OpenRaiser/Intern-Atlas huggingface dataset:
Computing the Expected Value and Variance of Geometric Measures
Venue: arXiv cs.DS 2016
Year: 2016

Let P be a set of points in R^d, and let M be a function that maps any subset of P to a positive real number. We examine the problem of computing the exact mean and variance of M when a subset of points in P is selected according to a well-defined random distribution. We consider two distributions; in the first distribution (which we call the Bernoulli distribution), each point p in P is included in the random subset independently, with probability pi(p). In the second distribution (the fixed-size distribution), a subset of exactly s points is selected uniformly at random among all possible subsets of s points in P. This problem is a crucial part of modern ecological analyses; each point in P represents a species in d-dimensional trait space, and the goal is to compute the statistics of a geometric measure on this trait space, when subsets of species are selected under random processes. We present efficient exact algorithms for computing the mean and variance of several geometric measures when point sets are selected under one of the described random distributions. More specifically, we provide algorithms for the following measures: the bounding box volume, the convex hull volume, the mean pairwise distance (MPD), the squared Euclidean distance from the centroid, and the diameter of the minimum enclosing disk. We also describe an efficient (1-e)-approximation algorithm for computing the mean and variance of the mean pairwise distance. We implemented three of our algorithms and we show that our implementations can provide major speedups compared to the existing imprecise approaches.
High Dimensional Uncertainty Quantification for an Electrothermal Field Problem using Stochastic Collocation on Sparse Grids and Tensor Train Decompositions
Venue: arXiv cs.CE 2016
Year: 2016

The temperature developed in bondwires of integrated circuits (ICs) is a possible source of malfunction, and has to be taken into account during the design phase of an IC. Due to manufacturing tolerances, a bondwire's geometrical characteristics are uncertain parameters, and as such their impact has to be examined with the use of uncertainty quantification (UQ) methods. Sampling methods, like the Monte Carlo (MC), converge slowly, while efficient alternatives scale badly with respect to the number of considered uncertainties. Possible remedies to this, so-called, curse of dimensionality are sought in the application of stochastic collocation (SC) on sparse grids (SGs) and of the recently emerged low-rank tensor decomposition methods, with emphasis on the tensor train (TT) decomposition.
Straintronic magneto-tunneling-junction based ternary content addressable memory
Venue: arXiv cs.ET 2016
Year: 2016

Straintronic magneto-tunneling junction (s-MTJ) switches, whose resistances are controlled with voltage-generated strain in the magnetostrictive free layer of the MTJ, are extremely energy-efficient switches that would dissipate a few aJ of energy during switching. Unfortunately, they are also relatively error-prone and have low resistance on/off ratio. This suggests that as computing elements, they are best suited for non-Boolean architectures. Here, we propose and analyze a ternary content addressable memory implemented with s-MTJs and some transistors. It overcomes challenges encountered by traditional all-transistor implementations, resulting in exceptionally high cell density.
A Survey of Voice Translation Methodologies - Acoustic Dialect Decoder
Venue: arXiv cs.CL 2016
Year: 2016

Speech Translation has always been about giving source text or audio input and waiting for system to give translated output in desired form. In this paper, we present the Acoustic Dialect Decoder (ADD) - a voice to voice ear-piece translation device. We introduce and survey the recent advances made in the field of Speech Engineering, to employ in the ADD, particularly focusing on the three major processing steps of Recognition, Translation and Synthesis. We tackle the problem of machine understanding of natural language by designing a recognition unit for source audio to text, a translation unit for source language text to target language text, and a synthesis unit for target language text to target language speech. Speech from the surroundings will be recorded by the recognition unit present on the ear-piece and translation will start as soon as one sentence is successfully read. This way, we hope to give translated output as and when input is being read. The recognition unit will use Hidden Markov Models (HMMs) Based Tool-Kit (HTK), hybrid RNN systems with gated memory cells, and the synthesis unit, HMM based speech synthesis system HTS. This system will initially be built as an English to Tamil translation device.
Morphisms on infinite alphabets, countable states automata and regular sequences
Venue: arXiv cs.FL 2016
Year: 2016

