Miracle-dz/UniFAR-FTUs huggingface dataset:
Discretely entropy stable weight-adjusted discontinuous Galerkin methods on curvilinear meshes

We construct entropy conservative and entropy stable high order accurate discontinuous Galerkin (DG) discretizations for time-dependent nonlinear hyperbolic conservation laws on curvilinear meshes. The resulting schemes preserve a semi-discrete quadrature approximation of a continuous global entropy inequality. The proof requires the satisfaction of a discrete geometric conservation law, which we enforce through an appropriate polynomial approximation. We extend the construction of entropy conservative and entropy stable DG schemes to the case when high order accurate curvilinear mass matrices are approximated using low-storage weight-adjusted approximations, and describe how to retain global conservation properties under such an approximation. The theoretical results are verified through numerical experiments for the compressible Euler equations on triangular and tetrahedral meshes.
Robust PCA via ℓ0-ℓ1 Regularization

We study the problem of low-rank and sparse decomposition from possibly noisy observations, known as Robust PCA, where the sparse component can be seen as outliers. We first propose a modified objective function where the nuclear norm captures the low-rank term, $\ell _0$ -“norm” addresses the sparse outlier term, and an $\ell _1$ -norm to deal with the additive noise term. The associated algorithm, termed sparsity regularized principal component pursuit (SRPCP), is shown to converge. Under certain model and algorithm parameter settings, it is shown that SRPCP can recover the low-rank component and sparse component exactly in the noiseless case. In the noisy case, we first prove that the widely used principal component pursuit (PCP) method, which was designed for the noiseless case, is actually stable to dense noise. Then, we show that SRPCP has smaller estimation error bound, and can identify outlier entries without any false alarm. Another important byproduct of our analysis is the result that PCP with missing entries is also stable to dense noise. We further propose another objective function which replaces the above nuclear norm by the log-determinant. The proposed algorithm, termed iterative reweighted sparsity regularized principal component pursuit, is also shown to converge. In each iteration, it solves a weighted nuclear norm regularized robust matrix completion problem. We propose an alternating direction method of multipliers algorithm to solve this nonconvex subproblem, which also converges. Empirical studies demonstrate the efficacy of the proposed $\ell _{0}$ - $\ell _{1}$ regularization framework to deal with the outliers as well as its advantage over the existing state-of-the-art methods.
Hardware Virtualization with Xen

S) Summarized by Geoffrey Lefebvre Making Exception Handling Work Bruno Cabral and Paulo Marques, University of Coimbra, Portugal Presented by Bruno Cabrel Exceptions are the standard mechanism for error handling in modern programming languages. Unfortunately, dealing with exceptions is a tedious process. Programmers often avoid the issue by writing empty handlers to save time. Programmers who take the effort to write proper exception-handling code see their productivity impaired. The authors argue that exception handling should not interfere with normal programming tasks but instead become a system issue. In this scenario, the run-time environment provides a set of generic exception handlers and deals with exceptions automatically. The programmer only has to wrap the code with try blocks at the appropriate locations. Because the system may have to try multiple handlers when an exception occurs, this approach requires that try blocks be resumable and appear atomic. Speculations: Providing Faulttolerance and Recoverability in Distributed Environments Cristian Tapus and Jason Hickey, California Institute of Technology Presented by Cristian Tapus Cristian began his talk by noting that distributed systems are ubiquitous. It is now mandatory that we build safe and reliable systems. Failures are frequent in highly parallel machines. It is critical that systems expected to run for a long time support fault tolerance. Unfortunately, traditional checkpoint mechanisms are application-specific, their implementation is expensive in terms of man-hours, and they are also error-prone. To address these issues, the authors present a new programming model based on speculative execution. The approach separates fault recovery code from computation. Fault recovery becomes transparent and automated and the design of distributed systems is simplified. Speculations are implemented as an extension to the Linux kernel. The implementation provides system calls to begin, abort, and commit speculative executions. Outbound messages sent while executing speculatively are marked accordingly. A process automatically switches to speculative execution when it receives a message marked as speculative. Discrete Control for Dependable IT
A Weighted DTW Approach for Similarity Matching over Uncertain Time Series

