Miracle-dz/UniFAR-FTUs huggingface dataset:
GPU Simulation of Finite Element Facial Soft-Tissue Models

Physically-based animation techniques enable more realistic and accurate animation to be created. We present a GPU-based finite element (FE) simulation and interactive visualisation system for efficiently producing realisticlooking animations of facial movement, including expressive wrinkles. It is optimised for simulating multi-layered voxel-based models using the total Lagrangian explicit dynamic (TLED) FE method. The flexibility of our system enables detailed animations of gross and fine-scale soft-tissue movement to be easily produced with different muscle structures and material parameters. While we focus on the forehead, the system can be used to animate any multi-material soft body.
Change Fault Localization Using Test Execution Profiles

Regression tests ensure that software systems retain correctness in their existing functionalities while the systems evolve. During development, regression tests allow the programmer to quickly identify problems. If there are any new failed tests, since those failures surfaced after new changes to the code base, we need to identify where this failure originated in our code changes. In this paper, we combine regression test results with changes in both the source code and test execution profiles to narrow down and identify the changes that may have caused test failures.
FTMF: Recommendation in social network with Feature Transfer and Probabilistic Matrix Factorization

It is well-known that recommendation system which is widely used in many e-commerce platforms to recommend items to the right users suffers from data sparsity, imbalanced rating and cold start problems. Matrix factorization is a good way to deal with the sparsity and imbalance problems, which is however unable to make prediction for new users due to the lack of auxiliary information. With the advent of online social networks, the trust relation in the social network can be utilized as auxiliary data to solve the aforementioned problems since consumers' buying behavior is usually affected by the people around them. This paper reports a study of exploiting the trust relationship in social network for personalized recommendation. Although previous studies have paid attention to this topic, we improve the quality of recommendation further and solve the cold start problem better. To this end, we propose a recommendation system in social network with Feature Transfer and Probabilistic Matrix Factorization (FTMF). The auxiliary data and matrix factorization technique are integrated to learn a social latent feature vector of users which represents the features transferred from trusted people. And an adaptive firm factor is introduced to balance the impact from user's own factors and trusted people on buying behavior for each user. The experimental results show that our model can effectively use the auxiliary data and outperforms the existing state-of-the-art social network based recommendation algorithms.
Reperfusion‐induced sustained ventricular tachycardia, leading to ventricular fibrillation, in chronically instrumented, intact, conscious mice

Reperfusion‐induced lethal ventricular arrhythmias are observed during relief of coronary artery spasm, with unstable angina, exercise‐induced ischemia, and silent ischemia. Accordingly, significant efforts are underway to understand the mechanisms responsible for reperfusion‐induced lethal arrhythmias and mice have become increasingly important in these efforts. However, although reperfusion‐induced sustained ventricular tachycardia leading to ventricular fibrillation (VF) has been recorded in many models, reports in mice are sparse and of limited success. Importantly, none of these studies were conducted in intact, conscious mice. Accordingly, a chronically instrumented, intact, conscious murine model of reperfusion‐induced lethal arrhythmias has the potential to be of major importance for advancing the concepts and methods that drive cardiovascular therapies. Therefore, we describe, for the first time, the use of an intact, conscious, murine model of reperfusion‐induced lethal arrhythmias. Male mice (n = 9) were instrumented to record cardiac output and the electrocardiogram. In addition, a snare was placed around the left main coronary artery. Following recovery, the susceptibility to sustained ventricular tachycardia produced by 3 min of occlusion and reperfusion of the left main coronary artery was determined in conscious mice by pulling on the snare. Reperfusion culminated in sustained ventricular tachycardia, leading to VF, in all nine conscious mice. The procedures conducted in conscious C57BL/6J mice, a strain commonly used in transgenic studies, can be utilized in genetically modified models to enhance our understanding of single gene defects on reperfusion‐induced lethal ventricular arrhythmias in intact, conscious, and complex animals.
An AODV-based Clustering Approach for Efficient Routing in MANET

In recent communication technologies and services the Mobile ad hoc network place an increasingly vital role, that dynamically establish the connection whenever required for the communication network resulting in a quickly changing in topology of the network and increase communication overheads. This dynamic topology, leads to significant routing overhead, scalability problems and battery power consumptions in MANETs. Consider this problem, in this paper a clustering approach have been proposed for ad hoc networks. In Clustering approach, the cluster head election is call upon for the constructing the path, reduce the communication over heads and scalability. For the path construction cluster-AODV routing protocol is applied and also the design goals of clustering algorithms are presented. General Terms Routing Protocols, Clusters.
A qualitative study on performance bugs

Software performance is one of the important qualities that makes software stand out in a competitive market. However, in earlier work we found that performance bugs take more time to fix, need to be fixed by more experienced developers and require changes to more code than non-performance bugs. In order to be able to improve the resolution of performance bugs, a better understanding is needed of the current practice and shortcomings of reporting, reproducing, tracking and fixing performance bugs. This paper qualitatively studies a random sample of 400 performance and non-performance bug reports of Mozilla Firefox and Google Chrome across four dimensions (Impact, Context, Fix and Fix validation). We found that developers and users face problems in reproducing performance bugs and have to spend more time discussing performance bugs than other kinds of bugs. Sometimes performance regressions are tolerated as a tradeoff to improve something else.
Effects of balance training on muscle coactivation during postural control in older adults: a randomized controlled trial.

