pinecone/core-2020-05-10-deduplication huggingface dataset:
X-ray Clusters of Galaxies as Cosmological Tools

This paper was written as part of a book entitled: "Questions of Modern
Cosmology - Galileo's Legacy" which is a celebrative book dedicated to Galileo
Galilei. The book is published in 2009, the International Year of Astronomy,
since it is intended to be a modern tribute to the astronomer who, 400 years
ago, first pointed a telescope towards the night sky. The book is written in
the form of interviews between the editors and many physicists, astrophysicists
and cosmologists from all over the world. The editors engaged in several
discussions on the formation and evolution of the Universe with the aim of
summarizing the most important and significative advances made by cosmology
over the past century and at the beginning of the new millennium. This paper
deals with X-ray clusters of galaxies and how they can be used to constrain
fundamental cosmological parameters.Comment: (La)Tex, 21 pages, 7 black and white figures, 2 color figure
Sparse Multiscale Pacthes for Image Processing

International audienceThis paper presents a framework to define an objective measure of the similarity (or dissimilarity) between two images for image processing. The problem is twofold: 1) define a set of features that capture the information contained in the image relevant for the given task and 2) define a similarity measure in this feature space. In this paper, we propose a feature space as well as a statistical measure on this space. Our feature space is based on a global descriptor of the image in a multiscale transformed domain. After decomposition into a Laplacian pyramid, the coefficients are arranged in intrascale/interscale/interchannel patches which reflect the dependencies of neighboring coefficients in presence of specific structures or textures. At each scale, the probability density function (pdf) of these patches is used as a descriptor of the relevant information. Because of the sparsity of the multiscale transform, the most significant patches, called Sparse Multiscale Patches (SMP), describe efficiently these pdfs. We propose a statistical measure (the Kullback-Leibler divergence) based on the comparison of these probability density functions. Interestingly, this measure is estimated via the nonparametric, k-th nearest neighbor framework without explicitly building the pdfs. This framework is applied to a query-by-example image retrieval method. Experiments on two publicly available databases showed the potential of our SMP approach for this task. In particular, it performed comparably to a SIFT-based retrieval method and two versions of a fuzzy segmentation-based method (the UFM and CLUE methods), and it exhibited some robustness to different geometric and radiometric deformations of the images
Recent advance in image search

International audienceThis paper introduces recent methods for large scale image search. State-of-the-art methods build on the bag-of-features image representation. We first analyze bag-of-features in the framework of approximate nearest neighbor search. This shows the sub-optimality of such a representation for matching descriptors and leads us to derive a more precise representation based on 1) Hamming embedding (HE) and 2) weak geometric consistency constraints (WGC). HE provides binary signatures that refine the matching based on visual words. WGC filters matching descriptors that are not consistent in terms of angle and scale. HE and WGC are integrated within an inverted file and are efficiently exploited for all images, even in the case of very large datasets. Experiments performed on a dataset of one million of images show a significant improvement due to the binary signature and the weak geometric consistency constraints, as well as their efficiency. Estimation of the full geometric transformation, i.e., a re-ranking step on a short list of images, is complementary to our weak geometric consistency constraints and allows to further improve the accuracy
Semantic spaces : measuring the distance between different subspaces

Semantic Space models, which provide a numerical representation of words’ meaning extracted from corpus of documents, have been formalized in terms of Hermitian operators over real valued Hilbert spaces by Bruza et al. [1]. The collapse of a word into a particular meaning has been investigated applying the notion of quantum collapse of superpositional states [2]. While the semantic association between words in a Semantic Space can be computed by means of the Minkowski distance [3] or the cosine of the angle between the vector representation of each pair of words, a new procedure is needed in order to establish relations between two or more Semantic Spaces. We address the question: how can the distance between different Semantic Spaces be computed? By representing each Semantic Space as a subspace of a more general Hilbert space, the relationship between Semantic Spaces can be computed by means of the subspace distance. Such distance needs to take into account the difference in the dimensions between subspaces. The availability of a distance for comparing different Semantic Subspaces would enable to achieve a deeper understanding about the geometry of Semantic Spaces which would possibly translate into better effectiveness in Information Retrieval tasks
Extracting Spooky-activation-at-a-distance from considerations of entanglement

Following an early claim by Nelson & McEvoy \cite{Nelson:McEvoy:2007} suggesting that word associations can display `spooky action at a distance behaviour', a serious investigation of the potentially quantum nature of such associations is currently underway. This paper presents a simple quantum model of a word association system. It is shown that a quantum model of word entanglement can recover aspects of both the Spreading Activation equation and the Spooky-activation-at-a-distance equation, both of which are used to model the activation level of words in human memory
Fault Attacks on RSA Public Keys: Left-To-Right Implementations are also Vulnerable

