LIPN: Introducing a new Geographical Context Similarity Measure and a Statistical Similarity Measure based on the Bhattacharyya coefficient

International audienceThis paper describes the system used by the LIPN team in the task 10, Multilingual Semantic Textual Similarity, at SemEval 2014, in both the English and Spanish sub-tasks. The system uses a support vector regression model, combining different text similarity measures as features. With respect to our 2013 participation, we included a new feature to take into account the geographical context and a new semantic distance based on the Bhattacharyya distance calculated on co-occurrence distributions derived from the Spanish Google Books n-grams dataset

Analyzing the neocortical fine-structure

Cytoarchitectonic fields of the human neocortex are defined by characteristic variations in the composition of a general six-layer structure. It is commonly accepted that these fields correspond to functionally homogeneous entities. Diligent techniques were developed to characterize cytoarchitectonic fields by staining sections of post-mortem brains and subsequent statistical evaluation. Fields were found to show a considerable interindividual variability in extent and relation to macroscopic anatomical landmarks. With upcoming new high-resolution magnetic resonance imaging (MRI) protocols, it appears worthwhile to examine the feasibility of characterizing the neocortical fine-structure from anatomical MRI scans, thus, defining neocortical fields by in vivo techniques. A fixated brain hemisphere was scanned at a resolution of approximately 0.3 mm. After correcting for intensity inhomogeneities in the dataset, the cortex boundaries (the white/grey matter and grey matter/background interfaces) were determined as a triangular mesh. Radial intensity profiles following the shortest path through the cortex were computed and characterized by a sparse set of features. A statistical similarity measure between features of different regions was defined, and served to define the extent of Brodmann’s Areas 4, 17, 44 and 45 in this dataset

Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package

We introduce the \texttt{pyunicorn} (Pythonic unified complex network and recurrence analysis toolbox) open source software package for applying and combining modern methods of data analysis and modeling from complex network theory and nonlinear time series analysis. \texttt{pyunicorn} is a fully object-oriented and easily parallelizable package written in the language Python. It allows for the construction of functional networks such as climate networks in climatology or functional brain networks in neuroscience representing the structure of statistical interrelationships in large data sets of time series and, subsequently, investigating this structure using advanced methods of complex network theory such as measures and models for spatial networks, networks of interacting networks, node-weighted statistics or network surrogates. Additionally, \texttt{pyunicorn} provides insights into the nonlinear dynamics of complex systems as recorded in uni- and multivariate time series from a non-traditional perspective by means of recurrence quantification analysis (RQA), recurrence networks, visibility graphs and construction of surrogate time series. The range of possible applications of the library is outlined, drawing on several examples mainly from the field of climatology.Comment: 28 pages, 17 figure

ACTIVITY-BASED MODELING FRAMEWORK FOR TRAVEL DEMAND AND BEHAVIOUR

In all developed and developing countries of the world, the government transportation’s policies aimed at controlling aggregate phenomena such as congestion, emissions and land use patterns. These are achieved through the provision of employer- based commute programs, single occupant vehicle regulation, road pricing, multimodal facilities and transit oriented land development. But these policies affect the aggregate phenomen indirectly through the behaviour of individuals. Furthermore, individuals adjust their behaviour in complex ways, motivated by a desire to achieve their activity objectives. This paper examines the activity based modeling framework for travel demand and behaviour, the concepts underlying the methods and modeling approaches. Finally, it identified three classes of model systems, which are econometric model systems, hybrid simulation systems and the theory of planned behaviour model, and also look at some examples in each class, considering how they work, and their particular strengths and weaknesses, and above all, looking at the big picture.  Article visualizations