OpenRaiser/Intern-Atlas huggingface dataset:
Near-Infrared Coloring via a Contrast-Preserving Mapping Model
Venue: arXiv cs.CV 2016
Year: 2016

Near-infrared gray images captured together with corresponding visible color images have recently proven useful for image restoration and classification. This paper introduces a new coloring method to add colors to near-infrared gray images based on a contrast-preserving mapping model. A naive coloring method directly adds the colors from the visible color image to the near-infrared gray image; however, this method results in an unrealistic image because of the discrepancies in brightness and image structure between the captured near-infrared gray image and the visible color image. To solve the discrepancy problem, first we present a new contrast-preserving mapping model to create a new near-infrared gray image with a similar appearance in the luminance plane to the visible color image, while preserving the contrast and details of the captured near-infrared gray image. Then based on the proposed contrast-preserving mapping model, we develop a method to derive realistic colors that can be added to the newly created near-infrared gray image. Experimental results show that the proposed method can not only preserve the local contrasts and details of the captured near-infrared gray image, but transfers the realistic colors from the visible color image to the newly created near-infrared gray image. Experimental results also show that the proposed approach can be applied to near-infrared denoising.
Rain Removal via Shrinkage-Based Sparse Coding and Learned Rain Dictionary
Venue: arXiv cs.CV 2016
Year: 2016

This paper introduces a new rain removal model based on the shrinkage of the sparse codes for a single image. Recently, dictionary learning and sparse coding have been widely used for image restoration problems. These methods can also be applied to the rain removal by learning two types of rain and non-rain dictionaries and forcing the sparse codes of the rain dictionary to be zero vectors. However, this approach can generate unwanted edge artifacts and detail loss in the non-rain regions. Based on this observation, a new approach for shrinking the sparse codes is presented in this paper. To effectively shrink the sparse codes in the rain and non-rain regions, an error map between the input rain image and the reconstructed rain image is generated by using the learned rain dictionary. Based on this error map, both the sparse codes of rain and non-rain dictionaries are used jointly to represent the image structures of objects and avoid the edge artifacts in the non-rain regions. In the rain regions, the correlation matrix between the rain and non-rain dictionaries is calculated. Then, the sparse codes corresponding to the highly correlated signal-atoms in the rain and non-rain dictionaries are shrunk jointly to improve the removal of the rain structures. The experimental results show that the proposed shrinkage-based sparse coding can preserve image structures and avoid the edge artifacts in the non-rain regions, and it can remove the rain structures in the rain regions. Also, visual quality evaluation confirms that the proposed method outperforms the conventional texture and rain removal methods.
On the Empirical Effect of Gaussian Noise in Under-sampled MRI Reconstruction
Venue: arXiv cs.CV 2016
Year: 2016

In Fourier-based medical imaging, sampling below the Nyquist rate results in an underdetermined system, in which linear reconstructions will exhibit artifacts. Another consequence of under-sampling is lower signal to noise ratio (SNR) due to fewer acquired measurements. Even if an oracle provided the information to perfectly disambiguate the underdetermined system, the reconstructed image could still have lower image quality than a corresponding fully sampled acquisition because of the reduced measurement time. The effects of lower SNR and the underdetermined system are coupled during reconstruction, making it difficult to isolate the impact of lower SNR on image quality. To this end, we present an image quality prediction process that reconstructs fully sampled, fully determined data with noise added to simulate the loss of SNR induced by a given under-sampling pattern. The resulting prediction image empirically shows the effect of noise in under-sampled image reconstruction without any effect from an underdetermined system. We discuss how our image quality prediction process can simulate the distribution of noise for a given under-sampling pattern, including variable density sampling that produces colored noise in the measurement data. An interesting consequence of our prediction model is that we can show that recovery from underdetermined non-uniform sampling is equivalent to a weighted least squares optimization that accounts for heterogeneous noise levels across measurements. Through a series of experiments with synthetic and in vivo datasets, we demonstrate the efficacy of the image quality prediction process and show that it provides a better estimation of reconstruction image quality than the corresponding fully-sampled reference image.
An Arabic-Hebrew parallel corpus of TED talks
Venue: arXiv cs.CL 2016
Year: 2016

