OpenRaiser/Intern-Atlas huggingface dataset:
A GEMM interface and implementation on NVIDIA GPUs for multiple small matrices
Venue: arXiv cs.MS 2013
Year: 2013

We present an interface and an implementation of the General Matrix Multiply (GEMM) routine for multiple small matrices processed simultaneously on NVIDIA graphics processing units (GPUs). We focus on matrix sizes under 16. The implementation can be easily extended to larger sizes. For single precision matrices, our implementation is 30% to 600% faster than the batched cuBLAS implementation distributed in the CUDA Toolkit 5.0 on NVIDIA Tesla K20c. For example, we obtain 104 GFlop/s and 216 GFlop/s when multiplying 100,000 independent matrix pairs of size 10 and 16, respectively. Similar improvement in performance is obtained for other sizes, in single and double precision for real and complex types, and when the number of matrices is smaller. Apart from our implementation, our different function interface also plays an important role in the improved performance. Applications of this software include Finite Element computation on GPUs.
Synthesis of neural networks for spatio-temporal spike pattern recognition and processing
Venue: arXiv cs.NE 2013
Year: 2013

The advent of large scale neural computational platforms has highlighted the lack of algorithms for synthesis of neural structures to perform predefined cognitive tasks. The Neural Engineering Framework offers one such synthesis, but it is most effective for a spike rate representation of neural information, and it requires a large number of neurons to implement simple functions. We describe a neural network synthesis method that generates synaptic connectivity for neurons which process time-encoded neural signals, and which makes very sparse use of neurons. The method allows the user to specify, arbitrarily, neuronal characteristics such as axonal and dendritic delays, and synaptic transfer functions, and then solves for the optimal input-output relationship using computed dendritic weights. The method may be used for batch or online learning and has an extremely fast optimization process. We demonstrate its use in generating a network to recognize speech which is sparsely encoded as spike times.
Pulmonary Vascular Tree Segmentation from Contrast-Enhanced CT Images
Venue: arXiv cs.CV 2013
Year: 2013

We present a pulmonary vessel segmentation algorithm, which is fast, fully automatic and robust. It uses a coarse segmentation of the airway tree and a left and right lung labeled volume to restrict a vessel enhancement filter, based on an offset medialness function, to the lungs. We show the application of our algorithm on contrast-enhanced CT images, where we derive a clinical parameter to detect pulmonary hypertension (PH) in patients. Results on a dataset of 24 patients show that quantitative indices derived from the segmentation are applicable to distinguish patients with and without PH. Further work-in-progress results are shown on the VESSEL12 challenge dataset, which is composed of non-contrast-enhanced scans, where we range in the midfield of participating contestants.
The automorphism group of a self-dual [72,36,16] code is not an elementary abelian group of order 8
Venue: arXiv cs.IT 2013
Year: 2013

The existence of an extremal self-dual binary linear code C of length 72 is a long-standing open problem. We continue the investigation of its automorphism group: looking at the combination of the subcodes fixed by different involutions and doing a computer calculation with Magma, we prove that Aut(C) is not isomorphic to the elementary abelian group of order 8. Combining this with the known results in the literature one obtains that Aut(C) has order at most 5.
In the sight of my wearable camera: Classifying my visual experience
Venue: arXiv cs.CV 2013
Year: 2013

We introduce and we analyze a new dataset which resembles the input to biological vision systems much more than most previously published ones. Our analysis leaded to several important conclusions. First, it is possible to disambiguate over dozens of visual scenes (locations) encountered over the course of several weeks of a human life with accuracy of over 80%, and this opens up possibility for numerous novel vision applications, from early detection of dementia to everyday use of wearable camera streams for automatic reminders, and visual stream exchange. Second, our experimental results indicate that, generative models such as Latent Dirichlet Allocation or Counting Grids, are more suitable to such types of data, as they are more robust to overtraining and comfortable with images at low resolution, blurred and characterized by relatively random clutter and a mix of objects.
Relation-algebraic and Tool-supported Control of Condorcet Voting
Venue: arXiv cs.GT 2013
Year: 2013

We present a relation-algebraic model of Condorcet voting and, based on it, relation-algebraic solutions of the constructive control problem via the removal of voters. We consider two winning conditions, viz. to be a Condorcet winner and to be in the (Gilles resp. upward) uncovered set. For the first condition the control problem is known to be NP-hard; for the second condition the NP-hardness of the control problem is shown in the paper. All relation-algebraic specifications we will develop in the paper immediately can be translated into the programming language of the BDD-based computer system RelView. Our approach is very flexible and especially appropriate for prototyping and experimentation, and as such very instructive for educational purposes. It can easily be applied to other voting rules and control problems.
An Improved Approach for Word Ambiguity Removal
Venue: arXiv cs.CL 2013
Year: 2013

