Miracle-dz/UniFAR-FTUs huggingface dataset:
Makeup-robust face verification

We investigate in this paper the problem of face verification in the presence of face makeups. To our knowledge, this problem has less formally addressed in the literature. A key challenge is how to increase the measured similarity between face images of the same person without and with makeups. In this paper, we propose a novel approach for makeup-robust face verification, by measuring correlations between face images in a meta subspace. The meta subspace is learned using canonical correlation analysis (CCA), with the objective that intra-personal sample correlations are maximized. Subsequently, discriminative learning with the support vector machine (SVM) classifier is applied to verify faces based on the low-dimensional features in the learned meta subspace. Experimental results on our dataset are presented to demonstrate the efficacy of our approach.
The role of transthoracic ultrasonography in predicting the outcome of community-acquired pneumonia in hospitalized children

Transthoracic ultrasound (TUS) has recently become a valuable tool in the diagnosis of community-acquired pneumonia (CAP). This study assessed the association between TUS findings and clinical outcome in children with CAP. The medical records of pediatric patients hospitalized with CAP who underwent transthoracic ultrasonography within 48 hours of admission were retrospectively reviewed. Associations between the TUS findings and patient outcome were analyzed, including intensive care unit (ICU) admission, length of hospital stay, and tube thoracotomy. The study enrolled 142 patients (median age, 60 months): 28 (19.7%) required ICU admission, 14 (9.89%) underwent tube thoracotomy, and 26 (18.3%) had a hospital stay > 9 days. Multifocal involvement seen by TUS were independently associated with ICU admission, a prolonged hospital stay, and tube thoracotomy (p = 0.0027, p = 0.02, and p = 0.0262, respectively). A pleural effusion and fluid bronchogram were independent predictors of a longer hospital stay (p = 0.003 and p = 0.006, respectively). In addition, a fluid bronchogram was an independent predictor of tube thoracotomy (p = 0.0262). Conclusion TUS findings of fluid bronchogram, multifocal involvement, and pleural effusion were associated with adverse outcomes, including longer hospital stay, ICU admission, and tube thoracotomy in hospitalized CAP children. Therefore, TUS is a novel tool for prognostic stratifications of CAP in hospitalized children.
Performance analysis of javaspace in a mobile environment

-In this paper we present an evaluation of shared virtual memory spaces such as Jini’s JavaSpace in the context of mobile/wireless computing. We envision the creation of federations of mobile and fixed devices forming collaborative environments on the fly where devices can offload computationalmemoryand powerconsuming tasks to other devices. Our experimental framework shows how JavaSpace struggles to cope with issues raised by the unpredictable behavior of mobile devices (in terms of sudden lost of connectivity) and by device heterogeneity. We conclude that current approaches to shared virtual memory, though effective in relatively static computational environments, do not suite the requirements of mobile computing, elaborating on the need to couple tuple spaces with appropriate resource management strategies. Key-Words: -JavaSpace, mobile/wireless computing, resource management
New HDTV Signal Processing System for Extended Gamut Transmission

A new HDTV signal processing system for extended gamut signal transmission is proposed in this paper. The system has determined the gamut of Pointer colors as target gamut, and transmit all Pointer colors by designing a new set of chroma signal. Moreover, the capability of decreasing color difference of the new system is evaluated. Compared with the system of ITU-R BT.1361, the average color difference ΔE*00 was calculated according to CIEDE2000, and ΔE*00 is 0.2755 for the new system, while ΔE*00 is 0.5011 for the system with ITU-R BT.1361.
Flesh, flab, and fascia simulation on zootopia

We present the latest character simulation techniques developed for Disney's Zootopia. In this film, we created herds of anthropomorphic mammals whose art direction called for the subtle, detailed motion distinctive to the real animal world coupled with the stylized, non-physical aesthetics characteristic of animated feature films. This required a strong partnership between technology and production to productize our flesh simulation research to meet the unique challenges of this show. Our techniques scaled from several hero characters to many secondary and tertiary characters, and also accommodated two characters with special requirements.
Goodreads Versus Amazon: The Effect of Decoupling Book Reviewing And Book Selling

