Miracle-dz/UniFAR-FTUs huggingface dataset:
Physical Activity and Sedentary Behaviors of Children in Family Child Care Homes: Are There Opportunities for Improvement?

PURPOSE Many children attend family child care homes (FCCHs), an important setting to influence children's physical activity (PA) and sedentary behavior (SB). This study assessed children's PA and SB while in FCCHs, characteristics of the FCCH PA environment, and relationships between the environment and child PA and SB. METHOD Children ages 1.5-4.0 years (n = 495) were recruited from 165 FCCHs in North Carolina. Children's moderate to vigorous PA and SB were measured via accelerometry for 3 days. FCCH PA environments were assessed over 2 days using the Environment and Policy Assessment and Observation for FCCHs. Ten subscores and an overall PA environment score (possible range: 0-30) were calculated; higher scores indicate better quality. RESULTS Children accumulated 30 (13) minutes of moderate to vigorous PA and 143 (42) minutes of SB in FCCHs daily. FCCHs scored low on the Environment and Policy Assessment and Observation for FCCHs, with an average overall score of 13 (2). FCCHs scored highest on screen time and screen time practices subscores, and lowest on PA education/professional development and PA policy subscores. Although no statistically significant associations were observed, some large Cohen d effect sizes were noted (eg, outdoor playtime subscore and moderate to vigorous PA). CONCLUSIONS This study highlights opportunities to improve FCCHs and increase children's behaviors (eg, providing adequate time and outdoor play spaces).
ZCU-NTIS Speaker Diarization System for the DIHARD 2018 Challenge

In this paper, we present the system developed by the team from the New Technologies for the Information Society (NTIS) research center of the University of West Bohemia, for the First DIHARD Speech Diarization Challenge. The base of our sys-tem follows the currently-standard approach of segmentation, i-vector extraction, clustering, and resegmentation. Here, we describe the modiﬁcations to the system which allowed us to apply it to data from a range of different domains. The main contribution to our achievement is a Neural Network (NN) based domain classiﬁer, which categorizes each conversation into one of the ten domains present in the development set. This classiﬁ-cation determines the speciﬁc system conﬁguration, such as the expected number of speakers and the stopping criterion for the hierarchical clustering. At the time of writing of this abstract, our best submission achieves a DER of 26.90% and an MI of 8.34 bits on the evaluation set (gold speech/nonspeech segmentation).
Self-esteem in the deaf who have become cochlear implant users as adults

Objective Self-esteem is a good predictor of mental health and is crucial for well-being and psychological functioning. It is especially important in situations where there are potential mental health problems, such as in people suffering from hearing loss or total deafness. This study aims to gauge the level of self-esteem in adults with hearing problems, in particular those who, in adulthood, had received a cochlear implant (CI). The subjects had different onset (pre-lingual/post-lingual) and amount (deafness/partial deafness) of hearing loss, and their current level of self-esteem was compared to that of the general population. The association of self-esteem with other deafness-related variables (e.g. satisfaction with their CI or whether they also used a hearing aid) and sociodemographic factors was also investigated. Methods Data were obtained from questionnaires mailed to patients who, when adult, had received a CI. The subjects were divided into four subgroups: subjects with pre-lingual deafness, post-lingual deafness, pre-lingual partial deafness, and post-lingual partial deafness. To evaluate their self-esteem, the Rosenberg Self-Esteem Scale (RSES) was used. For data on sociodemographic status and information related to deafness and CI, we used our own Information Inquiry form. For statistical analysis of the results, we compared means (t-test, ANOVA), investigated correlations, and applied linear regression. Results The self-esteem of deaf and partially deaf CI users was significantly lower than in the general population, especially for post-lingually deafened subjects. The only factor related to deafness and CIs that explained self-esteem was self-rated satisfaction with the CI–meaning that higher satisfaction was associated with higher self-esteem. The major sociodemographic factor that explained self-esteem was marital/partnership status (being in a relationship was helpful). Also men had higher self-esteem than women. Those with higher levels of education, and those working or studying, had higher self-esteem than those who did not. RSES was found to have a single-factor structure. Conclusion Deafness and partial deafness appear to be risk factors for lower self-esteem, a finding that rehabilitation, medical, educational, and employment communities should be made aware of. Medical intervention in the form of a CI supplies the person with improved hearing, but it is not a panacea: their self-esteem is still vulnerable, and reinforcement of self-esteem is an aspect that professionals should focus on. Psychological, psycho-educational, and psychotherapeutic interventions have important roles to play for CI recipients.
Investigation on Bandwidth Extension for Speaker Recognition

