pinecone/core-2020-05-10-deduplication huggingface dataset:
The bayesian pursuit algorithm: A new family of estimator learning automata

Published version of a chapter in the book: Modern Approaches in Applied Intelligence. Also available from the publisher at http://dx.doi.org/10.1007/978-3-642-21827-9_53The fastest Learning Automata (LA) algorithms currently available come from the family of estimator algorithms. The Pursuit algorithm (PST), a pioneering scheme in the estimator family, obtains its superior learning speed by using Maximum Likelihood (ML) estimates to pursue the action currently perceived as being optimal. Recently, a Bayesian LA (BLA) was introduced, and empirical results that demonstrated its advantages over established top performers, including the PST scheme, were reported. The BLA scheme is inherently Bayesian in nature, but it succeeds in avoiding the computational intractability by merely relying on updating the hyper-parameters of sibling conjugate priors, and on random sampling from the resulting posteriors. In this paper, we integrate the foundational learning principles motivating the design of the BLA, with the principles of the PST. By doing this, we have succeeded in obtaining a completely novel, and rather pioneering, approach to solving LA-like problems, namely, by designing the Bayesian Pursuit algorithm (BPST). As in the BLA, the estimates are truly Bayesian (as opposed to ML) in nature. However, the action selection probability vector of the PST is used for its exploration purposes. Also, unlike the ML estimate, which is usually a single value, the use of a posterior distribution permits us to choose any one of a spectrum of values in the posterior, as the appropriate estimate. Thus, in this paper, we have chosen a 95% percentile value of the posterior (instead of the mean) to pursue the most promising actions. Further, as advocated in [7], the pursuit has been done using both the Linear Reward-Penalty and Reward-Inaction philosophies, leading to the corresponding BPST RP and BPST RI schemes respectively. It turns out that the BPST is superior to the PST, with the BPST RI being even more robust than the BPST RP . Moreover, by controlling the learning speed of the BPST, the BPST schemes perform either better or comparable to the BLA. We thus believe that the BPST constitutes a new avenue of research, in which the performance benefits of the PST and the BLA are mutually augmented, opening up for improved performance in a number of applications, currently being tested
A two-armed bandit based scheme for accelerated decentralized learning

Published version of a chapter from the book: Modern Approaches in Applied Intelligence. Also available at SpringerLink: http://dx.doi.org/10.1007/978-3-642-21827-9_54The two-armed bandit problem is a classical optimization problem where a decision maker sequentially pulls one of two arms attached to a gambling machine, with each pull resulting in a random reward. The reward distributions are unknown, and thus, one must balance between exploiting existing knowledge about the arms, and obtaining new information. Bandit problems are particularly fascinating because a large class of real world problems, including routing, QoS control, game playing, and resource allocation, can be solved in a decentralized manner when modeled as a system of interacting gambling machines. Although computationally intractable in many cases, Bayesian methods provide a standard for optimal decision making. This paper proposes a novel scheme for decentralized decision making based on the Goore Game in which each decision maker is inherently Bayesian in nature, yet avoids computational intractability by relying simply on updating the hyper parameters of sibling conjugate priors, and on random sampling from these posteriors. We further report theoretical results on the variance of the random rewards experienced by each individual decision maker. Based on these theoretical results, each decision maker is able to accelerate its own learning by taking advantage of the increasingly more reliable feedback that is obtained as exploration gradually turns into exploitation in bandit problem based learning. Extensive experiments demonstrate that the accelerated learning allows us to combine the benefits of conservative learning, which is high accuracy, with the benefits of hurried learning, which is fast convergence. In this manner, our scheme outperforms recently proposed Goore Game solution schemes, where one has to trade off accuracy with speed. We thus believe that our methodology opens avenues for improved performance in a number of applications of bandit based decentralized decision making
Forward Analysis and Model Checking for Trace Bounded WSTS

We investigate a subclass of well-structured transition systems (WSTS), the
bounded---in the sense of Ginsburg and Spanier (Trans. AMS 1964)---complete
deterministic ones, which we claim provide an adequate basis for the study of
forward analyses as developed by Finkel and Goubault-Larrecq (Logic. Meth.
Comput. Sci. 2012). Indeed, we prove that, unlike other conditions considered
previously for the termination of forward analysis, boundedness is decidable.
Boundedness turns out to be a valuable restriction for WSTS verification, as we
show that it further allows to decide all $\omega$-regular properties on the
set of infinite traces of the system
Social communication between virtual characters and children with autism

