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Cosmological perturbations in the energy-momentum squared gravity theory:constraints from gravitational wave standard sirens and redshift space distortions

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Chinese Physics C 2025年第4期49卷 199-208页

作者： Qi-Ming Fu Xin Zhang Institute of Physics Shaanxi University of TechnologyHanzhong 723000China Lanzhou Center for Theoretical Physics Key Laboratory of Theoretical Physics of Gansu Provinceand Key Laboratory of Quantum Theory and Applications of MoELanzhou UniversityLanzhou 730000China Key Laboratory of Cosmology and Astrophysics(Liaoning)&College of Sciences Northeastern UniversityShenyang 110819China Key Laboratory of Data Analytics and Optimization for Smart Industry(Ministry of Education) Northeastern UniversityShenyang 110819China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern UniversityShenyang 110819China

We investigate the linear cosmological perturbations in the context of the so-called energy-momentum squared gravity(EMSG)*** research shows that the EMSG theory can reproduce a viable background cosmological evolution comparable toΛCDM,whereas the matter-dominated era exhibits slight *** this paper,we focus on power-law EMSG models and derive the equations for the linear cosmological *** explore the propagation of the gravitational wave(GW)and the growth of matter density perturbation at the first order,and we estimate the model parameters from the simulated GW and observed redshift space distortion *** analysis reveals that the model parameters should be small and positive in the confidence interval,which indicates that the theory agrees closely with the observational data and can be considered an alternative to the standard cosmological model.

关键词： energy-momentum squared gravity cosmological perturbations gravitational wave cosmological parameters

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Identification of Core Technological Topics in the New Energy Vehicle Industry: The SAO-BERTopic Topic Modeling Approach Based on Patent Text Mining 5

Identification of Core Technological Topics in the New Energ...

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Joint Workshop of the 5th Extraction and Evaluation of knowledge Entities from Scientific Documents and the 4th AI + Informetrics, EEKE-AII 2024

作者： Zhu, Jianxin Chuang, Yutong Wang, Zhinan Li, Yunke Harbin Engineering University School of Economics and Management 150001 China Key Laboratory of Big Data and Business Intelligence Technology Ministry of Industry and Information Technology China

In the new energy vehicle industry, precise identification of core technologies is the key to promoting innovation and maintaining market competitiveness. In this article, a comprehensive approach combining information weight method and SAO-BERTopic topic model is proposed to extract and analyze core technologies from large-scale patent data. Through in-depth analysis of Incopat patent database, we use the information weight method to select high-quality core patents from four dimensions: technological, strategic, law and market value. These selected patents form the basis of the research data, which is then applied to the SAO-BERTopic model, which combines the advanced semantic understanding capabilities of BERTopic with the fine-structured characteristics of SAO analysis, greatly improving the efficiency and accuracy of identifying complex technical topics. This innovation of this research is not only applicable to the analysis of the technological development of the new energy vehicle industry, but also can provide valuable reference for other high-tech industries such as biomedicine, renewable energy and information technology. These fields also need to identify core technologies from a large number of patents. SAO-BERTopic's structured analysis framework can help these industries to insight into technology development trends, identify innovation opportunities, and provide data-driven decision support for enterprises, research institutions and governments, thus playing an important role in technology planning and market commercialization. © Copyright 2024 for this paper by its authors.

关键词： Commerce

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Advances in intelligent computing approaches for solving problems related to photonic crystal fibers: Advances in Intelligent..: D. Yang et al.

