ppxscal/embedding_network huggingface dataset:
Facts, Events, and Inflection: When Language and Memory Dissociate
Authors: Michele Miozzo, Peter Gordon
Venue: Journal of Cognitive Neuroscience (2005)

We report on two brain-damaged patients who show contrasting patterns of deficits in memory and language functioning. One patient (AW) suffers from a lexical retrieval deficit and failed to produce many irregularly inflected words such as spun, forgotten, and mice, but demonstrated intact production of regularly inflected words such as walked and rats. She also had preserved declarative memory for facts and events. The other patient (VP) presented with a severe declarative memory deficit but showed no signs of impairment in producing either regular or irregular inflections. These patterns of deficits reveal that the retrieval of irregular inflections proceeds relatively autonomously with respect to declarative memory. We interpret these deficits with reference to three current theories of lexical structure: (a) Pinker's "words and rules" account, which assumes distinct mechanisms for processing regular and irregular inflections and proposes that lexical and semantic processing are subserved by distinct but interacting cognitive systems; (b) Ullman's "declarative/procedural" model, which assumes that mechanisms for the retrieval of irregular inflections are part of declarative memory; (c) Joanisse and Seidenberg's connectionist model, in which semantic information is critical for the retrieval of irregular inflections.
Using Gaussians Functions to Determine Representative Clustering Prototypes
Authors: Minyar Sassi, Amel Grissa Touzi, Habib Ounelli
Venue: Proceedings of the 17th International Conference on Database and Expert Systems Applications (2006)

Clustering is a process for grouping a set of objects into classes or clusters so that the objects within a cluster have high similarity, but are very dissimilar to objects in other clusters. Choosing cluster centers is crucial during clustering process. In this paper, we propose an improved fuzzy clustering approach, named FGWC (Fuzzy Gaussian Weights Clustering). We compared FGWC with an Enhanced Fuzzy C-Means (EFCM) clustering approach that we already presented in [1]. The EFCM determines automatically the number of clusters which is a user-defined parameter for FCM, and uses the fuzzy weights to compute cluster prototypes, but does nor take into account the distribution of the clusters. FGWC uses Gaussian functions for determining clustering prototypes. The generated cluster centers are more representative and accurate with FGWC than with EFCM.
Dynamic Dopamine Modulation in the Basal Ganglia: A Neurocomputational Account of Cognitive Deficits in Medicated and Nonmedicated Parkinsonism
Authors: Michael J. Frank
Venue: Journal of Cognitive Neuroscience (2005)

Dopamine (DA) depletion in the basal ganglia (BG) of Parkinson's patients gives rise to both frontal-like and implicit learning impairments. Dopaminergic medication alleviates some cognitive deficits but impairs those that depend on intact areas of the BG, apparently due to DA "overdose." These findings are difficult to accommodate with verbal theories of BG/DA function, owing to complexity of system dynamics: DA dynamically modulates function in the BG, which is itself a modulatory system. This article presents a neural network model that instantiates key biological properties and provides insight into the underlying role of DA in the BG during learning and execution of cognitive tasks. Specifically, the BG modulates the execution of "actions" (e.g., motor responses and working memory updating) being considered in different parts of the frontal cortex. Phasic changes in DA, which occur during error feedback, dynamically modulate the BG threshold for facilitating/suppressing a cortical command in response to particular stimuli. Reduced dynamic range of DA explains Parkinson and DA overdose deficits with a single underlying dysfunction, despite overall differences in raw DA levels. Simulated Parkinsonism and medication effects provide a theoretical basis for behavioral data in probabilistic classification and reversal tasks. The model also provides novel testable predictions for neuropsychological and pharmacological studies, and motivates further investigation of BG/DA interactions with the prefrontal cortex in working memory.
From structures and functors to modules and units
Authors: Scott Owens, Matthew Flatt
Venue: Proceedings of the eleventh ACM SIGPLAN international conference on Functional programming (2006)

