Sparse visual models for biologically inspired sensorimotor control

Given the importance of using resources efficiently in the competition for survival, it is reasonable to think that natural evolution has discovered efficient cortical coding strategies for representing natural visual information. Sparse representations have intrinsic advantages in terms of fault-tolerance and low-power consumption potential, and can therefore be attractive for robot sensorimotor control with powerful dispositions for decision-making. Inspired by the mammalian brain and its visual ventral pathway, we present in this paper a hierarchical sparse coding network architecture that extracts visual features for use in sensorimotor control. Testing with natural images demonstrates that this sparse coding facilitates processing and learning in subsequent layers. Previous studies have shown how the responses of complex cells could be sparsely represented by a higher-order neural layer. Here we extend sparse coding in each network layer, showing that detailed modeling of earlier stages in the visual pathway enhances the characteristics of the receptive fields developed in subsequent stages. The yield network is more dynamic with richer and more biologically plausible input and output representation

Multiscale Information Decomposition: Exact Computation for Multivariate Gaussian Processes

Exploiting the theory of state space models, we derive the exact expressions of the information transfer, as well as redundant and synergistic transfer, for coupled Gaussian processes observed at multiple temporal scales. All of the terms, constituting the frameworks known as interaction information decomposition and partial information decomposition, can thus be analytically obtained for different time scales from the parameters of the VAR model that fits the processes. We report the application of the proposed methodology firstly to benchmark Gaussian systems, showing that this class of systems may generate patterns of information decomposition characterized by mainly redundant or synergistic information transfer persisting across multiple time scales or even by the alternating prevalence of redundant and synergistic source interaction depending on the time scale. Then, we apply our method to an important topic in neuroscience, i.e., the detection of causal interactions in human epilepsy networks, for which we show the relevance of partial information decomposition to the detection of multiscale information transfer spreading from the seizure onset zone

DLocalMotif: a discriminative approach for discovering local motifs in protein sequences

Motivation: Local motifs are patterns of DNA or protein sequences that occur within a sequence interval relative to a biologically defined anchor or landmark. Current protein motif discovery methods do not adequately consider such constraints to identify biologically significant motifs that are only weakly over-represented but spatially confined. Using negatives, i.e. sequences known to not contain a local motif, can further increase the specificity of their discovery

Why Information Matters: A Foundation for Resilience

Embracing Change: The Critical Role of Information, a research project by the Internews' Center for Innovation & Learning, supported by the Rockefeller Foundation, combines Internews' longstanding effort to highlight the important role ofinformation with Rockefeller's groundbreaking work on resilience. The project focuses on three major aspects:- Building knowledge around the role of information in empowering communities to understand and adapt to different types of change: slow onset, long-term, and rapid onset / disruptive;- Identifying strategies and techniques for strengthening information ecosystems to support behavioral adaptation to disruptive change; and- Disseminating knowledge and principles to individuals, communities, the private sector, policymakers, and other partners so that they can incorporate healthy information ecosystems as a core element of their social resilience strategies

Interactive probabilistic post-mining of user-preferred spatial co-location patterns

© 2018 IEEE. Spatial co-location pattern mining is an important task in spatial data mining. However, traditional mining frameworks often produce too many prevalent patterns of which only a small proportion may be truly interesting to end users. To satisfy user preferences, this work proposes an interactive probabilistic post-mining method to discover user-preferred co-location patterns from the early-round of mined results by iteratively involving user's feedback and probabilistically refining preferred patterns. We first introduce a framework of interactively post-mining preferred co-location patterns, which enables a user to effectively discover the co-location patterns tailored to his/her specific preference. A probabilistic model is further introduced to measure the user feedback-based subjective preferences on resultant co-location patterns. This measure is used to not only select sample co-location patterns in the iterative user feedback process but also rank the results. The experimental results on real and synthetic data sets demonstrate the effectiveness of our approach

The interdependence between biodiversity and socioeconomic variables on a local level: evidence for german counties

This paper explores possible interdependence of biodiversity and several socioeconomic and political factors at the county level. It is aimed at the empirical identification of direct and indirect effects between biodiversity (loss) and their theoretical major impact factors. To date, research shows that in addition to geography, agriculture is one major determinant of biodiversity status. However, the impact of regional socioeconomic structures on biodiversity should not be underestimated. Specifically, in regard to biodiversity loss, the socioeconomic structure counteracts political measures instituted to protect biodiversity and change agricultural practice.biodiversity, socioeconomic interdependence, Bavaria, Thuringia