检索结果-内蒙古大学图书馆

IEEE International Conference on big data and Cloud computing (BdCloud)

作者： Wentao Hu Dawei Jiang Sai Wu Ke Chen Gang Chen The Key Laboratory of Big Data Intelligent Computing Zhejiang Province Zhejiang University

Automatic constraint discovery from a relational database is beneficial for domain experts in fraud detection and intelligent auditing. Its objective is to discover a set of inherent constraints underlying the database such that tuples violating them are considered anomalous. In this paper, we propose Ice-Berg as the first system to simultaneously detect anomalous tuples and discover the associated human-readable constraints. The backbone of IceBerg is a novel generative network, namely KD-VAE, that integrates Kernel Density estimation with Variational AutoEncoder. KD-VAE is expected to learn the distributions of normal tuples. We can perform anomalous data detection by calculating the likelihood that the tuple fits the distributions of normal tuples and abnormality interpretation by comparing the detected anomalous tuples with their generated normal counterparts. We empirically compare the proposed method with several state-of-the-art outlier detection methods on 13 real-world datasets. The results show that IceBerg outperforms its competitors in most cases, especially for complex datasets with high-dimensional features.

关键词： Estimation Relational databases Probability distribution Fraud Kernel Task analysis Anomaly detection

来源：评论

学校读者我要写书评

暂无评论

Research on WNN Greenhouse Temperature Prediction Method Based on GA

引用

Phyton-International Journal of Experimental Botany 2022年第10期91卷 2283-2296页

作者： Wenbin Dai Lina Wang Binrui Wang Xiaohong Cui Xue Li College of Mechanical and Electronic Engineering China Jiliang UniversityHangzhou310018China Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province China Jiliang UniversityHangzhou310018China

Temperature in agricultural production has a direct impact on the growth of *** emergence of greenhouses has improved the impact of the original unpredictable changes in temperature,but the temperature modeling of greenhouses is still the main direction at *** network modeling relies on sufficient actual data to model greenhouses,but there is a widening gap in the application of different neural *** paper proposes a greenhouse temperature prediction model based on wavelet neural network with genetic algorithm(GA-WNN).With the simple network structure and the nonlinear adaptability of the wavelet basis function,wavelet neural network(WNN)improved model training speed and accuracy of prediction results compared with back propagation neural networks(BPNN),which was conducive to the prediction and control of short-term greenhouse temperature *** the same time,the genetic algorithm(GA)was introduced to globally optimize the initial weights of the original model,which improved the insensitivity of the model to the initial weights and thresholds,and improved the training speed and stability of the ***,simulation results for the greenhouse showed that the model training speed,prediction results accuracy and model stability of the GA-WNN in the greenhouse were improved in comparison to results obtained by the WNN and BPNN in the greenhouse.

关键词： Greenhouse temperature greenhouse modeling wavelet neural network genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

Knowledge circuits in pretrained transformers 24

Knowledge circuits in pretrained transformers

引用

Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Yunzhi Yao Ningyu Zhang Zekun Xi Mengru Wang Ziwen Xu Shumin Deng Huajun Chen Zhejiang University National University of Singapore NUS-NCS Joint Lab Singapore Zhejiang University and Zhejiang Key Laboratory of Big Data Intelligent Computing

ISBN: (纸本)9798331314385

The remarkable capabilities of modern large language models are rooted in their vast repositories of knowledge encoded within their parameters, enabling them to perceive the world and engage in reasoning. The inner workings of how these models store knowledge have long been a subject of intense interest and investigation among researchers. To date, most studies have concentrated on isolated components within these models, such as the Multilayer Perceptrons and attention head. In this paper, we delve into the computation graph of the language model to uncover the knowledge circuits that are instrumental in articulating specific knowledge. The experiments, conducted with GPT2 and TinyLLAMA, have allowed us to observe how certain information heads, relation heads, and Multilayer Perceptrons collaboratively encode knowledge within the model. Moreover, we evaluate the impact of current knowledge editing techniques on these knowledge circuits, providing deeper insights into the functioning and constraints of these editing methodologies. Finally, we utilize knowledge circuits to analyze and interpret language model behaviors such as hallucinations and in-context learning. We believe the knowledge circuits hold potential for advancing our understanding of Transformers and guiding the improved design of knowledge editing. Code and data are available in https://***/zjunlp/KnowledgeCircuits.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Association of Thallium Exposure with Decreased Renal Function among Chinese Adults—China,2017-2018

