OpenRaiser/Intern-Atlas huggingface dataset:
A Java Implementation of the SGA, UMDA, ECGA, and HBOA
Venue: arXiv cs.NE 2015
Year: 2015

The Simple Genetic Algorithm, the Univariate Marginal Distribution Algorithm, the Extended Compact Genetic Algorithm, and the Hierarchical Bayesian Optimization Algorithm are all well known Evolutionary Algorithms. In this report we present a Java implementation of these four algorithms with detailed instructions on how to use each of them to solve a given set of optimization problems. Additionally, it is explained how to implement and integrate new problems within the provided set. The source and binary files of the Java implementations are available for free download at https://github.com/JoseCPereira/2015EvolutionaryAlgorithmsJava.
Precoding in Multibeam Satellite Communications: Present and Future Challenges
Venue: arXiv cs.IT 2015
Year: 2015

Whenever multibeam satellite systems target very aggressive frequency reuse in their coverage area, inter-beam interference becomes the major obstacle for increasing the overall system throughput. As a matter of fact, users located at the beam edges suffer from a very large interference for even a moderately aggressive planning of reuse-2. Although solutions for inter-beam interference management have been investigated at the satellite terminal, it turns out that the performance improvement does not justify the increased terminal complexity and cost. In this article, we pay attention to interference mitigation techniques that take place at the transmitter (i.e. the gateway). Based on this understanding, we provide our vision on advanced precoding techniques and user clustering methods for multibeam broadband fixed satellite communications. We also discuss practical challenges to deploy precoding schemes and the support introduced in the recently published DVB-S2X standard. Future challenges for novel configurations employing precoding are also provided.
Modelling of directional data using Kent distributions
Venue: arXiv cs.LG 2015
Year: 2015

The modelling of data on a spherical surface requires the consideration of directional probability distributions. To model asymmetrically distributed data on a three-dimensional sphere, Kent distributions are often used. The moment estimates of the parameters are typically used in modelling tasks involving Kent distributions. However, these lack a rigorous statistical treatment. The focus of the paper is to introduce a Bayesian estimation of the parameters of the Kent distribution which has not been carried out in the literature, partly because of its complex mathematical form. We employ the Bayesian information-theoretic paradigm of Minimum Message Length (MML) to bridge this gap and derive reliable estimators. The inferred parameters are subsequently used in mixture modelling of Kent distributions. The problem of inferring the suitable number of mixture components is also addressed using the MML criterion. We demonstrate the superior performance of the derived MML-based parameter estimates against the traditional estimators. We apply the MML principle to infer mixtures of Kent distributions to model empirical data corresponding to protein conformations. We demonstrate the effectiveness of Kent models to act as improved descriptors of protein structural data as compared to commonly used von Mises-Fisher distributions.
Nonnegative Matrix Factorization applied to reordered pixels of single images based on patches to achieve structured nonnegative dictionaries
Venue: arXiv cs.CV 2015
Year: 2015

Recent improvements in computing allow for the processing and analysis of very large datasets in a variety of fields. Often the analysis requires the creation of low-rank approximations to the datasets leading to efficient storage. This article presents and analyzes a novel approach for creating nonnegative, structured dictionaries using NMF applied to reordered pixels of single, natural images. We reorder the pixels based on patches and present our approach in general. We investigate our approach when using the Singular Value Decomposition (SVD) and Nonnegative Matrix Factorizations (NMF) as low-rank approximations. Peak Signal-to-Noise Ratio (PSNR) and Mean Structural Similarity Index (MSSIM) are used to evaluate the algorithm. We report that while the SVD provides the best reconstructions, its dictionary of vectors lose both the sign structure of the original image and details of localized image content. In contrast, the dictionaries produced using NMF preserves the sign structure of the original image matrix and offer a nonnegative, parts-based dictionary.
Data-driven Approaches for Social Video Distribution
Venue: arXiv cs.MM 2015
Year: 2015

