检索结果-内蒙古大学图书馆

arXiv 2023年

作者： Caruana, Adrian Bandara, Madhushi Musial, Katarzyna Catchpoole, Daniel Kennedy, Paul J. Australian Artificial Intelligence Institute Faculty of Engineering and IT University of Technology Sydney Australia Complex Adaptive Systems Lab Data Science Institute Faculty of Engineering and IT University of Technology Sydney Australia Biospecimen Research Services The Children's Cancer Research Unit The Children's Hospital Westmead Australia Joint Research Centre in AI for Health and Wellness University of Technology Sydney Australia Ontario Tech University Canada

Machine learning provides many powerful and effective techniques for analysing heterogeneous electronic health records (EHR). Administrative Health Records (AHR) are a subset of EHR collected for administrative purposes, and the use of machine learning on AHRs is a growing subfield of EHR analytics. Existing reviews of EHR analytics emphasise that the data-modality of the EHR limits the breadth of suitable machine learning techniques, and pursuable healthcare applications. Despite emphasising the importance of data modality, the literature fails to analyse which techniques and applications are relevant to AHRs. AHRs contain uniquely well-structured, categorically encoded records which are distinct from other data-modalities captured by EHRs, and they can provide valuable information pertaining to how patients interact with the healthcare system. This paper systematically reviews AHR-based research, analysing 70 relevant studies and spanning multiple databases. We identify and analyse which machine learning techniques are applied to AHRs and which health informatics applications are pursued in AHR-based research. We also analyse how these techniques are applied in pursuit of each application, and identify the limitations of these approaches. We find that while AHR-based studies are disconnected from each other, the use of AHRs in health informatics research is substantial and accelerating. Our synthesis of these studies highlights the utility of AHRs for pursuing increasingly complex and diverse research objectives despite a number of pervading data- and technique-based limitations. Finally, through our findings, we propose a set of future research directions that can enhance the utility of AHR data and machine learning techniques for health informatics research. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Thermal Decomposition Mechanism of 1,3,5-Trinitroperhydro-1,3,5-Triazine: Experiments and Reaction Kinetic Modeling

SSRN

引用

SSRN 2022年

作者： Xu, Yabei Chu, Qingzhao Wang, He Wang, Shengkai Xu, Shengliang Chen, Dongping State Key Lab of Explosion Science and Technology Beijing Institute of Technology Beijing100081 China State Key Laboratory of Turbulence and Complex Systems College of Engineering Peking University Beijing100871 China Science and Technology on Aerospace Chemical Power Laboratory Hubei Institute of Aerospace Chemotechnology Xiangyang441003 China

This study investigates the thermal decomposition of 1,3,5- trinitroperhydro-1,3,5-triazine (RDX) based on combined thermogravimetric (TG) measurements and chemical reaction neural network (CRNN) modeling. Two compact kinetic models that can accurately capture the mass change time-history during the decomposition of RDX are introduced, with one consisting of three substances and a single global reaction (denoted as the one-step model) and the other consisting of four substances and four reactions (4-4 model). In the one-step model, an activation energy of 169.71 kJ mol -1 is calculated from best-fitting the CRNN to experimental data and the value is in good agreement with the existing value determined independently from classical methods. As for the 4-4 model, more constraints from molecular simulations, as well as experimental observations, are carefully introduced. The substances and reactions in 4-4 models are assigned based on a skeleton mechanism involving reactions of N-N and C-N bond rupture and HONO elimination. To the authors’ knowledge, the current work presents the first detailed mechanism of RDX decomposition that is reported publicly and validated against experiments. Extension of the CRNN framework to kinetic modeling of other solid-phase energetic materials is anticipated in future studies. © 2022, The Authors. All rights reserved.

