检索结果-内蒙古大学图书馆

Sensitive vPSA - Exploring Sensitivity in Visual Parameter Space Analysis

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Fröhler, Bernhard Elberfeld, Tim Möller, Torsten Hege, Hans-Christian Maurer, Julia Heinzl, Christoph the Research Group X-Ray Computed Tomography University of Applied Sciences Upper Austria Austria the imec-Vision Lab University of Antwerp Belgium Data Science @ Uni Vienna the Faculty of Computer Science University of Vienna Austria Visual and Data-Centric Computing Department Zuse Institut Berlin Germany

The sensitivity of parameters in computational science problems is difficult to assess, especially for algorithms with multiple input parameters and diverse outputs. This work seeks to explore sensitivity analysis in the visualization domain, introducing novel techniques for respective visual analyses of parameter sensitivity in multi-dimensional algorithms. First, the sensitivity analysis background is revisited, highlighting the definition of sensitivity analysis and approaches analyzing global and local sensitivity as well as the differences of sensitivity analysis to the more common uncertainty analysis. We introduce and explore parameter sensitivity using visualization techniques from overviews to details on demand, covering the analysis of all aspects of sensitivity in a prototypical implementation. The respective visualization techniques outline the algorithmic in- and outputs including indications, on how sensitive an input is with regard to the outputs. The detailed sensitivity information is communicated through constellation plots for the exploration of input and output spaces. A matrix view is discussed for localized information on the sensitivity of specific outputs to specific inputs. A 3D view provides the link of the parameter sensitivity to the spatial domain, in which the results of the multi-dimensional algorithms are embedded. The proposed sensitivity analysis techniques are implemented and evaluated in a prototype called Sensitivity Explorer. We show that Sensitivity Explorer reliably identifies the most influential parameters and provides insights into which of the output characteristics these affect as well as to which extent. © 2022, CC BY-NC-SA.

关键词： Sensitivity analysis

CaloScore v2: Single-shot Calorimeter Shower Simulation with Diffusion Models

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Mikuni, Vinicius Nachman, Benjamin National Energy Research Scientific Computing Center Berkeley Lab BerkeleyCA94720 United States Physics Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Berkeley Institute for Data Science University of California BerkeleyCA94720 United States

Diffusion generative models are promising alternatives for fast surrogate models, producing high-fidelity physics simulations. However, the generation time often requires an expensive denoising process with hundreds of function evaluations, restricting the current applicability of these models in a realistic setting. In this work, we report updates on the CaloScore architecture, detailing the changes in the diffusion process, which produces higher quality samples, and the use of progressive distillation, resulting in a diffusion model capable of generating new samples with a single function evaluation. We demonstrate these improvements using the Calorimeter Simulation Challenge 2022 dataset. © 2023, CC BY.

关键词： Function evaluation

High-dimensional and Permutation Invariant Anomaly Detection

学校读者我要写书评

暂无评论

arXiv 2023年

Methods for anomaly detection of new physics processes are often limited to low-dimensional spaces due to the difficulty of learning high-dimensional probability densities. Particularly at the constituent level, incorporating desirable properties such as permutation invariance and variable-length inputs becomes difficult within popular density estimation methods. In this work, we introduce a permutation-invariant density estimator for particle physics data based on diffusion models, specifically designed to handle variable-length inputs. We demonstrate the efficacy of our methodology by utilizing the learned density as a permutation-invariant anomaly detection score, effectively identifying jets with low likelihood under the background-only hypothesis. To validate our density estimation method, we investigate the ratio of learned densities and compare to those obtained by a supervised classification algorithm. © 2023, CC BY.

关键词： Anomaly detection

Predicting Alzheimer’s Disease Diagnosis Risk over Time with Survival Machine Learning on the ADNI Cohort

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Musto, Henry Stamate, Daniel Pu, Ida Stahl, Daniel Data Science & Soft Computing Lab Computing Department Goldsmiths College University of London United Kingdom Division of Population Health Health Services Research & Primary Care School of Health Sciences University of Manchester United Kingdom Institute of Psychiatry Psychology and Neuroscience Biostatistics and Health Info-matics Department King’s College London London United Kingdom

The rise of Alzheimer’s Disease worldwide has prompted a search for efficient tools which can be used to predict deterioration in cognitive decline leading to dementia. In this paper, we explore the potential of survival machine learning as such a tool for building models capable of predicting not only deterioration but also the likely time to deterioration. We demonstrate good predictive ability (0.86 C-Index), lending support to its use in clinical investigation and prediction of Alzheimer’s Disease risk. © 2023, CC BY.