We describe an Arabic-Hebrew parallel corpus of TED talks built upon WIT3, the Web inventory that repurposes the original content of the TED website in a way which is more convenient for MT researchers. The benchmark consists of about 2,000 talks, whose subtitles in Arabic and Hebrew have been accurately aligned and rearranged in sentences, for a total of about 3.5M tokens per language. Talks have been partitioned in train, development and test sets similarly in all respects to the MT tasks of the IWSLT 2016 evaluation campaign. In addition to describing the benchmark, we list the problems encountered in preparing it and the novel methods designed to solve them. Baseline MT results and some measures on sentence length are provided as an extrinsic evaluation of the quality of the benchmark.
Secure Massive MIMO Systems with Limited RF Chains
Venue: arXiv cs.IT 2016
Year: 2016

In future practical deployments of massive multi-input multi-output (MIMO) systems, the number of radio frequency (RF) chains at the base stations (BSs) may be much smaller than the number of BS antennas to reduce the overall expenditure. In this paper, we propose a novel design framework for joint data and artificial noise (AN) precoding in a multiuser massive MIMO system with limited number of RF chains, which improves the wireless security performance. With imperfect channel state information (CSI), we analytically derive an achievable lower bound on the ergodic secrecy rate of any mobile terminal (MT), for both analog and hybrid precoding schemes. The closed-form lower bound is used to determine optimal power splitting between data and AN that maximizes the secrecy rate through simple one-dimensional search. Analytical and numerical results together reveal that the proposed hybrid precoder, although suffers from reduced secrecy rate compared with theoretical full-dimensional precoder, is free of the high computational complexity of large-scale matrix inversion and null-space calculations, and largely reduces the hardware cost.
Kernel Selection using Multiple Kernel Learning and Domain Adaptation in Reproducing Kernel Hilbert Space, for Face Recognition under Surveillance Scenario
Venue: arXiv cs.CV 2016
Year: 2016

Face Recognition (FR) has been the interest to several researchers over the past few decades due to its passive nature of biometric authentication. Despite high accuracy achieved by face recognition algorithms under controlled conditions, achieving the same performance for face images obtained in surveillance scenarios, is a major hurdle. Some attempts have been made to super-resolve the low-resolution face images and improve the contrast, without considerable degree of success. The proposed technique in this paper tries to cope with the very low resolution and low contrast face images obtained from surveillance cameras, for FR under surveillance conditions. For Support Vector Machine classification, the selection of appropriate kernel has been a widely discussed issue in the research community. In this paper, we propose a novel kernel selection technique termed as MFKL (Multi-Feature Kernel Learning) to obtain the best feature-kernel pairing. Our proposed technique employs a effective kernel selection by Multiple Kernel Learning (MKL) method, to choose the optimal kernel to be used along with unsupervised domain adaptation method in the Reproducing Kernel Hilbert Space (RKHS), for a solution to the problem. Rigorous experimentation has been performed on three real-world surveillance face datasets : FR\_SURV, SCface and ChokePoint. Results have been shown using Rank-1 Recognition Accuracy, ROC and CMC measures. Our proposed method outperforms all other recent state-of-the-art techniques by a considerable margin.
A thermodynamic approach to nonlinear ultrasonics for material state awareness and prognosis
Venue: arXiv cs.CE 2016
Year: 2016

