OpenRaiser/Intern-Atlas huggingface dataset:
Convex Structured Controller Design
Venue: arXiv cs.SY 2013
Year: 2013

We consider the problem of synthesizing optimal linear feedback policies subject to arbitrary convex constraints on the feedback matrix. This is known to be a hard problem in the usual formulations ($\Htwo,\Hinf,\LQR$) and previous works have focused on characterizing classes of structural constraints that allow efficient solution through convex optimization or dynamic programming techniques. In this paper, we propose a new control objective and show that this formulation makes the problem of computing optimal linear feedback matrices convex under arbitrary convex constraints on the feedback matrix. This allows us to solve problems in decentralized control (sparsity in the feedback matrices), control with delays and variable impedance control. Although the control objective is nonstandard, we present theoretical and empirical evidence that it agrees well with standard notions of control. We also present an extension to nonlinear control affine systems. We present numerical experiments validating our approach.
Maximizing Utility Among Selfish Users in Social Groups
Venue: arXiv cs.NI 2013
Year: 2013

We consider the problem of a social group of users trying to obtain a "universe" of files, first from a server and then via exchange amongst themselves. We consider the selfish file-exchange paradigm of give-and-take, whereby two users can exchange files only if each has something unique to offer the other. We are interested in maximizing the number of users who can obtain the universe through a schedule of file-exchanges. We first present a practical paradigm of file acquisition. We then present an algorithm which ensures that at least half the users obtain the universe with high probability for $n$ files and $m=O(\log n)$ users when $n\rightarrow\infty$, thereby showing an approximation ratio of 2. Extending these ideas, we show a $1+ε_1$ - approximation algorithm for $m=O(n)$, $ε_1>0$ and a $(1+z)/2 +ε_2$ - approximation algorithm for $m=O(n^z)$, $z>1$, $ε_2>0$. Finally, we show that for any $m=O(e^{o(n)})$, there exists a schedule of file exchanges which ensures that at least half the users obtain the universe.
Bibliometric-enhanced Retrieval Models for Big Scholarly Information Systems
Venue: arXiv cs.DL 2013
Year: 2013

Bibliometric techniques are not yet widely used to enhance retrieval processes in digital libraries, although they offer value-added effects for users. In this paper we will explore how statistical modelling of scholarship, such as Bradfordizing or network analysis of coauthorship network, can improve retrieval services for specific communities, as well as for large, cross-domain large collections. This paper aims to raise awareness of the missing link between information retrieval (IR) and bibliometrics / scientometrics and to create a common ground for the incorporation of bibliometric-enhanced services into retrieval at the digital library interface.
Gradient-Domain Processing for Large EM Image Stacks
Venue: arXiv cs.GR 2013
Year: 2013

We propose a new gradient-domain technique for processing registered EM image stacks to remove the inter-image discontinuities while preserving intra-image detail. To this end, we process the image stack by first performing anisotropic diffusion to smooth the data along the slice axis and then solving a screened-Poisson equation within each slice to re-introduce the detail. The final image stack is both continuous across the slice axis (facilitating the tracking of information between slices) and maintains sharp details within each slice (supporting automatic feature detection). To support this editing, we describe the implementation of the first multigrid solver designed for efficient gradient domain processing of large, out-of-core, voxel grids.
Covering sets for limited-magnitude errors
Venue: arXiv cs.IT 2013
Year: 2013

For a set $\cM=\{-μ,-μ+1,\ldots, λ\}\setminus\{0\}$ with non-negative integers $λ,μ<q$ not both 0, a subset $\cS$ of the residue class ring $\Z_q$ modulo an integer $q\ge 1$ is called a $(λ,μ;q)$-\emph{covering set} if $$ \cM \cS=\{ms \bmod q : m\in \cM,\ s\in \cS\}=\Z_q. $$ Small covering sets play an important role in codes correcting limited-magnitude errors. We give an explicit construction of a $(λ,μ;q)$-covering set $\cS$ which is of the size $q^{1 + o(1)}\max\{λ,μ\}^{-1/2}$ for almost all integers $q\ge 1$ and of optimal size $p\max\{λ,μ\}^{-1}$ if $q=p$ is prime. Furthermore, using a bound on the fourth moment of character sums of Cochrane and Shi we prove the bound $$ω_{λ,μ}(q)\le q^{1+o(1)}\max\{λ,μ\}^{-1/2},$$ for any integer $q\ge 1$, however the proof of this bound is not constructive.
The 4-error linear complexity distribution for $2^n$-periodic binary sequences
Venue: arXiv cs.CR 2013
Year: 2013

