Miracle-dz/UniFAR-FTUs huggingface dataset:
An adaptive finite element DtN method for the open cavity scattering problems

Consider the scattering of a time-harmonic electromagnetic plane wave by an open cavity which is embedded in a perfectly electrically conducting infinite ground plane. This paper concerns the numerical solutions of the open cavity scattering problems in both transverse magnetic and transverse electric polarizations. Based on the Dirichlet-to-Neumann (DtN) map for each polarization, a transparent boundary condition is imposed to reduce the scattering problem equivalently into a boundary value problem in a bounded domain. An a posteriori error estimate based adaptive finite element DtN method is proposed. The estimate consists of the finite element approximation error and the truncation error of the DtN operator, which is shown to decay exponentially with respect to the truncation parameter. Numerical experiments are presented for both polarizations to illustrate the competitive behavior of the adaptive method.
High-Dimensional Uncertainty Quantification of Fluid-Structure Interaction

This work combines an example of a multi-physics application, namely fluid-structure interaction, with uncertainty quantification. This is motivated by the goal of obtaining more realistic simulations via a comprehensive understanding of the impact of input uncertainties in the outcomes. Furthermore, performing multi-physics simulations is a must, as most of the real-world problems are complex, comprising components with individual physical descriptions. Due to the challenges posed by both domains, their combination leads to a multi-challenge, which implies that it is imperative to find suitable methods to deal with multi-physics, multi-dimensionality, and multi-core problems. In the current work the multi-challenge is tackled by employing high performance computing and sophisticated mathematics. The simulations are performed on multi-core architectures using two levels of parallelism, one at the level of an individual run and the other at the level of the entire set of runs. The high dimensional problems are approached via the pseudo-spectral approach in combination with sparse grids interpolation. This methodology is non-intrusive and once the simulation results are available, they can be reused. The post-processing step comprises statistics estimation, probability density function estimation, and variance based sensitivity analysis. The testing is performed using two simulation scenarios. For both scenarios, uncertainty is modelled in five input parameters: density and dynamic viscosity of the fluid, density of the structure, Young’s modulus, and Poisson’s ratio. The first scenario is a two dimensional wall-mounted flap and it is tested for stochastic dimensionality one, two, and five. The pseudo-spectral approach is validated using the Monte Carlo algorithm for dimensionality one and five. The post-processing step of the first two settings shows that the uncertainty in two out of the five input parameters is the most significant for the resulting uncertainty. Moreover, the analysis of the five dimensional case reveals that the same two parameters together with their interaction are the most significant for the outcomes, implying that the five dimensional problem could be reduced to a two dimensional one. The second scenario represents an established fluidstructure interaction benchmark, for which the five dimensional stochastic setting is simulated. Employing a sparse grid of level 3 for the interior domain and one of level 0 for the boundary, a total of 103 points is used. All simulations were performed on the MAC Cluster from the Munich Center of Advanced Computing in a time span of eight days, employing between 10 and 18 nodes. The total compute hours were 24h × 103, with 16 cores/simulation. The post-processing reveals that uncertainty in all five input parameters has a significant impact in the movement of the structure. Moreover, from the sensitivity analysis, it results that uncertainty in all input parameters is relevant in the outcomes, meaning that in this scenario, the stochastic dimensionality cannot be reduced.
Analysis of English free association network reveals mechanisms of efficient solution of the Remote Association Tests

In this paper we study the connection between the structure and properties of the so-called free association network of the English language, and the solution of psycholiguistical Remote Association Tests (RATs). We show that average hardness of individual RATs is largely determined by the relative positions of the test words (stimuli and response) on the free association network. We argue that solution of RATs can be interpreted as a first passage search problem on a free association network and study a variety of different search algorithms. We demonstate that in easy RATs (those solved by more than 64% subjects in 15 seconds) there are strong links directly connecting stimuli and response, and thus an efficient strategy consist in activating these direct links. In turn, the most efficient mechanism of solving medium and hard RATs consists of preferentially following what we call "moderately weak" associations.
A POISSON-POISSON MODEL TO ANALYZE CONGESTION DATA