Word ambiguity removal is a task of removing ambiguity from a word, i.e. correct sense of word is identified from ambiguous sentences. This paper describes a model that uses Part of Speech tagger and three categories for word sense disambiguation (WSD). Human Computer Interaction is very needful to improve interactions between users and computers. For this, the Supervised and Unsupervised methods are combined. The WSD algorithm is used to find the efficient and accurate sense of a word based on domain information. The accuracy of this work is evaluated with the aim of finding best suitable domain of word.
Web graph compression with fast access
Venue: arXiv cs.DS 2013
Year: 2013

In recent years studying the content of the World Wide Web became a very important yet rather difficult task. There is a need for a compression technique that would allow a web graph representation to be put into the memory while maintaining random access time competitive to the time needed to access uncompressed web graph on a hard drive. There are already available techniques that accomplish this task, but there is still room for improvements and this thesis attempts to prove it. It includes a comparison of two methods contained in state of art of this field (BV and k2partitioned) to two already implemented algorithms (rewritten, however, in C++ programming language to maximize speed and resource management efficiency), which are LM and 2D, and introduces the new variant of the latter one, called 2D stripes. This thesis serves as well as a proof of concept. The final considerations show positive and negative aspects of all presented methods, expose the feasibility of the new variant as well as indicate future direction for development.
Stopping Sets of Algebraic Geometry Codes
Venue: arXiv cs.IT 2013
Year: 2013

Stopping sets and stopping set distribution of a linear code play an important role in the performance analysis of iterative decoding for this linear code. Let $C$ be an $[n,k]$ linear code over $\f$ with parity-check matrix $H$, where the rows of $H$ may be dependent. Let $[n]=\{1,2,...,n\}$ denote the set of column indices of $H$. A \emph{stopping set} $S$ of $C$ with parity-check matrix $H$ is a subset of $[n]$ such that the restriction of $H$ to $S$ does not contain a row of weight 1. The \emph{stopping set distribution} $\{T_{i}(H)\}_{i=0}^{n}$ enumerates the number of stopping sets with size $i$ of $C$ with parity-check matrix $H$. Denote $H^{*}$ the parity-check matrix consisting of all the non-zero codewords in the dual code $C^{\bot}$. In this paper, we study stopping sets and stopping set distributions of some residue algebraic geometry (AG) codes with parity-check matrix $H^*$. First, we give two descriptions of stopping sets of residue AG codes. For the simplest AG codes, i.e., the generalized Reed-Solomon codes, it is easy to determine all the stopping sets. Then we consider AG codes from elliptic curves. We use the group structure of rational points of elliptic curves to present a complete characterization of stopping sets. Then the stopping sets, the stopping set distribution and the stopping distance of the AG code from an elliptic curve are reduced to the search, counting and decision versions of the subset sum problem in the group of rational points of the elliptic curve, respectively. Finally, for some special cases, we determine the stopping set distributions of AG codes from elliptic curves.
Base-Station Assisted Device-to-Device Communications for High-Throughput Wireless Video Networks
Venue: arXiv cs.NI 2013
Year: 2013

We propose a new scheme for increasing the throughput of video files in cellular communications systems. This scheme exploits (i) the redundancy of user requests as well as (ii) the considerable storage capacity of smartphones and tablets. Users cache popular video files and - after receiving requests from other users - serve these requests via device-to-device localized transmissions. The file placement is optimal when a central control knows a priori the locations of wireless devices when file requests occur. However, even a purely random caching scheme shows only a minor performance loss compared to such a genie-aided scheme. We then analyze the optimal collaboration distance, trading off frequency reuse with the probability of finding a requested file within the collaboration distance. We show that an improvement of spectral efficiency of one to two orders of magnitude is possible, even if there is not very high redundancy in video requests.
Semi-supervised Eigenvectors for Large-scale Locally-biased Learning
Venue: arXiv cs.LG 2013
Year: 2013

In many applications, one has side information, e.g., labels that are provided in a semi-supervised manner, about a specific target region of a large data set, and one wants to perform machine learning and data analysis tasks "nearby" that prespecified target region. For example, one might be interested in the clustering structure of a data graph near a prespecified "seed set" of nodes, or one might be interested in finding partitions in an image that are near a prespecified "ground truth" set of pixels. Locally-biased problems of this sort are particularly challenging for popular eigenvector-based machine learning and data analysis tools. At root, the reason is that eigenvectors are inherently global quantities, thus limiting the applicability of eigenvector-based methods in situations where one is interested in very local properties of the data. In this paper, we address this issue by providing a methodology to construct semi-supervised eigenvectors of a graph Laplacian, and we illustrate how these locally-biased eigenvectors can be used to perform locally-biased machine learning. These semi-supervised eigenvectors capture successively-orthogonalized directions of maximum variance, conditioned on being well-correlated with an input seed set of nodes that is assumed to be provided in a semi-supervised manner. We show that these semi-supervised eigenvectors can be computed quickly as the solution to a system of linear equations; and we also describe several variants of our basic method that have improved scaling properties. We provide several empirical examples demonstrating how these semi-supervised eigenvectors can be used to perform locally-biased learning; and we discuss the relationship between our results and recent machine learning algorithms that use global eigenvectors of the graph Laplacian.
Monoidify! Monoids as a Design Principle for Efficient MapReduce Algorithms
Venue: arXiv cs.DC 2013
Year: 2013

