Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Video‐Based Performance Cloning

We present a new video‐based performance cloning technique. After training a deep generative network using a reference video capturing the appearance and dynamics of a target actor, we are able to generate videos where this actor reenacts other performances. All of the training data and the driving performances are provided as ordinary video segments, without motion capture or depth information. Our generative model is realized as a deep neural network with two branches, both of which train the same space‐time conditional generator, using shared weights. One branch, responsible for learning to generate the appearance of the target actor in various poses, uses paired training data, self‐generated from the reference video. The second branch uses unpaired data to improve generation of temporally coherent video renditions of unseen pose sequences. Through data augmentation, our network is able to synthesize images of the target actor in poses never captured by the reference video. We demonstrate a variety of promising results, where our method is able to generate temporally coherent videos, for challenging scenarios where the reference and driving videos consist of very different dance performances.
Machine Learning for Spatiotemporal Sequence Forecasting: A Survey

Spatiotemporal systems are common in the real-world. Forecasting the multi-step future of these spatiotemporal systems based on the past observations, or, Spatiotemporal Sequence Forecasting (STSF), is a significant and challenging problem. Although lots of real-world problems can be viewed as STSF and many research works have proposed machine learning based methods for them, no existing work has summarized and compared these methods from a unified perspective. This survey aims to provide a systematic review of machine learning for STSF. In this survey, we define the STSF problem and classify it into three subcategories: Trajectory Forecasting of Moving Point Cloud (TF-MPC), STSF on Regular Grid (STSF-RG) and STSF on Irregular Grid (STSF-IG). We then introduce the two major challenges of STSF: 1) how to learn a model for multi-step forecasting and 2) how to adequately model the spatial and temporal structures. After that, we review the existing works for solving these challenges, including the general learning strategies for multi-step forecasting, the classical machine learning based methods for STSF, and the deep learning based methods for STSF. We also compare these methods and point out some potential research directions.
The science of teaching science

This paper explores the relationship between various science teaching strategies and students’ science-related outcomes. The focus is on enquiry-based science teaching, teacher-directed instruction, adaptive teaching and teacher feedback. The outcomes of interest include students’ science performance, and students’ dispositions and attitudes towards science. The findings show that the negative association between enquiry-based science teaching and science performance is greatly attenuated when lessons are delivered in disciplined science classes. This approach could help close the gender gap between girls and boys when it comes to attitudes towards science and to the decision to pursue a career in STEM-related fields. The results also show that teacher-directed instruction is a reliable strategy that is positively associated with students’ science outcomes regardless of school climate and resources. Adaptive teaching is positively correlated with science performance in the majority of countries, particularly in countries known for the use of personalised learning approaches, while teacher feedback is weakly but positively associated with science performance once students’ achievement in mathematics and reading is accounted for. In general, all teaching strategies have the potential to foster enjoyment of and interest in science, and students’ epistemic beliefs, self-efficacy in science and expectations of a career in science.
Out-of-Distribution Detection using Multiple Semantic Label Representations

Deep Neural Networks are powerful models that attained remarkable results on a variety of tasks. These models are shown to be extremely efficient when training and test data are drawn from the same distribution. However, it is not clear how a network will act when it is fed with an out-of-distribution example. In this work, we consider the problem of out-of-distribution detection in neural networks. We propose to use multiple semantic dense representations instead of sparse representation as the target label. Specifically, we propose to use several word representations obtained from different corpora or architectures as target labels. We evaluated the proposed model on computer vision, and speech commands detection tasks and compared it to previous methods. Results suggest that our method compares favorably with previous work. Besides, we present the efficiency of our approach for detecting wrongly classified and adversarial examples.
Gene regulatory networks for lignin biosynthesis in switchgrass (Panicum virgatum)

