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arXiv 2025年

作者： Sauro, Herbert M. Agmon, Eran Blinov, Michael L. Gennari, John H. Hellerstein, Joe Heydarabadipour, Adel Hunter, Peter Jardine, Bartholomew E. May, Elebeoba Nickerson, David P. Smith, Lucian P. Bader, Gary D. Bergmann, Frank Boyle, Patrick M. Dräger, Andreas Faeder, James R. Feng, Song Freire, Juliana Fröhlich, Fabian Glazier, James A. Gorochowski, Thomas E. Helikar, Tomas Hoops, Stefan Imoukhuede, Princess Keating, Sarah M. Konig, Matthias Laubenbacher, Reinhard Loew, Leslie M. Lopez, Carlos F. Lytton, William W. McCulloch, Andrew Mendes, Pedro Myers, Chris J. Myers, Jerry G. Mulugeta, Lealem Niarakis, Anna van Niekerk, David D. Olivier, Brett G. Patrie, Alexander A. Quardokus, Ellen M. Radde, Nicole Rohwer, Johann M. Sahle, Sven Schaff, James C. Sego, T.J. Shin, Janis Snoep, Jacky L. Vadigepalli, Rajanikanth Wiley, H. Steve Waltemath, Dagmar Moraru, Ion Department of Bioengineering University of Washington SeattleWA98195-5061 United States Intelligent Systems Engineering and Biocomplexity Institute Indiana University Street BloomingtonIN47408 United States Center for Cardiovascular Biology University of Washington SeattleWA98195-5061 United States eScience Institute University of Washington SeattleWA98195-5061 United States Institute for Stem Cell and Regenerative Medicine University of Washington SeattleWA98195-5061 United States School of Biological Sciences University of Bristol 24 Tyndall Avenue BristolBS8 1TQ United Kingdom Departments of Physiology & Pharmacology Neurology Downstate Health Science University BrooklynNY11203 United States Department of Neurology Kings County Hospital BrooklynNY11203 United States NASA-John H. Glenn Research Center MS 110-3 21000 Brookpark Road ClevelandOH44135 United States Center for Cell Analysis and Modeling UConn Health 263 Farmington Avenue FarmingtonCT06030-6406 United States Amsterdam Institute for Life and Environment Vrije Universiteit Amsterdam De Boelelaan 1108 Amsterdam1081 HZ Netherlands Department of Pathology and Genomic Medicine Thomas Jefferson University 1020 Locust St PhiladelphiaPA19107 United States Biological Sciences Division Pacific Northwest National Laboratory 902 Battelle Blvd RichlandWA99354 United States Auckland Bioengineering Institute University of Auckland Auckland1010 New Zealand Center of Integrative Biology University of Toulouse III-Paul Sabatier 165 Rue Marianne Grunberg-Manago Toulouse31400 France Lifeware Group Inria Building Alan Turing 1 Rue Honoré d’Estienne d’Orves Palaiseau91120 France Multiscale Modeling Group Altos Labs Redwood CityCA94065 United States partner site Tübingen Tübingen Germany Ottfried-Müller-Str. 37 Tübingen72076 Germany 06120 Germany Department of Computer Science Center for Data Science New York University New YorkNY11201 United States Department of Bi

Guidelines for managing scientific data have been established under the FAIR principles requiring that data be Findable, Accessible, Interoperable, and Reusable. In many scientific disciplines, especially computational biology, both data and models are key to progress. For this reason, and recognizing that such models are a very special type of "data", we argue that computational models, especially mechanistic models prevalent in medicine, physiology and systems biology, deserve a complementary set of guidelines. We propose the CURE principles, emphasizing that models should be Credible, Understandable, Reproducible, and Extensible. We delve into each principle, discussing verification, validation, and uncertainty quantification for model credibility;the clarity of model descriptions and annotations for understandability;adherence to standards and open science practices for reproducibility;and the use of open standards and modular code for extensibility and reuse. We outline recommended and baseline requirements for each aspect of CURE, aiming to enhance the impact and trustworthiness of computational models, particularly in biomedical applications where credibility is paramount. Our perspective underscores the need for a more disciplined approach to modeling, aligning with emerging trends such as Digital Twins and emphasizing the importance of data and modeling standards for interoperability and reuse. Finally, we emphasize that given the non-trivial effort required to implement the guidelines, the community moves to automate as many of the guidelines as possible. © 2025, CC BY.