In this work, we investigate training speaker recognition systems on wideband (WB) features and compare their performance with narrowband (NB) baselines. NIST speaker recognition evaluations have mainly driven speaker recognition research in the past years. Because of the target application of these evaluations, most data available to train speaker recognition systems is NB telephone speech. Meanwhile, WB data have been more scarce not being enough to train factor analysis and PLDA models. Thus, the usual practice when dealing with WB speech consists in downsampling the signal to 8 kHz, which implies potential loss of useful information. Instead, we experimented upsampling the training telephone data and leaving the WB data unchanged. We adopt two techniques to upsample telephone data: (1) using a feed-forward neural network, termed Bandwidth Extension (BWE) network, to predict WB features given NB features as input; and (2) using basic up-sampling with a low-pass ﬁlter interpolator. While the former intends to estimate the high frequency information, the latter does not. The upsampled features are used to train state-of-the art i-vector and recently proposed x-vector models. We evaluated the systems on Speakers In The Wild (SITW) database obtaining 11.5% relative improvement in detection cost function (DCF) with x-vector model.
Feature Dimensionality Reduction with Graph Embedding and Generalized Hamming Distance

Principal component analysis (PCA) and linear discriminant analysis (LDA) are the most well-known methods to reduce the dimensionality of feature vectors. However, both methods face challenges when used on multilabel data—each data point may be associated to multiple labels. PCA does not take advantage of label information thus the performance is sacrificed. LDA can exploit class information for multiclass data, but cannot be directly applied to multilabel problems. In this paper, we propose a novel dimensionality reduction method for multilabel data. We first introduce the generalized Hamming distance that measures the distance of two data points in the label space. Then the proposed distance is used in the graph embedding framework for feature dimension reduction. We verified the proposed method using three multilabel benchmark datasets and one large image dataset. The results show that the proposed feature dimensionality reduction method consistently outperforms PCA and other competing methods.
Progressive Sub-Aperture Image Recovery for Interactive Light Field Data Streaming

Due to the large size of a light field image, compressing and transmitting the entire data to a client before rendering any image for observation would incur a significant startup delay. In response, in interactive light field streaming (ILFS) a server synthesizes and transmits a new viewpoint image as a combination of sub-aperture images (SAIs) per user request. However, in so doing the client relies entirely on the server for reconstruction of every requested image. In this paper, we extend a previous proposal of progressive light field data transmission strategy, where the client can incrementally learn SAIs over time. Specifically, requested focal-point images are synthesized using carefully chosen weighted linear combinations of SAIs, so that recovery of SAIs amounts to inversion of a lower-triangular weight matrix-a matrix structure that enables SAI recovery without amplifying quantization noise due to lossy image coding. We design an objective function to encourage specific combinations of SAIs to increase rank of the lower-triangular weight matrix for fast SAI recovery. This new proposal reduces the size of the initial user's cache and the total number of transmitted images compared to our previous work. Experimental results show that our scheme can outperform ILFS by up to 70% in terms of BD-rate.
A Correlation Maximization Approach for Cross Domain Co-Embeddings