Summary Cell wall recalcitrance is the major challenge to improving saccharification efficiency in converting lignocellulose into biofuels. However, information regarding the transcriptional regulation of secondary cell wall biogenesis remains poor in switchgrass (Panicum virgatum), which has been selected as a biofuel crop in the United States. In this study, we present a combination of computational and experimental approaches to develop gene regulatory networks for lignin formation in switchgrass. To screen transcription factors (TFs) involved in lignin biosynthesis, we developed a modified method to perform co‐expression network analysis using 14 lignin biosynthesis genes as bait (target) genes. The switchgrass lignin co‐expression network was further extended by adding 14 TFs identified in this study, and seven TFs identified in previous studies, as bait genes. Six TFs (PvMYB58/63, PvMYB42/85, PvMYB4, PvWRKY12, PvSND2 and PvSWN2) were targeted to generate overexpressing and/or down‐regulated transgenic switchgrass lines. The alteration of lignin content, cell wall composition and/or plant growth in the transgenic plants supported the role of the TFs in controlling secondary wall formation. RNA‐seq analysis of four of the transgenic switchgrass lines revealed downstream target genes of the secondary wall‐related TFs and crosstalk with other biological pathways. In vitro transactivation assays further confirmed the regulation of specific lignin pathway genes by four of the TFs. Our meta‐analysis provides a hierarchical network of TFs and their potential target genes for future manipulation of secondary cell wall formation for lignin modification in switchgrass.
CoT: Cooperative Training for Generative Modeling

We propose Cooperative Training (CoT) for training generative models that measure a tractable density function for target data. CoT coordinately trains a generator $G$ and an auxiliary predictive mediator $M$. The training target of $M$ is to estimate a mixture density of the learned distribution $G$ and the target distribution $P$, and that of $G$ is to minimize the Jensen-Shannon divergence estimated through $M$. CoT achieves independent success without the necessity of pre-training via Maximum Likelihood Estimation or involving high-variance algorithms like REINFORCE. This low-variance algorithm is theoretically proved to be unbiased for both generative and predictive tasks. We also theoretically and empirically show the superiority of CoT over most previous algorithms, in terms of generative quality and diversity, predictive generalization ability and computational cost.
Asymptotic variability of (multilevel) multirater kappa coefficients

Agreement studies are of paramount importance in various scientific domains. When several observers classify objects on categorical scales, agreement can be quantified through multirater kappa coefficients. In most statistical packages, the standard error of these coefficients is only available under the null hypothesis that the coefficient is equal to zero, preventing the construction of confidence intervals in the general case. The aim of this paper is triple. First, simple analytic formulae for the standard error of multirater kappa coefficients will be given in the general case. Second, these formulae will be extended to the case of multilevel data structures. The formulae are based on simple matrix algebra and are implemented in the R package “multiagree”. Third, guidelines on the choice between the different mulitrater kappa coefficients will be provided.
Aldehyde Dehydrogenase 2 (ALDH2) in Rat Fatty Liver Cold Ischemia Injury

Institut George Lopez-1 (IGL-1) and Histidine-tryptophan-ketoglutarate (HTK) solutions are proposed as alternatives to UW (gold standard) in liver preservation. Their composition differs in terms of the presence/absence of oncotic agents such as HES or PEG, and is decisive for graft conservation before transplantation. This is especially so when fatty (steatotic) livers are used since these grafts are more vulnerable to ischemia insult during conservation. Their composition determines the extent of the subsequent reperfusion injury after transplantation. Aldehyde dehydrogenase-2 (ALDH2), a mitochondrial enzyme, has been reported to play a protective role in warm ischemia-reperfusion injury (IRI), but its potential in fatty liver cold ischemic injury has not yet been investigated. We evaluated the relevance of ALDH2 activity in cold ischemia injury when fatty liver grafts from Zucker Obese rats were preserved in UW, HTK, and IGL-1 solutions, in order to study the mechanisms involved. ALDH2 upregulation was highest in livers preserved in IGL-1. It was accompanied by a decrease in transaminases, apoptosis (Caspase 3 and TUNEL assay), and lipoperoxidation, which was concomitant with the effective clearance of toxic aldehydes such as 4-hydroxy-nonenal. Variations in ATP levels were also determined. The results were consistent with levels of NF-E2 p45-related factor 2 (Nrf2), an antioxidant factor. Here we report for the first time the relevance of mitochondrial ALDH2 in fatty liver cold preservation and suggest that ALDH2 could be considered a potential therapeutic target or regulator in clinical transplantation.
Pulmonary impairment after tuberculosis in a South African population

