OpenRaiser/Intern-Atlas huggingface dataset:
Question Dependent Recurrent Entity Network for Question Answering
Venue: arXiv cs.CL 2017
Year: 2017

Question Answering is a task which requires building models capable of providing answers to questions expressed in human language. Full question answering involves some form of reasoning ability. We introduce a neural network architecture for this task, which is a form of $Memory\ Network$, that recognizes entities and their relations to answers through a focus attention mechanism. Our model is named $Question\ Dependent\ Recurrent\ Entity\ Network$ and extends $Recurrent\ Entity\ Network$ by exploiting aspects of the question during the memorization process. We validate the model on both synthetic and real datasets: the $bAbI$ question answering dataset and the $CNN\ \&\ Daily\ News$ $reading\ comprehension$ dataset. In our experiments, the models achieved a State-of-The-Art in the former and competitive results in the latter.
Time Series Compression Based on Adaptive Piecewise Recurrent Autoencoder
Venue: arXiv cs.NE 2017
Year: 2017

Time series account for a large proportion of the data stored in financial, medical and scientific databases. The efficient storage of time series is important in practical applications. In this paper, we propose a novel compression scheme for time series. The encoder and decoder are both composed by recurrent neural networks (RNN) such as long short-term memory (LSTM). There is an autoencoder between encoder and decoder, which encodes the hidden state and input together and decodes them at the decoder side. Moreover, we pre-process the original time series by partitioning it into segments with various lengths which have similar total variation. The experimental study shows that the proposed algorithm can achieve competitive compression ratio on real-world time series.
Price and Profit Awareness in Recommender Systems
Venue: arXiv cs.IR 2017
Year: 2017

Academic research in the field of recommender systems mainly focuses on the problem of maximizing the users' utility by trying to identify the most relevant items for each user. However, such items are not necessarily the ones that maximize the utility of the service provider (e.g., an online retailer) in terms of the business value, such as profit. One approach to increasing the providers' utility is to incorporate purchase-oriented information, e.g., the price, sales probabilities, and the resulting profit, into the recommendation algorithms. In this paper we specifically focus on price- and profit-aware recommender systems. We provide a brief overview of the relevant literature and use numerical simulations to illustrate the potential business benefit of such approaches.
ShotgunWSD: An unsupervised algorithm for global word sense disambiguation inspired by DNA sequencing
Venue: arXiv cs.CL 2017
Year: 2017

In this paper, we present a novel unsupervised algorithm for word sense disambiguation (WSD) at the document level. Our algorithm is inspired by a widely-used approach in the field of genetics for whole genome sequencing, known as the Shotgun sequencing technique. The proposed WSD algorithm is based on three main steps. First, a brute-force WSD algorithm is applied to short context windows (up to 10 words) selected from the document in order to generate a short list of likely sense configurations for each window. In the second step, these local sense configurations are assembled into longer composite configurations based on suffix and prefix matching. The resulted configurations are ranked by their length, and the sense of each word is chosen based on a voting scheme that considers only the top k configurations in which the word appears. We compare our algorithm with other state-of-the-art unsupervised WSD algorithms and demonstrate better performance, sometimes by a very large margin. We also show that our algorithm can yield better performance than the Most Common Sense (MCS) baseline on one data set. Moreover, our algorithm has a very small number of parameters, is robust to parameter tuning, and, unlike other bio-inspired methods, it gives a deterministic solution (it does not involve random choices).
Resource-Efficient Common Randomness and Secret-Key Schemes
Venue: arXiv cs.CC 2017
Year: 2017

We study common randomness where two parties have access to i.i.d. samples from a known random source, and wish to generate a shared random key using limited (or no) communication with the largest possible probability of agreement. This problem is at the core of secret key generation in cryptography, with connections to communication under uncertainty and locality sensitive hashing. We take the approach of treating correlated sources as a critical resource, and ask whether common randomness can be generated resource-efficiently. We consider two notable sources in this setup arising from correlated bits and correlated Gaussians. We design the first explicit schemes that use only a polynomial number of samples (in the key length) so that the players can generate shared keys that agree with constant probability using optimal communication. The best previously known schemes were both non-constructive and used an exponential number of samples. In the amortized setting, we characterize the largest achievable ratio of key length to communication in terms of the external and internal information costs, two well-studied quantities in theoretical computer science. In the relaxed setting where the two parties merely wish to improve the correlation between the generated keys of length $k$, we show that there are no interactive protocols using $o(k)$ bits of communication having agreement probability even as small as $2^{-o(k)}$. For the related communication problem where the players wish to compute a joint function $f$ of their inputs using i.i.d. samples from a known source, we give a zero-communication protocol using $2^{O(c)}$ bits where $c$ is the interactive randomized public-coin communication complexity of $f$. This matches the lower bound shown previously while the best previously known upper bound was doubly exponential in $c$.
From Image to Text Classification: A Novel Approach based on Clustering Word Embeddings
Venue: arXiv cs.CL 2017
Year: 2017

