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15th International Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2024

作者： Ma, Jiquan Teng, Yujun Chen, Geng Jiang, Haotian Zhang, Kai Liu, Feihong Rekik, Islem Shen, Dinggang School of Computer Science and Technology Heilongjiang University Harbin China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an China School of Biomedical Engineering and State Key Laboratory of Advanced Medical Materials and Devices ShanghaiTech University Shanghai China School of Information Science and Technology Northwest University Xi’an China BASIRA Lab Imperial-X and Department of Computing Imperial College London London United Kingdom

ISBN: (纸本)9783031869198

Diffusion-Weighted Imaging (DWI) is a significant technique for studying white matter. However, it suffers from low-resolution obstacles in clinical settings. Post-acquisition Super-Resolution (SR) can enhance the resolution of DWIs and has gained increasing research interest in recent years. An advanced generative model, the Diffusion Model (DM), exhibits particularly promising performance in image SR. However, effective conditions are required to bootstrap the DM for DWI SR. To this end, we proposed the first DM-based DWI SR model with two effective conditions based on low-solution DWIs and Track Density Imaging (TDI) maps, which possess rich high-resolution prior knowledge Additionally, we consider another condition based on features from low-resolution DWIs. These two conditions are integrated into our model, which comprises three components: DWI Resolution Enhancer (DRE), DWI Feature Extractor (DFE), and TDI Feature Extractor (TFE). DRE combines low-resolution DWI features from DFE with TDI features from TFE to progressively generate high-resolution DWIs. We performed extensive experiments on DWIs of normal subjects from human connectome projects and patients with Parkinson’s disease. The results demonstrate that our model outperforms existing DWI SR models, both qualitatively and quantitatively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neurodegenerative diseases

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Improved Approximation Algorithm for Individual Fairness k-Median 17th

Improved Approximation Algorithm for Individual Fairness k-...

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17th International Conference on Combinatorial Optimization and Applications, COCOA 2024

作者： Wu, Di Feng, Qilong Xu, Jinhui Wang, Jianxin School of Computer Science and Engineering Central South University Changsha China Xiangjiang Laboratory Changsha410205 China Department of Computer Science and Engineering State University of New York at Buffalo BuffaloNY United States The Hunan Provincial Key Lab of Bioinformatics Central South University Changsha China

ISBN: (纸本)9789819644445

The individual fairness k-median problem is a frequently encountered problem in applications involving center location, which generalizes the standard k-median problem by assigning each point a neighborhood radius, allowing connections only to centers within a constant factor of this radius. In this paper, we present a randomized polynomial-time approximation scheme (PTAS) framework with (2+O(ϵ))-fairness violation for the individual fairness k-median problem, improving upon the previous best approximation ratio of (7.081+ϵ) and fairness violation of 3. We propose a new dynamic programming approach to deal with the challenges caused by the individual fairness requirements, which is the crucial step in getting the improved ratio. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Approximation algorithms

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SenseDSE: Sensitivity-Based Performance Evaluation for Design Space Exploration of Microarchitecture

SenseDSE: Sensitivity-Based Performance Evaluation for Desig...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Zheng Wu Xiaoling Yi Li Shang Fan Yang State Key Lab of Integrated Circuits and Systems School of Microelectronics Fudan University. Shanghai Key Laboratory of Data Science School of Computer Science Fudan University.

ISBN: (数字)9783981926385

ISBN: (纸本)9798350348606

The design of modern processors is driven by plenty of benchmarks. As processors evolve and applications expand, the complexity of benchmark programs grows, which increases the computational cost of architecture design space exploration (DSE). To accelerate performance evaluations of processors in DSE, we developed a sensitivity-based framework for performance evaluation of a large set of benchmarks. The framework avoids simulating the insensitive benchmarks to the adjusted parameters during the exploration of designs. We developed a sampling algorithm based on evolutionary strategies to provide learning data for the sensitivity analysis and enhance the performance of the fast performance evaluation algorithm. We integrated this framework into a RISe-V processor architecture exploration framework. Our experiments revealed that we could achieve a significant acceleration in runtime with negligible accuracy loss in DSE.

