ppxscal/embedding_network huggingface dataset:
Acceleration of lattice Boltzmann models through state extrapolation: a reaction--diffusion example
Authors: Christophe Vandekerckhove, Pieter Van Leemput, Dirk Roose
Venue: Applied Numerical Mathematics (2008)

Recently, several methods were proposed to accelerate a time integrator that uses a time step that is small compared to the dominant slow time scales of the dynamics of the system. In this paper, we apply these methods to accelerate a lattice Boltzmann model for the one-dimensional FitzHugh-Nagumo reaction-diffusion system. We compare the projective method [C.W. Gear, I.G. Kevrekidis, Projective methods for stiff differential equations: Problems with gaps in their eigenvalue spectrum, SIAM Journal on Scientific Computing 24 (4) (2003) 1091-1106] proposed by Gear and Kevrekidis to the related multistep scheme [C. Vandekerckhove, D. Roose, K. Lust, Numerical stability analysis of an acceleration scheme for step size constrained time integrators, Journal of Computational and Applied Mathematics 200 (2) (2007) 761-777] that we developed in an earlier paper. It is shown that the acceleration related error obtained with both methods is comparable and small compared to the discretization error of the lattice Boltzmann model itself. Therefore, a substantial speedup can be obtained, essentially without accuracy loss. Furthermore, it is shown that the accuracy obtained with these acceleration schemes is better than the accuracy of the lattice Boltzmann model with a larger time step. Finally, we illustrate that it is straightforward to combine the acceleration methods with traditional time integration tools such as adaptive step size control.
Smartcard-Based e-Coin for Electronic Payments on the (Mobile) Internet
Authors: A. Ruiz-Martínez, Ó. Cánovas, A. F. Gómez-Skarmeta
Venue: Proceedings of the 2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System (2007)

Nowadays e-purses are not being offered as payment method on the Internet. This is mainly due to the fact that vendors have to integrate in their devices a security application module (SAM) to exchange security messages between the e-purse and that module during the payment phase. In this paper we introduce a new payment method that combines the main advantages of e-purses and the use of e-coins to make payments. This proposal does not need either a SAM to make and verify payments on the Internet nor an on-line e-coin validation. Thus, we introduce the possibility that this e-purse can be easily integrated in payment applications that vendors offer on the Internet. Moreover, our proposal is based on the use of Javacard applets with the aim of being used in both traditional Javacard-compliant smartcards and SIM smartcards, which involves that our proposal might be considered as a mobile payment system.
Minimum Cardinality Matrix Decomposition into Consecutive-Ones Matrices: CP and IP Approaches
Authors: Davaatseren Baatar, Natashia Boland, Sebastian Brand, Peter J. Stuckey
Venue: Proceedings of the 4th international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems (2007)

We consider the problem of decomposing an integer matrix into a positively weighted sum of binary matrices that have the consecutive-ones property. This problem is well-known and of practical relevance. It has an important application in cancer radiation therapy treatment planning: the sequencing of multileaf collimators to deliver a given radiation intensity matrix, representing (a component of) the treatment plan.Two criteria characterise the efficacy of a decomposition: the beam-on time (length of time the radiation source is switched on during the treatment), and the cardinality (the number of machine set-ups required to deliver the planned treatment).Minimising the former is known to be easy. However finding a decomposition of minimal cardinality is NP-hard. Progress so far has largely been restricted to heuristic algorithms, mostly using linear programming, integer programming and combinatorial enumerative methods as the solving technologies. We present a novel model, with corresponding constraint programming and integer programming formulations. We compare these computationally with previous formulations, and we show that constraint programming performs very well by comparison.
Letters: A new approach to TS fuzzy modeling using dual kernel-based learning machines
Authors: Wei Li, Yupu Yang
Venue: Neurocomputing (2008)

This paper proposes a novel approach for structure identification of TS fuzzy model using dual kernel-based learning machines. Firstly, a convenient kernel fuzzy C-means clustering algorithm is developed to partition the data set into several clusters. Secondly, a new kernel function which is free of parameter selection is utilized to locate support vectors in each cluster. Finally, the model structure is further simplified by a combination strategy for support vectors. The experimental results show that the resulting model has concise structure and good generalization ability, especially its performance is insensitive to initial clustering number.
Correlated Active Appearance Models
Authors: Bernard Tiddeman, Jingying Chen
Venue: Proceedings of the 2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System (2007)

In this paper we describe a novel algorithm for active appearance model (AAM) alignment in which the normalized cross correlation (NCC) is maximized, rather than minimizing the sum of squared errors (SSE). We use an inverse compositional approach and derive an analytic solution rather than rely on an off-the-shelf non-linear optimization routine. We show that the algorithm performs better than traditional AAM fitting in terms of accuracy, stability and speed.
A uniform framework for weighted decision diagrams and its implementation
Authors: J&#x00f6;rn Ossowski, Christel Baier
Venue: International Journal on Software Tools for Technology Transfer (STTT) (2008)