Book reviewing is a commonplace activity on many ecommerce sites. However, it is nested within the broader context of book buying and selling. Goodreads, an online platform for social curation of book collections, provides an opportunity to observe on-line book reviewing in an environment that is not (at least overtly) focused on commercialization. In this study, we perform a careful comparative study of reviewer behavior and engagement in Goodreads and Amazon.com, constrained to a single genre (biography), including 21,394 books and 2.5 million reviews. We discover marked differences between the platforms that suggest disparate population composition and objectives of review-writing across the two platforms. Our findings suggest an important and generalizable principle: that two platforms engaging users on the same task (e.g., book review writing) may elicit quite different behavior depending on the implicit or explicit context and motivation present.
An improved boosting scheme based ensemble of Fuzzy Neural Networks for nonlinear time series prediction

This paper proposed a Modified AdaBoostRT (AdaBoost Regression and Threshold) algorithm based on Fuzzy Neural Networks (FNNs) and its application to the accurate prediction of complex nonlinear time-series. The algorithm is validated by using four typical time-series data, namely Lorenz, Mackey-Glass, Sunspot and Dow Jones Indices data. The performance comparison of the proposed method and several existing approaches is also performed to show its advantages for nonlinear time series prediction problems.
Minimizing calibration time of single-trial recognition of error potentials in brain-computer interfaces

One of the main problems of both synchronous and asynchronous EEG-based BCIs is the need of an initial calibration phase before the system can be used. This phase is necessary due to the high non-stationarity of the EEG, since it changes between sessions and users. The calibration limits the BCI systems to scenarios where the outputs are very controlled, and makes these systems non-friendly and exhausting for the users. Although it has been studied how to reduce calibration time for asynchronous signals, it is still an open issue for event- related potentials. Here, we analyze the differences between users for single-trial error-related potentials, and propose the design of classifiers based on inter-subject features to either remove or minimize the calibration time. The results show that it is possible to have a classifier with a high performance from the beginning of the experiment, which is able to adapt itself without the user noticing.
The Prognostic Value of Peak Cardiac Power Output in Chinese Patients with Chronic Heart Failure

Background Cardiopulmonary exercise testing has been widely used to risk stratify patients with chronic heart failure (CHF). Peak oxygen consumption (peakVO2) was regarded as a powerful predictor of survival, as it is a surrogate for peak cardiac output (CO), which by most is considered the “true” measure of heart failure. Therefore, it is reasonable to hypothesize that CO is an even stronger predictor than peak VO2. The present study is aimed to investigate the prognostic value of peak cardiac power output (peak CPO) in comparison with peakVO2 in Chinese patients with CHF. Methods Participants provided written informed consent to participate in this study. Totally 129 patients with CHF underwent symptom-limited cardiopulmonary exercise testing (CPET), with mean age 59.1±11.4 years, 87.6% male, 57.4% ischemic etiology, body mass index (BMI) 24.7±3.7 kg/m2 and LVEF 38±9%. CO was measured using an inert gas rebreathing method. The primary endpoints are cardiac deaths. Results Over median 33.7-month follow-up, 19 cardiac deaths were reported. Among peak VO2,VE/VCO2 slope and Peak CPO, their area under ROC were 0.64, 0.67, 0.68, respectively (Ρ<0.05). The optimal thresholds for predicting cardiac deaths were peak VO2≤13.4 ml.kg-1.min-1, and VE/VCO2 slope≥39.3 and peak CPO≤ 1.1 respectively by ROC analysis. Finally, in patients with a peak VO2≤13.4 ml.kg-1.min-1 those with peak CPO>1.1W had better survival than those with peak CPO ≤ 1.1W. However, by multivariate analysis adjusted for age, sex, BMI, resting heart rate, LVMI, LVEF, Peak CPO was not an independent predictor of cardiac deaths (P> 0.05). Conclusions Peak CPO was not a predictor of cardiac death in Chinese CHF patients.
Bulk filling branes and the baryon density in AdS/QCD with gravity back-reaction

