Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimization Research on Deep Learning and Temporal Segmentation Algorithm of Video Shot in Basketball Games

The analysis of the video shot in basketball games and the edge detection of the video shot are the most active and rapid development topics in the field of multimedia research in the world. Video shots' temporal segmentation is based on video image frame extraction. It is the precondition for video application. Studying the temporal segmentation of basketball game video shots has great practical significance and application prospects. In view of the fact that the current algorithm has long segmentation time for the video shot of basketball games, the deep learning model and temporal segmentation algorithm based on the histogram for the video shot of the basketball game are proposed. The video data is converted from the RGB space to the HSV space by the boundary detection of the video shot of the basketball game using deep learning and processing of the image frames, in which the histogram statistics are used to reduce the dimension of the video image, and the three-color components in the video are combined into a one-dimensional feature vector to obtain the quantization level of the video. The one-dimensional vector is used as the variable to perform histogram statistics and analysis on the video shot and to calculate the continuous frame difference, the accumulated frame difference, the window frame difference, the adaptive window's mean, and the superaverage ratio of the basketball game video. The calculation results are combined with the set dynamic threshold to optimize the temporal segmentation of the video shot in the basketball game. It can be seen from the comparison results that the effectiveness of the proposed algorithm is verified by the test of the missed detection rate of the video shots. According to the test result of the split time, the optimization algorithm for temporal segmentation of the video shot in the basketball game is efficiently implemented.
Virtual Reality Exercises in an Interdisciplinary Rehabilitation Programme for Persons with Chronic Neck Pain: a Feasibility Study

Objective To investigate the feasibility of a virtual reality exercise intervention within an interdisciplinary rehabilitation programme for persons with chronic neck pain. The effects of the intervention on symptom severity, variables related to chronic neck pain, and patients’ experience of exercises were assessed. Methods Nine women and 3 men participated in a 6-week virtual reality exercise intervention during an interdisciplinary rehabilitation programme. Symptom severity was rated before and after each session of virtual reality exercise, using questionnaires before and after the interdisciplinary rehabilitation programme, and questions about participants’ experiences. Results Neck pain symptoms increased temporarily during the exercises, but no lasting deterioration was found after the interdisciplinary rehabilitation programme. Depression, pain interference, pain control, sleep and kinesiophobia improved significantly after the programme. Participants experienced that the virtual reality exercises increased motivation to exercise and provided a focus other than pain. However, the equipment was heavy; and exercising was tiring and reminded them of their challenges. Conclusion This study indicates that virtual reality exercises as part of an interdisciplinary rehabilitation programme are feasible and safe for patients with chronic neck pain. Pain symptoms may increase temporarily during the exercises. Virtual reality exercises may support participants by increasing motivation to exercise and providing helpful feedback. Further research into the added value of virtual reality exercises in an interdisciplinary rehabilitation programme for patients with chronic neck pain is warranted. LAY ABSTRACT Approximately 20% of the general population have such severe pain that it has a major impact on their daily life. Chronic neck pain is a common diagnosis. For treatment of chronic neck pain, current knowledge supports the use of interdisciplinary rehabilitation programmes; health professionals working in a team together with the patient. Such programmes include education, exercise and psychological intervention. It is possible that exercising with the use of virtual reality might stimulate physical activity and facilitate exercise performance through instant feedback. This study investigated the feasibility of using virtual reality exercises within an interdisciplinary rehabilitation programme for patients with chronic neck pain. The results show that it is possible and safe for patients with chronic neck pain to incorporate virtual reality exercises as part of an interdisciplinary rehabilitation programme. Neck pain symptoms may increase temporarily during the exercises, but no long-lasting deterioration in symptoms was found. No adverse effects occurred due to the interdisciplinary rehabilitation programme. Virtual reality exercises may support participants in increasing motivation and by providing helpful, direct, feedback of their performance during the exercise session.
Public willingness to adhere to COVID-19 precautionary measures in Sudan: an application of the Health Belief Model

