OpenRaiser/Intern-Atlas huggingface dataset:
On the Nanocommunications at THz Band in Graphene-Enabled Wireless Network-on-Chip
Venue: arXiv cs.DC 2017
Year: 2017

One of the main challenges towards the growing computation-intensive applications with scalable bandwidth requirement is the deployment of a dense number of on-chip cores within a chip package. To this end, this paper investigates the Wireless Network- on-Chip (WiNoC), which is enabled by graphene-based nanoantennas (GNAs) in Terahertz frequency band. We first develop a channel model between the GNAs taking into account the practical issues of the propagation medium, such as transmission frequency, operating temperature, ambient pressure, and distance between the GNAs. In the Terahertz band, not only dielectric propagation loss but also molecular absorption attenuation (MAA) caused by various molecules and their isotopologues within the chip package constitutes the signal transmission loss. We further propose an optimal power allocation to achieve the channel capacity. The proposed channel model shows that the MAA significantly degrades the performance at certain frequency ranges compared to the conventional channel model, even when the GNAs are very closely located. More specifically, at transmission frequency of 1 THz, the channel capacity of the proposed model is shown to be much lower than that of the conventional model over the whole range of temperature and ambient pressure of up to 26.8% and 25%, respectively.
Simple and Near-Optimal Distributed Coloring for Sparse Graphs
Venue: arXiv cs.DS 2017
Year: 2017

Graph coloring is one of the central problems in distributed graph algorithms. Much of the research on this topic has focused on coloring with $Δ+1$ colors, where $Δ$ denotes the maximum degree. Using $Δ+1$ colors may be unsatisfactory in sparse graphs, where not all nodes have such a high degree; it would be more desirable to use a number of colors that improves with sparsity. A standard measure that captures sparsity is arboricity, which is the smallest number of forests into which the edges of the graph can be partitioned. We present simple randomized distributed algorithms that, with high probability, color any $n$-node $α$-arboricity graph: - using $(2+\varepsilon)\cdot α$ colors, for constant $\varepsilon>0$, in $O(\log n)$ rounds, if $α=\tildeΩ(\log n)$, or - using $O(α\log α)$ colors, in $O(\log n)$ rounds, or - using $O(α)$ colors, in $O(\log n \cdot \min\{\log\log n,\; \log α\})$ rounds. These algorithms are nearly-optimal, as it is known by results of Linial [FOCS'87] and Barenboim and Elkin [PODC'08] that coloring with $Θ(α)$ colors, or even poly$(α)$ colors, requires $Ω(\log_α n)$ rounds. The previously best-known $O(\log n)$-time result was a deterministic algorithm due to Barenboim and Elkin [PODC'08], which uses $Θ(α^2)$ colors. Barenboim and Elkin stated improving this number of colors as an open problem in their Distributed Graph Coloring Book.
A survey on fiber nonlinearity compensation for 400 Gbps and beyond optical communication systems
Venue: arXiv cs.NI 2017
Year: 2017

Optical communication systems represent the backbone of modern communication networks. Since their deployment, different fiber technologies have been used to deal with optical fiber impairments such as dispersion-shifted fibers and dispersion-compensation fibers. In recent years, thanks to the introduction of coherent detection based systems, fiber impairments can be mitigated using digital signal processing (DSP) algorithms. Coherent systems are used in the current 100 Gbps wavelength-division multiplexing (WDM) standard technology. They allow the increase of spectral efficiency by using multi-level modulation formats, and are combined with DSP techniques to combat the linear fiber distortions. In addition to linear impairments, the next generation 400 Gbps/1 Tbps WDM systems are also more affected by the fiber nonlinearity due to the Kerr effect. At high input power, the fiber nonlinear effects become more important and their compensation is required to improve the transmission performance. Several approaches have been proposed to deal with the fiber nonlinearity. In this paper, after a brief description of the Kerr-induced nonlinear effects, a survey on the fiber nonlinearity compensation (NLC) techniques is provided. We focus on the well-known NLC techniques and discuss their performance, as well as their implementation and complexity. An extension of the inter-subcarrier nonlinear interference canceler approach is also proposed. A performance evaluation of the well-known NLC techniques and the proposed approach is provided in the context of Nyquist and super-Nyquist superchannel systems.
Distributed Apportioning in a Power Network for providing Demand Response Services
Venue: arXiv cs.SY 2017
Year: 2017

