OpenRaiser/Intern-Atlas huggingface dataset:
A Multi-Smartwatch System for Assessing Speech Characteristics of People with Dysarthria in Group Settings
Venue: arXiv cs.CY 2016
Year: 2016

Speech-language pathologists (SLPs) frequently use vocal exercises in the treatment of patients with speech disorders. Patients receive treatment in a clinical setting and need to practice outside of the clinical setting to generalize speech goals to functional communication. In this paper, we describe the development of technology that captures mixed speech signals in a group setting and allows the SLP to analyze the speech signals relative to treatment goals. The mixed speech signals are blindly separated into individual signals that are preprocessed before computation of loudness, pitch, shimmer, jitter, semitone standard deviation and sharpness. The proposed method has been previously validated on data obtained from clinical trials of people with Parkinson disease and healthy controls.
Verification of railway interlocking - Compositional approach with OCRA
Venue: arXiv cs.SE 2016
Year: 2016

In the railway domain, an electronic interlocking is a computerised system that controls the railway signalling components (e.g. switches or signals) in order to allow a safe operation of the train traffic. Interlockings are controlled by a software logic that relies on a generic software and a set of application data particular to the station under control. The verification of the application data is time consuming and error prone as it is mostly performed by human testers. In the first stage of our research, we built a model of a small Belgian railway station and we performed the verification of the application data with the nusmv model checker. However, the verification of larger stations fails due to the state space explosion problem. The intuition is that large stations can be split into smaller components that can be verified separately. This concept is known as compositional verification. This article explains how we used the ocra tool in order to model a medium size station and how we verified safety properties by mean of contracts. We also took advantage of new algorithms (k-liveness and ic3) recently implemented in nuxmv in order to verify LTL properties on our model.
Constant Envelope Precoding for MIMO Systems
Venue: arXiv cs.IT 2016
Year: 2016

Constant envelope (CE) precoding is an appealing transmission technique, which enables highly efficient power amplification, and is realizable with a single radio frequency (RF) chain at the multi-antenna transmitter. In this paper, we study the transceiver design for a point-to-point multiple-input multiple-output (MIMO) system with CE precoding. Both single-stream transmission (i.e., beamforming) and multi-stream transmission (i.e., spatial multiplexing) are considered. For single-stream transmission, we optimize the receive beamforming vector to minimize the symbol error rate (SER) for any given channel realization and desired constellation at the combiner output. By reformulating the problem as an equivalent quadratically constrained quadratic program (QCQP), we propose an efficient semi-definite relaxation (SDR) based algorithm to find an approximate solution. Next, for multi-stream transmission, we propose a new scheme based on antenna grouping at the transmitter and minimum mean squared error (MMSE) or zero-forcing (ZF) based beamforming at the receiver. The transmit antenna grouping and receive beamforming vectors are then jointly designed to minimize the maximum SER over all data streams. Finally, the error-rate performance of single- versus multi-stream transmission is compared via simulations under different setups.
On the Robustness of Decision Tree Learning under Label Noise
Venue: arXiv cs.LG 2016
Year: 2016

In most practical problems of classifier learning, the training data suffers from the label noise. Hence, it is important to understand how robust is a learning algorithm to such label noise. This paper presents some theoretical analysis to show that many popular decision tree algorithms are robust to symmetric label noise under large sample size. We also present some sample complexity results which provide some bounds on the sample size for the robustness to hold with a high probability. Through extensive simulations we illustrate this robustness.
The Price of Fog: a Data-Driven Study on Caching Architectures in Vehicular Networks
Venue: arXiv cs.NI 2016
Year: 2016