International audienceAfter attacking the RSA by injecting fault and corresponding countermeasures, works appear now about the need for protecting RSA public elements against fault attacks. We provide here an extension of a recent attack based on the public modulus corruption. The difficulty to decompose the "Left-To-Right" exponentiation into partial multiplications is overcome by modifying the public modulus to a number with known factorization. This fault model is justified here by a complete study of faulty prime numbers with a fixed size. The good success rate of this attack combined with its practicability raises the question of using faults for changing algebraic properties of finite field based cryptosystems
Optimal privacy-aware path in hippocratic databases

Privacy becomes a major concern for both customers and enterprises in today's corporate marketing strategies, many research efforts have been put into developing new privacy-aware technologies. Among them, Hippocratic databases are one of the important mechanisms to guarantee the respect of privacy principles in data management, which adopt purpose as a central concept associated with each piece of data stored in the databases. The proposed mechanism provides basic principles for future database systems protecting privacy of data as a founding tenet. However, Hippocratic databases do not allow to distinguish which particular method is used for fulfilling a purpose. Especially, the issues like purpose hierarchies, task delegations and minimal privacy cost are missing from the proposed mechanism. In this paper, we extend these mechanisms in order to support inter- organizational business processes in Hippocratic databases. A comprehensive approach for negotiation of personal information between customers and enterprises based on user preferences is developed when\ud
enterprises offer their clients a number of ways to fulfill a service. We organize purposes into purpose directed graphs through AND/OR decomposition, which supports task delegations and distributed authorizations. Specially, customers have controls of deciding how to get a service fulfilled on the basis of their personal feeling of trust for any service customization. Quantitative analysis is performed to characterize privacy penalties dealing with privacy cost and customer's trust. Finally, efficient algorithms are given to guarantee the minimal privacy cost and maximal customer's trust involved in a business process
newYAWL : Towards Workflow 2.0

The field of workflow technology has burgeoned in recent years providing a variety of means of automating business processes. It is a great source of opportunity for organisations seeking to streamline and optimise their operations. Despite these advantages however, the current generation of workflow technologies are subject to a variety of criticisms, in terms of their restricted view of what comprises a business process, their imprecise definition and their general inflexibility. As a remedy to these potential difficulties, in this paper we propose a series of development goals for the next generation of workflow technology. We also present newYAWL, a formally defined, multi-perspective reference language for workflow systems
Linux kernel compaction through cold code swapping

There is a growing trend to use general-purpose operating systems like Linux in embedded systems. Previous research focused on using compaction and specialization techniques to adapt a general-purpose OS to the memory-constrained environment, presented by most, embedded systems. However, there is still room for improvement: it has been shown that even after application of the aforementioned techniques more than 50% of the kernel code remains unexecuted under normal system operation. We introduce a new technique that reduces the Linux kernel code memory footprint, through on-demand code loading of infrequently executed code, for systems that support virtual memory. In this paper, we describe our general approach, and we study code placement algorithms to minimize the performance impact of the code loading. A code, size reduction of 68% is achieved, with a 2.2% execution speedup of the system-mode execution time, for a case study based on the MediaBench II benchmark suite
A process calculus analysis of compensations

Conversations in service-oriented computation are frequently long running. In such a setting, traditional ACID properties of transactions cannot be reasonably implemented, and compensation mechanisms seem to provide convenient techniques to, at least, approximate them. In this paper, we investigate the representation and analysis of structured compensating transactions within a process calculus model, by embedding in the Conversation Calculus certain structured compensation programming abstractions inspired by the ones proposed by Butler, Ferreira, and Hoare. We prove the correctness of the embedding after developing a general notion of stateful model for structured compensations and related results, and showing that the embedding induces such a model
Galaxies and Cladistics

Talk given at the "12th Evolutionary Biology Meeting" held in Marseille, France, Sept. 24-26, 2008.The Hubble tuning fork diagram, based on morphology and established in the 1930s, has always been the preferred scheme for classification of galaxies. However, the current large amount of multiwavelength data, most often spectra, for objects up to very high distances, asks for more sophisticated statistical approaches. Interpreting formation and evolution of galaxies as a ‘transmission with modification' process, we have shown that the concepts and tools of phylogenetic systematics can be heuristically transposed to the case of galaxies. This approach, which we call ‘astrocladistics', has successfully been applied on several samples. Many difficulties still remain, some of them being specific to the nature of both galaxies and their diversification processes, some others being classical in cladistics, like the pertinence of the descriptors in conveying any useful evolutionary information
Towards Effective Portability of Packet Handling Applications Across Heterogeneous Hardware Platforms

