OpenRaiser/Intern-Atlas huggingface dataset:
Statistical Estimation with Strategic Data Sources in Competitive Settings
Venue: arXiv cs.GT 2017
Year: 2017

In this paper, we introduce a preliminary model for interactions in the data market. Recent research has shown ways in which a data aggregator can design mechanisms for users to ensure the quality of data, even in situations where the users are effort-averse (i.e. prefer to submit lower-quality estimates) and the data aggregator cannot observe the effort exerted by the users (i.e. the contract suffers from the principal-agent problem). However, we have shown that these mechanisms often break down in more realistic models, where multiple data aggregators are in competition. Under minor assumptions on the properties of the statistical estimators in use by data aggregators, we show that there is either no Nash equilibrium, or there is an infinite number of Nash equilibrium. In the latter case, there is a fundamental ambiguity in who bears the burden of incentivizing different data sources. We are also able to calculate the price of anarchy, which measures how much social welfare is lost between the Nash equilibrium and the social optimum, i.e. between non-cooperative strategic play and cooperation.
Storing complex data sharing policies with the Min Mask Sketch
Venue: arXiv cs.DS 2017
Year: 2017

More data is currently being collected and shared by software applications than ever before. In many cases, the user is asked if either all or none of their data can be shared. We hypothesize that in some cases, users would like to share data in more complex ways. In order to implement the sharing of data using more complicated privacy preferences, complex data sharing policies must be used. These complex sharing policies require more space to store than a simple "all or nothing" approach to data sharing. In this paper, we present a new probabilistic data structure, called the Min Mask Sketch, to efficiently store these complex data sharing policies. We describe an implementation for the Min Mask Sketch in PostgreSQL and analyze the practicality and feasibility of using a probabilistic data structure for storing complex data sharing policies.
Dynamic Base Station Repositioning to Improve Spectral Efficiency of Drone Small Cells
Venue: arXiv cs.IT 2017
Year: 2017

With recent advancements in drone technology, researchers are now considering the possibility of deploying small cells served by base stations mounted on flying drones. A major advantage of such drone small cells is that the operators can quickly provide cellular services in areas of urgent demand without having to pre-install any infrastructure. Since the base station is attached to the drone, technically it is feasible for the base station to dynamic reposition itself in response to the changing locations of users for reducing the communication distance, decreasing the probability of signal blocking, and ultimately increasing the spectral efficiency. In this paper, we first propose distributed algorithms for autonomous control of drone movements, and then model and analyse the spectral efficiency performance of a drone small cell to shed new light on the fundamental benefits of dynamic repositioning. We show that, with dynamic repositioning, the spectral efficiency of drone small cells can be increased by nearly 100\% for realistic drone speed, height, and user traffic model and without incurring any major increase in drone energy consumption.
Estimation of Tissue Microstructure Using a Deep Network Inspired by a Sparse Reconstruction Framework
Venue: arXiv cs.CV 2017
Year: 2017

Diffusion magnetic resonance imaging (dMRI) provides a unique tool for noninvasively probing the microstructure of the neuronal tissue. The NODDI model has been a popular approach to the estimation of tissue microstructure in many neuroscience studies. It represents the diffusion signals with three types of diffusion in tissue: intra-cellular, extra-cellular, and cerebrospinal fluid compartments. However, the original NODDI method uses a computationally expensive procedure to fit the model and could require a large number of diffusion gradients for accurate microstructure estimation, which may be impractical for clinical use. Therefore, efforts have been devoted to efficient and accurate NODDI microstructure estimation with a reduced number of diffusion gradients. In this work, we propose a deep network based approach to the NODDI microstructure estimation, which is named Microstructure Estimation using a Deep Network (MEDN). Motivated by the AMICO algorithm which accelerates the computation of NODDI parameters, we formulate the microstructure estimation problem in a dictionary-based framework. The proposed network comprises two cascaded stages. The first stage resembles the solution to a dictionary-based sparse reconstruction problem and the second stage computes the final microstructure using the output of the first stage. The weights in the two stages are jointly learned from training data, which is obtained from training dMRI scans with diffusion gradients that densely sample the q-space. The proposed method was applied to brain dMRI scans, where two shells each with 30 gradient directions (60 diffusion gradients in total) were used. Estimation accuracy with respect to the gold standard was measured and the results demonstrate that MEDN outperforms the competing algorithms.
Dynamic Conjunctive Queries
Venue: arXiv cs.LO 2017
Year: 2017

