OpenRaiser/Intern-Atlas huggingface dataset:
Pixelated VLC-backscattering for Self-charging Indoor IoT Devices
Venue: arXiv cs.IT 2016
Year: 2016

Visible light communication (VLC) backscatter has been proposed as a wireless access option for Internet of Things (IoT). However, the throughput of the state-of-the-art VLC backscatter is limited by simple single-carrier pulsed modulation scheme, such as on-off keying (OOK). In this paper, a novel pixelated VLC backscatter is proposed and implemented to overcome the channel capacity limitation. In particular, multiple smaller VLC backscatters, switching on or off, are integrated to generate multi-level signals, which enables the usage of more advanced modulation schemes than OOK. Based on experimental results, rate adaptation at different communication distances can be employed to enhance the achievable data rate. Compared to OOK, the data rate can be tripled when 8-PAM is used at 2 meters. In general, $n$-fold throughput enhancement is realized by utilizing $n$ smaller VLC backscatters while incurring negligible additional energy using the same device space as that of a single large backscatter.
Solving and Verifying the boolean Pythagorean Triples problem via Cube-and-Conquer
Venue: arXiv cs.DM 2016
Year: 2016

The boolean Pythagorean Triples problem has been a longstanding open problem in Ramsey Theory: Can the set N = $\{1, 2, ...\}$ of natural numbers be divided into two parts, such that no part contains a triple $(a,b,c)$ with $a^2 + b^2 = c^2$ ? A prize for the solution was offered by Ronald Graham over two decades ago. We solve this problem, proving in fact the impossibility, by using the Cube-and-Conquer paradigm, a hybrid SAT method for hard problems, employing both look-ahead and CDCL solvers. An important role is played by dedicated look-ahead heuristics, which indeed allowed to solve the problem on a cluster with 800 cores in about 2 days. Due to the general interest in this mathematical problem, our result requires a formal proof. Exploiting recent progress in unsatisfiability proofs of SAT solvers, we produced and verified a proof in the DRAT format, which is almost 200 terabytes in size. From this we extracted and made available a compressed certificate of 68 gigabytes, that allows anyone to reconstruct the DRAT proof for checking.
Learning from Binary Labels with Instance-Dependent Corruption
Venue: arXiv cs.LG 2016
Year: 2016

Suppose we have a sample of instances paired with binary labels corrupted by arbitrary instance- and label-dependent noise. With sufficiently many such samples, can we optimally classify and rank instances with respect to the noise-free distribution? We provide a theoretical analysis of this question, with three main contributions. First, we prove that for instance-dependent noise, any algorithm that is consistent for classification on the noisy distribution is also consistent on the clean distribution. Second, we prove that for a broad class of instance- and label-dependent noise, a similar consistency result holds for the area under the ROC curve. Third, for the latter noise model, when the noise-free class-probability function belongs to the generalised linear model family, we show that the Isotron can efficiently and provably learn from the corrupted sample.
Estimating software reliability in maintenance phase through ann and statistics
Venue: arXiv cs.SE 2016
Year: 2016

Maintenance is the last and the most critical phase of the software development life cycle. It involves debugging of errors and different types of enhancements which are requested by the user. Software reliability regarding maintenance is the most crucial part as it depends upon the time and cost to correct the errors and make enchantements. It is often felt that software errors or correction takes time to be removed. The maintenance time depends upon the nature of the occurred errors and requested enhancements. In this research work we predict the software reliability in terms of time taken to maintain the errors and enhancements. Artificial Neural Network is used to analyze and predict the software reliability of the maintenance phase. At the end statistical results and proposed neural network results are also compared to make sure that forecasted results are equal to the output results. These results are compared to show that the ANN can understand the relationship between data in a better way. This research work also shows how much it is difficult to understand the nature of maintenance data.
Online Learning of Commission Avoidant Portfolio Ensembles
Venue: arXiv cs.AI 2016
Year: 2016

