OpenRaiser/Intern-Atlas huggingface dataset:
Fast Constraint Propagation for Image Segmentation
Venue: arXiv cs.CV 2015
Year: 2015

This paper presents a novel selective constraint propagation method for constrained image segmentation. In the literature, many pairwise constraint propagation methods have been developed to exploit pairwise constraints for cluster analysis. However, since most of these methods have a polynomial time complexity, they are not much suitable for segmentation of images even with a moderate size, which is actually equivalent to cluster analysis with a large data size. Considering the local homogeneousness of a natural image, we choose to perform pairwise constraint propagation only over a selected subset of pixels, but not over the whole image. Such a selective constraint propagation problem is then solved by an efficient graph-based learning algorithm. To further speed up our selective constraint propagation, we also discard those less important propagated constraints during graph-based learning. Finally, the selectively propagated constraints are exploited based on $L_1$-minimization for normalized cuts over the whole image. The experimental results demonstrate the promising performance of the proposed method for segmentation with selectively propagated constraints.
Using temporal abduction for biosignal interpretation: A case study on QRS detection
Venue: arXiv cs.AI 2015
Year: 2015

In this work, we propose an abductive framework for biosignal interpretation, based on the concept of Temporal Abstraction Patterns. A temporal abstraction pattern defines an abstraction relation between an observation hypothesis and a set of observations constituting its evidence support. New observations are generated abductively from any subset of the evidence of a pattern, building an abstraction hierarchy of observations in which higher levels contain those observations with greater interpretative value of the physiological processes underlying a given signal. Non-monotonic reasoning techniques have been applied to this model in order to find the best interpretation of a set of initial observations, permitting even to correct these observations by removing, adding or modifying them in order to make them consistent with the available domain knowledge. Some preliminary experiments have been conducted to apply this framework to a well known and bounded problem: the QRS detection on ECG signals. The objective is not to provide a new better QRS detector, but to test the validity of an abductive paradigm. These experiments show that a knowledge base comprising just a few very simple rhythm abstraction patterns can enhance the results of a state of the art algorithm by significantly improving its detection F1-score, besides proving the ability of the abductive framework to correct both sensitivity and specificity failures.
OS-level Failure Injection with SystemTap
Venue: arXiv cs.OS 2015
Year: 2015

Failure injection in distributed systems has been an important issue to experiment with robust, resilient distributed systems. In order to reproduce real-life conditions, parts of the application must be killed without letting the operating system close the existing network communications in a "clean" way. When a process is simply killed, the OS closes them. SystemTap is a an infrastructure that probes the Linux kernel's internal calls. If processes are killed at kernel-level, they can be destroyed without letting the OS do anything else. In this paper, we present a kernel-level failure injection system based on SystemTap. We present how it can be used to implement deterministic and probabilistic failure scenarios.
Position Paper: Provenance Data Visualisation for Neuroimaging Analysis
Venue: arXiv cs.DB 2015
Year: 2015

Visualisation facilitates the understanding of scientific data both through exploration and explanation of visualised data. Provenance contributes to the understanding of data by containing the contributing factors behind a result. With the significant increase in data volumes and algorithm complexity, clinical researchers are struggling with information tracking, analysis reproducibility and the verification of scientific output. Data coming from various heterogeneous sources (multiple sources with varying level of trust) in a collaborative environment adds to the uncertainty of the scientific output. Systems are required that offer provenance data capture and visualisation support for analyses. We present an account for the need to visualise provenance information in order to aid the process of verification of scientific outputs, comparison of analyses,progression and evolution of results for neuroimaging analysis.
In an Uncertain World: Distributed Optimization in MIMO Systems with Imperfect Information
Venue: arXiv cs.IT 2015
Year: 2015

