OpenRaiser/Intern-Atlas huggingface dataset:
Tracking mm-Wave Channel Dynamics: Fast Beam Training Strategies under Mobility
Venue: arXiv cs.NI 2016
Year: 2016

In order to cope with the severe path loss, millimeter-wave (mm-wave) systems exploit highly directional communication. As a consequence, even a slight beam misalignment between two communicating devices (for example, due to mobility) can generate a significant signal drop. This leads to frequent invocations of time-consuming mechanisms for beam re-alignment, which deteriorate system performance. In this paper, we propose smart beam training and tracking strategies for fast mm-wave link establishment and maintenance under node mobility. We leverage the ability of hybrid analog-digital transceivers to collect channel information from multiple spatial directions simultaneously and formulate a probabilistic optimization problem to model the temporal evolution of the mm-wave channel under mobility. In addition, we present for the first time a beam tracking algorithm that extracts information needed to update the steering directions directly from data packets, without the need for spatial scanning during the ongoing data transmission. Simulation results, obtained by a custom simulator based on ray tracing, demonstrate the ability of our beam training/tracking strategies to keep the communication rate only 10% below the optimal bound. Compared to the state of the art, our approach provides a 40% to 150% rate increase, yet requires lower complexity hardware.
Nonlinear FM Waveform Design to Reduction of sidelobe level in Autocorrelation Function
Venue: arXiv cs.IT 2016
Year: 2016

This paper will design non-linear frequency modulation (NLFM) signal for Chebyshev, Kaiser, Taylor, and raised-cosine power spectral densities (PSDs). Then, the variation of peak sidelobe level with regard to mainlobe width for these four different window functions are analyzed. It has been demonstrated that reduction of sidelobe level in NLFM signal can lead to increase in mainlobe width of autocorrelation function. Furthermore, the results of power spectral density obtained from the simulation and the desired PSD are compared. Finally, error percentage between simulated PSD and desired PSD for different peak sidelobe level are illustrated. The stationary phase concept is the possible source for this error.
Blind restoration for non-uniform aerial images using non-local Retinex model and shearlet-based higher-order regularization
Venue: arXiv cs.CV 2016
Year: 2016

Aerial images are often degraded by space-varying motion blur and simultaneous uneven illumination. To recover high-quality aerial image from its non-uniform version, we propose a novel patch-wise restoration approach based on a key observation that the degree of blurring is inevitably affected by the illuminated conditions. A non-local Retinex model is developed to accurately estimate the reflectance component from the degraded aerial image. Thereafter the uneven illumination is corrected well. And then non-uniform coupled blurring in the enhanced reflectance image is alleviated and transformed towards uniform distribution, which will facilitate the subsequent deblurring. For constructing the multi-scale sparsified regularizer, the discrete shearlet transform is improved to better represent anisotropic image features in term of directional sensitivity and selectivity. In addition, a new adaptive variant of total generalized variation is proposed for the structure-preserving regularizer. These complementary regularizers are elegantly integrated into an objective function. The final deblurred image with uniform illumination can be extracted by applying the fast alternating direction scheme to solve the derived function. The experimental results demonstrate that our algorithm can not only remove both the space-varying illumination and motion blur in the aerial image effectively but also recover the abundant details of aerial scenes with top-level objective and subjective quality, and outperforms other state-of-the-art restoration methods.
Correlation Preserving Sparse Coding Over Multi-level Dictionaries for Image Denoising
Venue: arXiv cs.CV 2016
Year: 2016

In this letter, we propose a novel image denoising method based on correlation preserving sparse coding. Because the instable and unreliable correlations among basis set can limit the performance of the dictionary-driven denoising methods, two effective regularized strategies are employed in the coding process. Specifically, a graph-based regularizer is built for preserving the global similarity correlations, which can adaptively capture both the geometrical structures and discriminative features of textured patches. In particular, edge weights in the graph are obtained by seeking a nonnegative low-rank construction. Besides, a robust locality-constrained coding can automatically preserve not only spatial neighborhood information but also internal consistency present in noisy patches while learning overcomplete dictionary. Experimental results demonstrate that our proposed method achieves state-of-the-art denoising performance in terms of both PSNR and subjective visual quality.
UDBMS: Road to Unification for Multi-model Data Management
Venue: arXiv cs.DB 2016
Year: 2016

