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arXiv 2022年

作者： Chen, Yulong Deng, Naihao Liu, Yang Zhang, Yue School of Engineering Westlake University China School of Computer Science and Engineering University of Michigan United States Institute of Advanced Technology Westlake Institute for Advanced Study China

We report the results of DialogSum Challenge, the shared task on summarizing real-life scenario dialogues at INLG 2022. Four teams participate in this shared task and three submit their system reports, exploring different methods to improve the performance of dialogue summarization. Although there is a great improvement over the baseline models regarding automatic evaluation metrics, such as ROUGE scores, we find that there is a salient gap between model generated outputs and human annotated summaries by human evaluation from multiple aspects. These findings demonstrate the difficulty of dialogue summarization and suggest that more fine-grained evaluatuion metrics are in need. © 2022, CC BY-SA.

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Efficient Cardinality and Cost Estimation with Bidirectional Compressor-based Ensemble Learning

Efficient Cardinality and Cost Estimation with Bidirectional...

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IEEE International Conference on Data Mining (ICDM)

作者： Zibo Liang Xu Chen Yan Zhao Jiandong Xie Kai Zeng Kai Zheng University of Electronic Science and Technology of China China Aalborg University Denmark Huawei Technologies Co. Ltd China Yangtze Delta Region Institute (Quzhou) School of Computer Science and Engineering and Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China

Query optimization is of great importance for the performance of a database, in which cardinality and cost estimation have a pivotal role. To enable accurate cardinality and cost estimation, we propose a novel framework based on bidirectional compressor and ensemble networks called BICE. In particular, we design a feature extractor composed of four sub-encoders, which can extract various types of information in a query plan tree and hybrid learning strategies for encoding. We encode joins based on a graph embedding method and design parallel networks for filters to improve the encoding efficiency. Then we propose a bidirectional LSTM-based compressor to learn the encoding and obtain fixed-length vectors, reducing the learning difficulty of the estimation model. Finally, we propose different data sampling strategies based on Bayesian neural networks and active learning, and an ensemble model is established based on transfer learning, which enables accurate estimation and adaptation to large-scale data queries. Extensive experiments offer insight into the effectiveness and efficiency of the proposed framework.

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Privacy-Preserving Personalized Federated Learning for Distributed Photovoltaic Disaggregation under Statistical Heterogeneity

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Chen, Xiaolu Huang, Chenghao Zhang, Yanru Wang, Hao University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China Monash University Faculty of IT Monash Energy Institute Department of Data Science and AI MelbourneVIC3800 Australia Shenzhen Institute for Advanced Study of UESTC Shenzhen China

The rapid expansion of distributed photovoltaic (PV) installations worldwide, many being behind-the-meter systems, has significantly challenged energy management and grid operations, as unobservable PV generation further complicates the supply-demand balance. Therefore, estimating this generation from net load, known as PV disaggregation, is critical. Given privacy concerns and the need for large training datasets, federated learning becomes a promising approach, but statistical heterogeneity, arising from geographical and behavioral variations among prosumers, poses new challenges to PV disaggregation. To overcome these challenges, a privacy-preserving distributed PV disaggregation framework is proposed using Personalized Federated Learning (PFL). The proposed method employs a two-level framework that combines local and global modeling. At the local level, a transformer-based PV disaggregation model is designed to generate solar irradiance embeddings for representing local PV conditions. A novel adaptive local aggregation mechanism is adopted to mitigate the impact of statistical heterogeneity on the local model, extracting a portion of global information that benefits the local model. At the global level, a central server aggregates information uploaded from multiple data centers, preserving privacy while enabling cross-center knowledge sharing. Experiments on real-world data demonstrate the effectiveness of this proposed framework, showing improved accuracy and robustness compared to benchmark methods. © 1963-2012 IEEE.

