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arXiv 2019年

作者： Song, Xiaotao Sun, Hailong Wang, Xu Yan, Jiafei School of Software Taiyuan University of Technology China SKLSDE Lab School of Computer Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China

As an integral part of source code files, code comments help improve program readability and comprehension. However, developers sometimes do not comment on their program code adequately due to the incurred extra efforts, lack of relevant knowledge, unawareness of the importance of code commenting or some other factors. As a result, code comments can be inadequate, absent or even mismatched with source code, which affects the understanding, reusing and the maintenance of software. To solve these problems of code comments, researchers have been concerned with generating code comments automatically. In this work, we aim at conducting a survey of automatic code commenting researches. First, we generally analyze the challenges and research framework of automatic generation of program comments. Second, we present the classification of representative algorithms, the design principles, strengths and weaknesses of each category of algorithms. Meanwhile, we also provide an overview of the quality assessment of the generated comments. Finally, we summarize some future directions for advancing the techniques of automatic generation of code comments and the quality assessment of comments. Copyright © 2019, The Authors. All rights reserved.

关键词： Surveys

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Re-evaluation of the comparative effectiveness of bootstrapbased optimism correction methods in the development of multivariable clinical prediction models

arXiv

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arXiv 2020年

作者： Iba, Katsuhiro Shinozaki, Tomohiro Maruo, Kazushi Noma, Hisashi Department of Statistical Science School of Multidisciplinary Sciences Graduate University for Advanced Studies Tokyo Japan Office of Biostatistics Department of Biometrics Headquarters of Clinical Development Otsuka Pharmaceutical Co. Ltd. Tokyo Japan Department of Information and Computer Technology Faculty of Engineering Tokyo Singapore University of Science Tokyo Japan Department of Biostatistics Faculty of Medicine University of Tsukuba Ibaraki Japan Department of Data Science Institute of Statistical Mathematics Tokyo Japan

Background: Multivariable prediction models are important statistical tools for providing synthetic diagnosis and prognostic algorithms based on patients' multiple characteristics. Their apparent measures for predictive accuracy usually have overestimation biases (known as 'optimism') relative to the actual performances for external populations. Existing statistical evidence and guidelines suggest that three bootstrap-based bias correction methods are preferable in practice, namely Harrell's bias correction and the .632 and .632+ estimators. Although Harrell's method has been widely adopted in clinical studies, simulation-based evidence indicates that the .632+ estimator may perform better than the other two methods. However, these methods' actual comparative effectiveness is still unclear due to limited numerical evidence. Methods: We conducted extensive simulation studies to compare the effectiveness of these three bootstrapping methods, particularly using various model building strategies: Conventional logistic regression, stepwise variable selections, Firth's penalized likelihood method, ridge, lasso, and elastic-net regression. We generated the simulation data based on the GUSTO-I trial dataset and considered how event per variable, event fraction, number of candidate predictors, and the regression coefficients of the predictors impacted the performances. The internal validity of C-statistics was evaluated. Results: Under relatively large sample settings (roughly, events per variable ≥ 10), the three bootstrap-based methods were comparable and performed well. However, all three methods had biases under small sample settings, and the directions and sizes of biases were inconsistent. In general, Harrell's and .632 methods had overestimation biases when event fraction become lager. Besides, .632+ method had a slight underestimation bias when event fraction was very small. Although the bias of the .632+ estimator was relatively small, its RMSE was comparable or som

关键词： Statistical mechanics

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On the Conformation of Dimeric Acceptors and Their Polymer Solar Cells with Efficiency over 18 %

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Angewandte Chemie 2023年第45期135卷