Component programming techniques encourage abstraction and reuse through external linking. Some parts of a program, however, must use concrete, internally specified references, so a pure component system is not a sufficient mechanism for structuring programs. We present the combination of a static, internally-linked module system and a purely abstractive component system. The latter extends our previous model of typed units to properly account for translucency and sharing. We also show how units and modules can express an SML-style system of structures and functors, and we explore the consequences for recursive structures and functors.
Multidimensional Filtering Approach Based on Contextual Information
Authors: Sunghoon Cho, Moohun Lee, Changbok Jang, Euiin Choi
Venue: Proceedings of the 2006 International Conference on Hybrid Information Technology - Volume 02 (2006)

Existing recommended systems offer calculation of recommendation for user-item with e-commerce. But these systems omit much available information. It is user's contextual information. Thus accuracy of recommender systems is relatively lower. Mostly excepted information is difficult to clearly define the attributes and to calculate the values as the numerical data. If this contextual information can be changed into calculable element in recommender system, it can become the improved recommendation technique . This paper proposes multidimensional approach additionally applying contextual information for 2 dimensions based on existing recommendation system.
Statistical properties of one-dimensional chaotic signals
Authors: S. H. Isabelle, G. W. Wornell
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1995. on International Conference - Volume 02 (1995)

Signals arising out of nonlinear dynamical systems are compelling models for a wide range of phenomena. We develop several properties of signals obtained from Markov maps, an important family of such systems, and present analytical techniques for computing their statistics. Among other results, we demonstrate that all Markov maps produce signals with rational spectra, and can therefore be viewed as "chaotic ARMA processes". Finally, we demonstrate how Markov maps can approximate to arbitrary precision any of a broad class of chaotic maps and their statistics.
Time-varying orthogonal filter banks without transient filters
Authors: J. Mau, J. Valot, D. Minaud
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1995. on International Conference - Volume 02 (1995)

We present a solution for the construction of orthogonal time-varying filter banks without transient filters. To reach this result the idea is the following: all the various filter banks used in the time-varying decomposition are not arbitrary, but are linked together and in fact are derived from an unique initial orthogonal filter bank. With this new technique, the perfect reconstruction (PR) property is always guaranteed even if we switch abruptly from one filter bank to an other without the use of transient filters. We will explain, by taking an initial M-band orthogonal filter bank which performs a regular M-band frequency splitting, how to derive various mutually orthogonal filter banks with almost any arbitrary time/frequency resolution, even able to perform irregular frequency splitting like for example in a wavelet decomposition.
Mainstream parallel programming
Authors: Michael-Jon Ainsley Hore
Venue: Linux Journal (2006)

Get a Beowulf cluster running without having to fight off Grendel.
Lattice Synchronization of Neural Oscillators for Scene Segmentation
Authors: Liang Zhao
Venue: Proceedings of the Ninth Brazilian Symposium on Neural Networks (2006)

Synchronization is an important mechanism for selforganization of neurons. In this work we give a rigorous analyze on the synchronization condition of coupled chaotic neural oscillators on a 2D lattice. Then, we show how this result can be applied to construct oscillatory correlation neural network for scene segmentation.
Robust Point-Location in Generalized Voronoi Diagrams
Authors: S. Bereg, Y. Zhang, M. L. Gavrilova
Venue: Proceedings of the 3rd International Symposium on Voronoi Diagrams in Science and Engineering (2006)

We address the problemof robust point-locationin a generalized -dimensional Voronoi diagram. The exact point location requires the solution for expressions of degree four. A natural question is what can be done using expression of smaller degree. We apply polyhedral metrics for this task. In general dimensions two Minkowski metrics can be used L_1(Manhattan metric) and l_r00. The approximation factor is \sqrt d and the computation uses expressions of degree one. We also show that a polygonal metric can be applied in two dimensions. The compuation involves only calls of the algorithm ESSA for detecting the sign of a sum using floating-point arithmetic.
A simultaneous maximum likelihood estimator based on a generalized matched filter
Authors: J.-O. Gustavsson, P. O. Borjesson
Venue: Proceedings of the Acoustics, Speech, and Signal Processing,1994. on IEEE International Conference - Volume 04 (1994)

