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SSRN 2024年

作者： Aksoy, Meltem Yanik, Seda Amasyali, Mehmet Fatih Department of Industrial Engineering Istanbul Technical University Sarıyer Istanbul Maslak34485 Turkey Department of Computer Science Research Center Trustworthy Data Science and Security Technical University Dortmund Joseph-vonFraunhofer-Straße 25 Dortmund44227 Germany Department of Computer Engineering Yildiz Technical University Davutpaşa Istanbul Esenler34420 Turkey

Reviewer assignment is a crucial task that involves selecting and assigning qualified reviewers to evaluate projects or paper proposals. The assignment should be based on the expertise of reviewers and the content of proposals to ensure accuracy and fairness of evaluation. This task is typically performed manually, which leads to subjective judgments and requires a significant amount of time and effort. Addressing the inefficiency of traditional manual reviewer assignments, this study introduces a decision support system that automates the assignment task. Utilizing information retrieval, natural language processing, and optimization techniques, it tackles the reviewer assignment problem through a structured, three-stage approach. Initially, it gathers diverse information from various sources to build a comprehensive database of proposals and reviewers. Then, it applies word embedding methods to convert multilingual proposal and reviewer texts into vector representations, and it uses the cosine similarity metric to determine content similarity between each proposal-reviewer pair. Concurrently, it assesses reviewer competency by analyzing their past evaluation performance and areas of expertise through predefined knowledge rules. In the final stage, a multi-objective integer linear programming model assigns reviewers to proposals, optimizing proposal-reviewer similarity and reviewer competency while avoiding conflicts of interest. Furthermore, it explores a max-min approach to improve outcomes for the least-advantaged proposals. This model is enhanced by two additional models to ensure balanced reviewer workloads. The performance of the system is tested with a real-world dataset from the project proposal evaluation process of a regional development agency. The results show that the proposed system significantly outperforms traditional methods. © 2024, The Authors. All rights reserved.

关键词： Integer programming

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LDConv: Linear deformable convolution for improving convolutional neural networks

arXiv

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arXiv 2023年

作者： Zhang, Xin Song, Yingze Song, Tingting Yang, Degang Ye, Yichen Zhou, Jie Zhang, Liming College of Computer and Information Science Chongqing Normal University Chongqing401331 China Chongqing Engineering Research Center of Educational Big Data Intelligent Perception and Application Chongqing401331 China College of Electronic and Information Engineering Southwest University Chongqing400715 China

Neural networks based on convolutional operations have achieved remarkable results in the field of deep learning, but there are two inherent flaws in standard convolutional operations. On the one hand, the convolution operation is confined to a local window, so it cannot capture information from other locations, and its sampled shapes is fixed. On the other hand, the size of the convolutional kernel are fixed to k × k, which is a fixed square shape, and the number of parameters tends to grow squarely with size. Although Deformable Convolution (Deformable Conv) address the problem of fixed sampling of standard convolutions, the number of parameters also tends to grow in a squared manner, and Deformable Conv do not explore the effect of different initial sample shapes on network performance. In response to the above questions, the Linear Deformable Convolution (LDConv) is explored in this work, which gives the convolution kernel an arbitrary number of parameters and arbitrary sampled shapes to provide richer options for the trade-off between network overhead and performance. In LDConv, a novel coordinate generation algorithm is defined to generate different initial sampled positions for convolutional kernels of arbitrary size. To adapt to changing targets, offsets are introduced to adjust the shape of the samples at each position. LDConv corrects the growth trend of the number of parameters for standard convolution and Deformable Conv to a linear growth. Compared to Deformable Conv, LDConv provides richer choices and can be equivalent to deformable convolution when the number of parameters of LDConv is set to the square of K. Differently, this paper also explores the effect of neural networks by using LDConv with the same size and different initial sampling shapes. LDConv completes the process of efficient feature extraction by irregular convolutional operations and brings more exploration options for convolutional sampled shapes. Object detection experiments on repre

关键词： Convolutional neural networks

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Experimenting on Markov Decision Processes with Local Treatments

arXiv

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arXiv 2024年

作者： Chen, Shuze Simchi-Levi, David Wang, Chonghuan Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200030 China Institute for Data Systems and Society Department of Civil and Environmental Engineering Operations Research Center Massachusetts Institute of Technology CambridgeMA02139 United States Center for Computational Science and Engineering Massachusetts Institute of Technology CambridgeMA02139 United States

