OpenRaiser/Intern-Atlas huggingface dataset:
Automatic Sequences and Zip-Specifications
Venue: arXiv cs.LO 2012
Year: 2012

We consider infinite sequences of symbols, also known as streams, and the decidability question for equality of streams defined in a restricted format. This restricted format consists of prefixing a symbol at the head of a stream, of the stream function `zip', and recursion variables. Here `zip' interleaves the elements of two streams in alternating order, starting with the first stream. For example, the Thue-Morse sequence is obtained by the `zip-specification' {M = 0 : X, X = 1 : zip(X,Y), Y = 0 : zip(Y,X)}. Our analysis of such systems employs both term rewriting and coalgebraic techniques. We establish decidability for these zip-specifications, employing bisimilarity of observation graphs based on a suitably chosen cobasis. The importance of zip-specifications resides in their intimate connection with automatic sequences. We establish a new and simple characterization of automatic sequences. Thus we obtain for the binary zip that a stream is 2-automatic iff its observation graph using the cobasis (hd,even,odd) is finite. The generalization to zip-k specifications and their relation to k-automaticity is straightforward. In fact, zip-specifications can be perceived as a term rewriting syntax for automatic sequences. Our study of zip-specifications is placed in an even wider perspective by employing the observation graphs in a dynamic logic setting, leading to an alternative characterization of automatic sequences. We further obtain a natural extension of the class of automatic sequences, obtained by `zip-mix' specifications that use zips of different arities in one specification. We also show that equivalence is undecidable for a simple extension of the zip-mix format with projections like even and odd. However, it remains open whether zip-mix specifications have a decidable equivalence problem.
Perfect Mannheim, Lipschitz and Hurwitz weight codes
Venue: arXiv cs.IT 2012
Year: 2012

In this paper, upper bounds on codes over Gaussian integers, Lipschitz integers and Hurwitz integers with respect to Mannheim metric, Lipschitz and Hurwitz metric are given.
Computing Puiseux Series for Algebraic Surfaces
Venue: arXiv cs.SC 2012
Year: 2012

In this paper we outline an algorithmic approach to compute Puiseux series expansions for algebraic surfaces. The series expansions originate at the intersection of the surface with as many coordinate planes as the dimension of the surface. Our approach starts with a polyhedral method to compute cones of normal vectors to the Newton polytopes of the given polynomial system that defines the surface. If as many vectors in the cone as the dimension of the surface define an initial form system that has isolated solutions, then those vectors are potential tropisms for the initial term of the Puiseux series expansion. Our preliminary methods produce exact representations for solution sets of the cyclic $n$-roots problem, for $n = m^2$, corresponding to a result of Backelin.
Detecting Priming News Events
Venue: arXiv cs.DB 2012
Year: 2012

We study a problem of detecting priming events based on a time series index and an evolving document stream. We define a priming event as an event which triggers abnormal movements of the time series index, i.e., the Iraq war with respect to the president approval index of President Bush. Existing solutions either focus on organizing coherent keywords from a document stream into events or identifying correlated movements between keyword frequency trajectories and the time series index. In this paper, we tackle the problem in two major steps. (1) We identify the elements that form a priming event. The element identified is called influential topic which consists of a set of coherent keywords. And we extract them by looking at the correlation between keyword trajectories and the interested time series index at a global level. (2) We extract priming events by detecting and organizing the bursty influential topics at a micro level. We evaluate our algorithms on a real-world dataset and the result confirms that our method is able to discover the priming events effectively.
On Natural Genetic Engineering: Structural Dynamism in Random Boolean Networks
Venue: arXiv cs.CE 2012
Year: 2012

This short paper presents an abstract, tunable model of genomic structural change within the cell lifecycle and explores its use with simulated evolution. A well-known Boolean model of genetic regulatory networks is extended to include changes in node connectivity based upon the current cell state, e.g., via transposable elements. The underlying behaviour of the resulting dynamical networks is investigated before their evolvability is explored using a version of the NK model of fitness landscapes. Structural dynamism is found to be selected for in non-stationary environments and subsequently shown capable of providing a mechanism for evolutionary innovation when such reorganizations are inherited.
Contractive Interference Functions and Rates of Convergence of Distributed Power Control Laws
Venue: arXiv cs.IT 2012
Year: 2012

The standard interference functions introduced by Yates have been very influential on the analysis and design of distributed power control laws. While powerful and versatile, the framework has some drawbacks: the existence of fixed-points has to be established separately, and no guarantees are given on the rate of convergence of the iterates. This paper introduces contractive interference functions, a slight reformulation of the standard interference functions that guarantees the existence and uniqueness of fixed-points along with linear convergence of iterates. We show that many power control laws from the literature are contractive and derive, sometimes for the first time, analytical convergence rate estimates for these algorithms. We also prove that contractive interference functions converge when executed totally asynchronously and, under the assumption that the communication delay is bounded, derive an explicit bound on the convergence time penalty due to increased delay. Finally, we demonstrate that although standard interference functions are, in general, not contractive, they are all para-contractions with respect to a certain metric. Similar results for two-sided scalable interference functions are also derived.
A PCA-Based Super-Resolution Algorithm for Short Image Sequences
Venue: arXiv cs.CV 2012
Year: 2012