BACKGROUND Recently, several studies have reported age-associated increases in muscle coactivation during postural control. A rigid posture induced by strong muscle coactivation reduces the degree of freedom to be organized by the postural control system. The purpose of this study was to clarify the effect of balance training on muscle coactivation during postural control in older adults. METHODS Forty-eight subjects were randomized into an intervention (mean age: 81.0 ± 6.9 years) and a control group (mean age: 81.6 ± 6.4 years). The control group did not receive any intervention. Postural control ability (postural sway during quiet standing, functional reach, and functional stability boundary) was assessed before and after the intervention. A cocontraction index was measured during the postural control tasks to assess muscle coactivation. RESULTS Cocontraction index values in the intervention group significantly decreased following the intervention phase for functional reach (p < .0125). Cocontraction index values had a tendency to decrease during functional stability boundary for forward and quiet standing tasks. Functional improvements were observed in some of the tasks after the intervention, that is, functional reach, functional stability boundary for forward, one-leg stance, and timed up and go (p < .05). CONCLUSIONS Our study raised the possibility that balance training for older adults was associated with decreases in muscle coactivation during postural control. Postural control exercise could potentially lead older adults to develop more efficient postural control strategies without increasing muscle coactivation. Further research is needed to clarify in greater detail the effects of changes in muscle coactivation.
Clinical significance of increased cerebellar default-mode network connectivity in resting-state patients with drug-naive somatization disorder

Abstract The cerebellum has been proven to be connected to the brain network, as in the default-mode network (DMN), among healthy subjects and patients with psychiatric disorders. However, whether or not abnormal cerebellar DMN connectivity exists and what its clinical significance is among drug-naive patients with somatization disorder (SD) at rest remain unclear. A total of 25 drug-naive patients with SD and 28 healthy controls were enrolled for a resting-state scan. The imaging data were analyzed using the seed-based functional connectivity (FC) method. Compared with the controls, patients with SD showed increased left/right Crus I-left/right angular gyrus (AG) connectivity and Lobule IX-left superior medial prefrontal cortex (MPFC) connectivity. The FC values of the left/right Crus I-right AG connectivity of the patients were positively correlated with their scores in the somatization subscale of the symptom checklist-90 (Scl-90). A trend level of correlations was observed between the FC values of the left Crus I-left AG connectivity of the patients and their scores for the somatization subscale of Scl-90, as well as between the FC values of their Lobule IX-left superior MPFC connectivity and their scores for the Eysenck personality questionnaire (EPQ) extraversion. Our findings show the increased cerebellar DMN connectivity in patients with SD and therefore highlight the importance of the DMN in the neurobiology of SD. Increased cerebellar DMN connectivities are also correlated with their somatization severity and personality, both of which bear clinical significance.
Cosh: Clear OS Data Sharing In An Incoherent World

This paper tackles the problem of providing familiar OS abstractions for I/O (such as pipes, network sockets, and a shared file system) to applications on heterogeneous cores including accelerators, co-processors, and offload engines. We aim to isolate the implementation of these facilities from the details of a platform's memory architecture, which is likely to include a combination of cache-coherent shared memory, non-cache-coherent shared memory, and non-shared memory, all in the same system. We propose coherence-oblivious sharing (Cosh), a new OS abstraction that provides inter-process sharing with clear semantics on such diverse hardware. We have implemented a prototype of Cosh for the Barrelfish multikernel. We describe how to build common OS functionality using Cosh, and evaluate its performance on a heterogeneous system consisting of commodity cache-coherent CPUs and prototype Intel many-core co-processors.
Fuzzy based tuning of a sensor fusion based low cost attitude estimator

A fuzzy based tuning mechanism of the Kalman filter (KF), for the purpose of attitude estimation using low cost Micro-Electro-Mechanical System (MEMS) sensors is proposed. The conceived system is fitted with three axis gyroscope, accelerometer and magnetometer as the sensor suite. The filter's dynamics is based on a quaternion based attitude propagation model using rate gyros; with the other two sensors providing a complementary attitude estimate. The measurement and process covariance of the filter are tuned using a Takagi-Sugeno based fuzzy logic model based on the perceived system dynamics and filter divergence indicators. The performance of the developed system shows better attitude estimation and convergence [9]properties than a conventional KF based attitude estimation system.
Semi-supervised Learning With Support Vector Machines

