Miracle-dz/UniFAR-FTUs huggingface dataset:
Teaching , Learning , and Exploration by Yiqun Yin

This thesis studies two problems in machine learning: on-line concept learning and on-line exploration in an unknown environment. In the first part of the thesis, we study two on-line concept learning models: teacher-directed learning and self-directed learning. In both models, the learner tries to identify an unknown concept based on examples of the concept presented one at a time. The learner predicts whether each example is positive or negative with immediate feedback, and the objective is to minimize the number of prediction mistakes. The examples are selected by the teacher in teacher-directed learning and by the learner itself in self-directed learning. We explore the power of a helpful teacher by investigating the number of mistakes in the two models. Roughly, teacher-directed learning reflects the scenario in which a teacher teaches a class of learners, and self-directed learning reflects the scenario in which a smart learner learns by itself. For all previously studied concept classes, the minimum number of mistakes in teacher-directed learning is always larger than that in self-directed learning. This raises an interesting question of whether teaching is helpful for all learners including the smart learner. Assuming the existence of one-way functions, we construct concept classes for which the minimum number of mistakes is linear in teacher-directed learning but superpolynomial in self-directed learning, demonstrating the power of a helpful teacher in a learning process. We next study the tradeoff between the number of mistakes and the number of queries in self-directed learning. We develop a new technique for reducing the number of queries significantly as the number of allowable mistakes increases. Using the technique, we construct self-directed learning algorithms that require only a polynomial number of queries. The results contrast with all previous algorithms that use O(ICl) queries, which is usually exponential. We also prove a general lower bound on the minimum number of queries needed when k or fewer mistakes are allowed in self-directed learning. For some concept classes, the lower bound matches the upper bound provided by our algorithms. In the second part of the thesis, we develop on-line strategies for exploring, with multiple robots, a unique goal that lies on one of many paths joined at an origin. The objective is to minimize the total distance traveled by all of the robots before the goal is found. Exploration strategies for a single robot have been studied intensively in recent years. However, the more challenging problem of exploring with multiple robots has not been well understood since an optimal algorithm must involve clear coordination among all robots. We present deterministic algorithms with optimal competitive ratios and efficient randomized algorithms that are provably optimal in some special cases. Our results completely characterize the condition under which randomization can help reduce the competitive ratios, and they settle some open questions in the literature. Thesis Supervisor: Michael Sipser Title: Professor of Applied Mathematics
Fusing generic objectness and deformable part-based models for weakly supervised object detection

In the context of lack of object-level annotation, we propose a model that enhances the weakly supervised deformable part model (DPM) by emphasizing the importance of size and aspect ratio of the initial class-specific root filter. For each image, to extract a reliable bounding box as this root filter estimate, we explore the generic objectness measurement to obtain a reference window based on the most salient region, and select a small set of candidate windows by adaptive thresholding and greedy Non-Maximum Suppression (NMS). The initial root filter estimate is decided by optimizing the score of overlap between the reference box and candidate boxes, as well as their corresponding objectness score. Then the derived window is treated as a positive training window for DPM training. Finally, we design a flexible enlarging-and-shrinking post-processing procedure to modify the output of DPM, which can effectively fit to the aspect ratio of the object and further improve the final accuracy. Experimental results on the challenging PASCAL VOC 2007 database demonstrate that our proposed framework is effective and competitive with the state-of-the-arts.
Face Recognition under Pose Variations

Dept. of E & TC Engineering, Maharashtra Institute of Technology, Pune, India Abstract -A Face Recognition system is computer application for automatically identifying or verifying a person from a digital image or a video frame. Due to the change of lighting condition, expression, occlusion, rotation, etc. the human face appearance could change considerably. In this project a model based approach is used. In this approach the Moment based feature extraction techniques (Hu’s Moments and Zernike Moments) are implemented on two different face databases containing different poses of the face. Extracted feature are classified by kNN classifier and Correct Recognition Rate is calculated.
Diferencias en ansiedad y depresión ante la muerte según edad y sexo en una muestra de universitarios españoles / Differences in anxiety and depression to the death according to age and sex in a sample of university students spanish

