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arXiv 2023年

作者： Lu, Ning Liu, Shengcai He, Rui Ong, Yew-Soon Wang, Qi Tang, Ke Guangdong Key Laboratory of Brain-Inspired Intelligent Computation China Department of Computer Science and Engineering Southern University of Science and Technology China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Singapore Singapore

Large language models (LLMs) have shown remarkable performance in various tasks and have been extensively utilized by the public. However, the increasing concerns regarding the misuse of LLMs, such as plagiarism and spamming, have led to the development of multiple detectors, including fine-tuned classifiers and statistical methods. In this study, we equip LLMs with prompts, rather than relying on an external paraphraser, to evaluate the vulnerability of these detectors. We propose a novel Substitution-based In-Context example Optimization method (SICO) to automatically construct prompts for evading the detectors. SICO is cost-efficient as it requires only 40 human-written examples and a limited number of LLM inferences to generate a prompt. Moreover, once a task-specific prompt has been constructed, it can be universally used against a wide range of detectors. Extensive experiments across three real-world tasks demonstrate that SICO significantly outperforms the paraphraser baselines and enables GPT-3.5 to successfully evade six detectors, decreasing their AUC by 0.5 on average. Furthermore, a comprehensive human evaluation show that the SICO-generated text achieves human-level readability and task completion rates, while preserving high imperceptibility. Finally, we propose an ensemble approach to enhance the robustness of detectors against SICO attack. Copyright © 2023, The Authors. All rights reserved.

关键词： Computational linguistics

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HPC-Atlas:Computationally Constructing A Comprehensive Atlas of Human Protein Complexes

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Genomics, Proteomics & Bioinformatics 2023年第5期21卷 976-990页

作者： Yuliang Pan Ruiyi Li Wengen Li Liuzhenghao Lv Jihong Guan Shuigeng Zhou Department of Computer Science and Technology College of Electronic and Information EngineeringTongji UniversityShanghai 201804China Translational Medical Center for Stem Cell Therapy Shanghai East HospitalSchool of MedicineTongji UniversityShanghai 200120China Shanghai Key Laboratory of Intelligent Information Processing School of Computer ScienceFudan UniversityShanghai 200433China

A fundamental principle of biology is that proteins tend to form complexes to play important roles in the core functions of *** a complete understanding of human cellular functions,it is crucial to have a comprehensive atlas of human protein ***,we still lack such a comprehensive atlas of experimentally validated protein complexes,which prevents us from gaining a complete understanding of the compositions and functions of human protein complexes,as well as the underlying biological *** fill this gap,we built Human Protein Complexes Atlas(HPC-Atlas),as far as we know,the most accurate and comprehensive atlas of human protein complexes available to *** integrated two latest protein interaction networks,and developed a novel computational method to identify nearly 9000 protein complexes,including many previously uncharacterized *** with the existing methods,our method achieved outstanding performance on both testing and independent ***,with HPC-Atlas we identified 751 severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)-affected human protein complexes,and 456 multifunctional proteins that contain many potential moonlighting *** results suggest that HPC-Atlas can serve as not only a computing framework to effectively identify biologically meaningful protein complexes by integrating multiple protein data sources,but also a valuable resource for exploring new biological *** HPCAtlas webserver is freely available at http://***/HPC-Atlas.

关键词： Human protein complex Protein interaction network SARS-CoV-2-affected complex Multifunctional protein Complex identification method

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DESIGN OF TWO CORE PHOTONIC CRYSTAL FIBER(TC-PCF) WITH CIRCULAR-ELLIPTICAL LATTICE STRUCTURE BASED TEMPERATURE SENSOR 8

DESIGN OF TWO CORE PHOTONIC CRYSTAL FIBER(TC-PCF) WITH CIRCU...

