Miracle-dz/UniFAR-FTUs huggingface dataset:
Incidence des leucémies de l'enfant en fonction de la proximité et des caractéristiques générales de diverses sources d'expositions environnementales

Le role de l'environnement dans l'etiologie des leucemies aigues de l’enfant (LA) fait aujourd'hui l'objet de recherches intenses. Dans ce contexte, le present travail a pour objectif d’etudier la relation entre l’incidence de LA et la proximite des centrales nucleaires de production d’electricite (CNPE) et des lignes a haute tension (LHT). Avant cette analyse fine, un premier travail a consiste a etudier les variations departementales de l'incidence de LA.Les cas inclus dans ces etudes sont toutes les LA du Registre National des Hemopathies malignes de l’Enfant sur la periode etudiee : 1990-2004 pour l’etude de l’incidence departementale et 2002-2007 pour les etudes de l’association avec les facteurs d’exposition environnementale. Dans l’approche cas-temoins principalement utilisee pour ces dernieres, les 30 000 sujets temoins constitue un echantillon representatif de la population pediatrique francaise sur la periode d’’interet. D’autre part, la geolocalisation des adresses des sujets et des sources d'exposition permet de definir des criteres de proximite en relation avec la probabilite et/ou l'intensite d'exposition aux facteurs d'interet. • L’etude des LA par departement n’a pas mis en evidence de tendance ni de structure spatiale dans l’incidence a ce niveau geographique : que ce soit globalement, par classe d’âge, par sexe ou par sous-type de leucemie. • Sur la periode 2002-2007 contrairement aux periodes precedentes, un quasi-doublement de l’incidence des LA a moins de 5 km des CNPE a ete mis en evidence, avec une approche cas-temoin comme avec l’etude d’incidence. Ce resultat n’etait pas specifique d’une CNPE ou d’un type de CNPE et non lie a la cartographie des emissions aeriennes de radioactivite par les CNPE. • L’association trouvee entre l’incidence de LA et la proximite aux LHT de plus de 225 kV (<50 m) semble restreinte aux enfants de moins de 5 ans ou n’habitant en milieu urbain ; aucune association n’a ete trouvee avec la proximite aux LHT de moins de 150 kV.
Estimation of random coefficients logit demand models: an application to the Brazilian fixed income fund market

Estimation of demand and supply in differentiated products markets is a central issue in Empirical Industrial Organization and has been used to study the effects of taxes, merges, introduction of new goods, market power, among others. Logit and Random Coefficients Logit are examples of demand models used to study these effects. For the supply side it is generally supposed a Nash equilibrium in prices. This work presents a detailed discussion of these models of demand and supply as well as the procedure for estimation. Lastly, is made an application to the Brazilian fixed income fund market.
Heteroscedastic Gaussian based correction term for fisher discriminant analysis and its kernel extension

Fisher discriminant analysis (FDA) is a very famous analysis method for classification. However, FDA does give an optimal projection only for Gaussian distributions with equal covariance matrices. In other words, FDA is not optimal in the case of heteroscedastic Gaussian distributions. In this paper, we propose a novel criterion for FDA including a correction term based on the Bhattacharyya distance which is closely related to classification rate. Furthermore, the Chernoff distance based criterion and its kernelized version are proposed as its extension. These proposed criteria have three strong points. The first one is that the correction term based on the Bhattacharyya distance can deal with heteroscedastic Gaussian distributions. The second one is that the correction term based on the Chernoff distance can handle easily the difference in the number of class samples. The third point is that their kernel extensions are easily implemented to be applied to non-Gaussian distributions. As a result, the proposed method is applicable in a wide variety of classification problems. Experimental results using toy simulations and nine kinds of UCI real-world datasets show marked advantages of the proposed method over the conventional FDA.
Challenger: Fine-Tuning the Odds Until Something Breaks

The Challenger disaster illustrates the effects of repeated successes, gradual acclimatization, and the differing responsibilities of engineers and managers. Past successes and acclimatization alter decision-makers' beliefs about probabilities of future success. Fine-tuning processes result from engineers' and managers' pursuing partially inconsistent goals while trying to learn from their experiences. Fine-tuning reduces probabilities of success, and it continues until a serious failure occurs.
Implementation of recursive digital filters into vector SIMD DSP architectures

Recently, digital signal processors featuring vector SIMD instructions have gained renewed attention, since they offer the potential to speed up the computation of digital signal processing algorithms. However, when implementing recursive algorithms the maximum achievable speed-up factors are upper bounded. In this paper, we investigate these performance limitations when pure recursive filters are implemented into parallel DSP architectures. We show that by applying algebraic transformations a block formulation of any recursive filter can be derived, which can be efficiently implemented into SIMD DSP architectures. We also show that the number of additional vector operations introduced by the transformation grows linearly with the level of parallelism and that it does not depend on the recursion order. These results enable the achievement of important speed-up factors even for low order recursions. Moreover, we introduce a suitable algebraic notation of the block formulation of the recursive filter, which reveals the processor instructions required to implement the algorithm into the SIMD DSP.
Adaptive Approach for Reducing Energy Consumption, Considering Nodes' Mission in Wireless Sensor Networks