Utilizing randomized experiments to evaluate the effect of short-term treatments on the short-term outcomes has been well understood and become the golden standard in industrial practice. However, as service systems become increasingly dynamical and personalized, much focus is shifting toward maximizing long-term cumulative outcomes, such as customer lifetime value, through lifetime exposure to interventions. To bridge this gap, we investigate the randomized experiments within dynamical systems modeled as Markov Decision Processes (MDPs). Our goal is to assess the impact of treatment and control policies on long-term cumulative rewards from relatively short-term observations. We first develop optimal inference techniques for assessing the effects of general treatment patterns. Furthermore, recognizing that many real-world treatments tend to be fine-grained and localized for practical efficiency and operational convenience, we then propose methods to harness this localized structure by sharing information on the non-targeted states. Our new estimator effectively overcomes the variance lower bound for general treatments while matching the more stringent lower bound incorporating the local treatment structure. Furthermore, our estimator can optimally achieve a linear reduction with the number of test arms for a major part of the variance. Finally, we explore scenarios with perfect knowledge of the control arm and design estimators that further improve inference efficiency. Copyright © 2024, The Authors. All rights reserved.

关键词： Efficiency

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Score-based generative modeling through stochastic evolution equations in hilbert spaces 23

Score-based generative modeling through stochastic evolution...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Sungbin Lim Eunbi Yoon Taehyun Byun Taewon Kang Seungwoo Kim Kyungjae Lee Sungjoon Choi Department of Statistics Korea University and LG AI Research and SNU-LG AI Research Center Department of Statistics Korea University Department of Artificial Intelligence Korea University Department of Computer Science and Engineering Korea University Artificial Intelligence Graduate School UNIST Department of Artificial Intelligence Chung-Ang University

Continuous-time score-based generative models consist of a pair of stochastic differential equations (SDEs)—a forward SDE that smoothly transitions data into a noise space and a reverse SDE that incrementally eliminates noise from a Gaussian prior distribution to generate data distribution samples—are intrinsically connected by the time-reversal theory on diffusion processes. In this paper, we investigate the use of stochastic evolution equations in Hilbert spaces, which expand the applicability of SDEs in two aspects: sample space and evolution operator, so they enable encompassing recent variations of diffusion models, such as generating functional data or replacing drift coefficients with image transformation. To this end, we derive a generalized time-reversal formula to build a bridge between probabilistic diffusion models and stochastic evolution equations and propose a score-based generative model called Hilbert Diffusion Model (HDM). Combining with Fourier neural operator, we verify the superiority of HDM for sampling functions from functional datasets with a power of kernel two-sample test of 4.2 on Quadratic, 0.2 on Melbourne, and 3.6 on Gridwatch, which outperforms existing diffusion models formulated in function spaces. Furthermore, the proposed method shows its strength in motion synthesis tasks by utilizing the Wiener process with values in Hilbert space. Finally, our empirical results on image datasets also validate a connection between HDM and diffusion models using heat dissipation, revealing the potential for exploring evolution operators and sample spaces.

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Predicting the physiological effects of multiple drugs using electronic health record

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computers in Biology and Medicine 2025年 184卷 109485-109485页

作者： Jeon, Junhyeok Hong, Eujin Kim, Jong-Yeup Lee, Suehyun Kim, Hyun Uk Daejeon34141 Korea Republic of Department of Biomedical Informatics College of Medicine Konyang University Daejeon35365 Korea Republic of Healthcare Data Science Center Konyang University Hospital Daejeon35365 Korea Republic of Department of Computer Engineering Gachon University Seongnam13120 Korea Republic of Graduate School of Engineering Biology KAIST Daejeon34141 Korea Republic of BioProcess Engineering Research Center and BioInformatics Research Center KAIST Daejeon34141 Korea Republic of

Various computational models have been developed to understand the physiological effects of drug-drug interactions, which can contribute to more effective drug treatments. However, they mostly focus on interactions of only two drugs, and do not consider the patient information. To address this challenge, we use publicly available electronic health record (EHR), MIMIC-IV, to develop machine learning models that predict the physiological effects of two or more drugs. This study involves extensive preprocessing of laboratory measurement data, prescription data and patient data. The resulting machine learning models predict potential abnormalities across 20 selected measurement items (e.g., concentrations of metabolites and blood cells) in the form of a sentence. Analysis of the model predictions showed that age, specific active pharmaceutical ingredients, and male/female appeared to be the most influential features. The model development process showcased in this study can be extended to other measurement items for a target EHR. © 2024 Elsevier Ltd

关键词： Electronic health record

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Development of Business Intelligence Framework for Open Government data Portal Usage Analysis: A Case Study of Thailand 13

Development of Business Intelligence Framework for Open Gove...