The article investigates classes of queries maintainable by conjunctive queries (CQs) and their extensions and restrictions in the dynamic complexity framework of Patnaik and Immerman. Starting from the basic language of quantifier-free conjunctions of positive atoms, it studies the impact of additional operators and features - such as union, atomic negation and quantification - on the dynamic expressiveness, for the standard semantics as well as for Delta-semantics. Although many different combinations of these features are possible, they basically yield five important fragments for the standard semantics, characterized by the addition of (1) arbitrary quantification and atomic negation, (2) existential quantification and atomic negation, (3) existential quantification, (4) atomic negation (but no quantification), and by (5) no addition to the basic language at all. While fragments (3), (4) and (5) can be separated, it remains open whether fragments (1), (2) and (3) are actually different. The fragments arising from Delta-semantics are also subsumed by the standard fragments (1), (2) and (4). The main fragments of DynFO that had been studied in previous work, DynQF and DynProp, characterized by quantifier-free update programs with or without auxiliary functions, respectively, also fit into this hierarchy: DynProp coincides with fragment (4) and DynQF is strictly above fragment (4) and within fragment (3). As a further result, all (statically) FO-definable queries are captured by fragment (2) and a complete characterization of these queries in terms of non-recursive dynamic programs with existential update formulas with one existential quantifier is given.
Tracking the Big NAT across Europe and the U.S
Venue: arXiv cs.NI 2017
Year: 2017

Carrier Grade NAT (CGN) mechanisms enable ISPs to share a single IPv4 address across multiple customers, thus offering an immediate solution to the IPv4 address scarcity problem. In this paper, we perform a large scale active measurement campaign to detect CGNs in fixed broadband networks using NAT Revelio, a tool we have developed and validated. Revelio enables us to actively determine from within residential networks the type of upstream network address translation, namely NAT at the home gateway (customer-grade NAT) or NAT in the ISP (Carrier Grade NAT). We demonstrate the generality of the methodology by deploying Revelio in the FCC Measuring Broadband America testbed operated by SamKnows and also in the RIPE Atlas testbed. We enhance Revelio to actively discover from within any home network the type of upstream NAT configuration (i.e., simple home NAT or Carrier Grade NAT). We ran an active large-scale measurement study of CGN usage from 5,121 measurement vantage points within over 60 different ISPs operating in Europe and the United States. We found that 10% of the ISPs we tested have some form of CGN deployment. We validate our results with four ISPs at the IP level and, reported to the ground truth we collected, we conclude that Revelio was 100% accurate in determining the upstream NAT configuration for all the corresponding lines. To the best of our knowledge, this represents the largest active measurement study of (confirmed) CGN deployments at the IP level in fixed broadband networks to date.
Finite-Time Stabilization of Longitudinal Control for Autonomous Vehicles via a Model-Free Approach
Venue: arXiv cs.SY 2017
Year: 2017

This communication presents a longitudinal model-free control approach for computing the wheel torque command to be applied on a vehicle. This setting enables us to overcome the problem of unknown vehicle parameters for generating a suitable control law. An important parameter in this control setting is made time-varying for ensuring finite-time stability. Several convincing computer simulations are displayed and discussed. Overshoots become therefore smaller. The driving comfort is increased and the robustness to time-delays is improved.
A new algorithm for Solving 3-CNF-SAT problem
Venue: arXiv cs.DS 2017
Year: 2017

NP-Complete problems have an important attribute that if one NP-Complete problem can be solved in polynomial time, all NP-Complete problems will have a polynomial solution. The 3-CNF-SAT problem is a NP-Complete problem and the primary method to solve it checks all values of the truth table. This task is of the Ω(2^n) time order. This paper shows that by changing the viewpoint towards the problem, it is possible to know if a 3-CNF-SAT problem is satisfiable in time O(n^10) or not? In this paper, the value of all clauses are considered as false. With this presumption, any of the values inside the truth table can be shown in string form in order to define the set of compatible clauses for each of the strings. So, rather than processing strings, their clauses will be processed implicating that instead of 2^n strings, (O(n^3)) clauses are to be processed; therefore, the time and space complexity of the algorithm would be polynomial.
Multi-Label Learning with Global and Local Label Correlation
Venue: arXiv cs.LG 2017
Year: 2017

It is well-known that exploiting label correlations is important to multi-label learning. Existing approaches either assume that the label correlations are global and shared by all instances; or that the label correlations are local and shared only by a data subset. In fact, in the real-world applications, both cases may occur that some label correlations are globally applicable and some are shared only in a local group of instances. Moreover, it is also a usual case that only partial labels are observed, which makes the exploitation of the label correlations much more difficult. That is, it is hard to estimate the label correlations when many labels are absent. In this paper, we propose a new multi-label approach GLOCAL dealing with both the full-label and the missing-label cases, exploiting global and local label correlations simultaneously, through learning a latent label representation and optimizing label manifolds. The extensive experimental studies validate the effectiveness of our approach on both full-label and missing-label data.
Non-Convex Weighted Lp Minimization based Group Sparse Representation Framework for Image Denoising
Venue: arXiv cs.CV 2017
Year: 2017