The computational cost in a wireless sensor network is usually considered neglected in comparison to the communication cost. While this assumption is considered true, it may fail if data volumes are significantly large. In such situations, it is more rational to consider the computational cost as well as the communication cost for energy related decisions in the network. In this paper, we want to reduce the energy consumed within a wireless sensor network, considering each node in charge of a mission. Every node, obtaining a trade-off between computation and communication energy, does its best to reduce the overall energy. In other words, each node endeavors to reduce network energy consumption adaptively, regarding application type, by means of making a trade-off between data compression at an appropriate level, considering spatial correlation, and sending row data. Simulation results, compared to similar work, show that our approach reduces energy consumption of nodes in the network.
Towards real-time emergency response using crowdsourcing

Crowdsourcing has emerged as an attractive paradigm in recent years for information collection for disaster response, which utilizes data received from the human crowd, to provide critical information collection and dissemination during emergency situations and visualize this data to generate emergency maps for the human crowd. In this paper we investigate the use of crowdsourcing mechanisms for real-time emergency response and describe our approach for developing a crowdsourcing tool that can be effectively used to formulate questions and seek answers from the human crowd using a MapReduce programming model, and integrate this information into a novel spatiotemporal data structure and create a visual emergency map. Our experimental evaluation shows that our approach is practical, efficient and can be used for applications with real-time demands.
Parallel Multilevel Diffusion Algorithms for Repartitioning of Adaptive Meshes

Graph partitioning has been shown to be an effective way to divide a large computation over an arbitrary number of processors. A good partitioning can ensure load balance and minimize the communication overhead of the computation by partitioning an irregular mesh into p equal parts while minimizing the number of edges cut by the partition. For a large class of irregular mesh applications, the structure of the graph changes from one phase of the computation to the next. Eventually, as the graph evolves, the adapted mesh has to be repartitioned to ensure good load balance. Failure to do so will lead to higher parallel run time. This repartitioning needs to maintain a low edge-cut in order to minimize communication overhead in the follow-on computation. It also needs to minimize the time for physically migrating data from one processor to another since this time can dominate overall run time. Finally, it must be fast and scalable since it may be necessary to repartition frequently. Partitioning the adapted mesh again from scratch with an existing graph partitioner can be done quickly and will result in a low edge-cut. However, it will lead to an excessive migration of data among processors. In this paper, we present new parallel algorithms for robustly computing repartitionings of adaptively refined meshes. These algorithms perform diffusion of vertices in a multilevel framework and minimize data movement without compromising the edge-cut. Furthermore, our parallel repartitioners include parameterized heuristics to specifically optimize edge-cut, total data migration, or the maximum amount of data migrated into and out of any one processor. Our results on a variety of synthetic meshes show that our parallel multilevel diffusion algorithms are highly robust schemes for repartitioning adaptive meshes. The resulting edge-cuts are close to those resulting from partitioning from scratch with a state-of-the-art graph partitioner, while data migration is substantially reduced. Furthermore, repartitioning can be done very fast. Our experiments show that meshes with around eight million vertices can be repartitioned on a 256-processor Cray T3D in only a couple of seconds.
Model selection in irregular problems: Applications to mapping quantitative trait loci

Two methods of model selection are discussed for changepoint-like problems, especially those arising in genetic linkage analysis. The first is a method that selects the model with the smallest p-value, while the second is a modification of the Bayes information criterion. The methods are compared theoretically and on examples from the literature. For these examples, they are roughly comparable although the p-value-based method is somewhat more liberal in selecting a high-dimensional model. Copyright 2004, Oxford University Press.
MIMO wireless systems: overcoming the problems of convenience

In this paper we consider the fundamental advantage of wireless communication - its convenience - and the inevitable problems of spatial dispersion that this gives rise to. We show that MIMO systems, and especially adaptive MIMO systems, can overcome a number of these disadvantages by exploiting the spatial dimension. Especially we note that an adaptive MIMO system can combine the capacity/diversity advantages of MIMO with the benefits of 'smart antenna' systems in a wireless network.
Statistical modeling of F0 dynamics in singing voices based on Gaussian processes with multiple oscillation bases

