Miracle-dz/UniFAR-FTUs huggingface dataset:
Élévation des marqueurs sériques cardiaques : diagnostics différentiels

Le syndrome coronarien aigu définit l’ensemble des douleurs d’allure angineuse survenant de novo, ou d’aggravation récente chez un patient avec ou sans antécédent coronarien. Il regroupe l’angor instable et 1’infarctus du myocarde qui ont la même origine physiopathologique : une réduction brutale du flux sanguin coronarien, le plus souvent par rupture ou érosion d’une plaque d’athérome. Ce spectre diagnostique est utile au développement de stratégies thérapeutiques.
Systematic Review and Meta-Analysis of 12 Randomized Controlled Trials Evaluating the Efficacy of Invasive Radiofrequency Treatment for Knee Pain and Function

Radiofrequency (RF) treatment is an invasive and promising procedure in the treatment of osteoarthritis (OA). A meta-analysis based on 12 RCT studies was to investigate whether invasive RF treatment is more effective in relieving knee pain and improving knee function. Relevant studies were searched on database of Pubmed, Embase, EBSCO, Cochrane library, Wanfang digital database, VIP database, and CNKI up to January 2018. A total of 841 participants from 12 publications were included. The weighted mean difference (WMD) and the corresponding 95% CIs were used to evaluate the difference in pain scores and OKS/WOMAC scores between RF treatment and control groups. The statistical analysis was performed by Stata 12.0. The pain scores (VAS) in the RF group were lower than those in the conservative treatment group after 1 week (WMD -1.77, 95% CI -2.93 to -0.61, P<0.01), 1 month (WMD -1.40, 95% CI -1.98 to -0.82, P<0.01), and 3 months (WMD -1.32, 95% CI -2.27 to -0.37, P<0.01) of treatment, while there was no significant improvement in knee function. In subgroup analyses by site of radiofrequency, RF mode showed some discrepancies in the WMD of VAS between the treatment and control groups. In addition, subgroup analysis and meta-regression showed that the efficacy of RF treatment for reducing pain is reversely related to female ratio, and we did not find any surgery-related adverse reactions. RF treatment significantly reduces the knee pain, but rarely improves the knee joint function. Radiofrequency ablation has better efficacy than pulsed radiofrequency ablation in reducing pain. Furthermore, subgroup analysis and meta-regression suggested that women are more sensitive to RF treatment than men.
Whole-body Control of Non-holonomic Mobile Manipulator Based on Hierarchical Quadratic Programming and Continuous Task Transition

Whole-body manipulation for a non-holonomic mobile manipulator has potential in various fields including service and industrial robotics because it can provide a great deal of manipulation capability and mobility. This paper presents the whole-body controller of the non-holonomic mobile manipulator for achieving complex tasks effectively. The proposed controller is designed based on Hierarchical Quadratic Programming (HQP). Thus, the controller can deal with both equality and inequality tasks. Moreover, by applying the continuous task transition strategy, our controller can compute the continuous control input when the priorities of the tasks are changed. The proposed control scheme was implemented on a mobile base with a 7 DoFs robotic arm and its performance was validated during various experimental scenarios.
Mechanism of leakage in flat seals

We present the results of a novel approach to estima te the fluid leakage in flat seals, based on the analogy between the seal-substrate interface a nd a porous medium. We assume that the interface is a random distribution of non-contact patch es (the pores) and contact spots (islands): leakage may occur only through the pores. The lateral size an d height of the pores are distributed according to a probability density function, that we calcul ate on the basis of Persson’s theory of contact mechanics. Our theoretical approach is based on a novel percolation scheme that can stimulate further theoretical or experimental investigations. With in this percolation scheme we apply critical path analysis (CPA) to calculate the hydraulic conductivit y of the medium. We also compare our predictions with other calculations very recently present ed to the scientific community.
Structurally adaptive RBF network in nonstationary time series prediction