his paper presents the Network Virtual Machine (NetVM), a virtual network processor optimized for implementation and execution of packet handling applications. As a Java Virtual Machine virtualizes a CPU, the NetVM virtualizes a network processor. The NetVM is expected to provide a unified layer for networking tasks (e.g., packet filtering, packet counting, string matching) performed by various network applications (firewalls, network monitors, intrusion detectors) so that they can be executed on any network device, ranging from high-end routers to small appliances. Moreover, the NetVM will provide efficient mapping of the elementary functionalities used to realize the above mentioned networking tasks onto specific hardware functional units (e.g., ASICs, FPGAs, and network processing elements) included in special purpose hardware systems possibly deployed to implement network device
Bounded Delay and Concurrency for Earliest Query Answering

International audienceEarliest query answering is needed for streaming XML processing with optimal memory management. We study the feasibility of earliest query answering for node selection queries. Tractable queries are distinguished by a bounded number of concurrently alive answer candidates at every time point, and a bounded delay for node selection. We show that both properties are decidable in polynomial time for queries defined by deterministic automata for unranked trees. Our results are obtained by reduction to the bounded valuedness problem for recognizable relations between unranked trees
Optimizing the DFCN Broadcast Protocol with a Parallel Cooperative Strategy of Multi-Objective Evolutionary Algorithms

Proceeding of: 5th International Conference, EMO 2009, Nantes, France, April 7-10, 2009This work presents the application of a parallel coopera- tive optimization approach to the broadcast operation in mobile ad-hoc networks (manets). The optimization of the broadcast operation im- plies satisfying several objectives simultaneously, so a multi-objective approach has been designed. The optimization lies on searching the best configurations of the dfcn broadcast protocol for a given manet sce- nario. The cooperation of a team of multi-objective evolutionary al- gorithms has been performed with a novel optimization model. Such model is a hybrid parallel algorithm that combines a parallel island- based scheme with a hyperheuristic approach. Results achieved by the algorithms in different stages of the search process are analyzed in order to grant more computational resources to the most suitable algorithms. The obtained results for a manets scenario, representing a mall, demon- strate the validity of the new proposed approach.This work has been supported by the ec (feder) and the Spanish Ministry of
Education and Science inside the ‘Plan Nacional de i+d+i’ (tin2005-08818-c04)
and (tin2008-06491-c04-02). The work of Gara Miranda has been developed under
grant fpu-ap2004-2290.Publicad
Statistical analysis of human boy movement and group interactions in response to music

Quantification of time series that relate to physiological data is challenging for empirical music research. Up to now, most studies have focused on time-dependent responses of individual subjects in controlled environments. However, little is known about time-dependent responses of between-subject interactions in an ecological context. This paper provides new findings on the statistical analysis of group synchronicity in response to musical stimuli. Different statistical techniques were applied to time-dependent data obtained from an experiment on embodied listening in individual and group settings. Analysis of inter group synchronicity are described. Dynamic Time Warping (DTW) and Cross Correlation Function (CCF) were found to be valid methods to estimate group coherence of the resulting movements. It was found that synchronicity of movements between individuals (human human interactions) increases significantly in the social context. Moreover, Analysis of Variance (ANOVA) revealed that the type of music is the predominant factor in both the individual and the social context
Data compression and regression based on local principal curves.

Frequently the predictor space of a multivariate regression problem of the type y = m(x_1, …, x_p ) + ε is intrinsically one-dimensional, or at least of far lower dimension than p. Usual modeling attempts such as the additive model y = m_1(x_1) + … + m_p (x_p ) + ε, which try to reduce the complexity of the regression problem by making additional structural assumptions, are then inefficient as they ignore the inherent structure of the predictor space and involve complicated model and variable selection stages. In a fundamentally different approach, one may consider first approximating the predictor space by a (usually nonlinear) curve passing through it, and then regressing the response only against the one-dimensional projections onto this curve. This entails the reduction from a p- to a one-dimensional regression problem.\ud
As a tool for the compression of the predictor space we apply local principal curves. Taking things on from the results presented in Einbeck et al. (Classification – The Ubiquitous Challenge. Springer, Heidelberg, 2005, pp. 256–263), we show how local principal curves can be parametrized and how the projections are obtained. The regression step can then be carried out using any nonparametric smoother. We illustrate the technique using data from the physical sciences.\u
A Flatness-Based Iterative Method for Reference Trajectory Generation in Constrained NMPC