In this paper, we propose a novel approach for text classification based on clustering word embeddings, inspired by the bag of visual words model, which is widely used in computer vision. After each word in a collection of documents is represented as word vector using a pre-trained word embeddings model, a k-means algorithm is applied on the word vectors in order to obtain a fixed-size set of clusters. The centroid of each cluster is interpreted as a super word embedding that embodies all the semantically related word vectors in a certain region of the embedding space. Every embedded word in the collection of documents is then assigned to the nearest cluster centroid. In the end, each document is represented as a bag of super word embeddings by computing the frequency of each super word embedding in the respective document. We also diverge from the idea of building a single vocabulary for the entire collection of documents, and propose to build class-specific vocabularies for better performance. Using this kind of representation, we report results on two text mining tasks, namely text categorization by topic and polarity classification. On both tasks, our model yields better performance than the standard bag of words.
A Unified Joint Matrix Factorization Framework for Data Integration
Venue: arXiv cs.CV 2017
Year: 2017

Nonnegative matrix factorization (NMF) is a powerful tool in data exploratory analysis by discovering the hidden features and part-based patterns from high-dimensional data. NMF and its variants have been successfully applied into diverse fields such as pattern recognition, signal processing, data mining, bioinformatics and so on. Recently, NMF has been extended to analyze multiple matrices simultaneously. However, a unified framework is still lacking. In this paper, we introduce a sparse multiple relationship data regularized joint matrix factorization (JMF) framework and two adapted prediction models for pattern recognition and data integration. Next, we present four update algorithms to solve this framework. The merits and demerits of these algorithms are systematically explored. Furthermore, extensive computational experiments using both synthetic data and real data demonstrate the effectiveness of JMF framework and related algorithms on pattern recognition and data mining.
A New Asymptotic Analysis Technique for Diversity Receptions Over Correlated Lognormal Fading Channels
Venue: arXiv cs.IT 2017
Year: 2017

Prior asymptotic performance analyses are based on the series expansion of the moment-generating function (MGF) or the probability density function (PDF) of channel coefficients. However, these techniques fail for lognormal fading channels because the Taylor series of the PDF of a lognormal random variable is zero at the origin and the MGF does not have an explicit form. Although lognormal fading model has been widely applied in wireless communications and free-space optical communications, few analytical tools are available to provide elegant performance expressions for correlated lognormal channels. In this work, we propose a novel framework to analyze the asymptotic outage probabilities of selection combining (SC), equal-gain combining (EGC) and maximum-ratio combining (MRC) over equally correlated lognormal fading channels. Based on these closed-form results, we reveal the followings: i) the outage probability of EGC or MRC becomes an infinitely small quantity compared to that of SC at large signal-to-noise ratio (SNR); ii) channel correlation can result in an infinite performance loss at large SNR. More importantly, the analyses reveal insights into the long-standing problem of performance analyses over correlated lognormal channels at high SNR, and circumvent the time-consuming Monte Carlo simulation and numerical integration.
Exploration of Pattern-Matching Techniques for Lossy Compression on Cosmology Simulation Data Sets
Venue: arXiv cs.IT 2017
Year: 2017

Because of the vast volume of data being produced by today's scientific simulations, lossy compression allowing user-controlled information loss can significantly reduce the data size and the I/O burden. However, for large-scale cosmology simulation, such as the Hardware/Hybrid Accelerated Cosmology Code (HACC), where memory overhead constraints restrict compression to only one snapshot at a time, the lossy compression ratio is extremely limited because of the fairly low spatial coherence and high irregularity of the data. In this work, we propose a pattern-matching (similarity searching) technique to optimize the prediction accuracy and compression ratio of SZ lossy compressor on the HACC data sets. We evaluate our proposed method with different configurations and compare it with state-of-the-art lossy compressors. Experiments show that our proposed optimization approach can improve the prediction accuracy and reduce the compressed size of quantization codes compared with SZ. We present several lessons useful for future research involving pattern-matching techniques for lossy compression.
A Comparative Analysis of XML Documents, XML Enabled Databases and Native XML Databases
Venue: arXiv cs.DB 2017
Year: 2017

