pinecone/core-2020-05-10-deduplication huggingface dataset:
Enhancing SPH using moving least-squares and radial basis functions

In this paper we consider two sources of enhancement for the meshfree
Lagrangian particle method smoothed particle hydrodynamics (SPH) by improving
the accuracy of the particle approximation. Namely, we will consider shape
functions constructed using: moving least-squares approximation (MLS); radial
basis functions (RBF). Using MLS approximation is appealing because polynomial
consistency of the particle approximation can be enforced. RBFs further appeal
as they allow one to dispense with the smoothing-length -- the parameter in the
SPH method which governs the number of particles within the support of the
shape function. Currently, only ad hoc methods for choosing the
smoothing-length exist. We ensure that any enhancement retains the conservative
and meshfree nature of SPH. In doing so, we derive a new set of
variationally-consistent hydrodynamic equations. Finally, we demonstrate the
performance of the new equations on the Sod shock tube problem.Comment: 10 pages, 3 figures, In Proc. A4A5, Chester UK, Jul. 18-22 200
Traffic of Molecular Motors

Molecular motors perform active movements along cytoskeletal filaments and
drive the traffic of organelles and other cargo particles in cells. In contrast
to the macroscopic traffic of cars, however, the traffic of molecular motors is
characterized by a finite walking distance (or run length) after which a motor
unbinds from the filament along which it moves. Unbound motors perform Brownian
motion in the surrounding aqueous solution until they rebind to a filament. We
use variants of driven lattice gas models to describe the interplay of their
active movements, the unbound diffusion, and the binding/unbinding dynamics. If
the motor concentration is large, motor-motor interactions become important and
lead to a variety of cooperative traffic phenomena such as traffic jams on the
filaments, boundary-induced phase transitions, and spontaneous symmetry
breaking in systems with two species of motors. If the filament is surrounded
by a large reservoir of motors, the jam length, i.e., the extension of the
traffic jams is of the order of the walking distance. Much longer jams can be
found in confined geometries such as tube-like compartments.Comment: 10 pages, latex, uses Springer styles (included), to appear in the
  Proceedings of "Traffic and Granular Flow 2005
DSLAM: Decoupled Localization and Mapping for Autonomous Robots

The main contribution of this paper is the reformulation of the simultaneous localization and mapping (SLAM) problem for mobile robots such that the mapping and localization can be treated as two concurrent yet separated processes: D-SLAM (decoupled SLAM). It is shown that SLAM can be decoupled into solving a non-linear static estimation problem for mapping and a low-dimensional dynamic estimation problem for localization. The mapping problem can be solved using an Extended Information Filter where the information matrix is shown to be exactly sparse. A significant saving in the computational effort can be achieved for large scale problems by exploiting the special properties of sparse matrices. An important feature of D-SLAM is that the correlation among landmarks are still kept and it is demonstrated that the uncertainty of the map landmarks monotonically decrease. The algorithm is illustrated through computer simulations and experiments
Experimental and CFD study of the effects of design parameters on Reynolds number in a short duration hypersonic test facility

The first phase of this paper is aimed to develop a standard test procedure to check the effects of the design parameters (e.g. diaphragm pressure ratio, speed of sound ratio, temperature ratios, and geometry) in a short-duration hypersonic test facility that build at the Universiti Tenaga Nasional “UNITEN” in Malaysia on Reynolds number. The facility has been designed, built, and commissioned for different values of diaphragm pressure ratios and Mach number. A theoretical model was developed to evaluate the Mach number values as a function of diaphragm pressure ratio for different working fluids. The second phase is to run experimental tests for different operating conditions. The calculated parameters which are pressure, temperature and velocity were very comparable to the practical results. A high precision in house made thermocouple was used to measure the temperature profile during the facility operation. A numerical transient heat transfer mathematical model was developed to evaluate the heat flux from the surface temperature history. Comparing these results with a CFD model using commercial software Fluent was found to be much matched. The principle of operation and the reasoning behind building such a facility are explained, and the governing equations for the shock tube are presented. The selection of the shock tube parameters is explained. \u
Quantum chromodynamics

The book is a self-contained introduction to perturbative and nonperturbative Quantum chromodynamics (QCD) with worked-out exercises for students of theoretical physics. It will be useful as a reference for research scientists as well. Starting with the hadron spectrum, the reader becomes familiar with the representations of SU(N). Relativistic quantum field theory is recapitulated, and scattering theory is discussed in the framework of scalar quantum electrodynamics. Then the gauge theory of quarks and gluons is introduced. In the more advanced chapters, perturbative and nonperturbative techniques in state-of-the-art QCD are discussed in great detail. The new third edition has been extensively revised and enlarged to cover all new aspects in Quantum chromodynamics
An adaptive model of asset price and wealth dynamics in a market with heterogeneous trading strategies