In this paper, we introduce a distributed algorithm that optimizes the Gaussian signal covariance matrices of multi-antenna users transmitting to a common multi-antenna receiver under imperfect and possibly delayed channel state information. The algorithm is based on an extension of exponential learning techniques to a semidefinite setting and it requires the same information as distributed water-filling methods. Unlike water-filling however, the proposed matrix exponential learning (MXL) algorithm converges to the system's optimum signal covariance profile under very mild conditions on the channel uncertainty statistics; moreover, the algorithm retains its convergence properties even in the presence of user update asynchronicities, random delays and/or ergodically changing channel conditions. In particular, by properly tuning the algorithm's learning rate (or step size), the algorithm converges within a few iterations, even for large numbers of users and/or antennas per user. Our theoretical analysis is complemented by numerical simulations which illustrate the algorithm's robustness and scalability in realistic network conditions.
Multidimensional Manhattan Sampling and Reconstruction
Venue: arXiv cs.IT 2015
Year: 2015

This paper introduces Manhattan sampling in two and higher dimensions, and proves sampling theorems. In two dimensions, Manhattan sampling, which takes samples densely along a Manhattan grid of lines, can be viewed as sampling on the union of two rectangular lattices, one dense horizontally, being a multiple of the fine spacing of the other. The sampling theorem shows that images bandlimited to the union of the Nyquist regions of the two rectangular lattices can be recovered from their Manhattan samples, and an efficient procedure for doing so is given. Such recovery is possible even though there is overlap among the spectral replicas induced by Manhattan sampling. In three and higher dimensions, there are many possible configurations for Manhattan sampling, each consisting of the union of special rectangular lattices called bi-step lattices. This paper identifies them, proves a sampling theorem showing that images bandlimited to the union of the Nyquist regions of the bi-step rectangular lattices are recoverable from Manhattan samples, and presents an efficient onion-peeling procedure for doing so. Furthermore, it develops a special representation for the bi-step lattices and an algebra with nice properties. It is also shown that the set of reconstructable images is maximal in the Landau sense. While most of the paper deals with continuous-space images, Manhattan sampling of discrete-space images is also considered, for infinite, as well as finite, support images.
Monitoring Term Drift Based on Semantic Consistency in an Evolving Vector Field
Venue: arXiv cs.CL 2015
Year: 2015

Based on the Aristotelian concept of potentiality vs. actuality allowing for the study of energy and dynamics in language, we propose a field approach to lexical analysis. Falling back on the distributional hypothesis to statistically model word meaning, we used evolving fields as a metaphor to express time-dependent changes in a vector space model by a combination of random indexing and evolving self-organizing maps (ESOM). To monitor semantic drifts within the observation period, an experiment was carried out on the term space of a collection of 12.8 million Amazon book reviews. For evaluation, the semantic consistency of ESOM term clusters was compared with their respective neighbourhoods in WordNet, and contrasted with distances among term vectors by random indexing. We found that at 0.05 level of significance, the terms in the clusters showed a high level of semantic consistency. Tracking the drift of distributional patterns in the term space across time periods, we found that consistency decreased, but not at a statistically significant level. Our method is highly scalable, with interpretations in philosophy.
Learning Efficient Anomaly Detectors from $K$-NN Graphs
Venue: arXiv cs.LG 2015
Year: 2015

We propose a non-parametric anomaly detection algorithm for high dimensional data. We score each datapoint by its average $K$-NN distance, and rank them accordingly. We then train limited complexity models to imitate these scores based on the max-margin learning-to-rank framework. A test-point is declared as an anomaly at $α$-false alarm level if the predicted score is in the $α$-percentile. The resulting anomaly detector is shown to be asymptotically optimal in that for any false alarm rate $α$, its decision region converges to the $α$-percentile minimum volume level set of the unknown underlying density. In addition, we test both the statistical performance and computational efficiency of our algorithm on a number of synthetic and real-data experiments. Our results demonstrate the superiority of our algorithm over existing $K$-NN based anomaly detection algorithms, with significant computational savings.
Bounded Verification with On-the-Fly Discrepancy Computation
Venue: arXiv cs.SY 2015
Year: 2015

