Miracle-dz/UniFAR-FTUs huggingface dataset:
Transient phenomena prediction using recurrent neural networks

To overcome the cost of numerical simulations of transient phenomena, the goal is to construct a robust spatio-temporal reduced model capable of long-term in time predictions. The construction proposed in this article has to deal with several constraints: the reference model is a black box with high dimensional inputs and outputs, long-term in time prediction, few learning samples available, non-linear behaviour and the construction time must remain reasonable while the prediction time must be negligible. Recurrent neural networks are predictive models adapted to this dynamic framework. The improvements of the construction methodology detailed in this paper are the weights optimization through a multilevel optimization approach, a robust construction based on cross-validation and an application of sensitivity analysis in order to reduce the input dimension of the network. Finally, this construction is validated on an industrial test case predicting the temperature of an electronic equipment located in the avionic bay and subjected to fluctuations of its boundary conditions.
Imperfect Recall and Counter Games

We study a class of ω-regular games with imperfect information and imperfect recall, for which we present an automata-based solution method. Furthermore, we show a reduction from a class of counter parity games to games with this kind of imperfect recall. By combining the two results, we obtain an elementary algorithm for solving counter parity games, which leads to improved complexity bounds for model checking quantitative variants of the μ-calculus.
On deformations of generalized Calabi-Yau, hyperK\"ahler, $G_2$ and Spin$(7)$ structures I

In this paper we will introduce a new notion of geometric structures defined by systems of closed differential forms in term of the Clifford algebra of the direct sum of the tangent bundle and the cotangent bundle on a manifold. We develop a unified approach of a deformation problem and establish a criterion of unobstructed deformations of the structures from a cohomological point of view. We construct the moduli spaces of the structures by using the action of b-fields and show that the period map of the moduli space is locally injective under a cohomological condition (the local Torelli type theorem). We apply our approach to generalized Calabi-Yau (metrical) structures and obtain an analog of the theorem by Bogomolov-Tian-Todorov. Further we prove that deformations of generalized Spin(7) structures are unobstructed.
Effect of electric field on activity index of Horseradish peroxidase

The effect of electric field on the activity index of Horseradish peroxidase (HRP) was investigated by a combination of Ultraviolet-visible, FT-infrared and Circular Dichroism spectroscopic techniques. The result showed that in comparison with the control, electric field could increase the activity index of HRP; Based on these changes, it can be concluded that the electric field could affect charge distribution of group of the amides in the polypeyide chains of HRP and thus cause the change in the secondary structure of HRP. It would change in the structure of the porphyrin group in the heme group of HRP and lead the change in the activity index of HRP. The result of this study has important meaning to explain the biological effect of electric treatment seeds.
A Maximum Entropy Approach to Text Summarization

A summary is a useful means of establishing the contents or ke y points of a document in a condensed form. Summaries are traditionally comp osed by humans who have read the document in question and selected the key point s that would make up the summary. Attempts have been made to automate this proces s f summarization. However the task is extremely difficult as it involves the use of natural language understanding techniques to process the text, and then natura l language generation to compose the summary. Instead the most important sentences of a document, that is t o say the ones that contain the key points, can be selected and collated to form a n extract, a type of summary. Statistical techniques have been used to select such s entences: a classifier labels or classifies each sentence in the document with a probabilit y of being extracted and of it not being selected. If the probability of the sentence b eing selected is highest the sentence then forms part of the extract. In this research lassifier novel to the task is employed: maximum entropy. The results are then comp ared to those of the naive Bayes classifier, used in previous research. The summa ries produced by these techniques are evaluated against an extract produced by a hu m n. An analysis of the results suggests that the maximum entropy ap roach appears to yield better results than naive Bayes.
Visual Skills and Cross‐Modal Plasticity in Deaf Readers

Most research on reading skill acquisition in deaf individuals has been conducted from the perspective of a hearing child learning to read. This approach may limit our understanding of how a deaf child approaches the task of learning to read and successfully acquires reading skills. An alternative approach is to consider how the cognitive skills that a deaf child brings to the reading task may influence the route by which he or she achieves reading fluency. A review of the literature on visual spatial attention suggests that deaf individuals are more distracted by visual information in the parafovea and periphery. We discuss how this may have an influence upon the perceptual processing of written text in deaf students.
Predictors of College Students’ Likelihood to Report Hypothetical Rape: Rape Myth Acceptance, Perceived Barriers to Reporting, and Self-Efficacy

