检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Zhou, Baojian Sun, Yifan Harikandeh, Reza Babanezhad Guo, Xingzhi Yang, Deqing Xiao, Yanghua The School of Data Science Fudan University China Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China Department of Computer Science Stony Brook University United States Samsung SAIT AI Lab Korea Republic of

Given the damping factor α and precision tolerance ϵ, Andersen et al. [2] introduced Approximate Personalized PageRank (APPR), the de facto local method for approximating the PPR vector, with runtime bounded by Θ(1/(αϵ)) independent of the graph size. Recently, Fountoulakis & Yang [12] asked whether faster local algorithms could be developed using Õ(1/(√αϵ)) operations. By noticing that APPR is a local variant of Gauss-Seidel, this paper explores the question of whether standard iterative solvers can be effectively localized. We propose to use the locally evolving set process, a novel framework to characterize the algorithm locality, and demonstrate that many standard solvers can be effectively localized. Let vol(St) and γt be the running average of volume and the residual ratio of active nodes St during the process. We show vol(St)/γt ≤ 1/ϵ and prove APPR admits a new runtime bound Õ(vol(St)/(αγt)) mirroring the actual performance. Furthermore, when the geometric mean of residual reduction is Θ(√α), then there exists c ∈ (0, 2) such that the local Chebyshev method has runtime Õ(vol(St)/(√α(2 − c))) without the monotonicity assumption. Numerical results confirm the efficiency of this novel framework and show up to a hundredfold speedup over corresponding standard solvers on real-world graphs. © 2024, CC BY.

关键词： Iterative methods

来源：评论

学校读者我要写书评

暂无评论

Efficient Photothermal Catalytic Oxidation Enabled by Three-Dimensional Nanochannel Substrates

引用

Environmental science and Technology 2024年第11期58卷 5153-5161页

作者： Li, Yifei Zhang, Qianpeng Chong, Yanan Huang, Wei-Hsiang Chen, Chi-Liang Jin, Xiaojing Chen, Guangxu Fan, Zhiyong Qiu, Yongcai Ye, Daiqi School of Environment and Energy State Key Laboratory of Luminescent Materials and Devices Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control South China University of Technology Guangdong Guangzhou510000 China State Key Laboratory of Photovoltaic Science and Technology Shanghai Frontiers Science Research Base of Intelligent Optoelectronics and Perception Institute of Optoelectronics Fudan University Shanghai200433 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Kowloon999077 Hong Kong State Key Laboratory of Advanced Displays and Optoelectronics Technologies The Hong Kong University of Science and Technology Clear Water Bay Kowloon999077 Hong Kong National Synchrotron Radiation Research Center 101 Hsin-Ann Road Hsinchu Science Park Hsinchu30076 Taiwan Taipei10607 Taiwan College of Light Chemical Industry and Materials Engineering Shunde Polytechnic Foshan528333 China

Photothermal catalysis exhibits promising prospects to overcome the shortcomings of high-energy consumption of traditional thermal catalysis and the low efficiency of photocatalysis. However, there is still a challenge to develop catalysts with outstanding light absorption capability and photothermal conversion efficiency for the degradation of atmospheric pollutants. Herein, we introduced the Co3O4 layer and Pt nanoclusters into the three-dimensional (3D) porous membrane through the atomic layer deposition (ALD) technique, leading to a Pt/Co3O4/AAO monolithic catalyst. The 3D ordered nanochannel structure can significantly enhance the solar absorption capacity through the light-trapping effect. Therefore, the embedded Pt/Co3O4 catalyst can be rapidly heated and the O2 adsorbed on the Pt clusters can be activated to generate sufficient O2- species, exhibiting outstanding activity for the diverse VOCs (toluene, acetone, and formaldehyde) degradation. Optical characterization and simulation calculation confirmed that Pt/Co3O4/AAO exhibited state-of-the-art light absorption and a notable localized surface plasmon resonance (LSPR) effect. In situ diffuse reflectance infrared Fourier transform spectrometry (in situ DRIFTS) studies demonstrated that light irradiation can accelerate the conversion of intermediates during toluene and acetone oxidation, thereby inhibiting byproduct accumulation. Our finding extends the application of AAO’s optical properties in photothermal catalytic degradation of air pollutants. © 2024 American Chemical Society.