Greater penetration of Distributed Energy Resources (DERs) in power networks requires coordination strategies that allow for self-adjustment of contributions in a network of DERs, owing to variability in generation and demand. In this article, a distributed scheme is proposed that enables a DER in a network to arrive at viable power reference commands that satisfies the DERs local constraints on its generation and loads it has to service, while, the aggregated behavior of multiple DERs in the network and their respective loads meet the ancillary services demanded by the grid. The Net-load Management system for a single unit is referred to as the Local Inverter System (LIS) in this article . A distinguishing feature of the proposed consensus based solution is the distributed finite time termination of the algorithm that allows each LIS unit in the network to determine power reference commands in the presence of communication delays in a distributed manner. The proposed scheme allows prioritization of Renewable Energy Sources (RES) in the network and also enables auto-adjustment of contributions from LIS units with lower priority resources (non-RES). The methods are validated using hardware-in-the-loop simulations with Raspberry PI devices as distributed control units, implementing the proposed distributed algorithm and responsible for determining and dispatching realtime power reference commands to simulated power electronics interface emulating LIS units for demand response.
STNet: Selective Tuning of Convolutional Networks for Object Localization
Venue: arXiv cs.CV 2017
Year: 2017

Visual attention modeling has recently gained momentum in developing visual hierarchies provided by Convolutional Neural Networks. Despite recent successes of feedforward processing on the abstraction of concepts form raw images, the inherent nature of feedback processing has remained computationally controversial. Inspired by the computational models of covert visual attention, we propose the Selective Tuning of Convolutional Networks (STNet). It is composed of both streams of Bottom-Up and Top-Down information processing to selectively tune the visual representation of Convolutional networks. We experimentally evaluate the performance of STNet for the weakly-supervised localization task on the ImageNet benchmark dataset. We demonstrate that STNet not only successfully surpasses the state-of-the-art results but also generates attention-driven class hypothesis maps.
Cold Fusion: Training Seq2Seq Models Together with Language Models
Venue: arXiv cs.CL 2017
Year: 2017

Sequence-to-sequence (Seq2Seq) models with attention have excelled at tasks which involve generating natural language sentences such as machine translation, image captioning and speech recognition. Performance has further been improved by leveraging unlabeled data, often in the form of a language model. In this work, we present the Cold Fusion method, which leverages a pre-trained language model during training, and show its effectiveness on the speech recognition task. We show that Seq2Seq models with Cold Fusion are able to better utilize language information enjoying i) faster convergence and better generalization, and ii) almost complete transfer to a new domain while using less than 10% of the labeled training data.
Weakly and Strongly Irreversible Regular Languages
Venue: arXiv cs.FL 2017
Year: 2017

Finite automata whose computations can be reversed, at any point, by knowing the last k symbols read from the input, for a fixed k, are considered. These devices and their accepted languages are called k-reversible automata and k-reversible languages, respectively. The existence of k-reversible languages which are not (k-1)-reversible is known, for each k>1. This gives an infinite hierarchy of weakly irreversible languages, i.e., languages which are k-reversible for some k. Conditions characterizing the class of k-reversible languages, for each fixed k, and the class of weakly irreversible languages are obtained. From these conditions, a procedure that given a finite automaton decides if the accepted language is weakly or strongly (i.e., not weakly) irreversible is described. Furthermore, a construction which allows to transform any finite automaton which is not k-reversible, but which accepts a k-reversible language, into an equivalent k-reversible finite automaton, is presented.
A New Sensing 5'-->3' Watson-Crick Automata Concept
Venue: arXiv cs.FL 2017
Year: 2017