We consider the gravity back reaction on the metric due to the baryon density in effective ads/qcd model by reconsidering the role of the charged AdS black hole. Previously it has been known that the U(1) charge is dual to the R-charge. Here we point out that if we consider the case where AdS5 is completely filled with Nf flavor branes, the gravity back reaction produces charged AdS black hole where the effect of charge on the metric is proportional to Nf/Nc. As a consequence, phase diagram changes qualitatively if we allow Nf/Nc finite: it closes at the finite density unlike the probe brane embedding approach. Another issue we discuss here is the question whether there is any chemical potential dependence in the confining phase. We consider this problem in the hard wall model with baryon charge. We conclude that there is a non-trivial dependence on the chemical potential in this case also.
SEMANTIC ROLE FRAMES GRAPH-BASED MULTIDOCUMENT SUMMARIZATION

Multi-document summarization is a process of automatic creation of a compressed version of the given collection of documents. Recently, the graph-based models and ranking algorithms have been extensively researched by the extractive document summarization community. While most work to date focuses on sentence-level relations in this paper we present graph model that emphasizes not only sentence level relations but also the influence of under sentence level relations (e.g. a part of sentence similarity). By using the proven cognitive psychology model (the Event-Indexing model) and semantic role parsing for generating the frame graph, we establish the bases for distinguishing the sentence level relations. Based on this model, we developed an iterative frame and sentence ranking algorithm, based on the existing well known PageRank algorithm. Experiments are conducted on the DUC 2004 data sets and the ROUGE (Recall-Oriented Understudy for Gisting Evaluation) evaluation results demonstrate the advantages of the proposed approach.
Unsupervised Learning of an IS-A Taxonomy from a Limited Domain-Specific Corpus

Taxonomies hierarchically organize concepts in a domain. Building and maintaining them by hand is a tedious and time-consuming task. This paper proposes a novel, unsupervised algorithm for automatically learning an IS-A taxonomy from scratch by analyzing a given text corpus. Our approach is designed to deal with infrequently occurring concepts, so it can effectively induce taxonomies even from small corpora. Algorithmically, the approach makes two important contributions. First, it performs inference based on clustering and the distributional semantics, which can capture links among concepts never mentioned together. Second, it uses a novel graph-based algorithm to detect and remove incorrect is-a relations from a taxonomy. An empirical evaluation on five corpora demonstrates the utility of our proposed approach.
Single-channel multi-talker-localization based on maximum likelihood

This paper presents a sound source (talker) localization method using only a single microphone based upon maximum likelihood. In our previous work, we proposed GMM (Gaussian Mixture Model) separation for estimation of the sound source direction, where the observed (reverberant) speech is separated into the acoustic transfer function and the clean speech GMM, and showed its effectiveness for the single-talker localization task. In this paper, we discuss a multi-talker localization method using GMM separation and model composition. Model composition is used to represent speech signals observed in a reverberant environment corresponding to every conceivable combination of positions of the sound sources, where composite models are obtained through composition of talker's speech model and acoustic transfer functions estimated using GMM separation. For each test data set, we find a maximum-likelihood model from among the composite models corresponding to each combination of talkers' positions. The effectiveness of this method has been confirmed by two-talker localization experiments performed in a room environment.
Functional Domains of the Cone-Rod Homeobox (CRX) Transcription Factor*