关键词： Performance evaluation Program processors Runtime Microarchitecture Sensitivity analysis computer architecture Benchmark testing

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Automated Order Dispatching Strategies Design Using Genetic Programming for Dynamic Ridesharing Problem

Automated Order Dispatching Strategies Design Using Genetic ...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Fan, Chong-Jiong Jia, Ya-Hui Chen, Wei-Neng School of Computer Science and Engineering South China University of Technology State Key Laboratory of Subtropical Building and Urban Science Guangzhou510006 China School of Future Technology South China University of Technology Guangzhou510641 China Pazhou Lab Guangzhou510330 China

ISBN: (纸本)9798350337020

Ridesharing is a popular transportation mode and has become an important part of smart city development, which helps alleviate the pressure of urban travel. The ridesharing problem (RSP) is mainly to match drivers to suitable passengers. In practice, passengers appear dynamically, and the departure and the destination locations of these subsequent orders are unknown, resulting in the dynamic RSP (DRSP). To solve this dynamic optimization problem, this paper develops a new genetic programming hyperheuristic (GPHH) method to evolve order dispatching rules (ODRs), which can guide drivers to match suitable passengers in real time. The proposed GPHH method contains a heuristic template for simulation-based hyper-heuristic optimization. The experiment results show that the proposed GPHH method outperforms the state-of-the-art methods. Further analysis revealed some valuable insights, such as the generalizability of the generated rules and the impact of some features on the results. © 2023 IEEE.

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A multimodal integration pipeline for accurate diagnosis, pathogen identification, and prognosis prediction of pulmonary infections

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The Innovation 2024年第4期5卷 63-73,62页

作者： Jun Shao Jiechao Ma Yizhou Yu Shu Zhang Wenyang Wang Weimin Li Chengdi Wang Department of Pulmonary and Critical Care Medicine State Key Laboratory of Respiratory Health and MultimorbidityWest China HospitalWest China School of MedicineSichuan UniversityChengdu 610041China Targeted Tracer Research and Development Laboratory Institute of Respiratory HealthFrontiers Science Center for Disease-related Molecular NetworkWest China HospitalSichuan UniversityChengdu 610213China Frontiers Medical Center Tianfu Jincheng LaboratoryChengdu 610213China AI Lab Deepwise HealthcareBeijing 100080China Department of Computer Science The University of Hong KongHong Kong SARChina

Pulmonary infections pose formidable challenges in clinical settings with high mortality rates across all age groups *** diagnosis and early intervention are crucial to improve patient *** intelligence(AI)has the capability to mine imaging features specific to different pathogens and fuse multimodal features to reach a synergistic diagnosis,enabling more precise investigation and individualized clinical *** this study,we successfully developed a multimodal integration(MMI)pipeline to differentiate among bacterial,fungal,and viral pneumonia and pulmonary tuberculosis based on a real-world dataset of 24,107 *** area under the curve(AUC)of the MMI system comprising clinical text and computed tomography(CT)image scans yielded 0.910(95%confidence interval[CI]:0.904–0.916)and 0.887(95%CI:0.867–0.909)in the internal and external testing datasets respectively,which were comparable to those of experienced ***,the MMI system was utilized to rapidly differentiate between viral subtypes with a mean AUC of 0.822(95%CI:0.805–0.837)and bacterial subtypes with a mean AUC of 0.803(95%CI:0.775–0.830).Here,the MMI system harbors the potential to guide tailored medication recommendations,thus mitigating the risk of antibiotic ***,the integration of multimodal factors in the AI-driven system also provided an evident advantage in predicting risks of developing critical illness,contributing to more informed clinical *** revolutionize medical care,embracing multimodal AI tools in pulmonary infections will pave the way to further facilitate early intervention and precise management in the foreseeable future.

关键词： diagnosis modal integration

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The Fair k-Center with Outliers Problem: FPT and Polynomial Approximations 1

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17th International Joint Conference on Theoretical computer science - Frontier of Algorithmic Wisdom, IJTCS-FAW 2023

作者： Wu, Xiaoliang Feng, Qilong Xu, Jinhui Wang, Jianxin School of Computer Science and Engineering Central South University Changsha410083 China Xiangjiang Laboratory Changsha410205 China Department of Computer Science and Engineering State University of New York at Buffalo New York14260-1660 United States Hunan Provincial Key Lab on Bioinformatics Central South University Changsha410083 China

ISBN: (数字)9783031393440

ISBN: (纸本)9783031393433

The fair k-center and k-center with outliers problems are two important variants of the k-center problem in computer science, which have attracted lots of attention. The previous best results for the above two problems are a 3-approximation (ICML 2020) and a 2-approximation (ICALP 2016), respectively. In this paper, we consider a common generalization of the two mentioned variants of the k-center problem, denoted as the fair k-center with outliers (FkCO) problem. For the FkCO problem, we are given a set X of points in a metric space and parameters k and z, where the points in X are divided into several groups, and each point is assigned a color to denote which group it belongs to. The goal is to find a subset C⊆ X of k centers and a set Z of at most z outliers such that C satisfies fairness constraints, and the objective maxx∈X\Zminc∈Cd(x, c) is minimized. In this paper, we study the Fixed-Parameter Tractability (FPT) approximation algorithm and polynomial approximation algorithm for the FkCO problem. Our main contributions are: (1) we propose a (1 + ϵ) -approximation algorithm in FPT time for the FkCO problem in a low-dimensional doubling metric space;(2) we achieve a polynomial 3-approximation algorithm for the FkCO problem with the reasonable assumptions that all optimal clusters are well separated and have size greater than z. © 2023, Springer Nature Switzerland AG.