We present a novel statistical model for dynamics of various singing behaviors, such as vibrato and overshoot, in a fundamental frequency (F0) contour. These dynamics are the important cues for perceiving individuality of a singer, and can be a useful measure for various applications, such as singing skill evaluation and singing voice synthesis. While most previous studies have modeled the dynamics using a second-order linear system, the automatic and accurate estimation of model parameters has yet to be accomplished. In this paper, we first develop a complete stochastic representation of the second-order system with Gaussian processes from parametric discretization, and propose a complete, efficient scheme for parameter estimation using the Expectation-Maximization (EM) algorithm. Experimental results show that the proposed method can decompose an F0 contour into a musical component and a dynamics component. Finally, we discuss estimating singing styles from the model parameters for each singer. Index Terms: Singing voices, Fundamental frequency (F0), Gaussian Processes, EM algorithm, Singing voice synthesis
Validation of the Simplified Acute Physiology Score 3 Scoring System in a Korean Intensive Care Unit

Purpose The Simplified Acute Physiology Score (SAPS) 3 was recently proposed to reflect contemporary changes in intensive care practices. SAPS 3 features customized equations for the prediction of mortality in different geographic regions. However, the usefulness of SAPS 3 and its customized equation (Australasia SAPS 3) have never been externally validated in Korea. This study was designed to validate SAPS 3 and Australasia SAPS 3 for mortality prediction in Korea. Materials and Methods A retrospective analysis of the prospective intensive care unit (ICU) registry was conducted in the medical ICU of Samsung Medical Center. Calibration and discrimination were determined by the Hosmer-Lemeshow test and area under the receiver operating characteristic (aROC) curve from 633 patients. Results The mortalities (%) predicted by SAPS 3, Australasia SAPS 3, and SAPS II were 42 ± 28, 39 ± 27 and 37 ± 31, respectively. The calibration of SAPS II was poor (p = 0.003). SAPS 3 and Australasia SAPS 3 were appropriate (p > 0.05). The discriminative power of all models yielded aROC values less than 0.8. Conclusion In Korea, mortality rates predicted using general SAPS 3 and Australasia SAPS 3 exhibited good calibration and modest discrimination. However, Australasia SAPS 3 did not improve the mortality prediction. To better predict mortality in Korean ICUs, a new equation may be needed specifically for Korea.
Socioeconomic disadvantage and adolescent women's sexual and reproductive behavior: the case of five developed countries.

CONTEXT Differences among developed countries in teenagers' patterns of sexual and reproductive behavior may partly reflect differences in the extent of disadvantage. However, to date, this potential contribution has received little attention. METHODS Researchers in Canada, France, Great Britain, Sweden and the United States used the most current survey and other data to study adolescent sexual and reproductive behavior. Comparisons were made within and across countries to assess the relationships between these behaviors and factors that may indicate disadvantage. RESULTS Adolescent childbearing is more likely among women with low levels of income and education than among their better-off peers. Levels of childbearing are also strongly related to race, ethnicity and immigrant status, but these differences vary across countries. Early sexual activity has little association with income, but young women who have little education are more likely to initiate intercourse during adolescence than those who are better educated. Contraceptive use at first intercourse differs substantially according to socioeconomic status in some countries but not in others. Within countries, current contraceptive use does not differ greatly according to economic status, but at each economic level, use is higher in Great Britain than in the United States. Regardless of their socioeconomic status, U.S. women are the most likely to give birth as adolescents. In addition, larger proportions of adolescents are disadvantaged in the United States than in other developed countries. CONCLUSIONS Comparatively widespread disadvantage in the United States helps explain why U.S. teenagers have higher birthrates andpregnancy rates than those in other developed countries. Improving U.S. teenagers' sexual and reproductive behavior requires strategies to reduce the numbers of young people growing up in disadvantaged conditions and to help those who are disadvantaged overcome the obstacles they face.
Adaptive Algorithms and Data-Dependent Guarantees for Bandit Convex Optimization

We present adaptive algorithms with strong data-dependent regret guarantees for the problem of bandit convex optimization. In the process, we develop a general framework from which the main previous results in this setting can be recovered. The key method is the introduction of adaptive regularization. By appropriately adapting the exploration scheme, we show that one can derive regret guarantees that can be significantly more favorable than those previously known. Moreover, our analysis also modularizes the problematic quantities in achieving the conjectured minimax optimal rates in the most general setting of the problem.
Intelligent search optimized edge potential function (EPF) approach to synthetic aperture radar (SAR) scene matching