This paper introduces a generic framework for OBDD variants with weighted edges. It covers many boolean and multi-valued OBDD-variants that have been studied in the literature and applied to the symbolic representation and manipulation of discrete functions. Our framework supports reasoning about reducedness and canonicity of new OBDD-variants and provides a platform for the implementation and comparison of OBDD-variants. Furthermore, we introduce a new multi-valued OBDD-variant, called normalized algebraic decision diagram, which supports min/max-operations and turns out to be very useful for, e.g., integer linear programming and model checking probabilistic systems. Finally, we explain the main features of an implementation of a newly developed BDD-package, called *JINC*, which relies on our generic notion of weighted decision diagrams, and realizes various synthesis algorithms, reordering techniques and transformation algorithms for boolean and multi-terminal OBDDs, with or without edge-attributes, and their zero-suppressed variants.
Robust Vessel Tree Modeling
Authors: M. Akif Gülsün, Hüseyin Tek
Venue: Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I (2008)

In this paper, we present a novel method for extracting center axis representations (centerlines) of blood vessels in contrast enhanced (CE)-CTA/MRA, robustly and accurately. This graph-based optimization algorithm which employs multi-scale medialness filters extracts vessel centerlines by computing the minimum-cost paths. Specifically, first, new medialness filters are designed from the assumption of circular/elliptic vessel cross-sections. These filters produce contrast and scale independent responses even the presence of nearby structures. Second, they are incorporated to the minimum-cost path detection algorithm in a novel way for the computational efficiency and accuracy. Third, the full vessel centerline tree is constructed from this optimization technique by assigning a saliency measure for each centerline from their length and radius information. The proposed method is computationally efficient and produces results that are comparable in quality to the ones created by experts. It has been tested on more than 100 coronary artery data set where the full coronary artery trees are extracted in 21 seconds in average on a 3.2GHz PC.
Plenary lecture VI: intelligent pattern recognition and biometrics
Authors: Patrick Wang
Venue: Proceedings of the American Conference on Applied Mathematics (2008)

This tutorial deals with advanced concepts of Artificial Intelligence (AI) and Pattern Recognition (PR), and their applications to solving real life problems including biometrics applications. It basically covers the following topics: (1) Overview of Pattern Recognition (PR),(2) Overview of Artificial Intelligence (AI),(3) The Relation Between PR and AI, (4) Analysis and Learning: Pattern Recognition Concept: Foundation and theories, (5) Importance of Ambiguity, and its Applications: Theory and applications, (6) An Overall Interactive Intelligent Pattern Recognition (IPR) System, (7) Concepts of Syntax, Semantics, and Pragmatics: Theories and Applications, (8) Importance of Ambiguity, and its Applications: Theory and Applications, (9) How it works: IPR and Applications to Solving Real Life Problems including Biometrics and Face Recognition, (10) Some More Illustrations, Discussions and Future Directions.
Restricted Sequential Floating Search Applied to Object Selection
Authors: J. Arturo Olvera-López, J. Francisco Martínez-Trinidad, J. Ariel Carrasco-Ochoa
Venue: Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition (2007)

The object selection is an important task for instance-based classifiers since through this process the size of a training set could be reduced and then the runtimes in both classification and training steps would be reduced. Several methods for object selection have been proposed but some methods discard relevant objects for the classification step. In this paper, we propose an object selection method which is based on the idea of sequential floating search. This method reconsiders the inclusion of relevant objects previously discarded. Some experimental results obtained by our method are shown and compared against some other object selection methods.
Self-consciousness for artificial entities using modular neural networks
Authors: Milton Martinez Luaces, Celina Gayoso Rocha, Juan Pazos Sierra, Alfonso Rodriguez Patón
Venue: Proceedings of the 9th WSEAS International Conference on Neural Networks (2008)

One of the most puzzling cognitive features is self-consciousness. A considerable amount of research has been conducted on this question in different fields like Psychology, Neurobiology and Cognitive Science. Self-consciousness implies not only self or group recognition, but also real knowledge of one's own identity. In this paper, a cognitive architecture of self-consciousness for autonomous artificial entities (holons) is proposed. This cognitive architecture includes: abstraction, self-representation, other individuals' representation, and action modules. It also includes a learning process of self-representation by direct (self-experience based) and observational learning (based on the observation of other individuals). For model implementation a new approach is taken using Modular Artificial Neural Networks (MANN) due to their biological inspiration, modularity and adaptability. We explain the cognitive architecture that enables dynamic self-representation. For model testing a multi-holon virtual environment was implemented. We analyse the effect of holon interaction, focusing on the evolution of the holon's abstract self-representation. Finally, the results are explained and analysed and conclusions drawn.
Spatio-temporal Dynamics during Perceptual Processing in an Oscillatory Neural Network
Authors: A. Ravishankar Rao, Guillermo Cecchi
Venue: Proceedings of the 18th international conference on Artificial Neural Networks, Part II (2008)

