Capturing Momentum: Tennis Match Analysis Using Machine Learning and Time Series Theory

This paper represents an analysis on the momentum of tennis match. And due to Generalization performance of it, it can be helpful in constructing a system to predict the result of sports game and analyze the performance of player based on the Technical statistics. We First use hidden markov models to predict the momentum which is defined as the performance of players. Then we use Xgboost to prove the significance of momentum. Finally we use LightGBM to evaluate the performance of our model and use SHAP feature importance ranking and weight analysis to find the key points that affect the performance of players.Comment: 16 pages, 18 figure

Predicting mutational effects on protein-protein binding via a side-chain diffusion probabilistic model

Many crucial biological processes rely on networks of protein-protein interactions. Predicting the effect of amino acid mutations on protein-protein binding is vital in protein engineering and therapeutic discovery. However, the scarcity of annotated experimental data on binding energy poses a significant challenge for developing computational approaches, particularly deep learning-based methods. In this work, we propose SidechainDiff, a representation learning-based approach that leverages unlabelled experimental protein structures. SidechainDiff utilizes a Riemannian diffusion model to learn the generative process of side-chain conformations and can also give the structural context representations of mutations on the protein-protein interface. Leveraging the learned representations, we achieve state-of-the-art performance in predicting the mutational effects on protein-protein binding. Furthermore, SidechainDiff is the first diffusion-based generative model for side-chains, distinguishing it from prior efforts that have predominantly focused on generating protein backbone structures

On the impact of government employment services on the quality of re-employment of unemployed fishermen in Yangtze River --A case study in Xiantao City of Hubei Province, China

In the transition period of the Yangtze River fishing ban policy, the retrenched fishermen are in an inferior position in the employment market due to the lack of their own employment capital, and it has become a problem to switch to employment. Based on the study of employment quality and employment capital theory, this paper uses the PLS-SEM model to process the questionnaire data on the effect of government employment services on the quality of fishermen's re-employment based on the establishment of government employment services-quality analysis system and questionnaire design and collection. The total effect of government employment services on re-employment quality was 0.685. The indirect effect of government employment services on the quality of re-employment through the mediating variable of employment conditions was smaller than the direct effect of government employment services on the quality of employment. This research may provide a basis for solving the problems of high-quality re-employment for unemployed fishermen and sustainable livelihoods for fishermen

BlendedMVS: A Large-scale Dataset for Generalized Multi-view Stereo Networks

While deep learning has recently achieved great success on multi-view stereo (MVS), limited training data makes the trained model hard to be generalized to unseen scenarios. Compared with other computer vision tasks, it is rather difficult to collect a large-scale MVS dataset as it requires expensive active scanners and labor-intensive process to obtain ground truth 3D structures. In this paper, we introduce BlendedMVS, a novel large-scale dataset, to provide sufficient training ground truth for learning-based MVS. To create the dataset, we apply a 3D reconstruction pipeline to recover high-quality textured meshes from images of well-selected scenes. Then, we render these mesh models to color images and depth maps. To introduce the ambient lighting information during training, the rendered color images are further blended with the input images to generate the training input. Our dataset contains over 17k high-resolution images covering a variety of scenes, including cities, architectures, sculptures and small objects. Extensive experiments demonstrate that BlendedMVS endows the trained model with significantly better generalization ability compared with other MVS datasets. The dataset and pretrained models are available at \url{https://github.com/YoYo000/BlendedMVS}.Comment: Accepted to CVPR202

Fluorescent Probes Design Strategies for Imaging Mitochondria and Lysosomes

Modern cellular biology faces several major obstacles, such as the determination of the concentration of active sites corresponding to chemical substances. In recent years, the popular small-molecule fluorescent probes have completely changed the understanding of cellular biology through their high sensitivity toward specific substances in various organisms. Mitochondria and lysosomes are significant organelles in various organisms, and their interaction is closely related to the development of various diseases. The investigation of their structure and function has gathered tremendous attention from biologists. The advanced nanoscopic technologies have replaced the diffraction-limited conventional imaging techniques and have been developed to explore the unknown aspects of mitochondria and lysosomes with a sub-diffraction resolution. Recent progress in this field has yielded several excellent mitochondria- and lysosome-targeted fluorescent probes, some of which have demonstrated significant biological applications. Herein, we review studies that have been carried out to date and suggest future research directions that will harness the considerable potential of mitochondria- and lysosome-targeted fluorescent probes

NeurRev:Train Better Sparse Neural Network Practically via Neuron Revitalization

Dynamic Sparse Training (DST) employs a greedy search mechanism to identify an optimal sparse subnetwork by periodically pruning and growing network connections during training. To guarantee effectiveness, DST algorithms rely on high search frequency, which consequently, requires large learning rate and batch size to enforce stable neuron learning. Such settings demand extreme memory consumption, as well as generating significant system overheads that limit the wide deployment of deep learning-based applications on resource-constraint platforms. To reconcile such, we propose on italization framework for DST (NeurRev), based on an innovative finding that dormant neurons exist with the presence of weight sparsity, and cannot be revitalized (i.e., activated for learning) even with high sparse mask search frequency. These dormant neurons produce a large quantity of zeros during training, which contribute relatively little to the outputs of succeeding layers or to the final results. Different from most existing DST algorithms that spare no effort designing weight growing criteria, NeurRev focuses on optimizing the long-neglected pruning part, which awakes dormant neurons by pruning and incurs no additional computation costs. As such, NeurRev advances more effective neuron learning, which not only achieves outperforming accuracy in a variety of networks and datasets, but also promoting a low-cost dynamism at system-level. Systematical evaluations on training speed and system overhead are conducted on the mobile devices, where the proposed NeurRev framework consistently outperforms representative state-of-the-arts. Code will be released