The paper discusses parameter estimation and detection in Laplace distributed noise. The received signal is modeled as r(/spl middot/)=As(/spl middot/,/spl theta/)+n(/spl middot/), where A is an unknown amplitude, /spl theta/ is the parameter vector to be estimated and n(/spl middot/) is independent Laplace distributed noise. The simultaneous maximum likelihood estimator of (A,/spl theta/) is derived. The derived estimator is based on a combination of a weighted median filter [Astola and Nuevo, 1992] and a generalized form of the ordinary matched filter [Gustavsson and Borjesson, 1992]. Examples of performance for four different detectors are given for a case of binary detection, when the amplitude A or the signal shape s(/spl middot/,/spl theta/) are varied. Simulations indicate that the performance of detectors based on the generalized matched filter is not particularly dependent on either the estimate of the amplitude A or the signal shape.
Detecting Cognitive States from fMRI Images by Machine Learning and Multivariate Classification
Authors: Yong Fan, Dinggang Shen, Christos Davatzikos
Venue: Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop (2006)

The major obstacle in building classifiers that robustly detect a particular cognitive state across different subjects using fMRI images has been the high inter-subject functional variability in brain activation patterns. To overcome this obstacle, firstly, the brain regions that are relevant to the problem under study are determined from the training data; then, statistical information of each brain region is extracted to form regional features, which are robust to inter-subject functional variations within the brain region; finally, the regional feature statistical variations across different samples are further alleviated by a PCA technique. To improve the generalization ability and efficiency of the classification, from the extracted regional features, a hybrid feature selection method is utilized to select the most discriminative features, which are used to train a SVM classifier for decoding brain states from fMRI images. The performance of this method is validated in a deception fMRI study. The proposed method yielded better results compared to other commonly used fMRI image classification methods.
A design framework for teaching/learning activities supporting Latino first graders' ten-structured thinking in urban classrooms
Authors: Karen C. Fuson, Steven T. Smith, Ana Maria Lo Cicero
Venue: Proceedings of the 1996 international conference on Learning sciences (1996)

This paper presents a design framework for teaching/learning activities during year-long classroom teaching experiments that sought to support first graders' ten-structured thinking in two predominantly-Latino low-SES urban classrooms (one English-speaking and one Spanish-speaking). The design framework outlines (1) theoretical perspectives on the teaching/learning domain of multidigit concepts and multidigit addition and subtraction, (2) theoretical perspectives on teaching and learning processes, and (3) the pragmatic context of the research. The particular elements within the framework generalize to other design tasks to various extents. The teaching/learning activities were successful in enabling children in these two classes to achieve above grade level and to look more like East Asian than U.S. children in their multidigit concepts.
Multivariate regression model selection from small samples using Kullback's symmetric divergence
Authors: Abd-Krim Seghouane
Venue: Signal Processing (2006)

The Kullback Information Criterion, KIC, and its univariate bias-corrected version, KICc, are two new developed criteria for model selection. The two criteria can be viewed as estimators of the expected Kullback symmetric divergence and they have a fixed bias corrected term. In this paper, a new small sample model selection criterion for multivariate regression models is developed. The proposed criterion is named KICvc, where the notation "vc" stands for vector correction, and it can be considered as an extension of KIC for multivariate regression models. KICvc adjusts KIC to be an unbiased estimator for the variant of the Kullback symmetric divergence, assuming that the true model is correctly specified or overfitted. Furthermore, KICvc provides better multivariate regression model order or dimension choices than KIC in small samples. Simulation results shows that the proposed criterion estimates the model order more accurately than other asymptotically efficient methods when applied to multivariate regression model selection in small samples. As a result, KICvc serves as an effective tool for selecting a multivariate regression model of appropriate dimension. A theoretical justification of the proposed criterion is presented.
Fractional generalized splines and signal processing
Authors: Rutuparna Panda, Madhumita Dash
Venue: Signal Processing (2006)