In this paper, we prove that a class of regular sequences can be viewed as projections of fixed points of uniform morphisms on a countable alphabet, and also can be generated by countable states automata. Moreover, we prove that the regularity of some regular sequences is invariant under some codings.
Compressive Cyclostationary Spectrum Sensing with a Constant False Alarm Rate
Venue: arXiv cs.IT 2016
Year: 2016

Spectrum sensing is a crucial component of opportunistic spectrum access schemes, which aim at improving spectrum utilization by allowing for the reuse of idle licensed spectrum. Sensing a spectral band before using it makes sure the legitimate users are not disturbed. Since information about these users' signals is not necessarily available, the sensor should be able to conduct so-called blind spectrum sensing. Historically, this has not been a feature of cyclostationarity-based algorithms. Indeed, in many application scenarios the information required for traditional cyclostationarity detection might not be available, hindering its practical applicability. In this work we propose two new cyclostationary spectrum sensing algorithms that make use of the inherent sparsity of the cyclic autocorrelation to make blind operation possible. Along with utilizing sparse recovery methods for estimating the cyclic autocorrelation, we take further advantage of its structure by introducing joint sparsity as well as general structure dictionaries into the recovery process. Furthermore, we extend a statistical test for cyclostationarity to accommodate sparse cyclic spectra. Our numerical results demonstrate that the new methods achieve a near constant false alarm rate behavior in contrast to earlier approaches from the literature.
Optimizing Communication and Computation for Multi-UAV Information Gathering Applications
Venue: arXiv cs.SY 2016
Year: 2016

Mobile agent networks, such as multi-UAV systems, are constrained by limited resources. In particular, limited energy affects system performance directly, such as system lifetime. It has been demonstrated in the wireless sensor network literature that the communication energy consumption dominates the computational and the sensing energy consumption. Hence, the lifetime of the multi-UAV systems can be extended significantly by optimizing the amount of communication data, at the expense of increasing computational cost. In this work, we aim at attaining an optimal trade-off between the communication and the computational energy. Specifically, we propose a mixed-integer optimization formulation for a multi-hop hierarchical clustering-based self-organizing UAV network incorporating data aggregation, to obtain an energy-efficient information routing scheme. The proposed framework is tested on two applications, namely target tracking and area mapping. Based on simulation results, our method can significantly save energy compared to a baseline strategy, where there is no data aggregation and clustering scheme.
Automatic View-Point Selection for Inter-Operative Endoscopic Surveillance
Venue: arXiv cs.CV 2016
Year: 2016

Esophageal adenocarcinoma arises from Barrett's esophagus, which is the most serious complication of gastroesophageal reflux disease. Strategies for screening involve periodic surveillance and tissue biopsies. A major challenge in such regular examinations is to record and track the disease evolution and re-localization of biopsied sites to provide targeted treatments. In this paper, we extend our original inter-operative relocalization framework to provide a constrained image based search for obtaining the best view-point match to the live view. Within this context we investigate the effect of: the choice of feature descriptors and color-space; filtering of uninformative frames and endoscopic modality, for view-point localization. Our experiments indicate an improvement in the best view-point retrieval rate to [92%,87%] from [73%,76%] (in our previous approach) for NBI and WL.
An Information Theoretic Feature Selection Framework for Big Data under Apache Spark
Venue: arXiv cs.AI 2016
Year: 2016

With the advent of extremely high dimensional datasets, dimensionality reduction techniques are becoming mandatory. Among many techniques, feature selection has been growing in interest as an important tool to identify relevant features on huge datasets --both in number of instances and features--. The purpose of this work is to demonstrate that standard feature selection methods can be parallelized in Big Data platforms like Apache Spark, boosting both performance and accuracy. We thus propose a distributed implementation of a generic feature selection framework which includes a wide group of well-known Information Theoretic methods. Experimental results on a wide set of real-world datasets show that our distributed framework is capable of dealing with ultra-high dimensional datasets as well as those with a huge number of samples in a short period of time, outperforming the sequential version in all the cases studied.
Fast, Scalable Phrase-Based SMT Decoding
Venue: arXiv cs.CL 2016
Year: 2016