The Internet has recently witnessed the convergence of online social network services and online video services: users import videos from content sharing sites, and propagate them along the social connections by re-sharing them. Such social behaviors have dramatically reshaped how videos are disseminated, and the users are now actively engaged to be part of the social ecosystem, rather than being passively consumers. Despite the increasingly abundant bandwidth and computation resources, the ever increasing data volume of user generated video content and the boundless coverage of socialized sharing have presented unprecedented challenges. In this paper, we first presents the challenges in social-aware video delivery. Then, we present a principal framework for data-driven social video delivery approaches. Moreover, we identify the unique characteristics of social-aware video access and the social content propagation, and closely reveal the design of individual modules and their integration towards enhancing users' experience in the social network context.
Temporal and Spatial Classification of Active IPv6 Addresses
Venue: arXiv cs.NI 2015
Year: 2015

There is striking volume of World-Wide Web activity on IPv6 today. In early 2015, one large Content Distribution Network handles 50 billion IPv6 requests per day from hundreds of millions of IPv6 client addresses; billions of unique client addresses are observed per month. Address counts, however, obscure the number of hosts with IPv6 connectivity to the global Internet. There are numerous address assignment and subnetting options in use; privacy addresses and dynamic subnet pools significantly inflate the number of active IPv6 addresses. As the IPv6 address space is vast, it is infeasible to comprehensively probe every possible unicast IPv6 address. Thus, to survey the characteristics of IPv6 addressing, we perform a year-long passive measurement study, analyzing the IPv6 addresses gleaned from activity logs for all clients accessing a global CDN. The goal of our work is to develop flexible classification and measurement methods for IPv6, motivated by the fact that its addresses are not merely more numerous; they are different in kind. We introduce the notion of classifying addresses and prefixes in two ways: (1) temporally, according to their instances of activity to discern which addresses can be considered stable; (2) spatially, according to the density or sparsity of aggregates in which active addresses reside. We present measurement and classification results numerically and visually that: provide details on IPv6 address use and structure in global operation across the past year; establish the efficacy of our classification methods; and demonstrate that such classification can clarify dimensions of the Internet that otherwise appear quite blurred by current IPv6 addressing practices.
Optimal Seed Solver: Optimizing Seed Selection in Read Mapping
Venue: arXiv cs.CE 2015
Year: 2015

Motivation: Optimizing seed selection is an important problem in read mapping. The number of non-overlapping seeds a mapper selects determines the sensitivity of the mapper while the total frequency of all selected seeds determines the speed of the mapper. Modern seed-and-extend mappers usually select seeds with either an equal and fixed-length scheme or with an inflexible placement scheme, both of which limit the potential of the mapper to select less frequent seeds to speed up the mapping process. Therefore, it is crucial to develop a new algorithm that can adjust both the individual seed length and the seed placement, as well as derive less frequent seeds. Results: We present the Optimal Seed Solver (OSS), a dynamic programming algorithm that discovers the least frequently-occurring set of x seeds in an L-bp read in $O(x \times L)$ operations on average and in $O(x \times L^{2})$ operations in the worst case. We compared OSS against four state-of-the-art seed selection schemes and observed that OSS provides a 3-fold reduction of average seed frequency over the best previous seed selection optimizations.
Occam's Gates
Venue: arXiv cs.LG 2015
Year: 2015

We present a complimentary objective for training recurrent neural networks (RNN) with gating units that helps with regularization and interpretability of the trained model. Attention-based RNN models have shown success in many difficult sequence to sequence classification problems with long and short term dependencies, however these models are prone to overfitting. In this paper, we describe how to regularize these models through an L1 penalty on the activation of the gating units, and show that this technique reduces overfitting on a variety of tasks while also providing to us a human-interpretable visualization of the inputs used by the network. These tasks include sentiment analysis, paraphrase recognition, and question answering.
Compressed Sensing Based Data Processing and MAC Protocol Design for Smartgrids
Venue: arXiv cs.IT 2015
Year: 2015

