ppxscal/embedding_network huggingface dataset:
Seqhound: an integrated bioinformatics resource and its applications
Authors: Katerina Michalickova
Venue:  (2005)

SeqHound is an integrated bioinformatics database system. SeqHound provides a resource of basic biological information and enables hypothesis driven bioinformatics research. The system facilitates simple, uniform and automated access to various biological data resources. It offers daily updated contents of all Entrez sequence databases, 3-D structural data and annotation. SeqHound holds links from sequences to sequence neighbours, equivalent sequences from other sources, 3-D structures, structural domains, taxonomy, complete genomes and functional annotation including Gene Ontology terms and literature links to PubMed. Currently, the SeqHound data collection is being enriched with molecule interaction data, literature mining data and an extensive collection of sequence identifiers from assorted specialized databases. SeqHound provides the functionality necessary to retrieve sequences, structures and structural domains using numerous entry points for querying. It is accessible via a web server through a remote Application Programming Interface (API) in Perl, Bioperl, Java, C or C++ programming languages or directly through an optimized local C API. The system is based on the National Center for Biotechnology Information data model and programming tools and offers equivalent functionality in terms of available data formats. Sequences may be retrieved in FASTA, GenBank, ASN.1 and XML formats. Structures are available in ASN.1, XML and PDB formats. The data held by SeqHound can be a starting point for any bioinformatics application. If an application or calculation is to be performed on an on-going basis, the underlying data must also be regularly updated. SeqHound removes the burden of keeping data up to date from the researcher allowing them to focus on developing their bioinformatics analysis. Several published applications ranging from lightweight GUI interfaces to robust data analysis applications were built using SeqHound. The thesis presents a family of species-specific scoring functions, PreBIND, BIND, FAST, Kangaroo and MMDBind systems and explains the way they utilize SeqHound.
Neuroscience instrumentation and distributed analysis of brain activity data: a case for eScience on global Grids: Research Articles
Authors: Rajkumar Buyya, Susumu Date, Yuko Mizuno-Matsumoto, Srikumar Venugopal, David Abramson
Venue: Concurrency and Computation: Practice Experience (2005)

The distribution of knowledge (by scientists) and data sources (advanced scientific instruments), and the need for large-scale computational resources for analyzing massive scientific data are two major problems commonly observed in scientific disciplines. Two popular scientific disciplines of this nature are brain science and high-energy physics. The analysis of brain-activity data gathered from the MEG (magnetoencephalography) instrument is an important research topic in medical science since it helps doctors in identifying symptoms of diseases. The data needs to be analyzed exhaustively to efficiently diagnose and analyze brain functions and requires access to large-scale computational resources. The potential platform for solving such resource intensive applications is the Grid. This paper presents the design and development of MEG data analysis system by leveraging Grid technologies, primarily Nimrod-G, Gridbus, and Globus. It describes the composition of the neuroscience (brain-activity analysis) application as parameter-sweep application and its on-demand deployment on global Grids for distributed execution. The results of economic-based scheduling of analysis jobs for three different optimizations scenarios on the world-wide Grid testbed resources are presented along with their graphical visualization. Copyright &copy; 2005 John Wiley & Sons, Ltd.
A neural network scheme for transparent surface modelling
Authors: Mohamad Ivan Fanany, Itsuo Kumazawa, Kiichi Kobayashi
Venue: Proceedings of the 3rd international conference on Computer graphics and interactive techniques in Australasia and South East Asia (2005)