Rape myth acceptance (RMA), perceived barriers, and self-efficacy were examined as predictors of likelihood to report different types of rape to law enforcement among 409 undergraduates. Participants had lower likelihood to report incapacitated compared to physically forced rape. Men had lower reporting likelihood than women for rape perpetrated by the same and opposite sex and were more likely to perceive several barriers. RMA and perceived barriers predicted a lower likelihood to report several types of rape. Among men, higher self-efficacy predicted increased reporting likelihood. Targeting RMA and decreasing perceived barriers is imperative to increase college students’ likelihood to report rape to local and campus law enforcement.
Magnetic Force Microscope Contrast Simulation for Low-Coercive Ferromagnetic and Superparamagnetic Nanoparticles in an External Magnetic Field

We report the results of simulations of magnetic force microscope (MFM) contrast for low-coercive ferromagnetic and superparamagnetic nanoparticles. We show that two types of MFM contrast in the form of gaussian and ring distributions can be observed because of probe-particle interaction. We discuss stabilization of the magnetic moment of nanoparticles by an external magnetic field. We have calculated the values of stabilizing magnetic fields and their dependence on probe parameters and scanning heights.
The Study on Extraction Methods of Color Tree Image

The purpose of tree image extraction is to identify an individual tree from its surrounding scenery in the photography image of the woodland, which provides basic data and technical support for the applications of computer technology in forestry field. There is a lot of information in the image which is shot in the natural scenery. And at the same time, the image is influenced by environment such as the conditions, lights, weather and noise. In this paper, the tree image is named tree image in the complex background which is shot in the natural scenery and the background is uncertain. There is important practical value and realistic significance to extract tree image from complex background. In recent years, image segmentation and natural image matting are the main techniques used to extract tree image. In this paper, the author expounds the two methods in detail, analyzes and compares the advantages and limitations of them at the same time. At the end of the paper, the central contents in tree image extraction research field in the future are put forward. This paper proposes that image segmentation technique and natural images matting algorithm should be reasonable combined for tree image features. The design of an accurate, rapid, real-time tree image extraction technology is the focus and difficult problem in future research.
EEG-fMRI Based Information Theoretic Characterization of the Human Perceptual Decision System

The modern metaphor of the brain is that of a dynamic information processing device. In the current study we investigate how a core cognitive network of the human brain, the perceptual decision system, can be characterized regarding its spatiotemporal representation of task-relevant information. We capitalize on a recently developed information theoretic framework for the analysis of simultaneously acquired electroencephalography (EEG) and functional magnetic resonance imaging data (fMRI) (Ostwald et al. (2010), NeuroImage 49: 498–516). We show how this framework naturally extends from previous validations in the sensory to the cognitive domain and how it enables the economic description of neural spatiotemporal information encoding. Specifically, based on simultaneous EEG-fMRI data features from n = 13 observers performing a visual perceptual decision task, we demonstrate how the information theoretic framework is able to reproduce earlier findings on the neurobiological underpinnings of perceptual decisions from the response signal features' marginal distributions. Furthermore, using the joint EEG-fMRI feature distribution, we provide novel evidence for a highly distributed and dynamic encoding of task-relevant information in the human brain.
The Turkish Version of the Grandiose Narcissism Scale: A Validity and Reliability Study of Football Team Fans

Literature about narcissism has been the subject of studies in many different disciplines including sports sciences. The studies about narcissism in sport generally are for determining the level of athletes’ narcissism, but there are no studies which examines the level of football team fans narcissism. In light of the foregoing, there are two purpose of this study: 1) To examine the factor structure of GNS (Grandiose Narcissism Scale) in Turkish culture. 2) To reveal relationship between demographic variables and narcissism levels of sport fans. The study involved a quantitative research methodology and convenience sampling with a total sample of 275 sports fan. The data were subjected to, an exploratory factor analysis (EFA), confirmatory factor analysis (CFA), t-test, analysis of variance (ANOVA) and correlation analysis. After conducting the analyzes seven aspects of GNS revealed as in the original scale, which are authority, self-sufficiency, superiority, vanity, exhibitionism, entitlement and exploitativeness. According to results of ANOVA and t-tests, it is found out that there were significant differences between some demographic variables of football fans and authority, vanity dimension.
Value-Directed Human Behavior Analysis from Video Using Partially Observable Markov Decision Processes