Children with ASD have difficulty with social communication, particularly joint attention. Interaction in a virtual environment (VE) may be a means for both understanding these difficulties and addressing them. It is first necessary to discover how this population interacts with virtual characters, and whether they can follow joint attention cues in a VE. This paper describes a study in which 32 children with ASD used the ECHOES VE to assist a virtual character in selecting objects by following the character’s gaze and/or pointing. Both accuracy and reaction time data suggest that children were able to successfully complete the task, and qualitative data further suggests that most children perceived the character as an intentional being with relevant, mutually directed behaviour
Providing Performance Guarantees to Virtual Machines Using Real-Time Scheduling

In this paper we tackle the problem of providing Quality of Service guarantees to virtualized applications, focusing on computing and networking guarantees. We propose a mechanism for providing temporal isolation based on a CPU real time scheduling strategy. This allows not only to have control over the individual virtual machine throughput, but also on the activation latency and response-time by which virtualized software components react to external events. We show experimental results gathered on a real system validating the approach
Connecting geodata initiatives to contribute to a European spatial data infrastructure : the CRISOLA case for Malta and the project Plan4all

The Malta CRISOLA case study is investigated in terms of its analysis of the approaches taken to ensure that data in the physical and social domains are tackled within a reliable structure as that provided by the spatial domain through INSPIRE. The crime, social and landuse themes, being pivotal to the model, have served as a bridge across the landuse domains identified as fundamental in the INSPIRE Directive and as expressed in the Plan4all project. The case study is described in terms of its eventual take-up of functions developed by the Plan4all project which are essential for the future successful outcomes as identified in the CRISOLA model. The idea is to disseminate Maltese results to other geoportals including Plan4all geoserver which focuses on the interoperability and harmonisation of spatial planning data. The aims are to support holistic planning through the provision of data services across the spatial and social themes.peer-reviewe
A Speed-Up Technique for Distributed Shortest Paths Computation

International audienceWe propose a simple and practical speed-up technique, which can be combined with every distance vector routing algorithm based on shortest paths, allowing to reduce the total number of messages sent by that algorithm. We combine the new technique with two algorithms known in the literature: DUAL, which is part of CISCO's widely used EIGRP protocol, and the recent DUST, which has been shown to be very effective on networks with power law node degree distribution. We give experimental evidence that these combinations lead to an important gain in terms of the number of messages sent by DUAL and DUST at the price of a little increase in terms of space occupancy per node
Towards Combining Individual and Collaborative Work Spaces under a Unified E-Portfolio

Proceedings of: 11th International Conference on Computational Science and Applications (ICCSA 2011). Santander, Spain, June 20-23, 2011E-portfolios in learning environments have been attributed numerous benefits and their presence has been steadily increasing. And so has the variety of environments in which a student participates. Collaborative learning requires communication and resource sharing among team members. Students may participate in multiple teams throughout a long period of time, sometimes even simultaneously. Conventional eportfolios are oriented toward showcasing individual achievements, but they need to also equally reflect collaborative achievements. The approach described in this paper has the objective of offering students an e-portfolio as a local folder their personal computer containing a combined view of their individual and collaborative work spaces. The content of this folder can be synchronized with a remote server thus achieving resource sharing and publication of a clearly identified set of resources.Work partially funded by the Learn3 project, “Plan Nacional de I+D+I TIN2008- 05163/TSI”, the Consejo Social - Universidad Carlos III de Madrid, the Acción Integrada Ref. DE2009-0051, and the “Emadrid: Investigación y desarrollo de tecnologías para el e-learning en la Comunidad de Madrid” project (S2009/TIC-1650).Publicad
Towards Programming Languages for Machine Learning and Data Mining (Extended Abstract)

Today there is only little support for developing software
that incorporates a machine learning or a data mining component. To
alleviate this situation, we propose to develop programming languages
for machine learning and data mining. We also argue that such languages
should be declarative and should be based on constraint programming
modeling principles. In this way, one could declaratively specify the problem
of machine learning or data mining problem of interest in a high-level
modeling language and then translate it into a constraint satisfaction
or optimization problem, which could then be solved using particular
solvers. These ideas are illustrated on problems of constraint-based itemset
and pattern set mining.status: publishe
K-means based approaches to clustering nodes in annotated graphs

The goal of clustering is to form groups of similar elements. Quality criteria for clusterings, as well as the notion of similarity, depend strongly on the application domain, which explains the existence of many different clustering algorithms and similarity measures. In this paper we focus on the problem of clustering annotated nodes in a graph, when the similarity between nodes depends on both their annotations and their context in the graph ("hybrid" similarity), using k-means-like clustering algorithms. We show that, for the similarity measure we focus on, k-means itself cannot trivially be applied. We propose three alternatives, and evaluate them empirically on the Cora dataset. We ﬁnd that using these alternative clustering algorithms with the hybrid similarity can be advantageous over using standard k-means with a purely annotation-based similarity.status: publishe
Improving the accuracy of similarity measures by using link information

