Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Bayesian Models via Monte Carlo - An Introduction with Examples -

This report gives an introduction to a Bayesian probabilistic approach to modeling a dynamic system, with emphasis on stochastic methods for posterior inference. The Bayesian paradigm is a powerful tool to combine observed data along with prior knowledge to gain a current (probabilistic) understanding of unknown model parameters. In particular, it provides a very natural framework for updating the state of knowledge in a dynamic system. For complex systems, such updating needs to be carried out via stochastic sampling of unknown model parameters. An overview is given of the well established Markov chain Monte Carlo (MCMC) approach to achieve this and of the more recent sequential Monte Carlo (SMC) approach, which is better suited for dynamic systems. Examples are provided, including an application to event reconstruction for an atmospheric release.
PostgreSQL Developer's Handbook

From the Publisher: PostgreSQL Developer's Handbook provides a complete overview of the PostgreSQL database server and extensive coverage of its core features, including object orientation, PL/pgSQL, and the most important programming interfaces. The authors introduce the reader to the language and syntax of PostgreSQL and then move quickly into sophisticated programming topics. Readers will learn how to program efficient date calculations, to integrate PostgreSQL into the Microsoft Office environment and how to build dynamic Flash Web sites using PostgreSQL. The authors provide tested sample code, accompanied by detailed explanations, allowing developers to get up and running quickly with PostgreSQL and to solve common PostgreSQL problems.
General Purpose Computing on Graphics Processing Units for Robotic Applications

This paper deals with research related with the improvements of state of the art algorithms used in robotic applications based on parallel computation. The main goal is to decrease the computational complexity of 3D cloud of points processing in such applications as: data filtering, normal vector estimation, data registration and calculation of point feature histogram. The presented results efficiently improve the existing implementations with minimal lost of accuracy. The main contribution is a regular grid decomposition originally implemented for nearest neighborhood search. This data structure is used almost for all presented methods, it provides an efficient method for decreasing the time of computation. The results are compared with well-known robotic frameworks such as PCL and 3DTK.
Metodologie e problematiche per una biblioteca digitale. Il caso di digilibLT

Una biblioteca digitale, oggi, non e piu in se stessa un prodotto altamente innovativo, ma uno strumento consueto di lavoro e dunque deve per quanto possibile rispondere nel modo migliore a una molteplicita di esigenze e vincoli. digilibLT (digital library of late-antique latin texts; http://digiliblt.unipmn.it) e una biblioteca digitale nata di recente con lo scopo di raccogliere tutti i testi latini pagani tardi, a completamento della raccolta dei testi latini di epoca classica pubblicata nel PHI CDROM #5.3. La biblioteca e stata progettata in risposta ad un bando della Regione Piemonte, da cui e cofinanziata, con l’intento di rispondere alle esigenze piu avvertite tra gli studiosi e i bibliotecari. I testi antichi, che sono/saranno disponibili per il download in molteplici formati, da XML-TEI a testo, PDF ed epub (oltre al ‘vecchio formato’ Beta della raccolta PHI 5.3, per ragioni di compatibilita verso il passato), per rispondere alle esigenze di qualita degli studiosi provengono tutti da edizioni critiche recenti e selezionate dal comitato scientifico della biblioteca; introduzioni, apparati e commenti presenti nelle medesime edizioni sono invece del tutto assenti, in ossequio alla legge sul diritto d’autore. Il sistema e costruito interamente con free software, adottando senza limitazioni i principi digilibLT e le licenze Open Access per tutti i contenuti (pagine Web e testi).
Diseño e implementación de un sistema de seguridad vehicular mediante reconocimiento facial a través de visión artificial