Simulation-based verification algorithms can provide formal safety guarantees for nonlinear and hybrid systems. The previous algorithms rely on user provided model annotations called discrepancy function, which are crucial for computing reachtubes from simulations. In this paper, we eliminate this requirement by presenting an algorithm for computing piece-wise exponential discrepancy functions. The algorithm relies on computing local convergence or divergence rates of trajectories along a simulation using a coarse over-approximation of the reach set and bounding the maximal eigenvalue of the Jacobian over this over-approximation. The resulting discrepancy function preserves the soundness and the relative completeness of the verification algorithm. We also provide a coordinate transformation method to improve the local estimates for the convergence or divergence rates in practical examples. We extend the method to get the input-to-state discrepancy of nonlinear dynamical systems which can be used for compositional analysis. Our experiments show that the approach is effective in terms of running time for several benchmark problems, scales reasonably to larger dimensional systems, and compares favorably with respect to available tools for nonlinear models.
Online Convex Covering and Packing Problems
Venue: arXiv cs.DS 2015
Year: 2015

We study the online convex covering problem and online convex packing problem. The (offline) convex covering problem is modeled by the following convex program: $\min_{x \in R_+^n} f(x) \ \text{s.t}\ A x \ge 1$, where $f : R_+^n \mapsto R_+$ is a monotone and convex cost function, and $A$ is an $m \times n$ matrix with non-negative entries. Each row of the constraint matrix $A$ corresponds to a covering constraint. In the online problem, each row of $A$ comes online and the algorithm must maintain a feasible assignment $x$ and may only increase $x$ over time. The (offline) convex packing problem is modeled by the following convex program: $\max_{y\in R_+^m} \sum_{j = 1}^m y_j - g(A^T y)$, where $g : R_+^n \mapsto R_+$ is a monotone and convex cost function. It is the Fenchel dual program of convex covering when $g$ is the convex conjugate of $f$. In the online problem, each variable $y_j$ arrives online and the algorithm must decide the value of $y_j$ on its arrival. We propose simple online algorithms for both problems using the online primal dual technique, and obtain nearly optimal competitive ratios for both problems for the important special case of polynomial cost functions. For any convex polynomial cost functions with non-negative coefficients and maximum degree $τ$, we introduce an $O(τ\log n)^τ$-competitive online convex covering algorithm, and an $O(τ)$-competitive online convex packing algorithm, matching the known $Ω(τ\log n)^τ$ and $Ω(τ)$ lower bounds respectively. There is a large family of online resource allocation problems that can be modeled under this online convex covering and packing framework, including online covering and packing problems (with linear objectives), online mixed covering and packing, and online combinatorial auction. Our framework allows us to study these problems using a unified approach.
Etude des déterminants psychologiques de la persistance dans l'usage d'un jeu sérieux : évaluation de l'environnement optimal d'apprentissage avec Mecagenius?
Venue: arXiv cs.OH 2015
Year: 2015

The aim of this paper is to show the relevance of motivational key concepts in evaluating the use of serious game. This research involves 115 students training with Mecagenius (serious game in mechanical engineering). The results of the exploratory study also confirm the relevance of the use of flow in Education scale (EduFlow) to evaluate the optimal learning experienc ewith a serious game. It also appears that EduFlow is related to specific actions within the school context such as self-efficacy, motivational climate and interest.
Unsupervised Fusion Weight Learning in Multiple Classifier Systems
Venue: arXiv cs.LG 2015
Year: 2015

In this paper we present an unsupervised method to learn the weights with which the scores of multiple classifiers must be combined in classifier fusion settings. We also introduce a novel metric for ranking instances based on an index which depends upon the rank of weighted scores of test points among the weighted scores of training points. We show that the optimized index can be used for computing measures such as average precision. Unlike most classifier fusion methods where a single weight is learned to weigh all examples our method learns instance-specific weights. The problem is formulated as learning the weight which maximizes a clarity index; subsequently the index itself and the learned weights both are used separately to rank all the test points. Our method gives an unsupervised method of optimizing performance on actual test data, unlike the well known stacking-based methods where optimization is done over a labeled training set. Moreover, we show that our method is tolerant to noisy classifiers and can be used for selecting N-best classifiers.
Distributed Verification of Rare Properties using Importance Splitting Observers
Venue: arXiv cs.LO 2015
Year: 2015