关键词： Catalysis

来源：评论

学校读者我要写书评

暂无评论

Moisture stable and ultrahigh-rate Ni/Mn-based sodium-ion battery cathodes via K^(+)decoration

引用

Nano Research 2023年第5期16卷 6890-6902页

作者： Tao Yuan Yuanyuan Sun Siqing Li Haiying Che Qinfeng Zheng Yongjian Ni Yixiao Zhang Jie Zou Xiaoxian Zang Shi-Hao Wei Yuepeng Pang Shuixin Xia Shiyou Zheng Liwei Chen Zi-Feng Ma School of Materials and Chemistry University of Shanghai for Science and TechnologyShanghai 200093China Zhejiang Natrium Energy Co. Ltd.Shaoxing 312000China In-situ Center for Physical Sciences Shanghai Electrochemical Energy Device Research CenterFrontiers Science Center for Transformative MoleculesSchool of Chemistry and Chemical EngineeringShanghai Jiao Tong UniversityShanghai 200240China Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315000China Key Laboratory of Solar Energy Utilization&Energy Saving Technology of Zhejiang Province Zhejiang Energy R&D Institute Co.Ltd.Hangzhou 311121China

As one of the most promising cathodes for sodium-ion batteries(SIBs),the layered transition metal oxides have attracted great attentions due to their high specific capacities and facile ***,their applications are still hindered by the problems of poor moisture stability and sluggish Na^(+)diffusion caused by intrinsic structural Jahn–Teller ***,we demonstrate a new approach to settle the above issues through introducing K^(+)into the structures of Ni/Mn-based *** physicochemical characterizations reveal that K^(+)induces atomic surface reorganization to form the birnessite-type K_(2)Mn_(4)O_(8).Combining with the phosphate,the mixed coating layer protects the cathodes from moisture and hinders metal dissolution into the electrolyte ***,K^(+)substitution at Na site in the bulk structure can not only widen the lattice-spacing for favoring Na^(+)diffusion,but also work as the rivet to restrain the grain crack upon *** as achieved K^(+)-decorated P2-Na_(0.67)Mn_(0.75)Ni_(0.2)5O_(2)(NKMNO@KM/KP)cathodes are tested in both coin cell and pouch cell configurations using Na metal or hard carbon(HC)as ***,the NKMNO@KM/KP||Na half-cell demonstrates a high rate performance of 50 C and outstanding cycling performance of 90.1%capacity retention after 100 cycles at 5 ***,the NKMNO@KM/KP||HC fullcell performed a promising energy density of 213.9 Wh·kg^(−1).This performance significantly outperforms most reported state-ofthe-art ***,by adopting this strategy on O3-NaMn_(0.5)Ni_(0.5)O_(2),we further proved the universality of this method on layered cathodes for SIBs.

关键词： sodium-ion battery cathode layered oxide material K^(+)decoration

来源：评论

学校读者我要写书评

暂无评论

Holistic Sentence Embeddings for Better Out-of-Distribution Detection

Holistic Sentence Embeddings for Better Out-of-Distribution ...

引用

2022 Findings of the Association for Computational Linguistics: EMNLP 2022

作者： Chen, Sishuo Bi, Xiaohan Gao, Rundong Sun, Xu Center for Data Science Peking University China MOE Key Laboratory of Computational Linguistics School of Computer Science Peking University China

Detecting out-of-distribution (OOD) instances is significant for the safe deployment of NLP models. Among recent textual OOD detection works based on pretrained language models (PLMs), distance-based methods have shown superior performance. However, they estimate sample distance scores in the last-layer CLS embedding space and thus do not make full use of linguistic information underlying in PLMs. To address the issue, we propose to boost OOD detection by deriving more holistic sentence embeddings. On the basis of the observations that token averaging and layer combination contribute to improving OOD detection, we propose a simple embedding approach named Avg-Avg, which averages all token representations from each intermediate layer as the sentence embedding and significantly surpasses the state-of-the-art on a comprehensive suite of benchmarks by a 9.33% FAR95 margin. Furthermore, our analysis demonstrates that it indeed helps preserve general linguistic knowledge in fine-tuned PLMs and substantially benefits detecting background shifts. The simple yet effective embedding method can be applied to fine-tuned PLMs with negligible extra costs, providing a free gain in OOD detection. Our code is available at https://***/lancopku/Avg-Avg. © 2022 Association for Computational Linguistics.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

A Blackbox Fuzzing Based on Automated State Machine Extraction

A Blackbox Fuzzing Based on Automated State Machine Extracti...