Introduction coronavirus disease (COVID-19) is a highly infectious disease caused by the novel coronavirus (SARS-CoV-2). Several public health and social protective measures that may prevent or slow down the transmission of COVID-19 were introduced. However, these measures are unfortunately being neglected or deliberately ignored by some individuals. Methods a cross sectional online based survey was conducted to identify possible factors influencing public willingness to adhere to precautionary measures and preventive guidelines against COVID-19 during the lockdown periods in Sudan. The questionnaire was used to collect socio-demographic data of study participants, their health beliefs and willingness regarding adherence to precautionary measures against COVID-19 based on the constructs of the Health Belief Model. Results a total of 680 respondents completed and returned the online questionnaire. Significant predictors of the willingness to adhere to the precautionary measures against COVID-19 were gender (β= 3.34, P<0.001), self-efficacy (β= 0.476, P<0.001), perceived benefits (β= 0.349, P<0.001) and perceived severity (β= 0.113, P=0.005). These factors explained 43% of the variance in respondents' willingness to adhere to COVID-19 precautionary measures. Participants who were female, confident in their ability to adhere to the protective measures when available, believing in the benefits of the protective measures against COVID-19 and perceiving that the disease could have serious consequences were more likely to be willing to adhere to the protective measures. Conclusion female respondents and respondents having higher self-efficacy, higher perceived benefits and higher perceived severity were more likely to be willing to adhere to the protective measures against COVID-19 in Sudan.
Complete mitochondrial genome sequence and phylogenetic analysis of Arion ater (Stylommatophora: Arionidae)

Abstract Arion ater are a group of mollusks, almost all of which live in a dark and humid environment during their life cycle. In this study, the complete mitogenome of Arion ater was sequenced using next generation sequencing, analyzed and compared with other Stylommatophorans. The total length of the mitochondrial genome is 14,314 bp and consists of 13 protein-coding genes, 22 tRNA genes, 2 rRNA genes, and 1 control region. The genome has a typical mitochondrial gene sequence of Arion. Phylogenetic analysis of the mitochondrial genomes of 27 species of Stylommatophora showed that the Arion ater, Arion rufus and Arion vulgaris forms a monophyletic group with other species.
Dying within dyads: Stress, sense of security and support during palliative home care

Objectives To examine similarities and dissimilarities in patient and family caregiver dyads in their experience of stress, support, and sense of security. Methods 144 patients and their family caregivers participated. Patients were admitted to six Swedish specialist palliative home care units and diagnosed with a non-curable disease with an expected short survival. We analysed similarity patterns of answers within dyads (correlations) as well as dissimilarities, expressed as the difference between within-dyad responses. The latter were subjected to a model-building procedure using GLM, with 13 sociodemographic and clinical characteristics as independent variables. Results Within dyads, patients and family caregivers scored similar in their perception of support and sense of security with care. There was also dissimilarity within dyad responses in their perception of stress and support that could be attributed to sociodemographic or clinical characteristics. When patients scored higher levels of stress than family caregivers, the family caregiver was more likely to be male. Also family caregiver attachment style (attachment anxiety), patient age and the relationship of the family caregiver to the patient explained dissimilarities within the dyads. Conclusions Patients and family caregivers within the dyads often, but not always, had similar scores. We suggest that it is important that the healthcare staff identify situations in which perceptions within the dyads regarding stress and perception of support differ, such that they can recognise patients’ and family caregivers’ unique needs in different situations, to be able to provide adequate support and facilitate dyadic coping.
Improving Text Auto-Completion with Next Phrase Prediction

Language models such as GPT-2 have performed well on constructing syntactically sound sentences for text auto-completion task. However, such models often require considerable training effort to adapt to specific writing domains (e.g., medical). In this paper, we propose an intermediate training strategy to enhance pre-trained language models' performance in the text auto-completion task and fastly adapt them to specific domains. Our strategy includes a novel self-supervised training objective called Next Phrase Prediction (NPP), which encourages a language model to complete the partial query with enriched phrases and eventually improve the model's text auto-completion performance. Preliminary experiments have shown that our approach is able to outperform the baselines in auto-completion for email and academic writing domains.
Union: A Unified HW-SW Co-Design Ecosystem in MLIR for Evaluating Tensor Operations on Spatial Accelerators

To meet the extreme compute demands for deep learning across commercial and scientific applications, dataflow accelerators are becoming increasingly popular. While these “domain-specific” accelerators are not fully programmable like CPUs and GPUs, they retain varying levels of flexibility with respect to data orchestration, i.e., dataflow and tiling optimizations to enhance efficiency. There are several challenges when designing new algorithms and mapping approaches to execute the algorithms for a target problem on new hardware. Previous works have addressed these challenges individually. To address this challenge as a whole, in this work, we present a HW-SW codesign ecosystem for spatial accelerators called Union11https://github.com/union-codesign/union within the popular MLIR compiler infrastructure. Our framework allows exploring different algorithms and their mappings on several accelerator cost models. Union also includes a plug-and-play library of accelerator cost models and mappers which can easily be extended. The algorithms and accelerator cost models are connected via a novel mapping abstraction that captures the map space of spatial accelerators which can be systematically pruned based on constraints from the hardware, workload, and mapper. We demonstrate the value of Union for the community with several case studies which examine offloading different tensor operations (CONV/GEMM/Tensor Contraction) on diverse accelerator architectures using different mapping schemes.
A Three Step Training Approach with Data Augmentation for Morphological Inflection