关键词： Thermolysis

来源：评论

学校读者我要写书评

暂无评论

Inferring Actual Treatment Pathways from Patient Records

arXiv

引用

arXiv 2023年

作者： Wilkins-Caruana, Adrian Bandara, Madhushi Musial, Katarzyna Catchpoole, Daniel Kennedy, Paul J. Australian Artificial Intelligence Institute Faculty of Engineering and IT University of Technology Sydney Australia Complex Adaptive Systems Lab Data Science Institute Faculty of Engineering and IT University of Technology Sydney Australia Biospecimen Research Services The Children's Cancer Research Unit The Children's Hospital Westmead Australia Joint Research Centre in AI for Health and Wellness University of Technology Sydney Australia Ontario Tech University Canada

Objective: Treatment pathways are step-by-step plans outlining the recommended medical care for specific diseases;they get revised when different treatments are found to improve patient outcomes. Examining health records is an important part of this revision process, but inferring patients' actual treatments from health data is challenging due to complex event-coding schemes and the absence of pathway-related annotations. The objective of this study is to develop a method for inferring actual treatment steps for a particular patient group from administrative health records - a common form of tabular healthcare data - and address several technique- and methodology-based gaps in treatment pathway-inference research. Methods: We introduce Defrag, a method for examining health records to infer the real-world treatment steps for a particular patient group. Defrag learns the semantic and temporal meaning of healthcare event sequences, allowing it to reliably infer treatment steps from complex healthcare data. To our knowledge, Defrag is the first pathway-inference method to utilise a neural network (NN), an approach made possible by a novel, self-supervised learning objective. We also developed a testing and validation framework for pathway inference, which we use to characterise and evaluate Defrag's pathway inference ability, establish benchmarks, and compare against baselines. Results: We demonstrate Defrag's effectiveness by identifying best-practice pathway fragments for breast cancer, lung cancer, and melanoma in public healthcare records. Additionally, we use synthetic data experiments to demonstrate the characteristics of the Defrag inference method, and to compare Defrag to several baselines, where it significantly outperforms non-NN-based methods. Conclusions: Defrag offers an innovative and effective approach for inferring treatment pathways from complex health data. Defrag significantly outperforms several existing pathway-inference methods, but computationall

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Inferring spatial source of disease outbreaks using maximum entropy

引用

Physical Review E 2022年第1期106卷 014306-014306页

作者： Mehrad Ansari David Soriano-Paños Gourab Ghoshal Andrew D. White Department of Chemical Engineering University of Rochester Rochester New York 14627 USA Instituto Gulbenkian de Ciência (IGC) Oeiras 2780-156 Portugal GOTHAM Lab Institute for Biocomputation and Physics of Complex Systems University of Zaragoza E-50009 Zaragoza Spain Department of Physics and Astronomy and Computer Science University of Rochester Rochester New York 14627 USA

Mathematical modeling of disease outbreaks can infer the future trajectory of an epidemic, allowing for making more informed policy decisions. Another task is inferring the origin of a disease, which is relatively difficult with current mathematical models. Such frameworks, across varying levels of complexity, are typically sensitive to input data on epidemic parameters, case counts, and mortality rates, which are generally noisy and incomplete. To alleviate these limitations, we propose a maximum entropy framework that fits epidemiological models, provides calibrated infection origin probabilities, and is robust to noise due to a prior belief model. Maximum entropy is agnostic to the parameters or model structure used and allows for flexible use when faced with sparse data conditions and incomplete knowledge in the dynamical phase of disease-spread, providing for more reliable modeling at early stages of outbreaks. We evaluate the performance of our model by predicting future disease trajectories based on simulated epidemiological data in synthetic graph networks and the real mobility network of New York State. In addition, unlike existing approaches, we demonstrate that the method can be used to infer the origin of the outbreak with accurate confidence. Indeed, despite the prevalent belief on the feasibility of contact-tracing being limited to the initial stages of an outbreak, we report the possibility of reconstructing early disease dynamics, including the epidemic seed, at advanced stages.

关键词： Epidemic Spreading

来源：评论

学校读者我要写书评

暂无评论

Learning Latent Causal Factors from the Intricate Sensor Feedback of Contact-Rich Robotic Assembly Tasks

SSRN

引用

SSRN 2023年

作者： Chen, Yurou Yu, Jiyang Lin, Zhenyang Shen, Liancheng Liu, Zhiyong The State Key Lab of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China The Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai China Beijing Institute of Spacecraft System Engineering China Academy of Space Technology Beijing China

