OpenRaiser/Intern-Atlas huggingface dataset:
A Devil's Advocate against Termination of Direct Recursion
Venue: arXiv cs.PL 2017
Year: 2017

A devil's advocate is one who argues against a claim, not as a committed opponent but in order to determine the validity of the claim. We are interested in a devil's advocate that argues against termination of a program. He does so by producing a maleficent program that can cause the non-termination of the original program. By inspecting and running the malicious program, one may gain insight into the potential reasons for non-termination and produce counterexamples for termination. We introduce our method using the concurrent programming language Constraint Handling Rules (CHR). Like in other declarative languages, non-termination occurs through unbounded recursion. Given a self-recursive rule, we automatically generate one or more devil's rules from it. The construction of the devil's rules is straightforward and involves no guessing. The devil's rules can be simple. For example, they are non-recursive for rules with single recursion. We show that the devil's rules are maximally vicious in the following sense: For any program that contains the self-recursive rule and for any infinite computation through that rule in that program, there is a corresponding infinite computation with the recursive rule and the devil's rules alone. In that case, the malicious rules serve as a finite witness for non-termination. On the other hand, if the devil's rules do not exhibit an infinite computation, the recursive rule is unconditionally terminating. We also identify cases where the static analysis of the devil's rule decides termination or non-termination of the recursive rule.
What are the visual features underlying human versus machine vision?
Venue: arXiv cs.CV 2017
Year: 2017

Although Deep Convolutional Networks (DCNs) are approaching the accuracy of human observers at object recognition, it is unknown whether they leverage similar visual representations to achieve this performance. To address this, we introduce Clicktionary, a web-based game for identifying visual features used by human observers during object recognition. Importance maps derived from the game are consistent across participants and uncorrelated with image saliency measures. These results suggest that Clicktionary identifies image regions that are meaningful and diagnostic for object recognition but different than those driving eye movements. Surprisingly, Clicktionary importance maps are only weakly correlated with relevance maps derived from DCNs trained for object recognition. Our study demonstrates that the narrowing gap between the object recognition accuracy of human observers and DCNs obscures distinct visual strategies used by each to achieve this performance.
Distributed Estimation of Dynamic Fields over Multi-agent Networks
Venue: arXiv cs.IT 2017
Year: 2017

This work presents distributed algorithms for estimation of time-varying random fields over multi-agent/sensor networks. A network of sensors makes sparse and noisy local measurements of the dynamic field. Each sensor aims to obtain unbiased distributed estimates of the entire field with bounded mean-squared error (MSE) based on its own local observations and its neighbors' estimates. This work develops three novel distributed estimators: Pseudo-Innovations Kalman Filter (PIKF), Distributed Information Kalman Filter (DIKF) and Consensus+Innovations Kalman Filter (CIKF). We design the gain matrices such that the estimators achieve unbiased estimates with bounded MSE under minimal assumptions on the local observation and network communication models. This work establishes trade-offs between these three distributed estimators and demonstrates how they outperform existing solutions. We validate our results through extensive numerical evaluations.
On the Uniqueness of FROG Methods
Venue: arXiv cs.IT 2017
Year: 2017

The problem of recovering a signal from its power spectrum, called phase retrieval, arises in many scientific fields. One of many examples is ultra-short laser pulse characterization in which the electromagnetic field is oscillating with ~10^15 Hz and phase information cannot be measured directly due to limitations of the electronic sensors. Phase retrieval is ill-posed in most cases as there are many different signals with the same Fourier transform magnitude. To overcome this fundamental ill-posedness, several measurement techniques are used in practice. One of the most popular methods for complete characterization of ultra-short laser pulses is the Frequency-Resolved Optical Gating (FROG). In FROG, the acquired data is the power spectrum of the product of the unknown pulse with its delayed replica. Therefore the measured signal is a quartic function of the unknown pulse. A generalized version of FROG, where the delayed replica is replaced by a second unknown pulse, is called blind FROG. In this case, the measured signal is quadratic with respect to both pulses. In this letter we introduce and formulate FROG-type techniques. We then show that almost all band-limited signals are determined uniquely, up to trivial ambiguities, by blind FROG measurements (and thus also by FROG), if in addition we have access to the signals power spectrum.
Towards Decoding as Continuous Optimization in Neural Machine Translation
Venue: arXiv cs.CL 2017
Year: 2017