With the increasing popularity of XML data and a great need for a database management system able to store, retrieve and manipulate XML-based data in an efficient manner, database research communities and software industries have tried to respond to this requirement. XML-enabled database and native XML database are two approaches that have been proposed to address this challenge. These two approaches are a legacy database systems which are extended to store, retrieve and manipulate XML-based data. The major objective of this paper is to explore and compare between the two approaches and reach to some criteria to have a suitable guideline to select the best approach in each circumstance. In general, native XML database systems have more ability in comparison with XML-enabled database system for managing XML-based data
A General and Yet Efficient Scheme for Sub-Nyquist Radar Processing
Venue: arXiv cs.IT 2017
Year: 2017

We study the target parameter estimation for sub-Nyquist pulse-Doppler radar. Several past works have addressed this problem but either have low estimation accuracy for off-grid targets, take large computation load, or lack versatility for analog-to-information conversion (AIC) systems. To overcome these difficulties, we present a general and efficient estimation scheme. The scheme first formulates a general model in the sense that it is applicable to all AICs regardless of whether the targets are on or off the grids. The estimation of Doppler shifts and delays is performed sequentially, in which the Doppler estimation is formulated into a spatial spectrum estimation problem and the delay estimation is decomposed into a series of compressive parameter estimation problems with each corresponding to an estimated Doppler shift. By the sequential and decomposed processing, the computational complexity is substantially reduced, and by the parametric estimation techniques, the high accurate estimation is obtained. Theoretical analyses and numerical experiments show the effectiveness and the correctness of the proposed scheme.
Hybrid Precoding in Millimeter Wave Systems: How Many Phase Shifters Are Needed?
Venue: arXiv cs.IT 2017
Year: 2017

Hybrid precoding has been recently proposed as a cost-effective transceiver solution for millimeter wave (mm-wave) systems. The analog component in such precoders, which is composed of a phase shifter network, is the key differentiating element in contrast to conventional fully digital precoders. While a large number of phase shifters with unquantized phases are commonly assumed in existing works, in practice the phase shifters should be discretized with a coarse quantization, and their number should be reduced to a minimum due to cost and power consideration. In this paper, we propose a new hybrid precoder implementation using a small number of phase shifters with quantized and fixed phases, i.e., a fixed phase shifter (FPS) implementation, which significantly reduces the cost and hardware complexity. In addition, a dynamic switch network is proposed to enhance the spectral efficiency. Based on the proposed FPS implementation, an effective alternating minimization (AltMin) algorithm is developed with closed-form solutions in each iteration. Simulation results show that the proposed algorithm with the FPS implementation outperforms existing ones. More importantly, it needs much fewer phase shifters than existing hybrid precoder proposals, e.g., $\sim$10 fixed phase shifters are sufficient for practically relevant system settings.
Cascaded Scene Flow Prediction using Semantic Segmentation
Venue: arXiv cs.CV 2017
Year: 2017

Given two consecutive frames from a pair of stereo cameras, 3D scene flow methods simultaneously estimate the 3D geometry and motion of the observed scene. Many existing approaches use superpixels for regularization, but may predict inconsistent shapes and motions inside rigidly moving objects. We instead assume that scenes consist of foreground objects rigidly moving in front of a static background, and use semantic cues to produce pixel-accurate scene flow estimates. Our cascaded classification framework accurately models 3D scenes by iteratively refining semantic segmentation masks, stereo correspondences, 3D rigid motion estimates, and optical flow fields. We evaluate our method on the challenging KITTI autonomous driving benchmark, and show that accounting for the motion of segmented vehicles leads to state-of-the-art performance.
Declarative Sequential Pattern Mining of Care Pathways
Venue: arXiv cs.AI 2017
Year: 2017

Sequential pattern mining algorithms are widely used to explore care pathways database, but they generate a deluge of patterns, mostly redundant or useless. Clinicians need tools to express complex mining queries in order to generate less but more significant patterns. These algorithms are not versatile enough to answer complex clinician queries. This article proposes to apply a declarative pattern mining approach based on Answer Set Programming paradigm. It is exemplified by a pharmaco-epidemiological study investigating the possible association between hospitalization for seizure and antiepileptic drug switch from a french medico-administrative database.
Notes on optimal approximations for importance sampling
Venue: arXiv cs.GR 2017
Year: 2017

In this manuscript, we derive optimal conditions for building function approximations that minimize variance when used as importance sampling estimators for Monte Carlo integration problems. Particularly, we study the problem of finding the optimal projection $g$ of an integrand $f$ onto certain classes of piecewise constant functions, in order to minimize the variance of the unbiased importance sampling estimator $E_g[f/g]$, as well as the related problem of finding optimal mixture weights to approximate and importance sample a target mixture distribution $f = \sum_i α_i f_i$ with components $f_i$ in a family $\mathcal{F}$, through a corresponding mixture of importance sampling densities $g_i$ that are only approximately proportional to $f_i$. We further show that in both cases the optimal projection is different from the commonly used $\ell_1$ projection, and provide an intuitive explanation for the difference.
Discrete Geodesic Nets for Modeling Developable Surfaces
Venue: arXiv cs.GR 2017
Year: 2017