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8th International Conference on Computing in Engineering and Technology, ICCET 2023

作者： Pravesh, Ram Kumar, Dharmendra Pandey, Bramha P. Chaudhary, Vijay Shanker Kumar, Santosh Department of Electronics and Communication Engineering Madan Mohan Malaviya University of Technology UP Gorakhpur273010 India Department of Computer Science Engineering Galgotias College of Engineering and Technology UP Greater Noida201310 India Shandong Key Laboratory of Optical Communication Science and Technology School of Physics Science and Information Technology Liaocheng University Liaocheng252059 China

ISBN: (纸本)9781839539237

This paper proposes a unique two-core photonic crystal fibre-based temperature sensor that combines circular and elliptical air hole lattice structures. Version 5.6 of the commercially available COMSOL Multiphysics software is based on the finite element method (FEM) used to develop and explore the impact of different effects of the sensor's basic geometric characteristics on its sensing capabilities. Two solid cores that are separated by vertical elliptical air holes create two independent waveguides. Because the background material's refractive index varies with temperature, Temperature has an interference with the TC-PCF transmission spectrum. The proposed TC-PCF temperature sensor can detect temperature in the range of 0 °C to 600 °C with fiber length 16 cm long and calculated sensitivity is 20 pm/°C. As a result of mode coupling between two waveguides, light is transported between one core and another core along the fibre when one core of a two-core PCF receives optical light from the input side. At 600 °C the maximum coupling length is observed 2.801 mm with operating wavelength 1.54 urn for proposed PCF. © The Institution of Engineering & Technology 2023.

关键词： Photonic crystal fibers

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Incomplete multi-view clustering via local and global co-regularization

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science China(Information sciences) 2022年第5期65卷 96-111页

作者： Jiye LIANG Xiaolin LIU Liang BAI Fuyuan CAO Dianhui WANG Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education School of Computer and Information Technology Shanxi University Artificial Intelligence Research Institute China University of Mining and Technology Department of Computer Science and Computer Engineering La Trobe University

The incompleteness of multi-view data is a phenomenon associated with real-world data mining applications, which brings a huge challenge for multi-view clustering. Although various types of clustering methods, which try to obtain a complete and consensus clustering result from a latent subspace, have been developed to overcome this problem, most methods excessively rely on views-public instances to bridge the connection with view-private instances. When lacking sufficient views-public instances, existing methods fail to transmit the information among incomplete views effectively. To overcome this limitation, we propose an incomplete multi-view clustering algorithm via local and global co-regularization(IMVC-LG). In this algorithm, we define a new objective function that is composed of two terms: local clustering from each view and global clustering from multiple views, which constrain each other to exploit the local clustering information from different incomplete views and determine a global consensus clustering result, ***, an iterative optimization method is proposed to minimize the objective function. Finally, we compare the proposed algorithm with other state-of-the-art incomplete multi-view clustering methods on several benchmark datasets to illustrate its effectiveness.

关键词： incompleteness multi-view clustering local clustering global clustering co-regularization

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Beamspace Channel Estimation for Wideband Millimeter-Wave MIMO: A Model-Driven Unsupervised Learning Approach

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IEEE Transactions on Wireless Communications 2023年第3期22卷 1808-1822页

作者： He, Hengtao Wang, Rui Jin, Weijie Jin, Shi Wen, Chao-Kai Li, Geoffrey Ye Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hong Kong The Hong Kong Polytechnic University Department of Electronic and Information Engineering Hong Kong Hong Kong Imperial College London Department of Electrical and Electronic Engineering LondonSW7 2BX United Kingdom Southeast University National Mobile Communications Research Laboratory Nanjing210096 China

Millimeter-wave (mmWave) communications have been one of the promising technologies for future wireless networks that integrate a wide range of data-demanding applications. To compensate for the large channel attenuation in mmWave band and avoid high hardware cost, a lens-based beamspace massive multiple-input multiple-output (MIMO) system is considered. However, the spatial-wideband effect in wideband mmWave systems makes channel estimation very challenging, especially when the receiver is equipped with a limited number of radio-frequency (RF) chains. Furthermore, the real channel data cannot be obtained before the mmWave system is used in a new environment, which makes it impossible to train a deep learning (DL)-based channel estimator using real data set beforehand. To solve the problem, we propose a model-driven unsupervised learning network, named learned denoising-based generalized expectation consistent (LDGEC) signal recovery network. By utilizing the Stein's unbiased risk estimator loss, the LDGEC network can be trained only with limited measurements corresponding to the pilot symbols, instead of the real channel data. Even if designed for unsupervised learning, the LDGEC network can be supervisingly trained with the real channel via the denoiser-by-denoiser way. The numerical results demonstrate that the LDGEC-based channel estimator significantly outperforms state-of-the-art compressive sensing-based algorithms when the receiver is equipped with a small number of RF chains and low-resolution ADCs. © 2002-2012 IEEE.