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Progressive Refinement Bilateral Filter

Progressive Refinement Bilateral Filter

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IEEE International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)

作者： Chanyi Lu Yong Zhao Lin Wang Guiying Zhang Fujian Feng Li Zhang College of Data Science and Information Engineering Guizhou Minzu University Guiyang China School of Electronic and Computer Engineering Shenzhen Graduate School of Peking University Shenzhen China Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University Guiyang China Department of Medical Information Engineering Zunyi Medical University Zunyi China

ISBN: (纸本)9781538678626;9781538678619

In this paper, a coarse-to-fine framework for image noise removal is proposed. The bilateral filter is redefined by the manner of progressive refining to effectively eliminate noise, thus forming progressive refinement bilateral filter (PRBF), which estimates the pixel values of a noisy image through the method of progressive refinement with gradual recursion and its result retains more details, infinite close to the original image. PRBF is further integrated into the denoising model for the details processing again after denoising of the major part of the image. Our algorithm is evaluated and compared on two large standardized datasets and a number of selected images. The experimental results show that our algorithm consistently outperforms other approaches for image denoising and demonstrates the effectiveness of our framework for stereo matching.

关键词： Noise reduction Image denoising Mathematical model Filtering Filtering algorithms Kernel Biomedical imaging

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A Multilayer Framework for Online Metric Learning

arXiv

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arXiv 2018年

作者： Li, Wenbin Liu, Yanfang Huo, Jing Shi, Yinghuan Gao, Yang Wang, Lei Luo, Jiebo The State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China The College of Mathematics and Information Engineering Longyan University 364012 China The State Key Laboratory for Novel Software Technology National Institute of Healthcare Data Science Nanjing University Nanjing210023 China The School of Computing and Information Technology University of Wollongong Australia The Department of Computer Science University of Rochester RochesterNY14611 United States

Online metric learning has been widely applied in classification and retrieval. It can automatically learn a suitable metric from data by restricting similar instances to be separated from dissimilar instances with a given margin. However, the existing online metric learning algorithms have limited performance in real-world classifications, especially when data distributions are complex. To this end, this paper proposes a multilayer framework for online metric learning to capture the nonlinear similarities among instances. Different from the traditional online metric learning, which can only learn one metric space, the proposed Multi-Layer Online Metric Learning (MLOML) takes an online metric learning algorithm as a metric layer and learns multiple hierarchical metric spaces, where each metric layer follows a nonlinear layers for the complicated data distribution. Moreover, the forward propagation (FP) strategy and backward propagation (BP) strategy are employed to train the hierarchical metric layers. To build a metric layer of the proposed MLOML, a new Mahalanobis-based Online Metric Learning (MOML) algorithm is presented based on the passive-aggressive strategy and one-pass triplet construction strategy. Furthermore, in a progressively and nonlinearly learning way, MLOML has a stronger learning ability than traditional online metric learning in the case of limited available training data. To make the learning process more explainable and theoretically guaranteed, theoretical analysis is provided. The proposed MLOML enjoys several nice properties, indeed learns a metric progressively, and performs better on the benchmark datasets. Extensive experiments with different settings have been conducted to verify these properties of the proposed MLOML. Copyright © 2018, The Authors. All rights reserved.

关键词： Multilayers

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CuSAO:Parallel Optimization of Sample Adaptive Offset(SAO) for Video Encoder

CuSAO:Parallel Optimization of Sample Adaptive Offset(SAO) f...

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2017 2nd International Conference on Software, Multimedia and Communication Engineering（SMCE 2017)

作者： Qing-ge JI Ting ZHU Run-yang LIN Fan LIANG School of Data and Computer Science Sun Yat-sen University Guangdong Key Laboratory of Big Data Analysis and Processing School of Electronics and Information Technology Sun Yat-sen University

Conventional SAO module in encoder decides classification mode and offsets based on the statistical data of pixels for *** needs to traverse all pixels several times,which is extremely *** improving works for efficiency either reduce statistical data or concentrate only on SAO *** tackle this,a fast parallel algorithm for standard SAO in encoder named Cu SAO based on Graphics Processing Unit(GPU) is *** proposed a pixel grid – thread mapping method and adopted parallel computing of multi-granularity in different stages of SAO *** results demonstrate our method outperforms state-of-the-arts modules and shows impressive results in terms of efficiency,reaching a significant time saving by average 75% and up to more than 85%.