The utilization of statistical machine translation (SMT) has grown enormously over the last decade, many using open-source software developed by the NLP community. As commercial use has increased, there is need for software that is optimized for commercial requirements, in particular, fast phrase-based decoding and more efficient utilization of modern multicore servers. In this paper we re-examine the major components of phrase-based decoding and decoder implementation with particular emphasis on speed and scalability on multicore machines. The result is a drop-in replacement for the Moses decoder which is up to fifteen times faster and scales monotonically with the number of cores.
Tensor Computation: A New Framework for High-Dimensional Problems in EDA
Venue: arXiv cs.NA 2016
Year: 2016

Many critical EDA problems suffer from the curse of dimensionality, i.e. the very fast-scaling computational burden produced by large number of parameters and/or unknown variables. This phenomenon may be caused by multiple spatial or temporal factors (e.g. 3-D field solvers discretizations and multi-rate circuit simulation), nonlinearity of devices and circuits, large number of design or optimization parameters (e.g. full-chip routing/placement and circuit sizing), or extensive process variations (e.g. variability/reliability analysis and design for manufacturability). The computational challenges generated by such high dimensional problems are generally hard to handle efficiently with traditional EDA core algorithms that are based on matrix and vector computation. This paper presents "tensor computation" as an alternative general framework for the development of efficient EDA algorithms and tools. A tensor is a high-dimensional generalization of a matrix and a vector, and is a natural choice for both storing and solving efficiently high-dimensional EDA problems. This paper gives a basic tutorial on tensors, demonstrates some recent examples of EDA applications (e.g., nonlinear circuit modeling and high-dimensional uncertainty quantification), and suggests further open EDA problems where the use of tensor computation could be of advantage.
Perspectives on Surgical Data Science
Venue: arXiv cs.CY 2016
Year: 2016

The availability of large amounts of data together with advances in analytical techniques afford an opportunity to address difficult challenges in ensuring that healthcare is safe, effective, efficient, patient-centered, equitable, and timely. Surgical care and training stand to tremendously gain through surgical data science. Herein, we discuss a few perspectives on the scope and objectives for surgical data science.
Augmented Reality with Hololens: Experiential Architectures Embedded in the Real World
Venue: arXiv cs.GR 2016
Year: 2016

Early hands-on experiences with the Microsoft Hololens augmented/mixed reality device are reported and discussed, with a general aim of exploring basic 3D visualization. A range of usage cases are tested, including data visualization and immersive data spaces, in-situ visualization of 3D models and full scale architectural form visualization. Ultimately, the Hololens is found to provide a remarkable tool for moving from traditional visualization of 3D objects on a 2D screen, to fully experiential 3D visualizations embedded in the real world.
Automatic Generation of Equivalent Electrothermal SPICE Netlists from 3D Electrothermal Field Models
Venue: arXiv cs.CE 2016
Year: 2016

Starting from a 3D electrothermal field problem discretized by the Finite Integration Technique, the equivalence to a circuit description is shown by exploiting the analogy to the Modified Nodal Analysis approach. Using this analogy, an algorithm for the automatic generation of a monolithic SPICE netlist is presented. Joule losses from the electrical circuit are included as heat sources in the thermal circuit. The thermal simulation yields nodal temperatures that influence the electrical conductivity. Apart from the used field discretization, this approach applies no further simplifications. An example 3D chip package is used to validate the algorithm.
Civique: Using Social Media to Detect Urban Emergencies
Venue: arXiv cs.CL 2016
Year: 2016