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Optical and Quantum Electronics 2025年第1期57卷 1-31页

作者： Yang, Dan Zhao, Yuyu Liu, Hong Li, Yijin Tang, Chang Li, Shijun Lingye, Zhenglin College of Information Science & Engineering Northeastern University Shenyang110819 Thailand National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang110819 China Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education Northeastern University Shenyang110819 Thailand

Photonic crystal fiber (PCF) has shown a promising application in various fields of modern optics due to its excellent transmission characteristics. However, conventional numerical solution methods have limitations such as high computational effort and low efficiency in designing PCF structures and characterizing pulse propagation in PCFs. With the advancement of artificial intelligence technology, various types of intelligent computing approaches, especially intelligent algorithm and machine learning algorithm (IML), provide new perspectives to solve the above difficulties. Therefore, an increasing number of researchers are committed to using IML to break through the limitations of conventional methods and accordingly open up a new interdisciplinary research direction. This paper begins with a brief overview of common optical properties and typical algorithms. Subsequently, it reviews specific areas where PCF and IML are combined, including the prediction and optimization of optical properties, the design of polarization devices, fiber lasers and amplifiers, as well as the control and prediction of Supercontinuum generation. Finally, this paper offers insights into the future developments and challenges in this emerging interdisciplinary field. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Supercontinuum generation

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Synergy between CSST galaxy survey and gravitational-wave observation: Inferring the Hubble constant from dark standard sirens

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Science china(Physics,Mechanics & Astronomy) 2024年第3期67卷 7-19页

作者： Ji-Yu Song Ling-Feng Wang Yichao Li Ze-Wei Zhao Jing-Fei Zhang Wen Zhao Xin Zhang Key Laboratory of Cosmology and Astrophysics(Liaoning)&College of Sciences Northeastern UniversityShenyang 110819China CAS Key Laboratory for Researches in Galaxies and Cosmology Department of AstronomyUniversity of Science and Technology of ChinaChinese Academy of SciencesHefei 230026China School of Astronomy and Space Sciences University of Science and Technology of ChinaHefei 230026China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern UniversityShenyang 110819China Key Laboratory of Data Analytics and Optimization for Smart Industry(Ministry of Education) Northeastern Universty Shenyang 110819China

Gravitational waves(GWs) from compact binary coalescences encode the absolute luminosity distances of GW sources. Once the redshifts of GW sources are known, one can use the distance-redshift relation to constrain cosmological parameters. One way to obtain the redshifts is to localize GW sources by GW observations and then use galaxy catalogs to determine redshifts from a statistical analysis of redshift information of the potential host galaxies, commonly referred to as the dark siren method. The third-generation(3G) GW detectors are planned to work in the 2030s and will observe numerous compact binary *** these GW events as dark sirens requires high-quality galaxy catalogs from future sky survey projects. The china Space Station Telescope(CSST) will be launched in 2024 and will observe billions of galaxies within a 17500 deg^(2) survey area with redshift up to z ~ 4, providing photometric and spectroscopic galaxy catalogs. In this work, we simulate the CSST galaxy catalogs and the 5-year GW data from the 3G GW detectors and combine them to infer the Hubble constant(H_(0)). Our results show that the measurement precision of H0could reach the sub-percent level, meeting the standard of precision cosmology. We conclude that the synergy between CSST and the 3G GW detectors is of great significance in measuring the Hubble constant.

关键词： CSST galaxy survey galaxy catalogs gravitational waves dark sirens the Hubble constant

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STAGGER: Enabling All-in-One Subarray Sensing for Efficient Module-level Processing in Open-Bitline ReRAM 24

STAGGER: Enabling All-in-One Subarray Sensing for Efficient ...

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61st ACM/IEEE Design Automation Conference, DAC 2024

作者： Wang, Chengning Feng, Dan Hu, Yuchong Tong, Wei Liu, Jingning Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage Systems Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798400706011

Emerging resistive RAM (ReRAM) devices can in-situ execute vector-matrix-multiplication (VMM) and is able to achieve energy-efficient in-memory scientific computing. However, the peripheral separated S&Hs and ADCs for row buffering and sensing in conventional designs are the system bottleneck. We propose an ADC-less all-in-one processing-in-ReRAM design that enables the precharge once, readout multiple-bits (PORM) functionality for overlapping the tRCD latencies of different-significance result bits sensed out on the same bitline. Specifically, to support PORM sensing, we propose a cascaded-feedback bitline sensing architecture for VMM and a buffering-and-sensing-collocated sense amplifier elemental design with the bitline and the storage node fully decoupled for enabling conflict-free column accesses. We further propose cross-level inter-leaving mechanism for successive column VMM accesses to reduce the overall latency through improving the hardware spatiotemporal utilization. Experimental results show that our proposed design achieves 297% overall performance improvement and 85.8% energy reduction, compared with an aggressive baseline. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Feedback amplifiers