关键词： Training Load modeling Data centers Solar irradiance Data models Servers Adaptation models Accuracy Clouds Electricity

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On Creativity and Open-Endedness

arXiv

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arXiv 2024年

作者： Soros, L.B. Adams, Alyssa M. Kalonaris, Stefano Witkowski, Olaf Guckelsberger, Christian Department of Computer Science Barnard College United States Riken AIP Japan Cross Labs Cross Compass Ltd. Japan Graduate School of Arts and Sciences University of Tokyo Japan Earth-Life Science Institute Tokyo Institute of Technology Japan Institute for Advanced Study United States Department of Computer Science Aalto University Finland School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom

Artificial Life (ALife) as an interdisciplinary field draws inspiration and influence from a variety of perspectives. Scientific progress crucially depends, then, on concerted efforts to invite cross-disciplinary dialogue. The goal of this paper is to revitalize discussions of potential connections between the fields of Computational Creativity (CC) and ALife, focusing specifically on the concept of Open-Endedness (OE);the primary goal of CC is to endow artificial systems with creativity, and ALife has dedicated much research effort into studying and synthesizing OE and artificial innovation. However, despite the close proximity of these concepts, their use so far remains confined to their respective communities, and their relationship is largely unclear. We provide historical context for research in both domains, and review the limited work connecting research on creativity and OE explicitly. We then highlight specific questions to be investigated in future work, with the eventual goals of (i) decreasing conceptual ambiguity by highlighting similarities and differences between the concepts of OE and creativity, (ii) identifying synergy effects of a research agenda that encompasses both concepts, and (iii) establishing a dialogue between ALife and CC research. © 2024, CC BY.

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Soft-Orthogonal Constrained Dual-Stream Encoder with Self-Supervised Clustering Network

SSRN

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SSRN 2023年

作者： Lu, Hu Jin, TingTing Wei, Hui Nappi, Michele Li, Hu Wan, ShaoHua School of Computer Science and Communication Engineering Jiangsu University Jiangsu212013 China School of Computer Science Fudan University Shanghai200433 China Department of Computer Science University of Salerno Salerno Italy School of Information Management Shanghai Lixin University of Accounting and Finance Shanghai01209 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518110 China

In many brain network studies, brain functional connectivity data is extracted from neuroimaging data and then used for disease prediction. For now, brain disease data not only has a small sample but also has the problem of high dimensional and nonlinear. Therefore, deep clustering on brain functional connectivity data is very challenging. To solve these problems, we propose a Soft-orthogonal Constrained Dual-stream Encoder with Self-supervised clustering network (SSCDE), which consists of a pretext task and downstream task, which can fully mine the effective information in brain disease data. In the pretext task, we use two brain disease data under the same category to do cross-domain learning to obtain effective information from the same dataset. In the downstream task, to reduce redundancy and avoid negative coding, we propose a soft-orthogonal constrained dual-stream encoder to encode features separately. At the same time, we use the pseudo-labels given by the pretext task as prior information for self-supervised learning. We conduct experiments on different brain disease recognition tasks, and many experiments have proved that the proposed frame- © 2023, The Authors. All rights reserved.

关键词： Neuroimaging

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Nanosecond laser conditioning of multilayer dielectric gratings for picosecond–petawatt laser systems

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High Power Laser science and engineering 2023年第6期11卷 239-248页

作者： Kun Shuai Yuanan Zhao Xiaofeng Liu Xiangkun Lin Zhilin Xia Keqiang Qiu Dawei Li He Gong Yan Zhou Jian Sun Li Zhou Youen Jiang Yaping Dai Jianda Shao Laboratory of Thin Film Optics Shanghai Institute of Optics and Fine MechanicsChinese Academy of Sciences(CAS)ShanghaiChina Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of SciencesBeijingChina Key Laboratory of Materials for High Power Laser Chinese Academy of SciencesShanghaiChina School of Materials Science and Engineering Wuhan University of TechnologyWuhanChina National Synchrotron Radiation Laboratory University of Science and Technology of ChinaHefeiChina School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghaiChina National Laboratory on High Power Laser and Physics Shanghai Institute of Optics and Fine MechanicsCASShanghaiChina Research Center of Laser Fusion China Academy of Engineering PhysicsMianyangChina Hangzhou Institute for Advanced Study University of Chinese Academy of SciencesHangzhouChina

Multilayer dielectric gratings(MLDGs)are crucial for pulse compression in picosecond-petawatt laser *** nodular defects,embedded in coating stacks during multilayer deposition,influence the lithographic process and performance of the final MLDG *** this study,the integration of nanosecond laser conditioning(NLC)into different manufacturing stages of MLDGs was proposed for the first time on multilayer dielectric films(MLDFs)and final grating products to improve laser-induced damage *** results suggest that the remaining nodular ejection pits introduced by the two protocols exhibit a high nanosecond laser damage resistance,which remains stable when the irradiated laser fluence is more than twice the nanosecond-laser-induced damage threshold(nanosecond-LIDT)of the unconditioned ***,the picosecond-LIDT of the nodular ej ection pit conditioned on the MLDFs was approximately 40%higher than that of the nodular defects,and the loss of the grating structure surrounding the nodular defects was ***,NLC is an effective strategy for improving the laser damage resistance of MLDGs.