To measure uncertain time series similarity effectively and efficiently, in this paper, we propose a weighted DTW distance-based approach for uncertain time series with the expected distance. We introduce a weight function to assign weights to a reference point and a testing point. With this function and the WDTW, the accuracy of calculating uncertain time series similarity can be improved. Also, to reduce the storage space and time-consuming, we extend the lower bound function LB_Keogh for DTW into ULB_Keogh for our approach.
Automated Self-learning Chatbot Initially Build as a FAQs Database Information Retrieval System: Multi-level and Intelligent Universal Virtual Front-office Implementing Neural Network

In this paper we propose a novel approach of an automatic creation of a chatbot exploiting only a questions-answers archive. The described model is not bound to a particular context or language, and it allows to initially retrieve information from a FAQs database using natural language, by allowing successively the automation of customer support services for the optimization of human resources thus implementing a self-learning chatbot system. We also propose a solution based on dynamical information system capable of exploiting the potential of the proposed model in a universal virtual front-office, including the statistics and tests necessary to validate the solution, and a comparison between neural network and AIML results.
Continuous propositional modal logic

Abstract We introduce a propositional many-valued modal logic which is an extension of the Continuous Propositional Logic to a modal system. Otherwise said, we extend the minimal modal logic to a Continuous Logic system. After introducing semantics, axioms and deduction rules, we establish some preliminary results. Then we prove the equivalence between consistency and satisfiability. As straightforward consequences, we get compactness, an approximated completeness theorem, in the vein of Continuous Logic, and a Pavelka-style completeness theorem.
Alternating Iteratively Reweighted Least Squares Minimization for Low-Rank Matrix Factorization

Nowadays, the availability of large-scale data in disparate application domains urges the deployment of sophisticated tools for extracting valuable knowledge out of this huge bulk of information. In that vein, low-rank representations (LRRs), which seek low-dimensional embeddings of data have naturally appeared. In an effort to reduce computational complexity and improve estimation performance, LRR has been viewed via a matrix factorization (MF) perspective. Recently, low-rank MF (LRMF) approaches have been proposed for tackling the inherent weakness of MF, i.e., the unawareness of the dimension of the low-dimensional space where data reside. Herein, inspired by the merits of iterative reweighted schemes for sparse recovery and rank minimization, we come up with a generic low-rank promoting regularization function. Then, focusing on a specific instance of it, we propose a regularizer that imposes column-sparsity jointly on the two matrix factors that result from MF, thus promoting low-rankness on the optimization problem. The low-rank promoting properties of the resulting regularization term are brought to light by mathematically showing that it is actually a tight upper bound of a specific version of the weighted nuclear norm. The problems of denoising and matrix completion are redefined according to the new LRMF formulation and solved via efficient alternating iteratively reweighted least squares type algorithms. Theoretical analysis of the algorithms regarding the convergence and the rates of convergence to stationary points is provided. The effectiveness of the proposed algorithms is verified in diverse simulated and real data experiments.
Commodity Grid Computing with Amazon's S3 and EC2

Abstract : Amazon.com recently introduced two new storage and computing services that might fundamentally change the way that we provision equipment for both e-commerce and high-performance computing. I learned about these services a few days after I had started looking for quotes to purchase a multiblade server with 10-20 TB of storage to further my own research in computer forensics. Rather than moving ahead with that purchase, I decided to evaluate whether or not I could use Amazon's offering for this real-world problem. My conclusion is that Amazon's offering is good enough for my research and will probably save me tens of thousands of dollars, but the system isn't yet ready for hosting serious e-commerce customers.
Analog Circuit Fault Diagnosis Based on Deep Learning