In this paper, we consider the joint design of data compression and 802.15.4-based medium access control (MAC) protocol for smartgrids with renewable energy. We study the setting where a number of nodes, each of which comprises electricity load and/or renewable sources, report periodically their injected powers to a data concentrator. Our design exploits the correlation of the reported data in both time and space to perform efficient data compression using the compressed sensing (CS) technique and efficiently engineer the MAC protocol so that the reported data can be recovered reliably within minimum reporting time. Specifically, we perform the following design tasks: i) we employ the two-dimensional (2D) CS technique to compress the reported data in the distributed manner, ii) we propose to adapt the 802.15.4 MAC protocol frame structure to enable efficient data transmission and reliable data reconstruction, and iii) we develop an analytical model based on which we can obtain the optimal parameter configuration to minimize the reporting delay. Finally, numerical results are presented to demonstrate the effectiveness of our design.
Simultaneously Solving Computational Problems Using an Artificial Chemical Reactor
Venue: arXiv cs.ET 2015
Year: 2015

This paper is centered on using chemical reaction as a computational metaphor for simultaneously solving problems. An artificial chemical reactor that can simultaneously solve instances of three unrelated problems was created. The reactor is a distributed stochastic algorithm that simulates a chemical universe wherein the molecular species are being represented either by a human genomic contig panel, a Hamiltonian cycle, or an aircraft landing schedule. The chemical universe is governed by reactions that can alter genomic sequences, re-order Hamiltonian cycles, or reschedule an aircraft landing program. Molecular masses were considered as measures of goodness of solutions, and represented radiation hybrid (RH) vector similarities, costs of Hamiltonian cycles, and penalty costs for landing an aircraft before and after target landing times. This method, tested by solving in tandem with deterministic algorithms, has been shown to find quality solutions in finding the minima RH vector similarities of genomic data, minima costs in Hamiltonian cycles of the traveling salesman, and minima costs for landing aircrafts before or after target landing times.
Binary full adder, made of fusion gates, in sub-excitable Belousov-Zhabotinsky system
Venue: arXiv cs.ET 2015
Year: 2015

A sub-excitable BZ medium responds to asymmetric local perturbation by producing travelling localised excitation wave-fragments, distant relatives of dissipative solitons. The size and life span of an excitation wave-fragment depend on the illumination level of the medium. Under the right conditions the wave-fragments conserve their shape and velocity vectors for extended time periods. We interpret the wave-fragments as values of Boolean variables. When two or more wave-fragments collide they annihilate or merge into a new wave-fragment. States of the logic variables, represented by the wave-fragments, are changed in the result of the collision between the wave-fragments. Thus, a logical gate is implemented. Several theoretical designs and experimental laboratory implementations of Boolean logic gates have been proposed in the past but little has been done cascading the gates into binary arithmetical circuits. We propose a unique design of a binary one-bit full adder based on a fusion gate. A fusion gate is a two-input three-output logical device which calculates conjunction of the input variables and conjunction of one input variable with negation of another input variable. The gate is made of three channels: two channels cross each other at an angle, third channel starts at the junction. The channels contain BZ medium. When two excitation wave-fragments, travelling towards each other along input channels, collide at the junction they merge into a single wave-front travelling along the third channel. If there is just one wave-front in the input channel, the front continues its propagation undisturbed. We make a one-bit full adder by cascading two fusion gates. We show how to cascade the adder blocks into a many-bit full adder. We evaluate feasibility of our designs by simulating evolution of excitation in the gates and adders using numerical integration of Oregonator equations.
Deep-Plant: Plant Identification with convolutional neural networks
Venue: arXiv cs.CV 2015
Year: 2015

This paper studies convolutional neural networks (CNN) to learn unsupervised feature representations for 44 different plant species, collected at the Royal Botanic Gardens, Kew, England. To gain intuition on the chosen features from the CNN model (opposed to a 'black box' solution), a visualisation technique based on the deconvolutional networks (DN) is utilized. It is found that venations of different order have been chosen to uniquely represent each of the plant species. Experimental results using these CNN features with different classifiers show consistency and superiority compared to the state-of-the art solutions which rely on hand-crafted features.
Compressed Sensing of Multi-Channel EEG Signals: The Simultaneous Cosparsity and Low Rank Optimization
Venue: arXiv cs.IT 2015
Year: 2015