Nonlocal image representation or group sparsity has attracted considerable interest in various low-level vision tasks and has led to several state-of-the-art image denoising techniques, such as BM3D, LSSC. In the past, convex optimization with sparsity-promoting convex regularization was usually regarded as a standard scheme for estimating sparse signals in noise. However, using convex regularization can not still obtain the correct sparsity solution under some practical problems including image inverse problems. In this paper we propose a non-convex weighted $\ell_p$ minimization based group sparse representation (GSR) framework for image denoising. To make the proposed scheme tractable and robust, the generalized soft-thresholding (GST) algorithm is adopted to solve the non-convex $\ell_p$ minimization problem. In addition, to improve the accuracy of the nonlocal similar patches selection, an adaptive patch search (APS) scheme is proposed. Experimental results have demonstrated that the proposed approach not only outperforms many state-of-the-art denoising methods such as BM3D and WNNM, but also results in a competitive speed.
Effect of Super Resolution on High Dimensional Features for Unsupervised Face Recognition in the Wild
Venue: arXiv cs.CV 2017
Year: 2017

Majority of the face recognition algorithms use query faces captured from uncontrolled, in the wild, environment. Often caused by the cameras limited capabilities, it is common for these captured facial images to be blurred or low resolution. Super resolution algorithms are therefore crucial in improving the resolution of such images especially when the image size is small requiring enlargement. This paper aims to demonstrate the effect of one of the state-of-the-art algorithms in the field of image super resolution. To demonstrate the functionality of the algorithm, various before and after 3D face alignment cases are provided using the images from the Labeled Faces in the Wild (lfw). Resulting images are subject to testing on a closed set face recognition protocol using unsupervised algorithms with high dimension extracted features. The inclusion of super resolution algorithm resulted in significant improved recognition rate over recently reported results obtained from unsupervised algorithms.
Distributed Hypothesis Testing Over Noisy Channels
Venue: arXiv cs.IT 2017
Year: 2017

A distributed binary hypothesis testing problem, in which multiple observers transmit their observations to a detector over noisy channels, is studied. Given its own side information, the goal of the detector is to decide between two hypotheses for the joint distribution of the data. Single-letter upper and lower bounds on the optimal type 2 error exponent (T2-EE), when the type 1 error probability vanishes with the block-length are obtained. These bounds coincide and characterize the optimal T2-EE when only a single helper is involved. Our result shows that the optimal T2-EE depends on the marginal distributions of the data and the channels rather than their joint distribution. However, an operational separation between HT and channel coding does not hold, and the optimal T2-EE is achieved by generating channel inputs correlated with observed data.
Best Practices for Applying Deep Learning to Novel Applications
Venue: arXiv cs.SE 2017
Year: 2017

This report is targeted to groups who are subject matter experts in their application but deep learning novices. It contains practical advice for those interested in testing the use of deep neural networks on applications that are novel for deep learning. We suggest making your project more manageable by dividing it into phases. For each phase this report contains numerous recommendations and insights to assist novice practitioners.
Rhetorical relations for information retrieval
Venue: arXiv cs.IR 2017
Year: 2017

Typically, every part in most coherent text has some plausible reason for its presence, some function that it performs to the overall semantics of the text. Rhetorical relations, e.g. contrast, cause, explanation, describe how the parts of a text are linked to each other. Knowledge about this socalled discourse structure has been applied successfully to several natural language processing tasks. This work studies the use of rhetorical relations for Information Retrieval (IR): Is there a correlation between certain rhetorical relations and retrieval performance? Can knowledge about a document's rhetorical relations be useful to IR? We present a language model modification that considers rhetorical relations when estimating the relevance of a document to a query. Empirical evaluation of different versions of our model on TREC settings shows that certain rhetorical relations can benefit retrieval effectiveness notably (> 10% in mean average precision over a state-of-the-art baseline).
Vico-Greengard-Ferrando quadratures in the tensor solver for integral equations
Venue: arXiv cs.NA 2017
Year: 2017

Convolution with Green's function of a differential operator appears in a lot of applications e.g. Lippmann-Schwinger integral equation. Algorithms for computing such are usually non-trivial and require non-uniform mesh. However, recently Vico, Greengard and Ferrando developed method for computing convolution with smooth functions with compact support with spectral accuracy, requiring nothing more than Fast Fourier Transform (FFT). Their approach is very suitable for the low-rank tensor implementation which we develop using Quantized Tensor Train (QTT) decomposition.
Constructive Stabilization and Pole Placement by Arbitrary Decentralized Architectures
Venue: arXiv cs.SY 2017
Year: 2017