This paper presents a new neural network (NN) scheme for recovering three dimensional (3D) transparent surface. We view the transparent surface modeling, not as a separate problem, but as an extension of opaque surface modeling. The main insight of this work is we simulate transparency not only for generating visually realistic images, but for recovering the object shape. We construct a formulation of transparent surface modeling using ray tracing framework into our NN. We compared this ray tracing method, with a texture mapping method that simultaneously map the silhouette images and smooth shaded images (obtained form our NN), and textured images (obtained from the teacher image) to an initial 3D model. By minimizing the images error between the output images of our NN and the teacher images, observed in multiple views, we refine vertices position of the initial 3D model. We show that our NN can refine the initial 3D model obtained by polarization images and converge into more accurate surface.
System of Generalized Vector Quasi-Equilibrium Problems with Applications to Fixed Point Theorems for a Family of Nonexpansive Multivalued Mappings
Authors: Lai-Jiu Lin
Venue: Journal of Global Optimization (2006)

In this paper, we establish the existence theorems of the generalized vector quasi-equilibrium problems. From some existence theorem, we establish fixed point theorems for a family of lower semicontinuous or nonexpansive multivalued mappings. We also obtain the existence theorems of system of mixed generalized vector variational-like inequalities and existence theorems of the Debreu vector equilibrium problems and the Nash vector equilibrium problems.
Incremental Methods for Simple Problems in Time Series: Algorithms and Experiments
Authors: Xiaojian Zhao, Xin Zhang, Tyler Neylon, Dennis Shasha
Venue: Proceedings of the 9th International Database Engineering & Application Symposium (2005)

A time series (or equivalently a data stream) consists of data arriving in time order. Single or multiple data streams arise in fields including physics, finance, medicine, and music, to name a few. Often the data comes from sensors (in physics and medicine for example) whose data rates continue to improve dramatically as sensor technology improves and as the number of sensors increases. So fast algorithms become ever more critical in order to distill knowledge from the data. This paper presents our recent work regarding the incremental computation of various primitives: windowed correlation, matching pursuit, sparse null space discovery and elastic burst detection. The incremental idea reflects the fact that recent data is more important than older data. Our StatStream system contains an implementation of these algorithms, permitting us to do empirical studies on both simulated and real data.
Similarity Search in Ad Hoc Networks Using Semantic-Based Caching
Authors: Bo Yang, Ali R. Hurson
Venue: Proceedings of the The IEEE Conference on Local Computer Networks 30th Anniversary (2005)

Ad hoc networks build temporary wireless connections in environments where the stationary infrastructures are either destroyed or too expensive to construct. Most of the previous research in ad hoc networks focuses on routing protocols that adapt to the dynamic network topologies, and not much work has been done on data accessing. One important data accessing application is similarity search, which provides the foundation of content-based retrieval. Many traditional similarity search algorithms are based on centralized or flooding mechanisms, which are not effective in wireless ad hoc network environments due to the multiple limitations such as bandwidth and power. In this paper we tackle the problem of similarity search by using semantic-based caching to reflect the data content distribution in the network. The basic idea is analyzing the cached results of earlier queries and trying to resolve the later queries within a small collection of content-related mobile nodes. Based on a Hilbert space-filling curve, the data points in a multi-dimensional semantic space are described as a linear representation. These data points are further cached to facilitate query processing. Through extensive simulations, we show that our method can perform similarity search with improved performance in terms of search cost and response time.
Boundary behavior of a infinitesimal metric and intrinsic measure on domains and moduli space
Authors: Eric Wayne Overholser
Venue:  (2005)

We investigate the intrinsic volume (n, n)-form of Eisenman and Kobayashi and the complex Finder metric of Kobayashi and Royden. We show that the natural logarithm of this measure and metric satisfy a modified sub-mean value property near the boundary of particular bounded domains. Then we illustrate how a bounded strictly pseudoconvex domain is such a domain which exhibits this boundary behavior for the Kobayashi-Royden metric. Also, we show how the moduli space of Riemann surfaces of given genus larger than one is such a particular domain which exhibits this boundary behavior for the Eisenman-Kobayashi volume form. Last, we show the equivalence of the Carath&eacute;odory and Eisenman-Kobayashi volume forms on Teichm&uuml;ller space of Riemann surfaces of given genus larger than one.
Semi-Supervised Mixture of Kernels via LPBoost Methods
Authors: Jinbo Bi, Glenn Fung, Murat Dundar, Bharat Rao
Venue: Proceedings of the Fifth IEEE International Conference on Data Mining (2005)

