OpenRaiser/Intern-Atlas huggingface dataset:
Neighborhood Preserved Sparse Representation for Robust Classification on Symmetric Positive Definite Matrices
Venue: arXiv cs.CV 2016
Year: 2016

Due to its promising classification performance, sparse representation based classification(SRC) algorithm has attracted great attention in the past few years. However, the existing SRC type methods apply only to vector data in Euclidean space. As such, there is still no satisfactory approach to conduct classification task for symmetric positive definite (SPD) matrices which is very useful in computer vision. To address this problem, in this paper, a neighborhood preserved kernel SRC method is proposed on SPD manifolds. Specifically, by embedding the SPD matrices into a Reproducing Kernel Hilbert Space (RKHS), the proposed method can perform classification on SPD manifolds through an appropriate Log-Euclidean kernel. Through exploiting the geodesic distance between SPD matrices, our method can effectively characterize the intrinsic local Riemannian geometry within data so as to well unravel the underlying sub-manifold structure. Despite its simplicity, experimental results on several famous database demonstrate that the proposed method achieves better classification results than the state-of-the-art approaches.
CoVer-ability: Consistent Versioning for Concurrent Objects
Venue: arXiv cs.DC 2016
Year: 2016

An object type characterizes the domain space and the operations that can be invoked on an object of that type. In this paper we introduce a new property for concurrent objects, we call coverability, that aims to provide precise guarantees on the consistent evolution of an object. This new property is suitable for a variety of distributed objects including concurrent file objects that demand operations to manipulate the latest version of the object. We propose two levels of coverability: (i) strong coverability and (ii) weak coverability. Strong coverability requires that only a single operation can modify the latest version of the object, i.e. "covers" the latest version with a new version, imposing a total order on object modifications. Weak coverability relaxes the strong requirements of strong coverability and allows multiple operations to modify the same version of an object, where each modification leads to a different version. Weak coverability preserves consistent evolution of the object, by demanding any subsequent operation to only modify one of the newly introduced versions. Coverability combined with atomic guarantees yield to coverable atomic read/write registers. We also show that strongly coverable atomic registers are equivalent in power to consensus. Thus, we focus on weakly coverable registers, and we demonstrate their importance by showing that they cannot be implemented using similar types of registers, like ranked-registers. Furthermore we show that weakly coverable registers may be used to implement basic (weak) read-modify-write and file objects. Finally, we implement weakly coverable registers by modifying an existing MWMR atomic register implementation.
Monitoring in the Clouds: Comparison of ECO2Clouds and EXCESS Monitoring Approaches
Venue: arXiv cs.DC 2016
Year: 2016

With the increasing adoption of private cloud infrastructures by providers and enterprises, the monitoring of these infrastructures is becoming crucial. The rationale behind monitoring is manifold: reasons include saving energy, lowering costs, and better maintenance. In the e-Science sector, moreover, the collection of infrastructure and application-specific data at high resolutions is immanent. In this paper, we present two monitoring approaches implemented throughout two European projects: ECO2Clouds and EXCESS. The ECO2Clouds project aims to minimize CO2 emissions caused by the execution of applications on the cloud infrastructure. In order to allow for eco-aware deployment and scheduling of applications, the ECO2Clouds monitoring framework provides the necessary set of metrics on different layers including physical, virtual and application layer. In turn, the EXCESS project introduces new energy-aware execution models that improve energy-efficiency on a software level. Having in-depth knowledge about the energy consumption and overall behavior of applications on a given infrastructure, subsequent executions can be optimized to save energy. To achieve this goal, the EXCESS monitoring framework provides APIs allowing developers to collect application-specific data in addition to infrastructure data at run-time. We perform a comparative analysis of both monitoring approaches, and highlighting use cases including a hybrid approach which benefits from both monitoring solutions.
Nmag micromagnetic simulation tool - software engineering lessons learned
Venue: arXiv cs.SE 2016
Year: 2016

We review design and development decisions and their impact for the open source code Nmag from a software engineering in computational science point of view. We summarise lessons learned and recommendations for future computational science projects. Key lessons include that encapsulating the simulation functionality in a library of a general purpose language, here Python, provides great flexibility in using the software. The choice of Python for the top-level user interface was very well received by users from the science and engineering community. The from-source installation in which required external libraries and dependencies are compiled from a tarball was remarkably robust. In places, the code is a lot more ambitious than necessary, which introduces unnecessary complexity and reduces main- tainability. Tests distributed with the package are useful, although more unit tests and continuous integration would have been desirable. The detailed documentation, together with a tutorial for the usage of the system, was perceived as one of its main strengths by the community.
Multi-Object Reasoning with Constrained Goal Models
Venue: arXiv cs.AI 2016
Year: 2016