关键词： laser-induced damage threshold multilayer dielectric gratings nanosecond laser conditioning nodular defects picosecond-petawatt laser systems

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Generation of isolated attosecond electron sheet via relativistic spatiotemporal optical manipulation

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Physical Review Research 2024年第1期6卷 013075-013075页

作者： Fengyu Sun Wenpeng Wang Hao Dong Jianzhi He Zhiyong Shi Zhengxing Lv Qiwen Zhan Yuxin Leng Songlin Zhuang Ruxin Li State Key Laboratory of High Field Laser Physics and CAS Center for Excellence in Ultra-intense Laser Science Shanghai Institute of Optics and Fine Mechanics (SIOM) Chinese Academy of Sciences (CAS) Shanghai 201800 China School of Physical Science and Technology ShanghaiTech University Shanghai 201210 China University of Chinese Academy of Sciences Beijing 100049 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai 200093 China

Novel phase-locked acceleration of a relativistic isolated attosecond electron sheet was realized using a relativistic reflected spatiotemporal optical vortex (STOV) pulse in three-dimensional particle-in-cell simulations. Electrons can be trapped in a spatiotemporal phase singularity within the STOV pulse center, modulated into an approximately 600-attosecond duration sheet. Furthermore, electrons can be phase-locked accelerated to the order of GeV in the longitudinal direction. Such an isolated attosecond electron sheet driven by the intense STOV pulse can be used in isolated attosecond pulse generation, ultrashort x/γ-ray radiation, and attosecond electron diffraction.

关键词： High-energy-density plasmas Laser driven electron acceleration Laser-plasma interactions Plasma acceleration & new acceleration techniques

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A biosensing system using a multiparameter nonlinear dynamic analysis of cardiomyocyte beating for drug-induced arrhythmia recognition

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Microsystems & Nanoengineering 2022年第3期8卷 277-290页

作者： Hao Wang Yue Wu Quchao Zou Wenjian Yang Zhongyuan Xu Hao Dong Zhjing Zhu Depeng Wang Tanxing Wang Ning Hu Diming Zhang State Key Laboratory of Optoelectronic Materials and Technologies Guangdong Province Key Laboratory of Display Material and TechnologySchool of Electronics and Information TechnologySun Yat-sen UniversityGuangzhou 510006China Research Center for Itelligent Sensing Systems Zhejiang LabHangzhou 311121China ZJU-Hangzhou Global Scientific and Technological Innovation Center Department of ChemistryThe Second Affiliated Hospital Zhejiang University School of MedicineDepartment of Clinical Medical EngineeringZhejiang UniversityHangzhou 310058China Key Laboratory of Novel Target and Drug Study for Neural Repair of Zhejang Province School of MedicineSchool of Computer&Computing ScienceZhejiang University City CollegeHangzhou 310015China School of Brain Science and Brain Medicine Zhejiang UniversityHangzhou 310058China College of Energy and Power Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210016China E-LinkCare Meditech Co LtdHangzhou 310011China State Key Laboratory of Transducer Technology Chinese Academy of SciencesShanghai 200050China

Cardiovascular disease is the number one cause of death in ***,cardiotoxicity is one of the most important adverse effects assessed by arrhythmia recognition in drug ***,cell-based techniques developed for arhythmia recognition primarily employ linear methods such as time-domain analysis that detect and compare individual waveforms and thus fall short in some applications that require automated and efficient arrhythmia recognition from large *** carried out the frst report to develop a biosensing system that integrated impedance measurement and multiparameter nonlinear dynamic algorithm(MNDA)analysis for druginduced arrhythmia recognition and *** biosensing system cultured cardiomyocytes as physiologically relevant models,used interdigitated electrodes to detect the mechanical beating of the cardiomyocytes,and employed MNDA analysis to recognize drug-induced arrhythmia from the cardiomyocyte beating *** best performing MNDA parameter,approximate entropy,enabled the system to recognize the appearance of sertindoleand norepinephrine-induced arrhythmia in the *** MNDA reconstruction in phase space enabled the system to classify the different arrhythmias and quantify the severity of *** new biosensing system utilizing MNDA provides a promising and alternative method for drug-induced arrhythmia recognition and classification in cardiological and pharmaceutical applications.