This paper presents a method for learning decision theoretic models of human behaviors from video data. Our system learns relationships between the movements of a person, the context in which they are acting, and a utility function. This learning makes explicit that the meaning of a behavior to an observer is contained in its relationship to actions and outcomes. An agent wishing to capitalize on these relationships must learn to distinguish the behaviors according to how they help the agent to maximize utility. The model we use is a partially observable Markov decision process, or POMDP. The video observations are integrated into the POMDP using a dynamic Bayesian network that creates spatial and temporal abstractions amenable to decision making at the high level. The parameters of the model are learned from training data using an a posteriori constrained optimization technique based on the expectation-maximization algorithm. The system automatically discovers classes of behaviors and determines which are important for choosing actions that optimize over the utility of possible outcomes. This type of learning obviates the need for labeled data from expert knowledge about which behaviors are significant and removes bias about what behaviors may be useful to recognize in a particular situation. We show results in three interactions: a single player imitation game, a gestural robotic control problem, and a card game played by two people.
A self-organizing neural network using hierarchical particle swarm optimization

This paper introduces a hierarchical particle swarm optimization (HPSO) algorithm strategy for self-organizing neural network design. The proposed CHPSO can determine the structure of the neural network and tune the parameters in the neural network automatically. The structure learning is based on the genetic algorithm (GA) and the parameter learning is based on the particle swarm optimization (PSO). The advantages of the proposed learning algorithm can obtain fine structure and performance for neural network (NN). The prediction of simulation example has been given to illustrate the performance and effectiveness of the proposed model.
Discrete Lagrangian-Based Search for Solving MAX-SAT Problems

Weighted maximum satissability problems (MAX-SAT) are diicult to solve due to the large number of local minima in their search space. In this paper we propose a new discrete Lagrangian based search method (DLM) for solving these problems. Instead of restarting from a new point when the search reaches a local minimum, the Lagrange multipliers in DLM provide a force to lead the search out of the local minimum and move it in a direction provided by the multipliers. Since DLM has very few parameters to be tuned, it can be made de-terministic and the results, reproducible. We compare DLM with GRASP in solving a large set of test problems, and show that it nds better solutions and is substantially faster. DLM has a solid theoretical foundation that can be used as a systematic approach for solving general discrete optimization problems.
A Class of Algorithms for Distributed Constraint Optimization

Multi Agent Systems (MAS) have recently attracted a lot of interest because of their ability to model many real life scenarios where information and control are distributed among a set of different agents. Practical applications include planning, scheduling, distributed control, resource allocation, etc. A major challenge in such systems is coordinating agent decisions, such that a globally optimal outcome is achieved. Distributed Constraint Optimization Problems (DCOP) are a framework that recently emerged as one of the most successful approaches to coordination in MAS. This thesis addresses three major issues that arise in DCOP: efficient optimization algorithms, dynamic and open environments, and manipulations from self-interested users. We make significant contributions in all these directions: Efficiency-wise, we introduce a series of DCOP algorithms, which are based on dynamic programming, and largely outperform previous DCOP algorithms. The basis of this class of algorithms is DPOP, a distributed algorithm that requires only a linear number of messages, thus incurring low networking overhead. For dynamic environments we introduce self-stabilizing algorithms that can deal with changes and continuously update their solutions. For self interested users, we propose the M-DPOP algorithm, which is the first DCOP algorithm that makes honest behaviour an ex-post Nash equilibrium by implementing the VCG mechanism distributedly. We also discuss the issue of budget balance, and introduce two algorithms that allow for redistributing (some of) the VCG payments back to the agents, thus avoiding the welfare loss caused by wasting the VCG taxes.
Domain-driven design patterns: A metadata-based approach

Design pattern is the most common form of object oriented software reuse. In object oriented domain driven design, a number of high-level patterns have been identified and applied for over a decade. However, no concrete design patterns for domain modeling in this method have been published in the literature. A primary challenge in defining these design patterns is how to express their form in a way that eases their application to a specific domain that uses a specific object oriented programming platform. In this work, we propose a set of concrete design patterns, whose form is expressed in a domain class modeling language (DCML). DCML is based on UML and uses implementation-aware meta-attributes to define design metadata. We extend this language with new meta-attributes to support the proposed design patterns. Further, we discuss how domain-specific examples of these patterns are translated to physical class models for automatically generating software prototypes.
Multicores-periphery structure in networks

Abstract Many real-world networks exhibit a multicores-periphery structure, with densely connected vertices in multiple cores surrounded by a general periphery of sparsely connected vertices. Identification of the multicores-periphery structure can provide a new lens to understand the structures and functions of various real-world networks. This paper defines the multicores-periphery structure and introduces an algorithm to identify the optimal partition of multiple cores and the periphery in general networks. We demonstrate the performance of our algorithm by applying it to a well-known social network and a patent technology network, which are best characterized by the multicores-periphery structure. The analyses also reveal the differences between our multicores-periphery detection algorithm and two state-of-the-art algorithms for detecting the single core-periphery structure and community structure.
ECDKG : A Distributed Key Generation Protocol Based on Elliptic Curve Discrete Logarithm