Vehicular users are expected to consume large amounts of data, for both entertainment and navigation purposes. This will put a strain on cellular networks, which will be able to cope with such a load only if proper caching is in place, this in turn begs the question of which caching architecture is the best-suited to deal with vehicular content consumption. In this paper, we leverage a large-scale, crowd-collected trace to (i) characterize the vehicular traffic demand, in terms of overall magnitude and content breakup, (ii) assess how different caching approaches perform against such a real-world load, (iii) study the effect of recommendation systems and local contents. We define a price-of-fog metric, expressing the additional caching capacity to deploy when moving from traditional, centralized caching architectures to a "fog computing" approach, where caches are closer to the network edge. We find that for location-specific contents, such as the ones that vehicular users are most likely to request, such a price almost disappears. Vehicular networks thus make a strong case for the adoption of mobile-edge caching, as we are able to reap the benefit thereof -- including a reduction in the distance traveled by data, within the core network -- with little or no of the associated disadvantages.
Inter-Technology Coexistence in a Spectrum Commons: A Case Study of Wi-Fi and LTE in the 5 GHz Unlicensed Band
Venue: arXiv cs.NI 2016
Year: 2016

Spectrum sharing mechanisms need to be carefully designed to enable inter-technology coexistence in the unlicensed bands, as these bands are an instance of a spectrum commons where heterogeneous technologies and deployments must coexist. Unlike in licensed bands, where multiple technologies could coexist only in a primary-secondary DSA mode, a spectrum commons offers competition opportunities between multiple dominant technologies, such as Wi-Fi and the recently proposed LTE in the 5 GHz unlicensed band. In this paper we systematically study the performance of different spectrum sharing schemes for inter-technology coexistence in a spectrum commons. Our contributions are threefold. First, we propose a general framework for comparative analysis of spectrum sharing mechanisms in time and frequency, by studying the effect of key constituent parameters. Second, we propose a novel throughput and interference model for inter-technology coexistence, integrating different distributed MAC sharing mechanisms at the same level of abstraction. Finally, we present a case study of IEEE 802.11n Wi-Fi and LTE in the 5 GHz unlicensed band, in order to obtain generalizable insight into coexistence in a spectrum commons. Our extensive simulation results show that LTE/Wi-Fi coexistence in the 5 GHz band can be ensured simply through channel selection schemes, such that time-sharing MAC mechanisms are irrelevant. We also show that, in the general co-channel case, the coexistence performance of MAC sharing mechanisms strongly depends on the interference coupling in the network, as determined by building shielding. We thus identify two regimes: (i) low interference coupling, e.g. residential indoor scenarios, where duty cycle mechanisms outperform sensing-based LBT mechanisms; and (ii) high interference coupling, e.g. open-plan indoor or outdoor hotspot scenarios, where LBT outperforms duty cycle mechanisms.
TensorLog: A Differentiable Deductive Database
Venue: arXiv cs.AI 2016
Year: 2016

Large knowledge bases (KBs) are useful in many tasks, but it is unclear how to integrate this sort of knowledge into "deep" gradient-based learning systems. To address this problem, we describe a probabilistic deductive database, called TensorLog, in which reasoning uses a differentiable process. In TensorLog, each clause in a logical theory is first converted into certain type of factor graph. Then, for each type of query to the factor graph, the message-passing steps required to perform belief propagation (BP) are "unrolled" into a function, which is differentiable. We show that these functions can be composed recursively to perform inference in non-trivial logical theories containing multiple interrelated clauses and predicates. Both compilation and inference in TensorLog are efficient: compilation is linear in theory size and proof depth, and inference is linear in database size and the number of message-passing steps used in BP. We also present experimental results with TensorLog and discuss its relationship to other first-order probabilistic logics.
MobilitApp: Analysing mobility data of citizens in the metropolitan area of Barcelona
Venue: arXiv cs.CY 2016
Year: 2016