By using the sieve method of combinatorics, we study $k$-error linear complexity distribution of $2^n$-periodic binary sequences based on Games-Chan algorithm. For $k=4,5$, the complete counting functions on the $k$-error linear complexity of $2^n$-periodic balanced binary sequences (with linear complexity less than $2^n$) are presented. As a consequence of the result, the complete counting functions on the 4-error linear complexity of $2^n$-periodic binary sequences (with linear complexity $2^n$ or less than $2^n$) are obvious. Generally, the complete counting functions on the $k$-error linear complexity of $2^n$-periodic binary sequences can be obtained with a similar approach.
Cross-Recurrence Quantification Analysis of Categorical and Continuous Time Series: an R package
Venue: arXiv cs.CL 2013
Year: 2013

This paper describes the R package crqa to perform cross-recurrence quantification analysis of two time series of either a categorical or continuous nature. Streams of behavioral information, from eye movements to linguistic elements, unfold over time. When two people interact, such as in conversation, they often adapt to each other, leading these behavioral levels to exhibit recurrent states. In dialogue, for example, interlocutors adapt to each other by exchanging interactive cues: smiles, nods, gestures, choice of words, and so on. In order for us to capture closely the goings-on of dynamic interaction, and uncover the extent of coupling between two individuals, we need to quantify how much recurrence is taking place at these levels. Methods available in crqa would allow researchers in cognitive science to pose such questions as how much are two people recurrent at some level of analysis, what is the characteristic lag time for one person to maximally match another, or whether one person is leading another. First, we set the theoretical ground to understand the difference between 'correlation' and 'co-visitation' when comparing two time series, using an aggregative or cross-recurrence approach. Then, we describe more formally the principles of cross-recurrence, and show with the current package how to carry out analyses applying them. We end the paper by comparing computational efficiency, and results' consistency, of crqa R package, with the benchmark MATLAB toolbox crptoolbox. We show perfect comparability between the two libraries on both levels.
Protein Threading Based on Nonlinear Integer Programming
Venue: arXiv cs.DS 2013
Year: 2013

Protein threading is a method of computational protein structure prediction used for protein sequences which have the same fold as proteins of known structures but do not have homologous proteins with known structure. The most popular algorithm is based on linear integer programming. In this paper, we consider methods based on nonlinear integer programming. Actually, the existing linear integer programming is directly linearized from the original quadratic integer programming. We then develop corresponding efficient algorithms.
Modeling Information Diffusion in Online Social Networks with Partial Differential Equations
Venue: arXiv cs.SI 2013
Year: 2013

Online social networks such as Twitter and Facebook have gained tremendous popularity for information exchange. The availability of unprecedented amounts of digital data has accelerated research on information diffusion in online social networks. However, the mechanism of information spreading in online social networks remains elusive due to the complexity of social interactions and rapid change of online social networks. Much of prior work on information diffusion over online social networks has based on empirical and statistical approaches. The majority of dynamical models arising from information diffusion over online social networks involve ordinary differential equations which only depend on time. In a number of recent papers, the authors propose to use partial differential equations(PDEs) to characterize temporal and spatial patterns of information diffusion over online social networks. Built on intuitive cyber-distances such as friendship hops in online social networks, the reaction-diffusion equations take into account influences from various external out-of-network sources, such as the mainstream media, and provide a new analytic framework to study the interplay of structural and topical influences on information diffusion over online social networks. In this survey, we discuss a number of PDE-based models that are validated with real datasets collected from popular online social networks such as Digg and Twitter. Some new developments including the conservation law of information flow in online social networks and information propagation speeds based on traveling wave solutions are presented to solidify the foundation of the PDE models and highlight the new opportunities and challenges for mathematicians as well as computer scientists and researchers in online social networks.
Criminal Geographical Profiling: Using FCA for Visualization and Analysis of Crime Data
Venue: arXiv cs.CY 2013
Year: 2013