We develop a thermodynamic framework for modeling nonlinear ultrasonic damage sensing and prognosis in materials undergoing progressive damage. The framework is based on the internal variable approach and relies on the construction of a pseudo-elastic strain energy function that captures the energetics associated with the damage progression. The pseudo-elastic strain energy function is composed of two energy functions - one that describes how a material stores energy in an elastic fashion and the other describes how material dissipates energy or stores it in an inelastic fashion. Experimental motivation for the choice of the above two functionals is discussed and some specific choices pertaining to damage progression during fatigue and creep are presented. The thermodynamic framework is employed to model the nonlinear response of material undergoing stress relaxation and creep-like degradation. For each of the above cases, evolution of the nonlinearity parameter with damage as well as with macroscopic measurables like accumulated plastic strain are obtained.
Complexity of Left-Ideal, Suffix-Closed and Suffix-Free Regular Languages
Venue: arXiv cs.FL 2016
Year: 2016

A language $L$ over an alphabet $Σ$ is suffix-convex if, for any words $x,y,z\inΣ^*$, whenever $z$ and $xyz$ are in $L$, then so is $yz$. Suffix-convex languages include three special cases: left-ideal, suffix-closed, and suffix-free languages. We examine complexity properties of these three special classes of suffix-convex regular languages. In particular, we study the quotient/state complexity of boolean operations, product (concatenation), star, and reversal on these languages, as well as the size of their syntactic semigroups, and the quotient complexity of their atoms.
Can Ground Truth Label Propagation from Video help Semantic Segmentation?
Venue: arXiv cs.CV 2016
Year: 2016

For state-of-the-art semantic segmentation task, training convolutional neural networks (CNNs) requires dense pixelwise ground truth (GT) labeling, which is expensive and involves extensive human effort. In this work, we study the possibility of using auxiliary ground truth, so-called \textit{pseudo ground truth} (PGT) to improve the performance. The PGT is obtained by propagating the labels of a GT frame to its subsequent frames in the video using a simple CRF-based, cue integration framework. Our main contribution is to demonstrate the use of noisy PGT along with GT to improve the performance of a CNN. We perform a systematic analysis to find the right kind of PGT that needs to be added along with the GT for training a CNN. In this regard, we explore three aspects of PGT which influence the learning of a CNN: i) the PGT labeling has to be of good quality; ii) the PGT images have to be different compared to the GT images; iii) the PGT has to be trusted differently than GT. We conclude that PGT which is diverse from GT images and has good quality of labeling can indeed help improve the performance of a CNN. Also, when PGT is multiple folds larger than GT, weighing down the trust on PGT helps in improving the accuracy. Finally, We show that using PGT along with GT, the performance of Fully Convolutional Network (FCN) on Camvid data is increased by $2.7\%$ on IoU accuracy. We believe such an approach can be used to train CNNs for semantic video segmentation where sequentially labeled image frames are needed. To this end, we provide recommendations for using PGT strategically for semantic segmentation and hence bypass the need for extensive human efforts in labeling.
An overview about Networks-on-Chip with multicast suppor
Venue: arXiv cs.AR 2016
Year: 2016

Modern System-on-Chip (SoC) platforms typically consist of multiple processors and a communication interconnect between them. Network-on-Chip (NoC) arises as a solution to interconnect these systems, which provides a scalable, reusable, and an efficient interconnect. For these SoC platforms, multicast communication is significantly used for parallel applications. Cache coherency in distributed sharedmemory,clock synchronization, replication, or barrier synchronization are examples of these requests. This paper presents an overview of research on NoC with support for multicast communication and delineates the major issues addressed so far by the scientific community in this investigation area.
Real Time Fine-Grained Categorization with Accuracy and Interpretability
Venue: arXiv cs.CV 2016
Year: 2016