The paired-like homeodomain transcription factor CRX (cone-rod homeobox) is involved in regulating photoreceptor gene expression and rod outer segment development. Mutations in CRX have been associated with several retinal degenerative diseases. These conditions range from Leber congenital amaurosis (a severe cone and rod degeneration of childhood onset) to adult onset cone-rod dystrophy and retinitis pigmentosa (an adult onset condition that primarily affects rods). The goal of this study is to better understand the molecular basis of CRX function and to provide insight into how mutations in CRX cause such a variety of clinical phenotypes. We performed deletion analysis in conjunction with DNA binding and transient transfection-based transactivation studies to identify the functional domains within CRX. DNA binding requires a complete homeodomain. Furthermore, truncated proteins that did not contain an intact homeodomain failed to demonstrate detectable expression in tissue culture upon transfection. Transactivation analysis indicated that both the OTX tail and the WSP domain are important for controlling positive regulatory activity of CRX. Interestingly, the mapped CRX transactivation domains were also critical when coexpressed with NRL. Specifically, the synergy between CRX and NRL was constant regardless of which CRX variant was used.
Wisdom of Students: A Consistent Automatic Short Answer Grading Technique

Automatic short answer grading (ASAG) techniques are designed to automatically assess short answers written in natural language having a length of a few words to a few sentences. In this paper, we report an intriguing ﬁnding that the set of short answers to a question, collectively, share signiﬁcant lexical commonalities. Based on this ﬁnding, we propose an unsupervised ASAG technique that only requires sequential pattern mining in the ﬁrst step and an intuitive scoring process in the second step. We demonstrate, using multiple datasets, that the proposed technique effectively exploits wisdom of students to deliver comparable or better performance than prior ASAG techniques as well as distributional semantics-based approaches that require heavy training with a large corpus. Moreover, by virtue of being independent of instructor provided model answers, our technique offers consistency by overcoming the limitation of undesired variability in performance exhibited by existing unsupervised techniques.
A comparative simulation study of wavelet based denoising algorithms

In this paper, we present a comparative simulation study of three denoising algorithms using wavelets. The denoising algorithms (i.e., universal threshold, minimax threshold and rigorous SURE threshold) have been used to remove white Gaussian noise from synthetic and real signals. The analysis is done by applying soft and hard thresholds to signals with different sample sizes. The mean squared error (MSE) is used to evaluate the performance of these algorithms. The results show that the rigorous SURE algorithm with a hard threshold has a better performance than other algorithms in synthetic signals. On the other hand, the universal threshold algorithm with a soft threshold shows the best performance in real signals when using the Daubechies wavelet with 5 vanishing moments.
Introducing DVFS-Management in a Full-System Simulator

Dynamic Voltage and Frequency Scaling (DVFS) is an essential part of controlling the power consumption of any computer system, ranging from mobile phones to servers. DVFS efficiency relies on hardware-software co-optimization, thus using existing hardware cannot reveal the full optimization potential beyond the current implementation's characteristics. To explore the vast design space for DVFS efficiency, that straddles software and hardware, a simulation infrastructure must provide features that are not readily available today, for example: software controllable clock and voltage domains, support for the OS and the frequency scaling module of it, and an online power estimation methodology. As the main contribution, this work enables DVFS studies in a full-system simulator. We extend the gem5 simulator to support full-system DVFS modeling. By doing so, we enable energy-efficiency experiments to be performed in gem5 and we showcase such studies. Finally, we show that both existing and novel frequency governors for Linux and Android can be effortlessly integrated in the framework, and we evaluate the efficiency of different DVFS schemes.
Crystal Structure of the Substrate-Binding Domain from Listeria monocytogenes Bile-Resistance Determinant BilE

BilE has been reported as a bile resistance determinant that plays an important role in colonization of the gastrointestinal tract by Listeria monocytogenes, the causative agent of listeriosis. The mechanism(s) by which BilE mediates bile resistance are unknown. BilE shares significant sequence similarity with ATP-binding cassette (ABC) importers that contribute to virulence and stress responses by importing quaternary ammonium compounds that act as compatible solutes. Assays using related compounds have failed to demonstrate transport mediated by BilE. The putative substrate-binding domain (SBD) of BilE was expressed in isolation and the crystal structure solved at 1.5 A. Although the overall fold is characteristic of SBDs, the binding site varies considerably relative to the well-characterized homologs ProX from Archaeoglobus fulgidus and OpuBC and OpuCC from Bacillus subtilis. This suggests that BilE may bind an as-yet unknown ligand. Elucidation of the natural substrate of BilE could reveal a novel bile resistance mechanism.
A Comparison of Approaches to Web Service Evolution

