ITMO University Fellowship program aims to provide outstanding researchers and scientists, who are, or have the potential to become, leaders in their chosen fields, with the opportunity to build an independent research career. Our intention is to help to develop next generation of researchers with the greatest potential in their postdoctoral and early career stages.

Source: fellowship.ifmo.ru

Shannon information theory has provided rigorous ways to capture our intuitive notions regarding uncertainty and information, and made an enormous impact in doing so. One of the fundamental measures here is mutual information, which captures the average information contained in one variable about another, and vice versa. If we have two source variables and a target, for example, we can measure the information held by one source about the target, the information held by the other source about the target, and the information held by those sources together about the target. Any other notion about the directed information relationship between these variables, which can be captured by classical information-theoretic measures (e.g., conditional mutual information terms) is linearly redundant with those three quantities.

However, intuitively, there is strong desire to measure further notions of how this directed information interaction may be decomposed, e.g., how much information the two source variables hold redundantly about the target, how much each source variable holds uniquely, and how much information can only be discerned by synergistically examining the two sources together. These notions go beyond the traditional information-theoretic view of a channel serving the purpose of reliable communication, considering now the situation of multiple communication streams converging on a single target. This is a common situation in biology, and in particular in neuroscience, where, say, the ability of a target to synergistically fuse multiple information sources in a non-trivial fashion is likely to have its own intrinsic value, independently of reliability of communication.

The absence of measures for such decompositions into redundant, unique and synergistic information is arguably the most fundamental missing piece in classical information theory. Triggered by the formulation of the Partial Information Decomposition framework by Williams and Beer in 2010, the past few years have witnessed a concentration of work by the community in proposing, contrasting, and investigating new measures to capture these notions of information decomposition. Other theoretical developments consider how these measures relate to concepts of information processing in terms of storage, transfer and modification. Meanwhile, computational neuroscience has emerged as a primary application area due to significant interest in questions surrounding how target neurons integrate information from large numbers of sources, as well as the availability of data sets to investigate these questions on.

This Special Issue seeks to bring together these efforts, to capture a snapshot of the current research, as well as to provide impetus for and focused scrutiny on newer work. We also seek to present progress to the wider community and attract further research. We welcome research articles proposing new measures or pointing out future directions, review articles on existing approaches, commentary on properties and limitations of such approaches, philosophical contributions on how such measures may be used or interpreted, applications to empirical data (e.g., neural imaging data), and more.

Dr. Joseph Lizier
Dr. Nils Bertschinger
Prof. Michael Wibral
Prof. Juergen Jost
Guest Editors

Source: www.mdpi.com

The Thomas J. Watson School of Engineering and Applied Science at Binghamton University is seeking nominations and applications for the George J. Klir Endowed Professor in Systems Science position. This is a newly-created position made possible by a gift to honor the memory and work of the late George J. Klir, a Distinguished Professor Emeritus of Systems Science at Binghamton University.
The candidate should demonstrate leading academic fortitude in the fields inspired by Dr. Klir’s work including, but not limited to: complex systems; artificial intelligence; fuzzy set theory, fuzzy logic and fuzzy systems; general systems concepts and theory; generalized information theory; probabilistic and possibilistic theory; soft computing; systems problem solving; uncertainty theory; and fields and disciplines that develop from this work. The position will carry the rank of full professor in the Department of Systems Science and Industrial Engineering, Thomas J. Watson School of Engineering and Applied Science.

Source: binghamton.interviewexchange.com

Today we’re launching a new tool to help people read research literature, instead of getting stuck behind paywalls. It’s an extension for Chrome and Firefox that links you to free full-text as you browse research articles. Hit a paywall? No problem: click the green tab and read it free!

Source: blogs.lse.ac.uk

We are looking for a Postdoctoral Researcher in the area of blockchain technology with a particular focus on applications in the fields of IoT and marketplaces for digital currencies.
The ideal candidate shall pursue exciting research & development in systems, blockchain, decentralized networks and IoT and some of the following areas like cryptocurrencies, financial markets, the sharing economy and digital marketplaces. The work will be performed within the European project “FuturICT 2.0“, which explores the future of digital societies and markets over the coming three years. During this period, we will build a pilot application and aim at integrating it with the nervousnet.

Source: m.refline.ch