A well-designed fine-grained categorization system usually has three contradictory requirements: accuracy (the ability to identify objects among subordinate categories); interpretability (the ability to provide human-understandable explanation of recognition system behavior); and efficiency (the speed of the system). To handle the trade-off between accuracy and interpretability, we propose a novel "Deeper Part-Stacked CNN" architecture armed with interpretability by modeling subtle differences between object parts. The proposed architecture consists of a part localization network, a two-stream classification network that simultaneously encodes object-level and part-level cues, and a feature vectors fusion component. Specifically, the part localization network is implemented by exploring a new paradigm for key point localization that first samples a small number of representable pixels and then determine their labels via a convolutional layer followed by a softmax layer. We also use a cropping layer to extract part features and propose a scale mean-max layer for feature fusion learning. Experimentally, our proposed method outperform state-of-the-art approaches both in part localization task and classification task on Caltech-UCSD Birds-200-2011. Moreover, by adopting a set of sharing strategies between the computation of multiple object parts, our single model is fairly efficient running at 32 frames/sec.
Chinese Event Extraction Using DeepNeural Network with Word Embedding
Venue: arXiv cs.CL 2016
Year: 2016

A lot of prior work on event extraction has exploited a variety of features to represent events. Such methods have several drawbacks: 1) the features are often specific for a particular domain and do not generalize well; 2) the features are derived from various linguistic analyses and are error-prone; and 3) some features may be expensive and require domain expert. In this paper, we develop a Chinese event extraction system that uses word embedding vectors to represent language, and deep neural networks to learn the abstract feature representation in order to greatly reduce the effort of feature engineering. In addition, in this framework, we leverage large amount of unlabeled data, which can address the problem of limited labeled corpus for this task. Our experiments show that our proposed method performs better compared to the system using rich language features, and using unlabeled data benefits the word embeddings. This study suggests the potential of DNN and word embedding for the event extraction task.
Isometrically Self-dual Cyclic Codes
Venue: arXiv cs.IT 2016
Year: 2016

General isometries of cyclic codes, including multipliers and translations, are introduced; and isometrically self-dual cyclic codes are defined. In terms of Type-I duadic splittings given by multipliers and translations, a necessary and sufficient condition for the existence of isometrically self-dual cyclic codes is obtained. A program to construct isometrically self-dual cyclic codes is provided, and illustrated by several examples. In particular, a class of isometrically self-dual MDS cyclic codes, which are alternant codes from a class of generalized Reed-Solomon codes, is presented.
Self-Dual Codes over $\mathbb{Z}_2\times (\mathbb{Z}_2+u\mathbb{Z}_2)$
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we study self-dual codes over $\mathbb{Z}_2 \times (\mathbb{Z}_2+u\mathbb{Z}_2) $, where $u^2=0$. Three types of self-dual codes are defined. For each type, the possible values $α,β$ such that there exists a code $\mathcal{C}\subseteq \mathbb{Z}_{2}^α\times (\mathbb{Z}_2+u\mathbb{Z}_2)^β$ are established. We also present several approaches to construct self-dual codes over $\mathbb{Z}_2 \times (\mathbb{Z}_2+u\mathbb{Z}_2) $. Moreover, the structure of two-weight self-dual codes is completely obtained for $α\cdotβ\neq 0$.
Decentralized Motion Planning with Collision Avoidance for a Team of UAVs under High Level Goals
Venue: arXiv cs.RO 2016
Year: 2016

This paper addresses the motion planning problem for a team of aerial agents under high level goals. We propose a hybrid control strategy that guarantees the accomplishment of each agent's local goal specification, which is given as a temporal logic formula, while guaranteeing inter-agent collision avoidance. In particular, by defining 3-D spheres that bound the agents' volume, we extend previous work on decentralized navigation functions and propose control laws that navigate the agents among predefined regions of interest of the workspace while avoiding collision with each other. This allows us to abstract the motion of the agents as finite transition systems and, by employing standard formal verification techniques, to derive a high-level control algorithm that satisfies the agents' specifications. Simulation and experimental results with quadrotors verify the validity of the proposed method.
Reducing Opacity to Linearizability: A Sound and Complete Method
Venue: arXiv cs.LO 2016
Year: 2016