Rare properties remain a challenge for statistical model checking (SMC) due to the quadratic scaling of variance with rarity. We address this with a variance reduction framework based on lightweight importance splitting observers. These expose the model-property automaton to allow the construction of score functions for high performance algorithms. The confidence intervals defined for importance splitting make it appealing for SMC, but optimising its performance in the standard way makes distribution inefficient. We show how it is possible to achieve equivalently good results in less time by distributing simpler algorithms. We first explore the challenges posed by importance splitting and present an algorithm optimised for distribution. We then define a specific bounded time logic that is compiled into memory-efficient observers to monitor executions. Finally, we demonstrate our framework on a number of challenging case studies.
Shannon and Renyi Entropy of Wavelets
Venue: arXiv cs.IT 2015
Year: 2015

This paper reports a new reading for wavelets, which is based on the classical 'De Broglie' principle. The wave-particle duality principle is adapted to wavelets. Every continuous basic wavelet is associated with a proper probability density, allowing defining the Shannon entropy of a wavelet. Further entropy definitions are considered, such as Jumarie or Renyi entropy of wavelets. We proved that any wavelet of the same family has the same Shannon entropy of its mother wavelet. Finally, the Shannon entropy for a few standard wavelet families is determined.
Simultaneous Time-Space Upper Bounds for Certain Problems in Planar Graphs
Venue: arXiv cs.CC 2015
Year: 2015

In this paper, we show that given a weighted, directed planar graph $G$, and any $ε>0$, there exists a polynomial time and $O(n^{\frac{1}{2}+ε})$ space algorithm that computes the shortest path between two fixed vertices in $G$. We also consider the {\RB} problem, which states that given a graph $G$ whose edges are colored either red or blue and two fixed vertices $s$ and $t$ in $G$, is there a path from $s$ to $t$ in $G$ that alternates between red and blue edges. The {\RB} problem in planar DAGs is {\NL}-complete. We exhibit a polynomial time and $O(n^{\frac{1}{2}+ε})$ space algorithm (for any $ε>0$) for the {\RB} problem in planar DAG. In the last part of this paper, we consider the problem of deciding and constructing the perfect matching present in a planar bipartite graph and also a similar problem which is to find a Hall-obstacle in a planar bipartite graph. We show the time-space bound of these two problems are same as the bound of shortest path problem in a directed planar graph.
Comparison of Algorithms for Compressed Sensing of Magnetic Resonance Images
Venue: arXiv cs.CV 2015
Year: 2015

Magnetic resonance imaging (MRI) is an essential medical tool with inherently slow data acquisition process. Slow acquisition process requires patient to be long time exposed to scanning apparatus. In recent years significant efforts are made towards the applying Compressive Sensing technique to the acquisition process of MRI and biomedical images. Compressive Sensing is an emerging theory in signal processing. It aims to reduce the amount of acquired data required for successful signal reconstruction. Reducing the amount of acquired image coefficients leads to lower acquisition time, i.e. time of exposition to the MRI apparatus. Using optimization algorithms, satisfactory image quality can be obtained from the small set of acquired samples. A number of optimization algorithms for the reconstruction of the biomedical images is proposed in the literature. In this paper, three commonly used optimization algorithms are compared and results are presented on the several MRI images.
Optimal Relay Selection with Non-negligible Probing Time
Venue: arXiv cs.IT 2015
Year: 2015