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13th International Conference Knowledge and Systems engineering, KSE 2021

作者： Sripramong, Sahaporn Anutariya, Chutiporn Buranarach, Marut Tumsangthong, Patipat Wutthitasarn, Theerawat School of Engineering and Technology Asian Institute of Technology Ict Department Thailand National Electronics and Computer Technology Center Data Science and Analytics Research Group Thailand

ISBN: (纸本)9781665499750

The usage of Open Government data (OGD) can enable the government transparency for better governance, increase citizen collaboration, and promote innovation through public service improvement. OGD initiatives involve different stakeholders and complex publishing guidelines. In general, governments initiate OGD in their countries and provide the government-run portals to support the provision and consumption process of OGD. Insufficient usage of OGD and limited provision by the government is a great challenge to OGD development. Monitoring the operation and constantly analyzing the usage can be beneficial for OGD policymakers to gain insight into usage, to plan, and to enact the law to support the OGD initiative. This study proposes a Business Intelligence (BI) framework to analyze OGD portal datasets and its usage. The data from Thailand's OGD (ThOGD) portal was derived to validate our framework. The work focuses on designing and implementing the BI framework. We demonstrated the features of the prototyped BI system in supporting the analysis through various data visualization. © 2021 IEEE.

关键词： Open data

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Novel deep recurrent neural structure with Bayesian distributed backpropagation for biomaterial model involving ferro–copper/blood nanofluids

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European Physical Journal Plus 2024年第1期139卷 42-42页

作者： Khan, Maryam Pervaiz Mukhtar, Roshana Chang, Chuan-Yu Chaudhary, Naveed Ishtiaq Shoaib, Muhammad Raja, Muhamad Asif Zahoor Department of Computer Science and Information Engineering Graduate School of Engineering Science and Technology National Yunlin University of Science and Technology Yunlin 64002 Taiwan Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin 64002 Taiwan Future Technology Research Center National Yunlin University of Science and Technology Yunlin 64002 Taiwan AI center Yuan Ze University Taoyuan 320 Taiwan

The aim of the presented study is to portray the numerical treatment for biomaterial fluid flow models (BFFMs) involving magnetized ferro and copper nanoparticles dispersed in blood between two rotating disks by exploiting the knacks of layered recurrent neural networks with Bayesian distributed backpropagation (LRNN-BDB). Mathematical formulation of BFFMs is governed with PDEs that are renovated into nonlinear ordinary differential system by operating suitable transformations. The synthetic data are acquired numerically by simulated the implicit backward differentiation formulas to measure the impact of porosity permeability, Darcy–Forchheimer, and Joule heating for BFFMs involving both ferro and copper nanomaterial mixed with blood as a base fluid. The supervised computing paradigm of LRNN-BDB is executed on acquired datasets, and outcomes of LRNN-BDB are regularly overlapping with numerical observations for each variant of BFFMs with negligible error. The stupendous knacks of LRNN-BDB procedures are efficaciously depicted through mean squared error-based iterative convergence plots, adaptive controlling metrics of learning, error distribution statistics on histogram illustrations, and regression measures for exhaustive experimentation on BFFMs. © 2024, The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature.

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Testing the Inference Accuracy of Accelerator-Friendly Approximate Phylogeny Tracking

Testing the Inference Accuracy of Accelerator-Friendly Appro...