The traditional asset-pricing models such as the capital asset pricing model (CAPM) of [42] and [34], the arbitrage pricing theory (APT) of [40], or the intertemporal capital asset pricing model (ICAPM) of [38] have as one of their important assumptions, investor homogeneity. In particular the paradigm of the representative agent assumes that all agents are homogeneous with regard to their preferences, their expectations and their investment strategies.1 However, as already argued by Keynes in the 1930s, agents do not have sufficient knowledge of the structure of the economy to form correct mathematical expectations that would be held by all agent
On the Construction of Time-Symmetric Black Hole Initial Data

We review in a pedagogical fashion the 3+1-split which serves to put
Einstein's equations into the form of a dynamical system with constraints. We
then discuss the constraint equations under the simplifying assumption of
time-symmetry. Multi-Black-Hole data are presented and more explicitly
described in the case of two holes. The effect of different topologies is
emphasized.Comment: 18 pages, Latex, uses Springer style-file lamuphys.sty. To appear in
  "Black Holes: Theory and Observation", edited by F. Hehl, C. Kiefer and R.
  Metzler, Springer 199
Towards a Full Quantum Theory of Black Holes

This review gives an introduction to various attempts to understand the
quantum nature of black holes. The first part focuses on thermodynamics of
black holes, Hawking radiation, and the interpretation of entropy. The second
part is devoted to the detailed treatment of black holes within canonical
quantum gravity. The last part adds a brief discussion of black holes in string
theory and quantum cosmology.Comment: 32 pages, needs macro lamuphys.sty, additional comments and
  references added, to appear in ``Black Holes: Theory and Observation''
  (Springer 1998
Mass Extinctions vs. Uniformitarianism in Biological Evolution

It is usually believed that Darwin's theory leads to a smooth gradual
evolution, so that mass extinctions must be caused by external shocks. However,
it has recently been argued that mass extinctions arise from the intrinsic
dynamics of Darwinian evolution. Species become extinct when swept by
intermittent avalanches propagating through the global ecology. These ideas are
made concrete through studies of simple mathematical models of coevolving
species. The models exhibit self-organized criticality and describe some
general features of the extinction pattern in the fossil record.Comment: 17 pages uuencoded with style file lamuphys.sty. 9 figures not
  included but can be obtained via bak@cmth.phy.bnl.gov. to appear in ``Physics
  of Biological Systems'' Lecture Notes in Physics (Springer-Verlag, Heidelberg
  , 1996
Explicit memory schemes for evolutionary algorithms in dynamic environments

Copyright @ 2007 Springer-VerlagProblem optimization in dynamic environments has atrracted a growing interest from the evolutionary computation community in reccent years due to its importance in real world optimization problems. Several approaches have been developed to enhance the performance of evolutionary algorithms for dynamic optimization problems, of which the memory scheme is a major one. This chapter investigates the application of explicit memory schemes for evolutionary algorithms in dynamic environments. Two kinds of explicit memory schemes: direct memory and associative memory, are studied within two classes of evolutionary algorithms: genetic algorithms and univariate marginal distribution algorithms for dynamic optimization problems. Based on a series of systematically constructed dynamic test environments, experiments are carried out to investigate these explicit memory schemes and the performance of direct and associative memory schemes are campared and analysed. The experimental results show the efficiency of the memory schemes for evolutionary algorithms in dynamic environments, especially when the environment changes cyclically. The experimental results also indicate that the effect of the memory schemes depends not only on the dynamic problems and dynamic environments but also on the evolutionary algorithm used
Genetic algorithms with self-organizing behaviour in dynamic environments

Copyright @ 2007 Springer-VerlagIn recent years, researchers from the genetic algorithm (GA) community have developed several approaches to enhance the performance of traditional GAs for dynamic optimization problems (DOPs). Among these approaches, one technique is to maintain the diversity of the population by inserting random immigrants into the population. This chapter investigates a self-organizing random immigrants scheme for GAs to address DOPs, where the worst individual and its next neighbours are replaced by random immigrants. In order to protect the newly introduced immigrants from being replaced by fitter individuals, they are placed in a subpopulation. In this way, individuals start to interact between themselves and, when the fitness of the individuals are close, one single replacement of an individual can affect a large number of individuals of the population in a chain reaction. The individuals in a subpopulation are not allowed to be replaced by individuals of the main population during the current chain reaction. The number of individuals in the subpopulation is given by the number of individuals created in the current chain reaction. It is important to observe that this simple approach can take the system to a self-organization behaviour, which can be useful for GAs in dynamic environments.Financial support was obtained from FAPESP (Proc. 04/04289-6)
Comparison of different classification techniques using WEKA for breast cancer