A traditional database systems is organized around a single data model that determines how data can be organized, stored and manipulated. But the vision of this paper is to develop new principles and techniques to manage multiple data models against a single, integrated backend. For example, semi-structured, graph and relational models are examples of data models that may be supported by a new system. Having a single data platform for managing both well-structured data and NoSQL data is beneficial to users; this approach significantly reduces integration, migration, development, maintenance and operational issues. The problem is challenging: the existing database principles mainly work for a single model and the research on multi-model data management is still at an early stage. In this paper, we envision a UDBMS (Unified Database Management System) for multi-model data management in one platform. UDBMS will provide several new features such as unified data model and flexible schema, unified query processing, unified index structure and cross-model transaction guarantees. We discuss our vision as well as present multiple research challenges that we need to address.
Secure Transmissions Using Artificial Noise in MIMO Wiretap Interference Channel: A Game Theoretic Approach
Venue: arXiv cs.IT 2016
Year: 2016

We consider joint optimization of artificial noise (AN) and information signals in a MIMO wiretap interference network, wherein the transmission of each link may be overheard by several MIMO-capable eavesdroppers. Each information signal is accompanied with AN, generated by the same user to confuse nearby eavesdroppers. Using a noncooperative game, a distributed optimization mechanism is proposed to maximize the secrecy rate of each link. The decision variables here are the covariance matrices for the information signals and ANs. However, the nonconvexity of each link's optimization problem (i.e., best response) makes conventional convex games inapplicable, even to find whether a Nash Equilibrium (NE) exists. To tackle this issue, we analyze the proposed game using a relaxed equilibrium concept, called quasi-Nash equilibrium (QNE). Under a constraint qualification condition for each player's problem, the set of QNEs includes the NE of the proposed game. We also derive the conditions for the existence and uniqueness of the resulting QNE. It turns out that the uniqueness conditions are too restrictive, and do not always hold in typical network scenarios. Thus, the proposed game often has multiple QNEs, and convergence to a QNE is not always guaranteed. To overcome these issues, we modify the utility functions of the players by adding several specific terms to each utility function. The modified game converges to a QNE even when multiple QNEs exist. Furthermore, players have the ability to select a desired QNE that optimizes a given social objective (e.g., sum-rate or secrecy sum-rate). Depending on the chosen objective, the amount of signaling overhead as well as the performance of resulting QNE can be controlled. Simulations show that due to the QNE selection mechanism, we can achieve a significant improvement in terms of secrecy sum-rate and power efficiency.
Cooperative Access Schemes for Efficient SWIPT Transmissions in Cognitive Radio Networks
Venue: arXiv cs.NI 2016
Year: 2016

We investigate joint information and energy cooperative schemes in a slotted-time cognitive radio network with a primary transmitter-receiver pair and a set of secondary transmitter-receiver pairs. The primary transmitter is assumed to be an energy-harvesting node. We propose a three-stage cooperative transmission protocol. During the first stage, the primary user releases a portion of its time slot to the secondary nodes to send their data and to power the energy-harvesting primary transmitter from the secondary radio-frequency signals. During the second stage, the primary transmitter sends its data to its destination and to the secondary nodes. During the third stage, the secondary nodes amplify and forward the primary data. We propose five different schemes for secondary access and powering the primary transmitter. We derive closed-form expressions for the primary and secondary rates for all the proposed schemes. Two of the proposed schemes use distributed beamforming to power the primary transmitter. We design a sparsity-aware relay-selection scheme based on the compressive sensing principles. Our numerical results demonstrate the gains of our proposed schemes for both the primary and secondary systems.
On Spectral Analysis of Directed Signed Graphs
Venue: arXiv cs.SI 2016
Year: 2016

It has been shown that the adjacency eigenspace of a network contains key information of its underlying structure. However, there has been no study on spectral analysis of the adjacency matrices of directed signed graphs. In this paper, we derive theoretical approximations of spectral projections from such directed signed networks using matrix perturbation theory. We use the derived theoretical results to study the influences of negative intra cluster and inter cluster directed edges on node spectral projections. We then develop a spectral clustering based graph partition algorithm, SC-DSG, and conduct evaluations on both synthetic and real datasets. Both theoretical analysis and empirical evaluation demonstrate the effectiveness of the proposed algorithm.
Can Modular Finger Control for In-Hand Object Stabilization be accomplished by Independent Tactile Feedback Control Laws?
Venue: arXiv cs.RO 2016
Year: 2016

