, much quoted examples including the cell, a network of chemicals linked by chemical reactions, or the Internet, a network of routers and computers connected by physical links. While traditionally these systems were modeled as random graphs, it is increasingly recognized that the topology and evolution of real networks is governed by robust organizing principles. Here we review the recent advances in the field of complex networks, focusing on the statistical mechanics of network topology and dynamics. After reviewing the empirical data that motivated the recent interest in networks, we discuss the main models and analytical tools, covering random graphs, small-world and scale-free networks, as well as the interplay between topology and the network's robustness against failures and attacks.Comment: 54 pages, submitted to Reviews of Modern Physic

Statistical learnability of nuclear masses

After more than 80 years from the seminal work of Weizs\"acker and the liquid drop model of the atomic nucleus, deviations from experiments of mass models ($\sim$ MeV) are orders of magnitude larger than experimental errors ($\lesssim$ keV). Predicting the mass of atomic nuclei with precision is extremely challenging. This is due to the non--trivial many--body interplay of protons and neutrons in nuclei, and the complex nature of the nuclear strong force. Statistical theory of learning will be used to provide bounds to the prediction errors of model trained with a finite data set. These bounds are validated with neural network calculations, and compared with state of the art mass models. Therefore, it will be argued that the nuclear structure models investigating ground state properties explore a system on the limit of the knowledgeable, as defined by the statistical theory of learning

Quantitative Analysis of Complex Tropical Forest Stands: A Review

The importance of data analysis in quantitative assessment of natural resources remains significant in the sustainable management of complex tropical forest resources. Analyses of data from complex tropical forest stands have not been easy or clear due to improper data management. It is pivotal to practical researches and discovery that promote development in forestry and many related disciplines. Many quantitative methods andapproaches are strongly dependent on the source, nature, and quality of the data. However, many issues related to data analysis in the tropical complex forests are inimical and may render quantitative methods impossible if not resolved. Data collection in many complex tropical forests is very difficult and oftentimes results in data violating simple assumptions of statistical models. The use of relevant data transformation proffers significant solution to this perennial challenge within the complex tropical forests. This paper therefore reviews statistical issues related to quantitative data collection and analyses in the complex tropical forests and provides pragmatic approaches for solving data analysis challenges in complex tropical forests’ management and planning.Keywords: data issues, analysis, complex stands, forestr

Characteristics of Real Futures Trading Networks

Futures trading is the core of futures business, and it is considered as one of the typical complex systems. To investigate the complexity of futures trading, we employ the analytical method of complex networks. First, we use real trading records from the Shanghai Futures Exchange to construct futures trading networks, in which nodes are trading participants, and two nodes have a common edge if the two corresponding investors appear simultaneously in at least one trading record as a purchaser and a seller respectively. Then, we conduct a comprehensive statistical analysis on the constructed futures trading networks. Empirical results show that the futures trading networks exhibit features such as scale-free behavior with interesting odd-even-degree divergence in low-degree regions, small-world effect, hierarchical organization, power-law betweenness distribution, disassortative mixing, and shrinkage of both the average path length and the diameter as network size increases. To the best of our knowledge, this is the first work that uses real data to study futures trading networks, and we argue that the research results can shed light on the nature of real futures business.Comment: 18 pages, 9 figures. Final version published in Physica