ArXiv Pub Date : 2024-02-29

Wenxiang Cong, Wenjun Xia, Ge Wang

{"title":"Tomographic Image Reconstruction Using an Advanced Score Function (ADSF).","authors":"Wenxiang Cong, Wenjun Xia, Ge Wang","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Computed tomography (CT) reconstructs volumetric images using X-ray projection data acquired from multiple angles around an object. For low-dose or sparse-view CT scans, the classic image reconstruction algorithms often produce severe noise and artifacts. To address this issue, we develop a novel iterative image reconstruction method based on maximum a posteriori (MAP) estimation. In the MAP framework, the score function, i.e., the gradient of the logarithmic probability density distribution, plays a crucial role as an image prior in the iterative image reconstruction process. By leveraging the Gaussian mixture model, we derive a novel score matching formula to establish an advanced score function (ADSF) through deep learning. Integrating the new ADSF into the image reconstruction process, a new ADSF iterative reconstruction method is developed to improve image reconstruction quality. The convergence of the ADSF iterative reconstruction algorithm is proven through mathematical analysis. The performance of the ADSF reconstruction method is also evaluated on both public medical image datasets and clinical raw CT datasets. Our results show that the ADSF reconstruction method can achieve better denoising and deblurring effects than the state-of-the-art reconstruction methods, showing excellent generalizability and stability.</p>","PeriodicalId":8425,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10312904/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10117027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Generative Representational Instruction Tuning 生成表征指令调整

ArXiv Pub Date : 2024-02-15 DOI: 10.48550/arXiv.2402.09906

Niklas Muennighoff, Hongjin Su, Liang Wang, Nan Yang, Furu Wei, Tao Yu, Amanpreet Singh, Douwe Kiela

引用次数: 0

Multi-Stage Algorithm for Group Testing with Prior Statistics 利用先验统计数据进行分组测试的多阶段算法

ArXiv Pub Date : 2024-02-15 DOI: 10.48550/arXiv.2402.10018

Ayelet C. Portnoy, Alejandro Cohen

引用次数: 0

Not Just Novelty: A Longitudinal Study on Utility and Customization of AI Workflows 不仅仅是新奇：关于人工智能工作流程的实用性和定制化的纵向研究

ArXiv Pub Date : 2024-02-15 DOI: 10.48550/arXiv.2402.09894

Tao Long, Katy Ilonka Gero, Lydia B. Chilton

{"title":"Not Just Novelty: A Longitudinal Study on Utility and Customization of AI Workflows","authors":"Tao Long, Katy Ilonka Gero, Lydia B. Chilton","doi":"10.48550/arXiv.2402.09894","DOIUrl":"https://doi.org/10.48550/arXiv.2402.09894","url":null,"abstract":"Generative AI brings novel and impressive abilities to help people in everyday tasks. There are many AI workflows that solve real and complex problems by chaining AI outputs together with human interaction. Although there is an undeniable lure of AI, it's uncertain how useful generative AI workflows are after the novelty wears off. Additionally, tools built with generative AI have the potential to be personalized and adapted quickly and easily, but do users take advantage of the potential to customize? We conducted a three-week longitudinal study with 12 users to understand the familiarization and customization of generative AI tools for science communication. Our study revealed that the familiarization phase lasts for 4.3 sessions, where users explore the capabilities of the workflow and which aspects they find useful. After familiarization, the perceived utility of the system is rated higher than before, indicating that the perceived utility of AI is not just a novelty effect. The increase in benefits mainly comes from end-users' ability to customize prompts, and thus appropriate the system to their own needs. This points to a future where generative AI systems can allow us to design for appropriation.","PeriodicalId":8425,"journal":{"name":"ArXiv","volume":"29 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139962463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

System-level Impact of Non-Ideal Program-Time of Charge Trap Flash (CTF) on Deep Neural Network 电荷陷阱闪存 (CTF) 非理想编程时间对深度神经网络的系统级影响