作者： Jingnan Wu Zhaoheng Ling Leandro R. Franco Sang Young Jeong Zewdneh Genene Josué Mena Si Chen Cailing Chen Prof. C. Moyses Araujo Prof. Cleber F. N. Marchiori Joost Kimpel Xiaoming Chang Furkan H. Isikgor Qiaonan Chen Hendrik Faber Prof. Yu Han Frédéric Laquai Prof. Maojie Zhang Prof. Han Young Woo Prof. Donghong Yu Prof. Thomas D. Anthopoulos Prof. Ergang Wang Department of Chemistry and Chemical Engineering Chalmers University of Technology 41296 Göteborg Sweden Department of Chemistry and Bioscience Aalborg University 9220 Aalborg Denmark Contribution: Conceptualization (lead) Data curation (lead) Formal analysis (lead) Writing - original draft (lead) Writing - review & editing (lead) King Abdullah University of Science and Technology (KAUST) KAUST Solar Center Thuwal 23955 Saudi Arabia Contribution: Data curation (lead) Formal analysis (lead) Writing - original draft (equal) Writing - review & editing (equal) Department of Engineering and Physics Karlstad University 65188 Karlstad Sweden Contribution: Data curation (equal) Writing - review & editing (equal) Department of Chemistry Korea University Seoul 02841 (Republic of Korea Contribution: Data curation (equal) Formal analysis (equal) Advanced Membranes and Porous Materials Center Physical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal 23955-6900 Saudi Arabia Contribution: Data curation (equal) Methodology (equal) Materials Theory Division Department of Physics and Astronomy Uppsala University 75120 Uppsala Sweden Contribution: Data curation (equal) Funding acquisition (equal) Software (equal) Writing - review & editing (equal) Contribution: Data curation (equal) Methodology (equal) Writing - review & editing (equal) Contribution: Data curation (equal) Writing - review & editing (supporting) Contribution: Data curation (equal) Methodology (supporting) Contribution: Data curation (equal) Contribution: Data curation (supporting) Funding acquisition (supporting) National Engineering Research Center for Colloidal Materials School of Chemistry & Chemical Engineering Shandong University Jinan Shandong 250100 China Contribution: Data curation (equal) Funding acquisition (supporting) Contribution: Data curation (equal) Funding acquisition (supporting) Writing - review & editing (supporting) Contribution: Data curation (equal) Fu

The determination of molecular conformations of oligomeric acceptors (OAs) and their impact on molecular packing are crucial for understanding the photovoltaic performance of their resulting polymer solar cells (PSCs) but have not been well studied yet. Herein, we synthesized two dimeric acceptor materials, DIBP3F-Se and DIBP3F-S, which bridged two segments of Y6-derivatives by selenophene and thiophene, respectively. Theoretical simulation and experimental 1D and 2D NMR spectroscopic studies prove that both dimers exhibit O -shaped conformations other than S - or U -shaped counter-ones. Notably, this O -shaped conformation is likely governed by a distinctive “conformational lock” mechanism, arising from the intensified intramolecular π–π interactions among their two terminal groups within the dimers. PSCs based on DIBP3F-Se deliver a maximum efficiency of 18.09 %, outperforming DIBP3F-S-based cells (16.11 %) and ranking among the highest efficiencies for OA-based PSCs. This work demonstrates a facile method to obtain OA conformations and highlights the potential of dimeric acceptors for high-performance PSCs.

关键词： Acceptor Molecular Conformation Oligomer Selenophene Solar Cells

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RL Perceptron: Generalization Dynamics of Policy Learning in High Dimensions

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Physical Review X 2025年第2期15卷 021051-021051页

作者： Nishil Patel Sebastian Lee Stefano Sarao Mannelli Sebastian Goldt Andrew Saxe Gatsby Computational Neuroscience Unit University College London Gower Street London WC1E 6BT United Kingdom Imperial College London Exhibition Road South Kensington London SW7 2AZ United Kingdom Data Science and AI Computer Science and Engineering Chalmers University of Technology and University of Gothenburg Gothenburg Sweden School of Computer Science and Applied Mathematics University of the Witwatersrand Johannesburg South Africa International School of Advanced Studies (SISSA) Via Bonomea 265 34136 Trieste Italy Sainsbury Wellcome Centre University College London 25 Howland Street London W1T 4JG United Kingdom

Reinforcement learning (RL) algorithms have transformed many domains of machine learning. To tackle real-world problems, RL often relies on neural networks to learn policies directly from pixels or other high-dimensional sensory input. By contrast, many theories of RL have focused on discrete state spaces or worst-case analysis, and fundamental questions remain about the dynamics of policy learning in high-dimensional settings. Here, we propose a solvable high-dimensional RL model that can capture a variety of learning protocols, and we derive its typical policy learning dynamics as a set of closed-form ordinary differential equations. We obtain optimal schedules for the learning rates and task difficulty—analogous to annealing schemes and curricula during training in RL—and show that the model exhibits rich behavior, including delayed learning under sparse rewards, a variety of learning regimes depending on reward baselines, and a speed-accuracy trade-off driven by reward stringency. Experiments on variants of the Procgen game “Bossfight” and Arcade Learning Environment game “Pong” also show such a speed-accuracy trade-off in practice. Together, these results take a step toward closing the gap between theory and practice in high-dimensional RL.