Resumen El proposito de este estudio era evaluar las diferencias en ansiedad y depresion ante la muerte relacionadas con la edad y el sexo en una muestra de universitarios espanoles, segun el modelo desarrollado por Templer (1970). Participaron 1172 estudiantes en diferentes universidades espanolas, de edades comprendidas entre 17 y 28 anos. El diseno del estudio fue transversal y el metodo descriptivo correlacional. Se aplicaron los instrumentos de medida DAS (Death Anxiety Scale) y DDS-R (Death Depression Scale). Se calculan medias y desviaciones tipicas, ANOVA y prueba Scheffe. Los resultados muestran diferencias significativas asociadas a sexo y edad en ansiedad y depresion ante la muerte, siguiendo un patron decreciente en funcion de la edad y mostrando mayor prevalencia en mujeres. Finalmente, se reflexiona acerca de las consecuencias en el contexto de la formacion universitaria. Palabras clave : Ansiedad, depresion, muerte, jovenes, educacion universitaria. Abstract The purpose of this study wasto evaluate the differences in death anxiety and death depression related to age and sex in a sample of Spanish university students, according to the model developed by Templer (1970). 1172 students participated from different Spanish universities, aged between 17 and 28 years old. The study design was cross-sectional and the method was descriptive correlational. The DAS (Death Anxiety Scale) and DDS-R (Death Depression Scale) were applied. Means and typical deviations, ANOVA and Scheffe test are calculated. The results show significant differences associated with sex and age in death anxiety and death depression, so that those decrease according to age and are more pronounced in women. Finally, we reflect on the consequences in higher education. Keywords : Anxiety, depression, death, youth, higher education. DOI: https://doi.org/10.17398/2340-4256.13. 259
Mapping surface EMG activation patterns with neuromuscular disorders by an implanted multi-channel electrode array

This preliminary study aims to testify the utility of a new surface EMG measurement technique using an implanted multi-channel electrode array, in characterization of different neuromuscular pathologies in animal models. Obvious difference in muscle activation patterns were found in surface EMG topographies of different conditions of neuromuscular systems. Further research is needed to explain the relationship between the surface EMG topographical changes and the underlying pathological status.
Network Maximal Correlation

We introduce Network Maximal Correlation (NMC) as a multivariate measure of nonlinear association among random variables. NMC is defined via an optimization that infers transformations of variables by maximizing aggregate inner products between transformed variables. For finite discrete and jointly Gaussian random variables, we characterize a solution of the NMC optimization using basis expansion of functions over appropriate basis functions. For finite discrete variables, we propose an algorithm based on alternating conditional expectation to determine NMC. Moreover we propose a distributed algorithm to compute an approximation of NMC for large and dense graphs using graph partitioning. For finite discrete variables, we show that the probability of discrepancy greater than any given level between NMC and NMC computed using empirical distributions decays exponentially fast as the sample size grows. For jointly Gaussian variables, we show that under some conditions the NMC optimization is an instance of the Max-Cut problem. We then illustrate an application of NMC in inference of graphical model for bijective functions of jointly Gaussian variables. Finally, we show NMC’s utility in a data application of learning nonlinear dependencies among genes in a cancer dataset.
Validación de la versión española del Ámsterdam preoperative anxiety and information scale para la evaluación de la ansiedad preoperatoria.

RESUMEN Introduccion: La ansiedad preoperatoria es un problema frecuente que perjudica el correcto desarrollo del procedimiento quirurgico. Su prevencion y tratamiento depende de una correcta evaluacion por lo que se hace necesaria la implementacion de instrumentos de medida validos y fiables. Objetivos: El objetivo de este estudio es llevar a cabo la adaptacion transcultural y la validacion psicometrica de The Amsterdam Preoperative Anxiety and Information Scale (APAIS) para evaluar la ansiedad preoperatoria, en poblacion espanola. Metodologia: Se realizo una adaptacion transcultural, mediante la tecnica de traduccion-retro traduccion de la escala APAIS. Ambito: Bloque quirurgico de la Agencia Publica Sanitaria Costa del Sol (Espana). Pacientes: La version adaptada al espanol del cuestionario se administro a una muestra de 529 pacientes, sometidos a procedimientos quirurgicos programados, de edades comprendidas entre 18 y 70 anos. Analisis: Se considero la consistencia interna mediante el calculo del coeficiente Alfa de Cronbach y se evaluo la validez de criterio y de constructo. Se determino tanto la correlacion inter item, como el indice de homogeneidad y el coeficiente de correlacion intraclase realizandose posteriormente analisis factorial confirmatorio. Resultados: La estructura factorial de la version espanola de APAIS evidencio un modelo unifactorial con unos buenos indices de ajuste (Root Mean Square Error of Approximation: 0,05 Normed-fit index: 0,99 Goodness-of-fit statistic: 0,99). El cuestionario presento una elevada consistencia interna (Cronbach’s alpha: 0,84) y una buena correlacion con la escala de ansiedad de Goldberg (CCI: 0,62 [95% CI: 0,55 to 0,68]). Conclusion: La version espanola de APAIS es valida y fiable, presentando propiedades psicometricas similares a las obtenidas en estudios previos. Palabras claves: Ansiedad preoperatoria; cuestionario; estudio de validacion
ANÁLISIS MORFOTECTÓNICO EN EL PIEDEMONTE SUROCCIDENTAL DEL ANTEPAIS ANDINO, SIERRAS PAMPEANAS OCCIDENTALES, SAN JUAN, ARGENTINA