It is well known that since the sort/shuffle stage in MapReduce is costly, local aggregation is one important principle to designing efficient algorithms. This short paper represents an attempt to more clearly articulate this design principle in terms of monoids, which generalizes the use of combiners and the in-mapper combining pattern.
Remote Mentoring Young Females in STEM through MAGIC
Venue: arXiv cs.CY 2013
Year: 2013

The limited representation of women in STEM workforce is a concerning national issue. It has been found that the gender stratification is not due to the lack of talent amongst young females, but due to the lack of access to female role models. To this end, "remote mentoring" is an effective way to offer nation-wide personalized STEM mentoring to young females from all segments of the society. In this paper, we introduce MAGIC, an organization dedicated to mentoring young females in STEM through remote methods. We conduct a retrospective study of MAGIC's formative years and present our experience in remotely establishing 23 highly tailored mentor-mentee pairs. We provide several key findings on STEM remote mentoring, such as popular communication tools, frequently sought STEM skills among girls, and projects that could be accomplished through remote mentoring. Furthermore, we present key challenges faced by mentors and mentees, notable outcomes, and lessons learnt about remote mentoring.
Fractal structures in Adversarial Prediction
Venue: arXiv cs.LG 2013
Year: 2013

Fractals are self-similar recursive structures that have been used in modeling several real world processes. In this work we study how "fractal-like" processes arise in a prediction game where an adversary is generating a sequence of bits and an algorithm is trying to predict them. We will see that under a certain formalization of the predictive payoff for the algorithm it is most optimal for the adversary to produce a fractal-like sequence to minimize the algorithm's ability to predict. Indeed it has been suggested before that financial markets exhibit a fractal-like behavior. We prove that a fractal-like distribution arises naturally out of an optimization from the adversary's perspective. In addition, we give optimal trade-offs between predictability and expected deviation (i.e. sum of bits) for our formalization of predictive payoff. This result is motivated by the observation that several time series data exhibit higher deviations than expected for a completely random walk.
Novel Network Coding-based Techniques for Multi-layer Video Delivery over Multi-hop Wireless testbed
Venue: arXiv cs.NI 2013
Year: 2013

The actual performance of video delivery over wireless networks is best captured through experimental studies over real testbed, which is crucial to uncover the technical problems and practical challenges of realizing the proposed video delivery techniques. Network coding (NC) provides a paradigm shift to optimize the network resources for enhancing the video quality as a result of delivery over multi-hop wireless networks. In this paper, we propose a novel framework for evaluating the performance of NC-based video delivery over multi-hop wireless networks. Based on an experimental testbed implementation of inter-layer video coding and delivery, we propose a new generalized technique for multi-hop interlayer NC in enhancing video delivery quality. We evaluate the trade-off between complexity of multi-hop NC, and video quality gain. We provide clear recommendations for best design choices of NC-based inter-layer video coding and delivery over multi-hop wireless mesh network considering link conditions and Packet Delivery Ratio (PDR). The results show that using NC improves the layered video performance in single and multi-hop network. The average number of decoded layers when using NC in single hop outperforms that when using NC in multi-hop.
Counting small cuts in a graph
Venue: arXiv cs.DS 2013
Year: 2013

We study the minimum cut problem in the presence of uncertainty and show how to apply a novel robust optimization approach, which aims to exploit the similarity in subsequent graph measurements or similar graph instances, without posing any assumptions on the way they have been obtained. With experiments we show that the approach works well when compared to other approaches that are also oblivious towards the relationship between the input datasets.
Ranking and Tradeoffs in Sponsored Search Auctions
Venue: arXiv cs.GT 2013
Year: 2013

In a sponsored search auction, decisions about how to rank ads impose tradeoffs between objectives such as revenue and welfare. In this paper, we examine how these tradeoffs should be made. We begin by arguing that the most natural solution concept to evaluate these tradeoffs is the lowest symmetric Nash equilibrium (SNE). As part of this argument, we generalise the well known connection between the lowest SNE and the VCG outcome. We then propose a new ranking algorithm, loosely based on the revenue-optimal auction, that uses a reserve price to order the ads (not just to filter them) and give conditions under which it raises more revenue than simply applying that reserve price. Finally, we conduct extensive simulations examining the tradeoffs enabled by different ranking algorithms and show that our proposed algorithm enables superior operating points by a variety of metrics.
Optimised hybrid parallelisation of a CFD code on Many Core architectures
Venue: arXiv cs.DC 2013
Year: 2013