We propose an algorithmto construct classification models with a mixture of kernels from labeled and unlabeled data. The derived classifier is a mixture of models, each based on one kernel choice from a library of kernels. The sparse-favoring 1-norm regularization method is employed to restrict the complexity of mixture models and to achieve the sparsity of solutions. By modifying the column generation boosting algorithm LPBoost to a more general linear programming formulation, we are able to efficiently solve mixture-of-kernel problems and automatically select kernel basis functions centered at labeled data as well as unlabeled data. The effectiveness of the proposed approach is proved by experimental results on benchmark datasets.
Least Squares Fitting Articulated Objects
Authors: C. Gronwall, Pierre Anderson, Fredrik Gustafsson
Venue: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03 (2005)

In safety and security applications, one issue is target recognition. In some recognition processes, for vehicle recognition, we must take into account that the targetmaybe articulated. Vehicles can easily change shape by opening of a door, adding of load etc.This change of shape can be moddled as an articulation. if the articluation of model the target also can be moddled, the mathcing with library modles can be improved. In this paper we propose a method for modular least squares fitting of two 3D point scatters with points correspondence. A method for least squares fitting of a 3D pointscatter and a CAD (face) models is also proposed. An example pf modular leatssquares fittingtwo #D point scatters,based on simulated data, is known.
D&eacute;finition du contexte: fonctions de co&ucirc;t et m&eacute;thodes de s&eacute;lection
Authors: Stéphane Lavirotte, Diane Lingrand, Jean-Yves Tigli
Venue: Proceedings of the 2nd French-speaking conference on Mobility and ubiquity computing (2005)

This paper targets with applications running on mobile devices. We aim at developing a formalism and a framework to simplify the development of applications taking into account the context in which they are running.Following previous studies from other authors, we extend the notion of context area using distance function with cost function on context space dealing on different modes of selection.We extend the context area formalism to encompass more applications. We demonstrate on a mobile applications: the museum visit.
On the development of in-flight autonomous integrity monitoring of stored geo-spatial data using forward-looking remote sensing technology
Authors: Steven D. Young
Venue:  (2005)

Synthetic Vision Systems have been proposed that integrate traditional information provided by Primary Flight Displays with depictions of terrain and other geo-spatial features. As validation is impractical, the geo-spatial models used by an SVS typically have no inherent integrity. This has been a primary constraint to approved use as other than &ldquo;advisory-only&rdquo; in civil aviation. This dissertation describes a general approach to using forward-looking remote sensing technology as part of an in-flight monitor that provides a bounded level of integrity for geo-spatial feature data. After reviewing a taxonomy of sensor technologies and applications relevant to the premise, a specific proof-of-concept implementation using X-band radar is discussed. Terrain shadowing features, as seen by the radar, are compared in a statistical manner against estimated shadow features extracted from a stored terrain model as viewed from the perspective of the airborne observer. A test statistic is defined that enables detection of errors as small as the range resolution of the radar. Experimental results obtained from multiple aircraft platforms hosting certified commercial-off-the-shelf X-band radars test the premise.
Accuracy Improvement for Handwritten Japanese Word Recognition by Combination of Character and Word Recognizer
Authors: Yoshinobu Hotta, Hiroaki Takebe, Misako Suwa, Satoshi Naoi
Venue: Proceedings of the Eighth International Conference on Document Analysis and Recognition (2005)

This paper proposes a combination method of character recognizer (CR) and word recognizer (WR) for Japanese handwritten words. CR is composed of character segmentation, isolated character recognition, and postprocessing. On the other hand, WR recognizes word images holistically based on a given word lexicon. These methods have been used as key modules in handwritten address recognition but have not been compared quantitatively up to now. In this paper, CR and WR are first empirically compared with each other using synthesized words generated from character images. Based on the comparison, a combination method is then proposed. Experimental results show that the combination approach can achieve better recognition performance than the other two methods
Multi-Target Tracking in Clutter with Histogram Probabilistic Multi-Hypothesis Tracker
Authors: Ahmet G. Pakfiliz, Murat Efe
Venue: Proceedings of the 18th International Conference on Systems Engineering (2005)