We present the BME submission for the SIGMORPHON 2021 Task 0 Part 1, Generalization Across Typologically Diverse Languages shared task. We use an LSTM encoder-decoder model with three step training that is first trained on all languages, then fine-tuned on each language families and finally finetuned on individual languages. We use a different type of data augmentation technique in the first two steps. Our system outperformed the only other submission. Although it remains worse than the Transformer baseline released by the organizers, our model is simpler and our data augmentation techniques are easily applicable to new languages. We perform ablation studies and show that the augmentation techniques and the three training steps often help but sometimes have a negative effect.
Fast Extraction of Word Embedding from Q-contexts

The notion of word embedding plays a fundamental role in natural language processing (NLP). However, pre-training word embedding for very large-scale vocabulary is computationally challenging for most existing methods. In this work, we show that with merely a small fraction of contexts (Q-contexts) which are typical in the whole corpus (and their mutual information with words), one can construct high-quality word embedding with negligible errors. Mutual information between contexts and words can be encoded canonically as a sampling state, thus, Q-contexts can be fast constructed. Furthermore, we present an efficient and effective WEQ method, which is capable of extracting word embedding directly from these typical contexts. In practical scenarios, our algorithm runs 11 ~ 13 times faster than well-established methods. By comparing with well-known methods such as matrix factorization, word2vec, GloVe and fasttext, we demonstrate that our method achieves comparable performance on a variety of downstream NLP tasks, and in the meanwhile maintains run-time and resource advantages over all these baselines.
Piezotronic effect in 1D van der Waals solid of elemental tellurium nanobelt for smart adaptive electronics

Emerging technologies in wearable systems demand that functional devices can adaptively interact with the human body, where mechanical stimuli are ubiquitous and abundant. However, the electrical manipulation of charge carriers underpins the operations of state-of-the-art devices, and the effective control of interfacial energetics for charge carriers by the dynamic mechanical stimuli is still a relatively unexplored degree of freedom for semiconductor nanodevices. Piezotronic effect in nanostructured piezoelectric semiconductors offers exciting opportunities in addressing the above challenges. Here we report the first experimental exploration of piezotronic effect in 1D van der Waals solid of p-type tellurium nanobelt and systematically investigate the strain-gated charge carriers transport properties. The strain-induced polarization charges at the [ 10 1 ¯ 0 ] surfaces of Te nanobelt can modulate the electronic transport through the interfacial effect on the Schottky contacts and the volumetric effect on the conducting channel. The competing phenomenon between interfacial and volumetric effects has been studied for the first time in piezotronics. Our research allows the access to a broad range of characterization and application of Te nanomaterials for piezotronics and could guide the future study of piezotronic effect in other materials. This progress in piezotronics, together with emerging methods for deterministic production and assembly of nanomaterials, leads to compelling opportunities for research from basic studies of piezoelectricity and semiconductor properties in functional nanomaterials to the development of ‘smarter’ electronics and optoelectronics.
Searching for More Efficient Dynamic Programs

Computational models of human language often involve combinatorial problems. For instance, a probabilistic parser may marginalize over exponentially many trees to make predictions. Algorithms for such problems often employ dynamic programming and are not always unique. Finding one with optimal asymptotic runtime can be unintuitive, time-consuming, and error-prone. Our work aims to automate this laborious process. Given an initial correct declarative program, we search for a sequence of semantics-preserving transformations to improve its running time as much as possible. To this end, we describe a set of program transformations, a simple metric for assessing the efficiency of a transformed program, and a heuristic search procedure to improve this metric. We show that in practice, automated search -- like the mental search performed by human programmers -- can find substantial improvements to the initial program. Empirically, we show that many common speed-ups described in the NLP literature could have been discovered automatically by our system.
Creating jackknife and bootstrap estimates of the covariance matrix for the two-point correlation function