We present a novel online ensemble learning strategy for portfolio selection. The new strategy controls and exploits any set of commission-oblivious portfolio selection algorithms. The strategy handles transaction costs using a novel commission avoidance mechanism. We prove a logarithmic regret bound for our strategy with respect to optimal mixtures of the base algorithms. Numerical examples validate the viability of our method and show significant improvement over the state-of-the-art.
Using Two Types of Computer Algebra Systems to Solve Maxwell Optics Problems
Venue: arXiv cs.SC 2016
Year: 2016

To synthesize Maxwell optics systems, the mathematical apparatus of tensor and vector analysis is generally employed. This mathematical apparatus implies executing a great number of simple stereotyped operations, which are adequately supported by computer algebra systems. In this paper, we distinguish between two stages of working with a mathematical model: model development and model usage. Each of these stages implies its own computer algebra system. As a model problem, we consider the problem of geometrization of Maxwell's equations. Two computer algebra systems---Cadabra and FORM---are selected for use at different stages of investigation.
Hierarchical Modeling of Multidimensional Data in Regularly Decomposed Spaces: Implementation on Computer
Venue: arXiv cs.CG 2016
Year: 2016

The described works have been carried out in the framework of a mid-term study initiated by the Centre Electronique de l'Armement, then by an advanced study launched by the Direction de la Recherche et des Etudes Technologiques in France in the aim to develop new techniques for multidimensional hierarchical modeling and to port them on parallel architecture computers for satisfying the future needs in processing huge numerical data bases. Following the first tome describing the modeling principles, the second tome details the way used for developing the modeling software and for porting it on different computers, especially on parallel architecture computers. In addition to these works, it is gone through new algorithms that have been developed after those that have been presented in the former tome and that are described in pseudo-code in annex of the present document: - operators for constructive geometry (building simple shapes, Boolean operators, slice handling); - integral transformations (epigraph, hypograph, convex hull) ; - homotopic transformations (boundary, erosion, dilation, opening, closing) ; - median transformations (median filtering, thinning, median set, intrinsic dimension) ; - transformations of (hyper-)surface manifolds (median filtering, extension, polynomial fitting of a simple function). The present publication is ending with the software porting on two distributed memory parallel computers: - a thin-grained synchronous computer ; - a coarse-grained asynchronous computer.
Tracking Dengue Epidemics using Twitter Content Classification and Topic Modelling
Venue: arXiv cs.SI 2016
Year: 2016

Detecting and preventing outbreaks of mosquito-borne diseases such as Dengue and Zika in Brasil and other tropical regions has long been a priority for governments in affected areas. Streaming social media content, such as Twitter, is increasingly being used for health vigilance applications such as flu detection. However, previous work has not addressed the complexity of drastic seasonal changes on Twitter content across multiple epidemic outbreaks. In order to address this gap, this paper contrasts two complementary approaches to detecting Twitter content that is relevant for Dengue outbreak detection, namely supervised classification and unsupervised clustering using topic modelling. Each approach has benefits and shortcomings. Our classifier achieves a prediction accuracy of about 80\% based on a small training set of about 1,000 instances, but the need for manual annotation makes it hard to track seasonal changes in the nature of the epidemics, such as the emergence of new types of virus in certain geographical locations. In contrast, LDA-based topic modelling scales well, generating cohesive and well-separated clusters from larger samples. While clusters can be easily re-generated following changes in epidemics, however, this approach makes it hard to clearly segregate relevant tweets into well-defined clusters.
Gaussian Two-Way Channels With Discrete Inputs and Quantized Outputs
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, Gaussian two-way channel with uniform output quantization is studied. For Gaussian inputs, the optimum uniform finite-level quantizer is determined numerically for different values of Signal-to-Noise Ratio (SNR). The two-way channel with constellation-based transmitters is then investigated. A formulation for the so-called Shannon achievable region of this channel is developed and numerical computations of this region are presented for particular constellations. It is shown that if one transmitter utilizes a rotated version of the constellation used at the other transmitter, the Shannon achievable region can be enlarged
Online Machine Learning Techniques for Predicting Operator Performance
Venue: arXiv cs.LG 2016
Year: 2016