Fighting criminal activities in our modern societies required the engagement of intelligent information systems that can analyze crime data geographically and enable new concepts to be deduced from it. These information systems should be able to create visualization of such data as well as have the capability of giving new incite of information, if data is updated whilst maintaining the previously predicted patterns. This paper proposed the use of Formal Concept Analysis, or Galois Lattices, a data analysis technique grounded on Lattice Theory and Propositional Calculus, for the visualization and analysis of crime data. This method considered the set of common and distinct attributes of crimes in such a way that categorization are done based on related crime types, geographical locations and the persons involved.
Phase Retrieval for Sparse Signals
Venue: arXiv cs.IT 2013
Year: 2013

The aim of this paper is to build up the theoretical framework for the recovery of sparse signals from the magnitude of the measurement. We first investigate the minimal number of measurements for the success of the recovery of sparse signals without the phase information. We completely settle the minimality question for the real case and give a lower bound for the complex case. We then study the recovery performance of the $\ell_1$ minimization. In particular, we present the null space property which, to our knowledge, is the first sufficient and necessary condition for the success of $\ell_1$ minimization for $k$-sparse phase retrievable.
Event-triggered transmission for linear control over communication channels
Venue: arXiv cs.SY 2013
Year: 2013

We consider an exponentially stable closed loop interconnection of a continuous linear plant and a continuous linear controller, and we study the problem of interconnecting the plant output to the controller input through a digital channel. We propose a family of "transmission-lazy" sensors whose goal is to transmit the measured plant output information as little as possible while preserving closed-loop stability. In particular, we propose two transmission policies, providing conditions on the transmission parameters. These guarantee global asymptotic stability when the plant state is available or when an estimate of the state is available (provided by a classical continuous linear observer). Moreover, under a specific condition, they guarantee global exponential stability
Efficient Search (RES) for One-Hop Destination over Wireless Sensor Network
Venue: arXiv cs.NI 2013
Year: 2013

The revolution of wireless sensors networks (WSNs) has highly augmented the expectations of people to get the work done efficiently, but there is little bit impediment to deal with deployed nodes in WSNs. The nature of used routing and medium access control (MAC) protocols in WSNs is completely different from wireless adhoc network protocols. Sensor nodes do not have enough capability to synchronize with robust way, in resulting causes of longer delay and waste of energy. In this paper, we deploy efficientenergy consuming sensors and to find one hop robust and efficient destination search in WSNs. We firstly deploy BT (Bluetooth enabled) sensors, which offer passive and active sensing capability to save energy. This work is a continuation of previous published work in [2]. The BT node is supported with efficient searchmethodss. The main objective of this contribution is to control different types of objects from remote places using cellular phone. To validate our proposed methodology,simulation is done with network simulator (ns2) to examine the behavior of WSNs. Based on simulation results, we claim that our approach saves 62% energy spent for finding best one- hop destination as compared with existing techniques.
Full-Rank Perfect Codes over Finite Fields
Venue: arXiv cs.IT 2013
Year: 2013

In this paper, we propose a construction of full-rank q-ary 1-perfect codes over finite fields. This construction is a generalization of the Etzion and Vardy construction of full-rank binary 1-perfect codes (1994). Properties of i-components of q-ary Hamming codes are investigated and the construction of full-rank q-ary 1-perfect codes is based on these properties. The switching construction of 1-perfect codes are generalized for the q-ary case. We give a generalization of the concept of i-component of 1-perfect codes and introduce the concept of (i,σ)-components of q-ary 1-perfect codes. We also present a generalization of the Lindström and Schönheim construction of q-ary 1-perfect codes and provide a lower bound on the number of pairwise distinct q-ary 1-perfect codes of length n.
Compression of animated 3D models using HO-SVD
Venue: arXiv cs.GR 2013
Year: 2013