关键词： Differential equations

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Interpretable Prediction of Protein-Ligand Interaction by Convolutional Neural Network

Interpretable Prediction of Protein-Ligand Interaction by Co...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Fan Hu Jiaxin Jiang Peng Yin Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China School of Software Engineering University of Science and Technology of China Hefei China

Evaluation of protein-ligand interaction is a crucial step in the process of drug discovery. Recently, several methods based on deep learning have gained impressive binary classification performance on protein-ligand binding prediction. However, lack of three-dimensional complex data still limits the accuracy and robustness of evaluation of protein-ligand binding affinity, as well as the prediction of their binding sites. In this paper, we propose a novel convolutional neural network based method for estimating the binding affinity between protein and ligand using only 1D sequence data. Even with the same amount of sample size, this model outperforms other structure-dependent traditional and machine learning based methods in terms of both binary classification and regression task. Furthermore, we use this model to identify the key amino acid residues of protein that are vital for binding interaction, which provides biological interpretation.

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Efficient Block Pruning Based on Kernel and Feature Stablization

Efficient Block Pruning Based on Kernel and Feature Stabliza...

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Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA)

作者： Sheng Xu Hanlin Chen Kexin Liu Jinhu Lii Baochang Zhang School of Automatic Science and Electrical Engineering Beihang University China State Key Laboratory of Software Development Environment Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University China

With the development of computer vision research, the architecture of convolutional neural network becomes more and more complex to reach the state-of-the-art performance. Is the complexity of the model necessarily proportional to its accuracy? To answer this, the compression of the network has attracted much attention in the academy and industry. Existing network pruning methods mostly rely on the scoring mechanism of complexity or diversity of kernels to compress the network, and then build the network model after removing the kernels by tuning or training on the input data. These methods are cumbersome and depend on a well-trained pre-trained model. In this paper, we propose an end-to-end block pruning method based on kernel and feature stability by pruning blocks efficiently. To accomplish this, we firstly introduce a mask to scale the output of the blocks, and the L1 regularization term to monitor the mask update. Second, we introduce the Center Loss to guarantee that the feature does not deviate greatly during learning. To converge fast, we introduce fast iterative shrinkage-thresholding algorithm (FISTA) to optimize the mask, by which a more fast and reliable pruning process is achieved. We implement experiments on different datasets, including CIFAR-10 and ImageNet ILSVRC2012. All the experiments have achieved the state-of-the-art accuracy.

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Coherent H ∞ control for Markovian jump linear quantum systems

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IFAC-PapersOnLine 2020年第2期53卷 269-274页

作者： Yanan Liu Daoyi Dong Ian R. Petersen Qing Gao Steven X. Ding Hidehiro Yonezawa School of Engineering and Information Technology University of New South Wales Canberra ACT 2600 Australia Center for Quantum Computation and Communication Technology Australian Research Council Canberra ACT 2600 Australia Institute for Automatic Control and Complex Systems (AKS) University of Duisburg-Essen. 47057 Duisburg Germany Research School of Electrical Energy and Materials Engineering The Australian National University Canberra ACT 2601 Australia School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment Beijing and also with Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing 100191 China

The purpose of this paper is to design a coherent feedback controller for a Markovian jump linear quantum system suffering from a fault signal. The control objective is to bound the effect of the disturbance input on the output for the time-varying quantum system. We prove the relation between the H ∞ control problem, the dissipation properties, and the solutions of Riccati differential equations, by which the H ∞ controller of the Markovian jump linear quantum system is given by the solutions of Linear Matrix Inequalities (LMIs).

