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2022 ◽  
Vol 28 (1) ◽  
pp. 65-67
Bo Yu

ABSTRACT Introduction: The performance of basketball players is based on physical function and quality. In addition to genetic factors, physical function can also be improved through acquired training. Objective: The article analyzes the concept of body movement through literature data and a questionnaire survey. Methods: This article analyzes the mechanical characteristics of basketball technology from the perspective of physiology and proposes methods to develop the strength of basketball players. Results: Through the activation of different training actions, controlling the muscles that maintain the stability of the limbs to adjust body balance is beneficial to improvement of the coordination and sensitivity of the muscles. Conclusion: Pay attention to the principle of incremental load, the SAID principle, and comprehensiveness in strength training. The training method adopted is helpful to the improvement of the athlete’s aerobic metabolism. Level of evidence II; Therapeutic studies - investigation of treatment results.

Soumyashee Soumyaprakash Panda ◽  
Ravi Hegde

Abstract Free-space diffractive optical networks are a class of trainable optical media that are currently being explored as a novel hardware platform for neural engines. The training phase of such systems is usually performed in a computer and the learned weights are then transferred onto optical hardware ("ex-situ training"). Although this process of weight transfer has many practical advantages, it is often accompanied by performance degrading faults in the fabricated hardware. Being analog systems, these engines are also subject to performance degradation due to noises in the inputs and during optoelectronic conversion. Considering diffractive optical networks (DON) trained for image classification tasks on standard datasets, we numerically study the performance degradation arising out of weight faults and injected noises and methods to ameliorate these effects. Training regimens based on intentional fault and noise injection during the training phase are only found marginally successful at imparting fault tolerance or noise immunity. We propose an alternative training regimen using gradient based regularization terms in the training objective that are found to impart some degree of fault tolerance and noise immunity in comparison to injection based training regimen.

Information ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 5
Teerapong Panboonyuen ◽  
Sittinun Thongbai ◽  
Weerachai Wongweeranimit ◽  
Phisan Santitamnont ◽  
Kittiwan Suphan ◽  

Due to the various sizes of each object, such as kilometer stones, detection is still a challenge, and it directly impacts the accuracy of these object counts. Transformers have demonstrated impressive results in various natural language processing (NLP) and image processing tasks due to long-range modeling dependencies. This paper aims to propose an exceeding you only look once (YOLO) series with two contributions: (i) We propose to employ a pre-training objective to gain the original visual tokens based on the image patches on road asset images. By utilizing pre-training Vision Transformer (ViT) as a backbone, we immediately fine-tune the model weights on downstream tasks by joining task layers upon the pre-trained encoder. (ii) We apply Feature Pyramid Network (FPN) decoder designs to our deep learning network to learn the importance of different input features instead of simply summing up or concatenating, which may cause feature mismatch and performance degradation. Conclusively, our proposed method (Transformer-Based YOLOX with FPN) learns very general representations of objects. It significantly outperforms other state-of-the-art (SOTA) detectors, including YOLOv5S, YOLOv5M, and YOLOv5L. We boosted it to 61.5% AP on the Thailand highway corpus, surpassing the current best practice (YOLOv5L) by 2.56% AP for the test-dev data set.

Retos ◽  
2021 ◽  
Vol 44 ◽  
pp. 464-476
Cristián Andrés Mateluna Núñez ◽  
Juan Pablo Zavala-Crichton ◽  
Matías Monsalves-Álvarez ◽  
Jorge Olivares-Arancibia ◽  
Rodrigo Yáñez-Sepúlveda