关键词： Approximation algorithms

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Wavelength-division-multiplexed Transmission and Dispersion Compensation Enabled by Soliton Microcombs and Cascaded Microrings

Wavelength-division-multiplexed Transmission and Dispersion ...

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CLEO: science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

作者： Liu, Yuanbin Zhang, Hongyi Liu, Jiacheng Lu, Liangjun Du, Jiangbing Li, Yu He, Zuyuan Chen, Jianping Zhou, Linjie Poon, Andrew W. State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Key Lab of Navigation and Location Services Shanghai Institute for Advanced Communication and Data Science Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China SJTU-Pinghu Institute of Intelligent Optoelectronics Pinghu314200 China Photonic Device Laboratory Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Hong Kong

We demonstrate wavelength-division-multiplexed data transmission and dispersion compensation of 25 Gb/s × 9 on-off-keying signals over a 20-km singlemode fiber using an integrated single-soliton microcomb and a cascaded microring resonator-based reconfigurable dispersion compensator. © Optica Publishing Group 2024

关键词： Dispersion compensation

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Wavelength-division-multiplexed Transmission and Dispersion Compensation Enabled by Soliton Microcombs and Cascaded Microrings

Wavelength-division-multiplexed Transmission and Dispersion ...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

ISBN: (纸本)9781957171395

关键词： Optical fibers

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Bridging Distinct Spaces in Graph-Based Machine Learning 1

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7th Asian Conference on Pattern Recognition, ACPR 2023

作者： Jia, Linlin Ning, Xiao Gaüzère, Benoit Honeine, Paul Riesen, Kaspar Institute of Computer Science University of Bern Bern3012 Switzerland State Key Laboratory of Bioelectronics School of Biological Science and Medical Engineering Southeast University 2 Sipailou Nanjing210096 China The LITIS Lab INSA Rouen Normandie Rouen France The LITIS Lab Université de Rouen Normandie Rouen France

ISBN: (数字)9783031476372

ISBN: (纸本)9783031476365

Graph-based machine learning, encompassing Graph Edit Distances (GEDs), Graph Kernels, and Graph Neural Networks (GNNs), offers extensive capabilities and exciting potential. While each model possesses unique strengths for graph challenges, interrelations between their underlying spaces remain under-explored. In this paper, we introduce a novel framework for bridging these distinct spaces via GED cost learning. A supervised metric learning approach serves as an instance of this framework, enabling space alignment through pairwise distances and the optimization of edit costs. Experiments reveal the framework’s potential for enhancing varied tasks, including regression, classification, and graph generation, heralding new possibilities in these fields. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Machine learning

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EGODE: An Event-attended Graph ODE Framework for Modeling Rigid Dynamics 38

EGODE: An Event-attended Graph ODE Framework for Modeling Ri...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Yuan, Jingyang Sun, Gongbo Xiao, Zhiping Zhou, Hang Luo, Xiao Luo, Junyu Zhao, Yusheng Ju, Wei Zhang, Ming School of Computer Science State Key Laboratory for Multimedia Information Processing PKU-Anker LLM Lab Peking University China University of Wisconsin-Madison United States University of Washington United States University of California Davis United States University of California Los Angeles United States

This paper studies the problem of rigid dynamics modeling, which has a wide range of applications in robotics, graphics, and mechanical design. The problem is partly solved by graph neural network (GNN) simulators. However, these approaches cannot effectively handle the relationship between intrinsic continuity and instantaneous changes in rigid dynamics. Moreover, they usually neglect hierarchical structures across mesh nodes and objects in systems. In this paper, we propose a novel approach named Event-attend Graph ODE (EGODE) for effective rigid dynamics modeling. In particular, we describe the rigid system using both mesh node representations and object representations. To model continuous dynamics across hierarchical structures, we use a coupled graph ODE framework for the evolution of both types of representations over a long period. In addition, to capture instantaneous changes during the collision, we introduce an event module, which can effectively estimate the occurrence of the collision and update the states of both mesh node and object representations during evolution. Extensive experiments on a range of benchmark datasets validate the superiority of the proposed EGODE compared to various state-of-the-art baselines. The source code can be found at https://***/yuanjypku/EGODE. © 2024 Neural information processing systems foundation. All rights reserved.

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