A sequentially adaptive radial basis function (RBF) network is applied to the nonstationary, time series prediction. Sequential adaptation of parameters and structure is achieved using an extended Kalman filter criterion for network growing is obtained from the Kalman filter's consistency test. The Optimal Brain Surgeon and Optimal Brain Damage pruning methods are derived for networks which parameters are estimated by the EKF. Criteria for neurons/connections pruning are based on the statistical parameter significance test. Prediction of the nonstationary logistic map and Lorenz time series is considered.
On analog decoders and digitally corrected converters

In recent years, the demand for efficient and reliable communication networks has greatly increased. To satisfy this need, powerful error correcting codes were introduced. The (iterative) algorithms used for decoding such modern codes are computationally very demanding and need great computing power to deliver real-time results. Mobile users, however, ask for low-power electronics; the combination of both demands led to an increased interest in analog communication circuits, e.g., in analog decoders for error correcting codes. The first part of this thesis discusses various implementations of analog decoders. An analog decoder can be understood as a coderepresenting (factor) graph mapped on analog silicon, whereas the decoding algorithm (e.g., the sum-product algorithm) corresponds to the settling behavior of the analog circuit. The performance gain of analog decoders compared to digital implementations in terms of speed or power-consumption is believed to be at least a factor of 100. The following implementations of such analog decoders are discussed: Hamming decoders built out of two generations of discrete softgates, an integrated Hamming decoder and an integrated Reed-Muller decoder are presented. An extensive collection of measured error-rate curves of all decoders under various operating conditions prove their full functionality and demonstrate their behavior under transistor mismatch. Furthermore, a novel circuit to compute the soft symbols for a PAM or QAM signal is presented. This simple transistor network blends in nicely with analog decoders—its outputs are currents proportional to the symbol-likelihoods. vii
International Conference on Concentrating Solar Power and Chemical Energy Systems , SolarPACES 2014 Thermal analysis of a receiver for Linear Fresnel reflectors

This paper presents an advanced methodology for the detailed modeling of the heat transfer and dynamics phenomena in Linear Fresnel receivers. The present work aims at modeling Linear Fresnel receiver by proposing a parallel modular object-oriented methodology which considers the different elements of the receiver (e.g. Insulation material, glass cover, tube, pipe, etc.). The global model is composed of 4 sub models (heat conduction, two/single-phase flow, thermal radiation and natural convection) which are described. Results of the numerical model obtained so far are also presented and discussed. © 2015 The Authors. Published by Elsevier Ltd. Peer review by the scientific conference committee of SolarPACES 2014 under responsibility of PSE AG.
Feasibility of Platelet Indices as Possible Biomarkers in Evaluation of Initial Vascular Risks in Non-Diabetic Subjects .

A possible role of platelet indices in non-diabetic subjects was explored for its possible association with hematopoietic and renal biochemical indices. Hypothesis was that non-diabetic disease burden is closely associated with change in serum levels of lipoproteins, urea, uric acid, creatinine, microalbumin and hematological parameters as consequence of platelet dysfunction manifested as dyslipidemia, nephrosis and thrombosis. A total of 97 non-diabetic patients out of 225 were analyzed for correlation and contribution of serum sugar, lipoproteins, urea, uric acid, creatinine, microalbumin and hematological parameters with platelet dysfunction as ‘pleiotropic risk index’. Results showed that ‘pleiotropic risk index’ may reflect the initial trigger of dyslipidemia, followed by progressive renal complications with growing risk of thrombosis in non-diabetic subjects. Males in their fifties age were at high risk of pleiotropic effects. Change in serum levels of sugar, lipoproteins, urea, uric acid, creatinine and microalbumin contribute as pleiotropic factors with increased risks in the order of lipids < platelet indices < urea < creatinine < microalbumin < hematological parameters < uric acid. Deranged platelet indices were closely associated with initial vascular and renal complication in nondiabetic subjects. In conclusion, non-diabetic pleiotropic effects associated with platelet indices, biochemical and hematopoietic biomarkers served as better risk indicators of progressive vascular complications in nondiabetic patients having chronic kidney disease. -------------------------------------------------------------------------------------------------------------------------------------Date of Submission: 20-12-2018 Date of acceptance: 06-01-2019 -------------------------------------------------------------------------------------------------------------------------------------
Search , product variety and monopolistic competition in a pure monetary economy

