OpenRaiser/Intern-Atlas huggingface dataset:
Evolution of Gi Fi Technology Over Other Technologies
Venue: arXiv cs.NI 2013
Year: 2013

Gi-Fi stands for Gigabit Wireless. Gi-Fi is a wireless transmission system which is ten times faster than other technology and its chip delivers short-range multigigabit data transfer in a local environment. Gi-Fi is a wireless technology which promises high speed short range data transfers with speeds of up to 5 Gbps within a range of 10 meters. The Gi-Fi operates on the 60GHz frequency band. This frequency band is currently mostly unused. It is manufactured using (CMOS) technology. This wireless technology named as Gi-Fi. The benefits and features of this new technology can be helpful for use in development of the next generation of devices and places. In this paper, the comparison is perform between Gi-Fi and some of existing technologies with very high speed large files transfers within seconds it is expected that Gi-Fi to be the preferred wireless technology used in home and office of future.
On Secure Source Coding with Side Information at the Encoder
Venue: arXiv cs.IT 2013
Year: 2013

We consider a secure source coding problem with side information (S.I.) at the decoder and the eavesdropper. The encoder has a source that it wishes to describe with limited distortion through a rate limited link to a legitimate decoder. The message sent is also observed by the eavesdropper. The encoder aims to minimize both the distortion incurred by the legitimate decoder; and the information leakage rate at the eavesdropper. When the encoder has access to the uncoded S.I. at the decoder, we characterize the rate-distortion-information leakage rate (R.D.I.) region under a Markov chain assumption and when S.I. at the encoder does not improve the rate-distortion region as compared to the case when S.I. is absent. When the decoder also has access to the eavesdroppers S.I., we characterize the R.D.I. region without the Markov Chain condition. We then consider a related setting where the encoder and decoder obtain coded S.I. through a rate limited helper, and characterize the R.D.I. region for several special cases, including special cases under logarithmic loss distortion and for special cases of the Quadratic Gaussian setting. Finally, we consider the amplification measures of list or entropy constraint at the decoder, and show that the R.D.I. regions for the settings considered in this paper under these amplification measures coincide with R.D.I. regions under per symbol logarithmic loss distortion constraint at the decoder.
An Efficient Model Selection for Gaussian Mixture Model in a Bayesian Framework
Venue: arXiv cs.LG 2013
Year: 2013

In order to cluster or partition data, we often use Expectation-and-Maximization (EM) or Variational approximation with a Gaussian Mixture Model (GMM), which is a parametric probability density function represented as a weighted sum of $\hat{K}$ Gaussian component densities. However, model selection to find underlying $\hat{K}$ is one of the key concerns in GMM clustering, since we can obtain the desired clusters only when $\hat{K}$ is known. In this paper, we propose a new model selection algorithm to explore $\hat{K}$ in a Bayesian framework. The proposed algorithm builds the density of the model order which any information criterions such as AIC and BIC basically fail to reconstruct. In addition, this algorithm reconstructs the density quickly as compared to the time-consuming Monte Carlo simulation.
Software Engineering Process Theory: A Multi-Method Comparison of Sensemaking-CoevoIution-Implementation Theory and Function-Behavior-Structure Theory
Venue: arXiv cs.SE 2013
Year: 2013

Many academics have called for increasing attention to theory in software engineering. Consequently, this paper empirically evaluates two dissimilar software development process theories - one expressing a more traditional, methodical view (FBS) and one expressing an alternative, more improvisational view (SCI). A primarily quantitative survey of more than 1300 software developers is combined with four qualitative case studies to achieve a simultaneously broad and deep empirical evaluation. Case data analysis using a closed-ended, a priori coding scheme based on the two theories strongly supports SCI, as does analysis of questionnaire response distributions (p<0.001; chi-square goodness of fit test). Furthermore, case-questionnaire triangulation found no evidence that support for SCI varied by participants' gender, education, experience, nationality or the size or nature of their projects. This suggests that instead of iteration between weakly-coupled phases (analysis, design, coding, testing), it is more accurate and useful to conceptualize development as ad hoc oscillation between organizing perceptions of the project context (Sensemaking), simultaneously improving mental pictures of the context and design artifact (Coevolution) and constructing, debugging and deploying software artifacts (Implementation).
Byzantine Convex Consensus: Preliminary Version
Venue: arXiv cs.DC 2013
Year: 2013