This study presents a recently developed tracking algorithm, namely Histogram Probabilistic Multi-Hypothesis Tracker (H-PMHT), a modified version of PMHT, for multi-target tracking. Even though the theory of H-PMHT could be easily extended to multi-dimensional case, its applications have only been realized for one-dimensional cases. In this work the theory of H-PMHT has been extended into two-dimensional case and a its performance has been compared to that of Interacting Multi Model Probabilistic Data Association Filter (IMMPDAF) with Amplitude Information (IMMPDAF-AI). Simulation results reveal that H-PMHT Algorithm outperforms the IMMPDAF-AI under various conditions explained in the following sections.
Markov Random Fields for Handwritten Chinese Character Recognition
Authors: Jia Zeng, Zhi-Qiang Liu
Venue: Proceedings of the Eighth International Conference on Document Analysis and Recognition (2005)

In this paper, we propose a statistical-structural scheme for Chinese character modeling based on Markov random fields (MRFs). We use 2-D Gabor filters to extract directional stroke segments from images of Chinese characters, where each stroke segment is associated with a state in Markov random field models. The structural information is described by neighborhood system and pair-state clique potentials; meanwhile the statistical information is represented by single-state probability density functions (pdfs). Extensive experiments on similar characters have been carried out on the database ETL9B. The experimental results confirm that Markov random field models are effective in modeling both statistical and structural information of Chinese characters, and works well for handwritten Chinese character recognition.
A Software-Based Concurrent Error Detection Technique for PowerPC Processor-based Embedded Systems
Authors: Mahdi Fazeli, Reza Farivar, Seyed Ghassem Miremadi
Venue: Proceedings of the 20th IEEE International Symposium on Defect and Fault Tolerance in VLSI Systems (2005)

This paper presents a behavior-based error detection technique called Control Flow Checking using Branch Trace Exceptions for PowerPC processors family (CFCBTE). This technique is based on the branch trace exception feature available in the PowerPC processors family for debugging purposes. This technique traces the target addresses of program branches at run-time and compares them with reference target addresses to detect possible violations caused by transient faults. The reference target addresses are derived by a preprocessor from the source program. The proposed technique is experimentally evaluated on a 32-bit PowerPC microcontroller using software implemented fault injection (SWIFI). The results show that this technique detects about 91% of the injected control flow errors. The memory overhead is 39.16% on average, and the performance overhead varies between 110% and 304% depending on the workload used. This technique does not modify the program source code.
A Prescreener for 3D Face Recognition Using Radial Symmetry and the Hausdorff Fraction
Authors: Melissa L. Koudelka, Mark W. Koch, Trina D. Russ
Venue: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03 (2005)

Face recognition systems require the ability to efficiently scan an existing database of faces to locate a match for a newly acquired face. The large number of faces in real world databases makes computationally intensive algorithms impractical for scanning entire databases. We propose the use of more efficient algorithms to "prescreen" face databases, determining a limited set of likely matches that can be processed further to identify a match. We use both radial symmetry and shape to extract five features of interest on 3D range images of faces. These facial features determine a very small subset of discriminating points which serve as input to a prescreening algorithm based on a Hausdorff fraction. We show how to compute the Haudorff fraction in linear O(n) time using a range image representation. Our feature extraction and prescreening algorithms are verified using the FRGC v1.0 3D face scan data. Results show 97% of the extracted facial features are within 10 mm or less of manually marked ground truth, and the prescreener has a rank 6 recognition rate of 100%.
Propagating distributions on a hypergraph by dual information regularization
Authors: Koji Tsuda
Venue: Proceedings of the 22nd international conference on Machine learning (2005)

In the information regularization framework by Corduneanu and Jaakkola (2005), the distributions of labels are propagated on a hypergraph for semi-supervised learning. The learning is efficiently done by a Blahut-Arimoto-like two step algorithm, but, unfortunately, one of the steps cannot be solved in a closed form. In this paper, we propose a dual version of information regularization, which is considered as more natural in terms of information geometry. Our learning algorithm has two steps, each of which can be solved in a closed form. Also it can be naturally applied to exponential family distributions such as Gaussians. In experiments, our algorithm is applied to protein classification based on a metabolic network and known functional categories.
A middleware for experimentation on dynamic adaptation
Authors: Renato Maia, Renato Cerqueira, Fabio Kon
Venue: Proceedings of the 4th workshop on Reflective and adaptive middleware systems (2005)

This paper presents OiL, an adaptive middleware that aims at supporting experimentation with different models and techniques for dynamic adaptation of distributed systems. OiL is a CORBA implementation written completely in Lua, an interpreted language with several reflective and data description facilities. In addition to the support for dynamic adaptation, OiL was designed to be deployed on a wide range of platforms, from server machines to PDAs and mobile phones, enabling experimentation in different applications scenarios. In this paper, we report the results already achieved in this project and discuss future directions.
Nonsmooth Nonnegative Matrix Factorization (nsNMF)
Authors: Alberto Pascual-Montano, J. M. Carazo
Venue: IEEE Transactions on Pattern Analysis and Machine Intelligence (2006)

We propose a novel nonnegative matrix factorization model that aims at finding localized, part-based, representations of nonnegative multivariate data items. Unlike the classical nonnegative matrix factorization (NMF) technique, this new model, denoted "nonsmooth nonnegative matrix factorization” (nsNMF), corresponds to the optimization of an unambiguous cost function designed to explicitly represent sparseness, in the form of nonsmoothness, which is controlled by a single parameter. In general, this method produces a set of basis and encoding vectors that are not only capable of representing the original data, but they also extract highly localized patterns, which generally lend themselves to improved interpretability. The properties of this new method are illustrated with several data sets. Comparisons to previously published methods show that the new nsNMF method has some advantages in keeping faithfulness to the data in the achieving a high degree of sparseness for both the estimated basis and the encoding vectors and in better interpretability of the factors.
Interactive Sankey Diagrams
Authors: Patrick Riehmann, Manfred Hanfler, Bernd Froehlich
Venue: Proceedings of the Proceedings of the 2005 IEEE Symposium on Information Visualization (2005)

We present a system that allows users to interactively explore complex flow scenarios represented as Sankey diagrams. Our system provides an overview of the flow graph and allows users to zoom in and explore details on demand. The support for quantitative flow tracing across the flow graph as well as representations at different levels of detail facilitate the understanding of complex flow situations. The energy flow in a city serves as a sample scenario for our system. Different forms of energy are distributed within the city and they are transformed into heat, electricity, or other forms of energy. These processes are visualized and interactively explored. In addition our system can be used as a planning tool for the exploration of alternative scenarios by interactively manipulating different parameters in the energy flow network.
Scalable Multistage Network for Multiprocessor System-on-Chip Design
Authors: Sammy Meftali, Jean-luc Dekeyser, Isaac D. Scherson
Venue: Proceedings of the 8th International Symposium on Parallel Architectures,Algorithms and Networks (2005)

This paper presents a micro-network that is ageneric, scalable and multi-stage interconnect architecture for systems on a chip (SoC). Thenetwork architecture relies on a packet switching and point-to-point bi-directional links between the routers implementing the micro network. The NoC provides a configurable number of OCP compliant communication interfaces for both initiators (master) and targets (slave). This network has been used in a multiprocessor SoC with 16 initiators 16 slaves, and compared with an AMBA bus in terms of latency and saturation threshold.