MobilitApp is a platform designed to provide smart mobility services in urban areas. It is designed to help citizens and transport authorities alike. Citizens will be able to access the MobilitApp mobile application and decide their optimal transportation strategy by visualising their usual routes, their carbon footprint, receiving tips, analytics and general mobility information, such as traffic and incident alerts. Transport authorities and service providers will be able to access information about the mobility pattern of citizens to o er their best services, improve costs and planning. The MobilitApp client runs on Android devices and records synchronously, while running in the background, periodic location updates from its users. The information obtained is processed and analysed to understand the mobility patterns of our users in the city of Barcelona, Spain.
Functional Hashing for Compressing Neural Networks
Venue: arXiv cs.LG 2016
Year: 2016

As the complexity of deep neural networks (DNNs) trend to grow to absorb the increasing sizes of data, memory and energy consumption has been receiving more and more attentions for industrial applications, especially on mobile devices. This paper presents a novel structure based on functional hashing to compress DNNs, namely FunHashNN. For each entry in a deep net, FunHashNN uses multiple low-cost hash functions to fetch values in the compression space, and then employs a small reconstruction network to recover that entry. The reconstruction network is plugged into the whole network and trained jointly. FunHashNN includes the recently proposed HashedNets as a degenerated case, and benefits from larger value capacity and less reconstruction loss. We further discuss extensions with dual space hashing and multi-hops. On several benchmark datasets, FunHashNN demonstrates high compression ratios with little loss on prediction accuracy.
Gambler's Ruin Bandit Problem
Venue: arXiv cs.LG 2016
Year: 2016

In this paper, we propose a new multi-armed bandit problem called the Gambler's Ruin Bandit Problem (GRBP). In the GRBP, the learner proceeds in a sequence of rounds, where each round is a Markov Decision Process (MDP) with two actions (arms): a continuation action that moves the learner randomly over the state space around the current state; and a terminal action that moves the learner directly into one of the two terminal states (goal and dead-end state). The current round ends when a terminal state is reached, and the learner incurs a positive reward only when the goal state is reached. The objective of the learner is to maximize its long-term reward (expected number of times the goal state is reached), without having any prior knowledge on the state transition probabilities. We first prove a result on the form of the optimal policy for the GRBP. Then, we define the regret of the learner with respect to an omnipotent oracle, which acts optimally in each round, and prove that it increases logarithmically over rounds. We also identify a condition under which the learner's regret is bounded. A potential application of the GRBP is optimal medical treatment assignment, in which the continuation action corresponds to a conservative treatment and the terminal action corresponds to a risky treatment such as surgery.
Enhanced Playback of Automated Service Emulation Models Using Entropy Analysis
Venue: arXiv cs.SE 2016
Year: 2016

Service virtualisation is a supporting tool for DevOps to generate interactive service models of dependency systems on which a system-under-test relies. These service models allow applications under development to be continuously tested against production-like conditions. Generating these virtual service models requires expert knowledge of the service protocol, which may not always be available. However, service models may be generated automatically from network traces. Previous work has used the Needleman-Wunsch algorithm to select a response from the service model to play back for a live request. We propose an extension of the Needleman-Wunsch algorithm, which uses entropy analysis to automatically detect the critical matching fields for selecting a response. Empirical tests against four enterprise protocols demonstrate that entropy weighted matching can improve response accuracy.
Tangled up in Blue (A Survey on Connectivity, Decompositions, and Tangles)
Venue: arXiv cs.DM 2016
Year: 2016

We survey an abstract theory of connectivity, based on symmetric submodular set functions. We start by developing Robertson and Seymour's fundamental duality between branch decompositions (related to the better-known tree decompositions) and so-called tangles, which may be viewed as highly connected regions in a connectivity system. We move on to studying canonical decompositions of connectivity systems into their maximal tangles. Last, but not least, we will discuss algorithmic aspect of the theory.
Sparse Signal Reconstruction with Multiple Side Information using Adaptive Weights for Multiview Sources
Venue: arXiv cs.CV 2016
Year: 2016

