OpenRaiser/Intern-Atlas huggingface dataset:
Robust Beamforming for SWIPT Systems with Non-linear Energy Harvesting Model
Venue: arXiv cs.IT 2016
Year: 2016

This paper investigates resource allocation for simultaneous wireless information and power transfer (SWIPT) downlink systems based on a non-linear energy harvesting model. The resource allocation algorithm design is formulated as a non-convex optimization problem for the maximization of the total harvested power. The proposed problem formulation not only takes into account imperfect channel state information (CSI) but also guarantees the quality-of-service (QoS) of information transfer. A novel iterative algorithm is proposed to obtain the globally optimal solution of the considered non-convex optimization problem. In each iteration, a rank-constrained semidefinite program (SDP) is solved optimally by SDP relaxation. Simulation results demonstrate the significant gains in harvested power and the robustness against CSI imperfection for the proposed optimal resource allocation, compared to a baseline scheme designed for perfect CSI and the conventional linear energy harvesting model.
Fitting a 3D Morphable Model to Edges: A Comparison Between Hard and Soft Correspondences
Venue: arXiv cs.CV 2016
Year: 2016

We propose a fully automatic method for fitting a 3D morphable model to single face images in arbitrary pose and lighting. Our approach relies on geometric features (edges and landmarks) and, inspired by the iterated closest point algorithm, is based on computing hard correspondences between model vertices and edge pixels. We demonstrate that this is superior to previous work that uses soft correspondences to form an edge-derived cost surface that is minimised by nonlinear optimisation.
Learning Discriminative Features via Label Consistent Neural Network
Venue: arXiv cs.CV 2016
Year: 2016

Deep Convolutional Neural Networks (CNN) enforces supervised information only at the output layer, and hidden layers are trained by back propagating the prediction error from the output layer without explicit supervision. We propose a supervised feature learning approach, Label Consistent Neural Network, which enforces direct supervision in late hidden layers. We associate each neuron in a hidden layer with a particular class label and encourage it to be activated for input signals from the same class. More specifically, we introduce a label consistency regularization called "discriminative representation error" loss for late hidden layers and combine it with classification error loss to build our overall objective function. This label consistency constraint alleviates the common problem of gradient vanishing and tends to faster convergence; it also makes the features derived from late hidden layers discriminative enough for classification even using a simple $k$-NN classifier, since input signals from the same class will have very similar representations. Experimental results demonstrate that our approach achieves state-of-the-art performances on several public benchmarks for action and object category recognition.
The Second Reactive Synthesis Competition (SYNTCOMP 2015)
Venue: arXiv cs.LO 2016
Year: 2016

We report on the design and results of the second reactive synthesis competition (SYNTCOMP 2015). We describe our extended benchmark library, with 6 completely new sets of benchmarks, and additional challenging instances for 4 of the benchmark sets that were already used in SYNTCOMP 2014. To enhance the analysis of experimental results, we introduce an extension of our benchmark format with meta-information, including a difficulty rating and a reference size for solutions. Tools are evaluated on a set of 250 benchmarks, selected to provide a good coverage of benchmarks from all classes and difficulties. We report on changes of the evaluation scheme and the experimental setup. Finally, we describe the entrants into SYNTCOMP 2015, as well as the results of our experimental evaluation. In our analysis, we emphasize progress over the tools that participated last year.
Citations to articles citing Benford's law: a Benford analysis
Venue: arXiv cs.DL 2016
Year: 2016

The occurrence of first significant digits of numbers in large data is often governed by a logarithmically decreasing distribution called Benford's law (BL), reported first by S. Newcomb (SN) and many decades later independently by F. Benford (FB). Due to its counter-intuitiveness the law was ignored for decades as a mere curious observation. However, an indication of its remarkable resurgence is the huge swell in the number of citations received by the papers of SN/FB. The law has come a long way, from obscurity to now being a regular subject of books, peer reviewed papers, patents, blogs and news. Here, we use Google Scholar (GS) to collect the data on the number of citations received by the articles citing the original paper of SN/FB and then investigate whether the leading digits of this citations data are distributed according to the law they discovered. We find that the citations data of literature on BL is in remarkable agreement with the predictions of the law.
The Minimum Shared Edges Problem on Planar Graphs
Venue: arXiv cs.CC 2016
Year: 2016

We study the Minimum Shared Edges problem introduced by Omran et al. [Journal of Combinatorial Optimization, 2015] on planar graphs: Planar MSE asks, given a planar graph G = (V,E), two distinct vertices s,t in V , and two integers p, k, whether there are p s-t paths in G that share at most k edges, where an edges is called shared if it appears in at least two of the p s-t paths. We show that Planar MSE is NP-hard by reduction from Vertex Cover. We make use of a grid-like structure, where the alignment (horizontal/vertical) of the edges in the grid correspond to selection and validation gadgets respectively.
On the Relay-Fallback Tradeoff in Millimeter Wave Wireless System
Venue: arXiv cs.IT 2016
Year: 2016