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2025 IEEE Symposium on Computational Intelligence in Artificial Life and Cooperative Intelligent Systems, ALIFE-CIS 2025

作者： Moreno, Matthew Andres Ranjan, Anika Dolson, Emily Zaman, Luis University of Michigan Department of Ecology and Evolutionary Biology Ann Arbor United States Center for the Study of Complex Systems University of Michigan Ann Arbor United States Michigan Institute for Data and Ai in Society University of Michigan Ann Arbor United States Undergraduate Research Opportunities Program University of Michigan Ann Arbor United States Michigan State University Department of Computer Science and Engineering East Lansing United States Program in Ecology Evolution and Behavior Michigan State University East Lansing United States

ISBN: (纸本)9798331508371

computer simulations are an important tool for studying the mechanics of biological evolution. In particular, agent-based approaches provide an opportunity to collect high-quality records of ancestry relationships. Such phylogenies can provide insight into evolutionary dynamics within these simulations. Previous work generally tracks lineages directly, yielding an exact phylogenetic record of evolutionary history. However, challenges exist in scaling direct ancestrytracking approaches to highly-distributed, many-processor evolution in silico. An alternative approach is to estimate phylogenetic history via noncoding annotations on digital genomes, akin to how bioinformaticians build phylogenies by assessing genetic similarities between organisms. Recent work has extended this 'hereditary stratigraphy' approach to support powerful hardware accelerator platforms, such as the Cerebras Wafer-Scale Engine. Although these second-generation 'surface'-based hereditary stratigraphy algorithms have demonstrated order-ofmagnitude speedups over first-generation 'column'-based algorithms, it remains unknown how they impact the accuracy of reconstructed phylogenies. To address this question, we assessed reconstruction accuracy under alternative configurations across a matrix of evolutionary conditions varying in selection pressure, spatial structure, and ecological dynamics. Encouragingly, we find that the second-generation approaches provide higher reconstruction quality across most surveyed conditions. © 2025 IEEE.

关键词： Biostratigraphy

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KADEL: Knowledge-Aware Denoising Learning for Commit Message Generation

arXiv

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arXiv 2024年

作者： Tao, Wei Zhou, Yucheng Wang, Yanlin Zhang, Hongyu Wang, Haofen Zhang, Wenqiang Shanghai Engineering Research Center of AI and Robotics Academy for Engineering and Technology Fudan University Shanghai China State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau China School of Software Engineering Sun Yat-sen University Guangdong Zhuhai519082 China School of Big Data and Software Engineering Chongqing University Chongqing China College of Design and Innovation Tongji University Shanghai China Engineering Research Center of AI and Robotics Ministry of Education Academy for Engineering and Technology Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University 220 Handan Road Shanghai200433 China

Commit messages are natural language descriptions of code changes, which are important for software evolution such as code understanding and maintenance. However, previous methods are trained on the entire dataset without considering the fact that a portion of commit messages adhere to good practice (i.e., good-practice commits), while the rest do not. On the basis of our empirical study, we discover that training on good-practice commits significantly contributes to the commit message generation. Motivated by this finding, we propose a novel knowledge-aware denoising learning method called KADEL. Considering that good-practice commits constitute only a small proportion of the dataset, we align the remaining training samples with these good-practice commits. To achieve this, we propose a model that learns the commit knowledge by training on good-practice commits. This knowledge model enables supplementing more information for training samples that do not conform to good practice. However, since the supplementary information may contain noise or prediction errors, we propose a dynamic denoising training method. This method composes a distribution-aware confidence function and a dynamic distribution list, which enhances the effectiveness of the training process. Experimental results on the whole MCMD dataset demonstrate that our method overall achieves state-of-the-art performance compared with previous methods. © 2024, CC BY.

关键词： Sampling

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Technology Roadmap for Flexible Sensors

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ACS NANO 2023年第6期17卷 5211-5295页