引用

China CDC weekly 2024年第34期6卷 867-871,I0007-I0010页

作者： Zheng Zhang Miao Zhang Yingli Qu Feng Zhao Saisai Ji Zheng Li Bing Wu Chunxian Lyu Haocan Song Qi Sun Yawei Li Xu Zhang Xulin Zheng Yidan Qiu Zihan Lu Hui Fu Lanjing Xu Wenli Zhang Yufei Luo Fangyu Li Jiayi Cai Yuanduo Zhu Ying Zhu Zhaojin Cao Yuebin Lyu Xiaoming Shi China CDC Key Laboratory of Environment and Population Health National Institute of Environmental HealthChinese Center for Disease Control and PreventionBeijingChina Center for Global Health School of Public HealthNanjing Medical UniversityNanjing CityJiangsu ProvinceChina Department of Big Data in Health Science School of Public HealthZhejiang UniversityHangzhou CityZhejiang ProvinceChina Department of Occupational Health and Environment Health School of Public HealthAnhui Medical UniversityHefei CityAnhui ProvinceChina School of Public Health China Medical UniversityShenyang CityLiaoning ProvinceChina National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases(NITFID) National Institute of Environmental HealthChinese Center for Disease Control and PreventionBeijingChina

Summary What is already known about this topic?Thallium(Tl)is significantly more toxic than heavy metals such as lead,cadmium,and ***,previous studies examining the relationship between Tl exposure and the risk of chr... 详细信息

关键词： Renal kidney mercury

来源：评论

学校读者我要写书评

暂无评论

IMPRESS: an importance-informed multi-tier prefix KV storage system for large language model inference 25

IMPRESS: an importance-informed multi-tier prefix KV storage...

引用

Proceedings of the 23rd USENIX Conference on File and Storage Technologies

作者： Weijian Chen Shuibing He Haoyang Qu Ruidong Zhang Siling Yang Ping Chen Yi Zheng Baoxing Huai Gang Chen The State Key Laboratory of Blockchain and Data Security Zhejiang University and Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security and Zhejiang Key Laboratory of Big Data Intelligent Computing The State Key Laboratory of Blockchain and Data Security Zhejiang University Huawei Cloud

ISBN: (纸本)9781939133458

Modern advanced large language model (LLM) applications often prepend long contexts before user queries to improve model output quality. These contexts frequently repeat, either partially or fully, across multiple queries. Existing systems typically store and reuse the keys and values of these contexts (referred to as prefix KVs) to reduce redundant computation and time to first token (TTFT). When prefix KVs need to be stored on disks due to insufficient CPU memory, reusing them does not always reduce TTFT, as disk I/O latency is high. In this paper, we propose IMPRESS, an importance-informed multi-tier prefix KV storage system to reduce I/O delay for LLM inference by only loading important prefix KVs. IMPRESS first leverages the insight that there is significant similarity in important token index sets across attention heads and introduces an I/O-efficient important KV identification algorithm. It then optimizes prefix KV storage and caching through importance-informed KV management, reducing TTFT during model inference. Our experimental results show that IMPRESS can reduce TTFT by up to 2.8× compared to state-of-the-art systems, while maintaining comparable inference accuracy.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Agent planning with world knowledge model 24

Agent planning with world knowledge model

引用

Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Shuofei Qiao Runnan Fang Ningyu Zhang Yuqi Zhu Xiang Chen Shumin Deng Yong Jiang Pengjun Xie Fei Huang Huajun Chen Zhejiang University National University of Singapore NUS-NCS Joint Lab Alibaba Group Zhejiang University and Zhejiang Key Laboratory of Big Data Intelligent Computing

ISBN: (纸本)9798331314385

Recent endeavors towards directly using large language models (LLMs) as agent models to execute interactive planning tasks have shown commendable results. Despite their achievements, however, they still struggle with brainless trial-and-error in global planning and generating hallucinatory actions in local planning due to their poor understanding of the "real" physical world. Imitating humans' mental world knowledge model which provides global prior knowledge before the task and maintains local dynamic knowledge during the task, in this paper, we introduce parametric World Knowledge Model (WKM) to facilitate agent planning. Concretely, we steer the agent model to self-synthesize knowledge from both expert and sampled trajectories. Then we develop WKM, providing prior task knowledge to guide the global planning and dynamic state knowledge to assist the local planning. Experimental results on three complex real-world simulated datasets with three state-of-the-art open-source LLMs, Mistral-7B, Gemma-7B, and Llama-3-8B, demonstrate that our method can achieve superior performance compared to various strong baselines. Other interesting findings include: 1) our instance-level task knowledge can generalize better to unseen tasks, 2) weak WKM can guide strong agent model planning, and 3) unified WKM training has promising potential for further development. The code is available at https://***/zjunlp/WKM.

关键词：

来源：评论

学校读者我要写书评

暂无评论

LeapGNN: accelerating distributed GNN training leveraging feature-centric model migration 25

LeapGNN: accelerating distributed GNN training leveraging fe...