Much of the past work on asynchronous approximate Byzantine consensus has assumed scalar inputs at the nodes [3, 7]. Recent work has yielded approximate Byzantine consensus algorithms for the case when the input at each node is a d-dimensional vector, and the nodes must reach consensus on a vector in the convex hull of the input vectors at the fault-free nodes [8, 12]. The d-dimensional vectors can be equivalently viewed as points in the d-dimensional Euclidean space. Thus, the algorithms in [8, 12] require the fault-free nodes to decide on a point in the d-dimensional space. In this paper, we generalize the problem to allow the decision to be a convex polytope in the d-dimensional space, such that the decided polytope is within the convex hull of the input vectors at the fault-free nodes. We name this problem as Byzantine convex consensus (BCC), and present an asynchronous approximate BCC algorithm with optimal fault tolerance. Ideally, the goal here is to agree on a convex polytope that is as large as possible. While we do not claim that our algorithm satisfies this goal, we show a bound on the output convex polytope chosen by our algorithm.
Investigating the Detection of Adverse Drug Events in a UK General Practice Electronic Health-Care Database
Venue: arXiv cs.CE 2013
Year: 2013

Data-mining techniques have frequently been developed for Spontaneous reporting databases. These techniques aim to find adverse drug events accurately and efficiently. Spontaneous reporting databases are prone to missing information, under reporting and incorrect entries. This often results in a detection lag or prevents the detection of some adverse drug events. These limitations do not occur in electronic health-care databases. In this paper, existing methods developed for spontaneous reporting databases are implemented on both a spontaneous reporting database and a general practice electronic health-care database and compared. The results suggests that the application of existing methods to the general practice database may help find signals that have gone undetected when using the spontaneous reporting system database. In addition the general practice database provides far more supplementary information, that if incorporated in analysis could provide a wealth of information for identifying adverse events more accurately.
Mixed-Timescale Precoding and Cache Control in Cached MIMO Interference Network
Venue: arXiv cs.IT 2013
Year: 2013

Consider media streaming in MIMO interference networks whereby multiple base stations (BS) simultaneously deliver media to their associated users using fixed data rates. The performance is fundamentally limited by the cross-link interference. We propose a cache-induced opportunistic cooperative MIMO (CoMP) for interference mitigation. By caching a portion of the media files, the BSs opportunistically employ CoMP to transform the cross-link interference into spatial multiplexing gain. We study a mixed-timescale optimization of MIMO precoding and cache control to minimize the transmit power under the rate constraint. The cache control is to create more CoMP opportunities and is adaptive to the long-term popularity of the media files. The precoding is to guarantee the rate requirement and is adaptive to the channel state information and cache state at the BSs. The joint stochastic optimization problem is decomposed into a short-term precoding and a long-term cache control problem. We propose a precoding algorithm which converges to a stationary point of the short-term problem. Based on this, we exploit the hidden convexity of the long-term problem and propose a low complexity and robust solution using stochastic subgradient. The solution has significant gains over various baselines and does not require explicit knowledge of the media popularity.
On digital simply connected spaces and manifolds: a digital simply connected 3-manifold is the digital 3-sphere
Venue: arXiv cs.DM 2013
Year: 2013

In the framework of digital topology, we study structural and topological properties of digital n-dimensional manifolds. We introduce the notion of simple connectedness of a digital space and prove that if M and N are homotopy equivalent digital spaces and M is simply connected, then so is N. We show that a simply connected digital 2-manifold is the digital 2-sphere and a simply connected digital 3-manifold is the digital 3-sphere. This property can be considered as a digital form of the Poincaré conjecture for continuous three-manifolds.
Examining the Classification Accuracy of TSVMs with ?Feature Selection in Comparison with the GLAD Algorithm
Venue: arXiv cs.LG 2013
Year: 2013

