OpenRaiser/Intern-Atlas huggingface dataset:
Distributed Estimation of a Parametric Field: Algorithms and Performance Analysis
Venue: arXiv cs.IT 2014
Year: 2014

This paper presents a distributed estimator for a deterministic parametric physical field sensed by a homogeneous sensor network and develops a new transformed expression for the Cramer-Rao lower bound (CRLB) on the variance of distributed estimates. The proposed transformation reduces a multidimensional integral representation of the CRLB to an expression involving an infinite sum. Stochastic models used in this paper assume additive noise in both the observation and transmission channels. Two cases of data transmission are considered. The first case assumes a linear analog modulation of raw observations prior to their transmission to a fusion center. In the second case, each sensor quantizes its observation to $M$ levels, and the quantized data are communicated to a fusion center. In both cases, parallel additive white Gaussian channels are assumed. The paper develops an iterative expectation-maximization (EM) algorithm to estimate unknown parameters of a parametric field, and its linearized version is adopted for numerical analysis. The performance of the developed numerical solution is compared to the performance of a simple iterative approach based on Newton's approximation. While the developed solution has a higher complexity than Newton's solution, it is more robust with respect to the choice of initial parameters and has a better estimation accuracy. Numerical examples are provided for the case of a field modeled as a Gaussian bell, and illustrate the advantages of using the transformed expression for the CRLB. However, the distributed estimator and the derived CRLB are general and can be applied to any parametric field. The dependence of the mean-square error (MSE) on the number of quantization levels, the number of sensors in the network and the SNR of the observation and transmission channels are analyzed. The variance of the estimates is compared to the derived CRLB.
The Unreasonable Fundamental Incertitudes Behind Bitcoin Mining
Venue: arXiv cs.CR 2014
Year: 2014

Bitcoin is a "crypto currency", a decentralized electronic payment scheme based on cryptography which has recently gained excessive popularity. Scientific research on bitcoin is less abundant. A paper at Financial Cryptography 2012 conference explains that it is a system which "uses no fancy cryptography", and is "by no means perfect". It depends on a well-known cryptographic standard SHA-256. In this paper we revisit the cryptographic process which allows one to make money by producing bitcoins. We reformulate this problem as a Constrained Input Small Output (CISO) hashing problem and reduce the problem to a pure block cipher problem. We estimate the speed of this process and we show that the cost of this process is less than it seems and it depends on a certain cryptographic constant which we estimated to be at most 1.86. These optimizations enable bitcoin miners to save tens of millions of dollars per year in electricity bills. Miners who set up mining operations face many economic incertitudes such as high volatility. In this paper we point out that there are fundamental incertitudes which depend very strongly on the bitcoin specification. The energy efficiency of bitcoin miners have already been improved by a factor of about 10,000, and we claim that further improvements are inevitable. Better technology is bound to be invented, would it be quantum miners. More importantly, the specification is likely to change. A major change have been proposed in May 2013 at Bitcoin conference in San Diego by Dan Kaminsky. However, any sort of change could be flatly rejected by the community which have heavily invested in mining with the current technology. Another question is the reward halving scheme in bitcoin. The current bitcoin specification mandates a strong 4-year cyclic property. We find this property totally unreasonable and harmful and explain why and how it needs to be changed.
Monadic second-order definable graph orderings
Venue: arXiv cs.LO 2014
Year: 2014

We study the question of whether, for a given class of finite graphs, one can define, for each graph of the class, a linear ordering in monadic second-order logic, possibly with the help of monadic parameters. We consider two variants of monadic second-order logic: one where we can only quantify over sets of vertices and one where we can also quantify over sets of edges. For several special cases, we present combinatorial characterisations of when such a linear ordering is definable. In some cases, for instance for graph classes that omit a fixed graph as a minor, the presented conditions are necessary and sufficient; in other cases, they are only necessary. Other graph classes we consider include complete bipartite graphs, split graphs, chordal graphs, and cographs. We prove that orderability is decidable for the so called HR-equational classes of graphs, which are described by equation systems and generalize the context-free languages.
Tunable and Growing Network Generation Model with Community Structures
Venue: arXiv cs.SI 2014
Year: 2014

Recent years have seen a growing interest in the modeling and simulation of social networks to understand several social phenomena. Two important classes of networks, small world and scale free networks have gained a lot of research interest. Another important characteristic of social networks is the presence of community structures. Many social processes such as information diffusion and disease epidemics depend on the presence of community structures making it an important property for network generation models to be incorporated. In this paper, we present a tunable and growing network generation model with small world and scale free properties as well as the presence of community structures. The major contribution of this model is that the communities thus created satisfy three important structural properties: connectivity within each community follows power-law, communities have high clustering coefficient and hierarchical community structures are present in the networks generated using the proposed model. Furthermore, the model is highly robust and capable of producing networks with a number of different topological characteristics varying clustering coefficient and inter-cluster edges. Our simulation results show that the model produces small world and scale free networks along with the presence of communities depicting real world societies and social networks.
Subspace Thresholding Pursuit: A Reconstruction Algorithm for Compressed Sensing
Venue: arXiv cs.IT 2014
Year: 2014