引用

IEEE International Conference on data science in Cyberspace (DSC)

作者： Zhaowei Zhang Wenjing Yu Zibin Wang Youlin Xiang National Key Laboratory of Science and Technology on Information System Security Beijing China Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai China

Currently, protocol fuzzing techniques mainly employ two approaches: greybox fuzzing based on mutation and blackbox fuzzing based on generation. Greybox fuzzing techniques use message exchanges between the protocol server and actual clients as seeds, and generate test cases through mutation. Although this approach can provide coverage information of the SUT's code and state space through instrumentation and feedback, its drawback lies in the relatively random mutation strategy, which makes it challenging to validate the SUT's message verification process. This paper addresses this limitation by utilizing artificial intelligence techniques to extract protocol state machines. It aims to overcome the reliance on manual work in blackbox fuzzing based on generation and leverage its advantages to generate more effective fuzzing test cases. The study utilizes Prompt-Learning technology to analyze the semantic information in protocol RFC documents, obtain corresponding intermediate representations, and extract protocol state machines from these representations. Taking the BGP protocol as an experimental subject, the experimental results demonstrate a certain level of accuracy in the obtained protocol state machines and the ability to generate test cases from these state machines, thereby enhancing the automation level of protocol fuzzing.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Towards Stricter Black-box Integrity Verification of Deep Neural Network Models 24

Towards Stricter Black-box Integrity Verification of Deep Ne...

引用

32nd ACM International Conference on Multimedia, MM 2024

作者： He, Chaoxiang Bai, Xiaofan Ma, Xiaojing Zhu, Bin B. Hu, Pingyi Fu, Jiayun Jin, Hai Zhang, Dongmei Huazhong University of Science and Technology Hubei Wuhan China Shanghai Jiao Tong University Shanghai China Microsoft Corporation Beijing China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security School of Cyber Science and Engineering Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China

ISBN: (纸本)9798400706868

Cloud-based machine learning services offer significant advantages but also introduce the risk of tampering with cloud-deployed deep neural network (DNN) models. Black-box integrity verification (BIV) allows model owners and end-users to determine if a cloud-deployed DNN model has been tampered with by examining only the top-1 label responses. Fingerprinting generates fingerprint samples to query the model, achieving BIV with no impact on the model's accuracy. In this paper, we present BIVBench, the first comprehensive benchmark for BIV of DNN models. BIVBench covers 16 types of model modifications, providing extensive coverage of practical modification scenarios. Our analysis reveals that existing fingerprinting methods, which are typically focused on significant tampering, lack the sensitivity needed to effectively detect subtle yet common and potentially severe modifications. To address this limitation, we propose MiSentry (Model Integrity Sentry), a novel fingerprinting method that leverages meta-learning. MiSentry strategically incorporates a few subtly modified models into the meta-learning model zoo and maximizes the divergence of output predictions between the target model and the modified models in the model zoo to generate highly sensitive, generalizable, and effective fingerprint samples. Extensive evaluations using BIVBench demonstrate that MiSentry outperforms existing state-of-the-art methods overall and significantly surpasses them in detecting subtle modifications. The BIVBench and supplementary materials are available at: https://***/CGCL-codes/BIVBench. © 2024 ACM.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Expose Backdoors on the Way: A Feature-Based Efficient Defense against Textual Backdoor Attacks

Expose Backdoors on the Way: A Feature-Based Efficient Defen...

引用

2022 Findings of the Association for Computational Linguistics: EMNLP 2022

作者： Chen, Sishuo Yang, Wenkai Zhang, Zhiyuan Bi, Xiaohan Sun, Xu Center for Data Science Peking University China MOE Key Laboratory of Computational Linguistics School of Computer Science Peking University China

Natural language processing (NLP) models are known to be vulnerable to backdoor attacks, which poses a newly arisen threat to NLP models. Prior online backdoor defense methods for NLP models only focus on the anomalies at either the input or output level, still suffering from fragility to adaptive attacks and high computational cost. In this work, we take the first step to investigate the unconcealment of textual poisoned samples at the intermediate-feature level and propose a feature-based efficient online defense method. Through extensive experiments on existing attacking methods, we find that the poisoned samples are far away from clean samples in the intermediate feature space of a poisoned NLP model. Motivated by this observation, we devise a distance-based anomaly score (DAN) to distinguish poisoned samples from clean samples at the feature level. Experiments on sentiment analysis and offense detection tasks demonstrate the superiority of DAN, as it substantially surpasses existing online defense methods in terms of defending performance and enjoys lower inference costs. Moreover, we show that DAN is also resistant to adaptive attacks based on feature-level regularization. Our code is available at https://***/lancopku/DAN. © 2022 Association for Computational Linguistics.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Lightweight Network Based Real-time Anomaly Detection Method for Caregiving at Home 25

Lightweight Network Based Real-time Anomaly Detection Method...