Currently grip control during in-hand manipulation is usually modeled as part of a monolithic task, yielding complex controllers based on force control specialized for their situations. Such non-modular and specialized control approaches render the generalization of these controllers to new in-hand manipulation tasks difficult. Clearly, a grip control approach that generalizes well between several tasks would be preferable. We propose a modular approach where each finger is controlled by an independent tactile grip controller. Using signals from the human-inspired biotac sensor, we can predict future slip - and prevent it by appropriate motor actions. This slip-preventing grip controller is first developed and trained during a single-finger stabilization task. Subsequently, we show that several independent slip-preventing grip controllers can be employed together without any form of central communication. The resulting approach works for two, three, four and five finger grip stabilization control. Such a modular grip control approach has the potential to generalize across a large variety of inhand manipulation tasks, including grip change, finger gaiting, between-hands object transfer, and across multiple objects.
Throughput Maximization in Multi-Channel Cognitive Radio Systems with Delay Constraints
Venue: arXiv cs.IT 2016
Year: 2016

We study the throughput-vs-delay trade-off in an overlay multi-channel single-secondary-user cognitive radio system. Due to the limited sensing capabilities of the cognitive radio user, channels are sensed sequentially. Maximizing the throughput in such a problem is well-studied in the literature. Yet, in real-time applications, hard delay constraints need to be considered besides throughput. In this paper, optimal stopping rule and optimal power allocation are discussed to maximize the secondary user's throughput, subject to an average delay constraint. We provide a low complexity approach to the optimal solution of this problem. Simulation results show that this solution allows the secondary user to meet the delay constraint without sacrificing throughput significantly. It also shows the benefits of the optimal power allocation strategy over the constant power allocation strategy.
Power Control and Scheduling under Hard Deadline Constraints for On-Off Fading Channels
Venue: arXiv cs.IT 2016
Year: 2016

We consider the joint scheduling-and-power-allocation problem of a downlink cellular system. The system consists of two groups of users: real-time (RT) and non-real-time (NRT) users. Given some average power constraint on the base station, the problem is to find an algorithm that satisfies the RT and NRT quality-of-service (QoS) constraints. The RT QoS constraints guarantee the portion of RT packets that miss their deadline are no more than a pre-specified threshold. On the other hand, the NRT QoS is only to guarantee the stability of their queues. We propose a sum-rate-maximizing algorithm that satisfy all QoS and average power constraints. The proposed power allocation policy has a closed-form expression for the two groups of users. However, the power policy of the RT users differ in structure from the NRT users. The proposed algorithm is optimal for the on-off channel model with a polynomial-time scheduling complexity. Using extensive simulations, the throughput of the proposed algorithm is shown exceed existing approaches.
Image-Text Multi-Modal Representation Learning by Adversarial Backpropagation
Venue: arXiv cs.CV 2016
Year: 2016

We present novel method for image-text multi-modal representation learning. In our knowledge, this work is the first approach of applying adversarial learning concept to multi-modal learning and not exploiting image-text pair information to learn multi-modal feature. We only use category information in contrast with most previous methods using image-text pair information for multi-modal embedding. In this paper, we show that multi-modal feature can be achieved without image-text pair information and our method makes more similar distribution with image and text in multi-modal feature space than other methods which use image-text pair information. And we show our multi-modal feature has universal semantic information, even though it was trained for category prediction. Our model is end-to-end backpropagation, intuitive and easily extended to other multi-modal learning work.
Clustering Algorithms: A Comparative Approach
Venue: arXiv cs.LG 2016
Year: 2016

Many real-world systems can be studied in terms of pattern recognition tasks, so that proper use (and understanding) of machine learning methods in practical applications becomes essential. While a myriad of classification methods have been proposed, there is no consensus on which methods are more suitable for a given dataset. As a consequence, it is important to comprehensively compare methods in many possible scenarios. In this context, we performed a systematic comparison of 7 well-known clustering methods available in the R language. In order to account for the many possible variations of data, we considered artificial datasets with several tunable properties (number of classes, separation between classes, etc). In addition, we also evaluated the sensitivity of the clustering methods with regard to their parameters configuration. The results revealed that, when considering the default configurations of the adopted methods, the spectral approach usually outperformed the other clustering algorithms. We also found that the default configuration of the adopted implementations was not accurate. In these cases, a simple approach based on random selection of parameters values proved to be a good alternative to improve the performance. All in all, the reported approach provides subsidies guiding the choice of clustering algorithms.
A Sparse Nonlinear Classifier Design Using AUC Optimization
Venue: arXiv cs.AI 2016
Year: 2016