Although modern recommendation systems can exploit the structure in users' item feedback, most are powerless in the face of new users who provide no structure for them to exploit. In this paper we introduce ImplicitCE, an algorithm for recommending items to new users during their sign-up flow. ImplicitCE works by transforming users' implicit feedback towards auxiliary domain items into an embedding in the target domain item embedding space. ImplicitCE learns these embedding spaces and transformation function in an end-to-end fashion and can co-embed users and items with any differentiable similarity function. To train ImplicitCE we explore methods for maximizing the correlations between model predictions and users' affinities and introduce Sample Correlation Update, a novel and extremely simple training strategy. Finally, we show that ImplicitCE trained with Sample Correlation Update outperforms a variety of state of the art algorithms and loss functions on both a large scale Twitter dataset and the DBLP dataset.
Analyzing EEG Signals in Auditory Speech Comprehension Using Temporal Response Functions and Generalized Additive Models

Analyzing EEG signals recorded while participants are listening to continuous speech with the purpose of testing linguistic hypotheses is complicated by the fact that the signals simultaneously reﬂect exogenous acoustic excitation and endogenous linguistic processing. This makes it difﬁcult to trace subtle differences that occur in mid-sentence position. We apply an analysis based on multivariate temporal response functions to uncover subtle mid-sentence effects. This approach is based on a per-stimulus estimate of the response of the neural system to speech input. Analyzing EEG signals predicted on the basis of the response functions might then bring to light condition-speciﬁc differences in the ﬁltered signals. We validate this approach by means of an analysis of EEG signals recorded with isolated word stimuli. Then, we apply the validated method to the analysis of the responses to the same words in the middle of meaningful sentences.
Resilience of and recovery strategies for weighted networks

The robustness and resilience of complex networks have been widely studied and discussed in both research and industry because today, the diversity of system components and the complexity of the connection between units are increasingly influencing the reliability of complex systems. Previous studies have focused on node failure in networks, proposing several performance indicators. However, a single performance indicator cannot comprehensively measure all the performance aspects; thus, the selected performance indicators and recovery strategies lack consistency with respect to the targeted complex systems. This paper introduces a novel stress–strength-balanced weighted network model based on two network transmission hypotheses. Then, with respect to different concerns of the complex network, we propose two modified network performance measurement indicators and compare these indicators in terms of their trends in the attack process. In addition, we introduce several network recovery strategies and compare their efficiencies. Our findings are as follows: (1) The evaluation and judgment of the network performance depend on the performance measurement indicators we use. (2) Different recovery strategies exhibit distinct efficiencies in recovering different aspects of network performance, and no strategy exists that can improve all the network performance aspects simultaneously. (3) The timing of the recovery is proved to have a deep influence on the cost and efficiency of network recovery; thus, the optimal recovery strategy for a damaged network varies with the extent of the damage. From the results of the simulation of the attack-recovery process, we conclude that while defining and analyzing complex network models, we should adjust our network topology, weight assignment, and performance indicators in accordance with the focal characteristics of complex systems so that we can use the network model to build robust complex systems and efficient logistics and maintenance strategies.
Real-Time Registration of Rgb-D Image Pair for See-Through System

This paper presents a dense method of real-time registration of RGB-D image pair. So far, we have proposed the “see-through system”, in which multiple images acquired from RGB-D sensors are integrated to present images that is useful for confirming the shape or the positions of objects behind obstacles. However, errors of positional relation of sensors result in see-through images in which some objects are doubled or appear at incorrect positions. It is difficult to align the images because they are captured from distant viewpoints and there are few shared field of view. In the proposed method, positional relation of two RGB-D sensors is corrected using a new IRLS framework, fast and robust minimization strategy.
Contemporary management of ejaculatory dysfunction

Although erectile dysfunction is the most common disorder of male sexual health, ejaculatory dysfunction is the most common form of sexual dysfunction experienced by men. Ejaculatory dysfunction covers a broad range of disorders that we have divided into four main categories: premature ejaculation, delayed ejaculation (DE)/anorgasmia, unsatisfactory sensation of ejaculation (including painful ejaculation and ejaculatory anhedonia), and absent ejaculate (including retrograde ejaculation and aspermia). We also cover several special scenarios including hematospermia, spinal cord injury and fertility with anejaculation. In this paper, we will review the anatomy and pathophysiology of normal ejaculation to establish the baseline knowledge of how this pathway can go awry. We will then briefly review the critical diagnostic criteria, pertinent steps in evaluation, risk factors, and causes (if known) for each of the ejaculatory disorders. Finally, the bulk of the paper will discuss current management strategies of each disorder.
Waist Circumference and Waist-to-Hip Ratio in Law Enforcement Agency Recruits: Relationship to Performance in Physical Fitness Tests.