关键词： Channel estimation

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A RAG-BASED MULTI-AGENT LLM SYSTEM FOR NATURAL HAZARD RESILIENCE AND ADAPTATION

arXiv

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arXiv 2025年

作者： Xie, Yangxinyu Jiang, Bowen Mallick, Tanwi Bergerson, Joshua David Hutchison, John K. Verner, Duane R. Branham, Jordan Alexander, M. Ross Ross, Robert B. Feng, Yan Levy, Leslie-Anne Su, Weijie J. Taylor, Camillo J. Department of Statistics and Data Science University of Pennsylvania PhiladelphiaPA United States Department of Computer and Information Science University of Pennsylvania PhiladelphiaPA United States Mathematics and Computer Science Division Argonne National Laboratory LemontIL United States Decision and Infrastructure Sciences Division Argonne National Laboratory LemontIL United States Environmental Science Division Argonne National Laboratory LemontIL United States

Large language models (LLMs) are a transformational capability at the frontier of artificial intelligence and machine learning that can support decision-makers in addressing pressing societal challenges such as extreme natural hazard events. As generalized models, LLMs often struggle to provide context-specific information, particularly in areas requiring specialized knowledge. In this work we propose a retrieval-augmented generation (RAG)-based multi-agent LLM system to support analysis and decision-making in the context of natural hazards and extreme weather events. As a proof of concept, we present WildfireGPT, a specialized system focused on wildfire hazards. The architecture employs a user-centered, multi-agent design to deliver tailored risk insights across diverse stakeholder groups. By integrating natural hazard and extreme weather projection data, observational datasets, and scientific literature through an RAG framework, the system ensures both the accuracy and contextual relevance of the information it provides. Evaluation across ten expert-led case studies demonstrates that WildfireGPT significantly outperforms existing LLM-based solutions for decision support. © 2025, CC BY-NC-SA.

关键词： Expert systems

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Experimental and Theoretical Studies on the Self-assembled Nano-structure of Nonionic Surfactant/ionic Liquid Hybrid Systems 3

Experimental and Theoretical Studies on the Self-assembled N...

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2023 3rd International Conference on Fluid and Chemical Engineering, ICFCE 2023

作者： Zhu, Jingjing Hua, Er Harada, Masafumi Chemical Science and Engineering College Ningxia Key Laboratory of Solar Chemical Conversion Technology North Minzu University Yinchuan750021 China Department of Computer Science and Clothing Environment Faculty of Human Life and Environment Nara Women's University Nara630-8506 Japan

The self-assembled structure of various Brij series nonionic surfactants (such as Brij-30, Brij-52, Brij-56, Brij-58, Brij-35, and Brij-700) hybridized with room-temperature ionic liquids (RTILs) was studied using small-angle X-ray scattering (SAXS) measurements and DFT calculations. The detailed characterization of the obtained self-assembled structure in nonionic surfactant/RTILs hybrid system was carried out using the combination of SAXS and DFT calculations. The SAXS result indicates that the alkyl chain length dependence on the spatial correlation corresponds to the interference peak angle. The addition of Brij surfactant to the [BMIm][PF6] results in a drastic change in the interference peak position. The DFT calculation result shows that a weak hydrogen bonding was formed between the -CH, -F of RTILs ([BMIm][PF6] and [OMIm][PF6]) and the polar site -OH, -O - of surfactants (Brij-30 and Brij-56). When the mixing molar ratio of ILs/Brij is 1:1, the absolute value of interaction energy ( "E 0BSSE) is 8.10-1.43 and 4.22-0.90 kcal·mol-1 for ILs/Brij-30 and ILs/Brij-56, respectively. Meanwhile, in the case of the ILs/Brij molar ratio equal to 2:1, "E 0BSSE is 19.14-1.79 and 12.39-1.45 kcal·mol-1, respectively. It suggests that the stability of ILs/Brij hybridized system is higher for ILs/Brij-30 than for ILs/Brij56, and it has an increasing tendency to some extent with an increase of the molar fraction of ILs. © Published under licence by IOP Publishing Ltd.