关键词： arrhythmia cardio analysis

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Adversarial Purification by Consistency-aware Latent Space Optimization on Data Manifolds

arXiv

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arXiv 2024年

作者： Zhang, Shuhai Yang, Jiahao Luo, Hui Chen, Jie Wang, Li Liu, Feng Han, Bo Tan, Mingkui The School of Software Engineering South China University of Technology China the Pazhou Laboratory Guangzhou China The National Key Laboratory of Optical Field Manipulation Science and Technology CAS China the Institute of Optics and Electronics CAS Chengdu China the School of Electronic and Computer Engineering Peking University Beijing100871 China Peng Cheng Laboratory Shenzhen518066 China The Department of Mathematics The Department of Computer Science and Engineering University of Texas at Arlington ArlingtonTX76019 United States University of Melbourne Australia Department of Computer Science Hong Kong Baptist University Hong Kong

Deep neural networks (DNNs) are vulnerable to adversarial samples crafted by adding imperceptible perturbations to clean data, potentially leading to incorrect and dangerous predictions. Adversarial purification has been an effective means to improve DNNs robustness by removing these perturbations before feeding the data into the model. However, it faces significant challenges in preserving key structural and semantic information of data, as the imperceptible nature of adversarial perturbations makes it hard to avoid over-correcting, which can destroy important information and degrade model performance. In this paper, we break away from traditional adversarial purification methods by focusing on the clean data manifold. To this end, we reveal that samples generated by a well-trained generative model are close to clean ones but far from adversarial ones. Leveraging this insight, we propose Consistency Model-based Adversarial Purification (CMAP), which optimizes vectors within the latent space of a pre-trained consistency model to generate samples for restoring clean data. Specifically, 1) we propose a Perceptual consistency restoration mechanism by minimizing the discrepancy between generated samples and input samples in both pixel and perceptual spaces. 2) To maintain the optimized latent vectors within the valid data manifold, we introduce a Latent distribution consistency constraint strategy to align generated samples with the clean data distribution. 3) We also apply a Latent vector consistency prediction scheme via an ensemble approach to enhance prediction reliability. CMAP fundamentally addresses adversarial perturbations at their source, providing a robust purification. Extensive experiments on CIFAR-10 and ImageNet-100 show that our CMAP significantly enhances robustness against strong adversarial attacks while preserving high natural accuracy. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

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On the Representativeness Metric of Benchmark Problems in Numerical Optimization

SSRN

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SSRN 2024年

作者： Chen, Caifeng Liu, Qunfeng Jing, Yunpeng Zhang, Mingming Cheng, Shi Li, Yun School of Modern Information Industry Guangzhou College of Commerce Guangzhou511363 China School of Computer Science and Technology Dongguan University of Technology Dongguan523808 China Department of Mechanical and Mechatronics Engineering University of Auckland New Zealand School of Computer Science Shaanxi Normal University Xi’an710062 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518110 China i4AI Ltd LondonWCIN3AX United Kingdom

Numerical comparison on benchmark problems is often necessary in evaluating optimization algorithms with or without theoretical analysis. An implicit assumption is that the adopted set of benchmark problems is representative. However, to our knowledge, there are few results about how to evaluate the representativeness of a test suite, partly due to the difficulty of this issue. In this paper, we first define three different levels of representativeness, and open up a window for addressing step by step the issue of representativeness-measuring. Then we turn to address the type-III representativeness-measuring problem, and provide a methodology and metric for this problem. To illustrate how to use the proposed metric, the representativeness-measuring problem of benchmark problems for single-objective optimization is *** analysis covers as many as 1141 single-objective unconstrained continuous benchmark problems, primarily focusing on existing benchmark problems. Based on the defined the representativeness metric, some classical features and calculations are used to assess the representativeness of the benchmark problems. Assessment results show that most of benchmark problems of high representativeness are non-separable problems from the CEC and BBOB test suites. We select the top $5\%$ of most representative problems to build a new test suite, providing a more representative and rigorous reference in verifying the overall performance of the optimization algorithms. © 2024, The Authors. All rights reserved.

关键词： Optimization

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