A seminal result in decentralized control is the development of fixed modes by Wang and Davison in 1973 - that plant modes which cannot be moved with a static decentralized controller cannot be moved by a dynamic one either, and that the other modes which can be moved can be shifted to any chosen location with arbitrary precision. These results were developed for perfectly decentralized, or block diagonal, information structure, where each control input may only depend on a single corresponding measurement. Furthermore, the results were claimed after a preliminary step was demonstrated, omitting a rigorous induction for each of these results, and the remaining task is nontrivial. In this paper, we consider fixed modes for arbitrary information structures, where certain control inputs may depend on some measurements but not others. We provide a comprehensive proof that the modes which cannot be altered by a static controller with the given structure cannot be moved by a dynamic one either, and that the modes which can be altered by a static controller with the given structure can be moved by a dynamic one to any chosen location with arbitrary precision, thus generalizing and solidifying Wang and Davison's results. This shows that a system can be stabilized by a linear time-invariant controller with the given information structure as long as all of the modes which are fixed with respect to that structure are in the left half-plane; an algorithm for synthesizing such a stabilizing decentralized controller is then distilled from the proof.
Joint Trajectory and Communication Design for UAV-Enabled Multiple Access
Venue: arXiv cs.IT 2017
Year: 2017

Unmanned aerial vehicles (UAVs) have attracted significant interest recently in wireless communication due to their high maneuverability, flexible deployment, and low cost. This paper studies a UAV-enabled wireless network where the UAV is employed as an aerial mobile base station (BS) to serve a group of users on the ground. To achieve fair performance among users, we maximize the minimum throughput over all ground users by jointly optimizing the multiuser communication scheduling and UAV trajectory over a finite horizon. The formulated problem is shown to be a mixed integer non-convex optimization problem that is difficult to solve in general. We thus propose an efficient iterative algorithm by applying the block coordinate descent and successive convex optimization techniques, which is guaranteed to converge to at least a locally optimal solution. To achieve fast convergence and stable throughput, we further propose a low-complexity initialization scheme for the UAV trajectory design based on the simple circular trajectory. Extensive simulation results are provided which show significant throughput gains of the proposed design as compared to other benchmark schemes.
Prototyping and Experimentation of a Closed-Loop Wireless Power Transmission with Channel Acquisition and Waveform Optimization
Venue: arXiv cs.IT 2017
Year: 2017

A systematic design of adaptive waveform for Wireless Power Transfer (WPT) has recently been proposed and shown through simulations to lead to significant performance benefits compared to traditional non-adaptive and heuristic waveforms. In this study, we design the first prototype of a closed-loop wireless power transfer system with adaptive waveform optimization based on Channel State Information acquisition. The prototype consists of three important blocks, namely the channel estimator, the waveform optimizer, and the energy harvester. Software Defined Radio (SDR) prototyping tools are used to implement a wireless power transmitter and a channel estimator, and a voltage doubler rectenna is designed to work as an energy harvester. A channel adaptive waveform with 8 sinewaves is shown through experiments to improve the average harvested DC power at the rectenna output by 9.8% to 36.8% over a non-adaptive design with the same number of sinewaves.
Bilateral Proofs of Safety and Progress Properties of Concurrent Programs
Venue: arXiv cs.PL 2017
Year: 2017

This paper suggests a theomisra@utexas.edury of composable specification of concurrent programs that permits: (1) verification of program code for a given specification, and (2) composition of the specifications of the components to yield the specification of a program. The specification consists of both terminal properties that hold at the end of a program execution (if the execution terminates) and perpetual properties that hold throughout an execution. We devise (1) proof techniques for verification, and (2) composition rules to derive the specification of a program from those of its components. We employ terminal properties of components to derive perpetual properties of a program and conversely. Hence, this proof strategy is called bilateral. The compositional aspect of the theory is important in assembling a program out of components some of whose source code may not be available, as is increasingly the case with cross-vendor program integration.
Fixed versus Dynamic Co-Occurrence Windows in TextRank Term Weights for Information Retrieval
Venue: arXiv cs.IR 2017
Year: 2017

TextRank is a variant of PageRank typically used in graphs that represent documents, and where vertices denote terms and edges denote relations between terms. Quite often the relation between terms is simple term co-occurrence within a fixed window of k terms. The output of TextRank when applied iteratively is a score for each vertex, i.e. a term weight, that can be used for information retrieval (IR) just like conventional term frequency based term weights. So far, when computing TextRank term weights over co- occurrence graphs, the window of term co-occurrence is al- ways ?xed. This work departs from this, and considers dy- namically adjusted windows of term co-occurrence that fol- low the document structure on a sentence- and paragraph- level. The resulting TextRank term weights are used in a ranking function that re-ranks 1000 initially returned search results in order to improve the precision of the ranking. Ex- periments with two IR collections show that adjusting the vicinity of term co-occurrence when computing TextRank term weights can lead to gains in early precision.