We present the Civique system for emergency detection in urban areas by monitoring micro blogs like Tweets. The system detects emergency related events, and classifies them into appropriate categories like "fire", "accident", "earthquake", etc. We demonstrate our ideas by classifying Twitter posts in real time, visualizing the ongoing event on a map interface and alerting users with options to contact relevant authorities, both online and offline. We evaluate our classifiers for both the steps, i.e., emergency detection and categorization, and obtain F-scores exceeding 70% and 90%, respectively. We demonstrate Civique using a web interface and on an Android application, in realtime, and show its use for both tweet detection and visualization.
Access Control in Linked Data Using WebID
Venue: arXiv cs.CR 2016
Year: 2016

Linked Data technologies become increasingly important in many domains. Key factors for their breakthrough are security and trust. Classical means for access control lack granularity when parts of the Linked Data graph must be protected. The WebID, combining semantic web concepts with methods from certificate based authentication and authorization, seems promising to fulfill all requirements concerning security and trust in the semantic web. In the PerSemID project, we challenged the WebID technology with a fully implemented proof-of-concept (PoC) addressing a workflow coming from the domain of lifelong learning and student mobility. In our use case of study enrollment, we used WebIDs for authentication and to grant access to parts of triple stores, during cross domain triple store interactions to exchange data between stakeholders.
A Comprehensive Comparative Study of Word and Sentence Similarity Measures
Venue: arXiv cs.IR 2016
Year: 2016

Sentence similarity is considered the basis of many natural language tasks such as information retrieval, question answering and text summarization. The semantic meaning between compared text fragments is based on the words semantic features and their relationships. This article reviews a set of word and sentence similarity measures and compares them on benchmark datasets. On the studied datasets, results showed that hybrid semantic measures perform better than both knowledge and corpus based measures.
Are Accuracy and Robustness Correlated?
Venue: arXiv cs.CV 2016
Year: 2016

Machine learning models are vulnerable to adversarial examples formed by applying small carefully chosen perturbations to inputs that cause unexpected classification errors. In this paper, we perform experiments on various adversarial example generation approaches with multiple deep convolutional neural networks including Residual Networks, the best performing models on ImageNet Large-Scale Visual Recognition Challenge 2015. We compare the adversarial example generation techniques with respect to the quality of the produced images, and measure the robustness of the tested machine learning models to adversarial examples. Finally, we conduct large-scale experiments on cross-model adversarial portability. We find that adversarial examples are mostly transferable across similar network topologies, and we demonstrate that better machine learning models are less vulnerable to adversarial examples.
A Harmonic Mean Linear Discriminant Analysis for Robust Image Classification
Venue: arXiv cs.CV 2016
Year: 2016

Linear Discriminant Analysis (LDA) is a widely-used supervised dimensionality reduction method in computer vision and pattern recognition. In null space based LDA (NLDA), a well-known LDA extension, between-class distance is maximized in the null space of the within-class scatter matrix. However, there are some limitations in NLDA. Firstly, for many data sets, null space of within-class scatter matrix does not exist, thus NLDA is not applicable to those datasets. Secondly, NLDA uses arithmetic mean of between-class distances and gives equal consideration to all between-class distances, which makes larger between-class distances can dominate the result and thus limits the performance of NLDA. In this paper, we propose a harmonic mean based Linear Discriminant Analysis, Multi-Class Discriminant Analysis (MCDA), for image classification, which minimizes the reciprocal of weighted harmonic mean of pairwise between-class distance. More importantly, MCDA gives higher priority to maximize small between-class distances. MCDA can be extended to multi-label dimension reduction. Results on 7 single-label data sets and 4 multi-label data sets show that MCDA has consistently better performance than 10 other single-label approaches and 4 other multi-label approaches in terms of classification accuracy, macro and micro average F1 score.
Joint Transmit Power and Relay Two-Way Beamforming Optimization for Energy-Harvesting Full-Duplex Communications
Venue: arXiv cs.IT 2016
Year: 2016