Gene expression data sets are used to classify and predict patient diagnostic categories. As we know, it is extremely difficult and expensive to obtain gene expression labelled examples. Moreover, conventional supervised approaches cannot function properly when labelled data (training examples) are insufficient using Support Vector Machines (SVM) algorithms. Therefore, in this paper, we suggest Transductive Support Vector Machines (TSVMs) as semi-supervised learning algorithms, learning with both labelled samples data and unlabelled samples to perform the classification of microarray data. To prune the superfluous genes and samples we used a feature selection method called Recursive Feature Elimination (RFE), which is supposed to enhance the output of classification and avoid the local optimization problem. We examined the classification prediction accuracy of the TSVM-RFE algorithm in comparison with the Genetic Learning Across Datasets (GLAD) algorithm, as both are semi-supervised learning methods. Comparing these two methods, we found that the TSVM-RFE surpassed both a SVM using RFE and GLAD.
An investigation into the relationship between type-2 FOU size and environmental uncertainty in robotic control
Venue: arXiv cs.RO 2013
Year: 2013

It has been suggested that, when faced with large amounts of uncertainty in situations of automated control, type-2 fuzzy logic based controllers will out-perform the simpler type-1 varieties due to the latter lacking the flexibility to adapt accordingly. This paper aims to investigate this problem in detail in order to analyse when a type-2 controller will improve upon type-1 performance. A robotic sailing boat is subjected to several experiments in which the uncertainty and difficulty of the sailing problem is increased in order to observe the effects on measured performance. Improved performance is observed but not in every case. The size of the FOU is shown to be have a large effect on performance with potentially severe performance penalties for incorrectly sized footprints.
Discovering Sequential Patterns in a UK General Practice Database
Venue: arXiv cs.LG 2013
Year: 2013

The wealth of computerised medical information becoming readily available presents the opportunity to examine patterns of illnesses, therapies and responses. These patterns may be able to predict illnesses that a patient is likely to develop, allowing the implementation of preventative actions. In this paper sequential rule mining is applied to a General Practice database to find rules involving a patients age, gender and medical history. By incorporating these rules into current health-care a patient can be highlighted as susceptible to a future illness based on past or current illnesses, gender and year of birth. This knowledge has the ability to greatly improve health-care and reduce health-care costs.
Distributed Detection and Estimation in Wireless Sensor Networks
Venue: arXiv cs.DC 2013
Year: 2013

In this article we consider the problems of distributed detection and estimation in wireless sensor networks. In the first part, we provide a general framework aimed to show how an efficient design of a sensor network requires a joint organization of in-network processing and communication. Then, we recall the basic features of consensus algorithm, which is a basic tool to reach globally optimal decisions through a distributed approach. The main part of the paper starts addressing the distributed estimation problem. We show first an entirely decentralized approach, where observations and estimations are performed without the intervention of a fusion center. Then, we consider the case where the estimation is performed at a fusion center, showing how to allocate quantization bits and transmit powers in the links between the nodes and the fusion center, in order to accommodate the requirement on the maximum estimation variance, under a constraint on the global transmit power. We extend the approach to the detection problem. Also in this case, we consider the distributed approach, where every node can achieve a globally optimal decision, and the case where the decision is taken at a central node. In the latter case, we show how to allocate coding bits and transmit power in order to maximize the detection probability, under constraints on the false alarm rate and the global transmit power. Then, we generalize consensus algorithms illustrating a distributed procedure that converges to the projection of the observation vector onto a signal subspace. We then address the issue of energy consumption in sensor networks, thus showing how to optimize the network topology in order to minimize the energy necessary to achieve a global consensus. Finally, we address the problem of matching the topology of the network to the graph describing the statistical dependencies among the observed variables.
Detect adverse drug reactions for the drug Pravastatin
Venue: arXiv cs.CE 2013
Year: 2013