We propose a novel decoding approach for neural machine translation (NMT) based on continuous optimisation. We convert decoding - basically a discrete optimization problem - into a continuous optimization problem. The resulting constrained continuous optimisation problem is then tackled using gradient-based methods. Our powerful decoding framework enables decoding intractable models such as the intersection of left-to-right and right-to-left (bidirectional) as well as source-to-target and target-to-source (bilingual) NMT models. Our empirical results show that our decoding framework is effective, and leads to substantial improvements in translations generated from the intersected models where the typical greedy or beam search is not feasible. We also compare our framework against reranking, and analyse its advantages and disadvantages.
A sequent calculus for the Tamari order
Venue: arXiv cs.LO 2017
Year: 2017

We introduce a sequent calculus with a simple restriction of Lambek's product rules that precisely captures the classical Tamari order, i.e., the partial order on fully-bracketed words (equivalently, binary trees) induced by a semi-associative law (equivalently, tree rotation). We establish a focusing property for this sequent calculus (a strengthening of cut-elimination), which yields the following coherence theorem: every valid entailment in the Tamari order has exactly one focused derivation. One combinatorial application of this coherence theorem is a new proof of the Tutte-Chapoton formula for the number of intervals in the Tamari lattice $Y_n$. We also apply the sequent calculus and the coherence theorem to build a surprising bijection between intervals of the Tamari order and a certain fragment of lambda calculus, consisting of the $β$-normal planar lambda terms with no closed proper subterms.
Question Analysis for Arabic Question Answering Systems
Venue: arXiv cs.CL 2017
Year: 2017

The first step of processing a question in Question Answering(QA) Systems is to carry out a detailed analysis of the question for the purpose of determining what it is asking for and how to perfectly approach answering it. Our Question analysis uses several techniques to analyze any question given in natural language: a Stanford POS Tagger & parser for Arabic language, a named entity recognizer, tokenizer,Stop-word removal, Question expansion, Question classification and Question focus extraction components. We employ numerous detection rules and trained classifier using features from this analysis to detect important elements of the question, including: 1) the portion of the question that is a referring to the answer (the focus); 2) different terms in the question that identify what type of entity is being asked for (the lexical answer types); 3) Question expansion ; 4) a process of classifying the question into one or more of several and different types; and We describe how these elements are identified and evaluate the effect of accurate detection on our question-answering system using the Mean Reciprocal Rank(MRR) accuracy measure.
Explicit Time Integration of Transient Eddy Current Problems
Venue: arXiv cs.CE 2017
Year: 2017

For time integration of transient eddy current problems commonly implicit time integration methods are used, where in every time step one or several nonlinear systems of equations have to be linearized with the Newton-Raphson method due to ferromagnetic materials involved. In this paper, a generalized Schur-complement is applied to the magnetic vector potential formulation, which converts a differential-algebraic equation system of index 1 into a system of ordinary differential equations (ODE) with reduced stiffness. For the time integration of this ODE system of equations, the explicit Euler method is applied. The Courant-Friedrich-Levy (CFL) stability criterion of explicit time integration methods may result in small time steps. Applying a pseudo-inverse of the discrete curl-curl operator in nonconducting regions of the problem is required in every time step. For the computation of the pseudo-inverse, the preconditioned conjugate gradient (PCG) method is used. The cascaded Subspace Extrapolation method (CSPE) is presented to produce suitable start vectors for these PCG iterations. The resulting scheme is validated using the TEAM 10 benchmark problem.
Decoding with Finite-State Transducers on GPUs
Venue: arXiv cs.CL 2017
Year: 2017