  La capacidad de generar máxima potencia neuromuscular es el factor más importante y determinante en el rendimiento atlético. Debido a esto, el entrenamiento con movimientos de Halterofilia (EMH) y sus derivados es uno de los métodos más usados, ya que la evidencia muestra que genera adaptaciones de fuerza-potencia superiores comparadas con el entrenamiento de fuerza tradicional, de salto y de kettlebells. Objetivo: Identificar los efectos del EMH en la capacidad de salto, esprint y cambio de dirección (COD) en población deportista. Método: Se realizó una búsqueda exhaustiva en diferentes bases de datos, como PUBMED, Sportdiscus (EBSCO), Scopus y Web of Science (WOS) bajo modelo PRISMA. Los trabajos revisados fueron experimentales con y sin grupo de control, entre los años 2000 y 2020. Resultados: El EMH produce mejoras significativas en las capacidades de salto, de esprint y de COD en población deportista. Conclusión: El EMH genera mejoras significativas en el rendimiento de salto, carreras y cambio de dirección bajo distintos protocolos. Existe evidencia que sustenta la aplicación de EMH, recomendando sus derivados centrados en el segundo tirón y aquellos que utilicen el ciclo de estiramiento-acortamiento en sus variantes colgantes. Abstract: The ability to generate maximum power is the most important and determining neuromuscular function in sports performance. Therefore, weightlifting training (WT) and its derivatives is one of the most widely used methods, generating superior strength-power adaptations compared to traditional strength training, jumping and kettlebell training. Objective: To identify the effects of WT on the ability to jump, sprint and change of direction (COD) in athletes. Method: An exhaustive search was carried out in different databases, such as PUBMED, Sportdiscus (EBSCO), Scopus and Web of Science (WOS) under the PRISMA model. The reviewed papers were experimental with and without a control group, between the years 2000 and 2020. Results: The WT produces significant improvements in jump, sprint and in change of direction capacities in the sport population. Conclusion: WT generates significant improvements in jumping, running and change of direction performance under different protocols. There is evidence supporting the use of WT, suggesting its derivatives focused on the second pull and those that use the stretch-shortening cycle in their hanging variants.

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Changchang Zeng ◽  
Shaobo Li

Machine reading comprehension (MRC) is a challenging natural language processing (NLP) task. It has a wide application potential in the fields of question answering robots, human-computer interactions in mobile virtual reality systems, etc. Recently, the emergence of pretrained models (PTMs) has brought this research field into a new era, in which the training objective plays a key role. The masked language model (MLM) is a self-supervised training objective widely used in various PTMs. With the development of training objectives, many variants of MLM have been proposed, such as whole word masking, entity masking, phrase masking, and span masking. In different MLMs, the length of the masked tokens is different. Similarly, in different machine reading comprehension tasks, the length of the answer is also different, and the answer is often a word, phrase, or sentence. Thus, in MRC tasks with different answer lengths, whether the length of MLM is related to performance is a question worth studying. If this hypothesis is true, it can guide us on how to pretrain the MLM with a relatively suitable mask length distribution for MRC tasks. In this paper, we try to uncover how much of MLM’s success in the machine reading comprehension tasks comes from the correlation between masking length distribution and answer length in the MRC dataset. In order to address this issue, herein, (1) we propose four MRC tasks with different answer length distributions, namely, the short span extraction task, long span extraction task, short multiple-choice cloze task, and long multiple-choice cloze task; (2) four Chinese MRC datasets are created for these tasks; (3) we also have pretrained four masked language models according to the answer length distributions of these datasets; and (4) ablation experiments are conducted on the datasets to verify our hypothesis. The experimental results demonstrate that our hypothesis is true. On four different machine reading comprehension datasets, the performance of the model with correlation length distribution surpasses the model without correlation.

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0258400
Akiva Kleinerman ◽  
Ariel Rosenfeld ◽  
David Benrimoh ◽  
Robert Fratila ◽  
Caitrin Armstrong ◽  

Machine-assisted treatment selection commonly follows one of two paradigms: a fully personalized paradigm which ignores any possible clustering of patients; or a sub-grouping paradigm which ignores personal differences within the identified groups. While both paradigms have shown promising results, each of them suffers from important limitations. In this article, we propose a novel deep learning-based treatment selection approach that is shown to strike a balance between the two paradigms using latent-space prototyping. Our approach is specifically tailored for domains in which effective prototypes and sub-groups of patients are assumed to exist, but groupings relevant to the training objective are not observable in the non-latent space. In an extensive evaluation, using both synthetic and Major Depressive Disorder (MDD) real-world clinical data describing 4754 MDD patients from clinical trials for depression treatment, we show that our approach favorably compares with state-of-the-art approaches. Specifically, the model produced an 8% absolute and 23% relative improvement over random treatment allocation. This is potentially clinically significant, given the large number of patients with MDD. Therefore, the model can bring about a much desired leap forward in the way depression is treated today.