关键词： Sample adaptive offset Encoder parallelization HEVC CUDA

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GARCH-LSSVM Coupled Predication Model and Its Application on Stock Index Forecasting

GARCH-LSSVM Coupled Predication Model and Its Application on...

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2017 International Conference on Mathematics, Modelling and Simulation Technologies and Applications（MMSTA 2017)

作者： Xiao-xu HU Meng-qi ZHANG Xin JIANG Key Laboratory of Mathematics Informatics and Behavioral ScienceMinistry of EducationBeihang University School of Mathematics and Systems Science Beihang University Beijing Advanced Innovation Center for Big Data and Brain Computing

It is usually a challenge for traditional time series prediction models to combine the linear and non-linear factors effectively,which might cause a problem that the trend forecast is not *** this paper we propose a new prediction model based on GARCH and *** GARCH model is used to deal with the heteroscedasticity of the residual series of the closing price of the stock index *** the same time,a number of technical indicators are constructed to train the LSSVM model and the corresponding predicted values and residuals are ***,the predicted value and the residual obtained respectively from GARCH and LSSVM are used as the training set to modify the LSSVM model,with the predicted value of the logarithmic closing price of the stock index *** coupled model not only contains the linear trend of historical information,but also integrates the non-linear features such as market volatility information which is closely related to the target *** experimental results show that the prediction accuracy of the model is 98.22% on the testing data *** model performs also better than the GARCH 97.84% and the LSSVM 98.04% respectively in accuracy deviation.

关键词： Generalized auto-regressive conditional heteroscedastic(GARCH) Least square support vector machine(LSSVM) Coupled prediction algorithm Stock index forecasting Technical indicators

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Learning view-specific deep networks for person re-identification

arXiv

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arXiv 2018年

作者： Feng, Zhanxiang Lai, Jianhuang Xie, Xiaohua School of Electronics and Information Technology Sun Yat-Sen University Guangzhou510006 China Xin Hua College of Sun Yat-Sen University Guangzhou510006 China School of Data and Computer Science Sun Yat-Sen University Guangzhou510006 China Guangdong Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou510006 China

—In recent years, a growing body of research has focused on the problem of person re-identification (re-id). The re-id techniques attempt to match the images of pedestrians from disjoint non-overlapping camera views. A major challenge of re-id is the serious intra-class variations caused by changing viewpoints. To overcome this challenge, we propose a deep neural network-based framework which utilizes the view information in the feature extraction stage. The proposed framework learns a view-specific network for each camera view with a cross-view Euclidean constraint (CV-EC) and a cross-view center loss (CV-CL). We utilize CV-EC to decrease the margin of the features between diverse views and extend the center loss metric to a view-specific version to better adapt the re-id problem. Moreover, we propose an iterative algorithm to optimize the parameters of the view-specific networks from coarse to fine. The experiments demonstrate that our approach significantly improves the performance of the existing deep networks and outperforms the state-of-the-art methods on the VIPeR, CUHK01, CUHK03, SYSUmReId, and Market-1501 benchmarks. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep neural networks

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A novel fault diagnostic technique for photovoltaic systems based on cascaded forest 1

A novel fault diagnostic technique for photovoltaic systems ...

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1st Workshop on Smart Internet of Things, SmartIoT 2017

作者： Hu, Liangqing Ye, Jin Chang, Shengqiang Li, Hongtao Chen, Hongyu School of Computer Electronicsand Information Guangxi University Nanning China Innovation Center of Cloud Computing and Big Data Guilin University of Electronic Technology Guangxi Colleges and Universities Guilin China Key Laboratory of Cloud Computing and Complex Systems Guilin University of Electronic Technology Guilin China Shijiazhuang Kelin Electric Co. Ltd. Shijiazhuang China