Achieving precise robotic assembly is paramount for safety and performance in industrial settings. Conventional assembly methods require tedious manual adjustment of many parameters, and it is challenging to meet the assembly requirements with tight clearance. Robot learning has been at the forefront of research, showing potential for automation and intelligence in robotic operations. However, intricate information such as force feedback is indispensable for high-precision robot assembly tasks, and current learning methods grapple with processing this complex observation data due to discontinuous force changes during operation and the inherent noise from the force sensor. To alleviate the low learning efficiency from the problematic observations, we propose introducing the concept of latent causal factors that cause the sensor observations and truly matter in assembly tasks. This paper analyses the impact of discontinuous and noisy observations and offers two ways to infer causal factors. Based on the implied latent factors, we propose a method that learns high-precision assembly policies from interacting with the environment. The algorithm's performance is evaluated in simulated and real-world nut assembly environments, demonstrating significant improvements over the previous methods. This research also underscores the promise of causal inference in addressing intricate industrial challenges%. © 2023, The Authors. All rights reserved.

关键词： Robotic assembly

来源：评论

学校读者我要写书评

暂无评论

Relaxation dynamics in the alternating XY chain following a quantum quench

arXiv

引用

arXiv 2023年

作者： Cao, Kaiyuan Hu, Yayun Tong, Peiqing Yang, Guangwen Liu, Peng Zhejiang Lab Hangzhou311100 China Department of Physics Institute of Theoretical Physics Nanjing Normal University Nanjing210023 China Jiangsu Key Laboratory for Numerical Simulation of Large Scale Complex Systems Nanjing Normal University Nanjing210023 China Department of Computer Science and Technology Tsinghua University Haidian District Beijing100084 China College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China

We investigate the relaxation dynamics of the fermion two-point correlation function Cmn(t) = 〈ψ(t)|c†mcn|ψ(t)〉 in the XY chain with alternating nearest-neighbor hopping interaction after a quench. We find that the deviation δCmn(t) = Cmn(t) − Cmn(∞) decays with time following the power law behavior t−µ, where the exponent µ depends on whether the quench is to the commensurate phase (µ = 1) or incommensurate phase (µ = 1/2). This decay of δCmn(t) arises from the transient behavior of the double-excited quasiparticle occupations and the transitions between different excitation spectra. Furthermore, we find that the steady value Cmn(∞) only involves the average fermion occupation numbers (i.e. the average excited single particle) over the time-evolved state, which are different from the ground state expectation values. We also observe nonanalytic singularities in the steady value Cmn(∞) for the quench to the critical points of the quantum phase transitions (QPTs), suggesting its potential use as a signature of QPTs. Copyright © 2023, The Authors. All rights reserved.

关键词： Ground state

来源：评论

学校读者我要写书评

暂无评论

Beyond Instruction Following: Evaluating Inferential Rule Following of Large Language Models

arXiv

引用

arXiv 2024年

作者： Sun, Wangtao Zhang, Chenxiang Zhang, Xueyou Yu, Xuanqing Huang, Ziyang Xu, Haotian Chen, Pei He, Shizhu Zhao, Jun Liu, Kang The Laboratory of Cognition and Decision Intelligence for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China CAS Engineering Laboratory for Intelligent Industrial Vision Institute of Automation Chinese Academy of Sciences Beijing China Department of Computer Science and Engineering Texas A&M University United States Shanghai Artificial Intelligence Laboratory China Xiaohongshu Inc China AI Lab AIGility Cloud Innovation Beijing China

Although Large Language Models (LLMs) have demonstrated strong instruction-following ability, they are further supposed to be controlled and guided by inferential rules in real-world scenarios to be safe, accurate, and intelligent. This demands the possession of inferential rule-following capability of LLMs. However, no prior work has made a clear evaluation of the inferential rule-following capability of LLMs. Previous studies that try to evaluate the inferential rule-following capability of LLMs fail to distinguish the inferential rule-following scenarios from the instruction-following scenarios. Therefore, this paper first clarifies the concept of inferential rule-following and proposes a comprehensive benchmark, RuleBench, to evaluate a diversified range of inferential rule-following abilities. Our experimental results on a variety of LLMs show that they are still limited in following rules. Our analysis based on the evaluation results provides insights into the improvements for LLMs toward a better inferential rule-following intelligent agent. We further propose Inferential Rule-Following Tuning (IRFT). The experimental results show that through IRFT, LLMs can learn abstract inferential rule-following abilities from purely synthetic data and then generalize to RuleBench. The data and code can be found at: https://***/r/llm-rulefollowing-B3E3/ Copyright © 2024, The Authors. All rights reserved.