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Learned Video Compression With Refined Adaptive Flow Pyramid And Coordinate-Aware Attention

Learned Video Compression With Refined Adaptive Flow Pyramid...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Huang, Qian Liu, Wenting Li, Xin Wang, Yiming College of Computer Science and Software Engineering Hohai University Nanjing China Key Laboratory of Water Big Data Technology Ministry of Water Resources Hohai University Nanjing China

ISBN: (纸本)9798350368741

In video compression, motion estimation and motion compensation are critical for achieving efficient encoding. Although the commonly used SpyNet and bilinear interpolation have contributed in improving the compression efficiency, they still have limitations. SpyNet often loses details and fails to fully utilize the feature extraction capabilities of deep networks. Furthermore, bilinear interpolation inherently attenuates high-frequency information, leading to frame blurring and distortion. In this paper, we propose a novel video compression algorithm. To overcome the limitations of SpyNet, we propose a refined adaptive flow pyramid network. This network uses a multi-scale feature pyramid to capture more details. Moreover, we use an iterative cost volume refinement engine that improves the feature representation of the network and iteratively improve the accuracy of motion estimation. In addition, to overcome the limitation of bilinear interpolation, we propose a coordinate-aware attention module, which captures more high-frequency information to improve the accuracy of motion compensation. Experimental results show that our method outperforms VTM-13.2 (LDP) in terms of PSNR. © 2025 IEEE.

关键词： coordinate network cross attention flow pyramid learned video compression

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Improving Adaptive Runoff Forecasts in data-Scarce Watersheds Through Personalized Federated Learning 27th

Improving Adaptive Runoff Forecasts in Data-Scarce Watershe...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Xie, Zaipeng Zhang, Xiangqin Wang, Yunfei Jie, Xuanyao Fang, Wenhao Cai, Yanping Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China College of Computer Science and Software Engineering Hohai University Nanjing China

ISBN: (纸本)9783031781827

Runoff forecasting plays a crucial role in water resource management and flood mitigation, but it often faces significant challenges due to data deficiency and decentralized datasets. Inadequate hydrological data in many watersheds hinders the development of accurate prediction models, while organizational barriers and concerns about data privacy lead to information silos, preventing effective collaborative modeling. To address these issues, we propose FedHydroDSW, a novel personalized federated learning framework for runoff forecasting in data-scarce watersheds. Our approach enables decentralized model training across multiple organizations without requiring direct data sharing, thus preserving data privacy. FedHydroDSW incorporates unique model similarity metrics and parameter adaptation methods, utilizing insights from data-rich areas to refine forecasts in data-scarce regions. Experiments on the CAMELS dataset show that FedHydroDSW significantly outperforms standalone and conventional federated learning methods, with an average 30.1% increase in NSE, and reductions of 28% in RAE and 26.6% in RMSE across multiple data-scarce basins. By enabling secure cross-silo learning, our FedHydroDSW strategy paves the way toward more equitable runoff forecasting globally. The customized training and pattern recognition techniques represent major progress in deploying federated intelligence for impactful hydrological predictions. Our code is publicly available at https://***/xyjie37/FedHydroDSW. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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knowledge-augmented Self-training of A Question Rewriter for Conversational knowledge Base Question Answering

Knowledge-augmented Self-training of A Question Rewriter for...