Congestion in internet network often worsens the performance of the network. An analysis of congestion data plays a significant role in congestion detection and control. To detect and control congestion, analysis of number of packet arrival, packet inter arrival times, throughput, queuing delay, etc. are some important phenomena. Fitting probability distributions to packet arrival, throughput, inter arrival time, etc. are essential in order to control congestion in future, setting appropriate values of parameters beforehand (buffer size, number of packet arrival, inter arrival time, average queuing delay, etc.). Number of packet arrival within a certain time interval is a count random variable that follows a Poisson distribution. Similarly, throughput/number of packet departure within a certain time interval is also a Poisson random variable but it is correlated with the number of packet arrival. In most of the previous studies, univariate distributions have been used to identify the best fit probability distributions for congestion data. In this study, an attempt is made to use a Poisson-Poisson model for packet arrival and departure (throughput) from the network where correlation between the packet arrival and departure is considered. Using simulation, the performance of models in each situation (small/large sample size, low/heavy congestion) is evaluated. This technique is expected to simplify the analysis of big data stemming from the congestion in TCP/IP network.
General relativistic magnetohydrodynamic simulations of binary neutron star mergers

In this thesis I present results of my fully general relativistic magnetohydrodynamic (GRMHD) binary neutron star merger (BNS) simulations, conducted by using the numerical code "Whisky" under various conditions, such as, different Equation of State (EOS) for neutron matter (APR4, Ideal fluid and H4 EOSs), masses (with equal/unequal masses for two neutron stars), different magnetic field configurations (both fields of two neutron stars aligned with the inspiral axis, one aligned and one anti-aligned, and both anti-aligned) to investigate the effect of these parameters on the dynamics of the simulations and possibility of forming relativistic jets, which is thought to be one of the necessary conditions for the central engine of short gamma-ray bursts (SGRBs).
A LEVENBERG-MARQUARDT METHOD FOR NONSMOOTH 1 REGULARIZED LEAST SQUARES 2

We develop a Levenberg-Marquardt method for minimizing the sum of a smooth 4 nonlinear least-squares term f ( x ) = 12 ∥ F ( x ) ∥ 22 and a nonsmooth term h . Both f and h may be 5 nonconvex. Steps are computed by minimizing the sum of a regularized linear least-squares model 6 and a model of h using a first-order method such as the proximal gradient method. We establish 7 global convergence to a first-order stationary point of both a trust-region and a regularization variant 8 of the Levenberg-Marquardt method under the assumptions that F and its Jacobian are Lipschitz 9 continuous and h is proper and lower semi-continuous. In the worst case, both methods perform 10 O ( ϵ − 2 ) iterations to bring a measure of stationarity below ϵ ∈ (0 , 1). We report numerical results on 11 three examples: a group-lasso basis-pursuit denoise example, a nonlinear support vector machine, 12 and parameter estimation in neuron firing. For those examples to be implementable, we describe in 13 detail how to evaluate proximal operators for separable h and for the group lasso with trust-region 14 constraint. In all cases, the Levenberg-Marquardt methods perform fewer outer iterations than a 15 proximal-gradient method with adaptive step length and a quasi-Newton trust-region method, neither 16 of which exploit the least-squares structure of the problem. Our results also highlight the need for 17 more sophisticated subproblem solvers than simple first-order methods. 18
Perceiving accurate CSI phases with commodity WiFi devices