El documento consiste en un sistema de seguridad antirrobo de vehiculos basado en vision artificial mediante varias tecnicas de reconocimiento facial, el cual permite el encendido y la conduccion de personas autorizadas previamente establecida en una base de datos y niega el acceso a otras personas haciendo sonar alarmas, esto se logra mediante la activacion del rele de la bomba de gasolina, el reconocimiento se lo hace cada cierto tiempo para a cada momento verificar la identidad de la persona que esta conduciendo el vehiculo.
A Token-based Code Clone Detection Tool - CCFinder and Its Empirical Evaluation

A code clone is a code portion in source files that is identical or similar to another. Since code clones generally reduce maintainability of software, several code clone detection techniques and tools have been proposed. This paper proposes a new clone detection technique, which consists of transformation of input source text and token-by-token comparison. Based on the proposed code clone detection technique, we developed a tool named CCFinder, which extracts code clones in C/C++ or Java source files. As well metrics for code clones were developed. In order to evaluate the usefulness of the tool and metrics, we conducted several experiments. As the results, the tool found several subsystems in two operating systems, namely FreeBSD and Linux, that could be traced to the same original. As well, the proposed metrics found interesting clones in a Java library, JDK. †Graduate School of Engineering Science, Osaka University ‡Graduate School of Information Science, Nara Institute of Science and Technology Contact to: Toshihiro Kamiya c/o Katuro Inoue Division of Software Science Graduate School of Engineering Science, Osaka University, Machikaneyama-cho 1-3, Toyonaka-city, 860-8531, Japan Phone: +81-6-6850-6571, Fax: +81-6-6850-6574 e-mail: kamiya@ics.es. osaka-u.ac. jp
Realistic Performance Gain Measurements for XML Data Streaming with Meta Data

This report is part of our ongoing project on the optimization of stream processing for XPath queries on XML documents. The fact that XML documents can be large relative to query-processing memory is one of the reasons to favour streaming over in-core processing. But even in streaming mode query processing uses storage and hence time proportional to the depth of sub-documents. With the goal of querying large documents on mobile devices, or very large ones on normal machines, we have designed a scheme whereby exhaustive searching can be traded against streaming performance. Our scheme uses query meta-data which restricts the search to a subset of the document. In an earlier report we have measured the maximal theoretical gains possible with perfect ”chance” on choosing the meta-data for synthetic documents, and have found them attractive. In this report we verify two further necessary properties of our scheme, namely 1. the maximal theoretical gains are confirmed on a realistic data set 2. randomly-chosen metadata also leads to substantial performance gains, which we quantify against the percentage of actual solutions found for the query.
The Automated Serialization of Concurrent CSP Scripts Using Mathematica

This report discusses the design and implementation of a package of Mathematica-based tools for serializing scripts describing the behavior of concurrent systems in CSP. Under some conditions, serialization can yield code that is more efficient than multi-process versions and can better meet processor constraints. Under most conditions concurrent design is easier and more reliable so we prefer to design concurrently and only implement serially when necessary. The tools include a set of definitions and procedures to automatically change concurrent code to serial code. Conversions of the following step laws have been implemented thus far: external choice, generalized parallel, and alphabetized parallel; additional laws will be implemented in the next month or so. The tools support the input of the more formal typeset notation of CSP, which makes the script writing intuitive, and can convert the typeset version to the machine-readable version required by software such as FDR. The correctness of the conversions has been checked with the FDR package. This package should be of use to both those regularly working on concurrent systems and those learning CSP.
On the Future of Data Analysis

This contribution is based on a discussion on the future of data analysis which took place on the last day of the symposium IDA 2000. By different discussants the main problems of current data analysis as well as future developments were discussed. Moreover a network for cooperation and continuing education in the field of uncertainty analysis was initiated and the list of scientists who initiated this is given.
Analyzing relational databases using rough set based methods

One of the most important problems in KDD applications is a size of realworld databases. In practical problems data may contain millions of records in many data tables bounded by relations. On the other hand, most of theoretical works and practical applications concentrate on relatively small data sets collected in single tables. The paper proposes a new approach to the problem of analysis of relational databases. The proposed methodology woks in adaptive way: if a considered data table is not sufficient to create satisfactory set of rules, new attributes (generated by arithmetical operations or based on database relations) are added to information system.
Algorithmic game theory and the computation of market equilibria