Goal models have been widely used in Computer Science to represent software requirements, business objectives, and design qualities. Existing goal modelling techniques, however, have shown limitations of expressiveness and/or tractability in coping with complex real-world problems. In this work, we exploit advances in automated reasoning technologies, notably Satisfiability and Optimization Modulo Theories (SMT/OMT), and we propose and formalize: (i) an extended modelling language for goals, namely the Constrained Goal Model (CGM), which makes explicit the notion of goal refinement and of domain assumption, allows for expressing preferences between goals and refinements, and allows for associating numerical attributes to goals and refinements for defining constraints and optimization goals over multiple objective functions, refinements and their numerical attributes; (ii) a novel set of automated reasoning functionalities over CGMs, allowing for automatically generating suitable refinements of input CGMs, under user-specified assumptions and constraints, that also maximize preferences and optimize given objective functions. We have implemented these modelling and reasoning functionalities in a tool, named CGM-Tool, using the OMT solver OptiMathSAT as automated reasoning backend. Moreover, we have conducted an experimental evaluation on large CGMs to support the claim that our proposal scales well for goal models with thousands of elements.
Object-oriented Packet Caching for ICN
Venue: arXiv cs.NI 2016
Year: 2016

One of the most discussed features offered by Information-centric Networking (ICN) architectures is the ability to support packet-level caching at every node in the network. By individually naming each packet, ICN allows routers to turn their queueing buffers into packet caches, thus exploiting the network's existing storage resources. However, the performance of packet caching at commodity routers is restricted by the small capacity of their SRAM, which holds the index for the packets stored at the, slower, DRAM. We therefore propose Object-oriented Packet Caching (OPC), a novel caching scheme that overcomes the SRAM bottleneck, by combining object-level indexing in the SRAM with packet-level storage in the DRAM. We implemented OPC and experimentally evaluated it over various cache placement policies, showing that it can enhance the impact of ICN packet-level caching, reducing both network and server load.
Gondola: a Parametric Robot Infrastructure for Repeatable Mobile Experiments
Venue: arXiv cs.RO 2016
Year: 2016

When deploying a testbed infrastructure for Wireless Sensor Networks (WSNs), one of the most challenging feature is to provide repeatable mobility. Wheeled robots, usually employed for such tasks, strive to adapt to the wide range of environments where WSNs are deployed, from chaotic office spaces to potato fields in the farmland. For this reson, these robot systems often require expensive customization steps that, for example, adapt their localization and navigation system. To avoid these issues, in this paper we present the design of Gondola, a parametric robot infrastructure based on pulling wires, rather than wheels, that avoids the most common problems of wheeled robot and easily adapts to many WSN's scenarios. Different from wheeled robots, wich movements are constrained on a 2-dimensional plane, Gondola can easily move in 3-dimensional spaces with no need of a complex localization system and an accuracy that is comparable with off-the-shelf wheeled robots.
Unsupervised Learning in Neuromemristive Systems
Venue: arXiv cs.ET 2016
Year: 2016

Neuromemristive systems (NMSs) currently represent the most promising platform to achieve energy efficient neuro-inspired computation. However, since the research field is less than a decade old, there are still countless algorithms and design paradigms to be explored within these systems. One particular domain that remains to be fully investigated within NMSs is unsupervised learning. In this work, we explore the design of an NMS for unsupervised clustering, which is a critical element of several machine learning algorithms. Using a simple memristor crossbar architecture and learning rule, we are able to achieve performance which is on par with MATLAB's k-means clustering.
Equivalence of additive-combinatorial linear inequalities for Shannon entropy and differential entropy
Venue: arXiv cs.IT 2016
Year: 2016

This paper addresses the correspondence between linear inequalities of Shannon entropy and differential entropy for sums of independent group-valued random variables. We show that any balanced (with the sum of coefficients being zero) linear inequality of Shannon entropy holds if and only if its differential entropy counterpart also holds; moreover, any linear inequality for differential entropy must be balanced. In particular, our result shows that recently proved differential entropy inequalities by Kontoyiannis and Madiman \cite{KM14} can be deduced from their discrete counterparts due to Tao \cite{Tao10} in a unified manner. Generalizations to certain abelian groups are also obtained. Our proof of extending inequalities of Shannon entropy to differential entropy relies on a result of Rényi \cite{Renyi59} which relates the Shannon entropy of a finely discretized random variable to its differential entropy and also helps in establishing the entropy of the sum of quantized random variables is asymptotically equal to that of the quantized sum; the converse uses the asymptotics of the differential entropy of convolutions with weak additive noise.
Sufficiency on the Stock Market
Venue: arXiv cs.IT 2016
Year: 2016