This paper presents the concept of fractional generalized splines, which is an extension of the idea of Unser's fractional splines. The first part of this paper describes a method for construction of fractional generalized splines through evaluating fractional finite differences. The main key to our approach is to provide an additional tuning parameter α by using a generating function, which is the solution of the Laguerre's nth-order differential equation. The second part of the paper deals with characterization of these functions in both time and frequency domain and shows how to use these results for construction of wavelet bases in L2 for signal processing applications. This paper also present simulation results to reveal the suitability of the proposed basis functions for signal approximation.
Visual perception modulated by galvanic vestibular stimulation
Authors: Naohisa Nagaya, Maki Sugimoto, Hideaki Nii, Michiteru Kitazaki, Masahiko Inami
Venue: Proceedings of the 2005 international conference on Augmented tele-existence (2005)

Galvanic vestibular stimulation (GVS) induces a sensation of virtual acceleration as vestibular information and its application is expected as a wearable interface because it does not need a large device like a motion platform. In particular, GVS with alternating current (AC) can influence vision that mainly consists of a torsional component. This research aimed to quantitatively evaluate the effects of GVS on visual perception, and to infer its cause. To investigate these issues, we conducted psychophysical experiments during GVS with AC that consisted of three different image presentation methods; images fixed on spatial coordinate, head coordinate and retina coordinate systems. It is suggested that the visual motion perception induced by GVS is mediated by eye movements. As well, the stimulus frequency response of the current threshold, at which the subjects perceived visual motion, showed a U-shaped curve for stimulus frequency dependency.
A Presentation Feature Based Approach to Improving Interactive Web Service Discovery in Web Portals
Authors: Jingyu Song, Jun Wei, Shuchao Wan, Hua Zhong
Venue: Proceedings of the IEEE International Conference on e-Business Engineering (2006)

Presentation level integration now becomes an important and fast growing trend in enterprise computing and portal-based composite applications are the mainstream to realize it. Such applications use portlet and interactive web service, which usually offers several portlets, as their basic constituents. Hence, portlet description and discovery are the key issues that have to be considered. This paper proposes a novel concept POI (Presentation Oriented Interface) to describe the presentation features of a portlet, so that interactive web services may be extended to facilitate the selection and interoperation of portlets. Interactive web services discovery can be effectively achieved based on the calculation of POI similarity that considers both type and structure similarity. Experiments show that the proposed approach can improve the satisfaction of service discovery, thereby achieving better application integration at presentation level.
Multi-objective optimisation of bijective S-boxes
Authors: Joanne Fuller, William Millam, Ed Dawson
Venue: New Generation Computing (2005)

In this paper we investigate the heuristic construction of bijective s-boxes that satisfy a wide range of cryptographic criteria including algebraic complexity, high nonlinearity, low autocorrelation and have none of the known weaknesses including linear structures, fixed points or linear redundancy. We demonstrate that the power mappings can be evolved (by iterated mutation operators alone) to generate bijective s-boxes with the best known tradeoffs among the considered criteria. The s-boxes found are suitable for use directly in modern encryption algorithms.
A wideband fast multipole method for the Helmholtz equation in three dimensions
Authors: Hongwei Cheng, William Y. Crutchfield, Zydrunas Gimbutas, Leslie F. Greengard, J. Frank Ethridge, Jingfang Huang, Vladimir Rokhlin, Norman Yarvin, Junsheng Zhao
Venue: Journal of Computational Physics (2006)