In this paper an optimal relay selection algorithm with non-negligible probing time is proposed and analyzed for cooperative wireless networks. Relay selection has been introduced to solve the degraded bandwidth efficiency problem in cooperative communication. Yet complete information of relay channels often remain unavailable for complex networks which renders the optimal selection strategies impossible for transmission source without probing the relay channels. Particularly when the number of relay candidate is large, even though probing all relay channels guarantees the finding of the best relays at any time instant, the degradation of bandwidth efficiency due to non-negligible probing times, which was often neglected in past literature, is also significant. In this work, a stopping rule based relay selection strategy is determined for the source node to decide when to stop the probing process and choose one of the probed relays to cooperate with under wireless channels' stochastic uncertainties. This relay selection strategy is further shown to have a simple threshold structure. At the meantime, full diversity order and high bandwidth efficiency can be achieved simultaneously. Both analytical and simulation results are provided to verify the claims.
Semantics-based services for a low carbon society: An application on emissions trading system data and scenarios management
Venue: arXiv cs.AI 2015
Year: 2015

A low carbon society aims at fighting global warming by stimulating synergic efforts from governments, industry and scientific communities. Decision support systems should be adopted to provide policy makers with possible scenarios, options for prompt countermeasures in case of side effects on environment, economy and society due to low carbon society policies, and also options for information management. A necessary precondition to fulfill this agenda is to face the complexity of this multi-disciplinary domain and to reach a common understanding on it as a formal specification. Ontologies are widely accepted means to share knowledge. Together with semantic rules, they enable advanced semantic services to manage knowledge in a smarter way. Here we address the European Emissions Trading System (EU-ETS) and we present a knowledge base consisting of the EREON ontology and a catalogue of rules. Then we describe two innovative semantic services to manage ETS data and information on ETS scenarios.
An improved lower bound for one-dimensional online unit clustering
Venue: arXiv cs.DS 2015
Year: 2015

The online unit clustering problem was proposed by Chan and Zarrabi-Zadeh (WAOA2007 and Theory of Computing Systems 45(3), 2009), which is defined as follows: "Points" are given online in the $d$-dimensional Euclidean space one by one. An algorithm creates a "cluster," which is a $d$-dimensional rectangle. The initial length of each edge of a cluster is 0. An algorithm can extend an edge until it reaches unit length independently of other dimensions. The task of an algorithm is to cover a new given point either by creating a new cluster and assigning it to the point, or by extending edges of an existing cluster created in past times. The goal is to minimize the total number of created clusters. Chan and Zarrabi-Zadeh proposed some method to obtain a competitive algorithm for the $d$-dimensional case using an algorithm for the one-dimensional case, and thus the one-dimensional case has been extensively studied including some variants of the unit clustering problem. In this paper, we show a lower bound of $13/8 = 1.625$ on the competitive ratio of any deterministic online algorithm for the one-dimensional unit clustering, improving the previous lower bound $8/5 (=1.6)$ presented by Epstein and van Stee (WAOA2007 and ACM Transactions on Algorithms 7(1), 2010). Note that Ehmsen and Larsen (SWAT2010 and Theoretical Computer Science, 500, 2013) showed the current best upper bound of $5/3$, and conjectured that the exact competitive ratio in the one-dimensional case may be $13/8$.
Exact solutions to Super Resolution on semi-algebraic domains in higher dimensions
Venue: arXiv cs.IT 2015
Year: 2015

We investigate the multi-dimensional Super Resolution problem on closed semi-algebraic domains for various sampling schemes such as Fourier or moments. We present a new semidefinite programming (SDP) formulation of the 1 -minimization in the space of Radon measures in the multi-dimensional frame on semi-algebraic sets. While standard approaches have focused on SDP relaxations of the dual program (a popular approach is based on Gram matrix representations), this paper introduces an exact formulation of the primal 1 -minimization exact recovery problem of Super Resolution that unleashes standard techniques (such as moment-sum-of-squares hier-archies) to overcome intrinsic limitations of previous works in the literature. Notably, we show that one can exactly solve the Super Resolution problem in dimension greater than 2 and for a large family of domains described by semi-algebraic sets.