Goal: This paper deals with the problems that some EEG signals have no good sparse representation and single channel processing is not computationally efficient in compressed sensing of multi-channel EEG signals. Methods: An optimization model with L0 norm and Schatten-0 norm is proposed to enforce cosparsity and low rank structures in the reconstructed multi-channel EEG signals. Both convex relaxation and global consensus optimization with alternating direction method of multipliers are used to compute the optimization model. Results: The performance of multi-channel EEG signal reconstruction is improved in term of both accuracy and computational complexity. Conclusion: The proposed method is a better candidate than previous sparse signal recovery methods for compressed sensing of EEG signals. Significance: The proposed method enables successful compressed sensing of EEG signals even when the signals have no good sparse representation. Using compressed sensing would much reduce the power consumption of wireless EEG system.
Enabling On-Demand Database Computing with MIT SuperCloud Database Management System
Venue: arXiv cs.DB 2015
Year: 2015

The MIT SuperCloud database management system allows for rapid creation and flexible execution of a variety of the latest scientific databases, including Apache Accumulo and SciDB. It is designed to permit these databases to run on a High Performance Computing Cluster (HPCC) platform as seamlessly as any other HPCC job. It ensures the seamless migration of the databases to the resources assigned by the HPCC scheduler and centralized storage of the database files when not running. It also permits snapshotting of databases to allow researchers to experiment and push the limits of the technology without concerns for data or productivity loss if the database becomes unstable.
Joint Source-Channel Coding for Real-Time Video Transmission to Multi-homed Mobile Terminals
Venue: arXiv cs.NI 2015
Year: 2015

This study focuses on the mobile video delivery from a video server to a multi-homed client with a network of heterogeneous wireless. Joint Source-Channel Coding is effectively used to transmit video over bandwidth-limited, noisy wireless networks. But most existing JSCC methods only consider single path video transmission of the server and the client network. The problem will become more complicated when consider multi-path video transmission, because involving low-bandwidth, high-drop-rate or high-latency wireless network will only reduce the video quality. To solve this critical problem, we propose a novel Path Adaption JSCC (PA-JSCC) method that contain below characters: (1) path adaption, and (2) dynamic rate allocation. We use Exata to evaluate the performance of PA-JSCC and Experiment show that PA-JSCC has a good results in terms of PSNR (Peak Signal-to-Noise Ratio).
Secrecy Capacity Analysis over $κ-μ$ Fading Channels: Theory and Applications
Venue: arXiv cs.IT 2015
Year: 2015

In this paper, we consider the transmission of confidential information over a $κ$-$μ$ fading channel in the presence of an eavesdropper, who also observes $κ$-$μ$ fading. In particular, we obtain novel analytical solutions for the probability of strictly positive secrecy capacity (SPSC) and the lower bound of secure outage probability (SOP$^L$) for channel coefficients that are positive, real, independent and non-identically distributed ($i.n.i.d.$). We also provide a closed-form expression for the probability of SPSC when the $μ$ parameter is assumed to only take positive integer values. We then apply the derived results to assess the secrecy performance of the system in terms of the average signal-to-noise ratio (SNR) as a function of the $κ$ and $μ$ fading parameters. We observed that for fixed values of the eavesdropper's average SNR, increases in the average SNR of the main channel produce a higher probability of SPSC and a lower secure outage probability (SOP). It was also found that when the main channel experiences a higher average SNR than the eavesdropper's channel, the probability of SPSC improved while the SOP was found to decrease with increasing values of $κ$ and $μ$ for the legitimate channel. The versatility of the $κ$-$μ$ fading model, means that the results presented in this paper can be used to determine the probability of SPSC and SOP$^L$ for a large number of other fading scenarios such as Rayleigh, Rice (Nakagami-$n$), Nakagami-$m$, One-Sided Gaussian and mixtures of these common fading models. Additionally, due to the duality of the analysis of secrecy capacity and co-channel interference, the results presented here will also have immediate applicability in the analysis of outage probability in wireless systems affected by co-channel interference and background noise.
Accelerating DNA Sequence Analysis using Intel Xeon Phi
Venue: arXiv cs.DC 2015
Year: 2015