WiFi technology has gained a wide prevalence for not only wireless communication but also pervasive sensing. A wide variety of emerging applications leverage accurate measurements of the Channel State Information (CSI) information obtained from commodity WiFi devices. Due to hardware imperfection of commodity WiFi devices, the frequency response of internal signal processing circuit is mixed with the real channel frequency response in passband, which makes deriving accurate channel frequency response from CSI measurements a challenging task. In this paper, we identify non-negligible non-linear CSI phase errors and report that IQ imbalance is the root source of non-linear CSI phase errors. We conduct intensive analysis on the characteristics of such non-linear errors and find that such errors are prevalent among various WiFi devices. Furthermore, they are rather stable along time and the received signal strength indication (RSSI) but sensitive to frequency bands used between a transmission pair. Based on these key observations, we propose new calibration methods to compensate both non-linear and linear CSI phase errors. We demonstrate the efficacy of the proposed methods by applying them in CSI splicing. Results of extensive real-world experiments indicate that accurate CSI phase measurements can significantly improve the performance of splicing and the stability of the derived power delay profiles (PDPs).
MAXIMAL INEQUALITIES AND WEIGHTED BMO PROCESSES

For a general adapted integrable right-continuous with left limits (RCLL) process ( 𝑋 𝑡 ) 𝑡 ≥ 0 taking values in a metric space (E ,𝑑 ) , we show (among other things) that for every 𝑚 ∈ ( 1 , ∞) and every 𝜏 > 0
Apparel-invariant Feature Learning for Apparel-changed Person Re-identification

With the rise of deep learning methods, person Re-Identification (ReID) performance has been improved tremendously in many public datasets. However, most public ReID datasets are collected in a short time window in which persons' appearance rarely changes. In real-world applications such as in a shopping mall, the same person's clothing may change, and different persons may wearing similar clothes. All these cases can result in an inconsistent ReID performance, revealing a critical problem that current ReID models heavily rely on person's apparels. Therefore, it is critical to learn an apparel-invariant person representation under cases like cloth changing or several persons wearing similar clothes. In this work, we tackle this problem from the viewpoint of invariant feature representation learning. The main contributions of this work are as follows. (1) We propose the semi-supervised Apparel-invariant Feature Learning (AIFL) framework to learn an apparel-invariant pedestrian representation using images of the same person wearing different clothes. (2) To obtain images of the same person wearing different clothes, we propose an unsupervised apparel-simulation GAN (AS-GAN) to synthesize cloth changing images according to the target cloth embedding. It's worth noting that the images used in ReID tasks were cropped from real-world low-quality CCTV videos, making it more challenging to synthesize cloth changing images. We conduct extensive experiments on several datasets comparing with several baselines. Experimental results demonstrate that our proposal can improve the ReID performance of the baseline models.
User Records xi User Records with Protected Data Masked x ∗ i Learn Fair Representation Utility : xi ≈ x̃i Individual

People are rated and ranked, towards algorithmic decision making in an increasing number of applications, typically based on machine learning. Research on how to incorporate fairness into such tasks has prevalently pursued the paradigm of group fairness: ensuring that each ethnic or social group receives its fair share in the outcome of classifiers and rankings. In contrast, the alternative paradigm of individual fairness has received relatively little attention. This paper introduces a method for probabilistically clustering user records into a lowrank representation that captures individual fairness yet also achieves high accuracy in classification and regression models. Our notion of individual fairness requires that users who are similar in all task-relevant attributes such as job qualification, and disregarding all potentially discriminating attributes such as gender, should have similar outcomes. Since the case for fairness is ubiquitous across many tasks, we aim to learn general representations that can be applied to arbitrary downstream usecases. We demonstrate the versatility of our method by applying it to classification and learning-to-rank tasks on two real-world datasets. Our experiments show substantial improvements over the best prior work for this setting.
Structural insights into CUG repeats containing the ‘stretched U–U wobble’: implications for myotonic dystrophy