We propose a new iterative greedy algorithm for reconstructions of sparse signals with or without noisy perturbations in compressed sensing. The proposed algorithm, called \emph{subspace thresholding pursuit} (STP) in this paper, is a simple combination of subspace pursuit and iterative hard thresholding. Firstly, STP has the theoretical guarantee comparable to that of $\ell_1$ minimization in terms of restricted isometry property. Secondly, with a tuned parameter, on the one hand, when reconstructing Gaussian signals, it can outperform other state-of-the-art reconstruction algorithms greatly; on the other hand, when reconstructing constant amplitude signals with random signs, it can outperform other state-of-the-art iterative greedy algorithms and even outperform $\ell_1$ minimization if the undersampling ratio is not very large. In addition, we propose a simple but effective method to improve the empirical performance further if the undersampling ratio is large. Finally, it is showed that other iterative greedy algorithms can improve their empirical performance by borrowing the idea of STP.
How to Gamble Against All Odds
Venue: arXiv cs.GT 2014
Year: 2014

A decision maker observes the evolving state of the world while constantly trying to predict the next state given the history of past states. The ability to benefit from such predictions depends not only on the ability to recognize patters in history, but also on the range of actions available to the decision maker. We assume there are two possible states of the world. The decision maker is a gambler who has to bet a certain amount of money on the bits of an announced binary sequence of states. If he makes a correct prediction he wins his wager, otherwise he loses it. We compare the power of betting strategies (aka martingales) whose wagers take values in different sets of reals. A martingale whose wagers take values in a set $A$ is called an $A$-martingale. A set of reals $B$ anticipates a set $A$, if for every $A$-martingale there is a countable set of $B$-martingales, such that on every binary sequence on which the $A$-martingale gains an infinite amount at least one of the $B$-martingales gains an infinite amount, too. We show that for two important classes of pairs of sets $A$ and $B$, $B$ anticipates $A$ if and only if the closure of $B$ contains $rA$, for some positive $r$. One class is when $A$ is bounded and $B$ is bounded away from zero; the other class is when $B$ is well ordered (has no left-accumulation points). Our results generalize several recent results in algorithmic randomness and answer a question posed by Chalcraft et al. (2012).
Social Network Integration: Towards Constructing the Social Graph
Venue: arXiv cs.SI 2014
Year: 2014

In this work, we formulate the problem of social network integration. It takes multiple observed social networks as input and returns an integrated global social graph where each node corresponds to a real person. The key challenge for social network integration is to discover the correspondences or interlinks across different social networks. We engaged an in-depth analysis across three online social networks, AMiner, Linkedin, and Videolectures in order to address what reveals users' social identity, whether the social factors consistent across different social networks and how we can leverage these information to perform integration. We proposed a unified framework for the social network integration task. It crawls data from multiple social networks and further discovers accounts correspond to the same real person from the obtained networks. We use a probabilistic model to determine such correspondence, it incorporates features like the consistency of social status and social ties across different, as well as one-to-one mapping constraint and logical transitivity to jointly make the prediction. Empirical experiments verify the effectiveness of our method.
Are all Social Networks Structurally Similar? A Comparative Study using Network Statistics and Metrics
Venue: arXiv cs.SI 2014
Year: 2014

The modern age has seen an exponential growth of social network data available on the web. Analysis of these networks reveal important structural information about these networks in particular and about our societies in general. More often than not, analysis of these networks is concerned in identifying similarities among social networks and how they are different from other networks such as protein interaction networks, computer networks and food web. In this paper, our objective is to perform a critical analysis of different social networks using structural metrics in an effort to highlight their similarities and differences. We use five different social network datasets which are contextually and semantically different from each other. We then analyze these networks using a number of different network statistics and metrics. Our results show that although these social networks have been constructed from different contexts, they are structurally similar. We also review the snowball sampling method and show its vulnerability against different network metrics.
Protocol Design and Stability Analysis of Cooperative Cognitive Radio Users
Venue: arXiv cs.NI 2014
Year: 2014