Transactional memory is a mechanism that manages thread synchronisation on behalf of a programmer so that blocks of code execute with an illusion of atomicity. The main safety criterion for transactional memory is opacity, which defines conditions for serialising concurrent transactions. Proving opacity is complicated because it allows concurrent transactions to observe distinct memory states, while TM implementations are typically based on one single shared store. This paper presents a sound and complete method, based on coarse-grained abstraction, for reducing proofs of opacity to the relatively simpler correctness condition: linearizability. We use our methods to verify TML and NORec from the literature and show our techniques extend to relaxed memory models by showing that both are opaque under TSO without requiring additional fences. Our methods also elucidate TM designs at higher level of abstraction; as an application, we develop a variation of NORec with fast-path reads transactions. All our proofs have been mechanised, either in the Isabelle theorem prover or the PAT model checker.
DeepAlgebra - an outline of a program
Venue: arXiv cs.AI 2016
Year: 2016

We outline a program in the area of formalization of mathematics to automate theorem proving in algebra and algebraic geometry. We propose a construction of a dictionary between automated theorem provers and (La)TeX exploiting syntactic parsers. We describe its application to a repository of human-written facts and definitions in algebraic geometry (The Stacks Project). We use deep learning techniques.
A Game-Theoretic Approach to Robust Fusion and Kalman Filtering Under Unknown Correlations
Venue: arXiv cs.SY 2016
Year: 2016

This work addresses the problem of fusing two random vectors with unknown cross-correlations. We present a formulation and a numerical method for computing the optimal estimate in the minimax sense. We extend our formulation to linear measurement models that depend on two random vectors with unknown cross-correlations. As an application we consider the problem of decentralized state estimation for a group of agents. The proposed estimator takes cross-correlations into account while being less conservative than the widely used Covariance Intersection. We demonstrate the superiority of the proposed method compared to Covariance Intersection with numerical examples and simulations within the specific application of decentralized state estimation using relative position measurements.
Approximation Algorithms for Stochastic k-TSP
Venue: arXiv cs.DS 2016
Year: 2016

We consider the stochastic $k$-TSP problem where rewards at vertices are random and the objective is to minimize the expected length of a tour that collects reward $k$. We present an adaptive $O(\log k)$-approximation algorithm, and a non-adaptive $O(\log^2k)$-approximation algorithm. We also show that the adaptivity gap of this problem is between $e$ and $O(\log^2k)$.
Fast Image Classification by Boosting Fuzzy Classifiers
Venue: arXiv cs.CV 2016
Year: 2016

This paper presents a novel approach to visual objects classification based on generating simple fuzzy classifiers using local image features to distinguish between one known class and other classes. Boosting meta learning is used to find the most representative local features. The proposed approach is tested on a state-of-the-art image dataset and compared with the bag-of-features image representation model combined with the Support Vector Machine classification. The novel method gives better classification accuracy and the time of learning and testing process is more than 30% shorter.
FLOCK: Combating Astroturfing on Livestreaming Platforms
Venue: arXiv cs.SI 2016
Year: 2016

Livestreaming platforms have become increasingly popular in recent years as a means of sharing and advertising creative content. Popular content streamers who attract large viewership to their live broadcasts can earn a living by means of ad revenue, donations and channel subscriptions. Unfortunately, this incentivized popularity has simultaneously resulted in incentive for fraudsters to provide services to astroturf, or artificially inflate viewership metrics by providing fake "live" views to customers. Our work provides a number of major contributions: (a) formulation: we are the first to introduce and characterize the viewbot fraud problem in livestreaming platforms, (b) methodology: we propose FLOCK, a principled and unsupervised method which efficiently and effectively identifies botted broadcasts and their constituent botted views, and (c) practicality: our approach achieves over 98% precision in identifying botted broadcasts and over 90% precision/recall against sizable synthetically generated viewbot attacks on a real-world livestreaming workload of over 16 million views and 92 thousand broadcasts. FLOCK successfully operates on larger datasets in practice and is regularly used at a large, undisclosed livestreaming corporation.