: Deep learning is a new field in machine learning research, whose motivation is to build neural network simulating the human brain to analyze. Stacked autoencoder, which is a style of deep learning structure, is used to solve analog circuit fault diagnosis problem. An experiment is done, whose results show that the method proposed can effectively work on analog circuit fault diagnosis using neural network model based on the deep learning theory.
Fault Detection of Low Voltage Cable using Time-Frequency Correlation in SSTDR

This paper proposed an Spread Spectrum Time Domain Reflectometry (SSTDR) using time-frequency correlation analysis in order to have more accurate fault determination and location detection than classical SSTDR despite increased signal attenuation due to the long distance to cable fault location. The proposed method was validated through comparison with classical SSTDR methods in openand short-circuit fault detection experiments of low-voltage power cables. The experimental results showed that the proposed method can detect correlation coefficients at fault locations accurately despite reflected signal attenuation so that cable faults can be detected more accurately and clearly in comparison to existing methods.
Fundamentals of a Mereo-Operandi Theory to Support Transdisciplinary Modeling and Co-Design of Cyber-Physical Systems

The main statement of this paper is that synergetic modeling and co-design of the hardware, software and cyberware parts of complex cyber-physical systems (CPSs) are yet not solved, even from the perspective of an underpinning transdisciplinary theory. CPSs contain functionally tightly connected analog and digital hardware, control, and application software, and knowledge, data, and media contents as cyberware. The lack of a unified theoretical framework and an all-inclusive system conceptualization methodology can be traced back to professional, methodological and cultural differences between the abovementioned domains of development. The objective of our research is to make a step towards a theoretical framework that can support transdisciplinary modeling of CPSs. Architectural and operational modeling have been identified as two principal and interrelated dimensions of system modeling, and a mereo-operandi theory (MOT) has been identified as target. Mereotopology has been considered as the basis of architectural modeling. Operational modeling has been based on parameterized representation of the underlying physical principles, the morphological characteristics, the operation elements, and the overall operation flows. A demonstrative case study is presented to evidence the practical feasibility and utility of the proposed MOT. Our follow up research will focus on using this as a conceptual framework and computational basis for specification of system manifestation features and on a computational implementation to support embedded customization.Copyright © 2015 by ASME
Oral Findings in Brazilian Infants Born at Full Term

Objective: To evaluate the oral findings of Brazilian infants born at full term as well as to identify the presence of abnormalities at birth. Material and Methods: Cross-sectional study comprising 190 infants aged up to 2 days old. Data were obtained by applying a structured questionnaire to the mothers as well as by collecting information from the infants’ medical records. Clinical examination was conducted to evaluate the infants’ oral features and it included the relationship of alveolar ridges and shape of the gum pads and the insertion of the lingual and labial frenula. The presence of oral developmental abnormalities was also evaluated. Chi-square and Mann-Whitney U tests were used to analyze the infants’ abnormalities and gender (α = 0.05). Results: Most infants presented round upper gum pads (71.6%) and U-shaped lower one (52.6%). The mean discrepancy between the alveolar ridges was 3.33mm. Interference with tongue movement was found in 12.6% of the newborns. There was 50.0% prevalence of oral cysts. Among them, Epstein pearl was found in 27.4% of the cases, Bohn nodules in 26.3%, and dental lamina cysts in 11.1%. There was no significant difference between the occurrence of abnormalities and the infants’ gender. The normal features of the oral cavity meet the literature data. The herein found congenital developmental abnormalities were oral cysts and ankyloglossia. Conclusion: Different classification forms made it difficult to compare the current study data with those from other studies. Therefore, it suggests the need for standardization in order to evaluate these abnormalities.
Automatic Diagnosis of Breast Tumoursin Ultrasound Images