In this paper, we present a novel, learning-based, two-step super-resolution (SR) algorithm well suited to solve the specially demanding problem of obtaining SR estimates from short image sequences. The first step, devoted to increase the sampling rate of the incoming images, is performed by fitting linear combinations of functions generated from principal components (PC) to reproduce locally the sparse projected image data, and using these models to estimate image values at nodes of the high-resolution grid. PCs were obtained from local image patches sampled at sub-pixel level, which were generated in turn from a database of high-resolution images by application of a physically realistic observation model. Continuity between local image models is enforced by minimizing an adequate functional in the space of model coefficients. The second step, dealing with restoration, is performed by a linear filter with coefficients learned to restore residual interpolation artifacts in addition to low-resolution blurring, providing an effective coupling between both steps of the method. Results on a demanding five-image scanned sequence of graphics and text are presented, showing the excellent performance of the proposed method compared to several state-of-the-art two-step and Bayesian Maximum a Posteriori SR algorithms.
Combinatorial Modelling and Learning with Prediction Markets
Venue: arXiv cs.AI 2012
Year: 2012

Combining models in appropriate ways to achieve high performance is commonly seen in machine learning fields today. Although a large amount of combinatorial models have been created, little attention is drawn to the commons in different models and their connections. A general modelling technique is thus worth studying to understand model combination deeply and shed light on creating new models. Prediction markets show a promise of becoming such a generic, flexible combinatorial model. By reviewing on several popular combinatorial models and prediction market models, this paper aims to show how the market models can generalise different combinatorial stuctures and how they implement these popular combinatorial models in specific conditions. Besides, we will see among different market models, Storkey's \emph{Machine Learning Markets} provide more fundamental, generic modelling mechanisms than the others, and it has a significant appeal for both theoretical study and application.
Dynamic Shared Context Processing in an E-Collaborative Learning Environment
Venue: arXiv cs.HC 2012
Year: 2012

In this paper, we propose a dynamic shared context processing method based on DSC (Dynamic Shared Context) model, applied in an e-collaborative learning environment. Firstly, we present the model. This is a way to measure the relevance between events and roles in collaborative environments. With this method, we can share the most appropriate event information for each role instead of sharing all information to all roles in a collaborative work environment. Then, we apply and verify this method in our project with Google App supported e-learning collaborative environment. During this experiment, we compared DSC method measured relevance of events and roles to manual measured relevance. And we describe the favorable points from this comparison and our finding. Finally, we discuss our future research of a hybrid DSC method to make dynamical information shared more effective in a collaborative work environment.
The Call-by-need Lambda Calculus, Revisited
Venue: arXiv cs.PL 2012
Year: 2012

The existing call-by-need lambda calculi describe lazy evaluation via equational logics. A programmer can use these logics to safely ascertain whether one term is behaviorally equivalent to another or to determine the value of a lazy program. However, neither of the existing calculi models evaluation in a way that matches lazy implementations. Both calculi suffer from the same two problems. First, the calculi never discard function calls, even after they are completely resolved. Second, the calculi include re-association axioms even though these axioms are merely administrative steps with no counterpart in any implementation. In this paper, we present an alternative axiomatization of lazy evaluation using a single axiom. It eliminates both the function call retention problem and the extraneous re-association axioms. Our axiom uses a grammar of contexts to describe the exact notion of a needed computation. Like its predecessors, our new calculus satisfies consistency and standardization properties and is thus suitable for reasoning about behavioral equivalence. In addition, we establish a correspondence between our semantics and Launchbury's natural semantics.
About Instruction Sequence Testing
Venue: arXiv cs.SE 2012
Year: 2012

Software testing is presented as a so-called theme within which different authors and groups have defined different subjects each of these subjects having a different focus on testing. A uniform concept of software testing is non-existent and the space of possible coherent perspectives on software testing, each fitting within the theme, is viewed as being spanned by five dimensions, each dimension representing two opposite views with a variety of intermediate views in between. Instruction sequences are used as a simple theoretical conceptualization of computer programs. A theory of instruction sequence testing may serve as a model for a theory of software testing. Instruction sequences testing is considered a new topic for which definitions may be freely contemplated without being restricted by existing views on software testing. The problem of developing a theory of instruction sequence testing is posed. A survey is given of motivations and scenarios for developing a theory of instruction sequence testing.
Quantitative classical realizability
Venue: arXiv cs.LO 2012
Year: 2012