A single cognitive radio transmitter--receiver pair shares the spectrum with two primary users communicating with their respective receivers. Each primary user has a local traffic queue, whereas the cognitive user has three queues; one storing its own traffic while the other two are relaying queues used to store primary relayed packets admitted from the two primary users. A new cooperative cognitive medium access control protocol for the described network is proposed, where the cognitive user exploits the idle periods of the primary spectrum bands. Traffic arrival to each relaying queue is controlled using a tuneable admittance factor, while relaying queues service scheduling is controlled via channel access probabilities assigned to each queue based on the band of operation. The stability region of the proposed protocol is characterized shedding light on its maximum expected throughput. Numerical results demonstrate the performance gains of the proposed cooperative cognitive protocol.
Recognizing Image Style
Venue: arXiv cs.CV 2014
Year: 2014

The style of an image plays a significant role in how it is viewed, but style has received little attention in computer vision research. We describe an approach to predicting style of images, and perform a thorough evaluation of different image features for these tasks. We find that features learned in a multi-layer network generally perform best -- even when trained with object class (not style) labels. Our large-scale learning methods results in the best published performance on an existing dataset of aesthetic ratings and photographic style annotations. We present two novel datasets: 80K Flickr photographs annotated with 20 curated style labels, and 85K paintings annotated with 25 style/genre labels. Our approach shows excellent classification performance on both datasets. We use the learned classifiers to extend traditional tag-based image search to consider stylistic constraints, and demonstrate cross-dataset understanding of style.
Sparse Identification of Posynomial Models
Venue: arXiv cs.SY 2014
Year: 2014

Posynomials are nonnegative combinations of monomials with possibly fractional and both positive and negative exponents. Posynomial models are widely used in various engineering design endeavors, such as circuits, aerospace and structural design, mainly due to the fact that design problems cast in terms of posynomial objectives and constraints can be solved efficiently by means of a convex optimization technique known as geometric programming (GP). However, while quite a vast literature exists on GP-based design, very few contributions can yet be found on the problem of identifying posynomial models from experimental data. Posynomial identification amounts to determining not only the coefficients of the combination, but also the exponents in the monomials, which renders the identification problem numerically hard. In this draft, we propose an approach to the identification of multivariate posynomial models, based on the expansion on a given large-scale basis of monomials. The model is then identified by seeking coefficients of the combination that minimize a mixed objective, composed by a term representing the fitting error and a term inducing sparsity in the representation, which results in a problem formulation of the ``square-root LASSO'' type, with nonnegativity constraints on the variables. We propose to solve the problem via a sequential coordinate-descent scheme, which is suitable for large-scale implementations.
Stochastic processes and feedback-linearisation for online identification and Bayesian adaptive control of fully-actuated mechanical systems
Venue: arXiv cs.LG 2014
Year: 2014

This work proposes a new method for simultaneous probabilistic identification and control of an observable, fully-actuated mechanical system. Identification is achieved by conditioning stochastic process priors on observations of configurations and noisy estimates of configuration derivatives. In contrast to previous work that has used stochastic processes for identification, we leverage the structural knowledge afforded by Lagrangian mechanics and learn the drift and control input matrix functions of the control-affine system separately. We utilise feedback-linearisation to reduce, in expectation, the uncertain nonlinear control problem to one that is easy to regulate in a desired manner. Thereby, our method combines the flexibility of nonparametric Bayesian learning with epistemological guarantees on the expected closed-loop trajectory. We illustrate our method in the context of torque-actuated pendula where the dynamics are learned with a combination of normal and log-normal processes.
Routing Diverse Evacuees with Cognitive Packets
Venue: arXiv cs.OH 2014
Year: 2014

This paper explores the idea of smart building evacuation when evacuees can belong to different categories with respect to their ability to move and their health conditions. This leads to new algorithms that use the Cognitive Packet Network concept to tailor different quality of service needs to different evacuees. These ideas are implemented in a simulated environment and evaluated with regard to their effectiveness.
Dynamic Partitioning-based JPEG Decompression on Heterogeneous Multicore Architectures
Venue: arXiv cs.DC 2014
Year: 2014

