Instant Basketball Defensive Trajectory Generation

2022 ◽  
Vol 13 (1) ◽  
pp. 1-20
Author(s):  
Wen-Cheng Chen ◽  
Wan-Lun Tsai ◽  
Huan-Hua Chang ◽  
Min-Chun Hu ◽  
Wei-Ta Chu
Keyword(s):  
Domain Knowledge ◽  
State Of The Art ◽  
Trajectory Generation ◽  
Empty Space ◽  
Objective Evaluation ◽  
Training System ◽  
Loss Functions ◽  
Art Works ◽  
High Level ◽  
Average Position

Tactic learning in virtual reality (VR) has been proven to be effective for basketball training. Endowed with the ability of generating virtual defenders in real time according to the movement of virtual offenders controlled by the user, a VR basketball training system can bring more immersive and realistic experiences for the trainee. In this article, an autoregressive generative model for instantly producing basketball defensive trajectory is introduced. We further focus on the issue of preserving the diversity of the generated trajectories. A differentiable sampling mechanism is adopted to learn the continuous Gaussian distribution of player position. Moreover, several heuristic loss functions based on the domain knowledge of basketball are designed to make the generated trajectories assemble real situations in basketball games. We compare the proposed method with the state-of-the-art works in terms of both objective and subjective manners. The objective manner compares the average position, velocity, and acceleration of the generated defensive trajectories with the real ones to evaluate the fidelity of the results. In addition, more high-level aspects such as the empty space for offender and the defensive pressure of the generated trajectory are also considered in the objective evaluation. As for the subjective manner, visual comparison questionnaires on the proposed and other methods are thoroughly conducted. The experimental results show that the proposed method can achieve better performance than previous basketball defensive trajectory generation works in terms of different evaluation metrics.

DevNet: An Efficient CNN Architecture for Handwritten Devanagari Character Recognition

2020 ◽  
Vol 34 (12) ◽  
pp. 2052009 ◽  
Author(s):  
Riya Guha ◽  
Nibaran Das ◽  
Mahantapas Kundu ◽  
Mita Nasipuri ◽  
K. C. Santosh
Keyword(s):  
Character Recognition ◽  
Domain Knowledge ◽  
State Of The Art ◽  
A Priori ◽  
Image Data ◽  
Writing Style ◽  
Memory Consumption ◽  
Training Time ◽  
Memory Space ◽  
Art Works

The writing style is a unique characteristic of a human being as it varies from one person to another. Due to such diversity in writing style, handwritten character recognition (HCR) under the purview of pattern recognition is not trivial. Conventional methods used handcrafted features that required a-priori domain knowledge, which is always not feasible. In such a case, extracting features automatically could potentially attract more interests. For this, in the literature, convolutional neural network (CNN) has been a popular approach to extract features from the image data. However, state-of-the-art works do not provide a generic CNN model for character recognition, Devanagari script, for instance. Therefore, in this work, we first study several different CNN models on publicly available handwritten Devanagari characters and numerals datasets. This means that our study is primarily focusing on comparative study by taking trainable parameters, training time and memory consumption into account. Later, we propose and design DevNet, a modified CNN architecture that produced promising results, since computational complexity and memory space are our primary concerns in design.

scholarly journals State-of-the-art methods and devices for generation, exposure, and collection of aerosols from e-vapor products

2020 ◽  
Vol 4 ◽  
pp. 239784732097975
Author(s):  
Stéphanie Boué ◽  
Didier Goedertier ◽  
Julia Hoeng ◽  
Anita Iskandar ◽  
Arkadiusz K Kuczaj ◽  
...  
Keyword(s):  
Ad Hoc ◽  
State Of The Art ◽  
Physicochemical Characterization ◽  
Objective Evaluation ◽  
Vapor Generation ◽  
Characterization Methods ◽  
Clinical Exposure ◽  
Aerosol Generation

E-vapor products (EVP) have become popular alternatives for cigarette smokers who would otherwise continue to smoke. EVP research is challenging and complex, mostly because of the numerous and rapidly evolving technologies and designs as well as the multiplicity of e-liquid flavors and solvents available on the market. There is an urgent need to standardize all stages of EVP assessment, from the production of a reference product to e-vapor generation methods and from physicochemical characterization methods to nonclinical and clinical exposure studies. The objective of this review is to provide a detailed description of selected experimental setups and methods for EVP aerosol generation and collection and exposure systems for their in vitro and in vivo assessment. The focus is on the specificities of the product that constitute challenges and require development of ad hoc assessment frameworks, equipment, and methods. In so doing, this review aims to support further studies, objective evaluation, comparison, and verification of existing evidence, and, ultimately, formulation of standardized methods for testing EVPs.

scholarly journals Efficient End-to-End Sentence-Level Lipreading with Temporal Convolutional Networks

2021 ◽  
Vol 11 (15) ◽  
pp. 6975
Author(s):  
Tao Zhang ◽  
Lun He ◽  
Xudong Li ◽  
Guoqing Feng
Keyword(s):  
Performance Improvement ◽  
State Of The Art ◽  
Error Rates ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Sentence Level ◽  
End To End ◽  
High Level ◽  
Improved Accuracy ◽  
Talking Face