The notion of similarity is crucial to a number of tasks and methods in machine learning and data mining, including clustering and nearest neighbor classiﬁcation. In many contexts, there is on the one hand a natural (but not necessarily optimal) similarity measure deﬁned on the objects to be clustered or classiﬁed, but there is also information about which objects are linked together. This raises the question to what extent the information contained in the links can be used to obtain a more relevant similarity measure. Earlier research has already shown empirically that more accurate results can be obtained by including such link information, but it was not 
analyzed why this is the case. In this paper we provide such an analysis. We relate the extent to which improved results can be obtained to the notions of homophily in the network, transitivity of similarity, and content variability of objects. We explore this relationship using some randomly generated datasets, in which we vary the amount of homophily and content variability. The results show that within a fairly wide range of values for these parameters, the inclusion of link information in the similarity measure indeed yields improved results, as compared to computing the similarity of objects directly 
from their content.status: publishe
An Adapted Version of the Bentley-Ottmann Algorithm for Invariants of Plane Curves Singularities

International audienceWe report on an adapted version of the Bentley-Ottmann algorithm for computing all the intersection points among the edges of the projection of a three-dimensional graph. This graph is given as a set of vertices together with their space Euclidean coordinates, and a set of edges connecting them. More precisely, the three-dimensional graph represents the approximation of a closed and smooth implicitly defined space algebraic curve, that allows us a simplified treatment of the events encountered in the Bentley-Ottmann algorithm. As applications, we use the adapted algorithm to compute invariants for each singularity of a plane complex algebraic curve, i.e. the Alexander polynomial, the Milnor number, the delta-invariant, etc
Novel fish swarm heuristics for bound constrained global optimization problems

The heuristics herein presented are modified versions of the artificial fish swarm algorithm for global optimization. The new ideas aim to improve solution accuracy and reduce computational costs, in particular the number of function evaluations. The modifications also focus on special point movements, such as the random, search and the leap movements. A local search is applied to refine promising regions.
An extension to bound constrained problems is also presented. To assess the performance of the two proposed heuristics, we use the performance profiles as proposed by Dolan and More in 2002. A comparison with three stochastic methods from the literature is included.Fundação para a Ciência e a Tecnologia (FCT
Quaternions : a mathematica package for quaternionic analysis

This paper describes new issues of the Mathematica standard package Quaternions for implementing Hamilton's Quaternion Algebra.
This work attempts to endow the original package with the ability to perform operations on symbolic expressions involving quaternion-valued functions.
A collection of new functions is introduced in order to provide  basic mathematical tools necessary for dealing with regular functions in $\mathbb{R}^{n+1}$, for $n\geq2$. The performance of the package is illustrated by presenting several examples and applications.Centro de Investigação e Desenvolvimento em Matemática e Aplicações da Universidade de AveiroFundação para a Ciência e a Tecnologia (FCT)Centro de Matemática da Universidade do Minh
Modified differential evolution based on global competitive ranking for engineering design optimization problems

Engineering design optimization problems are formulated as large-scale mathematical programming problems with nonlinear objective function and constraints. Global optimization finds a solution while satisfying the constraints. Differential evolution is a population-based heuristic approach that is shown to be very efficient to solve global optimization problems with simple bounds. In this paper, we propose a modified differential evolution introducing self-adaptive control parameters, modified mutation, inversion operation and modified selection for obtaining global optimization. To handle constraints effectively, in modified selection we incorporate global competitive ranking which strikes the right balance between the objective function and the constraint violation. Sixteen well-known engineering design optimization problems are considered and the results compared with other solution methods. It is shown that our method is competitive when solving these problems.Fundação para a Ciência e a Tecnologia (FCT
Laguerre polynomials in several hypercomplex variables and their matrix representation

Recently the creation matrix, intimately related to the Pascal matrix and its generalizations, has been used to develop matrix representations of special polynomials, in particular Appell polynomials. In this paper we describe a matrix approach to polynomials in several hypercomplex variables based on special block matrices whose structures simulate the creation matrix and the Pascal matrix. We apply the approach to hypercomplex Laguerre polynomials, although it can be used for other Appell sequences, too
On generalized hypercomplex laguerre-type exponentials and applications

In hypercomplex context, we have recently constructed
Appell sequences with respect to a generalized
Laguerre derivative operator. This construction
is based on the use of a basic set of
monogenic polynomials which is particularly easy
to handle and can play an important role in applications.
Here we consider Laguerre-type exponentials
of order m and introduce Laguerre-type
circular and hyperbolic functions.Financial support from Center for research and development in Mathematics and Applications of the University of Aveiro, through the Portuguese Foundation for Science and Technology (FCT), is gratefully acknowledged
Nonlinear optimization for human-like movements of a high degree of freedom robotics arm-hand system