We examine the dynamics of object recognition in a multi-layer network of oscillatory elements. The derivation of network dynamics is based on principles of sparse representation, and results in system behavior that achieves binding through phase synchronization. We examine the behavior of the network during recognition of objects with missing contours. We observe that certain network units respond to missing contours with reduced amplitude and temporal delay, similar to neuroscientific findings. Furthermore, these units maintain synchronization with a high-level object representation only in the presence of feedback.Our results suggest that the illusory contour phenomena are formal consequences of a system that dynamically solves the binding problem, and highlight the functional relevance of feedback connections.
An Early Program Proof by Alan Turing
Authors: F. L. Morris, C. B. Jones
Venue: IEEE Annals of the History of Computing (1984)

The paper reproduces, with typographical corrections and comments, a 1949 paper by Alan Turing that foreshadows much subsequent work in program proving.
T2D: Generating Dialogues Between Virtual Agents Automatically from Text
Authors: Paul Piwek, Hugo Hernault, Helmut Prendinger, Mitsuru Ishizuka
Venue: Proceedings of the 7th international conference on Intelligent Virtual Agents (2007)

The Text2Dialogue (T2D) system that we are developing allows digital content creators to generate attractive multi-modal dialogues presented by two virtual agents--by simply providing textual information as input. We use Rhetorical Structure Theory (RST) to decompose text into segments and to identify rhetorical discourse relations between them. These are then "acted out" by two 3D agents using synthetic speech and appropriate conversational gestures. In this paper, we present version 1.0 of the T2D system and focus on the novel technique that it uses for mapping rhetorical relations to question---answer pairs, thus transforming (monological) text into a form that supports dialogues between virtual agents.
Statistical Appearance Models of Mammographic Masses
Authors: Michael Berks, Steven Caulkin, Rumana Rahim, Caroline Boggis, Susan Astley
Venue: Proceedings of the 9th international workshop on Digital Mammography (2008)

We present a method for building generative statistical appearance models of mammographic masses. We address several key issues that limited the performance of previous methods. In particular, we use MDL optimization to generate more compact shape correspondences; we describe a technique for the accurate estimation of the background tissue on which a mass is superimposed; and we highlight the importance of choosing suitable weighting between shape, texture and scale components in the final combined model. Improvements in the ability of the model to characterize a set of 101 mammographic masses are quantified using leave-one-out testing, showing a reduction in mean square error per pixel from 3.109 using a previous method to 1.262 using the new appearance model.
Kinaesthetic and Cutaneous Contributions to the Perception of Compressibility
Authors: Wouter M. Bergmann Tiest, Astrid M. Kappers
Venue: Proceedings of the 6th international conference on Haptics: Perception, Devices and Scenarios (2008)

Compressibility or hardness of objects is an important aspect in haptic perception. Both cutaneous and kinaesthetic information are used for the perception of compressibility. In this paper, the relative role of these contributions is investigated. This is done with psychophysical experiments using a purpose-made silicon rubber stimulus set. The fabrication and characterisation of the stimuli are described, as well as discrimination experiments with and without surface deformation of the stimuli. With the cutaneous cues of surface deformation present, the Weber fraction for hardness discrimination was 0.12. When surface deformation was removed and only kinaesthetic cues were available, the Weber fraction doubled, suggesting that the cutaneous sense contributes almost three quarters to hardness perception, and the kinaesthetic just over one quarter, if the information is integrated in a statistically optimal fashion.
Incremental Relevance Vector Machine with Kernel Learning
Authors: Dimitris Tzikas, Aristidis Likas, Nikolaos Galatsanos
Venue: Proceedings of the 5th Hellenic conference on Artificial Intelligence: Theories, Models and Applications (2008)

Recently, sparse kernel methods such as the Relevance Vector Machine (RVM) have become very popular for solving regression problems. The sparsity and performance of these methods depend on selecting an appropriate kernel function, which is typically achieved using a cross-validation procedure. In this paper we propose a modification to the incremental RVM learning method, that also learns the location and scale parameters of Gaussian kernels during model training. More specifically, in order to effectively model signals with different characteristics at various locations, we learn different parameter values for each kernel, resulting in a very flexible model. In order to avoid overfitting we use a sparsity enforcing prior that controls the effective number of parameters of the model. Finally, we apply the proposed method to one-dimensional and two-dimensional artificial signals, and evaluate its performance on two real-world datasets.
A Hybrid VNS with TS for the Single Machine Scheduling Problem to Minimize the Sum of Weighted Tardiness of Jobs
Authors: Xianpeng Wang, Lixin Tang
Venue: Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence (2008)