Adverse drug reaction (ADR) is widely concerned for public health issue. ADRs are one of most common causes to withdraw some drugs from market. Prescription event monitoring (PEM) is an important approach to detect the adverse drug reactions. The main problem to deal with this method is how to automatically extract the medical events or side effects from high-throughput medical data, which are collected from day to day clinical practice. In this study we propose an original approach to detect the ADRs using feature matrix and feature selection. The experiments are carried out on the drug Pravastatin. Major side effects for the drug are detected. The detected ADRs are based on computerized method, further investigation is needed.
Dynamic Decremental Approximate Distance Oracles with $(1+ε, 2)$ stretch
Venue: arXiv cs.DS 2013
Year: 2013

We provide a decremental approximate Distance Oracle that obtains stretch of $1+ε$ multiplicative and 2 additive and has $\hat{O}(n^{5/2})$ total cost (where $\hat{O}$ notation suppresses polylogarithmic and $n^{O(1)/\sqrt{n}}$ factors). The best previous results with $\hat{O}(n^{5/2})$ total cost obtained stretch $3+ε$.
A Beginners Guide to Systems Simulation in Immunology
Venue: arXiv cs.CE 2013
Year: 2013

Some common systems modelling and simulation approaches for immune problems are Monte Carlo simulations, system dynamics, discrete-event simulation and agent-based simulation. These methods, however, are still not widely adopted in immunology research. In addition, to our knowledge, there is few research on the processes for the development of simulation models for the immune system. Hence, for this work, we have two contributions to knowledge. The first one is to show the importance of systems simulation to help immunological research and to draw the attention of simulation developers to this research field. The second contribution is the introduction of a quick guide containing the main steps for modelling and simulation in immunology, together with challenges that occur during the model development. Further, this paper introduces an example of a simulation problem, where we test our guidelines.
Biomarker Clustering of Colorectal Cancer Data to Complement Clinical Classification
Venue: arXiv cs.LG 2013
Year: 2013

In this paper, we describe a dataset relating to cellular and physical conditions of patients who are operated upon to remove colorectal tumours. This data provides a unique insight into immunological status at the point of tumour removal, tumour classification and post-operative survival. Attempts are made to cluster this dataset and important subsets of it in an effort to characterize the data and validate existing standards for tumour classification. It is apparent from optimal clustering that existing tumour classification is largely unrelated to immunological factors within a patient and that there may be scope for re-evaluating treatment options and survival estimates based on a combination of tumour physiology and patient histochemistry.
Partitions of a Finite Partially Ordered Set
Venue: arXiv cs.DM 2013
Year: 2013

In this paper, we investigate the notion of partition of a finite partially ordered set (poset, for short). We will define three different notions of partition of a poset, namely, monotone, regular, and open partition. For each of these notions we will find three equivalent definitions, that will be shown to be equivalent. We start by defining partitions of a poset in terms of fibres of some surjection having the poset as domain. We then obtain combinatorial characterisations of such notions in terms of blocks, without reference to surjection. Finally, we give a further, equivalent definition of each kind of partition by means of analogues of equivalence relations.
Extracting the trustworthiest way to service provider in complex online social networks
Venue: arXiv cs.SI 2013
Year: 2013

In complex online social networks, it is crucial for a service consumer to extract the trustworthiest way to a target service provider from numerous social trust paths between them. The extraction of the trustworthiest way (namely, optimal social trust path (OSTP)) with multiple end-to-end quality of trust (QoT) constraints has been proved to be NP-Complete. Heuristic algorithms with polynomial and pseudo-polynomial-time complexities are often used to deal with this challenging problem. However, existing solutions cannot guarantee the efficiency of searching, that is, they can hardly avoid obtaining partial optimal solutions during searching process. Quantum annealing uses delocalization and tunneling to avoid falling into local minima without sacrifying execution time. It has been proved to be a promising way to many optimization problems in recently published literatures. In this paper, for the first time, QA based OSTP algorithms (QA_OSTP) is applied to the extraction of the trustworthiest way. The experiment results show that QA based algorithms have better performance than its heuristic opponents.
Simpler Sequential and Parallel Biconnectivity Augmentation
Venue: arXiv cs.DS 2013
Year: 2013

