Neural activity as vector fields

We consider the information transmission problem in neurons and its possible implications for learning in neural networks. Our approach is based on recent developments in statistical physics and complexity science. We also develop a method to try and select significant neural responses from the background activity and consider its wider applications. This would support temporal coding theory as a model for neural coding

Quantum Hamiltonian Complexity

Constraint satisfaction problems are a central pillar of modern computational complexity theory. This survey provides an introduction to the rapidly growing field of Quantum Hamiltonian Complexity, which includes the study of quantum constraint satisfaction problems. Over the past decade and a half, this field has witnessed fundamental breakthroughs, ranging from the establishment of a "Quantum Cook-Levin Theorem" to deep insights into the structure of 1D low-temperature quantum systems via so-called area laws. Our aim here is to provide a computer science-oriented introduction to the subject in order to help bridge the language barrier between computer scientists and physicists in the field. As such, we include the following in this survey: (1) The motivations and history of the field, (2) a glossary of condensed matter physics terms explained in computer-science friendly language, (3) overviews of central ideas from condensed matter physics, such as indistinguishable particles, mean field theory, tensor networks, and area laws, and (4) brief expositions of selected computer science-based results in the area. For example, as part of the latter, we provide a novel information theoretic presentation of Bravyi's polynomial time algorithm for Quantum 2-SAT.Comment: v4: published version, 127 pages, introduction expanded to include brief introduction to quantum information, brief list of some recent developments added, minor changes throughou

Neural Decoder for Topological Codes using Pseudo-Inverse of Parity Check Matrix

Recent developments in the field of deep learning have motivated many researchers to apply these methods to problems in quantum information. Torlai and Melko first proposed a decoder for surface codes based on neural networks. Since then, many other researchers have applied neural networks to study a variety of problems in the context of decoding. An important development in this regard was due to Varsamopoulos et al. who proposed a two-step decoder using neural networks. Subsequent work of Maskara et al. used the same concept for decoding for various noise models. We propose a similar two-step neural decoder using inverse parity-check matrix for topological color codes. We show that it outperforms the state-of-the-art performance of non-neural decoders for independent Pauli errors noise model on a 2D hexagonal color code. Our final decoder is independent of the noise model and achieves a threshold of $10 \%$. Our result is comparable to the recent work on neural decoder for quantum error correction by Maskara et al.. It appears that our decoder has significant advantages with respect to training cost and complexity of the network for higher lengths when compared to that of Maskara et al.. Our proposed method can also be extended to arbitrary dimension and other stabilizer codes.Comment: 12 pages, 12 figures, 2 tables, submitted to the 2019 IEEE International Symposium on Information Theor

An illustration of new methods in machine condition monitoring, Part I: Stochastic resonance

There have been many recent developments in the application of data-based methods to machine condition monitoring. A powerful methodology based on machine learning has emerged, where diagnostics are based on a two-step procedure: extraction of damage sensitive features, followed by unsupervised learning (novelty detection) or supervised learning (classification). The objective of the current pair of papers is simply to illustrate one state-of the-art procedure for each step, using synthetic data representative of reality in terms of size and complexity. The first paper in the pair will deal with feature extraction. Although some papers have appeared in the recent past considering stochastic resonance as a means of amplifying damage information in signals, they have largely relied on ad hoc specifications of the resonator used. In contrast, the current paper will adopt a principled optimisation-based approach to the resonator design. The paper will also show that a discrete dynamical system can provide all the benefits of a continuous system, but also provide a considerable speed-up in terms of simulation time in order to facilitate the optimisation approach

Software defined mobile multicast

Mobile multicast has been deployed in telecommunication networks for information dissemination applications such as IPTV and video conferencing. Recent studies of mobile multicast focused on fast handover protocols, and algorithms for multicast tree management have witnessed little improvement over the years. Shortest path trees represent the status quo of multicast topology in real-world systems. Steiner trees were investigated extensively in the theory community and are known to be bandwidth efficient, but come with an associated complexity. Recent developments in the Software Defined Networking (SDN) paradigm have shed light on implementing more sophisticated protocols for better routing performance. We propose an SDN-based design to combat the complexity vs. Performance dilemma in mobile multicast. We construct low-cost Steiner trees for multicastin a mobile network, employing an SDN controller for coordinating tree construction and morphing. Highlights of our design include a set of efficient online algorithms for tree adjustment when nodes arrive and depart on the fly, and an SDN rule update framework based on constraints expressed by boolean logic to ensure loop free rule updates. The algorithms are proven to achieve a constant competitive ratio against the offline optimal Steiner tree, with an amortized constant number of edge swaps per adjustment. Mininet-based implementation and evaluation further validate the efficacy of our design. © 2015 IEEE.postprin

Outside the Gates of Europe, the Weapons Speak : Metaphorical Conceptualizations of Ukraine and Russia in German Media Discourse

The world of global politics is composed of complex, interrelated and events. To obtain information, to form an opinion and to react to recent developments, policy makers as well as the public depend on news media. Decisions in the field of global politics, therefore, are based on perceptions and beliefs rather than on objective assessments. The following study takes the example of the German print media, analysing the German perception of Russia and Ukraine within the context of the ongoing Ukraine conflict. The current political development in Ukraine is complex, driven by geopolitical, economical and ideological factors. A mediation of these events by news media therefore requires a drastic reduction of complexity to inform readers and decision-makers on developments in this region. Through examination of conceptual metaphors used in the representation of the conflict, this study seeks to better understand how the two main state actors are comprehended and portrayed in German media discourse

Towards Collaborative Travel Recommender Systems

Collaborative filtering (CF) based recommender systems have been proven to be a promising solution to the problem of information overload. Such systems provide personalized recommendations to users based on their previously expressed preferences and that of other similar users. In the past decade, they have been successfully applied in various domains, such as the recommendation of books and movies, where items are simple, independent and single units. When applied in the tourism domain, however, CF falls short due to the simplicity of existing techniques and complexity of tourism products. In view of this, a study was carried out to review the research problems and opportunities. This paper details the results of the study, which includes a review on the recent developments in CF as well as recommender systems in tourism, and suggests future research directions for personalized recommendation of tourist destinations and products