In order to capture the demand externalities associated with taste for variety and firm entry, we integrate monopolistic competition and preferences for variety in the decentralized market of a Lagos-Wright setting, with an ex ante division of buyers and sellers. Matching is multilateral in that each buyer accesses a range of sellers who each supply a unique variety of the good. We investigate the efficiency properties of equilibrium, firm size, and welfare cost of inflation in a pure monetary economy. Markups arise from the taste for variety and create a rent-sharing externality which amplifies the cost of holding money induced by inflation. But with scale economies markups also help pay fixed costs of entry and thereby enable more variety. In the basic model without entry, consumption and firm size is too low and the Friedman rule is the optimal policy, though it implements the first best only as the taste for variety (and markups) approach zero. I extend the model in three important ways: free entry of sellers, free entry and variable markups, and variable search intensity. Equilibrium exists for each model under mild conditions and is generally unique except for the CES model with free entry. There, uniqueness fails to hold if taste for a variety is too high. In that case, with sufficiently low entry costs, there is a stable high-variety equilibrium and an unstable low-variety equilibrium, where the former Pareto dominates the latter. With CES and free entry, the optimal scale of a firm lies between the efficient scale and the equilibrium level depending on the elasticity of the matching function. The Dixit Stiglitz result that the optimal measure of sellers exceeds the equilibrium measure holds with sufficiently low search frictions, high nominal interest rates, and high elasticity of substitution, but does not hold generally. I compute the welfare costs of inflation in the former case using a compensated measure and use parameter values obtained by fitting the theoretical money demand to its empirical counterpart. For markups of 30%, the estimates are about 7.24% without entry and 9.25% with entry. Under variable markups introduced via additively separable preferences, markups decrease with interest rates and attenuate the welfare costs of inflation.
A Survey on Extractive Text Summarization Techniques

Text summarization is a technique for creating a short but useful information from a text document. Automatic text summarization plays a vital role in producing a summarized information from a large corpus. An extractive summarization is a technique that is based on extracting the sentences from the documents for the summary generation. Various systems are based on feature extraction method. The main goal of all the systems is to obtain relevant data and summarize them as per the users’ input. These systems differ in the main aspects of the features provided and the architecture used by them. This paper is consisted of the basics of multi-document summarization, then several approaches for extractive summarization and extractive methods, and then the various evaluation methods for the estimating the performance of the summarization techniques.
Active-learning and materials design: the example of high glass transition temperature polymers

Machine-learning (ML) approaches have proven to be of great utility in modern materials innovation pipelines. Generally, ML models are trained on predetermined past data and then used to make predictions for new test cases. Active-learning, however, is a paradigm in which ML models can direct the learning process itself through providing dynamic suggestions/queries for the “next-best experiment. ” In this work, the authors demonstrate how an active-learning framework can aid in the discovery of polymers possessing high glass transition temperatures ( T _g). Starting from an initial small dataset of polymer T _g measurements, the authors use Gaussian process regression in conjunction with an active-learning framework to iteratively add T _g measurements of candidate polymers to the training dataset. The active-learning framework employs one of three decision making strategies (exploitation, exploration, or balanced exploitation/exploration) for selection of the “next-best experiment. ” The active-learning workflow terminates once 10 polymers possessing a T _g greater than a certain threshold temperature are selected. The authors statistically benchmark the performance of the aforementioned three strategies (against a random selection approach) with respect to the discovery of high- T _g polymers for this particular demonstrative materials design challenge.
Automated Object Recognition FromHigh Resolution Satellite Images