Weighted finite automata and transducers (including hidden Markov models and conditional random fields) are widely used in natural language processing (NLP) to perform tasks such as morphological analysis, part-of-speech tagging, chunking, named entity recognition, speech recognition, and others. Parallelizing finite state algorithms on graphics processing units (GPUs) would benefit many areas of NLP. Although researchers have implemented GPU versions of basic graph algorithms, limited previous work, to our knowledge, has been done on GPU algorithms for weighted finite automata. We introduce a GPU implementation of the Viterbi and forward-backward algorithm, achieving decoding speedups of up to 5.2x over our serial implementation running on different computer architectures and 6093x over OpenFST.
Linear Search with Terrain-Dependent Speeds
Venue: arXiv cs.DS 2017
Year: 2017

We revisit the linear search problem where a robot, initially placed at the origin on an infinite line, tries to locate a stationary target placed at an unknown position on the line. Unlike previous studies, in which the robot travels along the line at a constant speed, we consider settings where the robot's speed can depend on the direction of travel along the line, or on the profile of the terrain, e.g. when the line is inclined, and the robot can accelerate. Our objective is to design search algorithms that achieve good competitive ratios for the time spent by the robot to complete its search versus the time spent by an omniscient robot that knows the location of the target. We consider several new robot mobility models in which the speed of the robot depends on the terrain.These include 1) different constant speeds for different directions, 2) speed with constant acceleration and/or variability depending on whether a certain segment has already been searched, 3) speed dependent on the incline of the terrain. We provide both upper and lower bounds on the competitive ratios of search algorithms for these models, and in many cases, we derive optimal algorithms for the search time.
Efficient Twitter Sentiment Classification using Subjective Distant Supervision
Venue: arXiv cs.SI 2017
Year: 2017

As microblogging services like Twitter are becoming more and more influential in today's globalised world, its facets like sentiment analysis are being extensively studied. We are no longer constrained by our own opinion. Others opinions and sentiments play a huge role in shaping our perspective. In this paper, we build on previous works on Twitter sentiment analysis using Distant Supervision. The existing approach requires huge computation resource for analysing large number of tweets. In this paper, we propose techniques to speed up the computation process for sentiment analysis. We use tweet subjectivity to select the right training samples. We also introduce the concept of EFWS (Effective Word Score) of a tweet that is derived from polarity scores of frequently used words, which is an additional heuristic that can be used to speed up the sentiment classification with standard machine learning algorithms. We performed our experiments using 1.6 million tweets. Experimental evaluations show that our proposed technique is more efficient and has higher accuracy compared to previously proposed methods. We achieve overall accuracies of around 80% (EFWS heuristic gives an accuracy around 85%) on a training dataset of 100K tweets, which is half the size of the dataset used for the baseline model. The accuracy of our proposed model is 2-3% higher than the baseline model, and the model effectively trains at twice the speed of the baseline model.
RUBER: An Unsupervised Method for Automatic Evaluation of Open-Domain Dialog Systems
Venue: arXiv cs.CL 2017
Year: 2017

Open-domain human-computer conversation has been attracting increasing attention over the past few years. However, there does not exist a standard automatic evaluation metric for open-domain dialog systems; researchers usually resort to human annotation for model evaluation, which is time- and labor-intensive. In this paper, we propose RUBER, a Referenced metric and Unreferenced metric Blended Evaluation Routine, which evaluates a reply by taking into consideration both a groundtruth reply and a query (previous user-issued utterance). Our metric is learnable, but its training does not require labels of human satisfaction. Hence, RUBER is flexible and extensible to different datasets and languages. Experiments on both retrieval and generative dialog systems show that RUBER has a high correlation with human annotation.
Deconstructing the Tail at Scale Effect Across Network Protocols
Venue: arXiv cs.NI 2017
Year: 2017