This work considers reconstructing a target signal in a context of distributed sparse sources. We propose an efficient reconstruction algorithm with the aid of other given sources as multiple side information (SI). The proposed algorithm takes advantage of compressive sensing (CS) with SI and adaptive weights by solving a proposed weighted $n$-$\ell_{1}$ minimization. The proposed algorithm computes the adaptive weights in two levels, first each individual intra-SI and then inter-SI weights are iteratively updated at every reconstructed iteration. This two-level optimization leads the proposed reconstruction algorithm with multiple SI using adaptive weights (RAMSIA) to robustly exploit the multiple SIs with different qualities. We experimentally perform our algorithm on generated sparse signals and also correlated feature histograms as multiview sparse sources from a multiview image database. The results show that RAMSIA significantly outperforms both classical CS and CS with single SI, and RAMSIA with higher number of SIs gained more than the one with smaller number of SIs.
Decidable models of integer-manipulating programs with recursive parallelism (technical report)
Venue: arXiv cs.LO 2016
Year: 2016

We study safety verification for multithreaded programs with recursive parallelism (i.e. unbounded thread creation and recursion) as well as unbounded integer variables. Since the threads in each program configuration are structured in a hierarchical fashion, our model is state-extended ground-tree rewrite systems equipped with shared unbounded integer counters that can be incremented, decremented, and compared against an integer constant. Since the model is Turing-complete, we propose a decidable underapproximation. First, using a restriction similar to context-bounding, we underapproximate the global control by a weak global control (i.e. DAGs possibly with self-loops), thereby limiting the number of synchronisations between different threads. Second, we bound the number of reversals between non-decrementing and non-incrementing modes of the counters. Under this restriction, we show that reachability becomes NP-complete. In fact, it is poly-time reducible to satisfaction over existential Presburger formulas, which allows one to tap into highly optimised SMT solvers. Our decidable approximation strictly generalises known decidable models including (i) weakly-synchronised ground-tree rewrite systems, and (ii) synchronisation/reversal-bounded concurrent pushdown systems systems with counters. Finally, we show that, when equipped with reversal-bounded counters, relaxing the weak control restriction by the notion of senescence results in undecidability.
A Riemannian gossip approach to decentralized matrix completion
Venue: arXiv cs.NA 2016
Year: 2016

In this paper, we propose novel gossip algorithms for the low-rank decentralized matrix completion problem. The proposed approach is on the Riemannian Grassmann manifold that allows local matrix completion by different agents while achieving asymptotic consensus on the global low-rank factors. The resulting approach is scalable and parallelizable. Our numerical experiments show the good performance of the proposed algorithms on various benchmarks.
DP-EM: Differentially Private Expectation Maximization
Venue: arXiv cs.LG 2016
Year: 2016

The iterative nature of the expectation maximization (EM) algorithm presents a challenge for privacy-preserving estimation, as each iteration increases the amount of noise needed. We propose a practical private EM algorithm that overcomes this challenge using two innovations: (1) a novel moment perturbation formulation for differentially private EM (DP-EM), and (2) the use of two recently developed composition methods to bound the privacy "cost" of multiple EM iterations: the moments accountant (MA) and zero-mean concentrated differential privacy (zCDP). Both MA and zCDP bound the moment generating function of the privacy loss random variable and achieve a refined tail bound, which effectively decrease the amount of additive noise. We present empirical results showing the benefits of our approach, as well as similar performance between these two composition methods in the DP-EM setting for Gaussian mixture models. Our approach can be readily extended to many iterative learning algorithms, opening up various exciting future directions.
Isotropic Dynamic Hierarchical Clustering
Venue: arXiv cs.DS 2016
Year: 2016