Introduced by Dal Lago and Hofmann, quantitative realizability is a technique used to define models for logics based on Multiplicative Linear Logic. A particularity is that functions are interpreted as bounded time computable functions. It has been used to give new and uniform proofs of soundness of several type systems with respect to certain time complexity classes. We propose a reformulation of their ideas in the setting of Krivine's classical realizability. The framework obtained generalizes Dal Lago and Hofmann's realizability, and reveals deep connections between quantitative realizability and a linear variant of Cohen's forcing.
On the intrinsic complexity of elimination problems in effective Algebraic Geometry
Venue: arXiv cs.CC 2012
Year: 2012

The representation of polynomials by arithmetic circuits evaluating them is an alternative data structure which allowed considerable progress in polynomial equation solving in the last fifteen years. We present a circuit based computation model which captures all known symbolic elimination algorithms in effective algebraic geometry and show the intrinsically exponential complexity character of elimination in this complexity model.
Distributed Data Storage in Large-Scale Sensor Networks Based on LT Codes
Venue: arXiv cs.IT 2012
Year: 2012

This paper proposes an algorithm for increasing data persistency in large-scale sensor networks. In the scenario considered here, k out of n nodes sense the phenomenon and produced ? information packets. Due to usually hazardous environment and limited resources, e.g. energy, sensors in the network are vulnerable. Also due to the large size of the network, gathering information from a few central hopes is not feasible. Flooding is not a desired option either due to limited memory of each node. Therefore the best approach to increase data persistency is propagating data throughout the network by random walks. The algorithm proposed here is based on distributed LT (Luby Transform) codes and it benefits from the low complexity of encoding and decoding of LT codes. In previous algorithms the essential global information (e.g., n and k) are estimated based on graph statistics, which requires excessive transmissions. In our proposed algorithm, these values are obtained without additional transmissions. Also the mixing time of random walk is enhanced by proposing a new scheme for generating the probabilistic forwarding table of random walk. The proposed method uses only local information and it is scalable to any network topology. By simulations the improved performance of developed algorithm compared to previous ones has been verified.
SLA-Oriented Resource Provisioning for Cloud Computing: Challenges, Architecture, and Solutions
Venue: arXiv cs.DC 2012
Year: 2012

Cloud computing systems promise to offer subscription-oriented, enterprise-quality computing services to users worldwide. With the increased demand for delivering services to a large number of users, they need to offer differentiated services to users and meet their quality expectations. Existing resource management systems in data centers are yet to support Service Level Agreement (SLA)-oriented resource allocation, and thus need to be enhanced to realize cloud computing and utility computing. In addition, no work has been done to collectively incorporate customer-driven service management, computational risk management, and autonomic resource management into a market-based resource management system to target the rapidly changing enterprise requirements of Cloud computing. This paper presents vision, challenges, and architectural elements of SLA-oriented resource management. The proposed architecture supports integration of marketbased provisioning policies and virtualisation technologies for flexible allocation of resources to applications. The performance results obtained from our working prototype system shows the feasibility and effectiveness of SLA-based resource provisioning in Clouds.
A further step forward in measuring journals' scientific prestige: The SJR2 indicator
Venue: arXiv cs.DL 2012
Year: 2012

A new size-independent indicator of scientific journal prestige, the SJR2 indicator, is proposed. This indicator takes into account not only the prestige of the citing scientific journal but also its closeness to the cited journal using the cosine of the angle between the vectors of the two journals' cocitation profiles. To eliminate the size effect, the accumulated prestige is divided by the fraction of the journal's citable documents, thus eliminating the decreasing tendency of this type of indicator and giving meaning to the scores. Its method of computation is described, and the results of its implementation on the Scopus 2008 dataset is compared with those of an ad hoc Journal Impact Factor, JIF(3y), and SNIP, the comparison being made both overall and within specific scientific areas. All three, the SJR2 indicator, the SNIP indicator and the JIF distributions, were found to fit well to a logarithmic law. Although the three metrics were strongly correlated, there were major changes in rank. In addition, the SJR2 was distributed more equalized than the JIF by Subject Area and almost as equalized as the SNIP, and better than both at the lower level of Specific Subject Areas. The incorporation of the cosine increased the values of the flows of prestige between thematically close journals.
Network Coding-Based Cooperative ARQ Scheme
Venue: arXiv cs.NI 2012
Year: 2012

In this paper we introduce a novel Automatic Repeat reQuest (ARQ) scheme for cooperative wireless networks. Our scheme adopts network coding techniques in order to enhance the total bandwidth of the network by minimizing the total number of transmissions. The performance of the proposed approach is evaluated by means of computer simulations and compared to other cooperative schemes, while an analytical solution is provided to validate the results.
Adaptive Shortest-Path Routing under Unknown and Stochastically Varying Link States
Venue: arXiv cs.NI 2012
Year: 2012