The design of autonomous robots, able to closely cooperate with human users in shared tasks, provides many new challenges for robotics research. Compared to industrial applications, robots working in human environments will need to have human-like abilities in their cognitive and motor behaviors. Here we present a model for generating
trajectories of a high degree of freedom robotics arm-hand system that reflects optimality principles of human motor control. The process of finding a human-like trajectory among all possible solutions is formalized as
a large-scale nonlinear optimization problem. We compare numerically three existing solvers, IPOPT, KNITRO and SNOPT, in terms of their real-time performance in different reach-to-grasp problems that are part
of a human-robot interaction task. The results show that the SQP methods obtain better results than the IP methods. SNOPT finds optimal solutions for all tested problems in competitive computational times, thus being the one that best serves our purpose.Fundação para a Ciência e a Tecnologia (FCT) - Bolsa SFRH/BD/23821/2005, CONC-REEQ/17/2001, Projeto JAST : Joint-Action Science and Technology (Ref. IST-2-003747-IP
Efficient model order reduction of large-scale systems on multi-core platforms

We propose an efficient implementation of the Balanced Truncation (BT) method for model order reduction when the state-space matrix is symmetric (positive definite). Most of the computational effort required by this method is due to the computation of matrix inverses. Two alternatives for the inversion of a symmetric positive definite matrix on multi-core platforms are studied and evaluated, the traditional approach based on the Cholesky factorization and the Gauss-Jordan elimination algorithm. Implementations of both methods have been developed and tested. Numerical experiments show the efficiency attained by the proposed implementations on the target architecture.Supported by the CICYT project TIN2008-06570-C0
The Microscopic Origin of the Macroscopic Dielectric Permittivity of Crystals: A Mathematical Viewpoint

Proceedings of the Workshop "Numerical Analysis of Multiscale Computations" at Banff International Research Station, December 2009.The purpose of this paper is to provide a mathematical analysis of the Adler-Wiser formula relating the macroscopic relative permittivity tensor to the microscopic structure of the crystal at the atomic level. The technical level of the presentation is kept at its minimum to emphasize the mathematical structure of the results. We also briefly review some models describing the electronic structure of finite systems, focusing on density operator based formulations, as well as the Hartree model for perfect crystals or crystals with a defect
MOSCFRA: A Multi-objective Genetic Approach for Simultaneous Clustering and Gene Ranking

International audienceMicroarray experiments generate a large amount of data which is used to discover the genetic background of diseases and to know the gene characteristics. Clustering the tissue samples is an important tool for partitioning the dataset according to co-expression patterns. This clustering task is even more difficult when we try to ﬁnd the rank of each gene (Gene Ranking) according to their abilities to distinguish different classes of samples. Finding clusters for samples and rank of each gene for a speciﬁc gene expression data in a single process is always better. In the literature many algorithms are available for ﬁnding the clusters and gene ranking or selection separately. A few algorithms for simultaneous clustering and feature selection are also available. In this article, we propose a new approach to cluster the samples and rank the genes, simultaneously. A novel encoding technique is proposed here for the problem of simultaneous clustering and ranking. Results have been demonstrated for both artiﬁcial and real-life gene expression data sets
Efficient Retrieval and Ranking of Undesired Package Cycles in Large Software Systems

International audienceMany design guidelines state that a software system architecture should avoid cycles between its packages. Yet such cycles appear again and again in many programs. We believe that the existing approaches for cycle detection are too coarse to assist the developers to remove cycles from their programs. In this paper, we describe an efficient algorithm that performs a fine-grained analysis of the cycles among the packages of an application. In addition, we define a metric to rank cycles by their level of undesirability, prioritizing the cycles that seems the more undesired by the developers. Our approach is validated on two large and mature software systems in Java and Smalltalk
The Unitary Fermi Gas: From Monte Carlo to Density Functionals

In this chapter, we describe three related studies of the universal physics
of two-component unitary Fermi gases with resonant short-ranged interactions.
First we discuss an ab initio auxiliary field quantum Monte Carlo technique for
calculating thermodynamic properties of the unitary gas from first principles.
We then describe in detail a Density Functional Theory (DFT) fit to these
thermodynamic properties: the Superfluid Local Density Approximation (SLDA) and
its Asymmetric (ASLDA) generalization. We present several applications,
including vortex structure, trapped systems, and a supersolid
Larkin-Ovchinnikov (FFLO/LOFF) state. Finally, we discuss the time-dependent
extension to the density functional (TDDFT) which can describe quantum dynamics
in these systems, including non-adiabatic evolution, superfluid to normal
transitions and other modes not accessible in traditional frameworks such as a
Landau-Ginzburg, Gross-Pitaevskii, or quantum hydrodynamics.Comment: 73 pages, 20 figures. Chapter 9 in "The BCS-BEC Crossover and the
  Unitary Fermi Gas" edited by W. Zwerger (Springer, 2012). Updated to match
  published versio