作者： Luo, Yifei Abidian, Mohammad Reza Ahn, Jong-Hyun Akinwande, Deji Andrews, Anne M. Antonietti, Markus Bao, Zhenan Berggren, Magnus Berkey, Christopher A. Bettinger, Christopher John Chen, Jun Chen, Peng Cheng, Wenlong Cheng, Xu Choi, Seon-Jin Chortos, Alex Dagdeviren, Canan Dauskardt, Reinhold H. Di, Chong-an Dickey, Michael D. Duan, Xiangfeng Facchetti, Antonio Fan, Zhiyong Fang, Yin Feng, Jianyou Feng, Xue Gao, Huajian Gao, Wei Gong, Xiwen Guo, Chuan Fei Guo, Xiaojun Hartel, Martin C. He, Zihan Ho, John S. Hu, Youfan Huang, Qiyao Huang, Yu Huo, Fengwei Hussain, Muhammad M. Javey, Ali Jeong, Unyong Jiang, Chen Jiang, Xingyu Kang, Jiheong Karnaushenko, Daniil Khademhosseini, Ali Kim, Dae-Hyeong Kim, Il-Doo Kireev, Dmitry Kong, Lingxuan Lee, Chengkuo Lee, Nae-Eung Lee, Pooi See Lee, Tae-Woo Li, Fengyu Li, Jinxing Liang, Cuiyuan Lim, Chwee Teck Lin, Yuanjing Lipomi, Darren J. Liu, Jia Liu, Kai Liu, Nan Liu, Ren Liu, Yuxin Liu, Yuxuan Liu, Zhiyuan Liu, Zhuangjian Loh, Xian Jun Lu, Nanshu Lv, Zhisheng Magdassi, Shlomo Malliaras, George G. Matsuhisa, Naoji Nathan, Arokia Niu, Simiao Pan, Jieming Pang, Changhyun Pei, Qibing Peng, Huisheng Qi, Dianpeng Ren, Huaying Rogers, John A. Rowe, Aaron Schmidt, Oliver G. Sekitani, Tsuyoshi Seo, Dae-Gyo Shen, Guozhen Sheng, Xing Shi, Qiongfeng Someya, Takao Song, Yanlin Stavrinidou, Eleni Su, Meng Sun, Xuemei Takei, Kuniharu Tao, Xiao-Ming Tee, Benjamin C. K. Thean, Aaron Voon-Yew Trung, Tran Quang Wan, Changjin Wang, Huiliang Wang, Joseph Wang, Ming Wang, Sihong Wang, Ting Wang, Zhong Lin Weiss, Paul S. Wen, Hanqi Xu, Sheng Xu, Tailin Yan, Hongping Yan, Xuzhou Yang, Hui Yang, Le Yang, Shuaijian Yin, Lan Yu, Cunjiang Yu, Guihua Yu, Jing Yu, Shu-Hong Yu, Xinge Zamburg, Evgeny Zhang, Haixia Zhang, Xiangyu Zhang, Xiaosheng Zhang, Xueji Zhang, Yihui Zhang, Yu Zhao, Siyuan Zhao, Xuanhe Zheng, Yuanjin Zheng, Yu-Qing Zheng, Zijian Zhou, Tao Zhu, Bowen Zhu, Ming Zhu, Rong Zhu, Yangzhi Zhu, Yong Zou, Guijin Chen, Xiaodong 08-03 Innovis Singapore 138634 Republic of Singapore Innovative Centre for Flexible Devices (iFLEX) School of Materials Science and Engineering Nanyang Technological University Singapore 639798 Singapore Department of Biomedical Engineering University of Houston Houston Texas 77024 United States School of Electrical and Electronic Engineering Yonsei University Seoul 03722 Republic of Korea Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 United States Microelectronics Research Center The University of Texas at Austin Austin Texas 78758 United States Department of Chemistry and Biochemistry California NanoSystems Institute and Department of Psychiatry and Biobehavioral Sciences Semel Institute for Neuroscience and Human Behavior and Hatos Center for Neuropharmacology University of California Los Angeles Los Angeles California 90095 United States Colloid Chemistry Department Max Planck Institute of Colloids and Interfaces 14476 Potsdam Germany Department of Chemical Engineering Stanford University Stanford California 94305 United States Laboratory of Organic Electronics Department of Science and Technology Campus Norrköping Linköping University 83 Linköping Sweden Wallenberg Initiative Materials Science for Sustainability (WISE) and Wallenberg Wood Science Center (WWSC) SE-100 44 Stockholm Sweden Department of Materials Science and Engineering Stanford University Stanford California 94301 United States Department of Biomedical Engineering and Department of Materials Science and Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 United States Department of Bioengineering University of California Los Angeles Los Angeles California 90095 United States School of Chemistry Chemical Engineering and Biotechnology Nanyang Technological University Singapore 637457 Singapore Nanobionics Group Department of Chemical and Biological Engineering Monash University Clayton Australia 3800 Monash

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze continued...challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative

关键词： soft materials mechanics engineering flexible electronics conformable sensors bioelectronics human-machine interfaces body area sensor networks technology translation sustainable electronics

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