With the emergence of social networks and improvements in computational photography, billions of JPEG images are shared and viewed on a daily basis. Desktops, tablets and smartphones constitute the vast majority of hardware platforms used for displaying JPEG images. Despite the fact that these platforms are heterogeneous multicores, no approach exists yet that is capable of joining forces of a system's CPU and GPU for JPEG decoding. In this paper we introduce a novel JPEG decoding scheme for heterogeneous architectures consisting of a CPU and an OpenCL-programmable GPU. We employ an offline profiling step to determine the performance of a system's CPU and GPU with respect to JPEG decoding. For a given JPEG image, our performance model uses (1) the CPU and GPU performance characteristics, (2) the image entropy and (3) the width and height of the image to balance the JPEG decoding workload on the underlying hardware. Our run-time partitioning and scheduling scheme exploits task, data and pipeline parallelism by scheduling the non-parallelizable entropy decoding task on the CPU, whereas inverse cosine transformations (IDCTs), color conversions and upsampling are conducted on both the CPU and the GPU. Our kernels have been optimized for GPU memory hierarchies. We have implemented the proposed method in the context of the libjpeg-turbo library, which is an industrial-strength JPEG encoding and decoding engine. Libjpeg-turbo's hand-optimized SIMD routines for ARM and x86 constitute a competitive yardstick for the comparison to the proposed approach. Retro-fitting our method with libjpeg-turbo provided insights on the software-engineering aspects of re-engineering legacy code for heterogeneous multicores.
Loosening the notions of compliance and sub-behaviour in client/server systems
Venue: arXiv cs.LO 2014
Year: 2014

In the context of "session behaviors" for client/server systems, we propose a weakening of the compliance and sub-behaviour relations where the bias toward the client (whose "requests" must be satisfied) is pushed further with respect to the usual definitions, by admitting that "not needed" output actions from the server side can be "skipped" by the client. Both compliance and sub-behaviour relations resulting from this weakening remain decidable, though the proof of the duals-as-minima property for servers, on which the decidability of the sub-behaviour relation relies, requires a tighter analysis of client/server interactions.
Tipping Points in Schelling Segregation
Venue: arXiv cs.GT 2014
Year: 2014

One of the earliest agent-based economical models, Schelling's spacial proximity model illustrated how global segregation can emerge, often unwanted, from the actions of agents of two races acting in accordance with their individual local preferences. Here a 1-dimensional unperturbed variant of the model is studied, which is additionally open in the sense that agents may enter and exit the model. Following the authors' previous work in [1] and that of Brandt, Immorlica, Kamath, and Kleinberg in [2], rigorous results are established, whose statements are asymptotic in both the model and neighbourhood sizes. The current model's openness allows one race or the other to take over almost everywhere in a measure-theoretic sense. Tipping points are identified between the two regions of takeover and the region of staticity, in terms of the parameters of the model. In a significant generalization from previous work, the parameters comprise the initial proportions of the two races, along with independent values of the tolerance for each race.
A Primal-Dual Method for Training Recurrent Neural Networks Constrained by the Echo-State Property
Venue: arXiv cs.LG 2014
Year: 2014

We present an architecture of a recurrent neural network (RNN) with a fully-connected deep neural network (DNN) as its feature extractor. The RNN is equipped with both causal temporal prediction and non-causal look-ahead, via auto-regression (AR) and moving-average (MA), respectively. The focus of this paper is a primal-dual training method that formulates the learning of the RNN as a formal optimization problem with an inequality constraint that provides a sufficient condition for the stability of the network dynamics. Experimental results demonstrate the effectiveness of this new method, which achieves 18.86% phone recognition error on the TIMIT benchmark for the core test set. The result approaches the best result of 17.7%, which was obtained by using RNN with long short-term memory (LSTM). The results also show that the proposed primal-dual training method produces lower recognition errors than the popular RNN methods developed earlier based on the carefully tuned threshold parameter that heuristically prevents the gradient from exploding.
Improved approximation algorithm for Fault-Tolerant Facility Placement
Venue: arXiv cs.DS 2014
Year: 2014

We consider the Fault-Tolerant Facility Placement problem ($FTFP$), which is a generalization of the classical Uncapacitated Facility Location problem ($UFL$). In the $FTFP$ problem we have a set of clients $C$ and a set of facilities $F$. Each facility $i \in F$ can be opened many times. For each opening of facility $i$ we pay $f_i \geq 0$. Our goal is to connect each client $j \in C$ with $r_j \geq 1$ open facilities in a way that minimizes the total cost of open facilities and established connections. In a series of recent papers $FTFP$ was essentially reduced to $FTFL$ and then to $UFL$ showing it could be approximated with ratio $1.575$. In this paper we show that $FTFP$ can actually be approximated even better. We consider approximation ratio as a function of $r = min_{j \in C} r_j$ (minimum requirement of a client). With increasing $r$ the approximation ratio of our algorithm $λ_r$ converges to one. Furthermore, for $r > 1$ the value of $λ_r$ is less than 1.463 (hardness of approximation of $UFL$). We also show a lower bound of 1.278 for the approximability of the Fault-Tolerant Facility Location problem ($FTFL$) for arbitrary $r$. Already for $r > 3$ we obtain that $FTFP$ can be approximated with ratio 1.275, showing that under standard complexity theoretic assumptions $FTFP$ is strictly better approximable than $FTFL$.