COSA is a novel CFD system based on the compressible Navier-Stokes model for unsteady aerodynamics and aeroelasticity of fixed structures, rotary wings and turbomachinery blades. It includes a steady, time domain, and harmonic balance flow solver. COSA has primarily been parallelised using MPI, but there is also a hybrid parallelisation that adds OpenMP functionality to the MPI parallelisation to enable larger number of cores to be utilised for a given simulation as the MPI parallelisation is limited to the number of geometric partitions (or blocks) in the simulation, or to exploit multi-threaded hardware where appropriate. This paper outlines the work undertaken to optimise these two parallelisation strategies, improving the efficiency of both and therefore reducing the computational time required to compute simulations. We also analyse the power consumption of the code on a range of leading HPC systems to further understand the performance of the code.
Markovian models for one dimensional structure estimation on heavily noisy imagery
Venue: arXiv cs.CV 2013
Year: 2013

Radar (SAR) images often exhibit profound appearance variations due to a variety of factors including clutter noise produced by the coherent nature of the illumination. Ultrasound images and infrared images have similar cluttered appearance, that make 1 dimensional structures, as edges and object boundaries difficult to locate. Structure information is usually extracted in two steps: first, building and edge strength mask classifying pixels as edge points by hypothesis testing, and secondly estimating from that mask, pixel wide connected edges. With constant false alarm rate (CFAR) edge strength detectors for speckle clutter, the image needs to be scanned by a sliding window composed of several differently oriented splitting sub-windows. The accuracy of edge location for these ratio detectors depends strongly on the orientation of the sub-windows. In this work we propose to transform the edge strength detection problem into a binary segmentation problem in the undecimated wavelet domain, solvable using parallel 1d Hidden Markov Models. For general dependency models, exact estimation of the state map becomes computationally complex, but in our model, exact MAP is feasible. The effectiveness of our approach is demonstrated on simulated noisy real-life natural images with available ground truth, while the strength of our output edge map is measured with Pratt's, Baddeley an Kappa proficiency measures. Finally, analysis and experiments on three different types of SAR images, with different polarizations, resolutions and textures, illustrate that the proposed method can detect structure on SAR images effectively, providing a very good start point for active contour methods.
A Dynamic Axiomatic Approach to First-Price Auctions
Venue: arXiv cs.GT 2013
Year: 2013

The first-price auction is popular in practice for its simplicity and transparency. Moreover, its potential virtues grow in complex settings where incentive compatible auctions may generate little or no revenue. Unfortunately, the first-price auction is poorly understood in theory because equilibrium is not {\em a priori} a credible predictor of bidder behavior. We take a dynamic approach to studying first-price auctions: rather than basing performance guarantees solely on static equilibria, we study the repeated setting and show that robust performance guarantees may be derived from simple axioms of bidder behavior. For example, as long as a loser raises her bid quickly, a standard first-price auction will generate at least as much revenue as a second-price auction. We generalize this dynamic technique to complex pay-your-bid auction settings and show that progressively stronger assumptions about bidder behavior imply progressively stronger guarantees about the auction's performance. Along the way, we find that the auctioneer's choice of bidding language is critical when generalizing beyond the single-item setting, and we propose a specific construction called the {\em utility-target auction} that performs well. The utility-target auction includes a bidder's final utility as an additional parameter, identifying the single dimension along which she wishes to compete. This auction is closely related to profit-target bidding in first-price and ascending proxy package auctions and gives strong revenue guarantees for a variety of complex auction environments. Of particular interest, the guaranteed existence of a pure-strategy equilibrium in the utility-target auction shows how Overture might have eliminated the cyclic behavior in their generalized first-price sponsored search auction if bidders could have placed more sophisticated bids.
Challenges on Probabilistic Modeling for Evolving Networks
Venue: arXiv cs.SI 2013
Year: 2013

With the emerging of new networks, such as wireless sensor networks, vehicle networks, P2P networks, cloud computing, mobile Internet, or social networks, the network dynamics and complexity expands from system design, hardware, software, protocols, structures, integration, evolution, application, even to business goals. Thus the dynamics and uncertainty are unavoidable characteristics, which come from the regular network evolution and unexpected hardware defects, unavoidable software errors, incomplete management information and dependency relationship between the entities among the emerging complex networks. Due to the complexity of emerging networks, it is not always possible to build precise models in modeling and optimization (local and global) for networks. This paper presents a survey on probabilistic modeling for evolving networks and identifies the new challenges which emerge on the probabilistic models and optimization strategies in the potential application areas of network performance, network management and network security for evolving networks.