Millimeter wave (mmWave) communications systems are promising candidate to support extremely high data rate services in future wireless networks. MmWave communications exhibit high penetration loss (blockage) and require directional transmissions to compensate for severe channel attenuations and for high noise powers. When blockage occurs, there are at least two simple prominent options: 1) switching to the conventional microwave frequencies (fallback option) and 2) using an alternative non-blocked path (relay option). However, currently it is not clear under which conditions and network parameters one option is better than the other. To investigate the performance of the two options, this paper proposes a novel blockage model that allows deriving maximum achievable throughput and delay performance of both options. A simple criterion to decide which option should be taken under which network condition is provided. By a comprehensive performance analysis, it is shown that the right option depends on the payload size, beam training overhead, and blockage probability. For a network with light traffic and low probability of blockage in the direct link, the fallback option is throughput- and delay-optimal. For a network with heavy traffic demands and semi-static topology (low beam-training overhead), the relay option is preferable.
"Draw My Topics": Find Desired Topics fast from large scale of Corpus
Venue: arXiv cs.CL 2016
Year: 2016

We develop the "Draw My Topics" toolkit, which provides a fast way to incorporate social scientists' interest into standard topic modelling. Instead of using raw corpus with primitive processing as input, an algorithm based on Vector Space Model and Conditional Entropy are used to connect social scientists' willingness and unsupervised topic models' output. Space for users' adjustment on specific corpus of their interest is also accommodated. We demonstrate the toolkit's use on the Diachronic People's Daily Corpus in Chinese.
Some Pairs Problems
Venue: arXiv cs.DB 2016
Year: 2016

A common form of MapReduce application involves discovering relationships between certain pairs of inputs. Similarity joins serve as a good example of this type of problem, which we call a "some-pairs" problem. In the framework of Afrati et al. (VLDB 2013), algorithms are measured by the tradeoff between reducer size (maximum number of inputs a reducer can handle) and the replication rate (average number of reducers to which an input must be sent. There are two obvious approaches to solving some-pairs problems in general. We show that no general-purpose MapReduce algorithm can beat both of these two algorithms in the worst case. We then explore a recursive algorithm for solving some-pairs problems and heuristics for beating the lower bound on common instances of the some-pairs class of problems.
Latent-Class Hough Forests for 6 DoF Object Pose Estimation
Venue: arXiv cs.CV 2016
Year: 2016

In this paper we present Latent-Class Hough Forests, a method for object detection and 6 DoF pose estimation in heavily cluttered and occluded scenarios. We adapt a state of the art template matching feature into a scale-invariant patch descriptor and integrate it into a regression forest using a novel template-based split function. We train with positive samples only and we treat class distributions at the leaf nodes as latent variables. During testing we infer by iteratively updating these distributions, providing accurate estimation of background clutter and foreground occlusions and, thus, better detection rate. Furthermore, as a by-product, our Latent-Class Hough Forests can provide accurate occlusion aware segmentation masks, even in the multi-instance scenario. In addition to an existing public dataset, which contains only single-instance sequences with large amounts of clutter, we have collected two, more challenging, datasets for multiple-instance detection containing heavy 2D and 3D clutter as well as foreground occlusions. We provide extensive experiments on the various parameters of the framework such as patch size, number of trees and number of iterations to infer class distributions at test time. We also evaluate the Latent-Class Hough Forests on all datasets where we outperform state of the art methods.
Investigating the Necessity of Distribution Markets in Accomodating High Penetration Microgrids
Venue: arXiv cs.SY 2016
Year: 2016

The increased need for reliable, resilient, and high quality power combined with a falling cost of distributed generation technologies has resulted in a rapid growth of microgrid in power systems. Although providing multitude of benefits, the microgrid power transfer with the main grid, which is commonly obtained using economy and reliability consideration, may result in major operational drawbacks, most notably, a large mismatch between actual and forecasted system loads. This paper investigates the impact of high penetration microgrids on the power system net load, and further proposes three paradigms that can be adopted to address the emerging operational issues. The IEEE 6-bus test system is used for numerical studies and to further support the discussions.
TopCom: Index for Shortest Distance Query in Directed Graph
Venue: arXiv cs.DB 2016
Year: 2016