Lockie, RG, Ruvalcaba, TR, Stierli, M, Dulla, JM, Dawes, JJ, and Orr, RM. Waist circumference and waist-to-hip ratio in law enforcement agency recruits: relationship to performance in physical fitness tests. J Strength Cond Res XX(X): 000-000, 2018-Law enforcement agencies (LEAs) use tests to assess recruit physical fitness. Body fat can influence test performance but is difficult to measure during academy because of time, equipment constraints, and instructor knowledge. This study examined relationships between waist circumference (WC) and waist-to-hip ratio (WHR), practical measures of fat distribution, and fitness test performance. Retrospective analysis of 267 LEA recruits (age: ∼28 years; height: ∼1.73 m; and body mass: ∼80 kg; 219 males and 48 females) was conducted. The tests included: WC and WHR; grip strength; push-ups, sit-ups, and arm ergometer revolutions in 60 seconds; vertical jump (VJ); medicine ball throw; 75-yd pursuit run (75PR); and multistage fitness test (MSFT) shuttles. Partial correlations, controlling for sex, calculated relationships between WC, WHR, and the fitness tests. Recruits were split into quartile groups (based on the sample size) for WC and WHR (group 1 had the lowest WC and WHR; and group 4 the highest). A 1-way multivariate analysis of variance, with sex as a covariate and Bonferroni post hoc, compared between-group test performance. A greater WC related to lesser push-up, sit-up, VJ, 75PR, and MSFT performance (p ≤ 0.024). When recruits were split into WC groups, group 4 had lesser performance in push-ups, sit-ups, VJ, and the 75PR compared with all groups (p ≤ 0.038). When split into WHR groups, group 4 performed less push-ups than group 1, less MSFT shuttles than group 3, and had a lower VJ compared with all groups (p ≤ 0.042). Recruits with a greater WC tended to have poorer fitness test performance.
Networks Effectively Utilizing 2D Spatial Information for Accurate 3D Hand Pose Estimation

In this work, we propose a new method for accurate 3D hand pose estimation from a single depth map using convolutional neural networks (CNN). Our method effectively makes use of 2D spatial information to improve the performance by two means. Firstly, we formulate 3D hand pose estimation as a two-task (2D joints detection and depth regression) problem so that we can directly utilize the ability of hourglass module on processing multi-scale information for estimating 2D joint coordinates. Secondly, 2D spatial information is used to help depth regression by introducing the spatial attention mechanism to our method. The experimental results demonstrate that our method achieves the state-of-the-art performance on ICVL hand posture dataset and a comparable performance with the state-of-the-arts on NYU dataset.
Porous Nanoparticles With Self-Adjuvanting M2e-Fusion Protein and Recombinant Hemagglutinin Provide Strong and Broadly Protective Immunity Against Influenza Virus Infections