关键词： X ray scattering

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Impact of Stress During COVID-19 Pandemic

Impact of Stress During COVID-19 Pandemic

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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： Hindu Angalakuditi Biswajit Bhowmik BRICS Laboratory Department of Computer Science and Engineering National Institute of Technology Karnataka Surathkal Mangalore Bharat

The COVID-19 pandemic has affected our lives in many ways. Many people faced different challenges during the pandemic to accomplish their daily activities. Many people faced various challenges during the pandemic might have been very stressful, overwhelming, and disgusting. Therefore, it is common to feel stress, irritation, mood swings, and anxiety during the pandemic. Different methodologies by medical practitioners are being taken. Additionally, researchers from academia are also trying to strengthen the methods. Unfortunately, the way for automatic, continuous, and invisible stress detection by the researchers are insufficient and not studied in depth. It becomes essential in the post-pandemic scenario due to COVID-19 disease. This paper studies the impact of stress on people during the COVID-19 pandemic. The study includes origin, classification, impact on health, prevention solutions, etc. Further statistics on the affected people by the stress during the period are provided.

关键词： COVID-19 Pandemics Mood Communication systems Anxiety disorders Organizations Curing

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GNNFORMER: A GRAPH-BASED FRAMEWORK FOR CYTOPATHOLOGY REPORT GENERATION

arXiv

引用

arXiv 2023年

作者： Zhou, Yang-Fan Yao, Kai-Lang Li, Wu-Jun National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University China

Cytopathology report generation is a necessary step for the standardized examination of pathology images. However, manually writing detailed reports brings heavy workloads for pathologists. To improve efficiency, some existing works have studied automatic generation of cytopathology reports, mainly by applying image caption generation frameworks with visual encoders originally proposed for natural images. A common weakness of these works is that they do not explicitly model the structural information among cells, which is a key feature of pathology images and provides significant information for making diagnoses. In this paper, we propose a novel graph-based framework called GNNFormer, which seamlessly integrates graph neural network (GNN) and Transformer into the same framework, for cytopathology report generation. To the best of our knowledge, GNNFormer is the first report generation method that explicitly models the structural information among cells in pathology images. It also effectively fuses structural information among cells, fine-grained morphology features of cells and background features to generate high-quality reports. Experimental results on the NMI-WSI dataset show that GNNFormer can outperform other state-of-the-art baselines. Copyright © 2023, The Authors. All rights reserved.

关键词： Cells

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Colorizing Monochromatic Radiance Fields

arXiv

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arXiv 2024年

作者： Cheng, Yean Wan, Renjie Weng, Shuchen Zhu, Chengxuan Chang, Yakun Shi, Boxin National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University China National Engineering Research Center of Visual Technology School of Computer Science Peking University China AI Innovation Center School of Computer Science Peking University China Department of Computer Science Hong Kong Baptist University Hong Kong National Key Lab of General AI School of Intelligence Science and Technology Peking University China

Though Neural Radiance Fields (NeRF) can produce colorful 3D representations of the world by using a set of 2D images, such ability becomes non-existent when only monochromatic images are provided. Since color is necessary in representing the world, reproducing color from monochromatic radiance fields becomes crucial. To achieve this goal, instead of manipulating the monochromatic radiance fields directly, we consider it as a representation-prediction task in the Lab color space. By first constructing the luminance and density representation using monochromatic images, our prediction stage can recreate color representation on the basis of an image colorization module. We then reproduce a colorful implicit model through the representation of luminance, density, and color. Extensive experiments have been conducted to validate the effectiveness of our approaches. Our project page: https://***/color-nerf. Copyright © 2024, The Authors. All rights reserved.

关键词： Color

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