We face a need of discovering a pattern in locations of a great number of points in a high-dimensional space. Goal is to group the close points together. We are interested in a hierarchical structure, like a B-tree. B-Trees are hierarchical, balanced, and they can be constructed dynamically. B-Tree approach allows to determine the structure without any supervised learning or a priori knowlwdge. The space is Euclidean and isotropic. Unfortunately, there are no B-Tree implementations processing indices in a symmetrical and isotropical way. Some implementations are based on constructing compound asymmetrical indices from point coordinates; and the others split the nodes along the coordinate hyper-planes. We need to process tens of millions of points in a thousand-dimensional space. The application has to be scalable. Ideally, a cluster should be an ellipsoid, but it would require to store O(n2) ellipse axes. So, we are using multi-dimensional balls defined by the centers and radii. Calculation of statistical values like the mean and the average deviation, can be done in an incremental way. While adding a point to a tree, the statistical values for nodes recalculated in O(1) time. We support both, brute force O(2n) and greedy O(n2) split algorithms. Statistical and aggregated node information also allows to manipulate (to search, to delete) aggregated sets of closely located points. Hierarchical information retrieval. When searching, the user is provided with the highest appropriate nodes in the tree hierarchy, with the most important clusters emerging in the hierarchy automatically. Then, if interested, the user may navigate down the tree to more specific points. The system is implemented as a library of Java classes representing Points, Sets of points with aggregated statistical information, B-tree, and Nodes with a support of serialization and storage in a MySQL database.
Free Deterministic Equivalents for the Analysis of MIMO Multiple Access Channel
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, a free deterministic equivalent is proposed for the capacity analysis of the multi-input multi-output (MIMO) multiple access channel (MAC) with a more general channel model compared to previous works. Specifically, a MIMO MAC with one base station (BS) equipped with several distributed antenna sets is considered. Each link between a user and a BS antenna set forms a jointly correlated Rician fading channel. The analysis is based on operator-valued free probability theory, which broadens the range of applicability of free probability techniques tremendously. By replacing independent Gaussian random matrices with operator-valued random variables satisfying certain operator-valued freeness relations, the free deterministic equivalent of the considered channel Gram matrix is obtained. The Shannon transform of the free deterministic equivalent is derived, which provides an approximate expression for the ergodic input-output mutual information of the channel. The sum-rate capacity achieving input covariance matrices are also derived based on the approximate ergodic input-output mutual information. The free deterministic equivalent results are easy to compute, and simulation results show that these approximations are numerically accurate and computationally efficient.
A Formal Evaluation of PSNR as Quality Measurement Parameter for Image Segmentation Algorithms
Venue: arXiv cs.CV 2016
Year: 2016

Quality evaluation of image segmentation algorithms are still subject of debate and research. Currently, there is no generic metric that could be applied to any algorithm reliably. This article contains an evaluation for the PSRN (Peak Signal-To-Noise Ratio) as a metric which has been used to evaluate threshold level selection as well as the number of thresholds in the case of multi-level segmentation. The results obtained in this study suggest that the PSNR is not an adequate quality measurement for segmentation algorithms.
Genetic Architect: Discovering Genomic Structure with Learned Neural Architectures
Venue: arXiv cs.LG 2016
Year: 2016

Each human genome is a 3 billion base pair set of encoding instructions. Decoding the genome using deep learning fundamentally differs from most tasks, as we do not know the full structure of the data and therefore cannot design architectures to suit it. As such, architectures that fit the structure of genomics should be learned not prescribed. Here, we develop a novel search algorithm, applicable across domains, that discovers an optimal architecture which simultaneously learns general genomic patterns and identifies the most important sequence motifs in predicting functional genomic outcomes. The architectures we find using this algorithm succeed at using only RNA expression data to predict gene regulatory structure, learn human-interpretable visualizations of key sequence motifs, and surpass state-of-the-art results on benchmark genomics challenges.
The bitwise operations in relation to obtaining Latin squares
Venue: arXiv cs.OH 2016
Year: 2016

The main thrust of the article is to provide interesting example, useful for students of using bitwise operations in the programming languages C ++ and Java. As an example, we describe an algorithm for obtaining a Latin square of arbitrary order. We will outline some techniques for the use of bitwise operations.