Object Recognition, is an important element of various computer vision areas such as image retrieval, video surveillance etc. A great deal of research has been performed to advance the field and to enhance the capability and robustness of object detection systems. In this research, an efficient method of automated object recognition from satellite image is constructed and for detection of roads mainly Hough Transformation is used. The subject of this proposal is object detection. In short, it is the problem of localising instances of object classes in digital images. Object detection through template matching is defined as follows: The input is a single image and an object image. The goal of the methodology is to localise subimage in the image that should be portrayed with rectangular bounding boxes. Subsequently, give the main image and some knowledge of the object image our problem is to report that object is present where in the scene image.
J ul 2 01 9 A CONVERGENT FV – FEM SCHEME FOR THE STATIONARY COMPRESSIBLE NAVIER-STOKES EQUATIONS

In this paper, we propose a discretization of the multi-dimensional stationary compressible Navier-Stokes equations combining finite element and finite volume techniques. As the mesh size tends to 0, the numerical solutions are shown to converge (up to a subsequence) towards a weak solution of the continuous problem for ideal gas pressure laws p(ρ) = aρ , with γ > 3/2 in the three-dimensional case. It is the first convergence result for a numerical method with adiabatic exponents γ less than 3 when the space dimension is three. The present convergence result can be seen as a discrete counterpart of the construction of weak solutions established by P.-L. Lions and by S. Novo, A. Novotný.
BEES ALGORITHM-BASED DOCUMENT CLUSTERING

Document clustering is one of data mining fields which mainly aims to automatically group the relevant documents into clusters. This paper introduces the application of the Bees Algorithm in optimizing the document clustering problem. The Bees Algorithm simulates the foraging behavior of honey bees swarms in collecting nectar from flower patches by performing global and local search simultaneously; this improves avoidance of local minima convergence. Genetic Algorithm and the K-means algorithm are used for comparisons as they are among the most commonly used techniques to solve the problem. Many experiments were performed on a corpus of 818 documents from 4 different fields and the results have shown that the Bees algorithm outperforms the Genetic Algorithm and the K-means by 15% and 50% respectively, in terms of solutions fitness, with an acceptable increase in the processing time.
Automatic program parallelization using stateless parallel processing architecture

This thesis investigates a new approach for automatic sequential-to-parallel program translation for distributed-memory multicomputers by leveraging a dataflow computation model. It is well-known that dataflow computation models allow automatic dependency uncovering without complex static dependency analysis. This dissertation focuses on a coarse-grain dataflow model supported by a stateless parallel processing architecture. Under this model, the user discovers an overall dependency pattern. This pattern is used to direct program partition and data distribution strategies. Non-linear, indirect and conditional dependencies are automatically uncovered at runtime. In contrast, other approaches, such as the PARADIGM project at UIUC, have encountered insurmountable difficulties when attempting to solve nonlinear and other dependencies at compile time. In this thesis, the dataflow computation model is provided by the Synergy system—a preliminary implementation of the stateless parallel processing architecture using multiple networked computers. This approach includes a new Parallelization Markup Language (PML). It is used to describe an overall dependency pattern by marking sequential program segments. A PML compiler translates the marked sequential program into multiple parallel programs using the extracted dependency pattern. Based on XML technique, PML is portable and extensible. It is completely independent from programming languages. The PML tags are also powerful enough to describe very complex dependency patterns (thus data partition strategies). Theoretically, this methodology is applicable to all types of iterative compute-intense applications. In this thesis, we have chosen four well-recognized numerical applications to illustrate the practical utility and efficiency of this method: Matrix Multiplication, Laplacian Solver using Gauss-Siedel iterations, Ion Generation Simulator and Block LU Factorization. We show that performance measurements from generated programs compare favorably against manually written parallel programs. (Abstract shortened by UMI.)
SKIVA: Flexible and Modular Side-channel and Fault Countermeasures