Lipreading aims to recognize sentences being spoken by a talking face. In recent years, the lipreading method has achieved a high level of accuracy on large datasets and made breakthrough progress. However, lipreading is still far from being solved, and existing methods tend to have high error rates on the wild data and have the defects of disappearing training gradient and slow convergence. To overcome these problems, we proposed an efficient end-to-end sentence-level lipreading model, using an encoder based on a 3D convolutional network, ResNet50, Temporal Convolutional Network (TCN), and a CTC objective function as the decoder. More importantly, the proposed architecture incorporates TCN as a feature learner to decode feature. It can partly eliminate the defects of RNN (LSTM, GRU) gradient disappearance and insufficient performance, and this yields notable performance improvement as well as faster convergence. Experiments show that the training and convergence speed are 50% faster than the state-of-the-art method, and improved accuracy by 2.4% on the GRID dataset.

Caged-electron States and Split-electron States in Endohedral Alkali C60

2021 ◽  
Author(s):  
yifan yang ◽  
Lorenz S Cederbaum
Keyword(s):  
State Of The Art ◽  
Electronic States ◽  
Electron States ◽  
The Common ◽  
High Level

The low-lying electronic states of neutral X@C60(X=Li, Na, K, Rb) have been computed and analyzed by employing state-of-the-art high level many-electron methods. Apart from the common charge-separated states, well known...

scholarly journals Assessing the Impact of the Loss Function, Architecture and Image Type for Deep Learning-Based Wildfire Segmentation

2021 ◽  
Vol 11 (15) ◽  
pp. 7046
Author(s):  
Jorge Francisco Ciprián-Sánchez ◽  
Gilberto Ochoa-Ruiz ◽  
Lucile Rossi ◽  
Frédéric Morandini
Keyword(s):  
Deep Learning ◽  
Loss Function ◽  
State Of The Art ◽  
Fire Detection ◽  
Loss Functions ◽  
Wildfire Spread ◽  
Combine Information ◽  
The Impact ◽  
Image Type ◽  
Segmentation Models

Wildfires stand as one of the most relevant natural disasters worldwide, particularly more so due to the effect of climate change and its impact on various societal and environmental levels. In this regard, a significant amount of research has been done in order to address this issue, deploying a wide variety of technologies and following a multi-disciplinary approach. Notably, computer vision has played a fundamental role in this regard. It can be used to extract and combine information from several imaging modalities in regard to fire detection, characterization and wildfire spread forecasting. In recent years, there has been work pertaining to Deep Learning (DL)-based fire segmentation, showing very promising results. However, it is currently unclear whether the architecture of a model, its loss function, or the image type employed (visible, infrared, or fused) has the most impact on the fire segmentation results. In the present work, we evaluate different combinations of state-of-the-art (SOTA) DL architectures, loss functions, and types of images to identify the parameters most relevant to improve the segmentation results. We benchmark them to identify the top-performing ones and compare them to traditional fire segmentation techniques. Finally, we evaluate if the addition of attention modules on the best performing architecture can further improve the segmentation results. To the best of our knowledge, this is the first work that evaluates the impact of the architecture, loss function, and image type in the performance of DL-based wildfire segmentation models.

scholarly journals France’s State of the Art Distributed Optical Fibre Sensors Qualified for the Monitoring of the French Underground Repository for High Level and Intermediate Level Long Lived Radioactive Wastes

Sensors ◽  
2017 ◽  
Vol 17 (6) ◽  
pp. 1377 ◽  
Author(s):  
Sylvie Delepine-Lesoille ◽  
Sylvain Girard ◽  
Marcel Landolt ◽  
Johan Bertrand ◽  
Isabelle Planes ◽  
...  
Keyword(s):  
Optical Fibre ◽  
State Of The Art ◽  
Radioactive Wastes ◽  
Intermediate Level ◽  
Optical Fibre Sensors ◽  
High Level

scholarly journals Saliency Detection by Multilevel Deep Pyramid Model

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Hai Wang ◽  
Lei Dai ◽  
Yingfeng Cai ◽  
Long Chen ◽  
Yong Zhang
Keyword(s):  
Background Noise ◽  
State Of The Art ◽  
Saliency Detection ◽  
Saliency Map ◽  
Multiple Features ◽  
Low Level ◽  
Pyramid Model ◽  
High Level ◽  
Different Levels ◽  
Better Than

Traditional salient object detection models are divided into several classes based on low-level features and contrast between pixels. In this paper, we propose a model based on a multilevel deep pyramid (MLDP), which involves fusing multiple features on different levels. Firstly, the MLDP uses the original image as the input for a VGG16 model to extract high-level features and form an initial saliency map. Next, the MLDP further extracts high-level features to form a saliency map based on a deep pyramid. Then, the MLDP obtains the salient map fused with superpixels by extracting low-level features. After that, the MLDP applies background noise filtering to the saliency map fused with superpixels in order to filter out the interference of background noise and form a saliency map based on the foreground. Lastly, the MLDP combines the saliency map fused with the superpixels with the saliency map based on the foreground, which results in the final saliency map. The MLDP is not limited to low-level features while it fuses multiple features and achieves good results when extracting salient targets. As can be seen in our experiment section, the MLDP is better than the other 7 state-of-the-art models across three different public saliency datasets. Therefore, the MLDP has superiority and wide applicability in extraction of salient targets.