关键词： Machine learning

Refining Fast Calorimeter Simulations with a Schrödinger Bridge

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Diefenbacher, Sascha Mikuni, Vinicius Nachman, Benjamin Physics Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States National Energy Research Scientific Computing Center Berkeley Lab BerkeleyCA94720 United States Berkeley Institute for Data Science University of California BerkeleyCA94720 United States

Machine learning-based simulations, especially calorimeter simulations, are promising tools for approximating the precision of classical high energy physics simulations with a fraction of the generation time. Nearly all methods proposed so far learn neural networks that map a random variable with a known probability density, like a Gaussian, to realistic-looking events. In many cases, physics events are not close to Gaussian and so these neural networks have to learn a highly complex function. We study an alternative approach: Schrödinger bridge Quality Improvement via Refinement of Existing Lightweight Simulations (SQuIRELS). SQuIRELS leverages the power of diffusion-based neural networks and Schrödinger bridges to map between samples where the probability density is not known explicitly. We apply SQuIRELS to the task of refining a classical fast simulation to approximate a full classical simulation. On simulated calorimeter events, we find that SQuIRELS is able to reproduce highly non-trivial features of the full simulation with a fraction of the generation time. Copyright © 2023, The Authors. All rights reserved.

关键词： Gaussian distribution

Editorial: Temporal structure of neural processes coupling sensory, motor and cognitive functions of the brain, volume II

学校读者我要写书评

暂无评论

Frontiers in Computational Neuroscience 2023年 17卷 1291744页

作者： Gupta, Daya Shankar Banerjee, Arpan Piras, Federica Roy, Dipanjan School of Pharmacy South University SavannahGA United States Cognitive Brain Dynamics Lab National Brain Research Center Gurugram India Laboratory of Neuropsychiatry Department of Clinical Neuroscience and Neurorehabilitation IRCCS Santa Lucia Foundation Rome Italy Centre for Brain Science and Applications School of Artificial Intelligence and Data Science Indian Institute of Technology Jodhpur India

Boenke et al. reported “on the group level: (1) Veridical auditory TOJs, relative to non-veridical, were associated with higher (pre-stimulus) 20 Hz (beta) power over central electrodes, and (2) veridical visual TOJs showed higher (pre-stimulus) 10, 15 Hz (alpha beta) power over parieto-occipital electrodes.” [...]in this study, “the individual-level modulation pattern was variable and included activations opposite to the group mean.” [...]they argue that there are different strategies (reflected in opposite directions of modulations of brain oscillations) used by the brain leading to the same outcome, namely veridical TOJs in their study. [...]as advised by the authors, a general description of brain activity during the processing of incoming information should account for variability in modulation directions at both the group and individual levels. Furthermore, desynchronization frees up neurons from the restriction of spiking only during a certain phase of oscillations. [...]desynchronization will promote the temporal coupling between neuronal activities, given the constraints of interactions involving external stimuli. The spatiotemporal separation, representing three groups of features, was about 90%. Since spatiotemporal separation does not exceed 100% in the above example, this mechanism may not lead to an increase in entropy, given the completion of a task.

关键词： consciousness perception binding voluntary control temporal motor noise active inference embodied embedded cognition

Large Language Models for Generative Information Extraction: A Survey

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Xu, Derong Chen, Wei Peng, Wenjun Zhang, Chao Xu, Tong Zhao, Xiangyu Wu, Xian Zheng, Yefeng Wang, Yang Chen, Enhong State Key Laboratory of Cognitive Intelligence University of Science and Technology of China Hefei230000 China Department of Data Science City University of Hong Kong Hongkong999077 Hong Kong Jarvis Research Center Tencent YouTu Lab Beijing100029 China Anhui Conch Information Technology Engineering Co. Ltd. Wuhu241000 China