关键词： Coherent quantum feedback H∞ control fault-tolerant control Markovian jump linear quantum systems

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Correction: Artificial intelligence for surgical safety during laparoscopic gastrectomy for gastric cancer: Indication of anatomical landmarks related to postoperative pancreatic fistula using deep learning

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Surgical endoscopy 2024年第10期38卷 6203页

作者： Yoshimasa Aoyama Yusuke Matsunobu Tsuyoshi Etoh Kosuke Suzuki Shunsuke Fujita Takayuki Aiba Hajime Fujishima Shinichiro Empuku Yohei Kono Yuichi Endo Yoshitake Ueda Hidefumi Shiroshita Toshiya Kamiyama Takemasa Sugita Kenichi Morishima Kohei Ebe Tatsushi Tokuyasu Masafumi Inomata Department of Gastroenterological and Pediatric Surgery Faculty of Medicine Oita University Oita Japan. Department of Information Systems and Engineering Faculty of Information Engineering Fukuoka Institute of Technology Fukuoka Japan. Department of Healthcare AI Data Science Faculty of Medicine Oita University Oita Japan. Department of Gastroenterological and Pediatric Surgery Faculty of Medicine Oita University Oita Japan. teto@oita-u.ac.jp. Research Center for GLOBAL and LOCAL Infectious Diseases Oita University 1-1 Idaigaoka Hasama-Machi Oita Oita 879-5593 Japan. teto@oita-u.ac.jp. Faculty of Medicine Oita University Oita Japan. Department of Comprehensive Surgery for Community Medicine Faculty of Medicine Oita University Oita Japan. Advanced AI Technology Research Advanced Software Technology Research Olympus Corporation Tokyo Japan. Information Aided Medical Solutions Development Application Software Engineering Olympus Medical Systems Corporation Tokyo Japan. Clinical Engineering Research Center Faculty of Medicine Oita University Oita Japan.

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Adaptive NMS: Refining pedestrian detection in a crowd

arXiv

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arXiv 2019年

作者： Liu, Songtao Huang, Di Wang, Yunhong Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University State Key Laboratory of Software Development Environment Beihang University School of Computer Science and Engineering Beihang University Beijing100191

Pedestrian detection in a crowd is a very challenging issue. This paper addresses this problem by a novel NonMaximum Suppression (NMS) algorithm to better refine the bounding boxes given by detectors. The contributions are threefold: (1) we propose adaptive-NMS, which applies a dynamic suppression threshold to an instance, according to the target density;(2) we design an efficient subnetwork to learn density scores, which can be conveniently embedded into both the single-stage and two-stage detectors;and (3) we achieve state of the art results on the CityPersons and CrowdHuman benchmarks. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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Organic Zinc-Ion Battery: Planar, π-Conjugated Quinone-Based Polymer Endows Ultrafast Ion Diffusion Kinetics

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Angewandte Chemie 2022年第51期134卷

作者： Dr. Fei Ye Qiang Liu Dr. Hongliang Dong Kailin Guan Zhaoyang Chen Na Ju Prof. Linfeng Hu School of Materials Science and Engineering Southeast University Nanjing 211189 China Contribution: Conceptualization (supporting) Data curation (lead) Investigation (lead) Writing - original draft (lead) Contribution: Data curation (equal) Formal analysis (equal) Software (lead) Center for High Pressure Science and Technology Advanced Research Shanghai 201203 P. R. China Contribution: Investigation (equal) Methodology (equal) Resources (equal) Validation (lead) Contribution: Formal analysis (equal) Investigation (lead) Methodology (equal) Contribution: Investigation (equal) Software (equal) Validation (lead) Visualization (lead) Contribution: Data curation (equal) Formal analysis (equal) Writing - original draft (supporting) Contribution: Conceptualization (lead) Data curation (lead) Formal analysis (lead) Funding acquisition (lead) Project administration (lead) Supervision (lead) Writing - original draft (lead) Writing - review & editing (lead)

A novel poly(phenazine-alt-pyromellitic anhydride) (PPPA) has been successfully designed and synthesized via a condensation polymerization strategy as promising cathode material in organic zinc-ion batteries. Electrochemical quartz crystal microbalance (EQCM), FTIR and XPS characterizations verify a reversible Zn 2+ -coordination mechanism in our PPPA cathode. Intriguingly, an ultrahigh Zn 2+ diffusion coefficient of 1.2×10 −7 cm 2 s −1 was found in this large π-conjugated system, which is the highest one among all organic cathode materials for zinc-ion batteries. Theoretical calculations reveal the extended π-conjugated plane in our PPPA sample results in a significant reduction on energy gap, effectively accelerating intramolecular electron transfer during charge/discharge process. Our finding provides insights to achieve high zinc-ion transport kinetics by a design strategy on planar polymer system.

关键词： Energy Gap Linear Polymer Organic Zinc-Ion Batteries Quinone-Based Polymer Zn2+ Diffusion Coefficient

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