Background In South Africa, pulmonary tuberculosis (PTB) remains a problem of epidemic proportions. Despite evidence demonstrating persistent lung impairment after PTB cure, few population-based South African studies have investigated this finding. Pulmonary rehabilitation post-cure is not routinely received. Objectives To determine the effects of PTB on lung function in adults with current or past PTB. To determine any association between PTB and chronic obstructive pulmonary disease (COPD). Methods This study was observational and cross-sectional in design. Participants (n = 55) were included if they were HIV positive on treatment, had current PTB and were on treatment, and/or had previous PTB and completed treatment or if they were healthy adult subjects with no history of PTB. A sample of convenience was used with participants coming from a similar socio-economic background and undergoing spirometry testing. Multiple regression analyses were conducted on each lung function variable. Results Compared to normal percentage-predicted values, forced expiratory volume in 1 second (FEV1), forced vital capacity (FVC) and FEV1:FVC were significantly reduced in those with current PTB by 23.39%, 15.99% and 6.4%, respectively. Both FEV1 and FVC were significantly reduced in those with past PTB by 11.76% and 10.79%, respectively. There was no association between PTB and COPD – those with previous PTB having a reduced FEV1:FVC (4.88% less than the norm), which was just short of significance (p = 0.059). Conclusions Lung function is reduced both during and after treatment for PTB and these deficits may persist. This has implications regarding the need for pulmonary rehabilitation even after medical cure.
Discovering Highly Potent Molecules from an Initial Set of Inactives Using Iterative Screening

The versatility of similarity searching and quantitative structure-activity relationships to model the activity of compound sets within given bioactivity ranges (i.e., interpolation) is well established. However, their relative performance in the common scenario in early stage drug discovery where lots of inactive data but no active data points are available (i.e., extrapolation from the low-activity to the high-activity range) has not been thoroughly examined yet. To this aim, we have designed an iterative virtual screening strategy which was evaluated on 25 diverse bioactivity data sets from ChEMBL. We benchmark the efficiency of random forest (RF), multiple linear regression, ridge regression, similarity searching, and random selection of compounds to identify a highly active molecule in the test set among a large number of low-potency compounds. We use the number of iterations required to find this active molecule to evaluate the performance of each experimental setup. We show that linear and ridge regression often outperform RF and similarity searching, reducing the number of iterations to find an active compound by a factor of 2 or more. Even simple regression methods seem better able to extrapolate to high-bioactivity ranges than RF, which only provides output values in the range covered by the training set. In addition, examination of the scaffold diversity in the data sets used shows that in some cases similarity searching and RF require two times as many iterations as random selection depending on the chemical space covered in the initial training data. Lastly, we show using bioactivity data for COX-1 and COX-2 that our framework can be extended to multitarget drug discovery, where compounds are selected by concomitantly considering their activity against multiple targets. Overall, this study provides an approach for iterative screening where only inactive data are present in early stages of drug discovery in order to discover highly potent compounds and the best experimental set up in which to do so.
Angiotensin-Converting Enzyme Inhibitors Provide Better Long-Term Survival Benefits to Patients With AMI Than Angiotensin II Receptor Blockers After Survival Hospital Discharge