We describe SKIVA, a customized 32-bit processor enabling the design of software countermeasures for a broad range of implementation attacks covering fault injection and side-channel analysis of timing-based and power-based leakage. We design the countermeasures as variants of bitslice programming. Our protection scheme is ﬂexible and modular, allowing us to combine higher-order masking – fending oﬀ side-channel analysis – with complementary spatial and temporal redundancy – protecting against fault injection. Multiple conﬁgurations of side-channel and fault protection enable the programmer to select the desired number of shares and the desired redundancy level for each slice. Recurring and security-sensitive operations are supported in hardware through a custom instruction set extension. The new instructions support bitslicing, secret-share generation, redundant logic computation, and fault detection. We demonstrate and analyze multiple versions of AES from a side-channel analysis and a fault-injection perspective, in addition to providing a detailed performance evaluation of the protected designs.
FDR 2019-1006 3 D RECONSTRUCTION FOR UNDERWATER INVESTIGATION AT FUKUSHIMA DAIICHI NUCLEAR POWER STATION USING REFRACTIVE STRCTURE FROM MOTION

Three-dimensional (3D) reconstruction of the Primary Containment Vessel (PCV) internal structure provides useful information for cleanup of the fuel and debris submerged in the coolant. This internal structure can be recovered by Structure from Motion (SfM) technique using the investigation video sequences, such as the video captured by an underwater robot at Unit 3 PCV. However, the inside of Unit 3 PCV is filled with coolant. This underwater environment is different from the air environment. Thus, the camera equipped on the robot is confined in a waterproof camera housing. Light rays entering to the camera are refracted twice in this situation. One happens at the interface of water and camera housing, and the other happens at the interface of camera housing and the air. The refraction will result in geometric distortion in 3D reconstruction. In this paper, we propose a Refractive Structure from Motion (RSfM) approach to solve the refractive distortion problem during the 3D reconstruction. The approach includes camera system modeling, camera housing calibration, camera system pose estimation and geometric reconstruction. The camera system modeling method based on ray tracing is proposed to model the camera system of the underwater robot sent to Unit 3 PCV. A new camera housing calibration is based on back-projection error is proposed to achieve accurate modeling. Furthermore, camera system pose estimation based on modeled camera system is proposed for the geometric reconstruction. Finally, the 3D reconstruction can be obtained by triangulation. The effectiveness of proposed approach is confirmed using the video captured by the underwater robot at Unit 3 PCV of Fukushima Daiichi Nuclear Power Station. The geometric reconstruction results show that the proposed method can reduce the refractive distortion effectively and achieve better results than the conventional SfM method.
COMPARATIVE ANALYSIS OF BER AND SNR BY USING MULTIPLE CHANNELS AND STBC CODE FOR MIMO HEMANGI

The paper presents the comparative analysis of Space-Time Block Codes (STBC) used in Multiple-Input Multiple-Output (MIMO) systems to assure transmit diversity. The receive diversity is resolved with a Maximum Ratio Combining technique. For a fixed number of transmit antennas, the performances of different STBC codes are analyzed in terms of Bit Error Rate (BER) and SNR for a quasi-static Rayleigh flat fading channel. Finally, it is shown that by increasing the number of transmit/receive antennas, the system performances increase, and also error performance analysis of multiple rate space-time-block code (STBC) for MIMO wireless network. The design aspect of wireless system aims at improvement in spectral efficiency and coverage area with reliable performance. This necessitates that the MIMO system must be capable to overcome data rate limitation and there must have options open for future improvement. Error performance and spectral efficiency can further be improved by incorporating STBC with MIMO system. Recently many researchers have been working on MIMO systems with STBC to improve the performance of system without additional bandwidth or transmit power requirements. We have considered here the STBC schemes in Rayleigh fading environment using various combinations of numbers of transmit and receive antennas. The simulations results have been obtained in MATLAB platform.