Finding shortest distance between two vertices in a graph is an important problem due to its numerous applications in diverse domains, including geo-spatial databases, social network analysis, and information retrieval. Classical algorithms (such as, Dijkstra) solve this problem in polynomial time, but these algorithms cannot provide real-time response for a large number of bursty queries on a large graph. So, indexing based solutions that pre-process the graph for efficiently answering (exactly or approximately) a large number of distance queries in real-time is becoming increasingly popular. Existing solutions have varying performance in terms of index size, index building time, query time, and accuracy. In this work, we propose T OP C OM , a novel indexing-based solution for exactly answering distance queries. Our experiments with two of the existing state-of-the-art methods (IS-Label and TreeMap) show the superiority of T OP C OM over these two methods considering scalability and query time. Besides, indexing of T OP C OM exploits the DAG (directed acyclic graph) structure in the graph, which makes it significantly faster than the existing methods if the SCCs (strongly connected component) of the input graph are relatively small.
Distributed Power Allocation and Channel Access Probability Assignment for Cognitive Radio
Venue: arXiv cs.NI 2016
Year: 2016

In this paper, we present a framework for distributively optimizing the transmission strategies of secondary users in an ad hoc cognitive radio network. In particular, the proposed approach allows secondary users to set their transmit powers and channel access probabilities such that, on average, the quality of service of both the primary and secondary networks are satisfied. The system under consideration assumes several primary and secondary transceiver pairs and assumes no cooperation or information exchange between neither primary and secondary users nor among secondary users. The outage probability, and consequently the connection probability, is derived for the system and is used in defining a new performance metric in the optimization problem using tools from stochastic geometry. We refer to this metric as the spatial density of successful transmission. We corroborate our derivations through numerical evaluations. We further demonstrate that even in the absence of any form of cooperation, an acceptable quality of service can be attained in the cognitive radio environment.
Orientations of Simplices Determined by Orderings on the Coordinates of their Vertices
Venue: arXiv cs.DM 2016
Year: 2016

Provided n points in an (n-1)-dimensional affine space, and one ordering of the points for each coordinate, we address the problem of testing whether these orderings determine if the points are the vertices of a simplex (i.e. are affinely independent), regardless of the real values of the coordinates. We also attempt to determine the orientation of this simplex. In other words, given a matrix whose columns correspond to affine points, we want to know when the sign (or the non-nullity) of its determinant is implied by orderings given to each row for the values of the row. We completely solve the problem in dimensions 2 and 3. We provide a direct combinatorial characterization, along with a formal calculus method. It can also be viewed as a decision algorithm, and is based on testing the existence of a suitable inductive cofactor expansion of the determinant. We conjecture that our method generalizes in higher dimensions. This work aims to be part of a study on how oriented matroids encode shapes of 3-dimensional landmark-based objects. Specifically, applications include the analysis of anatomical data for physical anthropology and clinical research.
Buffer-aware Worst Case Timing Analysis of Wormhole Network On Chip
Venue: arXiv cs.NI 2016
Year: 2016

A buffer-aware worst-case timing analysis of wormhole NoC is proposed in this paper to integrate the impact of buffer size on the different dependencies relationship between flows, i.e. direct and indirect blocking flows, and consequently the timing performance. First, more accurate definitions of direct and indirect blocking flows sets have been introduced to take into account the buffer size impact. Then, the modeling and worst-case timing analysis of wormhole NoC have been detailed, based on Network Calculus formalism and the newly defined blocking flows sets. This introduced approach has been illustrated in the case of a realistic NoC case study to show the trade off between latency and buffer size. The comparative analysis of our proposed Buffer-aware timing analysis with conventional approaches is conducted and noticeable enhancements in terms of maximum latency have been proved.
A short note on Merlin-Arthur protocols for subset sum
Venue: arXiv cs.CC 2016
Year: 2016

In the subset sum problem we are given n positive integers along with a target integer t. A solution is a subset of these integers summing to t. In this short note we show that for a given subset sum instance there is a proof of size $O^*(\sqrt{t})$ of what the number of solutions is that can be constructed in $O^*(t)$ time and can be probabilistically verified in time $O^*(\sqrt{t})$ with at most constant error probability. Here, the $O^*()$ notation omits factors polynomial in the input size $n\log(t)$.
On the Discovery of Success Trajectories of Authors
Venue: arXiv cs.DL 2016
Year: 2016

Understanding the qualitative patterns of research endeavor of scientific authors in terms of publication count and their impact (citation) is important in order to quantify success trajectories. Here, we examine the career profile of authors in computer science and physics domains and discover at least six different success trajectories in terms of normalized citation count in longitudinal scale. Initial observations of individual trajectories lead us to characterize the authors in each category. We further leverage this trajectory information to build a two-stage stratification model to predict future success of an author at the early stage of her career. Our model outperforms the baseline with an average improvement of 15.68% for both the datasets.
An Achievable Rate-Distortion Region for Multiple Descriptions Source Coding Based on Coset Codes
Venue: arXiv cs.IT 2016
Year: 2016