This thesis explores a number of online machine learning algorithms. From a theoret- ical perspective, it assesses their employability for a particular function approximation problem where the analytical models fall short. Furthermore, it discusses the applica- tion of theoretically suitable learning algorithms to the function approximation problem at hand through an efficient implementation that exploits various computational and mathematical shortcuts. Finally, this thesis work evaluates the implemented learning algorithms according to various evaluation criteria through rigorous testing.
Newer method of string comparison: the Modified Moving Contracting Window Pattern Algorithm
Venue: arXiv cs.DS 2016
Year: 2016

This paper presents a new algorithm, the Modified Moving Contracting Window Pattern Algorithm (CMCWPM), for the calculation of field similarity. It strongly relies on previous work by Yang et al. (2001), correcting previous work in which characters marked as inaccessible for further pattern matching were not treated as boundaries between subfields, occasionally leading to higher than expected scores of field similarity. A reference Python implementation is provided.
Dualized Simple Type Theory
Venue: arXiv cs.LO 2016
Year: 2016

We propose a new bi-intuitionistic type theory called Dualized Type Theory (DTT). It is a simple type theory with perfect intuitionistic duality, and corresponds to a single-sided polarized sequent calculus. We prove DTT strongly normalizing, and prove type preservation. DTT is based on a new propositional bi-intuitionistic logic called Dualized Intuitionistic Logic (DIL) that builds on Pinto and Uustalu's logic L. DIL is a simplification of L by removing several admissible inference rules while maintaining consistency and completeness. Furthermore, DIL is defined using a dualized syntax by labeling formulas and logical connectives with polarities thus reducing the number of inference rules needed to define the logic. We give a direct proof of consistency, but prove completeness by reduction to L.
Information Flows in Encrypted Databases
Venue: arXiv cs.DB 2016
Year: 2016

In encrypted databases, sensitive data is protected from an untrusted server by encrypting columns using partially homomorphic encryption schemes, and storing encryption keys in a trusted client. However, encrypting columns and protecting encryption keys does not ensure confidentiality - sensitive data can leak during query processing due to information flows through the trusted client. In this paper, we propose SecureSQL, an encrypted database that partitions query processing between an untrusted server and a trusted client while ensuring the absence of information flows. Our evaluation based on OLTP benchmarks suggests that SecureSQL can protect against explicit flows with low overheads (< 30%). However, protecting against implicit flows can be expensive because it precludes the use of key databases optimizations and introduces additional round trips between client and server.
Optimal DoF Region for the Asymmetric Two-Pair MIMO Two-Way Relay Channel
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we study the optimal degrees of freedom (DoF) region for the two-pair MIMO two-way relay channel (TWRC) with asymmetric antenna setting, where two pairs of users exchange information with the help of a common relay. Each user $i$ is equipped with $M_i$ antennas, for $i=1,2,3,4$, and the relay is equipped with $N$ antennas. First, we derive an outer bound of the DoF region by using the cut-set theorem and the genie-message approach. Then, we propose a new transmission scheme to achieve the outer bound of the DoF region. Due to the asymmetric data exchange, where the two users in each pair can communicate a different number of data streams, we not only need to form the network-coded symbols but also need to process the additional asymmetric data streams at the relay. This is realized through the joint design of relay compression matrix and source precoding matrices. After obtaining the optimal DoF region, we study the optimal sum DoF by solving a linear programming problem. From the optimal DoF region of this channel, we show that in the asymmetric antenna setting, some antennas at certain source nodes are redundant and cannot contribute to enlarge the DoF region. We also show that there is no loss of optimality in terms of the sum DoF by enforcing symmetric data exchange, where the two users in each pair are restricted to communicate the same number of data streams.
Waveform Optimization for Large-Scale Multi-Antenna Multi-Sine Wireless Power Transfer
Venue: arXiv cs.IT 2016
Year: 2016