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2022 Findings of the Association for Computational Linguistics: EMNLP 2022

作者： Ke, Xirui Zhang, Jing Lv, Xin Xu, Yiqi Cao, Shulin Li, Cuiping Chen, Hong Li, Juanzi School of Information Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education China Department of Computer Science and Technology Tsinghua University Beijing China

The recent rise of conversational applications such as online customer service systems and intelligent personal assistants has promoted the development of conversational knowledge base question answering (ConvKBQA). Different from the traditional single-turn KBQA, ConvKBQA usually explores multi-turn questions around a topic, where ellipsis and coreference pose great challenges to the single-turn KBQA systems which require self-contained questions. In this paper, we propose a rewrite- and-reason framework to first produce a full-fledged rewritten question based on the conversation history and then reason the answer by existing single-turn KBQA models. To overcome the absence of the rewritten supervision signals, we introduce a knowledge-augmented self-training mechanism to transfer the question rewriter from another dataset to adapt to the current knowledge base. Our question rewriter is decoupled from the subsequent QA process, which makes it easy to be united with either retrieval-based or semantic parsing-based KBQA models. Experiment results demonstrate the effectiveness of our method and a new state-of-the-art result is achieved. The code and dataset are available online now. © 2022 Association for Computational Linguistics.

关键词： knowledge based systems

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Plus ça change, plus c'est la même chose – The Australian Records Management Case 18th

Plus ça change, plus c'est la même chose – The Australian...

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18th International Conference on Information for a Better World: Normality, Virtuality, Physicality, Inclusivity, iConference 2023

作者： Xie, Sherry L. Gao, Yubao Ma, Linqing School of Information Resource Management Renmin University of China Beijing100872 China Center for Digital Records Management Research Renmin University of China Beijing100872 China Key Laboratory of Data Engineering and Data Knowledge of the Ministry of Education of China Beijing100872 China Center for Archival Undertakings Renmin University of China Beijing100872 China

ISBN: (纸本)9783031280344

Intrigued by the information management replacing records management phenomenon, this study aimed at shedding light on it. Relying on preserved websites, the study examined relevant contents from 2007 to early 2022. It discovered the battle between records and information, and later on information asset. This battle incurred dazzling changes in managerial considerations, yet the challenges to management – either towards records, information, information and records, or information asset – remained the same. So were the unsatisfactory outcomes. A case seemed perfect to tell the plus ça change, plus c'est la même chose story. It is our hope that future studies can gather mighty strengthen and thus settle down the records-information battle once for all. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Information management

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Ensemble Classifier Design Based on Perturbation Binary Salp Swarm Algorithm for Classification

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Computer Modeling in engineering & Sciences 2023年第4期135卷 653-671页

作者： Xuhui Zhu Pingfan Xia Qizhi He Zhiwei Ni Liping Ni School of Management Hefei University of TechnologyHefei230009China School of Statistics Zhejiang Gongshang UniversityHangzhou310018China Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of EducationHefei230009China Collaborative Innovation Center for Statistical Data Engineering Technology and Application ZhejiangGongshang UniversityHangzhou310018China

Multiple classifier system exhibits strong classification capacity compared with single classifiers,but they require significant computational *** ensemble system aims to attain equivalent or better classification accuracy with fewer ***,current methods fail to identify precise solutions for constructing an ensemble *** this study,we propose an ensemble classifier design technique based on the perturbation binary salp swarm algorithm(ECDPB).Considering that extreme learning machines(ELMs)have rapid learning rates and good generalization ability,they can serve as the basic classifier for creating multiple candidates while using fewer computational ***,we introduce a combined diversity measure by taking the complementarity and accuracy of ELMs into account;it is used to identify the ELMs that have good diversity and low *** addition,we propose an ECDPB with powerful optimizing ability;it is employed to find the optimal subset of *** selected ELMs can then be used to forman ensemble *** on 10 benchmark datasets have been conducted,and the results demonstrate that the proposed ECDPB delivers superior classification capacity when compared with alternative methods.

关键词： Ensemble classifier salp swarmalgorithm diversity measure multiple classifiers system extreme learning machine

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