Aim: Renin–angiotensin–aldosterone system inhibitors (RASIs) are widely used in high-risk cardiovascular (CV) diseases, including acute myocardial infarction (AMI). However, it is not yet clear which class of RASIs provides specific benefits to patients with AMI. The present study aimed to evaluate whether angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin II type 1 receptor blockers (ARBs) had any different effects on long-term CV and all-cause mortality in patients with AMI who received either agent from admission and were discharged alive from the hospital. Methods: We analyzed data of patients with AMI from the Korea Acute Myocardial Infarction Registry-National Institutes of Health registry. Cardiovascular and all-cause mortality at 12 months after AMI were assessed. Results: Among 12 481 patients with AMI who were discharged alive, RASI treatment was as follows: ACEIs (n = 5910), ARBs (n = 4009), and no RASI (n = 2562). After adjustment for multiple factors, compared with no RASI therapy, ACEI therapy was associated with lower hazard ratios (HRs) for 1-year CV and total mortality rates, whereas ARB therapy was not. In a direct comparison, compared with ARB treatment, ACEI treatment was associated with lower HRs (95% confidence interval) for CV and total mortality: 0.562 (0.420-0.753) and 0.567 (0.451-0.713), respectively. The superiority of ACEI to ARB was also observed across several subgroups. The mortality differences between the 2 treatment groups were reproduced in a propensity-score matched analysis (n = 2855 each). Conclusions: Our study of a recent AMI registry data revealed that ACEI therapy in patients with AMI was associated with better long-term survival benefits than ARB therapy.
Adversarial training for multi-context joint entity and relation extraction

Adversarial training (AT) is a regularization method that can be used to improve the robustness of neural network methods by adding small perturbations in the training data. We show how to use AT for the tasks of entity recognition and relation extraction. In particular, we demonstrate that applying AT to a general purpose baseline model for jointly extracting entities and relations, allows improving the state-of-the-art effectiveness on several datasets in different contexts (i.e., news, biomedical, and real estate data) and for different languages (English and Dutch).
A Review of the Literature on the Functional Analysis of Inappropriate Mealtime Behavior

A functional analysis is a well-established and widely used assessment in the treatment of challenging behavior. Given the fact that functional analysis results can inform treatment selection and procedures, a review of the current literature regarding functional analysis of inappropriate mealtime behavior (IMB) is warranted as it may guide researchers in conducting a functional analysis prior to treating IMB. This review summarizes the literature regarding functional analysis of IMB. Fourteen studies that implemented a functional analysis of IMB were identified and summarized based on procedural components of the functional analysis, results, and treatment selection. Results indicate functional analysis of IMB to be effective in the identification of variables maintaining IMB. Although functional analysis of IMB has been found to be helpful, several areas for further research and evaluation are identified.
Micro‐RNAs as critical regulators of matrix metalloproteinases in cancer

Metastasis is known to be one of the important factors associated with cancer‐related deaths worldwide. Several cellular and molecular targets are involved in the metastasis process. Among these targets, matrix metalloproteinases (MMPs) play central roles in promoting cancer metastasis. MMPs could contribute toward tumor growth, angiogenesis, migration, and invasion via degradation of the extracellular matrix and activation of pre‐pro–growth factors. Therefore, identification of various cellular and molecular pathways that affect MMPs could contribute toward a better understanding of the metastatic pathways involved in various tumors. Micro‐RNAs are important targets that could affect MMPs. Multiple lines of evidence have indicated that deregulation of various micro‐RNAs, including miR‐9, Let‐7, miR‐10b, and miR‐15b, affects metastasis of tumor cells via targeting MMPs.
Towards Light-Weight Deep Learning Based Malware Detection