This paper presents a hybrid variable neighborhood search (VNS) with tabu search for the single machine scheduling problem to minimize the sum of weighted tardiness of jobs, a well-known strongly NP-hard problem. In this algorithm, a pool of many solutions is used in the VNS to simultaneously generate multiple trial solutions to improve the search diversification and a tabu search is incorporated in the local search procedure of VNS to improve the search intensification. Computational results show that the proposed VNS algorithm can obtain good solutions for all standard benchmark problems.
Automated Unsupervised Geomorphometric Classification of Earth Surface for Landslide Susceptibility Assessment
Authors: Maria Ioannilli, Alessandro Paregiani
Venue: Proceeding sof the international conference on Computational Science and Its Applications, Part I (2008)

The aim of this work is to define an automated method of terrain classification in order to evaluate the correlation degree between topographic forms of the analyzed territory and registered landslide phenomena with a Landslide Inventory and DEMs as unique input data. A reliable procedure that identifies areas subject to different levels of susceptibility by a geomorphometric approach is presented. The main objective is reached by means of intermediate steps. The first step is the individuation of a set of measures, a geometric signature, that describes topographic form to distinguish among geomorphically different landscapes; the identified parameters are slope gradient, aspect, plan and section curvatures, local convexity and surface texture, computed from a 30x30m square-grid digital elevation model (DEM). The second step is the classification of the analyzed territory in eleven classes using the geometric signature tool. Finally, the eleven classes are statistically correlated with the Landslide Inventory of the analyzed territory. This work represents a useful tool in large-scale landslide susceptibility analysis. In fact, the application of this repeatable and reliable procedure may return the best results in a short time and with low economic resources, providing specific useful information in planning Civil Protection investigations and operations.
Generating Traffic Time Series Based on Generalized Cauchy Process
Authors: Ming Li, S. C. Lim, Huamin Feng
Venue: Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007 (2007)

Generating traffic time series (traffic for short) is important in networking, e.g., simulating the Internet. In this aspect, it is desired to generate a time series according to a given correlation structure that may well reflect the statistics of real traffic. Recent research of traffic modeling exhibits that traffic is well modeled by a type of Gaussian process called the generalized Cauchy (GC) process indexed by two parameters that separately characterize the self-similarity (SS), which is local property described by fractal dimension D, and long-range dependence (LRD), which is a global feature that can be measured by the Hurst parameter H, instead of using the linear relationship D = 2 ¿ H as that used in traditional traffic model with a single parameter such as fractional Gaussian noise (FGN). This paper presents a computational method to generate series based on the correlation form of GC process indexed by 2 parameters. Hence, the present model can be used to simulate realizations that flexibly capture the fractal phenomena of real traffic for both short-term lags and long-term lags.
Mixed-Integer Evolution Strategies with Dynamic Niching
Authors: Rui Li, Jeroen Eggermont, Ofer M. Shir, Michael T. Emmerich, Thomas Bäck, Jouke Dijkstra, Johan H. Reiber
Venue: Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X (2008)

Mixed-Integer Evolution Strategies (MIES) are a natural extension of standard Evolution Strategies (ES) for addressing optimization of various types of variables --- continuous, ordinal integer, and nominal discrete --- at the same time. Like most Evolutionary Algorithms (EAs), they experience problems in obtaining the global optimum in highly multimodal search landscapes. Niching methods, the extension of EAs to multimodal domains, are designed to treat this issue. In this study we present a dynamic niching technique for Mixed-Integer Evolution Strategies, based upon an existing ES niching approach, which was developed recently and successfully applied to continuous landscapes. The new approach is based on the heterogeneous distance measure that addresses search space similarity in a way consistent with the mutation operators of the MIES. We apply the proposed Dynamic Niching MIES framework to a test-bed of artificial landscapes and show the improvement on the global convergence in comparison to the standard MIES algorithm.
Motion Primitives of Dancing
Authors: Raphaela Groten, Jens Hölldampf, Massimiliano Luca, Marc Ernst, Martin Buss
Venue: Proceedings of the 6th international conference on Haptics: Perception, Devices and Scenarios (2008)

In this work, we analyze whether oscillatory motion between two extreme positions could be used to create a robotic dancing partner that provides natural haptic feedback. To this end, we compared the pattern of hand movements performed following a pacing signal while participants were instructed to either move rhythmically or to dance. Furthermore, we analyzed the influence of the frequency and type of pacing signal on the two kinds of movements. Trajectories were analyzed in terms of: frequency of movement, spatial and temporal synchronization, and jerk.Results indicate that it is easier to perform synchronized movements while dancing, even though these movements partially deviate from the pacing frequency. Dance movements are in fact more complex than the ones produced to keep the rhythm and for this reason they should be modeled accordingly in order to provide realistic haptic feedback.