Wireless power transfer (WPT) is expected to be a technology reshaping the landscape of low-power applications such as the Internet of Things, machine-to-machine communications and radio frequency identification networks. Although there has been some progress towards multi-antenna multi-sine WPT design, the large-scale design of WPT, reminiscent of massive multiple-input multiple-output (MIMO) in communications, remains an open problem. Considering the nonlinear rectifier model, a multiuser waveform optimization algorithm is derived based on successive convex approximation (SCA). A lower-complexity algorithm is derived based on asymptotic analysis and sequential approximation (SA). It is shown that the difference between the average output voltage achieved by the two algorithms can be negligible provided the number of antennas is large enough. The performance gain of the nonlinear model based design over the linear model based design can be large, in the presence of a large number of tones.
On the evaluation of uncertainties for state estimation with the Kalman filter
Venue: arXiv cs.SY 2016
Year: 2016

The Kalman filter is an established tool for the analysis of dynamic systems with normally distributed noise, and it has been successfully applied in numerous application areas. It provides sequentially calculated estimates of the system states along with a corresponding covariance matrix. For nonlinear systems, the extended Kalman filter is often used which is derived from the Kalman filter by linearization around the current estimate. A key issue in metrology is the evaluation of the uncertainty associated with the Kalman filter state estimates. The "Guide to the Expression of Uncertainty in Measurements" (GUM) and its supplements serve as the de facto standard for uncertainty evaluation in metrology. We explore the relationship between the covariance matrix produced by the Kalman filter and a GUM-compliant uncertainty analysis. In addition, also the results of a Bayesian analysis are considered. For the case of linear systems with known system matrices, we show that all three approaches are compatible. When the system matrices are not precisely known, however, or when the system is nonlinear, this equivalence breaks down and different results can be reached then. Though for precisely known nonlinear systems the result of the extended Kalman filter still corresponds to the linearized uncertainty propagation of GUM. The extended Kalman filter can suffer from linearization and convergence errors. These disadvantages can be avoided to some extent by applying Monte Carlo procedures, and we propose such a method which is GUM-compliant and can also be applied online during the estimation. We illustrate all procedures in terms of a two-dimensional dynamic system and compare the results with those obtained by particle filtering, which has been proposed for the approximate calculation of a Bayesian solution. Finally, we give some recommendations based on our findings.
Coded Caching for Networks with the Resolvability Property
Venue: arXiv cs.IT 2016
Year: 2016

Coded caching is a recently proposed technique for dealing with large scale content distribution over the Internet. As in conventional caching, it leverages the presence of local caches at the end users. However, it considers coding in the caches and/or coded transmission from the central server and demonstrates that huge savings in transmission rate are possible when the server and the end users are connected via a single shared link. In this work, we consider a more general topology where there is a layer of relay nodes between the server and the users, e.g., combination networks studied in network coding are an instance of these networks. We propose novel schemes for a class of such networks that satisfy a so-called resolvability property and demonstrate that the performance of our scheme is strictly better than previously proposed schemes.
Modeling Rich Contexts for Sentiment Classification with LSTM
Venue: arXiv cs.CL 2016
Year: 2016

Sentiment analysis on social media data such as tweets and weibo has become a very important and challenging task. Due to the intrinsic properties of such data, tweets are short, noisy, and of divergent topics, and sentiment classification on these data requires to modeling various contexts such as the retweet/reply history of a tweet, and the social context about authors and relationships. While few prior study has approached the issue of modeling contexts in tweet, this paper proposes to use a hierarchical LSTM to model rich contexts in tweet, particularly long-range context. Experimental results show that contexts can help us to perform sentiment classification remarkably better.
SCOUT: simultaneous time segmentation and community detection in dynamic networks
Venue: arXiv cs.SI 2016
Year: 2016