引用

Proceedings of the 23rd USENIX Conference on File and Storage Technologies

作者： Weijian Chen Shuibing He Haoyang Qu Xuechen Zhang The State Key Laboratory of Blockchain and Data Security Zhejiang University and Zhejiang Lab and Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security and Zhejiang Key Laboratory of Big Data Intelligent Computing Washington State University Vancouver

ISBN: (纸本)9781939133458

Distributed training of graph neural networks (GNNs) has become a crucial technique for processing large graphs. Prevalent GNN frameworks are model-centric, necessitating the transfer of massive graph vertex features to GNN models, which leads to a significant communication bottleneck. Recognizing that the model size is often significantly smaller than the feature size, we propose LeapGNN, a feature-centric framework that reverses this paradigm by bringing GNN models to vertex features. To make it truly effective, we first propose a micrograph-based training strategy that leverages a refined structure to enhance locality, combined with the model migration technique, to minimize remote feature retrieval. Then, we devise a feature pre-gathering approach that merges multiple fetch operations into a single one to eliminate redundant feature transmissions. Finally, we employ a micrograph-based merging method that adjusts the number of micrographs for each worker to minimize kernel switches and synchronization overhead. Our experimental results demonstrate that LeapGNN achieves a performance speedup of up to 4.2× compared to the state-of-the-art method, namely P3.

关键词：

来源：评论

学校读者我要写书评

暂无评论

AN APPROXIMATION SCHEME FOR SOLVING QUASI-VARIATIONAL INCLUSIONS AND QUASIMONOTONE VARIATIONAL INEQUALITIES

引用

UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2022年第3期84卷 41-52页

作者： Yu, Youli Chen, Kun Zhu, Li-Jun School of Electronics and Information Engineering Taizhou University Linhai317000 China Computer Center Taizhou Hospital of Zhejiang Province Linhai China The Key Laboratory of Intelligent Information and Big Data Processing of NingXia North Minzu University Yinchuan750021 China

In this article, we discuss iterative schemes for solving quasi-variational inclusions and quasimonotone variational inequalities in Hilbert spaces. An iterative scheme is presented which consists of resolvent technique, extragradient-type algorithm and selfadaptive linear search rule. Under several appropriate conditions, we prove the convergence of the investigated scheme. © 2022, Politechnica University of Bucharest. All rights reserved.

关键词： Variational techniques

来源：评论

学校读者我要写书评

暂无评论

THE RIEMANN PROBLEM WITH DELTA INITIAL data FOR THE NON-ISENTROPIC IMPROVED AW-RASCLE-ZHANG MODEL

引用

Acta Mathematica Scientia 2023年第1期43卷 237-258页

作者：蒋伟峰陈停停李彤王振 College of Science Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang ProvinceChina Jiliang UniversityHangzhou310018China School of Mathematics and Physics China University of GeosciencesWuhan430074China Department of Mathematics The University of IowaIowaIA52246USA Center for Mathematical Sciences and Department of Mathematics Wuhan University of TechnologyWuhan430070China

In this paper,we study the Radon measure initial value problem for the nonisentropic improved Aw-Rascle-Zhang *** arbitrary convex F(u)in this model we construct the Riemann solutions by elementary waves andδ-shock waves using the method of generalized characteristic *** obtain the solutions constructively for initial data containing the Dirac measure by taking the limit of the solutions for that with three piecewise ***,we analyze different kinds of wave interactions,including the interactions of theδ-shock waves with elementary waves.

关键词： Riemann problem non-isentropic improved Aw-Rascle-Zhang model δ-shock wave wave interactions traffic flow

来源：评论

学校读者我要写书评

暂无评论

Knowledge Circuits in Pretrained Transformers

arXiv

引用

arXiv 2024年

作者： Yao, Yunzhi Zhang, Ningyu Xi, Zekun Wang, Mengru Xu, Ziwen Deng, Shumin Chen, Huajun Zhejiang University China National University of Singapore NUS-NCS Joint Lab Singapore Zhejiang Key Laboratory of Big Data Intelligent Computing China

The remarkable capabilities of modern large language models are rooted in their vast repositories of knowledge encoded within their parameters, enabling them to perceive the world and engage in reasoning. The inner workings of how these models store knowledge have long been a subject of intense interest and investigation among researchers. To date, most studies have concentrated on isolated components within these models, such as the Multilayer Perceptrons and attention head. In this paper, we delve into the computation graph of the language model to uncover the knowledge circuits that are instrumental in articulating specific knowledge. The experiments, conducted with GPT2 and TinyLLAMA, have allowed us to observe how certain information heads, relation heads, and Multilayer Perceptrons collaboratively encode knowledge within the model. Moreover, we evaluate the impact of current knowledge editing techniques on these knowledge circuits, providing deeper insights into the functioning and constraints of these editing methodologies. Finally, we utilize knowledge circuits to analyze and interpret language model behaviors such as hallucinations and in-context learning. We believe the knowledge circuits hold potential for advancing our understanding of Transformers and guiding the improved design of knowledge editing1 Copyright © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

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：