This paper proposes a novel methodology that combines the differential flatness formalism for trajectory generation of nonlinear systems, and the use of a model predictive control (MPC) strategy for constraint handling. The methodology consists of a trajectory generator that generates a reference trajectory parameterised by splines, and with the property that it satisfies performance objectives. The reference trajectory is generated iteratively in accordance with information received from the MPC formulation. This interplay with MPC guarantees that the trajectory generator receives feedback from present and future constraints for real-time trajectory generation
Public Policy Processes and Sustainability in the Minerals and Energy Industries

This chapter considers the extent to which public policy processes are geared towards sustainability in mineral industries, and how they may need to change to take the requirements for sustainability into account. It begins with a general discussion of sustainability in mineral industries, drawing on examples from three countries. As these examples show mining is neither inherently sustainable nor unsustainable, but rather is made sustainable or unsustainable,  in large part by public policies applied to the mining industry. The nature and content of public policies reflect, in turn, the policy making processes utilised in formulating and implementing policy, and so it is critical to consider the links between policy processes and sustainability. Nine specific variables are identified that shape public these processes, including the identity and number of interests or groups involved in making policy and how much influence they exert; the range of policy alternatives considered;  time frames and information sources for policy making; the time periods over which the impacts of policy alternatives are considered; and the criteria applied by policy makers. The chapter argues that current policy  making processes in relation to mineral industries are often incompatible with the pursuit of sustainability and that radical changes are required if public policies are to support sustainabilty in mineral and energy industries.Griffith Business School, School of Government and International RelationsFull Tex
Evolving high-speed, easy-to-understand network intrusion detection rules with genetic programming

Proceeding of: EvoWorkshops 2009: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG, Tübingen, Germany, April 15-17, 2009An ever-present problem in intrusion detection technology is how to construct the patterns of (good, bad or anomalous) behaviour upon which an engine have to make decisions regarding the nature of the activity observed in a system. This has traditionally been one of the central areas of research in the field, and most of the solutions proposed so far have relied in one way or another upon some form of data mining–with the exception, of course, of human-constructed patterns. In this paper, we explore the use of Genetic Programming (GP) for such a purpose. Our approach is not new in some aspects, as GP has already been partially explored in the past. Here we show that GP can offer at least two advantages over other classical mechanisms: it can produce very lightweight detection rules (something of extreme importance for high-speed networks or resource-constrained applications) and the simplicity of the patterns generated allows to easily understand the semantics of the underlying attack.Publicad
An immune system based genetic algorithm using permutation-based dualism for dynamic traveling salesman problems

Copyright @ Springer-Verlag Berlin Heidelberg 2009.In recent years, optimization in dynamic environments has attracted a growing interest from the genetic algorithm community due to the importance and practicability in real world applications. This paper proposes a new genetic algorithm, based on the inspiration from biological immune systems, to address dynamic traveling salesman problems. Within the proposed algorithm, a permutation-based dualism is introduced in the course of clone process to promote the population diversity. In addition, a memory-based vaccination scheme is presented to further improve its tracking ability in dynamic environments. The experimental results show that the proposed diversification and memory enhancement methods can greatly improve the adaptability of genetic algorithms for dynamic traveling salesman problems.This work was supported by the Key Program of National Natural Science Foundation (NNSF) of China under Grant No. 70431003 and Grant No. 70671020, the Science Fund for Creative Research Group of NNSF of China under GrantNo. 60521003, the National Science and Technology Support Plan of China under Grant No. 2006BAH02A09 and the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant No. EP/E060722/1
An experimental study on fitness distributions of tree shapes in GP with one-point crossover

Proceeding of: 12th European Conference, EuroGP 2009, Tübingen, Germany, April 15-17In Genetic Programming (GP), One-Point Crossover is an alternative to the destructive properties and poor performance of Standard Crossover. One-Point Crossover acts in two phases, first making the population converge to a common tree shape, then looking for the best individual within that shape. So, we understand that One-Point Crossover is making an implicit evolution of tree shapes. We want to know if making this evolution explicit could lead to any improvement in the search power of GP. But we first need to define how this evolution could be performed. In this work we made an exhaustive study of fitness distributions of tree shapes for 6 different GP problems. We were able to identify common properties on distributions, and we propose a method to explicitly evaluate tree shapes. Based on this method, in the future, we want to implement a new genetic operator and a novel representation system for GP.This work has been funded by the Spanish Ministry of Education and Science and FEDER under contract TIN2005-08818-C04 (the OPLINK project) and by Comunidad de Madrid under contract 2008/00035/001 (Técnicas de Aprendizaje Automático Aplicadas al Interfaz Cerebro-Ordenador)Publicad