引用

25th IEEE International Conference on computer Supported Cooperative Work in Design, CSCWD 2022

作者： Wang, Bin Wu, Xingjiao Gong, Miaomiao Zhao, Jin Sun, Yuling School of Computer Science Fudan University Shanghai China Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China School of Computer Science and Technology East China Normal University Shanghai China

ISBN: (纸本)9781665405270

Using data-driven technologies to support the healthcare of the elderly has been largely celebrated as an effective means. This paper focuses on the issue of using video-based sensing technologies to remotely monitor the activities and conditions of the elderly. Although it is a widely explored field, the high cost and high infrastructural requirements of most existing technologies usually challenge their effectiveness and efficiency in practical caregiving context. To address these challenges, we propose a lightweight network based real-time anomaly detection system, which consists of video-based ADL sensing and pre-processing, AI streaming aggregating and cluster computing. We examine our method by implementing and deploying it into a real-world care facility for the elderly in shanghai China. The results show that our method has good performance in expansibility, reliability, bandwidth availability, accuracy and privacy protection. © 2022 IEEE.

关键词： Fall detection

来源：评论

学校读者我要写书评

暂无评论

BBT-Fin: Comprehensive Construction of Chinese Financial Domain Pre-trained Language Model, Corpus and Benchmark

arXiv

引用

arXiv 2023年

作者： Lu, Dakuan Liang, Jiaqing Xu, Yipei He, Qianyu Geng, Yipeng Han, Mengkun Xin, Yingsi Wu, Hengkui Xiao, Yanghua Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China School of Data Science Fudan University China SuperSymmetry Technologies China

To advance Chinese financial natural language processing (NLP), we introduce BBT-FinT5, a new Chinese financial pre-training language model based on the T5 model. To support this effort, we have built BBT-FinCorpus, a large-scale financial corpus with approximately 300GB of raw text from four different sources. In general domain NLP, comprehensive benchmarks like GLUE and SuperGLUE have driven significant advancements in language model pre-training by enabling head-to-head comparisons among models. Drawing inspiration from these benchmarks, we propose BBT-CFLEB, a Chinese Financial Language understanding and generation Evaluation Benchmark, which includes six datasets covering both understanding and generation tasks. Our aim is to facilitate research in the development of NLP within the Chinese financial domain. Our model, corpus and benchmark are released at https://***/ssymmetry/ BBT-FinCUGE-Applications. Our work belongs to the Big Bang Transformer (BBT), a large-scale pre-trained language model project. © 2023, CC BY-NC-SA.

关键词： Benchmarking

来源：评论

学校读者我要写书评

暂无评论

Neighbors Are Not Strangers: Improving Non-Autoregressive Translation under Low-Frequency Lexical Constraints

Neighbors Are Not Strangers: Improving Non-Autoregressive Tr...

引用

2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2022

作者： Zeng, Chun Chen, Jiangjie Zhuang, Tianyi Xu, Rui Yang, Hao Qin, Ying Tao, Shimin Xiao, Yanghua Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China Huawei Translation Services Center Fudan-Aishu Cognitive Intelligence Joint Research Center China

ISBN: (纸本)9781955917711

Lexically constrained neural machine translation (NMT) draws much industrial attention for its practical usage in specific domains. However, current autoregressive approaches suffer from high latency. In this paper, we focus on non-autoregressive translation (NAT) for this problem for its efficiency advantage. We identify that current constrained NAT models, which are based on iterative editing, do not handle low-frequency constraints well. To this end, we propose a plug-in algorithm for this line of work, i.e., Aligned Constrained Training (ACT), which alleviates this problem by familiarizing the model with the source-side context of the constraints. Experiments on the general and domain datasets show that our model improves over the backbone constrained NAT model in constraint preservation and translation quality, especially for rare constraints. © 2022 Association for Computational Linguistics.

关键词： Neural machine translation

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：