Many evolving complex systems can be modeled via dynamic networks. An important problem in dynamic network research is community detection, which identifies groups of topologically related nodes. Typically, this problem is approached by assuming either that each time point has a distinct community organization or that all time points share one community organization. In reality, the truth likely lies between these two extremes, since some time periods can have community organization that evolves while others can have community organization that stays the same. To find the compromise, we consider community detection in the context of the problem of segment detection, which identifies contiguous time periods with consistent network structure. Consequently, we formulate a combined problem of segment community detection (SCD), which simultaneously partitions the network into contiguous time segments with consistent community organization and finds this community organization for each segment. To solve SCD, we introduce SCOUT, an optimization framework that explicitly considers both segmentation quality and partition quality. SCOUT addresses limitations of existing methods that can be adapted to solve SCD, which typically consider only one of segmentation quality or partition quality. In a thorough evaluation, SCOUT outperforms the existing methods in terms of both accuracy and computational complexity.
Constant Envelope Pilot-Based Low-Complexity CFO Estimation in Massive MU-MIMO Systems
Venue: arXiv cs.IT 2016
Year: 2016

In this paper we consider a constant envelope pilot signal based carrier frequency offset (CFO) estimation in massive multiple-input multiple-output (MIMO) systems. The proposed algorithm performs spatial averaging on the periodogram of the received pilots across the base station (BS) antennas. Our study reveals that the proposed algorithm has complexity only linear in $M$ (the number of BS antennas). Further our analysis and numerical simulations also reveal that with fixed number of users and a fixed pilot length, the minimum required transmit pilot power decreases as $\frac{1}{\sqrt{M}}$ with increasing $M$, while maintaining a fixed desired mean squared error (MSE) of CFO estimation.
On the Aloha throughput-fairness tradeoff
Venue: arXiv cs.IT 2016
Year: 2016

A well-known inner bound of the stability region of the slotted Aloha protocol on the collision channel with n users assumes worst-case service rates (all user queues non-empty). Using this inner bound as a feasible set of achievable rates, a characterization of the throughput--fairness tradeoff over this set is obtained, where throughput is defined as the sum of the individual user rates, and two definitions of fairness are considered: the Jain-Chiu-Hawe function and the sum-user alpha-fair (isoelastic) utility function. This characterization is obtained using both an equality constraint and an inequality constraint on the throughput, and properties of the optimal controls, the optimal rates, and the fairness as a function of the target throughput are established. A key fact used in all theorems is the observation that all contention probability vectors that extremize the fairness functions take at most two non-zero values.
Bifurcation Analysis of Reaction Diffusion Systems on Arbitrary Surfaces
Venue: arXiv cs.CE 2016
Year: 2016

In this paper we present computational techniques to investigate the solutions of two-component, nonlinear reaction-diffusion (RD) systems on arbitrary surfaces. We build on standard techniques for linear and nonlinear analysis of RD systems, and extend them to operate on large-scale meshes for arbitrary surfaces. In particular, we use spectral techniques for a linear stability analysis to characterize and directly compose patterns emerging from homogeneities. We develop an implementation using surface finite element methods and a numerical eigenanalysis of the Laplace-Beltrami operator on surface meshes. In addition, we describe a technique to explore solutions of the nonlinear RD equations using numerical continuation. Here, we present a multiresolution approach that allows us to trace solution branches of the nonlinear equations efficiently even for large-scale meshes. Finally, we demonstrate the working of our framework for two RD systems with applications in biological pattern formation: a Brusselator model that has been used to model pattern development on growing plant tips, and a chemotactic model for the formation of skin pigmentation patterns. While these models have been used previously on simple geometries, our framework allows us to study the impact of arbitrary geometries on emerging patterns.