The explosive amount of malware continues threating the security of operating systems and networks. Traditional malware detection approaches fail to meet the requirements of detecting polymorphic and new samples. Existing neural network based detection approaches performs better, but consuming much more time in both feature extraction and training. In this paper, we propose a light-weight PC malware detection system which is based on deep convolutional neural network (CNN). The raw inputs of our system are sequences of grouped instructions, which were generated by our Instruction Analyzer in according to different functionalities of the instructions. The network will automatically learn features of malware from the grouped instruction sequences. The experiment results suggest that in a large dataset which contains roughly 70,000 samples, our detection system can achieve an overall accuracy of 95\%. The training time of our system with single convolutional layer was only about 10 hours, which is one order of magnitude less than traditional methods.
Antioxidant Metabolism, Photosystem II, and Fatty Acid Composition of Two Tall Fescue Genotypes With Different Heat Tolerance Under High Temperature Stress

Tall fescue (Festuca arundinacea Schreb.) is a typical and widely used cool-season turf grass. High temperature is a key factor that limits its utility. The objectives of this study were to investigate the behaviors of fatty acid composition and its gene expression patterns in heat-resistant genotype “TF71” and heat-sensitive genotype “TF133” exposed to heat stress (40/35°C, 14/10 h), and to broaden our comprehension about the relationship between heat tolerance and fatty acid function. The result showed that heat stress increased the malondialdehyde (MDA) content and relative electrolyte leakage (EL), but decreased the level of chlorophyll and the activity of superoxide dismutase (SOD) and peroxidase (POD) when compared to the controls, to a greater extent in “TF133. ” This result proved that “TF71” had superior high-temperature resistance. Furthermore, comparing the changes in the composition of fatty acid and the expression of the genes involved in its synthesis between the two different genotypes under heat stress, we found that heat stress increased the degree of unsaturation, UFA/SFA, and double bond index (DBI) in “TF71. ” Moreover, quantitative RT-PCR revealed that heat stress altered the expression of the genes involved in fatty acid synthesis, including ACAC, FabD, FabF, FabH, FabI, and FatA. According to these findings, we can speculate that increasing the unsaturation degree of fatty acid or controlling the equilibrium ratio of UFA/SFA might be closely associated with the improving of the heat resistance in tall fescue.
Noninvasive Cuffless Blood Pressure Estimation Using Pulse Transit Time and Impedance Plethysmography

<italic>Objective:</italic> To demonstrate the feasibility of everaging impedance plethysmography (IPG) for detection of pulse transit time (PTT) and estimation of blood pressure (BP). <italic>Methods:</italic> We first established the relationship between BP, PTT, and arterial impedance (<italic>i.e.</italic>, the IPG observations). The IPG sensor was placed on the wrist while the photoplethysmography sensor was attached to the index finger to measure the PTT. With a cuff-based BP monitoring system placed on the upper arm as a reference, our proposed methodology was evaluated on 15 young, healthy human subjects leveraging handgrip exercises to manipulate BP/PTT and compared to several conventional PTT models to assess the efficacy of PTT/BP detections. <italic>Results:</italic> The proposed model correlated with BP fairly well with group average correlation coefficients of <inline-formula><tex-math notation="LaTeX">${\text{0.88}}\pm {\text{0.07}}$</tex-math></inline-formula> for systolic BP (SBP) and <inline-formula><tex-math notation="LaTeX">${\text{0.88}}\pm {\text{0.06}}$</tex-math></inline-formula> for diastolic BP (DBP). In comparison with the other PTT methods, PTT-IPG-based BP estimation provided a lower root-mean-squared-error of <inline-formula><tex-math notation="LaTeX">${\text{8.47}}\pm {\text{0.91}}\,{\text{mmHg}}$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">${\text{5.02}}\pm {\text{0.73}}\,{\text{mmHg}}$</tex-math></inline-formula> for SBP and DBP, respectively. <italic>Conclusion:</italic> We conclude that the measurement of arterial impedance via IPG methods is an adequate indicator to estimate BP. The proposed method appears to offer superiority compared to the conventional PTT estimation approaches. <italic>Significance:</italic> Using impedance magnitude to estimate PTT offers promise to realize wearable and cuffless BP devices.