We present correction terms that allow delete-one Jackknife and Bootstrap methods to be used to recover unbiased estimates of the data covariance matrix of the two-point correlation function $\xi \left(\mathbf {r}\right)$. We demonstrate the accuracy and precision of this new method using a large set of 1000 QUIJOTE simulations that each cover a comoving volume of $1\rm {\left[h^{-1}Gpc\right]^3}$. The corrected resampling techniques recover the correct amplitude and structure of the data covariance matrix as represented by its principal components to within ∼10 per cent, the level of error achievable with the size of the sample of simulations used for the test. Our corrections for the internal resampling methods are shown to be robust against the intrinsic clustering of the cosmological tracers both in real- and redshift space using two snapshots at z = 0 and z = 1 that mimic two samples with significantly different clustering. We also analyse two different slicing of the simulation volume into nsv = 64 or 125 sub-samples and show that the main impact of different nsv is on the structure of the covariance matrix due to the limited number of independent internal realisations that can be made given a fixed nsv .
Sentiment Analysis in Poems in Misurata Sub-dialect - A Sentiment Detection in an Arabic Sub-dialect

Over the recent decades, there has been a significant increase and development of resources for Arabic natural language processing. This includes the task of exploring Arabic Language Sentiment Analysis (ALSA) from Arabic utterances in both Modern Standard Arabic (MSA) and different Arabic dialects. This study focuses on detecting sentiment in poems written in Misurata Arabic sub-dialect spoken in Misurata, Libya. The tools used to detect sentiment from the dataset are Sklearn as well as Mazajak sentiment tool1. Logistic Regression, Random Forest, Naive Bayes (NB), and Support Vector Machines (SVM) classifiers are used with Sklearn, while the Convolutional Neural Network (CNN) is implemented with Mazajak. The results show that the traditional classifiers score a higher level of accuracy as compared to Mazajak which is built on an algorithm that includes deep learning techniques. More research is suggested to analyze Arabic sub-dialect poetry in order to investigate the aspects that contribute to sentiments in these multi-line texts; for example, the use of figurative language such as metaphors.
The Robustness of Synthetic Observations in Producing Observed Core Properties: Predictions for the TolTEC Clouds to Cores Legacy Survey

We use hydrodynamical simulations of star-forming gas with stellar feedback and sink particles—proxies for young stellar objects (YSOs)—to produce and analyze synthetic 1.1 mm continuum observations at different distances (150–1000 pc) and ages (0.49–1.27 Myr). We characterize how the inferred core properties, including mass, size, and clustering with respect to diffuse natal gas structure, change with distance, cloud evolution, and the presence of YSOs. We find that atmospheric filtering and core segmentation treatments have distance-dependent impacts on the resulting core properties for d < 300 pc and 500 pc, respectively, which dominate over evolutionary differences. Concentrating on synthetic observations at further distances (650–1000 pc), we find a growing separation between the inferred sizes and masses of cores with and without YSOs in the simulations, which is not seen in recent observations of the Monoceros R2 (Mon R2) cloud at 860 pc. We find that the synthetic cores cluster in smaller groups, and that their mass densities are correlated with gas column density over a much narrower range, than those in the Mon R2 observations. Such differences limit the applicability of the evolutionary predictions we report here, but will motivate our future efforts to adapt our synthetic observation and analysis framework to next generation simulations, such as Star Formation in Gaseous Environments (STARFORGE). These predictions and systematic characterizations will help to guide the analysis of cores on the upcoming TolTEC Clouds to Cores Legacy Survey on the Large Millimeter Telescope Alfonso Serrano.
Guaranteed Safe Spacecraft Docking With Control Barrier Functions

This letter presents a strategy for control of a spacecraft docking with a non-maneuvering target in the presence of safety constraints and bounded disturbances. The presence of disturbances prevents convergence to a unique docking state, so in our formulation, docking is defined as occurring within a set constructed using prescribed tolerances. Safety is ensured via application of Robust Control Barrier Functions to render a designated safe set forward invariant for any allowable disturbance. However, this safety strategy necessarily presumes a worst-case disturbance, and thus restricts trajectories to a subset of the safe set when a worst-case disturbance is not present. The presented controller accounts for this restriction, and guarantees that the spacecraft both remains safe and achieves docking in finite time for any allowable disturbance. The controller is then validated in simulation for a spacecraft landing on an asteroid, and two spacecraft docking in low Earth orbit.
Regressive Ensemble for Machine Translation Quality Evaluation

This work introduces a simple regressive ensemble for evaluating machine translation quality based on a set of novel and established metrics. We evaluate the ensemble using a correlation to expert-based MQM scores of the WMT 2021 Metrics workshop. In both monolingual and zero-shot cross-lingual settings, we show a significant performance improvement over single metrics. In the cross-lingual settings, we also demonstrate that an ensemble approach is well-applicable to unseen languages. Furthermore, we identify a strong reference-free baseline that consistently outperforms the commonly-used BLEU and METEOR measures and significantly improves our ensemble’s performance.