Watson-Crick (WK) finite automata are working on a Watson-Crick tape, that is, on a DNA molecule. Therefore, it has two reading heads. While in traditional WK automata both heads read the whole input in the same physical direction, in 5'->3' WK automata the heads start from the two extremes and read the input in opposite direction. In sensing 5'->3' WK automata the process on the input is finished when the heads meet. Since the heads of a WK automaton may read longer strings in a transition, in previous models a so-called sensing parameter took care for the proper meeting of the heads (not allowing to read the same positions of the input in the last step). In this paper, a new model is investigated, which works without the sensing parameter (it is done by an appropriate change of the concept of configuration). Consequently, the accepted language classes of the variants are also changed. Various hierarchy results are proven in the paper.
Large-Scale User Modeling with Recurrent Neural Networks for Music Discovery on Multiple Time Scales
Venue: arXiv cs.IR 2017
Year: 2017

The amount of content on online music streaming platforms is immense, and most users only access a tiny fraction of this content. Recommender systems are the application of choice to open up the collection to these users. Collaborative filtering has the disadvantage that it relies on explicit ratings, which are often unavailable, and generally disregards the temporal nature of music consumption. On the other hand, item co-occurrence algorithms, such as the recently introduced word2vec-based recommenders, are typically left without an effective user representation. In this paper, we present a new approach to model users through recurrent neural networks by sequentially processing consumed items, represented by any type of embeddings and other context features. This way we obtain semantically rich user representations, which capture a user's musical taste over time. Our experimental analysis on large-scale user data shows that our model can be used to predict future songs a user will likely listen to, both in the short and long term.
Towards a Formal, Visual Framework of Emergent Cognitive Development of Scholars
Venue: arXiv cs.DL 2017
Year: 2017

Understanding the cognitive evolution of researchers as they progress in the academia is an important but complex problem, a problem belonging to a class of problems, which often require the development of models for gaining further understanding in the intricacies of the domain. The research question that we address in this paper is how to effectively model this temporal cognitive mental development of prolific researchers. Our proposed solution to this problem is based on noting that the academic progression and notability of a researcher are linked with a progressive increase in the citation count for the scholar's refereed publications quantified using indices such as the Hirsch index. In other words, we propose the use of yearly cognitive increment of a scholar's cognition to be quantifiable using a function of the scholar's citation index, thereby considering the index as an indicator of the discrete approximation of the scholar's cognitive development. Using validated agent-based modeling, a paradigm presented as part of our previous work i.e. Cognitive Agent-based Computing framework, we present both formal as well as visual agent-based complex network representations for this cognitive evolution in the form of a Temporal Cognitive Level Network (TCLN) model. As a proof of the effectiveness of this approach, we demonstrate validation of the model using historic data of citations.
A Self-Stabilizing General De Bruijn Graph
Venue: arXiv cs.DC 2017
Year: 2017

Searching for other participants is one of the most important operations in a distributed system. We are interested in topologies in which it is possible to route a packet in a fixed number of hops until it arrives at its destination. Given a constant $d$, this paper introduces a new self-stabilizing protocol for the $q$-ary $d$-dimensional de Bruijn graph ($q = \sqrt[d]{n}$) that is able to route any search request in at most $d$ hops w.h.p., while significantly lowering the node degree compared to the clique: We require nodes to have a degree of $\mathcal O(\sqrt[d]{n})$, which is asymptotically optimal for a fixed diameter $d$. The protocol keeps the expected amount of edge redirections per node in $\mathcal O(\sqrt[d]{n})$, when the number of nodes in the system increases by factor $2^d$. The number of messages that are periodically sent out by nodes is constant.
Secure two-way communication via a wireless powered untrusted relay and friendly jammer
Venue: arXiv cs.CR 2017
Year: 2017