We present a discrete theory for modeling developable surfaces as quadrilateral meshes satisfying simple angle constraints. The basis of our model is a lesser known characterization of developable surfaces as manifolds that can be parameterized through orthogonal geodesics. Our model is simple, local, and, unlike previous works, it does not directly encode the surface rulings. This allows us to model continuous deformations of discrete developable surfaces independently of their decomposition into torsal and planar patches or the surface topology. We prove and experimentally demonstrate strong ties to smooth developable surfaces, including a theorem stating that every sampling of the smooth counterpart satisfies our constraints up to second order. We further present an extension of our model that enables a local definition of discrete isometry. We demonstrate the effectiveness of our discrete model in a developable surface editing system, as well as computation of an isometric interpolation between isometric discrete developable shapes.
Deep Interactive Region Segmentation and Captioning
Venue: arXiv cs.CV 2017
Year: 2017

With recent innovations in dense image captioning, it is now possible to describe every object of the scene with a caption while objects are determined by bounding boxes. However, interpretation of such an output is not trivial due to the existence of many overlapping bounding boxes. Furthermore, in current captioning frameworks, the user is not able to involve personal preferences to exclude out of interest areas. In this paper, we propose a novel hybrid deep learning architecture for interactive region segmentation and captioning where the user is able to specify an arbitrary region of the image that should be processed. To this end, a dedicated Fully Convolutional Network (FCN) named Lyncean FCN (LFCN) is trained using our special training data to isolate the User Intention Region (UIR) as the output of an efficient segmentation. In parallel, a dense image captioning model is utilized to provide a wide variety of captions for that region. Then, the UIR will be explained with the caption of the best match bounding box. To the best of our knowledge, this is the first work that provides such a comprehensive output. Our experiments show the superiority of the proposed approach over state-of-the-art interactive segmentation methods on several well-known datasets. In addition, replacement of the bounding boxes with the result of the interactive segmentation leads to a better understanding of the dense image captioning output as well as accuracy enhancement for the object detection in terms of Intersection over Union (IoU).
Updating Singular Value Decomposition for Rank One Matrix Perturbation
Venue: arXiv cs.LG 2017
Year: 2017

An efficient Singular Value Decomposition (SVD) algorithm is an important tool for distributed and streaming computation in big data problems. It is observed that update of singular vectors of a rank-1 perturbed matrix is similar to a Cauchy matrix-vector product. With this observation, in this paper, we present an efficient method for updating Singular Value Decomposition of rank-1 perturbed matrix in $O(n^2 \ \text{log}(\frac{1}ε))$ time. The method uses Fast Multipole Method (FMM) for updating singular vectors in $O(n \ \text{log} (\frac{1}ε))$ time, where $ε$ is the precision of computation.
The Advantage of Evidential Attributes in Social Networks
Venue: arXiv cs.AI 2017
Year: 2017

Nowadays, there are many approaches designed for the task of detecting communities in social networks. Among them, some methods only consider the topological graph structure, while others take use of both the graph structure and the node attributes. In real-world networks, there are many uncertain and noisy attributes in the graph. In this paper, we will present how we detect communities in graphs with uncertain attributes in the first step. The numerical, probabilistic as well as evidential attributes are generated according to the graph structure. In the second step, some noise will be added to the attributes. We perform experiments on graphs with different types of attributes and compare the detection results in terms of the Normalized Mutual Information (NMI) values. The experimental results show that the clustering with evidential attributes gives better results comparing to those with probabilistic and numerical attributes. This illustrates the advantages of evidential attributes.
Confidentiality enforcement by hybrid control of information flows
Venue: arXiv cs.CR 2017
Year: 2017

An information owner, possessing diverse data sources, might want to offer information services based on these sources to cooperation partners and to this end interact with these partners by receiving and sending messages, which the owner on his part generates by program execution. Independently from data representation or its physical storage, information release to a partner might be restricted by the owner's confidentiality policy on an integrated, unified view of the sources. Such a policy should even be enforced if the partner as an intelligent and only semi-honest attacker attempts to infer hidden information from message data, also employing background knowledge. For this problem of inference control, we present a framework for a unified, holistic control of information flow induced by program-based processing of the data sources to messages sent to a cooperation partner. Our framework expands on and combines established concepts for confidentiality enforcement and its verification and is instantiated in a Java environment. More specifically, as a hybrid control we combine gradual release of information via declassification, enforced by static program analysis using a security type system, with a dynamic monitoring approach. The dynamic monitoring employs flow tracking for generalizing values to be declassified under confidentiality policy compliance.