In public key based cryptosystems, key distribution has been a crucial issue of protocol design. Many attack methods are orchestrated by making use of some weakness at the stage of key distribution. In this report, we also propose a distributed key generation protocol based on elliptic curve discrete logarithm, called ECDKG. Existing distributed key generation protocols use either cryptosystems based on discrete logarithm over a finite field or Integer factorization as a building block. The secrecy has been proven and the secrecy is as strong as the building block systems. However, there are subexponential algorithms for solving this discrete logarithm problem over a field field and the integer factorization problem. Elliptic curve cryptosystem provides a promising alternative for building a distributed key generation scheme and no known subexponential algorithms solve a general version of the discrete logarithm over an additive group derived from a point on an elliptic curve. Main advantages using this building block include the space and time efficiency and flexibility. We also propose the adaptive version and proactive version of ECDKG. An example on how to use ECDKG on ElGamal based message encryption is also given. We will also address some practical implementation issues.
A methodology for rapid prototyping microfluidic devices with sophisticated functionality

Microfluidic laboratory-on-a-chip (LOC) systems based on a modular architecture are presented. A multi-layer approach segregates components belonging to two fundamental categories: passive fluidic components (channels and reaction chambers) and active electromechanical control structures (sensors and actuators). Components belonging to these two categories are built separately on different physical layers and can communicate fluidically via cross-layer interconnects. We demonstrated the utility of this architecture by developing systems for two separate biochemical applications: one for detection of protein markers of cancer and another for detection of metal ions.
INTRODUCTION At the turn of the century , when the nascent discipline of psychology sought o appraise consciousness in both its mentalistic

INTRODUCTION . 23 PARADIGM VARIATIONS A DBASIC MODELS . 25 Procedural, Epistemic, and Conceptual Divergence . 26 Goal Selection . 27 Goal Cognition . 28 Strategic, On-Line Goal Coordination: The Systems Base . 29 PROXIMAL VO ITIONAL REGULATORS . 30 Goal (Standard) Setting . 31 Self-Monitoring. 33 The Activation and Use of Standards . 34 Discrepancy Detection, Self-Evaluative Judgment, Self-Consequation . 35 The Implementation f Discrepancy-Reduction Skills . 36 Self-Efficacy . 37 METASKILLS ANDBOUNDARY CONDITIONS . 38 Metaskills . 39 Boundary Conditions . 41 SELF-REGULATORY FAILURE: CONCEPTS AND DYNAMICS . 42 FINAL COMMENT . 44
Is cancer mortality increasing in France?

Long-term trends in cancer mortality are reported by site. Overall, cancer mortality has been decreasing in France since 1987 in the male population and since 1968 in the female population. Improvement in treatments and diagnosis should lead to persistently declining mortality rates, unless the tobacco epidemic reverses the trend in female mortality. © 2001 Cancer Research Campaign http://www.bjcancer. com
Accurate closed-form approximations to the sum of generalized random variables and applications

Accurate closed-form approximations to the sum of independent identically distributed eta-mu and kappa-mu random variables are provided. The proposed approximations turn out to be simple, precise and find applicability in obtaining important performance metrics of communications systems where sums of variates arise. In particular, outage probability and average bit error rate of some modulation schemes of multibranch equal-gain combining receivers are attained to illustrate the usefulness of the approximations. In passing, based on the fading models of the corresponding fading scenarios, exact expressions for these metrics for multibranch maximal-ratio combining receivers are attained that present the same functional form of the corresponding expressions for a single branch.
Weighted fairness guaranteed data aggregation scheduling algorithm in wireless sensor networks

Data aggregation from a set of sensors to a common sink over a tree-based routing topology is a fundamental traffic pattern in wireless sensor networks (WSNs). The time division multiple access (TDMA)-based scheduling protocols are suitable for such data aggregation applications. Most of the previously proposed TDMA schemes focus on a particular objective, such as minimizing schedule delay, latency, and energy consumption etc. In this paper, we investigate another important aspect of aggregation, i.e., weighted fairness, and propose a novel weighted fairness guaranteed aggregation scheduling algorithm (WAS) under the protocol interference model. We use node weight to reflect the importance of each node, and introduce the weighted delay as the weighted fairness guaranteed metric. Then the problem of weighted fairness guaranteed can be translated into minimizing the weighted delay. In order to achieve this purpose, WAS utilizes the approximate maximum weighted set to allocate time slots for each node according to different accumulative weight. The results of simulation demonstrate that the weighted fairness of WAS is much better than the existing TDMA scheduling algorithm. In particular, the proposed scheduling scheme can achieve a good trade-off between weighted fairness guarantee and aggregation delay.