关键词： Intelligent agents

来源：评论

学校读者我要写书评

暂无评论

PPSS: A Privacy-Preserving Supervision Scheme Based on Blockchain

PPSS: A Privacy-Preserving Supervision Scheme Based on Block...

引用

IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Ming Chen Peichang Shi Xiang Fu Feng Jiang Fei Gao Penghui Ma Jinzhu Kong National Key Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex Systems College of Computer Science National University of Defense Technology Changsha China Kylin Software Co.LTD Changsha China Zhejiang Lab Hangzhou China

In JointCloud Computing, multi-party participation introduces complexity and uncertainty. For all participants in JointCloud Computing, both continuous supervision and necessary privacy protection are required. Traditional supervision methods usually adopt the centralized information interaction mode, which has such defects as collusion of interests, single point of failure, privacy disclosure, etc. Building a decentralized supervision mechanism has become a new research direction. In this paper, we propose PPSS, a privacy-preserving supervision scheme based on blockchain, which decentralizes the supervision of the participants in JointCloud Computing, and combines the “double encryptions” and “threshold encryption” technologies to provide privacy protection. While making full use of the decentralization of the blockchain, a committee is established to carry out the analysis and decision-making tasks in terms of supervision and privacy protection. Experimental results indicate that PPSS can balance performance and security by reasonably configuring the committee.

关键词： Privacy Uncertainty Decision making Buildings Reliability engineering Blockchains complexity theory

来源：评论

学校读者我要写书评

暂无评论

Keep it Consistent: Topic-Aware Storytelling from an Image Stream via Iterative Multi-agent Communication 28

Keep it Consistent: Topic-Aware Storytelling from an Image S...

引用

28th International Conference on Computational Linguistics, COLING 2020

作者： Wang, Ruize Wei, Zhongyu Cheng, Ying Li, Piji Shan, Haijun Zhang, Ji Zhang, Qi Huang, Xuanjing Academy for Engineering and Technology Fudan University China School of Data Science Fudan University China Research Institute of Intelligent and Complex Systems Fudan University China Tencent AI Lab. China Zhejiang Lab. China School of Computer Science Fudan University China

ISBN: (纸本)9781952148279

Visual storytelling aims to generate a narrative paragraph from a sequence of images automatically. Existing approaches construct text description independently for each image and roughly concatenate them as a story, which leads to the problem of generating semantically incoherent content. In this paper, we propose a new way for visual storytelling by introducing a topic description task to detect the global semantic context of an image stream. A story is then constructed with the guidance of the topic description. In order to combine the two generation tasks, we propose a multi-agent communication framework that regards the topic description generator and the story generator as two agents and learn them simultaneously via iterative updating mechanism. We validate our approach on VIST dataset, where quantitative results, ablations, and human evaluation demonstrate our method’s good ability in generating stories with higher quality compared to state-of-the-art methods. © 2020 COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference. All rights reserved.

关键词： Iterative methods

来源：评论

学校读者我要写书评

暂无评论

Model-Based and Data-Driven Control of Event- and Self-Triggered Discrete-Time LTI systems

arXiv

引用

arXiv 2022年

作者： Wang, Xin Berberich, Julian Sun, Jian Wang, Gang Allgöwer, Frank Chen, Jie The Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology Beijing10081 China The Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China The University of Stuttgart Institute for Systems Theory and Automatic Control Stuttgart70550 Germany The Department of Control Science and Engineering Tongji University Shanghai201804 China The State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China

The present paper considers the model-based and data-driven control of unknown linear time-invariant discretetime systems under event-triggering and self-triggering transmission schemes. To this end, we begin by presenting a dynamic event-triggering scheme (ETS) based on periodic sampling, and a discrete-time looped-functional approach, through which a model-based stability condition is derived. Combining the model-based condition with a recent data-based system representation, a data-driven stability criterion in the form of linear matrix inequalities (LMIs) is established, which also offers a way of co-designing the ETS matrix and the controller. To further alleviate the sampling burden of ETS due to its continuous/periodic detection, a self-triggering scheme (STS) is developed. Leveraging pre-collected input-state data, an algorithm for predicting the next transmission instant is given, while achieving system stability. Finally, numerical simulations showcase the efficacy of ETS and STS in reducing data transmissions as well as of the proposed co-design methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Linear matrix inequalities

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：