This work presents an analysis of Higher Order Singular Value Decomposition (HO-SVD) applied to lossy compression of 3D mesh animations. We describe strategies for choosing a number of preserved spatial and temporal components after tensor decomposition. Compression error is measured using three metrics (MSE, Hausdorff, MSDM). Results are compared with a method based on Principal Component Analysis (PCA) and presented on a set of animations with typical mesh deformations.
The Generalized Degrees of Freedom of the Interference Relay Channel with Strong Interference
Venue: arXiv cs.IT 2013
Year: 2013

The interference relay channel (IRC) under strong interference is considered. A high-signal-to-noise ratio (SNR) generalized degrees of freedom (GDoF) characterization of the capacity is obtained. To this end, a new GDoF upper bound is derived based on a genie-aided approach. The achievability of the GDoF is based on cooperative interference neutralization. It turns out that the relay increases the GDoF even if the relay-destination link is weak. Moreover, in contrast to the standard interference channel, the GDoF is not a monotonically increasing function of the interference strength in the strong interference regime.
Stability and Delay Analysis of EPON Registration Protocol
Venue: arXiv cs.NI 2013
Year: 2013

The Ethernet passive optical network (EPON) has recently emerged as the mainstream of broadband access networks. The registration process of EPON, defined by the IEEE 802.3av standard, is a multi-point control protocol (MPCP) within the media access control (MAC) layer. As with other contention-based channel access methods, such as ALOHA and CSMA, stability and delay are critical issues concerning the performances of implementing the protocol on systems with finite channel capacity. In this paper, the registration process of an EPON subscriber, called optical network units (ONUs), is modeled as a discrete-time Markov chain, from which we derive the fundamental throughput equation of EPON that characterizes the registration processes. The solutions of this characteristic equation depend on the maximum waiting time. The aim of our stability analysis is to pinpoint the region of the maximum waiting time that can guarantee a stable registration throughput and a bounded registration delay. For a maximum waiting time selected from the stable region, we obtain the expression of registration delay experienced by an ONU attempting to register. All analytic results presented in this paper were verified by simulations.
Complexity Analysis in Presence of Control Operators and Higher-Order Functions (Long Version)
Venue: arXiv cs.LO 2013
Year: 2013

A polarized version of Girard, Scedrov and Scott's Bounded Linear Logic is introduced and its normalization properties studied. Following Laurent, the logic naturally gives rise to a type system for the lambda-mu-calculus, whose derivations reveal bounds on the time complexity of the underlying term. This is the first example of a type system for the lambda-mu-calculus guaranteeing time complexity bounds for typable programs.
A Fast Hadamard Transform for Signals with Sub-linear Sparsity in the Transform Domain
Venue: arXiv cs.IT 2013
Year: 2013

A new iterative low complexity algorithm has been presented for computing the Walsh-Hadamard transform (WHT) of an $N$ dimensional signal with a $K$-sparse WHT, where $N$ is a power of two and $K = O(N^α)$, scales sub-linearly in $N$ for some $0 < α< 1$. Assuming a random support model for the non-zero transform domain components, the algorithm reconstructs the WHT of the signal with a sample complexity $O(K \log_2(\frac{N}{K}))$, a computational complexity $O(K\log_2(K)\log_2(\frac{N}{K}))$ and with a very high probability asymptotically tending to 1. The approach is based on the subsampling (aliasing) property of the WHT, where by a carefully designed subsampling of the time domain signal, one can induce a suitable aliasing pattern in the transform domain. By treating the aliasing patterns as parity-check constraints and borrowing ideas from erasure correcting sparse-graph codes, the recovery of the non-zero spectral values has been formulated as a belief propagation (BP) algorithm (peeling decoding) over a sparse-graph code for the binary erasure channel (BEC). Tools from coding theory are used to analyze the asymptotic performance of the algorithm in the very sparse ($α\in(0,\frac{1}{3}]$) and the less sparse ($α\in(\frac{1}{3},1)$) regime.
A Game-Theoretic Approach to Energy Trading in the Smart Grid
Venue: arXiv cs.GT 2013
Year: 2013