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi137-vi138
Sara Ranjbar ◽  
Kyle Singleton ◽  
Deborah Boyett ◽  
Michael Argenziano ◽  
Jack Grinband ◽  

Abstract Glioblastoma (GBM) is a devastating primary brain tumor known for its heterogeneity with a median survival of 15 months. Clinical imaging remains the primary modality to assess brain tumor response, but it is nearly impossible to distinguish between tumor growth and treatment response. Ki67 is a marker of active cell proliferation that shows inter- and intra-patient heterogeneity and should change under many therapies. In this work, we assessed the utility of a semi-supervised deep learning approach for regionally predicting high-vs-low Ki67 in GBM patients based on MRI. We used both labeled and unlabeled datasets to train the model. Labeled data included 114 MRI-localized biopsies from 43 unique GBM patients with available immunohistochemistry Ki67 labels. Unlabeled data included nine repeat routine pretreatment paired scans of newly-diagnosed GBM patients acquired within three days. Data augmentation techniques were utilized to enhance the size of our data and increase generalizability. Data was split between training, validation, and testing sets using 65-15-20 percent ratios. Model inputs were 16x16x3 patches around biopsies on T1Gd and T2 MRIs for labeled data, and around randomly selected patches inside the T2 abnormal region for unlabeled data. The network was a 4-conv layered VGG-inspired architecture. Training objective was accurate prediction of Ki67 in labeled patches and consistency in predictions across repeat unlabeled patches. We measured final model accuracy on held-out test samples. Our promising preliminary results suggest potential for deep learning in deconvolving the spatial heterogeneity of proliferative GBM subpopulations. If successful, this model can provide a non-invasive readout of cell proliferation and reveal the effectiveness of a given cytotoxic therapy dynamically during the patient's routine follow up. Further, the spatial resolution of our approach provides insights into the intra-tumoral heterogeneity of response which can be related to heterogeneity in localization of therapies (e.g. radiation therapy, drug dose heterogeneity).

2021 ◽  
pp. 1-36
Chenze Shao ◽  
Yang Feng ◽  
Jinchao Zhang ◽  
Fandong Meng ◽  
Jie Zhou

Abstract In recent years, Neural Machine Translation (NMT) has achieved notable results in various translation tasks. However, the word-by-word generation manner determined by the autoregressive mechanism leads to high translation latency of the NMT and restricts its low-latency applications. Non-Autoregressive Neural Machine Translation (NAT) removes the autoregressive mechanism and achieves significant decoding speedup through generating target words independently and simultaneously. Nevertheless, NAT still takes the word-level cross-entropy loss as the training objective, which is not optimal because the output of NAT cannot be properly evaluated due to the multimodality problem. In this article, we propose using sequence-level training objectives to train NAT models, which evaluate the NAT outputs as a whole and correlates well with the real translation quality. Firstly, we propose training NAT models to optimize sequence-level evaluation metrics (e.g., BLEU) based on several novel reinforcement algorithms customized for NAT, which outperforms the conventional method by reducing the variance of gradient estimation. Secondly, we introduce a novel training objective for NAT models, which aims to minimize the Bag-of-Ngrams (BoN) difference between the model output and the reference sentence. The BoN training objective is differentiable and can be calculated efficiently without doing any approximations. Finally, we apply a three-stage training strategy to combine these two methods to train the NAT model.We validate our approach on four translation tasks (WMT14 En↔De, WMT16 En↔Ro), which shows that our approach largely outperforms NAT baselines and achieves remarkable performance on all translation tasks. The source code is available at