This paper studies the joint optimization problem of two-way relay beamforming, the receiver power splitting (PS) ratio as well as the transmit power at the sources to maximize the achievable sum-rate of a simultaneous wireless information and power transfer (SWIPT) system with a full-duplex (FD) multiple-input multiple-output (MIMO) amplify and forward (AF) relay, assuming perfect channel state information (CSI). In particular, our contribution is an iterative algorithm based on the difference of convex programming (DC) and one dimensional searching to achieve the joint solution. Simulation results are provided to demonstrate the effectiveness of the proposed algorithm.
A Distributed Parallel Algorithm for Minimum Spanning Tree Problem
Venue: arXiv cs.DC 2016
Year: 2016

In this paper we present and evaluate a parallel algorithm for solving a minimum spanning tree (MST) problem for supercomputers with distributed memory. The algorithm relies on the relaxation of the message processing order requirement for one specific message type compared to the original GHS (Gallager, Humblet, Spira) algorithm. Our algorithm adopts hashing and message compression optimization techniques as well. To the best of our knowledge, this is the first parallel implementation of the GHS algorithm that linearly scales to more than 32 nodes (256 cores) of Infiniband cluster.
CMOS-Memristor Hybrid Integrated Pixel Sensors
Venue: arXiv cs.ET 2016
Year: 2016

Increase in image resolution require the ability of image sensors to pack an increased number of circuit components in a given area. On the the other hand a high speed processing of signals from the sensors require the ability of pixel to carry out pixel parallel operations. In the paper, we propose a modified 3T and 4T CMOS wide dynamic range pixels, which we refer as 2T-M and 3T-M configurations, comprising of MOSFETS and memristors. The low leakage currents and low area of memristors helps to achieve the objective of reducing the area, while the possibility to create arrays of memristors and MOSFETs across different layers within the chip, ensure the possibility to scale the circuit architecture.
Reasoning in the Bernays-Schoenfinkel-Ramsey Fragment of Separation Logic
Venue: arXiv cs.LO 2016
Year: 2016

Separation Logic (SL) is a well-known assertion language used in Hoare-style modular proof systems for programs with dynamically allocated data structures. In this paper we investigate the fragment of first-order SL restricted to the Bernays-Schoenfinkel-Ramsey quantifier prefix $\exists^*\forall^*$, where the quantified variables range over the set of memory locations. When this set is uninterpreted (has no associated theory) the fragment is PSPACE-complete, which matches the complexity of the quantifier-free fragment. However, SL becomes undecidable when the quantifier prefix belongs to $\exists^*\forall^*\exists^*$ instead, or when the memory locations are interpreted as integers with linear arithmetic constraints, thus setting a sharp boundary for decidability within SL. We have implemented a decision procedure for the decidable fragment of $\exists^*\forall^*$SL as a specialized solver inside a DPLL($T$) architecture, within the CVC4 SMT solver. The evaluation of our implementation was carried out using two sets of verification conditions, produced by (i) unfolding inductive predicates, and (ii) a weakest precondition-based verification condition generator. Experimental data shows that automated quantifier instantiation has little overhead, compared to manual model-based instantiation.
Inferring Person-to-person Proximity Using WiFi Signals
Venue: arXiv cs.CY 2016
Year: 2016

Today's societies are enveloped in an ever-growing telecommunication infrastructure. This infrastructure offers important opportunities for sensing and recording a multitude of human behaviors. Human mobility patterns are a prominent example of such a behavior which has been studied based on cell phone towers, Bluetooth beacons, and WiFi networks as proxies for location. However, while mobility is an important aspect of human behavior, understanding complex social systems requires studying not only the movement of individuals, but also their interactions. Sensing social interactions on a large scale is a technical challenge and many commonly used approaches---including RFID badges or Bluetooth scanning---offer only limited scalability. Here we show that it is possible, in a scalable and robust way, to accurately infer person-to-person physical proximity from the lists of WiFi access points measured by smartphones carried by the two individuals. Based on a longitudinal dataset of approximately 800 participants with ground-truth interactions collected over a year, we show that our model performs better than the current state-of-the-art. Our results demonstrate the value of WiFi signals in social sensing as well as potential threats to privacy that they imply.