This project aims at investigating the possibility to distinguish between benign and malignant breast lesions in ultrasound (US) images. A total number of 327 images were collected, with 249 containing malignant cases and 78 containing benign ones. A classification was performed using an Artificial Neural Network (ANN) and the images were not fed the system directly. Instead image features were extracted from wavelet transformed US images. Several setups of ANN:s with different feature subsets were trained and tested. Each setup was run 1000 times and the mean sensitivity, specificity, accuracy and Area under ROC Curve (AUC) was calculated. The sensitivity of the systems varied between 93 − 98% and the specificity between 3 − 23%. Further, the accuracy varied between 74− 78%, and the AUC 0.60− 0.67. The conclusion is that all systems tend to overfit, i.e. benign cases are falsely classified as malignant ones in a high rate. None of the systems would be sufficient for diagnosis of breast cancer. False positives are highly unwanted in the health care due to the cost, and the stress put on the patient.
Quasistatic CV characterization: Overview and implementation of a complete measurement system with comparison to other CV methods

ii Tiivistelmä (in Finnish) . iii Acknowledgements . iv
SEGMENTAÇÕES MULTIRESOLUÇÃO EM IMAGENS DE ALTA RESOLUÇÃO ESPACIAL

O enfoque deste estudo é a análise de segmentações multiresolução em imagens de alta resolução do sensor Quickbird geradas a partir da fusão entre as três bandas multiespectrais com resolução espacial de 2,8 m e a pancromática do mesmo sensor com resolução espacial de 0,70 m, mediante os métodos de fusão por substituição por Componentes Principais (CP) e Transformação do espaço de cores RGB-IHS (RGB-IHS). Foram realizadas segmentações multiresolução com diferentes fatores de escala, forma, suavidade e compacidade para as imagens multiespectrais, pancromática e as com as fusões por CP e RGB-IHS. Os resultados mostraram que, a segmentação multiresolução na imagem com fusão por CP, agrupa melhor os objetos da cena, do que a segmentação multiresolução realizada na banda pancromática, na imagem fusionada RGB-IHS e na imagem multiespectral. A comparação entre as segmentações supracitadas foi realizada visualmente e mediante o Image Quality Index - IQI (Wang e Bovik, 2002), separando-se cada um dos três termos: perda da correlação, distorção da intensidade e do contraste. Conclui-se que, a imagem com a fusão, proporciona melhor resultado na segmentação multiresolução, igualmente em uma análise visual ou qualitativa realizada pelo intérprete e que a técnica de fusão influencia na segmentação.
Consonance-Based Spectral Mappings

This article presents a method of mapping the spectrum of a sound so as to make it tonally consonant with a given specified reference spectrum. One application is to transform inharmonic sounds into harmonic equivalents. Alternatively, this method can be used to create inharmonic instruments that retain much of the tonal quality of familiar (harmonic) instruments. Musical uses of such timbres are discussed, and forms of (inharmonic) modulation are presented. A series of sound examples demonstrate both the breadth and limitations of the method. [Sound examples will be included on the forthcoming compact disc for Volume 22 of Computer Music Journal. -Ed.]
The roles of TL1A and Pno1 in the pathogenesis of rheumatoid arthritis