Web services evolve over their life time. As the evolution occurs, corresponding structure and functions of Web service will change. The evolution of web service involves many reasons, such as changes of service's version, changes of service's interface, changes of behaviors, changes of requirements, problems of service's compatibility and so on. In this paper, we first analyze and compare the current solutions of Web service evolution by means of granularity of evolution, terminal of evolution, type of evolution, scalability and maintainability, then we discuss the future research directions.
Low-Complexity Variable Step-Size Mechanism for Code-Constrained Constant Modulus Stochastic Gradient Algorithms Applied to CDMA Interference Suppression

The code-constrained constant modulus algorithm (CCM) implemented with a stochastic gradient (SG) technique is a very effective and efficient blind approach for interference suppression when a communication channel is frequency-selective. In nonstationary wireless environments, users frequently enter and exit the system, making it very difficult for the receiver to compute a predetermined step-size. This suggests the deployment of mechanisms to automatically adjust the step-size in order to ensure good tracking of the interference and the channel. In this paper, the performance of blind CCM adaptive receivers for direct sequence code division multiple access (DS-CDMA) systems that employ stochastic gradient (SG) algorithms with variable step-size mechanisms is investigated. We propose a novel low-complexity variable step-size mechanism for blind CCM CDMA receivers. Convergence and tracking analyses of the proposed adaptation techniques are carried out for multipath channels. Finally, numerical experiments are presented for nonstationary environments, showing that the new mechanism achieves superior performance to previously reported methods at a reduced complexity.
Imperialist competitive algorithm for natural crack shape reconstruction from ECT signals

This paper presents a method to solve the problem of natural crack shape reconstruction from eddy current testing signals by means of imperialist competitive algorithm (ICA). ICA is a new meta-heuristic optimization and stochastic search strategy which is inspired from socio-political phenomenon of imperialistic competition. In order to evaluate the efficiency on solving crack shape inversion problem, ICA is compared with some heuristic algorithms such as genetic algorithm, particles swarm optimization and ant colony optimization. The reconstructed results verified the efficiency of neural network based forward model and the promising of imperialist competitive algorithm in crack shape inversion.
An incremental XSLT transformation processor for XML document manipulation

In this paper, we present an incremental transformation framework called incXSLT. This framework has been experimented for the XSLT language defined at the World Wide Web Consortium. For the currently available tools, designing the XML content and the transformation sheets is an inefficient, a tedious and an error prone experience. Incremental transformation processors such as incXSLT represent a better alternative to help in the design of both the content and the transformation sheets. We believe that such frameworks are a first step toward fully interactive transformation-based authoring environments.
Approximate Contagion Model of Common Knowledge on Facebook

Computational modeling of information exchange over social media is important for understanding coordination in online environments. Many contagion dynamics models that have been used to model Twitter, Facebook, and blog information transmission are polynomial-time computable, and hence can be efficiently simulated on networked populations. Game-theoretic models of collective action (i.e., coordination problems that require common knowledge among agents), however, have dynamics that are controlled in part by specific network structures such as cliques and bicliques. Contagion dynamics with these models cannot be efficiently computed because finding all bicliques in a graph, for example, is an NP-hard problem. We investigate a recent model of common knowledge dynamics that represents information spread on Facebook in which the biclique is the characterizing structure and convert the model into an efficiently computable one by using an approximation. We demonstrate this through experiments on seven different graphs for a total of 168 sets of conditions, including a 4-order of magnitude span in dynamics parameter values. Our approach speeds computations in two ways: (i) it obviates the need to find all bicliques in a social network, which is a very time- consuming task (computations can take 30 to 120 hours or more of wall clock time), and (ii) it reduces the time of simulation computations, in some cases by well over an order of magnitude. Our method also enables evaluation of much larger networks that are being mined from social media.