AUC (Area under the ROC curve) is an important performance measure for applications where the data is highly imbalanced. Learning to maximize AUC performance is thus an important research problem. Using a max-margin based surrogate loss function, AUC optimization problem can be approximated as a pairwise rankSVM learning problem. Batch learning methods for solving the kernelized version of this problem suffer from scalability and may not result in sparse classifiers. Recent years have witnessed an increased interest in the development of online or single-pass online learning algorithms that design a classifier by maximizing the AUC performance. The AUC performance of nonlinear classifiers, designed using online methods, is not comparable with that of nonlinear classifiers designed using batch learning algorithms on many real-world datasets. Motivated by these observations, we design a scalable algorithm for maximizing AUC performance by greedily adding the required number of basis functions into the classifier model. The resulting sparse classifiers perform faster inference. Our experimental results show that the level of sparsity achievable can be order of magnitude smaller than the Kernel RankSVM model without affecting the AUC performance much.
Accelerated Convolutions for Efficient Multi-Scale Time to Contact Computation in Julia
Venue: arXiv cs.CV 2016
Year: 2016

Convolutions have long been regarded as fundamental to applied mathematics, physics and engineering. Their mathematical elegance allows for common tasks such as numerical differentiation to be computed efficiently on large data sets. Efficient computation of convolutions is critical to artificial intelligence in real-time applications, like machine vision, where convolutions must be continuously and efficiently computed on tens to hundreds of kilobytes per second. In this paper, we explore how convolutions are used in fundamental machine vision applications. We present an accelerated n-dimensional convolution package in the high performance computing language, Julia, and demonstrate its efficacy in solving the time to contact problem for machine vision. Results are measured against synthetically generated videos and quantitatively assessed according to their mean squared error from the ground truth. We achieve over an order of magnitude decrease in compute time and allocated memory for comparable machine vision applications. All code is packaged and integrated into the official Julia Package Manager to be used in various other scenarios.
Joint Network Coding and Machine Learning for Error-prone Wireless Broadcast
Venue: arXiv cs.NI 2016
Year: 2016

Reliable broadcasting data to multiple receivers over lossy wireless channels is challenging due to the heterogeneity of the wireless link conditions. Automatic Repeat-reQuest (ARQ) based retransmission schemes are bandwidth inefficient due to data duplication at receivers. Network coding (NC) has been shown to be a promising technique for improving network bandwidth efficiency by combining multiple lost data packets for retransmission. However, it is challenging to accurately determine which lost packets should be combined together due to disrupted feedback channels. This paper proposes an adaptive data encoding scheme at the transmitter by joining network coding and machine learning (NCML) for retransmission of lost packets. Our proposed NCML extracts the important features from historical feedback signals received by the transmitter to train a classifier. The constructed classifier is then used to predict states of transmitted data packets at different receivers based on their corrupted feedback signals for effective data mixing. We have conducted extensive simulations to collaborate the efficiency of our proposed approach. The simulation results show that our machine learning algorithm can be trained efficiently and accurately. The simulation results show that on average the proposed NCML can correctly classify 90% of the states of transmitted data packets at different receivers. It achieves significant bandwidth gain compared with the ARQ and NC based schemes in different transmission terrains, power levels, and the distances between the transmitter and receivers.
Shamela: A Large-Scale Historical Arabic Corpus
Venue: arXiv cs.CL 2016
Year: 2016

Arabic is a widely-spoken language with a rich and long history spanning more than fourteen centuries. Yet existing Arabic corpora largely focus on the modern period or lack sufficient diachronic information. We develop a large-scale, historical corpus of Arabic of about 1 billion words from diverse periods of time. We clean this corpus, process it with a morphological analyzer, and enhance it by detecting parallel passages and automatically dating undated texts. We demonstrate its utility with selected case-studies in which we show its application to the digital humanities.
Signal to noise in matching markets
Venue: arXiv cs.GT 2016
Year: 2016

In many matching markets, one side "applies" to the other, and these applications are often expensive and time-consuming (e.g. students applying to college). It is tempting to think that making the application process easier should benefit both sides of the market. After all, the applicants could submit more applications, and the recipients would have more applicants to choose from. In this paper, we propose and analyze a simple model to understand settings where both sides of the market suffer from increased number of applications. The main insights of the paper are derived from quantifying the signal to noise tradeoffs in random matchings, as applications provide a signal of the applicants' preferences. When applications are costly the signal is stronger, as the act of making an application itself is meaningful. Therefore more applications may yield potentially better matches, but fewer applications create stronger signals for the receiving side to learn true preferences. We derive analytic characterizations of the expected quality of stable matchings in a simple utility model where applicants have an overall quality, but also synergy with specific possible partners. Our results show how reducing application cost without introducing an effective signaling mechanism might lead to inefficiencies for both sides of the market.