Electric storage units constitute a key element in the emerging smart grid system. In this paper, the interactions and energy trading decisions of a number of geographically distributed storage units are studied using a novel framework based on game theory. In particular, a noncooperative game is formulated between storage units, such as PHEVs, or an array of batteries that are trading their stored energy. Here, each storage unit's owner can decide on the maximum amount of energy to sell in a local market so as to maximize a utility that reflects the tradeoff between the revenues from energy trading and the accompanying costs. Then in this energy exchange market between the storage units and the smart grid elements, the price at which energy is traded is determined via an auction mechanism. The game is shown to admit at least one Nash equilibrium and a novel proposed algorithm that is guaranteed to reach such an equilibrium point is proposed. Simulation results show that the proposed approach yields significant performance improvements, in terms of the average utility per storage unit, reaching up to 130.2% compared to a conventional greedy approach.
Bayesian Optimization With Censored Response Data
Venue: arXiv cs.AI 2013
Year: 2013

Bayesian optimization (BO) aims to minimize a given blackbox function using a model that is updated whenever new evidence about the function becomes available. Here, we address the problem of BO under partially right-censored response data, where in some evaluations we only obtain a lower bound on the function value. The ability to handle such response data allows us to adaptively censor costly function evaluations in minimization problems where the cost of a function evaluation corresponds to the function value. One important application giving rise to such censored data is the runtime-minimizing variant of the algorithm configuration problem: finding settings of a given parametric algorithm that minimize the runtime required for solving problem instances from a given distribution. We demonstrate that terminating slow algorithm runs prematurely and handling the resulting right-censored observations can substantially improve the state of the art in model-based algorithm configuration.
Least Squares Revisited: Scalable Approaches for Multi-class Prediction
Venue: arXiv cs.LG 2013
Year: 2013

This work provides simple algorithms for multi-class (and multi-label) prediction in settings where both the number of examples n and the data dimension d are relatively large. These robust and parameter free algorithms are essentially iterative least-squares updates and very versatile both in theory and in practice. On the theoretical front, we present several variants with convergence guarantees. Owing to their effective use of second-order structure, these algorithms are substantially better than first-order methods in many practical scenarios. On the empirical side, we present a scalable stagewise variant of our approach, which achieves dramatic computational speedups over popular optimization packages such as Liblinear and Vowpal Wabbit on standard datasets (MNIST and CIFAR-10), while attaining state-of-the-art accuracies.
Fast Multi-Instance Multi-Label Learning
Venue: arXiv cs.LG 2013
Year: 2013

In many real-world tasks, particularly those involving data objects with complicated semantics such as images and texts, one object can be represented by multiple instances and simultaneously be associated with multiple labels. Such tasks can be formulated as multi-instance multi-label learning (MIML) problems, and have been extensively studied during the past few years. Existing MIML approaches have been found useful in many applications; however, most of them can only handle moderate-sized data. To efficiently handle large data sets, in this paper we propose the MIMLfast approach, which first constructs a low-dimensional subspace shared by all labels, and then trains label specific linear models to optimize approximated ranking loss via stochastic gradient descent. Although the MIML problem is complicated, MIMLfast is able to achieve excellent performance by exploiting label relations with shared space and discovering sub-concepts for complicated labels. Experiments show that the performance of MIMLfast is highly competitive to state-of-the-art techniques, whereas its time cost is much less; particularly, on a data set with 20K bags and 180K instances, MIMLfast is more than 100 times faster than existing MIML approaches. On a larger data set where none of existing approaches can return results in 24 hours, MIMLfast takes only 12 minutes. Moreover, our approach is able to identify the most representative instance for each label, and thus providing a chance to understand the relation between input patterns and output label semantics.