Information extraction (IE) aims to extract structural knowledge from plain natural language texts. Recently, generative Large Language Models (LLMs) have demonstrated remarkable capabilities in text understanding and generation. As a result, numerous works have been proposed to integrate LLMs for IE tasks based on a generative paradigm. To conduct a comprehensive systematic review and exploration of LLM efforts for IE tasks, in this study, we survey the most recent advancements in this field. We first present an extensive overview by categorizing these works in terms of various IE subtasks and techniques, and then we empirically analyze the most advanced methods and discover the emerging trend of IE tasks with LLMs. Based on a thorough review conducted, we identify several insights in technique and promising research directions that deserve further exploration in future studies. We maintain a public repository and consistently update related works and resources on GitHub (LLM4IE repository). Copyright © 2023, The Authors. All rights reserved.

关键词： Information retrieval

Survey on data Analysis in Social Media:A Practical Application Aspect

学校读者我要写书评

暂无评论

Big data Mining and Analytics 2020年第4期3卷 259-279页

作者： Qixuan Hou Meng Han Zhipeng Cai the College of Computing Georgia Institute of TechnologyAtlantaGA 30332USA Data-driven Intelligence Research(DIR)Lab of College of Computing and Software Engineering Kennesaw State UniversityKennesawGA 30060USA the Department of Computer Science Georgia State UniversityAtlantaGA 30303USA

Social media has more than three billion users sharing events,comments,and feelings throughout the *** serves as a critical information source with large volumes,high velocity,and a wide variety of *** previous studies on information spreading,relationship analyzing,and individual modeling,etc.,have been heavily conducted to explore the tremendous social and commercial values of social media *** survey studies the previous literature and the existing applications from a practical *** outline a commonly used pipeline in building social media-based applications and focus on discussing available analysis techniques,such as topic analysis,time series analysis,sentiment analysis,and network *** that,we present the impacts of such applications in three different areas,including disaster management,healthcare,and ***,we list existing challenges and suggest promising future research directions in terms of data privacy,5 G wireless network,and multilingual support.

关键词： social media topic analysis time series analysis sentiment analysis network analysis disaster management bio-surveillance business intelligence

Confidence-Regulated Generative Diffusion Models for Reliable AI Agent Migration in Vehicular Metaverses

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Kang, Yingkai Kang, Jiawen Wen, Jinbo Zhang, Tao Yang, Zhaohui Niyato, Dusit Zhang, Yan School of Automation Guangdong University of Technology Guangzhou510006 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing210016 China School of Cyberspace Science and Technology Beijing Jiaotong University Beijing100044 China College of Information Science and Electronic Engineering Zhejiang Provincial Key Lab of information processing communication and networking Zhejiang University Hangzhou310007 China College of Computing and Data Science Nanyang Technological University Singapore Department of Informatics University of Oslo the Simula Research Laboratory Norway

Vehicular metaverses are an emerging paradigm that merges intelligent transportation systems with virtual spaces, leveraging advanced digital twin and Artificial Intelligence (AI) technologies to seamlessly integrate vehicles, users, and digital environments. In this paradigm, vehicular AI agents are endowed with environment perception, decision-making, and action execution capabilities, enabling real-time processing and analysis of multi-modal data to provide users with customized interactive services. Since vehicular AI agents require substantial resources for real-time decision-making, given vehicle mobility and network dynamics conditions, the AI agents are deployed in RoadSide Units (RSUs) with sufficient resources and dynamically migrated among them. However, AI agent migration requires frequent data exchanges, which may expose vehicular metaverses to potential cyber attacks. To this end, we propose a reliable vehicular AI agent migration framework, achieving reliable dynamic migration and efficient resource scheduling through cooperation between vehicles and RSUs. Additionally, we design a trust evaluation model based on the theory of planned behavior to dynamically quantify the reputation of RSUs, thereby better accommodating the personalized trust preferences of users. We then model the vehicular AI agent migration process as a partially observable markov decision process and develop a Confidence-regulated Generative Diffusion Model (CGDM) to efficiently generate AI agent migration decisions. Numerical results demonstrate that the CGDM algorithm significantly outperforms baseline methods in reducing system latency and enhancing robustness against cyber attacks. Copyright © 2025, The Authors. All rights reserved.

关键词： Markov processes