Between 27° S and 33° S the Andean backarc is one of the most seismically active region in the world, being epicenter of the most important crustal earthquakes that affected Argentina. Associated with the seismicity an intense deformation occurs, causing changes on the land surface morphology. In the western sector of the Sierras Pampeanas Occidentales, in the Sierra de la Huerta, a GIS analysis was applied to define geomorphological units and obtain qualitative and quantitative parameters that allowed characterizing the identified geoforms and their relation with tectonic activity. Andean tectonics affect this region for the last 5 millions of years, controlling it landscape evolution. Three morphostructural units and quaternary active faulting were identify, presenting abundant morphotectonic markers, such as dislocated alluvial levels, changes in river pattern, diversion of river courses, increasing degree of incision, water springs, wind gap and inversion of the pattern drainage, among others. The quantitative and qualitative analysis of the morphotectonic parameters shows that deformation decrease from North to South and increase from East to West. Concluding, according to morphotectonics parameters deformation is still active, modifying the present-day landscape in the Western piedmont of La Huerta range.
Using Bayesian neural networks to classify forest scenes

We present results that compare the performance of Bayesian learning methods for neural networks on the task of classifying forest scenes into trees and background. Classification task is demanding due to the texture richness of the trees, occlusions of the forest scene objects and diverse lighting conditions under operation. This makes it difficult to determine which are optimal image features for the classification. A natural way to proceed is to extract many different types of potentially suitable features, and to evaluate their usefulness in later processing stages. One approach to cope with large number of features is to use Bayesian methods to control the model complexity. Bayesian learning uses a prior on model parameters, combines this with evidence from a training data, and the integrates over the resulting posterior to make predictions. With this method, we can use large networks and many features without fear of overfitting. For this classification task we compare two Bayesian learning methods for multi-layer perceptron (MLP) neural networks: (1) The evidence framework of MacKay uses a Gaussian approximation to the posterior weight distribution and maximizes with respect to hyperparameters. (2) In a Markov Chain Monte Carlo (MCMC) method due to Neal, the posterior distribution of the network parameters is numerically integrated using the MCMC method. As baseline classifiers for comparison we use (3) MLP early stop committee, (4) K-nearest-neighbor and (5) Classification And Regression Tree.
FINANCIAL INSTABILITY AND MONETARY REGULATION Is Europe well protected from systemic risk ?

INTRODUCTION: THE TOPICAL NATURE OF SYSTEMIC RISK I. SYSTEMIC RISK: RECENT THEORETICAL LESSONS I.1. Definition of systemic risk I.2. Why are financial systems so fragile? I.3. The hypotheses that the present study uses II. INSTABILITY AND INTERDEPENDENCY IN THE FINANCIAL MARKETS II.1. The financial markets’ instability II.2. Clearly recognisable systemic crises: the after-effects of the 1998 Russian default and 2001 terrorist attacks II.2. a The September-October 1998 crisis II.2. b The September 2001 crisis III. MICROECONOMIC BEHAVIOURS IN RISK MANAGEMENT III.1. Managing market risk and uncertainty about liquidity III.2. Managing credit risk and macro-economic interdependencies III.3. Extreme volatility and stress testing IV. THE CHANNELS OF CONTAGION IV.1. A general model of contagion IV.2. The role played by the over-the-counter derivative markets CONCLUSION: STAVING OFF SYSTEMIC RISK
A New, Kinematically Anomalous H I Component in the Spiral Galaxy NGC 2403

We discuss new, high-sensitivity H I observations of the spiral galaxy NGC 2403, which show extended emission at anomalous velocities with respect to the "cold" disk. This "anomalous" gas component (~1/10 of the total H I mass) is probably located in the region of the halo and rotates more slowly (~20-50 km s-1) than the gas in the disk. Moreover, it shows a distortion in the velocity field that we interpret as a large-scale radial motion (10-20 km s-1 inflow) toward the center of the galaxy. The most likely explanation for its origin and kinematics seems to be that of a galactic fountain. There is, however, a significant part of the anomalous gas that seems to be moving contrary to rotation and is difficult to understand in such a picture. These anomalous gas complexes discovered in NGC 2403 may be analogous to the high-velocity clouds of our Galaxy. They may be rather common in spiral galaxies and not have been detected yet for lack of sensitivity.
Microarray Classification Using Intelligent Techniques