Support Vector Machines are a modern technique in the field of machine learning and have been successfully used in different fields of application. In general they are used for some kind of classification task, where they learn from a randomly selected training set, which has been classified in advance and then are applied on unseen instances. To get a good classification result it is often necessary that this training set contains a huge set of labeled instances. But for humans labeling of data is a time-consuming and boring task. Some algorithms address this problem and overcome this by learning on both, a small amount of labeled and a huge amount of unlabeled instances. There the learner has access to the pool of unlabeled instances and requests the label for some specific instances from an user. Then the learner uses all labeled data to learn the model. The choice of the unlabeled instances which should be labeled next has a significant impact on the quality of the resulting model. This kind of learning is called semi-supervised learning or active learning. Currently there exist some different solutions for semi-supervised learning. This work focuses on the most known ones and gives an overview about them. KURZFASSUNG Support Vector Machines sind eine moderne Technik im Bereich vom maschinellen Lernen und wurden mittlerweile in verschiedenen Anwendungsgebieten erfolgreich eingesetzt. Generell werden sie für Klassifikationsaufgaben verwendet, wobei sie von einer zufällig gewählte Menge von schon vorklassifizierten Trainingsdaten lernen und dann auf noch unbekannte Daten angewendet werden. Um ein gutes Klassifikationsergebnis zu erhalten, ist es oft notwendig, eine große Menge von vorklassifizierten Trainingsdaten zum Training zu verwenden. Das manuelle Klassifizieren von den Daten durch Menschen ist oft eine zeitaufwendige und langweilige Aufgabe. Um dies zu erleichtern wurden Algorithmen entwickelt, um mit schon wenigen klassifizierten und vielen nichtklassifizierten Daten ein Modell zu erstellen. Dabei hat der Klassifikator Zugang zu dem Pool von nichtklassifizierten Daten und fragt einen Benutzer nach der Klasse für einige spezielle Instanzen. Dann benützt er alle klassifizierten Daten zum Erstellen des Modells. Die Wahl jener noch nicht klassifizierten Instanzen, die von einem Experten klassifiziert werden sollen, hat einen signifikanten Einfluss auf die Qualität des resultierenden Modells. Diese Art des maschinellen Lernens wird als Semi-überwachtes Lernen oder aktives Lernen) bezeichnet. Momentan existieren verschiedenste Ansätze für Semi-überwachtes Lernen. Diese Arbeit behandelt die bekanntesten und liefert eine Übersicht über die verschiedenen Ansätze.
Robust Face Recognition Based on Modified ICA without Training Sample of Test Subjects

At present there are many methods that could deal well with frontal view face recognition when there is sufficient number of representative training samples. There into, subspace learning method such as principal component analysis (PCA), independent component analysis (ICA), linear discriminant analysis (LDA) are a very hot research topic in this field. However, in some face recognition system, the needed recognition faces are different with different users and are often changed according users' requirement. So in this paper, we proposed to make use of some known face database, in which subjects will be different with the test subjects, for training (extract subspace) with a modified ICA method. The proposed modified ICA method can save much of computer time and memory, and at the same time, can obtain acceptable experimental results on a part of FERET face database. In addition, we validate that the accuracy rate with simple rotation images of logged faces as known data can be improved when the logged face is only one sample per subject.
Capacitive-loaded push-pull parallel-resonant converter

The results of a theoretical and experimental investigation of a capacitive-loaded push-pull parallel-resonant DC-DC converter (CL-PPRC) are presented and discussed. The converter is driven by a lower than resonance frequency, and the secondary voltage is rectified and smoothed by a capacitive filter. The CL-PPRC is shown to operate in the zero voltage switching (ZVS) mode with a boostlike DC transfer ratio which is approximately linear with the period of the switching frequency. Experimental results of a 180 W, high output voltage (1.8 kV) prototype are in good agreement with the analysis, models, and simulation results presented. The basic characteristic of ZVS, the fact that the resonant current is passing through the switches only during a fraction of the period, the high voltage transfer ratio, and the inherent input/output isolation, make the newly proposed topology a viable design alternative in avionic and aerospace applications. <<ETX>>
A Study of Visual MIMO Channel for Intelligent Vehicle

Due to the increasing adoptions of LED lights and CMOS image sensors for automobiles, intelligent vehicles could benefit from the visual MIMO, where optical transmissions by multiple transmitter elements are received by pixels in a camera. Safety applications in vehicular networks require reliable communications under potentially high co-channel interference. Such a visual MIMO structure allows reducing interference and noise from other light sources in the channel. Using MIMO techniques such as multiplexing and diversity can further enhance the data rates. In this paper the unique characteristics of the visual channel are exploited to show that visual MIMO can achieve significantly high data rates and robust consistency in data rates, which is important especially in mobile settings such as vehicle platoon.
Electronic Fraud Detection in the U.S. Medicaid Healthcare Program: Lessons Learned from other Industries