In this paper, we propose a self-dependent two-way secure communication where two sources exchange confidential messages via a wireless powered untrusted amplify-and-forward (AF) relay and friendly jammer (FJ). By adopting the time switching (TS) architecture at the relay, the data transmission is accomplished in three phases: Phase I) Energy harvesting by the untrusted relay and the FJ through non-information transmissions from the sources, Phase II) Information transmission by the sources and jamming transmissions from the FJ to reduce information leakage to the untrusted relay; and Phase III) Forwarding the scaled version of the received signal from the untrusted relay to the sources. For the proposed system, we derive a new closed-form lower bound expression for the ergodic secrecy sum rate (ESSR). Numerical examples are provided to demonstrate the impacts of different system parameters such as energy harvesting time, transmit signal-to-noise ratio (SNR) and the relay/FJ location on the secrecy performance. The numerical results illustrate that the proposed network with friendly jamming (WFJ) outperforms traditional one-way communication and the two-way without friendly jamming (WoFJ) policy.
S4: A New Secure Scheme for Enforcing Privacy in Cloud Data Warehouses
Venue: arXiv cs.DB 2017
Year: 2017

Outsourcing data into the cloud becomes popular thanks to the pay-as-you-go paradigm. However, such practice raises privacy concerns. The conventional way to achieve data privacy is to encrypt sensitive data before outsourcing. When data are encrypted, a trade-off must be achieved between security and efficient query processing. Existing solutions that adopt multiple encryption schemes induce a heavy overhead in terms of data storage and query performance, and are not suited for cloud data warehouses. In this paper, we propose an efficient additive encryption scheme (S4) based on Shamir's secret sharing for securing data warehouses in the cloud. S4 addresses the shortcomings of existing approaches by reducing overhead while still enforcing good data privacy. Experimental results show the efficiency of S4 in terms of computation and storage overhead with respect to existing solutions.
Automatic detection and decoding of honey bee waggle dances
Venue: arXiv cs.CV 2017
Year: 2017

The waggle dance is one of the most popular examples of animal communication. Forager bees direct their nestmates to profitable resources via a complex motor display. Essentially, the dance encodes the polar coordinates to the resource in the field. Unemployed foragers follow the dancer's movements and then search for the advertised spots in the field. Throughout the last decades, biologists have employed different techniques to measure key characteristics of the waggle dance and decode the information it conveys. Early techniques involved the use of protractors and stopwatches to measure the dance orientation and duration directly from the observation hive. Recent approaches employ digital video recordings and manual measurements on screen. However, manual approaches are very time-consuming. Most studies, therefore, regard only small numbers of animals in short periods of time. We have developed a system capable of automatically detecting, decoding and mapping communication dances in real-time. In this paper, we describe our recording setup, the image processing steps performed for dance detection and decoding and an algorithm to map dances to the field. The proposed system performs with a detection accuracy of 90.07\%. The decoded waggle orientation has an average error of -2.92° ($\pm$ 7.37° ), well within the range of human error. To evaluate and exemplify the system's performance, a group of bees was trained to an artificial feeder, and all dances in the colony were automatically detected, decoded and mapped. The system presented here is the first of this kind made publicly available, including source code and hardware specifications. We hope this will foster quantitative analyses of the honey bee waggle dance.
Grand Technologies for Grand Energy Challenges: A Futuristic Scenario for Solar Energy in the Age of Information
Venue: arXiv cs.CY 2017
Year: 2017

Instead of getting involved in either extremes of dispute around climate change as one of our grand challenges, we opened the space of potential policy responses from a systemic view and showed why current climate change mitigation policies are not successful as planned. Further, as a potential futuristic scenario, neither a projection nor a prediction, that attracts further discussions, we showed how solar based energy systems are different than other current energy systems and how we can conceive of them as grand technologies which dissolve the whole landscape of energy management in the age of information.
Activity Recognition based on a Magnitude-Orientation Stream Network
Venue: arXiv cs.CV 2017
Year: 2017