Microarray data classification is one of the most emerging clinical applications in the medical community. The classification process takes the detection of relevant and irrelevant probes into account, which is fundamental for subsequent classification. In this thesis, an efficient technique is proposed for the precise classification of microarray genes from the microarray gene expression dataset. The proposed classification technique performs the classification process with the aid of three phases namely, feature extraction, dimensionality reduction, and gene classification. Initially, Principal Component Analysis (PCA) is applied for dimension reduction and significant features are extracted from high dimension microarray data. The original data is projected to the lower dimension for selecting the eigenvector for the co-variance matrix shown cumulative variance up to a label of 100%. After the implementation of PCA, the reduced feature matrix so obtained is divided into two sets such as, training and test data set. Feed Forward Neural Network is a train using Back Propagation Algorithm and the performance accuracy of the classifier is tested using the test dataset (untrained samples). The total number of epochs required to reach the degree of accuracy is referred as the convergence rate. To check the efficiency of the algorithm the convergence rate is considered by plotting graphs between numbers of epochs vs. means square error (MSE). The two keys controlling parameters of BP algorithm are learning rate and momentum which affects the accuracy as well as the convergence rate. Varying the values of the parameters various graphs are plotted.
Performance Estimation of GPUs with Cache

Performance estimation of an application on any processor is becoming a essential task, specially when the processor is used for high performance computing. Our work here presents a model to estimate performance of various applications on a modern GPU. Recently, GPUs are getting popular in the area of high performance computing along with original application domain of graphics. We have chosen FERMI architecture from NVIDIA, as an example of modern GPU. Our work is divided into two basic parts, first we try to estimate computation time and then follow it up with estimation of memory access time. Instructions in the kernel contribute significantly to the computation time. We have developed a model to count the number of instructions in the kernel. We have found our instruction count methodology to give exact count. Memory access time is calculated in three steps, address trace generation, cache simulation and average memory latency per warp. Finally, computation time is combined with memory access time to predict the total execution time. This model has been tested with micro-benchmarks as well as real life kernels like blowfish encryption matrix multiplication and image smoothing. We have found that our average estimation errors for these applications range from -7.76% to 55%.
Construction of Fuzzy Ontology-Based Terrorism Event Extraction

Fuzzy ontology is based on the concept that each index object is related to every other object in the ontology, with a degree of membership assigned to that relationship based on fuzzy set theory. This paper proposes use cases based on the related process of the terrorism event extraction using fuzzy ontology, especially the terrorism fuzzy ontology construction methodology. The related use cases are represented using the Web Ontology Language (OWL) which is designed to support the representation ontology relation. Additionally, to make the proposed use cases more relevant to implementing the system, the paper presents linguistic variables which serve as a mean of approximate characterization of fuzzy phenomena concepts and also the appropriate characterization of the terrorism fuzzy relations in their properties.
A unified approach to models of synchronous parallel machines

A number of different models of synchronous, unbounded parallel computers have appeared in recent literature. Without exception, running time on these models has been shown to be polynomially related to the classical space complexity measure. The general applicability of this relationship is called “the parallel computation thesis” and strong evidence of its truth is given in this paper by introducing the notion of “conglomerates” - a very large class of parallel machines, including all those which could feasibly be built. Basic parallel machine models are also investigated, in an attempt to pin down the notion of parallel time to within a constant factor. To this end, a universal conglomerate structure is developed with can simulate any other basic model within linear time. This approach also leads to fair estimates of instruction execution times for various parallel models.
Local mirror symmetry: Calculations and interpretations

We describe local mirror symmetry from a mathematical point of view and make several A-model calculations using the mirror principle (localization). Our results agree with B-model computations from solutions of Picard-Fuchs differential equations constructed form the local geometry near a Fano surface within a Calabi-Yau manifold. We interpret the Gromov-Witten-type numbers from an enumerative point of view. We also describe the geometry of singular surfaces and show how the local invariants of singular surfaces agree with the smooth cases when they occur as complete intersections. † email: chi@math.harvard. edu, yau@math.harvard. edu ∗ email: klemm@ias.edu ∗∗ email: zaslow@math.nwu. edu
BDNF Regulates Rab11-Mediated Recycling Endosome Dynamics to Induce Dendritic Branching

Dendritic arborization of neurons is regulated by brain-derived neurotrophic factor (BDNF) together with its receptor, TrkB. Endocytosis is required for dendritic branching and regulates TrkB signaling, but how postendocytic trafficking determines the neuronal response to BDNF is not well understood. The monomeric GTPase Rab11 regulates the dynamics of recycling endosomes and local delivery of receptors to specific dendritic compartments. We investigated whether Rab11-dependent trafficking of TrkB in dendrites regulates BDNF-induced dendritic branching in rat hippocampal neurons. We report that TrkB in dendrites is a cargo for Rab11 endosomes and that both Rab11 and its effector, MyoVb, are required for BDNF/TrkB-induced dendritic branching. In addition, BDNF induces the accumulation of Rab11-positive endosomes and GTP-bound Rab11 in dendrites and the expression of a constitutively active mutant of Rab11 is sufficient to increase dendritic branching by increasing TrkB localization in dendrites and enhancing sensitization to endogenous BDNF. We propose that Rab11-dependent dendritic recycling provides a mechanism to retain TrkB in dendrites and to increase local signaling to regulate arborization.