It is well-known that there are a number of relations between theoretical finance theory and information theory. Some of these relations are exact and some are approximate. In this paper we will explore some of these relations and determine under which conditions the relations are exact. It turns out that portfolio theory always leads to Bregman divergences. The Bregman divergence is only proportional to information divergence in situations that are essentially equal to the type of gambling studied by Kelly. This can be related an abstract sufficiency condition.
Certified Context-Free Parsing: A formalisation of Valiant's Algorithm in Agda
Venue: arXiv cs.LO 2016
Year: 2016

Valiant (1975) has developed an algorithm for recognition of context free languages. As of today, it remains the algorithm with the best asymptotic complexity for this purpose. In this paper, we present an algebraic specification, implementation, and proof of correctness of a generalisation of Valiant's algorithm. The generalisation can be used for recognition, parsing or generic calculation of the transitive closure of upper triangular matrices. The proof is certified by the Agda proof assistant. The certification is representative of state-of-the-art methods for specification and proofs in proof assistants based on type-theory. As such, this paper can be read as a tutorial for the Agda system.
Topology Recognition and Leader Election in Colored Networks
Venue: arXiv cs.DC 2016
Year: 2016

Topology recognition and leader election are fundamental tasks in distributed computing in networks. The first of them requires each node to find a labeled isomorphic copy of the network, while the result of the second one consists in a single node adopting the label 1 (leader), with all other nodes adopting the label 0 and learning a path to the leader. We consider both these problems in networks whose nodes are equipped with not necessarily distinct labels called colors, and ports at each node of degree $d$ are arbitrarily numbered $0,1,\dots, d-1$. Colored networks are generalizations both of labeled networks and anonymous networks. In colored networks, topology recognition and leader election are not always feasible. Hence we study two more general problems. The aim of the problem TOP (resp. LE), for a colored network and for input $I$ given to its nodes, is to solve topology recognition (resp. leader election) in this network, if this is possible under input $I$, and to have all nodes answer "unsolvable" otherwise. We show that nodes of a network can solve problems TOP and LE, if they are given, as input $I$, an upper bound $k$ on the number of nodes of a given color, called the size of this color. On the other hand we show that, if the nodes are given an input that does not bound the size of any color, then the answer to TOP and LE must be "unsolvable", even for the class of rings. Under the assumption that nodes are given an upper bound $k$ on the size of a given color, we study the time of solving problems TOP and LE in the $LOCAL$. We give an algorithm to solve each of these problems in arbitrary $n$-node networks of diameter $D$ in time $O(kD+D\log(n/D))$. We also show that this time is optimal, by exhibiting classes of networks in which every algorithm solving problems TOP or LE must use time $Ω(kD+D\log(n/D))$.
Predicting Human Cooperation
Venue: arXiv cs.GT 2016
Year: 2016

The Prisoner's Dilemma has been a subject of extensive research due to its importance in understanding the ever-present tension between individual self-interest and social benefit. A strictly dominant strategy in a Prisoner's Dilemma (defection), when played by both players, is mutually harmful. Repetition of the Prisoner's Dilemma can give rise to cooperation as an equilibrium, but defection is as well, and this ambiguity is difficult to resolve. The numerous behavioral experiments investigating the Prisoner's Dilemma highlight that players often cooperate, but the level of cooperation varies significantly with the specifics of the experimental predicament. We present the first computational model of human behavior in repeated Prisoner's Dilemma games that unifies the diversity of experimental observations in a systematic and quantitatively reliable manner. Our model relies on data we integrated from many experiments, comprising 168,386 individual decisions. The computational model is composed of two pieces: the first predicts the first-period action using solely the structural game parameters, while the second predicts dynamic actions using both game parameters and history of play. Our model is extremely successful not merely at fitting the data, but in predicting behavior at multiple scales in experimental designs not used for calibration, using only information about the game structure. We demonstrate the power of our approach through a simulation analysis revealing how to best promote human cooperation.
Geo-distinctive Visual Element Matching for Location Estimation of Images
Venue: arXiv cs.MM 2016
Year: 2016