Genetic information is increasing exponentially, doubling every 18 months. Analyzing this information within a reasonable amount of time requires parallel computing resources. While considerable research has addressed DNA analysis using GPUs, so far not much attention has been paid to the Intel Xeon Phi coprocessor. In this paper we present an algorithm for large-scale DNA analysis that exploits thread-level and the SIMD parallelism of the Intel Xeon Phi. We evaluate our approach for various numbers of cores and thread allocation affinities in the context of real-world DNA sequences of mouse, cat, dog, chicken, human and turkey. The experimental results on Intel Xeon Phi show speed-ups of up to 10x compared to a sequential implementation running on an Intel Xeon processor E5.
Lithium-ion battery thermal-electrochemical model-based state estimation using orthogonal collocation and a modified extended Kalman filter
Venue: arXiv cs.SY 2015
Year: 2015

This paper investigates the state estimation of a high-fidelity spatially resolved thermal- electrochemical lithium-ion battery model commonly referred to as the pseudo two-dimensional model. The partial-differential algebraic equations (PDAEs) constituting the model are spatially discretised using Chebyshev orthogonal collocation enabling fast and accurate simulations up to high C-rates. This implementation of the pseudo-2D model is then used in combination with an extended Kalman filter algorithm for differential-algebraic equations to estimate the states of the model. The state estimation algorithm is able to rapidly recover the model states from current, voltage and temperature measurements. Results show that the error on the state estimate falls below 1 % in less than 200 s despite a 30 % error on battery initial state-of-charge and additive measurement noise with 10 mV and 0.5 K standard deviations.
Downlink Rate Analysis for Virtual-Cell based Large-Scale Distributed Antenna Systems
Venue: arXiv cs.IT 2015
Year: 2015

Despite substantial rate gains achieved by coordinated transmission from a massive amount of geographically distributed antennas, the resulting computational cost and channel measurement overhead could be unaffordable for a large-scale distributed antenna system (DAS). A scalable signal processing framework is therefore highly desirable, which, as recently demonstrated in \cite{Dai_TWireless}, could be established based on the concept of virtual cell. In a virtual-cell based DAS, each user chooses a few closest base-station (BS) antennas to form its virtual cell, that is, its own serving BS antenna set. In this paper, we focus on a downlink DAS with a large number of users and BS antennas uniformly distributed in a certain area, and aim to study the effect of the virtual cell size on the average user rate. Specifically, by assuming that maximum ratio transmission (MRT) is adopted in each user's virtual cell, the achievable ergodic rate of each user is derived as an explicit function of the large-scale fading coefficients from all the users to their virtual cells, and an upper-bound of the average user rate is established, based on which a rule of thumb is developed for determining the optimal virtual cell size to maximize the average user rate. The analysis is further extended to consider multiple users grouped together and jointly served by their virtual cells using zero-forcing beamforming (ZFBF). In contrast to the no-grouping case where a small virtual cell size is preferred, it is shown that by grouping users with overlapped virtual cells, the average user rate can be significantly improved by increasing the virtual cell size, though at the cost of a higher signal processing complexity.
Argumentation Semantics for Prioritised Default Logic
Venue: arXiv cs.AI 2015
Year: 2015

We endow prioritised default logic (PDL) with argumentation semantics using the ASPIC+ framework for structured argumentation, and prove that the conclusions of the justified arguments are exactly the prioritised default extensions. Argumentation semantics for PDL will allow for the application of argument game proof theories to the process of inference in PDL, making the reasons for accepting a conclusion transparent and the inference process more intuitive. This also opens up the possibility for argumentation-based distributed reasoning and communication amongst agents with PDL representations of mental attitudes.