We consider the problem of multiple descriptions (MD) source coding and propose new coding strategies involving both unstructured and structured coding layers. Previously, the most general achievable rate-distortion (RD) region for the $l$-descriptions problem was the Combinatorial Message Sharing with Binning (CMSB) region. The CMSB scheme utilizes unstructured quantizers and unstructured binning. In the first part of the paper, we show that this strategy can be improved upon using more general unstructured quantizers and a more general unstructured binning method. In the second part, structured coding strategies are considered. First, structured coding strategies are developed by considering specific MD examples involving three or more descriptions. We show that application of structured quantizers results in strict RD improvements when there are more than two descriptions. Furthermore, we show that structured binning also yields improvements. These improvements are in addition to the ones derived in the first part of the paper. This suggests that structured coding is essential when coding over more than two descriptions. Using the ideas developed through these examples we provide a new unified coding strategy by considering several structured coding layers. Finally, we characterize its performance in the form of an inner bound to the optimal rate-distortion region using computable single-letter information quantities. The new RD region strictly contains all of the previous known achievable regions.
On Feature based Delaunay Triangulation for Palmprint Recognition
Venue: arXiv cs.CV 2016
Year: 2016

Authentication of individuals via palmprint based biometric system is becoming very popular due to its reliability as it contains unique and stable features. In this paper, we present a novel approach for palmprint recognition and its representation. To extract the palm lines, local thresholding technique Niblack binarization algorithm is adopted. The endpoints of these lines are determined and a connection is created among them using the Delaunay triangulation thereby generating a distinct topological structure of each palmprint. Next, we extract different geometric as well as quantitative features from the triangles of the Delaunay triangulation that assist in identifying different individuals. To ensure that the proposed approach is invariant to rotation and scaling, features were made relative to topological and geometrical structure of the palmprint. The similarity of the two palmprints is computed using the weighted sum approach and compared with the k-nearest neighbor. The experimental results obtained reflect the effectiveness of the proposed approach to discriminate between different palmprint images and thus achieved a recognition rate of 90% over large databases.
YOURPRIVACYPROTECTOR, A recommender system for privacy settings in social networks
Venue: arXiv cs.CR 2016
Year: 2016

Ensuring privacy of users of social networks is probably an unsolvable conundrum. At the same time, an informed use of the existing privacy options by the social network participants may alleviate - or even prevent - some of the more drastic privacy-averse incidents. Unfortunately, recent surveys show that an average user is either not aware of these options or does not use them, probably due to their perceived complexity. It is therefore reasonable to believe that tools assisting users with two tasks: 1) understanding their social net behavior in terms of their privacy settings and broad privacy categories, and 2)recommending reasonable privacy options, will be a valuable tool for everyday privacy practice in a social network context. This paper presents YourPrivacyProtector, a recommender system that shows how simple machine learning techniques may provide useful assistance in these two tasks to Facebook users. We support our claim with empirical results of application of YourPrivacyProtector to two groups of Facebook users.
"Almost-stable" matchings in the Hospitals / Residents problem with Couples
Venue: arXiv cs.DS 2016
Year: 2016

The Hospitals / Residents problem with Couples (HRC) models the allocation of intending junior doctors to hospitals where couples are allowed to submit joint preference lists over pairs of (typically geographically close) hospitals. It is known that a stable matching need not exist, so we consider MIN BP HRC, the problem of finding a matching that admits the minimum number of blocking pairs (i.e., is "as stable as possible"). We show that this problem is NP-hard and difficult to approximate even in the highly restricted case that each couple finds only one hospital pair acceptable. However if we further assume that the preference list of each single resident and hospital is of length at most 2, we give a polynomial-time algorithm for this case. We then present the first Integer Programming (IP) and Constraint Programming (CP) models for MIN BP HRC. Finally, we discuss an empirical evaluation of these models applied to randomly-generated instances of MIN BP HRC. We find that on average, the CP model is about 1.15 times faster than the IP model, and when presolving is applied to the CP model, it is on average 8.14 times faster. We further observe that the number of blocking pairs admitted by a solution is very small, i.e., usually at most 1, and never more than 2, for the (28,000) instances considered.
Distributed Scenario-Based Optimization for Asset Management in a Hierarchical Decision Making Environment
Venue: arXiv cs.SY 2016
Year: 2016

Asset management attempts to keep the power system in working conditions. It requires much coordination between multiple entities and long term planning often months in advance. In this work we introduce a mid-term asset management formulation as a stochastic optimization problem, that includes three hierarchical layers of decision making, namely the mid-term, short-term and real-time. We devise a tractable scenario approximation technique for efficiently assessing the complex implications a maintenance schedule inflicts on a power system. This is done using efficient Monte-Carlo simulations that trade-off between accuracy and tractability. We then present our implementation of a distributed scenario-based optimization algorithm for solving our formulation, and use an updated PJM 5-bus system to show a solution that is cheaper than other maintenance heuristics that are likely to be considered by TSOs.