Due to the high risk of an outbreak of pandemic influenza, the development of a broadly protective universal influenza vaccine is highly warranted. The design of such a vaccine has attracted attention and much focus has been given to nanoparticle-based influenza vaccines which can be administered intranasally. This is particularly interesting since, contrary to injectable vaccines, mucosal vaccines elicit local IgA and lung resident T cell immunity, which have been found to correlate with stronger protection in experimental models of influenza virus infections. Also, studies in human volunteers have indicated that pre-existing CD4+ T cells correlate well to increased resistance against infection. We have previously developed a fusion protein with 3 copies of the ectodomain of matrix protein 2 (M2e), which is one of the most explored conserved influenza A virus antigens for a broadly protective vaccine known today. To improve the protective ability of the self-adjuvanting fusion protein, CTA1-3M2e-DD, we incorporated it into porous maltodextrin nanoparticles (NPLs). This proof-of-principle study demonstrates that the combined vaccine vector given intranasally enhanced immune protection against a live challenge infection and reduced the risk of virus transmission between immunized and unimmunized individuals. Most importantly, immune responses to NPLs that also contained recombinant hemagglutinin (HA) were strongly enhanced in a CTA1-enzyme dependent manner and we achieved broadly protective immunity against a lethal infection with heterosubtypic influenza virus. Immune protection was mediated by enhanced levels of lung resident CD4+ T cells as well as anti-HA and -M2e serum IgG and local IgA antibodies.
Solving Non-identifiable Latent Feature Models

Latent feature models (LFM)s are widely employed for extracting latent structures of data. While offering high, parameter estimation is difficult with LFMs because of the combinational nature of latent features, and non-identifiability is a particularly difficult problem when parameter estimation is not unique and there exists equivalent solutions. In this paper, a necessary and sufficient condition for non-identifiability is shown. The condition is significantly related to dependency of features, and this implies that non-identifiability may often occur in real-world applications. A novel method for parameter estimation that solves the non-identifiability problem is also proposed. This method can be combined as a post-process with existing methods and can find an appropriate solution by hopping efficiently through equivalent solutions. We have evaluated the effectiveness of the method on both synthetic and real-world datasets.
Multiresolution Contourlet Transform Fusion Based Depth Map Super Resolution

With the rapid advancement of 3D applications, depth map super resolution has been a serious problem to be solved. Many researches has proposed depth map super resolution methods focusing on spatial domain which can't produce clear edges in the super resolution results. However, different from color images, depth maps have clear edges along them with internal smoothness. In this paper, we propose a novel multiresolution contourlet transform fusion based depth map super resolution method to enhance the quality of depth maps with preserving more contour information. We first transform the depth maps of multiple views via contourlet transform (CT) into multiresolution coefficients. Then we fuse the coefficients of same resolution to get the fused coefficients. Finally the target depth map is upscaled utilizing the prevalent upscaling framework JBU or WMF. Depth map fusion in transform domain is first proposed to improve the quality of the target depth map. A CT based depth map fusion model can not only produce sharper edges, but alleviate the noise in the depth map. Experimental results demonstrate that our proposed method outperforms many state-of-the-art algorithms in both objectively and subjectively.
Mining Summaries for Knowledge Graph Search

Querying heterogeneous and large-scale knowledge graphs is expensive. This paper studies a graph summarization framework to facilitate knowledge graph search. (1) We introduce a class of <italic>reduced summaries</italic>. Characterized by approximate graph pattern matching, these summaries are capable of summarizing entities in terms of their neighborhood similarity up to a certain hop, using small and informative graph patterns. (2) We study a <italic> diversified graph summarization</italic> problem. Given a knowledge graph, it is to discover top-<inline-formula> <tex-math notation="LaTeX">$k$</tex-math><alternatives><inline-graphic xlink:href="song-ieq1-2807442.gif"/> </alternatives></inline-formula> summaries that maximize a bi-criteria function, characterized by both informativeness and diversity. We show that diversified summarization is feasible for large graphs, by developing both sequential and parallel summarization algorithms. (a) We show that there exists a 2-approximation algorithm to discover diversified summaries. We further develop an anytime sequential algorithm which discovers summaries under resource constraints. (b) We present a new parallel algorithm with quality guarantees. The algorithm is parallel scalable, which ensures its feasibility in distributed graphs. (3) We also develop a summary-based query evaluation scheme, which only refers to a small number of summaries. Using real-world knowledge graphs, we experimentally verify the effectiveness and efficiency of our summarization algorithms, and query processing using summaries.