We propose an image representation and matching approach that substantially improves visual-based location estimation for images. The main novelty of the approach, called distinctive visual element matching (DVEM), is its use of representations that are specific to the query image whose location is being predicted. These representations are based on visual element clouds, which robustly capture the connection between the query and visual evidence from candidate locations. We then maximize the influence of visual elements that are geo-distinctive because they do not occur in images taken at many other locations. We carry out experiments and analysis for both geo-constrained and geo-unconstrained location estimation cases using two large-scale, publicly-available datasets: the San Francisco Landmark dataset with $1.06$ million street-view images and the MediaEval '15 Placing Task dataset with $5.6$ million geo-tagged images from Flickr. We present examples that illustrate the highly-transparent mechanics of the approach, which are based on common sense observations about the visual patterns in image collections. Our results show that the proposed method delivers a considerable performance improvement compared to the state of the art.
Discontinuous Galerkin methods on graphics processing units for nonlinear hyperbolic conservation laws
Venue: arXiv cs.DC 2016
Year: 2016

We present a novel implementation of the modal discontinuous Galerkin (DG) method for hyperbolic conservation laws in two dimensions on graphics processing units (GPUs) using NVIDIA's Compute Unified Device Architecture (CUDA). Both flexible and highly accurate, DG methods accommodate parallel architectures well as their discontinuous nature produces element-local approximations. High performance scientific computing suits GPUs well, as these powerful, massively parallel, cost-effective devices have recently included support for double-precision floating point numbers. Computed examples for Euler equations over unstructured triangle meshes demonstrate the effectiveness of our implementation on an NVIDIA GTX 580 device. Profiling of our method reveals performance comparable to an existing nodal DG-GPU implementation for linear problems.
A Biologically Inspired Model of Distributed Online Communication Supporting Efficient Search and Diffusion of Innovation
Venue: arXiv cs.SI 2016
Year: 2016

We inhabit a world that is not only small but supports efficient decentralized search - an individual using local information can establish a line of communication with another completely unknown individual. Here we augment a hierarchical social network model with communication between and within communities. We argue that organization into communities would decrease overall decentralized search times. We take inspiration from the biological immune system which organizes search for pathogens in a hybrid modular strategy. Our strategy has relevance in search for rare amounts of information in online social networks. Our work also has implications for design of efficient online networks that could have an impact on networks of human collaboration, scientific collaboration and networks used in targeted manhunts. Real world systems, like online social networks, have high associated delays for long-distance links, since they are built on top of physical networks. Such systems have been shown to densify. Hence such networks will have a communication cost due to space and the requirement of maintaining connections. We have incorporated such a non-spatial cost to communication. We introduce the notion of a community size that increases with the size of the system, which is shown to reduce the time to search for information in networks. Our final strategy balances search times and participation costs and is shown to decrease time to find information in decentralized search in online social networks. Our strategy also balances strong-ties and weak-ties over long distances and may ultimately lead to more productive and innovative networks of human communication and enterprise. We hope that this work will lay the foundation for strategies aimed at producing global scale human interaction networks that are sustainable and lead to a more networked, diverse and prosperous society.
Re-designing knowledge management systems : Towards user-centred design methods integrating information architecture
Venue: arXiv cs.HC 2016
Year: 2016

The work presented in this paper focuses on the improvement of corporate knowledge management systems. For the implementation of such systems, companies deploy can important means for small gains. Indeed, management services often notice very limited use compared to what they actually expect. We present a five-step redesigning approach which takes into account different factors to increase the use of these systems. We use as an example the knowledge sharing platform implemented for the employees of Soci{é}t{é} du Canal de Provence (SCP). This system was taken into production but very occasionally used. We describe the reasons for this limited use and we propose a design methodology adapted to the context. Promoting the effective use of the system, our approach has been experimented and evaluated with a panel of users working at SCP.
Analysis of Distributed Snapshot Algorithms
Venue: arXiv cs.DC 2016
Year: 2016

Snapshot recording durations at each process contribute to the overall efficiency of the algorithm. In this paper we are presenting the observed variations in snapshot recording durations at processes in a distributed system. We conclude with key characteristics of a reliable and effective snapshot algorithm. Simulations were achieved using SimGrid Java API.
Channel Pre-Inversion and max-SINR Vector Perturbation for Large-Scale Broadcast Channels
Venue: arXiv cs.IT 2016
Year: 2016

We study channel pre-inversion and vector perturbation (VP) schemes for large-scale broadcast channels, wherein a transmitter has $M$ transmit antennas and is transmitting to $K$ single-antenna non-cooperating receivers. We provide results which predict the capacity of MMSE pre-inversion as $K\rightarrow\infty$. We construct a new VP strategy, max-SINR vector perturbation (MSVP), which maximizes a sharp estimate of the signal-to-interference-plus-noise ratio. We provide results which predict the performance of MSVP and demonstrate that MSVP outperforms other VP methods. Lastly, we combine MSVP with the low-complexity Sorted QR Precoding method to show that MSVP has the potential to efficiently deliver data to a very large number of users at close to channel capacity.