scholarly journals A Survey of Graphical Page Object Detection with Deep Neural Networks

2021 ◽  
Author(s):  
Jwalin Bhatt ◽  
Khurram Azeem Hashmi ◽  
Muhammad Zeshan Afzal ◽  
Didier Stricker
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Conceptual Understanding ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Learning Approaches ◽  
Document Images ◽  
Essential Information ◽  
Current State ◽  
High Level

In any document, graphical elements like tables, figures, and formulas contain essential information. The processing and interpretation of such information require specialized algorithms. Off-the-shelf OCR components cannot process this information reliably. Therefore, an essential step in document analysis pipelines is to detect these graphical components. It leads to a high-level conceptual understanding of the documents that makes digitization of documents viable. Since the advent of deep learning, the performance of deep learning-based object detection has improved many folds. In this work, we outline and summarize the deep learning approaches for detecting graphical page objects in the document images. Therefore, we discuss the most relevant deep learning-based approaches and state-of-the-art graphical page object detection in document images. This work provides a comprehensive understanding of the current state-of-the-art and related challenges. Furthermore, we discuss leading datasets along with the quantitative evaluation. Moreover, it discusses briefly the promising directions that can be utilized for further improvements.

scholarly journals Laboratory findings in SARS-CoV-2 infections: State of the art

2020 ◽  
Vol 66 (8) ◽  
pp. 1152-1156 ◽  
Author(s):  
Miguel Augusto Martins Pereira ◽  
Isabella Carolina de Almeida Barros ◽  
Ana Luiza Veríssimo Jacob ◽  
Mayara Lopes de Assis ◽  
Salim Kanaan ◽  
...  
Keyword(s):  
State Of The Art ◽  
Case Definitions ◽  
Laboratory Findings ◽  
Laboratory Markers ◽  
Tnf Α ◽  
The Subject ◽  
Ifn Γ ◽  
High Level ◽  
Laboratory Changes ◽  
Worse Prognosis

SUMMARY OBJECTIVE The scientific community is constantly assessing the clinical and laboratory manifestations of COVID-19 in the organism. In view of the fragmentation of the large amount of information, knowledge gaps in relation to laboratory markers, and scarcity of papers in Portuguese, we propose a Literature review on laboratory changes observed in patients infected with SARS-CoV-2. METHODS Analysis of articles published between December 2019 and May 2020 on the PubMed and SciELO databases. The articles were identified, filtered, and evaluated based on the approach to the subject, language, and impact. Then, the articles were subjected to a thorough reading, in full, by 4 (four) independent researchers. RESULTS Leukopenia and lymphopenia were included in most studies, even in case definitions. Platelet count and platelet-lymphocyte ratio, at peak platelet, were associated with advanced age and longer hospital stay. Eosinopenia showed a sensitivity of 74.7% and specificity of 68.7% and, together with increased CRP, these are one of the future prospects for screening for disease. A high level of procalcitonin may indicate bacterial co-infection, leading to a worse prognosis. COVID-19 manifests itself with increased levels of many inflammatory markers such as IL-1, IL-2, IL-6, IL-7, IL-12, IP10, IFN-γ, MIP1A, MCP1, GSCF, TNF-α, and MCP1/CCL2, as well as LDH, ESR, D-dimer, CK, ALT, and AST. CONCLUSION There is a need for further studies on the new SARS-CoV-2. So far, there is no consensus regarding laboratory findings and their usefulness, whether as a prognostic marker, mortality, or disease severity.

scholarly journals Towards High-Level Intrinsic Exploration in Reinforcement Learning

2020 ◽  
Author(s):  
Nicolas Bougie ◽  
Ryutaro Ichise
Keyword(s):  
Reinforcement Learning ◽  
Time Horizon ◽  
State Of The Art ◽  
Experimental Results ◽  
Prior Work ◽  
Extrinsic Rewards ◽  
Intrinsic Reward ◽  
Long Time ◽  
End To End ◽  
High Level

Deep reinforcement learning (DRL) methods traditionally struggle with tasks where environment rewards are sparse or delayed, which entails that exploration remains one of the key challenges of DRL. Instead of solely relying on extrinsic rewards, many state-of-the-art methods use intrinsic curiosity as exploration signal. While they hold promise of better local exploration, discovering global exploration strategies is beyond the reach of current methods. We propose a novel end-to-end intrinsic reward formulation that introduces high-level exploration in reinforcement learning. Our curiosity signal is driven by a fast reward that deals with local exploration and a slow reward that incentivizes long-time horizon exploration strategies. We formulate curiosity as the error in an agent’s ability to reconstruct the observations given their contexts. Experimental results show that this high-level exploration enables our agents to outperform prior work in several Atari games.

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