Tracks containing CUG repeats are abundant in human gene transcripts. Their biological role includes modulation of pre-mRNA splicing, mRNA transport and regulation of translation. Expanded forms of CUG runs are associated with pathogenesis of several neurodegenerative diseases, including myotonic dystrophy type 1. We have analysed two crystal structures of RNA duplexes containing the CUG repeats: G(CUG)2C and (CUG)6. The first of the structures, analysed at 1.23 Å resolution, is of an oligomer designed by us. The second model was obtained after ‘detwinning’ the 1.58 Å X-ray data previously deposited in the PDB. The RNA duplexes are in the A-form in which all the C–G pairs form Watson–Crick interactions while all the uridine pairs can be described as U•U cis wobble having only one hydrogen bond between the bases. The residue, which accepts the H-bond, is inclined towards the minor groove. This previously unreported base pairing can be described as ‘stretched U–U wobble’. The regular hydrogen-bonding pattern of interactions with the solvent, the electrostatic charge distribution and surface features indicate the ligand binding potential of the CUG tracks.
P ARAMETER EFFICIENT TRAINING OF DEEP CONVOLUTIONAL NEURAL NETWORKS BY DYNAMIC SPARSE REPARAMETERIZATION

Modern deep neural networks are highly overparameterized, and often of huge sizes. A number of post-training model compression techniques, such as distillation, pruning and quantization, can reduce the size of network parameters by a substantial fraction with little loss in performance. However, training a small network of the post-compression size de novo typically fails to reach the same level of accuracy achieved by compression of a large network, leading to a widely-held belief that gross overparameterization is essential to effective learning. In this work, we argue that this is not necessarily true. We describe a dynamic sparse reparameterization technique that closed the performance gap between a model compressed through iterative pruning and a model of the post-compression size trained de novo. We applied our method to training deep residual networks and showed that it outperformed existing reparameterization techniques, yielding the best accuracy for a given parameter budget for training. Compared to existing dynamic reparameterization methods that reallocate non-zero parameters during training, our approach achieved better performance at lower computational cost. Our method is not only of practical value for training under stringent memory constraints, but also potentially informative to theoretical understanding of generalization properties of overparameterized deep neural networks.
Nrc Publications Archive Archives Des Publications Du Cnrc the Oo Jdrew Reference Implementation of Ruleml the Oo Jdrew Reference Implementation of Ruleml * the Oo Jdrew Reference Implementation of Ruleml

L'accès à ce site Web et l'utilisation de son contenu sont assujettis aux conditions présentées dans le site LISEZ CES CONDITIONS ATTENTIVEMENT AVANT D'UTILISER CE SITE WEB. Access and use of this website and the material on it are subject to the Terms and Conditions set forth at Permission is granted to quote short excerpts and to reproduce figures and tables from this report, provided that the source of such material is fully acknowledged. Abstract. This paper presents the open source reference implementation of RuleML based on modular XML Schema definitions and bidirec-tional OO jDREW interpreters written in Java. For the family of RuleML sublanguages, schema modularization and RDF rules are discussed. The central bidirectional interpreters are introduced via jDREW principles, and explained w.r. t.
Graph Representation Forecasting of Patient's Medical Conditions: Toward a Digital Twin

Objective: Modern medicine needs to shift from a wait and react, curative discipline to a preventative, interdisciplinary science aiming at providing personalized, systemic, and precise treatment plans to patients. To this purpose, we propose a “digital twin” of patients modeling the human body as a whole and providing a panoramic view over individuals' conditions. Methods: We propose a general framework that composes advanced artificial intelligence (AI) approaches and integrates mathematical modeling in order to provide a panoramic view over current and future pathophysiological conditions. Our modular architecture is based on a graph neural network (GNN) forecasting clinically relevant endpoints (such as blood pressure) and a generative adversarial network (GAN) providing a proof of concept of transcriptomic integrability. Results: We tested our digital twin model on two simulated clinical case studies combining information at organ, tissue, and cellular level. We provided a panoramic overview over current and future patient's conditions by monitoring and forecasting clinically relevant endpoints representing the evolution of patient's vital parameters using the GNN model. We showed how to use the GAN to generate multi-tissue expression data for blood and lung to find associations between cytokines conditioned on the expression of genes in the renin–angiotensin pathway. Our approach was to detect inflammatory cytokines, which are known to have effects on blood pressure and have previously been associated with SARS-CoV-2 infection (e.g., CXCR6, XCL1, and others). Significance: The graph representation of a computational patient has potential to solve important technological challenges in integrating multiscale computational modeling with AI. We believe that this work represents a step forward toward next-generation devices for precision and predictive medicine.
Predicting Blood Pressure Response to Fluid Bolus Therapy Using Attention-Based Neural Networks for Clinical Interpretability Background Excessive positive fluid balance in critically ill patients in intensive care units (ICUs)