ISBN: (纸本)9781450355285

A variety of faults often occurduring the operation of PV arrays, which may seriously affect the normal operation of the system, the machine diagnosis of the types of fault has become a hotspot in the field of photovoltaic power generation. This paper proposes a novel fault diagnostic technique for photovoltaic systems based on Cascaded Forest. Through the in-depth analysis of the output of PV arrays froma data platform of ShijiazhuangKelin Electric Co, the input variables of the diagnosis model are obtained. Compared with other fault diagnosis methods for the PV array, the proposed method can work under a small number of tagged data and the system can be run online and real-time. Finally, the experimental results show that the fault diagnosis method for the PV array based on the cascading forests can effectively detect four types of fault for PV array such as short-circuit, open-circuit, abnormal degradation and partial shading. This method has a good value for the intelligent fault diagnosis of PV. © 2017 Association for computing Machinery.

关键词： Information management

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Cascade control for supply air temperature in a variable air volume system

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IOP Conference Series: Earth and Environmental science 2019年第1期238卷

作者： Junhua Zhuang Yimin Chen Jie Wu School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing China Department of Civil Architectural and Environmental Engineering Drexel University Philadelphia PA 19104 USA Beijing ZXT M&E Engineering Consultant Co. Ltd

Most supply air temperature in variable air volume systems is typically set at 55F(13 °C) that is design value in these systems. However, sometimes the load of cooling varies greatly in different zones, and it means the indoor temperature of low-load zones is lower than comfortable value, even if the supply air damper is at the minimum value that is not closed for minimum outside air requirement. Meanwhile, in transition season, the indoor temperature is also lower due to low outdoor temperature. These factors provided an opportunity to apply higher supply air temperature to reduce air handling unit (AHU) cooling load and box reheating. In this paper, a cascade control method is used to raise the supply air temperature. The external control loop controls the setting value of supply air temperature with the minimum supply air volume as the control target. And the internal control loop controls the supply air temperature by regulating valve. The cascade structure can solve the problem that the time constant of the system is too large. The simulation results show that the quality of control is good and the reheating can be cancelled.

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On link formation in heterogeneous information networks: A view based on multi-Label learning 9

On link formation in heterogeneous information networks: A v...

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9th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017

作者： Chen, Ke-Jia Xue, Shijun Li, Yun Liu, Bin Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing Jiangsu210023 China College of Computer Science Nanjing University of Posts and Telecommunications Nanjing Jiangsu210023 China Center for the Neural Basis of Cognition Carnegie Mellon University PittsburghPA15213 United States

ISBN: (纸本)9781450349932

This paper studies the problem of relationship prediction in heterogeneous information networks. Our goal is not only to predict links/relationships more accurately but also to provide more viable paths to facilitate the formation of new links/relationships. A relationship prediction method based on multi-label learning named ML3P is proposed. In ML3P, each meta-path between nodes is regarded as a type of relationship and is given a label. Under the framework of multi-label learning, any potential relationship including the target relationship can be predicted. The results of comparative experiments in DBLP and Twitter datasets show that ML3P better uses heterogeneous information in supervised learning process and thus achieves better performance. Moreover, our method can output the correlation between relationships. © 2017 Association for computing Machinery.

关键词： Forecasting

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Using Cost-Sensitive Ranking Loss to Improve Distant Supervised Relation Extraction

Using Cost-Sensitive Ranking Loss to Improve Distant Supervi...

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第十六届全国计算语言学学术会议暨第五届基于自然标注大数据的自然语言处理国际学术研讨会

作者： Daojian Zeng Junxin Zeng Yuan Dai Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation School of Computer and Communication EngineeringChangsha University of Science and TechnologyChangsha 410004P.R.China

ISBN: (纸本)9783319690049

Recently,many researchers have concentrated on using neu-ral networks to learn features for Distant Supervised Relation Extraction(DSRE).However,these approaches generally employ a softmax classi-fier with cross-entropy loss,and bring the noise of artificial class NA into classification ***,the class imbalance problem is serious in the automatically labeled data,and results in poor classification rates on minor classes in traditional *** this work,we exploit cost-sensitive ranking loss to improve *** first uses a Piecewise Convolutional Neural Network(PCNN)to embed the semantics of *** the features are fed into a classifier which takes into account both the ranking loss and ***-periments show that our method is effective and performs better than state-of-the-art methods.

关键词： Classifiers Neural network Semantics Noise Balance impairment Taxonomy embedding

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