Network latencies have become increasingly important for the performance of web servers and cloud computing platforms. Identifying network-related tail latencies and reasoning about their potential causes is especially important to gauge application run-time in online data-intensive applications, where the 99th percentile latency of individual operations can significantly affect the the overall latency of requests. This paper deconstructs the "tail at scale" effect across TCP-IP, UDP-IP, and RDMA network protocols. Prior scholarly works have analyzed tail latencies caused by extrinsic network parameters like network congestion and flow fairness. Contrary to existing literature, we identify surprising rare tails in TCP-IP round-trip measurements that are as enormous as 110x higher than the median latency. Our experimental design eliminates network congestion as a tail-inducing factor. Moreover, we observe similar extreme tails in UDP-IP packet exchanges, ruling out additional TCP-IP protocol operations as the root cause of tail latency. However, we are unable to reproduce similar tail latencies in RDMA packet exchanges, which leads us to conclude that the TCP/UDP protocol stack within the operating system kernel is likely the primary source of extreme latency tails.
Linear Disentangled Representation Learning for Facial Actions
Venue: arXiv cs.CV 2017
Year: 2017

Limited annotated data available for the recognition of facial expression and action units embarrasses the training of deep networks, which can learn disentangled invariant features. However, a linear model with just several parameters normally is not demanding in terms of training data. In this paper, we propose an elegant linear model to untangle confounding factors in challenging realistic multichannel signals such as 2D face videos. The simple yet powerful model does not rely on huge training data and is natural for recognizing facial actions without explicitly disentangling the identity. Base on well-understood intuitive linear models such as Sparse Representation based Classification (SRC), previous attempts require a prepossessing of explicit decoupling which is practically inexact. Instead, we exploit the low-rank property across frames to subtract the underlying neutral faces which are modeled jointly with sparse representation on the action components with group sparsity enforced. On the extended Cohn-Kanade dataset (CK+), our one-shot automatic method on raw face videos performs as competitive as SRC applied on manually prepared action components and performs even better than SRC in terms of true positive rate. We apply the model to the even more challenging task of facial action unit recognition, verified on the MPI Face Video Database (MPI-VDB) achieving a decent performance. All the programs and data have been made publicly available.
De-identification In practice
Venue: arXiv cs.CL 2017
Year: 2017

We report our effort to identify the sensitive information, subset of data items listed by HIPAA (Health Insurance Portability and Accountability), from medical text using the recent advances in natural language processing and machine learning techniques. We represent the words with high dimensional continuous vectors learned by a variant of Word2Vec called Continous Bag Of Words (CBOW). We feed the word vectors into a simple neural network with a Long Short-Term Memory (LSTM) architecture. Without any attempts to extract manually crafted features and considering that our medical dataset is too small to be fed into neural network, we obtained promising results. The results thrilled us to think about the larger scale of the project with precise parameter tuning and other possible improvements.
A practical efficient and effective method for the Hamiltonian cycle problem that runs on a standard computer
Venue: arXiv cs.DS 2017
Year: 2017

Given $N$ cities and $R < N^2 - N$ directed (unidirectional/one way) roads does there exist a tour of all $N$ cities stopping at each city exactly once using the given roads (a Hamiltonian cycle)? This Hamiltonian cycle problem (HCP) is an NP-complete problem, for which there is no known polynomial time solution algorithm. The HCP has important practical applications, for example, to logistical problems. It was claimed that an adiabatic quantum computer could solve an NP-complete problem faster than classical algorithms, but claim appears to have been debunked. Here we demonstrate an algorithm which runs on a standard computer that efficiently and effectively solves the HCP for at least up to 500 cities: We first optimized a simulated annealing based algorithm used for smaller sized HCP problems. Then we found that when a tour was deliberately inserted in a list of otherwise randomly chosen roads, crucially, if "extra" random roads are added to bring the total number of roads up to $0.58 N log_e N$ or more there is a 100% chance our algorithm will find a HC, but conversely when a list of roads does not include a pre-inserted tour random roads have to be added until there are $0.9 N log_e N$ roads to have a chance of finding a HC. We found similarly for a set of roads non-randomly chosen. Thus, the presence of a HC in a set of roads induces "connectivity" throughout the roads and a HC can be found with an insertion of a modest number of extra roads. Our algorithm also shows that only weakly non-local information is needed to find an HCP that is a global state.
On the Dynamics of Deterministic Epidemic Propagation over Networks
Venue: arXiv cs.SI 2017
Year: 2017