For a connected graph, a vertex separator is a set of vertices whose removal creates at least two components and a minimum vertex separator is a vertex separator of least cardinality. The vertex connectivity refers to the size of a minimum vertex separator. For a connected graph $G$ with vertex connectivity $k (k \geq 1)$, the connectivity augmentation refers to a set $S$ of edges whose augmentation to $G$ increases its vertex connectivity by one. A minimum connectivity augmentation of $G$ is the one in which $S$ is minimum. In this paper, we focus our attention on connectivity augmentation of trees. Towards this end, we present a new sequential algorithm for biconnectivity augmentation in trees by simplifying the algorithm reported in \cite{nsn}. The simplicity is achieved with the help of edge contraction tool. This tool helps us in getting a recursive subproblem preserving all connectivity information. Subsequently, we present a parallel algorithm to obtain a minimum connectivity augmentation set in trees. Our parallel algorithm essentially follows the overall structure of sequential algorithm. Our implementation is based on CREW PRAM model with $O(Δ)$ processors, where $Δ$ refers to the maximum degree of a tree. We also show that our parallel algorithm is optimal whose processor-time product is O(n) where $n$ is the number of vertices of a tree, which is an improvement over the parallel algorithm reported in \cite{hsu}.
Lifting Structural Tractability to CSP with Global Constraints
Venue: arXiv cs.AI 2013
Year: 2013

A wide range of problems can be modelled as constraint satisfaction problems (CSPs), that is, a set of constraints that must be satisfied simultaneously. Constraints can either be represented extensionally, by explicitly listing allowed combinations of values, or implicitly, by special-purpose algorithms provided by a solver. Such implicitly represented constraints, known as global constraints, are widely used; indeed, they are one of the key reasons for the success of constraint programming in solving real-world problems. In recent years, a variety of restrictions on the structure of CSP instances that yield tractable classes have been identified. However, many such restrictions fail to guarantee tractability for CSPs with global constraints. In this paper, we investigate the properties of extensionally represented constraints that these restrictions exploit to achieve tractability, and show that there are large classes of global constraints that also possess these properties. This allows us to lift these restrictions to the global case, and identify new tractable classes of CSPs with global constraints.
A Dynamic Algorithm for the Longest Common Subsequence Problem using Ant Colony Optimization Technique
Venue: arXiv cs.AI 2013
Year: 2013

We present a dynamic algorithm for solving the Longest Common Subsequence Problem using Ant Colony Optimization Technique. The Ant Colony Optimization Technique has been applied to solve many problems in Optimization Theory, Machine Learning and Telecommunication Networks etc. In particular, application of this theory in NP-Hard Problems has a remarkable significance. Given two strings, the traditional technique for finding Longest Common Subsequence is based on Dynamic Programming which consists of creating a recurrence relation and filling a table of size . The proposed algorithm draws analogy with behavior of ant colonies function and this new computational paradigm is known as Ant System. It is a viable new approach to Stochastic Combinatorial Optimization. The main characteristics of this model are positive feedback, distributed computation, and the use of constructive greedy heuristic. Positive feedback accounts for rapid discovery of good solutions, distributed computation avoids premature convergence and greedy heuristic helps find acceptable solutions in minimum number of stages. We apply the proposed methodology to Longest Common Subsequence Problem and give the simulation results. The effectiveness of this approach is demonstrated by efficient Computational Complexity. To the best of our knowledge, this is the first Ant Colony Optimization Algorithm for Longest Common Subsequence Problem.