The development of data-mining applications such as classification and clustering has shown the need for machine learning algorithms to be applied to large scale data. In this paper present the comparison of different classification techniques using Waikato Environment for Knowledge Analysis or in short, WEKA. WEKA is an open source software which consists of a collection of machine learning algorithms for data mining tasks. The aim of this paper is to investigate the performance of different classification or clustering methods for a set of large data. The algorithm or methods tested are Bayes Network, Radial Basis Function, Pruned Tree, Single Conjunctive Rule Learner and Nearest Neighbors Algorithm. A fundamental review on the selected technique is presented for introduction purposes. The data breast cancer data with a total data of 6291 and a dimension of 699 rows and 9 columns will be used to test and justify the differences between the classification methods or algorithms. Subsequently, the classification technique that has the potential to significantly improve the common or conventional methods will be suggested for use in large scale data, bioinformatics or other general applications
Selecting informative genes from leukemia gene expression data using a hybrid approach for cancer classification

The development of microarray-based high-throughput gene profiling has led to the hope that this technology could provide an efficient and accurate means of diagnosing and classifying cancers. However, the large amount of data generated by microarrays requires effective selection of informative genes for cancer classification. Key issue that needs to be addressed is a selection of small number of informative genes that contribute to a disease from the thousands of genes measured on microarrays. This work deals with finding the small subset of informative genes from gene expression microarray data which maximize the classification accuracy. We introduce an improved version of hybrid of genetic algorithm and support vector machine for genes selection and classification. We show that the classification accuracy of the proposed approach is superior to a number of current state-of-the-art methods of one widely used benchmark dataset. The informative genes from the best subset are validated and verified by comparing them with the biological results produced from biology and computer scientist researchers in order to explore the biological plausibility
Visualizing collaborative time-varying scientific datasets

Our perceptive of the scientific datasets has largely relied on numerical and statistical analysis of data from experimental dimension and computer simulation result [4][14][11][12][13]. In particular, we consider a simulated 3D time-varying model of scientific datasets and examine the temporal correlation among datasets. Our goal is to contrive effective visual representations to assist scientists in ascertaining temporal correlation among intricate and apparently chaotic scientific datasets. We propose a hybrid application with combination of streamline, global and local color scale and opacity scheme for spatio-temporal collaborative depiction. We illustrated also few images that can offer an effective tool for visually mining 3D time-varying scientific datasets
Hardness Evaluation of Porous Hydroxyapatite Coating

The extensive use of appropriate coatings to improve wear resistance, friction coefficient, electrical properties, corrosion resistance and biomedical application has stimulated a growing interest in their mechanical properties and especially hardness testing that is routinely used for coating evaluation. In this study Jönsson and Hogmark model is applied for the porous hydroxyapatite produced by plasma spraying on Ti6A14V substrate. Firstly, the effect of indentation load on hardness values of coating and substrate are studied. The modified Jönsson and Hogmark model is used to explain the composite hardness behavior and the effect of coating porosity
Identifying and improving reusability based on coupling patterns

Open Source Software (OSS) communities have not yet taken full advantage of reuse mechanisms. Typically many OSS projects which share the same application domain and topic, duplicate effort and code, without fully leveraging the vast amounts of available code.\ud
This study proposes the empirical evaluation of source code folders of OSS projects in order to determine their actual internal reuse and their potential as shareable, fine-grained and externally reusable software components by future projects.\ud
This paper empirically analyzes four OSS systems, identifies which components (in the form of folders) are currently being reused internally and studies their coupling characteristics. Stable components (i.e., those which act as service providers rather than service consumers) are shown to be more likely to be reusable. As a means of supporting replication of these successful instances of OSS reuse, source folders with similar patterns are extracted from the studied systems, and identified as externally reusable components
Characterising strongly normalising intuitionistic sequent terms

This paper gives a characterisation, via intersection types, of the strongly normalising terms of an intuitionistic sequent calculus (where LJ easily embeds). The soundness of the typing
system is reduced to that of a well known typing system with intersection types for the ordinary lambda-calculus. The completeness of the typing system is obtained from subject expansion at root position. This paper's sequent term calculus integrates smoothly the lambda-terms with generalised application or explicit substitution. Strong normalisability of these terms as
sequent terms characterises their typeability in certain "natural'' typing systems with intersection types. The latter are in the natural deduction format, like systems previously studied by Matthes and Lengrand et al., except that they do not  contain any extra, exceptional rules for typing generalised applications or substitution
A Grid-enabled Lattice-Boltzmann-based modelling system