It is estimated that between $600 and $850 billion annually is lost to fraud, waste, and abuse in the US healthcare system,with $125 to $175 billion of this due to fraudulent activity (Kelley 2009). Medicaid, a state-run, federally-matchedgovernment program which accounts for roughly one-quarter of all healthcare expenses in the US, has been particularlysusceptible targets for fraud in recent years. With escalating overall healthcare costs, payers, especially government-runprograms, must seek savings throughout the system to maintain reasonable quality of care standards. As such, the need foreffective fraud detection and prevention is critical. Electronic fraud detection systems are widely used in the insurance,telecommunications, and financial sectors. What lessons can be learned from these efforts and applied to improve frauddetection in the Medicaid health care program? In this paper, we conduct a systematic literature study to analyze theapplicability of existing electronic fraud detection techniques in similar industries to the US Medicaid program.
USING UTAGS FOR INCREMENTAL AND PARALLEL GENERATION

Exploiting an incremental and parallel processing scheme is useful to improve the performance of natural language generation systems. TAG‐GEN is a TAG‐based syntactic generator that realizes both principles. It is shown how the demands of incremental and parallel generation influence the definition, the design, and the processing of syntactic rules on the basis of tree‐adjoining grammars.
Separable Reversible Data Hiding in Encrypted Image

This work proposes a novel scheme for separable reversible data hiding in encrypted images. In the first phase, a content owner encrypts the original uncompressed image using an encryption key. Then, a data-hider may compress the least significant bits of the encrypted image using a data-hiding key to create a sparse space to accommodate some additional data. With an encrypted image containing additional data, if a receiver has the data-hiding key, he can extract the additional data though he does not know the image content. If the receiver has the encryption key, he can decrypt the received data to obtain an image similar to the original one, but cannot extract the additional data. If the receiver has both the data-hiding key and the encryption key, he can extract the additional data and recover the original content without any error by exploiting the spatial correlation in natural image when the amount of additional data is not too large.
Monetary Policy Trade-offs in the Open Economy

Recent work on monetary policy has focused on the trade-off between output volatility and inflation volatility. This literature is based largely on models of closed economies. Three conclusions about optimal policy characterize these models. First, the central bank faces an inflation-output gap volatility trade-off only in the presence of cost-push shocks. Second, aggregate demand shocks should be completely stabilized. Third, even in the absence of any under discretionary policy, there are still gains from commitment (or a conservative central banker), but these gains depend on the serial correlation properties of the cost-push shock only. In this paper, I show that none of these results carry over to the open economy.
Quality control in immunocytochemistry: experiences with the oestrogen receptor assay.

AIMS: To evaluate the feasibility of an interlaboratory quality control programme in immunohistochemistry. METHODS: Several pathology laboratories were asked to carry out immunohistochemical oestrogen receptor staining on a set of freeze dried cryostat sections of breast cancer tissue. The sections and protocols for staining and semi-quantitative scoring were mailed to the participating laboratories in two trials. The oestrogen receptor content of the breast cancer samples was determined by radioligand binding assay on the tumour cytosol. RESULTS: In the first trial 11 laboratories (response rate 60%) participated. Eight (73%) of the participants scored within a 95% confidence interval and all but one correctly classified the tumour as receptor positive. In the second trial all 20 participating laboratories (response rate 55%) correctly scored one tumour sample as negative and 18 of them (90% of respondents) correctly classified the two other tumour samples as receptor positive. In a quantitative evaluation a histochemical score within 95% confidence interval limits was provided by eight (40%) and 12 (60%) of the participants. CONCLUSIONS: Semiquantitative scoring of immunocytochemical staining is valuable for performing correlative inter-laboratory studies, although this scoring protocol may not be required for diagnosis or prognosis. Significant inter-laboratory variability exists, leading to qualitatively correct receptor classification in 100% of receptor negative and 80% of receptor positive cases, and quantitative agreement in only about half of the cases. The perceived variability is not caused by systematic differences in the choice of the immunocytochemical technique, or the mailing of freeze dried sections. Quality control programmes should be included in the standard procedures of each diagnostic immunohistochemistry laboratory.
Communicative Action Theory: An Approach to Understanding the Application of Information Systems

Habermas's theory of communicative action (TCA) has made a significant impact on information systems (IS) research. However, the expected benefit to IS application has lagged due to a gap between IS theory informed by Habermas’s ideas and IS practice. This paper re-states the concepts central to communicative action theory and from that perspective interprets the meaning of IS in organizational contexts. Four case studies, drawn from a Belgian retail company, are discussed to illustrate the roles of IS in support of social actions and to demonstrate the critical analysis of IS from a TCA perspective.