It is demonstrated that for certain markets where traders have constant elasticity of substitution utility (CES) functions, the existence of a price equilibrium can be determined in polynomial time. It is also shown that for a certain range of elasticity of substitution where the CES market does not satisfy gross subsitutability that price equilibira can be computed in polynomial time. It is also shown that for markets satisfying gross substitutability, equilibria can be computed in polynomial time even if the excess demand is a correspondence. On the experimental side, equilibrium computation algorithms from computer science without running time guarantees are shown to be competitive with software packages used in applied microeconomics. Simulations also lend support to the Nash equilibrium solution concept by showing that agents employing heuristics in a restricted form of Texas Holdem converge to an approximate equilibrium. Monte Carlo simulations also indicate the long run preponderance of skill over chance in Holdem tournaments. Abstract Approved: Thesis SupervisorApproved: Thesis Supervisor Title and Department
Head-Corner Parsing using Typed Feature Structures

In this report a description will be given of how typed feature structures can be specified. A specification language will be presented for the specification of types, words and grammar rules. An unification algorithm for typed feature structures as well as an algorithm to compute the least upper bound relation for a type lattice will be given. Finally, a head-corner parsing schema for typed feature structures will be presented.
Representation and registration in face recognition and medical imaging

This dissertation presents solutions to four problems from face recognition and medical imaging. The first problem identifies an unknown face from a large database of facial images. The algorithm is based on matching pursuit filters, a small set of facial features, and simple geometric model. The set of features consists of the nose and eye regions of the face, and the interior of the face at a reduced scale. The algorithm uses coarse to fine processing to estimate the location of the facial features. Based on the hypothesized locations of the facial features the identification module searches the database for the identity of the unknown face. The identification is made by matching pursuit filters--a self-organizing technique for creating efficient and compact models from data. This technique is based on an adapted wavelet expansion, which is adapted to both the data and the goals of the algorithm. Thus, the filters can automatically find the subtle differences between facial features needed to identify unknown individuals. The algorithm is demonstrated on a database of photographs of 311 individuals and on a database of infrared facial images. The second problem adjusts for illumination differences between two facial images. The algorithm transforms the histogram of pixel values on one face to the histogram of another face. The algorithm, which is computationally efficient, nonlinear, and data-driven, corrects for variations between two different facial images or changes within an image of a face. The third problem uses a sieve algorithm to find the correspondence between pairs of images taken with an electron microscope. A sieve algorithm uses a sequence of approximations to generate increasingly accurate estimates of the correspondence. Initially, the approximations are computationally inexpensive, and at later stages both accuracy and complexity increase. The fourth problem presents an automatic registration algorithm for MR and PET slices of the brain that does not require manual intervention. The algorithm takes an integrated approach and simultaneous segments the brain in both modalities and registers the slices. A sequence of templates from the PET slice is constructed and registered in the MR slice using an energy function. The template with minimum energy gives the final registration.
A Spatial Feature Selection Method Based on Maximum Entropy Theory

Feature selection has an important application in the field of pattern recognition and data mining etc. However, in real world domains, if there are spatial data operated in the application, the performance of feature selection will be decreased because of without considering the characteristic of spatial data. In this paper, a feature selection method from the point of the characteristic of spatial data, named MEFS (maximum entropy feature selection), is proposed. Based on the theory of maximum entropy, MEFS uses mutual information and Z-test technologies, and takes two-step method to execute feature selection. The first step is predicate selection, and the second step is to choose relevant dataset corresponding to each predicate. At last, the experiments between feature selection algorithms MEFS and RELIEF, and between ID3 classification algorithm and classification algorithm based on MEFS are carried out. The experimental results show that the MEFS algorithm not only saves feature selection and classification time, but also improves the quality of classification.
An XML Architecture for Technical Documentation: The Darwin Information Typing Architecture

DITA is an architecture for creating topicoriented, information-typed content that can be reused and single-sourced in a variety of ways. It is also an architecture for creating new information types and describing new information domains, allowing groups to create very specific, targeted document type definitions using a process called specialization, while at the same time reusing common output transforms and design rules. We discuss several methods that can be used to extend DITA’s basic topic types.
A Concurrent Validation Study of the Maryland Developmental Screen

Title of dissertation: A CONCURRENT VALIDATION STUDY OF THE MARYLAND DEVELOPMENTAL SCREEN Abigail McNinch. Doctor of Philosophy, 2005 Dissertation directed by: Professor Paula Beckman, Department of Special Education The purpose of this study was to examine the concurrent validity of a new screening instrument, the Maryland Developmental Screen (MDS), with the Bayley Scales of Infant Development, Second Edition (BSID-II). The MDS and BSID-II were concurrently administered at the University of Maryland (UMMS) Neonatal Intensive Care Unit (NICU) Follow Up Clinic to an age stratified sample of 81 infants and toddlers, born at 36 weeks gestation or younger, and whose corrected ages ranged from 18 days through 37 months of age. The sensitivity and the specificity rates were determined to be 88.2% and 90.1% respectively. The chance hit rate of 66.7% indicates that the overall hit rate of 90.1 was not due to chance alone. A relationship between the false negative and false positive outcomes in relation to the developmental domain of the child was determined. Evidence that the MDS has unique utility in identifying potential atypical development was also demonstrated. This study provides preliminary evidence that the MDS has validity in screening the development of infants and toddlers born prematurely. Further investigation of the instrument’s validity using larger and more diverse sample
Implementation of Bio-Informatics Applications on Various GPU Platforms

As of 2012, the world creates 2.5 quintillion bytes of data every day. Much of this data generated is what we refer to as Big Data. To explore how Big Data can create potential value and show the technical challenges accompanied with Big Data applications, we choose an application from bio-informatics: the Smith Waterman genetic database alignment algorithm, which is used for finding optimal genetic sequence alignments. The continuous increase in the volume of data in genetic databases leads to the exponential increase in the time required for comparing these genetic sequences. This thesis investigates the acceleration and optimization of the Smith Waterman algorithm using GPU platforms. The thesis uses DOPA, an existing implementation, which was optimized for the GTX275 GPU platform from NVIDIA. DOPA resulted in a huge performance gain compared to other implementations running sequentially on CPU. Our thesis aims to study and improve the behavior of this implementation on different NVIDIA GPUs: the Tesla C2075 and the GeForce GT640. We improved the cores occupancy of DOPA on different GPU cards resulting in an efficient workload distribution, thereby improving the performance by about 17% to 61%. We achieved 25 GCUPS performance on the C2075 and 11 GCUPS on the GT640 compared to a straight forward DOPA port on the same cards achieving 21.9 and 6.8 GCUPS, respectievly. To achieve considerable performance for Big Data application for different platforms, two important factors have to be taken into account: increase the parallelism in the software and increase the utilization on the hardware side. We evaluated and presented other metrics such as the cost in terms of euro and watt to be considered along with GPU performance.
Towards Compact and High Speed Silicon Modulators

Los moduladores son elementos claves para la transmision de la senal y el procesamiento de la informacion. Las tecnicas de fabricacion avanzadas "complementary metal-oxide semiconductor" (CMOS) permiten reducir drasticamente las dimensiones de estos dispositivos de interes para la implementacion a gran escala en un chip de silicito a bajo coste. El trabajo realizado en esta tesis se centra en el diseno, la fabricacion y la caracterizacion de estructuras de onda lenta con el objetivo de realizar moduladores compactos y eficientes integrados en un chip de silicio. El trabajo se divide en cuatro capitulos y un capitulo de conclusion y perspectivas. El capitulo uno introduce los fundamentos de fisica del estado solido y de los mecanismos basicos de propagacion guiada de la luz por reflexion total interna. El capitulo dos presenta los parametros importantes de los moduladroes electro-opticos asi como un trabajo de recopilacion de todos los mecanismos fisicos que pueden ser empleados para modular la luz en silicio. Ademas, se presenta el estado del arte de los moduladores basados en silicio. El capitulo tres presenta el diseno , fabricacion y caracterizacion de un modulador electro-optico en silicio compacto y eficiente basado en el efecto de onda lenta en una estructura periodica unidimensional integrada, cuya geometria, similar a la de una red de Bragg, permite reducir la velocidad de grupo de un paquetes de ondas. Dicho efecto, se emplea para incrementar la interaccion luz-materia y por lo tanto la eficiencia del modulador electro-optico. El capitulo cuatro demuestra experimentalmente que dicha guia unidimensional periodica puede ser mejorada a fin de conseguir que el efecto de baja velocidad de grupo suceda en un rango mayor de longitudes de onda para posibles aplicaciones como la multiplexacion por division de longitudinal de onda. En el capitulo cinco, se proporcionan conclusiones y perspectivas sobre el trabajo realizado.
Structural Cut Elimination in Linear Logic.

Abstract : We present a new proof of cut elimination for linear logic which proceeds by three nested structural inductions, avoiding the explicit use of multi-sets and termination measures on sequent derivations. The computational content of this proof is a non-deterministic algorithm for cut elimination which is amenable to an elegant implementation in Elf. We show this implementation in detail. (AN)
Epipolar Resampling of Stereo Image Base on Airbase in the Digital

SUMMARY The paper refers to the epipolar resampling method of stereo images. This method is based on the airbase and the exterior orientation parameters (EOP) of the left and the right original stereo images. The epipolar resampling and the correction of lens-distortion images are carried out simultaneously. The perspective centers of the normalized images will be the same as those of the original images. The nomalized images have the common rotation angles and the epipolar line in the left and the right images have same y coordinate whereas y paralax has been eliminated. The nomalized images assist the stereo display process as well as makes its easier and quicker in automatic matching in the digital photogrammetry.
Practical Use of Multiple Imputation

Multiple imputation is a flexible technique for handling missing data that is widely used in medical research. Its properties are understood well for some simple settings but less so for the complex settings in which it is typically applied. The three research topics considered in thesis consider incomplete continuous covariates when the analysis model involves nonlinear functions of one or more of these. Chapters 2–4 evaluate two imputation techniques known as predictive mean matching and local residual draws, which may protect against bias when the imputation model is misspecified. Following a review of the literature, I focus on how to match, the appropriate size of donor pool, and whether transformation can improve imputation. Neither method performs as well as hoped when the imputation model is misspecified but both can offer some protection against imputation model misspecification. Chapter 5 investigates strategies for imputing the ratio of two variables. Various ‘active’ and ‘passive’ strategies are critiqued, applied to two datasets and compared in a simulation study. (‘Active’ indicates the ratio is imputed directly within a model; ‘passive’ means it is calculated externally to the imputation model.) Without prior transformation, passive imputation after imputing the numerator and denominator should be avoided, but other methods require less caution. Chapter 6 proposes techniques for combining multiple imputation with (multivariable) fractional polynomial methods. A new technique for imputing dimension-one fractional polynomials is developed and nested in a chained-equations procedure. Two candidate methods for estimating exponents in the fractional polynomial model, using Wald statistics and log-likelihoods, are assessed via simulation. Finally, the type I error and power are compared for model selection procedures based on Wald and likelihood-ratio type tests. Both methods can out-perform complete cases analysis, with the Wald method marginally better than likelihood-ratio tests.