Rheumatoid Arthritis (RA) is a chronic autoimmune disease characterized by persistent inflammation of multiple small joints, which manifests pain, redness, swelling, and deformation. Studies with patients and animal models have found that autoantibodies, cytokines and tissue-destructive enzymes are important mediators of the pathogenesis of RA. In the past two decades, biologic disease-modifying antirheumatic drugs (DMARDs) have achieved great success in the treatment of RA. On the other hand, they are also associated with adverse effect like increasing the chance of opportunistic infections. The aim of present work was to investigate the roles of TNF-like molecule 1A (TL1A; TNFSF15) and partner of Nob1 (Pno1; YOR145c) in the pathogenesis of RA for developing novel drugs based on these molecules in the future. TL1A is a member of the TNF superfamily. It triggers costimulatory signals though death receptor 3 (DR3) and induces the proliferation and pro-inflammatory cytokine production in lymphocytes. Multiple lines of evidence suggest the implication of TL1A-DR3 signaling in several autoimmune diseases. Therefore, We hypothesized that 1) local TL1A production in the joints is a component of a vicious circle aggravating RA; 2) in RA, TL1A production in lymphoid organs enhances pathogenic autoantibody production. We demonstrated that the TL1A aggravates disease in murine collagen-induced arthritis (CIA). Moreover, we found elevated TL1A expression in RA-affected tissues, as well as in the draining lymph nodes (dLNs) of CIA mice. Mechanistically, we discovered that TL1A induces TNF-α and IL-17 production by T cells in vitro. These findings provided direct evidence that TL1A-DR3 signaling plays a critical role in the pathogenesis of RA. TL1A knockout (TL1A KO) mice were generated to further our study. We showed that TL1A KO mice have no visual anomaly, and no malfunction of immune system under a normal circumstance. However, they display ameliorated CIA and significantly reduced anti-Collagen II antibody levels in sera. We found that the draining lymph nodes (dLNs) from KO mice were smaller in size and lower in cellularity compared with their WT counterparts 14 days after immunization. Furthermore, we discovered that terminally differentiated plasma cells express DR3 and they survive better in the presence of TL1A. Our findings in this study present novel knowledge about the role of
Development of realistic head models for electromagnetic source imaging of the human brain

In this work, a methodology is developed to solve the forward problem of electromagnetic source imaging using realistic head models. For this purpose, first segmentation of the 3 dimensional MR head images is performed. Then triangular, quadratic meshes are formed for the interfaces of the tissues. Thus, realistic meshes, representing scalp, skull, CSF, brain and eye tissues, are formed. At least 2000 nodes for the scalp and 5000 for the cortex are needed to obtain reasonable geometrical approximation. Solution of the forward problem using our previous Boundary Element Method (BEM) formulation with quadratic elements remains to be made.
Towards Fast Iris Segmentation for Real-Time Recognition

this research describes a simpler and time efficient scheme for iris segmentation which is the core component of any iris based biometric recognition system. Time efficiency and simplicity is of much importance when the recognition system is to be used for real time applications. Segmentation starts from the pupil which is the darkest part, so lowest intensity pixels in the eye image belong to the pupil region. Segmentation starts from pupil using the state of the art circular Hough transform technique and then iris-sclera boundary is estimated using a very thin slab from the original image. The occlusions due to eyelids and eyelashes are avoided by selecting only a portion of segmented iris for biometric feature extraction. Keywords—, Pupil, Sclera, Biometric, Circular Hough Transform and Canny Edge map.
Infrared and visible image fusion based on random projection and sparse representation

A new image fusion approach for infrared and visible images is explored, combining fusion with data compression based on sparse representation and compressed sensing. The proposed approach first compresses the sensing data by random projection and then obtains sparse coefficients on compressed samples by sparse representation. Finally, the fusion coefficients are combined with the fusion impact factor and the fused image is reconstructed from the combined sparse coefficients. Experimental results validate its rationality and effectiveness, which can achieve comparable fusion quality on the less-compressed sensing data.
2011 Update of Scientific Statement for the Primary Prevention of Stroke: Dyslipidemia and Inflammation

Department of Neurology, Sanggye Paik Hospital, Inje University College of Medicine, Seoul, Korea Department of Neurology, Konkuk University College of Medicine, Seoul, Korea Department of Neurology, Eulji University College of Medicine, Seoul, Korea Department of Neurology, Catholic University College of Medicine, Seoul, Korea Department of Neurology, Ilsan Paik Hospital, Inje University College of Medicine, Ilsan, Korea Department of Neurology, Hallym University College of Medicine, Anyang, Korea Department of Neurology Inha University College of Medicine, Incheon, Korea Department of Neurology, Yonsei University College of Medicine, Seoul, Korea Department of Neurology, University of Ulsan College of Medicine, Seoul, Korea Department of Neurosurgery, Seoul National University College of Medicine, Seoul, Korea Department of Neurology, Seoul National University College of Medicine, Seoul, Korea Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea