Miracle-dz/UniFAR-FTUs huggingface dataset:
End-user perspectives on the development of an online intervention for parents of children on the autism spectrum

Strong evidence suggests that parents of children on the autism spectrum face increased stressors to their physical and mental wellbeing due to the demands of caregiving and paucity of support. The disparity between the services currently offered and unmet needs indicates that accessible and flexible support through online delivery is needed for parents. This study presents the ideas and perspectives collected from five focus groups of 17 parents of autistic children, on the optimal content and implementation strategy of an online intervention. Using thematic framework analysis, we derived themes under the categories: the need for online interventions; content and design; and implementation. Parents supported the development of a multicomponent online intervention which combines psychoeducation with peer-support and access to healthcare professionals. Lay abstract Parent caregivers play an essential role in the lives of individuals on the autism spectrum. The demands of caregiving can have negative effects on the mental and physical wellbeing of parents. Different types of formal support have been developed to help parents to cope with caregiving; however, many parents struggle to access services due to limited availability and busy schedules. The Internet could offer parents more accessible and flexible support. We asked 17 parents what content they would like to include in an online resource. Parents told us about their experiences trying to access and use existing formal support and websites. They overwhelmingly supported the development of an online resource informed by their suggestions. Parents emphasised the need for easier access to information through educational components and direct access to healthcare professionals online. Parents also wanted help with finding existing services and reliable, locally relevant information. Parents stressed the need for a safe environment to meet and chat with other parents online. This research forms the first stage in the development process of an online health resource for parents.
Sociodemographic Disparities in Access to Hospice and Palliative Care: An Integrative Review

Background: There is growing evidence of disparities in access to hospice and palliative care services to varying degrees by sociodemographic groups. Underlying factors contributing to access issues have received little systematic attention. Objective: To synthesize current literature on disparities in access to hospice and palliative care, highlight the range of sociodemographic groups affected by these inequities, characterize the domains of access addressed, and outline implications for research, policy, and clinical practice. Design: An integrative review comprised a systematic search of PubMed, Embase, and CINAHL databases, which was conducted from inception to March 2020 for studies outlining disparities in hospice and palliative care access in the United States. Data were analyzed using critical synthesis within the context of a health care accessibility conceptual framework. Included studies were appraised on methodological quality and quality of reporting. Results: Of the articles included, 80% employed non-experimental study designs. Study measures varied, but 70% of studies described differences in outcomes by race, ethnicity, or socioeconomic status. Others revealed disparate access based on variables such as age, gender, and geographic location. Overall synthesis highlighted evidence of disparities spanning 5 domains of access: Approachability, Acceptability, Availability, Affordability, and Appropriateness; 60% of studies primarily emphasized Acceptability, Affordability, and Appropriateness. Conclusions: This integrative review highlights the need to consider various stakeholder perspectives and attitudes at the individual, provider, and system levels going forward, to target and address access issues spanning all domains.
Ground Layer Microhabitats Influence Recruitment of Longleaf Pine in an Old-growth Pine Savanna

Abstract. Globally, savanna trees experience bottlenecks to recruitment. Likelihoods are low that juveniles, especially of nonclonal, reseeder species, will survive and reach sizes that survive recurrent fires. We hypothesized if ground layer vegetation within savannas contained patches with reduced fire effects, likelihoods of juvenile trees surviving fires would be increased. We refined our general hypothesis based on a field study in an old-growth southeastern pine savanna of North America, in which longleaf pine (Pinus palustris Mill.) is the most abundant tree. We hypothesized that recruitment of pines into the fire-resistant ‘grass stage’ may be more likely in three ground layer microhabitats (inside crowns of fallen pines, around pine tree stumps, and inside patches of oak/hardwood stems) than in surrounding groundcover located away from overstory pines. We measured the composition and abundance of ground layer vegetation and censused juvenile grass stages (< 1.5 m height) of longleaf pine in plots in replicated patches of these three microhabitats and in the surrounding ground layer matrix, all located away from large trees. Ground layer vegetation was less abundant inside than outside the three microhabitats and abundances of grasses and shrubs differed among microhabitats. A zero-inflated Poisson model indicated that occurrence of grass stage longleaf pines was >5 times more likely inside the three microhabitats than in the surrounding ground layer matrix. Recruitment was also more likely in pine than oak/hardwood microhabitats. We propose that altered microhabitats, especially those generated by death of large longleaf pines, likely facilitate recruitment into populations of this reseeding savanna tree.
A review of the genus Mimicoclytrina Bellamy, 2003 (Coleoptera: Buprestidae) and description of a new species from northwestern Venezuela

Abstract. The Neotropical genus Mimicoclytrina Bellamy, 2003 is revised and a new species from Venezuela, Mimicoclytrina bellamyi sp. nov., is described and compared to its most closely related species. A lectotype for Acherusia tristis Thomson, 1878 is designated, and the taxon is classified as a subspecies of M. piliventris (Saunders, 1869). Mimicoclytrina piliventris ssp. venezuelica (Cobos, 1980) is placed in synonymy with M. piliventris ssp. tristis (Thomson, 1878). The previously unavailable subgenus name Nelsonila Cobos, 1980 is redescribed as a new subgenus Antoniola sg. nov. and a type species is designated. All species of the genus are illustrated, including type specimens. A key to all known taxa is given, together with data on distribution, biology and taxonomic position.
COMPARISON OF TWO SURVEILLANCE COMPONENTS FOR INVESTIGATING THE EPIDEMIOLOGY OF CANINE DISTEMPER VIRUS IN RACCOONS (PROCYON LOTOR)

Abstract: Canine distemper virus (CDV) has a broad mammalian host range. In Ontario, Canada, CDV is frequently encountered in wild carnivores and is the most common infectious cause of death for raccoons (Procyon lotor). The isolation of wild-type CDV strains genetically distinct from vaccine strains in North America has renewed interest in the epidemiological patterns of this virus. However, wildlife surveillance is challenging and often utilizes a combination of surveillance methods with aggregation of data from multiple sources. Our objective was to compare raccoon CDV data generated through two separate surveillance components operated by the Ontario-Nunavut node of the Canadian Wildlife Health Cooperative. The raw data generated by each component in addition to the results of multilevel logistic regression and spatial scan statistics, were compared between the datasets. A total of 498 raccoons obtained via passive surveillance between 2007 and 2017 and 887 raccoons obtained via enhanced-passive surveillance between 2014 and 2017, were tested for CDV. The number and geographic distribution of reports, proportion of yearly reports classified as CDV-positive, and characteristics of CDV-positive raccoons differed between passive and enhanced-passive surveillance components. Geographical data demonstrated that CDV infection was present throughout southern Ontario. The geographic area of both surveillance components combined was more representative than either passive or enhanced-passive surveillance in isolation; but was restricted compared to the overall distribution of raccoons in Ontario. Regression analyses produced statistically significant associations between the presence of CDV and host and environmental variables that were at times discordant between the two datasets. Studying the properties of these datasets will inform future passive wildlife surveillance strategies and highlights the impact that a surveillance strategy can have on the results of epidemiological analyses.
Molecular Dynamics Simulation of the Formation of Polymer Networks

The random end linking of different amounts of trifunctional crosslinkers with 3 000 prepolymer linear chains, with length varying from 10 to 30 monomers, to form networks at different system number densities was dynamically simulated by the molecular dynamics method. Investigation of the crosslinking kinetics shows that, with a stoichiometric number of crosslinkers present, the time evolution of free ends fraction decays as a power law in time t -3/4 . This scaling behavior is different from the one in a dense polymer melt. Structural analyses of the resulting networks indicate that the imperfections of the network structure strongly depend on the initial synthesis conditions including the initial system number density and the ratio of crosslinkers to precursor polymer chain ends.
Variational Autoencoder Anomaly-Detection of Avalanche Deposits in Satellite SAR Imagery

This work demonstrates that deep unsupervised learning holds much promise for rare event detection, even when labeled data is limited. Rare event detection is challenging for traditional supervised learning approaches due to high class-imbalance. We demonstrate the efficacy of deep unsupervised learning, in particular a variational autoencoder (VAE), when used for anomaly detection, in an application to avalanche detection in the French Alps, from satellite SAR imagery and a limited on-the-ground survey. Remarkably, our results demonstrate that supervision (i.e., access to labeled data) is only needed in the validation phase of our training pipeline, to tune a hyper-parameter: the threshold on reconstruction error of the VAE (trained in an unsupervised fashion) that will be used to designate an observation as an anomaly, i.e., an avalanche in this context. Our method outperforms previous benchmarks in avalanche detection, including supervised learning a convolutional neural network on an artificially balanced version of the same data. To the best of our knowledge, this is the first step in exploring the potential of applying deep unsupervised learning methods to detect avalanche deposits. Our proposed semi-supervised learning pipeline is also promising for real-world settings in which the data is very unbalanced and labels are limited.
Attention-Based End-to-End Keywords Spotting

The rapid development of deep learning technology has significantly improved the performance of the keywords spotting, but at the same time it also requires more labeled data. This paper proposes a neural network based end-to-end (E2E) model for keywords spotting, which joints the attention mechanism as well as requires only a little supervision data. The proposed attention-based E2E Keywords spotting model consists of three main modules: a keyword embedding module, an acoustic module and a keywords spotting module. The keyword embedding module is used to obtain the embedding vector of the keyword. The acoustic module combines keyword embedding vector and audio feature to obtain the corresponding feature vector with attention mechanism. The keywords spotting module makes the corresponding feature vector as input to detect whether keyword occurs in the audio or not. We experiment with 13 and 20 different keywords on the AISHELL-2 datasets. The results show that the false alarm rates are 0.17 times per hour and 0.11 times per hour, and the false rejection rates are 3.98% and 3.36%, respectively.
Temporal-Spatial-Frequency Feature Selection of Brain-Computer Interface Based on BQPSO

The electroencephalography (EEG) signals can be identified and translated into control commands by brain-computer interface (BCI) systems. To improve the recognition results of the EEG signals, a temporal-spatial-frequency feature selection model based on binary quantum particle swarm optimization (BQPSO) is proposed. The signals are firstly divided into six segments according to time, and then they are bandpass filtered into six different frequency ranges, respectively. Temporal-spatial-frequency features are extracted by common spatial pattern (CSP). After selecting by BQPSO, the optimized features are classified by extreme learning machine. Two different data sets are used to validate the proposed model, and their average classification results are 84.7% and 81.4%, respectively. Compared with other feature selection algorithms, our proposed model achieves the best results. Better classification results can be obtained by the appropriate feature selection algorithm.
m6ABRP: Predicting m6A-YTHDF2 Binding Regions via Sequence-based Properties

m6A plays important roles in cell differentiation and tissue development via selectively binding with the YTH-containing proteins. However, the mechanism of the selectively binding events is largely unknown. The precise prediction of m6A-YTH binding regions across the transcriptome will be helpful to learn the molecular basis of m6A selectively binding with the YTH-containing proteins. In this study, we developed a machine learning-based model, named m6ABRP, for predicting m6A-YTHDF2 binding regions. Sequence-based features were extracted and five different algorithms, Support vector machine (SVM), RandomForest, ExtraTrees, GradientBoosting and AdaBoost, were implemented for model training. Among them, the SVM-based model received the best AUC of 0.920 on the training dataset using five-fold cross validation and received the best AUC of 0.910 on the independent test dataset. The results suggest that m6ABRP is a powerful tool for predicting m6A-YTHDF2 binding regions.
An Optic Disc Localization Method Based on Optic Disc Appearance Characteristics and Blood Vessel Structure

Detection of related tissues in the retinal fundus image of the human eye can diagnose the relevant eye diseases at an early stage. Fundus image processing and analysis is an important application of computer science in the medical field. Through the digital image processing method, automatic detection of the target related to the diagnosis of fundus diseases in the fundus image and screening of fundus diseases can reduce the workload of experts greatly, and is of great significance for the promotion of large-scale fundus screening. This paper explores image preprocessing as well as automatic localization methods suitable for automatic detection of optic disc with high accuracy and sensitivity, which lays a technical foundation for the promotion of fundus screening and large-scale fundus screening. On this basis, this paper studies the method of optic disc localization combined with the appearance characteristics of optic disc and retinal vascular structure, and improve it to test the effect of improved algorithm. Behind, the method of this research is validated and verified by using the open fundus image database. The effect of our method locating the optic disc and the causes of the optic disc localization error are analyzed.
Improved Traumatic Brain Injury Classification Approach Based on Deep Learning

Despite medical imaging diagnosis has made significant progress, accurate imaging diagnosis of traumatic brain injury (TBI) still remains a challenging task because of the extremely complex and diverse brain images in TBI. Deep learning has been proved to be an effective way for boosting medical image analysis performance. However, the current research in this direction is limited by the lack of a comprehensive TBI image dataset. This work contributes a new CT image dataset suitable for the detection of TBI, which includes 226 (TBI/normal: 175/51) subjects, 6780 slices in a hospital with a CT scan of the head and their ground truth classifications for TBI detection purpose given by the experienced radiologist. With this dataset, we propose a novel imaging diagnosis model of TBI based on convolutional neural network (CNN) combined with recurrent neural network (RNN) and embedded squeeze-and-excitation (SE) module. Besides, we introduce transfer learning to avoid the problems of local optimization and data insufficiency. Experimental results show that our model achieves 95.9% accuracy on the classification task of predicting whether there is damage at the slice level, which is more accurate than other commonly used classification networks. We believe that our current work can help doctors make a further clinical diagnosis.
Improving Object Detection of Remotely Sensed Multispectral Imagery Via Pan-sharpening

Pan-sharpening is used to fuse multispectral images with low spatial resolution and a panchromatic (Pan) image with high spatial resolution to generate synthesized images featured with high spatial and multispectral properties. The pan-sharpened images are assumed valuable for further application. However, there have been a few investigations on the effectiveness of the pan-sharpened products in practice (i.e. object detection), compared with the fact many algorithms for pan-sharpening have been developed. In this paper, improvements contributed by pan-sharpening process for the object detection in multispectral imagery were investigated. Original multispectral images along with the corresponding Pan images acquired by Gaojing-1 (SuperView-1, as the first sub-meter high-resolution commercial remote sensing satellite independently developed in China) satellite were used. Seven algorithms widely used in pan-sharpening were applied separately and compared, while the object detection experiments were done by implementing Faster RCNN. The preliminary findings show: (1) the pan-sharpened images present obviously positive contribution to object detection with Faster RCNN, compared to the original multispectral images; (2) detection results of the pan-sharpened images vary with the algorithms used in pan-sharpening process. Furthermore, this investigation suggests none of the pan-sharpening algorithms showed absolute advantages in image fusion to achieve better object detection consequently.
U-net for learning and inference of dense representation of multiple air pollutants from satellite imagery

Air pollution is an important topic on countless fronts and is an active area of research. The goal of this work is to provide a machine learning model for learning and inference of pollution concentrations and air quality measures, namely Particulate Matter 2.5, NO3 , Nitrate Pollution, and NH4 , Atmospheric Ammonium, with high granularity by using easily obtainable satellite imagery data. In order to achieve this, we propose the fully convolutional network U-net that, unlike previous work, can predict these pollutant values at a pixel-level high-resolution instead of being able only to predict a single value for an entire geographical region. We demonstrate that this approach can reconstruct the considered pollutant concentrations on ground-truth data and can predict the concentrations and their spatial structure reasonably well, even for data that the network has temporally not yet seen. Finally, we illustrate that the model’s pollutant predictions can offer valuable insights into the current COVID-19 pandemic.
A Deep Residual Sparse and Cross Domain Reconstruction Network for Dynamic MR Imaging

In this paper, we propose a deep residual sparse and cross-domain reconstruction network to reconstruct high-quality dynamic MR image sequences from undersampled data, which alternately learns to reconstruct signals in x-f domain and image domain in an iterative manner. Specifically, in the x-f domain, we decompose the unknown signal into the prediction signal and the residual signal. In order to sparsify the residual signal as much as possible, we exploit the powerful reasoning ability of neural networks to adaptively learn the residual signal and carry out the subsequent sparse coding processing. Furthermore, the spatiotemporal correlation of dynamic MR data in x-f space is used to recover the true signal from the aliasing signal. In the image domain, bidirectional ConvLSTM and dense connections have been incorporated in a U-net based reconstruction architecture to encourage the easier transfer of information. Gradually restoring sharp image details under different resolutions is realized in the decoding part of the U-net reconstruction architecture that is designed to avoid the over-smoothness issue. Experiments on vivo MR data confirm that the proposed method can achieve superior image reconstruction quality by preserving more fine details when compared to state-of-art methods.
Design and Implementation of a Time Prediction Model for LS-DYNA Cloud Computing Platform

Cloud computing can handle huge amounts of data in a very short time, so as to build a powerful network service capability. However, the existing cloud computing platform (e.g. LS-DYNA) cannot predict the calculation time and then provide appropriate reference conditions for users. Furthermore, the relationship between calculation time and hardware configuration are not always negative correlation. In this paper, the design idea of time prediction model is first put forward. Specifically, the correlation between the hardware conditions of the relevant historical model and the calculation time is set as the training set. Following this idea, a more accurate time prediction model is built and further improved. In this paper, regression analysis is adopted and four core steps are generally required to establish the regression equation, including curve estimation, automatic linear modeling, regression analysis and Durbin-Watson test. When compared with the original LS-DYNA, experimental results fully demonstrate the effectiveness of the proposed method for its small prediction error.
YOLOFKP: Dense Face Detection Based On YOLOv3 Key Point Network

With the rapid development of convolutional neural networks, the performance of face detection algorithms has been improved. However, the recall rate of the lightweight face detection algorithm is low when it is applied to the small-scale face and dense face. And high-speed and high-accuracy should be considered at the same time. To solve the problems, we propose a lightweight face detection algorithm based on the YOLOv3 key point. The contributions include: (1) A lightweight face detection algorithm, YOLOFKP, face detector, with scale invariance and enlarged receptive field, is proposed based on YOLOv3. (2) Use mosaic for data preprocessing. (3) The loss function Wing loss and activation function Mish for face detection are used in YOLOv3. (4) The classroom face dataset (CLASSROOMFACE) is made. The experimental results show that YOLOFKP has a fast detection speed and a small number of model parameters. It has robustness for small scale faces and dense faces and effectively improves the recall rate of the lightweight face detection algorithm. The test results on the WIDER FACE dataset show that the face recall rate is 91.19%, and the detection speed of 49 FPS is achieved on a single GTX 1080Ti GPU.