Lattice-Boltzmann (LB) methods are a well-known technique
in the context of computational fluid dynamics. By nature, they can eas-
ily be parallelized but their adaptation to the Grid environment is not
trivial due to hardware heterogeneity (CPU, memory. . . ) in a Grid. A
load balancing method to dynamically handle the differences in terms of
CPU number and power among the machines of a Grid is presented. The
CPU power is dynamically estimated using a benchmark. An estimation
method of execution time is also given.Peer reviewe
What is frequent in a single graph?

acceptance rate = 35%The standard, transactional setting of pattern mining assumes that data is subdivided in transactions; the aim is to find patterns that can be mapped onto at least a minimum number of transactions. However, this setting can be hard to apply when the aim is to find graph patterns in databases consisting of large graphs. For instance, the web,
or any social network, is a single large graph that one may not wish to split into small parts. The focus in network analysis is on finding structural regularities or anomalies in one network, rather than finding structural regularities common to a set of them. This requires us to revise
the definition of key concepts in pattern mining, such as support, in the single-graph setting. Our contribution is a support measure that we prove to be computationally less expensive and often closer to intuition than other measures proposed. Further we prove several properties between these measures and experimentally validate the efficiency of our measure.status: publishe
A novel approach for detecting symmetries in CSP models

While several powerful methods exist for automatically detecting symmetries in instances of constraint satisfaction problems (CSPs), current methods for detecting symmetries in CSP models are limited to the kind of symmetries that can be inferred from the global constraints present in the model. Herein, a new approach for detecting symmetries in CSP models is presented. The approach is based on first applying powerful methods to a sequence of problem instances, and then reasoning on the resulting instance symmetries to infer symmetries of the model. Our results show that this approach deserves further exploration.status: publishe
Forgetting Concepts in DL-Lite

To support the reuse and combination of ontologies in Semantic Web  applications, it is often necessary to obtain smaller ontologies  from existing larger ontologies.  In particular, applications may  require the omission of many terms, e.g., concept names and role  names, from an ontology.  However, the task of omitting terms from  an ontology is challenging because the omission of some terms may  affect the relationships between the remaining terms in complex  ways.  We present the first solution to this problem by adapting the  technique of forgetting, previously used in other domains.  Specifically, we present a semantic definition of forgetting for  description logics in general, which generalizes the standard  definition for classical logic.  We then introduce algorithms that  implement forgetting in both DL-Lite TBoxes and ABoxes, and in  DL-Lite knowledge bases.  We prove that the algorithms are correct  with respect to the semantic definition of forgetting, and that they  run in polynomial time.Griffith Sciences, School of Information and Communication TechnologyFull Tex
The creation and evaluation of iSPARQL strategies for matchmaking

This research explores a new method for Semantic Web service matchmaking based on iSPARQL strategies, which enables to query the Semantic Web with techniques from traditional information retrieval. The strategies for matchmaking that we developed and evaluated can make use of a plethora of similarity measures and combination functions from SimPack---our library of similarity measures. We show how our combination of structured and imprecise querying can be used to perform hybrid Semantic Web service matchmaking. We analyze our approach thoroughly on a large OWL-S service test collection and show how our initial strategies can be improved by applying machine learning algorithms to result in very effective strategies for matchmaking
Adding data mining support to SPARQL via statistical relational learning methods

Exploiting the complex structure of relational data enables to build better models by taking into account the additional information provided by the links between objects. We extend this idea to the Semantic Web by introducing our novel SPARQL-ML approach to perform data mining for Semantic Web data. Our approach is based on traditional SPARQL and statistical relational learning methods, such as Relational Probability Trees and Relational Bayesian Classifiers.\ud
\ud
We analyze our approach thoroughly conducting three sets of experiments on synthetic as well as real-world data sets. Our analytical results show that our approach can be used for any Semantic Web data set to perform instance-based learning and classification. A comparison to kernel methods used in Support Vector Machines shows that our approach is superior in terms of classification accuracy
Improving Interoperability Using Query Interpretation in Semantic Vector Spaces

International audienceIn semantic web applications where query initiators and information providers do not necessarily share the same ontology, semantic interoperability generally relies on ontology matching or schema mappings. Information exchange is then not only enabled by the established correspondences (the ``shared'' parts of the ontologies) but, in some sense, limited to them. Then, how the ``unshared'' parts can also contribute to and improve information exchange ? In this paper, we address this question by considering a system where documents and queries are represented by semantic vectors. We propose a specific query expansion step at the query initiator's side and a query interpretation step at the document provider's. Through these steps, unshared concepts contribute to evaluate the relevance of documents wrt. a given query. Our experiments show an important improvement of retrieval relevance when concepts of documents and queries are not shared. Even if the concepts of the initial query are not shared by the document provider, our method still ensures 90% of the precision and recall obtained when the concepts are shared