In this work we review a class of deterministic nonlinear models for the propagation of infectious diseases over contact networks with strongly-connected topologies. We consider network models for susceptible-infected (SI), susceptible-infected-susceptible (SIS), and susceptible-infected-recovered (SIR) settings. In each setting, we provide a comprehensive nonlinear analysis of equilibria, stability properties, convergence, monotonicity, positivity, and threshold conditions. For the network SI setting, specific contributions include establishing its equilibria, stability, and positivity properties. For the network SIS setting, we review a well-known deterministic model, provide novel results on the computation and characterization of the endemic state (when the system is above the epidemic threshold), and present alternative proofs for some of its properties. Finally, for the network SIR setting, we propose novel results for transient behavior, threshold conditions, stability properties, and asymptotic convergence. These results are analogous to those well-known for the scalar case. In addition, we provide a novel iterative algorithm to compute the asymptotic state of the network SIR system.
An Empirical Comparison of Simple Domain Adaptation Methods for Neural Machine Translation
Venue: arXiv cs.CL 2017
Year: 2017

In this paper, we propose a novel domain adaptation method named "mixed fine tuning" for neural machine translation (NMT). We combine two existing approaches namely fine tuning and multi domain NMT. We first train an NMT model on an out-of-domain parallel corpus, and then fine tune it on a parallel corpus which is a mix of the in-domain and out-of-domain corpora. All corpora are augmented with artificial tags to indicate specific domains. We empirically compare our proposed method against fine tuning and multi domain methods and discuss its benefits and shortcomings.
On the Successive Cancellation Decoding of Polar Codes with Arbitrary Linear Binary Kernels
Venue: arXiv cs.IT 2017
Year: 2017

A method for efficiently successive cancellation (SC) decoding of polar codes with high-dimensional linear binary kernels (HDLBK) is presented and analyzed. We devise a $l$-expressions method which can obtain simplified recursive formulas of SC decoder in likelihood ratio form for arbitrary linear binary kernels to reduce the complexity of corresponding SC decoder. By considering the bit-channel transition probabilities $W_{G}^{(\cdot)}(\cdot|0)$ and $W_{G}^{(\cdot)}(\cdot|1)$ separately, a $W$-expressions method is proposed to further reduce the complexity of HDLBK based SC decoder. For a $m\times m$ binary kernel, the complexity of straightforward SC decoder is $O(2^{m}N\log N)$. With $W$-expressions, we reduce the complexity of straightforward SC decoder to $O(m^{2}N\log N)$ when $m\leq 16$. Simulation results show that $16\times16$ kernel polar codes offer significant advantages in terms of error performances compared with $2\times2$ kernel polar codes under SC and list SC decoders.
Position Aided Beam Alignment for Millimeter Wave Backhaul Systems with Large Phased Arrays
Venue: arXiv cs.IT 2017
Year: 2017

Wireless backhaul communication has been recently realized with large antennas operating in the millimeter wave (mmWave) frequency band and implementing highly directional beamforming. In this paper, we focus on the alignment problem of narrow beams between fixed position network nodes in mmWave backhaul systems that are subject to small displacements due to wind flow or ground vibration. We consider nodes equipped with antenna arrays that are capable of performing only analog processing and communicate through wireless channels including a line-of-sight component. Aiming at minimizing the time needed to achieve beam alignment, we present an efficient method that capitalizes on the exchange of position information between the nodes to design their beamforming and combining vectors. Some numerical results on the outage probability with the proposed beam alignment method offer useful preliminary insights on the impact of some system and operation parameters.