Research on synthetic aperture radar (SAR) scene matching in the aircraft end-guidance has a significant value for both research and real-world application. The conventional scene matching methods, however, suffer many disadvantages such as heavy computation burden and low convergence rate so that these methods cannot meet the requirement of end-guidance system in terms of fast and real-time data processing. Furthermore, there are complex noises in the SAR image, which also compromise the effectiveness of using the conventional scene matching methods. To address the above issues, in this paper, the intelligent optimization method, Free Search with Adaptive Differential Evolution Exploitation and Quantum-Inspired Exploration, has been introduced to tackle the SAR scene matching problem. We first establish the effective similarity measurement function for target edge feature matching through introducing the edge potential function (EPF) model. Then, a new method, ADEQFS-EPF, has been proposed for SAR scene matching. In ADEQFS-EPF, the previous studied theoretical model, ADEQFS, is combined with EPF model. We also employed three recent proposed evolutionary algorithms to compare against the proposed method on optical and SAR datasets. The experiments based on Matlab simulation have verified the effectiveness of the application of ADEQFS and EPF model to the field of SAR scene matching.
Noise in RF-CMOS mixers: a simple physical model

Flicker noise in the mixer of a zero- or low-intermediate frequency (IF) wireless receiver can compromise overall receiver sensitivity. A qualitative physical model has been developed to explain the mechanisms responsible for flicker noise in mixers. The model simply explains how frequency translations take place within a mixer. Although developed to explain flicker noise, the model predicts white noise as well. Simple equations are derived to estimate the flicker and white noise at the output of a switching active mixer. Measurements and simulations validate the accuracy of the predictions, and the dependence of mixer noise on local oscillator (LO) amplitude and other circuit parameters.
Slepian-Wolf turbo coding of video with non-stationary side-information

In the early seventies, Slepian and Wolf first introduced source coding with side-information at the decoder. This coding principle allows for a low-complexity encoder, provided that the decoder has access to a sideinformation signal that is dependent on the source signal. In video source coding, we are able to generate this side-information by using a predictive video model. In the last decades, this possibility has been further explored, revealing that turbo codes are a serious candidate for this purpose. Turbo codes have been studied and used as an error correcting code for quite a long time now. In the light of recent research in Wyner-Ziv coding for video compression, the question has risen whether turbo code based compression systems will be capable to handle the inherent non-stationary noise introduced by the predictive video model generating the side-information at the decoder. State of the art motion estimation algorithms are very capable of error-free prediction of the larger part of the frame based only on previous frames. However, some situations, such as occlusions, will always remain a problem in frame prediction, causing location-specific noise. In this paper, we investigate the coding of non-stationary noise using a turbo code based Slepian-Wolf system. From the experiments described here, we can conclude that turbo code based systems have a limited capability of dealing with location-specific, badly described noise. We will also demonstrate that even relatively primitive information about the location and the underlying symbol probabilities will assist in the decoding process.
Efficient parallel programming in Poly/ML and Isabelle/ML

The ML family of languages and LCF-style interactive theorem proving have been closely related from their beginnings about 30 years ago. Here we report on a recent project to adapt both the Poly/ML compiler and the Isabelle theorem prover to current multicore hardware. Checking theories and proofs in typical Isabelle application takes minutes or hours, and users expect to make efficient use of "home machines" with 2-8 cores, or more. Poly/ML and Isabelle are big and complex software systems that have evolved over more than two decades. Faced with the requirement to deliver a stable and efficient parallel programming environment, many infrastructure layers had to be reworked: from low-level system threads to high-level principles of value-oriented programming. At each stage we carefully selected from the many existing concepts for parallelism, and integrated them in a way that fits smoothly into the idea of purely functional ML with the addition of synchronous exceptions and asynchronous interrupts. From the Isabelle/ML perspective, the main concept to manage parallel evaluation is that of "future values". Scheduling is implicit, but it is also possible to specify dependencies and priorities. In addition, block-structured groups of futures with propagation of exceptions allow for alternative functional evaluation (such as parallel search), without requiring user code to tackle concurrency. Our library also provides the usual parallel combinators for functions on lists, and analogous versions on prover tactics. Despite substantial reorganization in the background, only minimal changes are occasionally required in user ML code, and none at the Isabelle application level (where parallel theory and proof processing is fully implicit). The present implementation is able to address more than 8 cores effectively, while the earlier version of the official Isabelle2009 release works best for 2-4 cores. Scalability beyond 16 cores still poses some extra challenges, and will require further improvements of the Poly/ML runtime system (heap management and garbage collection), and additional parallelization of Isabelle application logic.
Reducing the branch penalty in pipelined processors

A probabilistic model is developed to quantify the performance effects of the branch penalty in a typical pipeline. The branch penalty is analyzed as a function of the relative number of branch instructions executed and the probability that a branch is taken. The resulting model shows the fraction of maximum performance achievable under the given conditions. Techniques to reduce the branch penalty include static and dynamic branch prediction, the branch target buffer, the delayed branch, branch bypassing and multiple prefetching, branch folding, resolution of branch decision early in the pipeline, using multiple independent instruction streams in a shared pipeline, and the prepare-to-branch instruction. <<ETX>>