We describe a wideband version of the Fast Multipole Method for the Helmholtz equation in three dimensions. It unifies previously existing versions of the FMM for high and low frequencies into an algorithm which is accurate and efficient for any frequency, having a CPU time of O(N) if low-frequency computations dominate, or O(N logN) if high-frequency computations dominate. The performance of the algorithm is illustrated with numerical examples.
Efficient Maximally Stable Extremal Region (MSER) Tracking
Authors: Michael Donoser, Horst Bischof
Venue: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1 (2006)

This paper introduces a tracking method for the well known local MSER (Maximally Stable Extremal Region) detector. The component tree is used as an efficient data structure, which allows the calculation of MSERs in quasi-linear time. It is demonstrated that the tree is able to manage the required data for tracking. We show that by means of MSER tracking the computational time for the detection of single MSERs can be improved by a factor of 4 to 10. Using a weighted feature vector for data association improves the tracking stability. Furthermore, the component tree enables backward tracking which further improves the robustness. The novel MSER tracking algorithm is evaluated on a variety of scenes. In addition, we demonstrate three different applications, tracking of license plates, faces and fibers in paper, showing in all three scenarios improved speed and stability.
Subspace Trust-Region Methods for Large Bound-Constrained Nonlinear Equations
Authors: Stefania Bellavia, Benedetta Morini
Venue: SIAM Journal on Numerical Analysis (2006)

Trust-region methods for solving large bound-constrained nonlinear systems are considered. These allow for spherical or elliptical trust regions where the search for an approximate solution is restricted to a low-dimensional space. A general formulation for these methods is introduced and global and superlinear/quadratic convergence is shown under standard assumptions. Viable approaches for implementation in conjunction with Krylov methods are discussed and the practical performance of the resulting algorithms is shown.
A causal approach to hierarchical decomposition in reinforcement learning
Authors: Andrew G. Barto
Venue:  (2006)

Reinforcement learning provides a means for autonomous agents to improve their action selection strategies without the need for explicit training information provided by an informed instructor. Theoretical and empirical results indicate that reinforcement learning algorithms can efficiently determine optimal or approximately optimal policies in tasks of limited size. However, as the size of a task grows, reinforcement learning algorithms become less consistent and less efficient at determining a useful policy. A key challenge in reinforcement learning is to develop methods that facilitate scaling reinforcement learning algorithms up to larger, more realistic tasks. We present a series of algorithms that take advantage of task structure to make reinforcement learning more efficient in realistic tasks that display such structure. In each algorithm, we assume that the state space of a task is factored; i.e., states are collections of values of a set of state variables. Our work combines hierarchical decomposition and state abstraction to reduce the size of a task prior to applying reinforcement learning. Hierarchical decomposition breaks a task into several subtasks that can be solved separately. For hierarchical decomposition to simplify learning, it is critical that each subtask is easier to solve than the overall task. To achieve the goal of simplifying the subtasks, we perform state abstraction separately for each subtask. We begin by presenting an algorithm that uses experience from the environment to dynamically perform state abstraction for each subtask in an existing hierarchy of subtasks. Since our goal is to automate hierarchical decomposition as well as state abstraction, a second algorithm uses a dynamic Bayesian network action representation to automatically decompose a task into a hierarchy of subtasks. In addition, the algorithm provides an efficient way to perform state abstraction for each resulting subtask. A third algorithm constructs compact representations of activities that represent solutions to the subtasks. These compact representations enable the use of planning to efficiently approximate solutions to higher-level subtasks without interacting with the environment. Our fourth and final algorithm provides a means to learn a dynamic Bayesian network representation of actions from experience in tasks for which the representation is not available prior to learning. The dissertation provides a detailed description of each algorithm as well as some theoretical results. We also present empirical results of each algorithm in a series of experiments. In tasks that display certain types of structure, the simplifications introduced by our algorithms significantly improve the performance of reinforcement learning. The results indicate that our algorithms provide a promising approach to make reinforcement learning better suited to solve realistic tasks in which these types of structure are present.