The temporal component of videos provides an important clue for activity recognition, as a number of activities can be reliably recognized based on the motion information. In view of that, this work proposes a novel temporal stream for two-stream convolutional networks based on images computed from the optical flow magnitude and orientation, named Magnitude-Orientation Stream (MOS), to learn the motion in a better and richer manner. Our method applies simple nonlinear transformations on the vertical and horizontal components of the optical flow to generate input images for the temporal stream. Experimental results, carried on two well-known datasets (HMDB51 and UCF101), demonstrate that using our proposed temporal stream as input to existing neural network architectures can improve their performance for activity recognition. Results demonstrate that our temporal stream provides complementary information able to improve the classical two-stream methods, indicating the suitability of our approach to be used as a temporal video representation.
Fractions, Projective Representation, Duality, Linear Algebra and Geometry
Venue: arXiv cs.GR 2017
Year: 2017

This contribution describes relationship between fractions, projective representation, duality, linear algebra and geometry. Many problems lead to a system of linear equations. This paper presents equivalence of the Cross-product operation and solution of a system of linear equations Ax=0 or Ax=b using projective space representation and homogeneous coordinates. It leads to conclusion that division operation is not required for a solution of a system of linear equations, if the projective representation and homogeneous coordinates are used. An efficient solution on CPU and GPU based architectures is presented with an application to barycentric coordinates computation as well.
Optimal and Scalable Caching for 5G Using Reinforcement Learning of Space-time Popularities
Venue: arXiv cs.NI 2017
Year: 2017

Small basestations (SBs) equipped with caching units have potential to handle the unprecedented demand growth in heterogeneous networks. Through low-rate, backhaul connections with the backbone, SBs can prefetch popular files during off-peak traffic hours, and service them to the edge at peak periods. To intelligently prefetch, each SB must learn what and when to cache, while taking into account SB memory limitations, the massive number of available contents, the unknown popularity profiles, as well as the space-time popularity dynamics of user file requests. In this work, local and global Markov processes model user requests, and a reinforcement learning (RL) framework is put forth for finding the optimal caching policy when the transition probabilities involved are unknown. Joint consideration of global and local popularity demands along with cache-refreshing costs allow for a simple, yet practical asynchronous caching approach. The novel RL-based caching relies on a Q-learning algorithm to implement the optimal policy in an online fashion, thus enabling the cache control unit at the SB to learn, track, and possibly adapt to the underlying dynamics. To endow the algorithm with scalability, a linear function approximation of the proposed Q-learning scheme is introduced, offering faster convergence as well as reduced complexity and memory requirements. Numerical tests corroborate the merits of the proposed approach in various realistic settings.
Towards a Holistic Integration of Spreadsheets with Databases: A Scalable Storage Engine for Presentational Data Management
Venue: arXiv cs.DB 2017
Year: 2017

Spreadsheet software is the tool of choice for interactive ad-hoc data management, with adoption by billions of users. However, spreadsheets are not scalable, unlike database systems. On the other hand, database systems, while highly scalable, do not support interactivity as a first-class primitive. We are developing DataSpread, to holistically integrate spreadsheets as a front-end interface with databases as a back-end datastore, providing scalability to spreadsheets, and interactivity to databases, an integration we term presentational data management (PDM). In this paper, we make a first step towards this vision: developing a storage engine for PDM, studying how to flexibly represent spreadsheet data within a database and how to support and maintain access by position. We first conduct an extensive survey of spreadsheet use to motivate our functional requirements for a storage engine for PDM. We develop a natural set of mechanisms for flexibly representing spreadsheet data and demonstrate that identifying the optimal representation is NP-Hard; however, we develop an efficient approach to identify the optimal representation from an important and intuitive subclass of representations. We extend our mechanisms with positional access mechanisms that don't suffer from cascading update issues, leading to constant time access and modification performance. We evaluate these representations on a workload of typical spreadsheets and spreadsheet operations, providing up to 20% reduction in storage, and up to 50% reduction in formula evaluation time.