Background Excessive positive fluid balance in critically ill patients in intensive care units (ICUs) has been proposed as a risk factor for severe organ dysfunctions, prolonged mechanical ventilation, longer length of stay in ICU, and increased mortality [10, 12, 15]. Fluid bolus therapy (FBT), the rapid infusion of fluid, has been recommended as the primary-line treatment for acute hypotensive episode (AHE) that occurs in about 41% of patients in ICU [13]. However, previous studies have reported that approximately one-third of AHE cases are not successfully resolved by FBT treatment [5, 9, 11]. Considering that FBT accounts for about 30-50% of the total fluid volume administrated for ICU patients [2], identifying patients for whom FBT would not likely resolve AHE may prevent an inappropriate increase of the total fluid volume. [9]. Previous studies have focused on predicting cardiac output (CO) as patients’ response to FBT since fluid infusion increases CO by increasing the venous return to heart [14]. A 15% increase in CO after FBT has been used as the success benchmark [6]. However, in clinical settings, only 11% of ICU clinicians use the increased CO as the patient’s positive response to FBT. 67% of them judged the patient’s response as positive when the patient’s blood pressure (BP) increases [3]. Of course CO is a gold-standard measure to evaluate patient’s physiological response to FBT, but the increased BP might be used as a practical alternative to evaluate the patient’s response to FBT in the ICU.
Hole-driven dynamics of a three-dimensional gravitational liquid curtain

Abstract It is known that the disintegration of vertical liquid curtains (sheets) is affected crucially by the amplification of free edge holes forming inside the curtain. This paper aims to investigate the influence of the hole expansion dynamics, driven by the so-called rim retraction, on the breakup of a liquid curtain, in both supercritical (Weber number $We > 1$) and subcritical ($We < 1$) conditions. The analysis is based on three-dimensional direct numerical simulations. For a selected supercritical configuration, the steady flow topology is first analysed. The investigation reveals the classic triangular shape regime of the steady curtain, due to the surface-tension-induced borders retraction towards its centre plane. The unsteady dynamics is then investigated as the curtain response to a hole perturbation introduced artificially in the steady flow configuration. The hole evolution determines a rim retraction phenomenon inside the curtain, which is influenced by both capillary and gravity forces. In supercritical conditions, the hole does not influence the curtain flow dynamics in the long-time limit. By reducing the Weber number slightly under the critical threshold ($We=1$), the initial amplification rate of the hole area increases, due to the stronger retraction effect of surface tension acting on the hole rims. The free hole expansion in fully subcritical conditions ($We < 1$) is investigated finally by simulating an edge-free curtain flow. As $We$ decreases progressively, the hole expands while it is convected downstream by gravity acceleration. In the range $0.4< We<1$, the subcritical curtain returns to the intact unperturbed configuration after the hole expulsion at the downstream outflow. For $We<0.4$, the surface tension force becomes strong enough to reverse the gravitational motion of the hole top point, which retracts upstream towards the sheet inlet section while expanding along the lateral directions. This last phenomenon causes finally the breakup of the curtain, which results in a columnar regime strictly resembling similar experimental findings of the literature.