2021 ◽  
Vol 19 (3) ◽  
pp. 1-6
Regina Queiroz Silva ◽  
Leonardo Carlos de Andrade ◽  
Isaac Neves de Lima ◽  
Katiane Dos Santos Costa

INTRODUÇÃO: Este artigo expõe uma sequência didática com o conteúdo práticas corporais de aventura, tomando como principal enfoque as articulações com o debate sobre saúde coletiva a partir da pedagogia histórico-crítica e dos princípios curriculares para o trato com o conhecimento da abordagem crítico-superadora. Compreendendo que a saúde coletiva está pautada em um aporte dialético e que toma a totalidade do indivíduo nessa particularidade histórica, defendemos que essa concepção de saúde tem envergadura suficiente para dialogar com o ensino das diferentes atividades da Cultura Corporal na busca por uma formação crítica. OBJETIVO: O objetivo é apresentar o caminho teórico-metodológico do ensino das práticas corporais de aventura considerando as possibilidades de usufruto e vivência em meio urbano, mais especificamente nos parques da cidade de Goiânia. MÉTODOS: A metodologia de exposição foi delineada a partir dos princípios de um relato de experiência, que visa apresentar detalhadamente uma proposta pedagógica já materializada em diálogo com a produção científica. Concomitantemente foi desenvolvido um debate acerca da saúde coletiva apresentando as determinações sociais do trabalho e as contradições dessa sociedade onde os trabalhadores não têm acesso à cultura corporal e ao tempo de lazer, impactando sua saúde. RESULTADOS: a) o processo de ensino e aprendizagem das práticas corporais de aventura na terra, no ar e na água, em suas modalidades arvorismo, slackline e stand up paddle e dimensões técnicas, históricas e de vertigem; b) Debate sobre as formas de acesso às práticas de aventura e exploração dos espaços públicos em Goiânia; c) Compreender as contradições acerca da particularidade de nossa sociedade (capitalismo); d) Construção de material alternativo. CONCLUSÃO: Os alunos compreenderam os conceitos das práticas corporais de aventura e os determinantes sociais da saúde, tecendo críticas à falta de políticas públicas de acesso à cultura corporal e apontando possibilidades alternativas (e provisórias) para a comunidade vivenciar e usufruir destes bens, mesmo à contragosto da lógica do capital.ABSTRACT. The teaching of adventure practices and the contextualization of the social determination of healthBACKGROUND: This article presents a didactic sequence with the content of corporal practices of adventure, taking as main focus the articulations with the debate on collective health from the historical pedagogy-critical and curricular principles for dealing with the knowledge of the critical-overcoming approach. Understanding that collective health is based on a dialectical contribution and that it takes the whole of the individual in this historical particularity, we defend that this concept of health has sufficient scope to dialogue with the teaching of the different activities of Body Culture in the search for a critical training.  OBJECTIVE: The objective is to present the theoretical-methodological path of the teaching of the corporal practices of adventure considering the possibilities of enjoyment and living in urban environment, more specifically in the parks of the city of Goiânia, GO, Brazil. METHODS: The methodology of exposition was outlined based on the principles of an experience report, which aims to present in detail a pedagogical proposal already materialized in dialogue with the scientific production. RESULTS: a) the process of teaching and learning the corporal practices of adventure on land, in the air and in the water, in their modalities tree climbing, slackline and stand up paddle and technical, historical and vertigo dimensions; b) Debate on the forms of access to the practices of adventure and exploration of public spaces in Goiânia; c) Understanding the contradictions about the particularity of our society (capitalism); d) Construction of alternative material. CONCLUSION: The students understood the concepts of the corporal practices of adventure and the social determinants of health, criticizing the lack of public policies of access to body culture and pointing out alternative (and provisional) possibilities for the community to experience and enjoy these goods, even against the logic of capital.

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