We consider the adaptive shortest-path routing problem in wireless networks under unknown and stochastically varying link states. In this problem, we aim to optimize the quality of communication between a source and a destination through adaptive path selection. Due to the randomness and uncertainties in the network dynamics, the quality of each link varies over time according to a stochastic process with unknown distributions. After a path is selected for communication, the aggregated quality of all links on this path (e.g., total path delay) is observed. The quality of each individual link is not observable. We formulate this problem as a multi-armed bandit with dependent arms. We show that by exploiting arm dependencies, a regret polynomial with network size can be achieved while maintaining the optimal logarithmic order with time. This is in sharp contrast with the exponential regret order with network size offered by a direct application of the classic MAB policies that ignore arm dependencies. Furthermore, our results are obtained under a general model of link-quality distributions (including heavy-tailed distributions) and find applications in cognitive radio and ad hoc networks with unknown and dynamic communication environments.
Effective Clustering Algorithms for Gene Expression Data
Venue: arXiv cs.CE 2012
Year: 2012

Microarrays are made it possible to simultaneously monitor the expression profiles of thousands of genes under various experimental conditions. Identification of co-expressed genes and coherent patterns is the central goal in microarray or gene expression data analysis and is an important task in Bioinformatics research. In this paper, K-Means algorithm hybridised with Cluster Centre Initialization Algorithm (CCIA) is proposed Gene Expression Data. The proposed algorithm overcomes the drawbacks of specifying the number of clusters in the K-Means methods. Experimental analysis shows that the proposed method performs well on gene Expression Data when compare with the traditional K- Means clustering and Silhouette Coefficients cluster measure.
Online Multi-Commodity Flow with High Demands
Venue: arXiv cs.DS 2012
Year: 2012

This paper deals with the problem of computing, in an online fashion, a maximum benefit multi-commodity flow (\ONMCF), where the flow demands may be bigger than the edge capacities of the network. We present an online, deterministic, centralized, all-or-nothing, bi-criteria algorithm. The competitive ratio of the algorithm is constant, and the algorithm augments the capacities by at most a logarithmic factor. The algorithm can handle two types of flow requests: (i) low demand requests that must be routed along a path, and (ii) high demand requests that may be routed using a multi-path flow. Two extensions are discussed: requests with known durations and machine scheduling.
Unsupervised Classification Using Immune Algorithm
Venue: arXiv cs.LG 2012
Year: 2012

Unsupervised classification algorithm based on clonal selection principle named Unsupervised Clonal Selection Classification (UCSC) is proposed in this paper. The new proposed algorithm is data driven and self-adaptive, it adjusts its parameters to the data to make the classification operation as fast as possible. The performance of UCSC is evaluated by comparing it with the well known K-means algorithm using several artificial and real-life data sets. The experiments show that the proposed UCSC algorithm is more reliable and has high classification precision comparing to traditional classification methods such as K-means.
On Constrained Spectral Clustering and Its Applications
Venue: arXiv cs.LG 2012
Year: 2012

Constrained clustering has been well-studied for algorithms such as $K$-means and hierarchical clustering. However, how to satisfy many constraints in these algorithmic settings has been shown to be intractable. One alternative to encode many constraints is to use spectral clustering, which remains a developing area. In this paper, we propose a flexible framework for constrained spectral clustering. In contrast to some previous efforts that implicitly encode Must-Link and Cannot-Link constraints by modifying the graph Laplacian or constraining the underlying eigenspace, we present a more natural and principled formulation, which explicitly encodes the constraints as part of a constrained optimization problem. Our method offers several practical advantages: it can encode the degree of belief in Must-Link and Cannot-Link constraints; it guarantees to lower-bound how well the given constraints are satisfied using a user-specified threshold; it can be solved deterministically in polynomial time through generalized eigendecomposition. Furthermore, by inheriting the objective function from spectral clustering and encoding the constraints explicitly, much of the existing analysis of unconstrained spectral clustering techniques remains valid for our formulation. We validate the effectiveness of our approach by empirical results on both artificial and real datasets. We also demonstrate an innovative use of encoding large number of constraints: transfer learning via constraints.
RT-SLAM: A Generic and Real-Time Visual SLAM Implementation
Venue: arXiv cs.RO 2012
Year: 2012

This article presents a new open-source C++ implementation to solve the SLAM problem, which is focused on genericity, versatility and high execution speed. It is based on an original object oriented architecture, that allows the combination of numerous sensors and landmark types, and the integration of various approaches proposed in the literature. The system capacities are illustrated by the presentation of an inertial/vision SLAM approach, for which several improvements over existing methods have been introduced, and that copes with very high dynamic motions. Results with a hand-held camera are presented.