ArXiv Pub Date : 2024-02-15 DOI: 10.48550/arXiv.2402.09792

S. Shrivastava, A. Biswas, S. Chakrabarty, G. Dash, V. Saraswat, U. Ganguly

{"title":"System-level Impact of Non-Ideal Program-Time of Charge Trap Flash (CTF) on Deep Neural Network","authors":"S. Shrivastava, A. Biswas, S. Chakrabarty, G. Dash, V. Saraswat, U. Ganguly","doi":"10.48550/arXiv.2402.09792","DOIUrl":"https://doi.org/10.48550/arXiv.2402.09792","url":null,"abstract":"Learning of deep neural networks (DNN) using Resistive Processing Unit (RPU) architecture is energy-efficient as it utilizes dedicated neuromorphic hardware and stochastic computation of weight updates for in-memory computing. Charge Trap Flash (CTF) devices can implement RPU-based weight updates in DNNs. However, prior work has shown that the weight updates (V_T) in CTF-based RPU are impacted by the non-ideal program time of CTF. The non-ideal program time is affected by two factors of CTF. Firstly, the effects of the number of input pulses (N) or pulse width (pw), and secondly, the gap between successive update pulses (t_gap) used for the stochastic computation of weight updates. Therefore, the impact of this non-ideal program time must be studied for neural network training simulations. In this study, Firstly, we propose a pulse-train design compensation technique to reduce the total error caused by non-ideal program time of CTF and stochastic variance of a network. Secondly, we simulate RPU-based DNN with non-ideal program time of CTF on MNIST and Fashion-MNIST datasets. We find that for larger N (~1000), learning performance approaches the ideal (software-level) training level and, therefore, is not much impacted by the choice of t_gap used to implement RPU-based weight updates. However, for lower N (<500), learning performance depends on T_gap of the pulses. Finally, we also performed an ablation study to isolate the causal factor of the improved learning performance. We conclude that the lower noise level in the weight updates is the most likely significant factor to improve the learning performance of DNN. Thus, our study attempts to compensate for the error caused by non-ideal program time and standardize the pulse length (N) and pulse gap (t_gap) specifications for CTF-based RPUs for accurate system-level on-chip training.","PeriodicalId":8425,"journal":{"name":"ArXiv","volume":"20 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139962495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

TOAD: Task-Oriented Automatic Dialogs with Diverse Response Styles TOAD：以任务为导向、响应风格多样的自动对话框

ArXiv Pub Date : 2024-02-15 DOI: 10.48550/arXiv.2402.10137

Yinhong Liu, Yimai Fang, David Vandyke, Nigel Collier

引用次数: 0

User Privacy Harms and Risks in Conversational AI: A Proposed Framework 人工智能对话中的用户隐私危害与风险：一个拟议框架

ArXiv Pub Date : 2024-02-15 DOI: 10.48550/arXiv.2402.09716

Ece Gumusel, Kyrie Zhixuan Zhou, M. Sanfilippo

引用次数: 0

A Systematic Evaluation of Evolving Highly Nonlinear Boolean Functions in Odd Sizes 对奇数大小高度非线性布尔函数进化的系统评估

ArXiv Pub Date : 2024-02-15 DOI: 10.48550/arXiv.2402.09937

C. Carlet, Marko Ðurasevic, D. Jakobović, S. Picek, L. Mariot

{"title":"A Systematic Evaluation of Evolving Highly Nonlinear Boolean Functions in Odd Sizes","authors":"C. Carlet, Marko Ðurasevic, D. Jakobović, S. Picek, L. Mariot","doi":"10.48550/arXiv.2402.09937","DOIUrl":"https://doi.org/10.48550/arXiv.2402.09937","url":null,"abstract":"Boolean functions are mathematical objects used in diverse applications. Different applications also have different requirements, making the research on Boolean functions very active. In the last 30 years, evolutionary algorithms have been shown to be a strong option for evolving Boolean functions in different sizes and with different properties. Still, most of those works consider similar settings and provide results that are mostly interesting from the evolutionary algorithm's perspective. This work considers the problem of evolving highly nonlinear Boolean functions in odd sizes. While the problem formulation sounds simple, the problem is remarkably difficult, and the related work is extremely scarce. We consider three solutions encodings and four Boolean function sizes and run a detailed experimental analysis. Our results show that the problem is challenging, and finding optimal solutions is impossible except for the smallest tested size. However, once we added local search to the evolutionary algorithm, we managed to find a Boolean function in nine inputs with nonlinearity 241, which, to our knowledge, had never been accomplished before with evolutionary algorithms.","PeriodicalId":8425,"journal":{"name":"ArXiv","volume":"20 17","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139962645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploiting Alpha Transparency In Language And Vision-Based AI Systems 在基于语言和视觉的人工智能系统中利用阿尔法透明度

ArXiv Pub Date : 2024-02-15 DOI: 10.48550/arXiv.2402.09671

David A. Noever, Forrest McKee

引用次数: 0

User Modeling and User Profiling: A Comprehensive Survey 用户建模和用户分析：全面调查

ArXiv Pub Date : 2024-02-15 DOI: 10.48550/arXiv.2402.09660

Erasmo Purificato, Ludovico Boratto, Ernesto William De Luca

{"title":"User Modeling and User Profiling: A Comprehensive Survey","authors":"Erasmo Purificato, Ludovico Boratto, Ernesto William De Luca","doi":"10.48550/arXiv.2402.09660","DOIUrl":"https://doi.org/10.48550/arXiv.2402.09660","url":null,"abstract":"The integration of artificial intelligence (AI) into daily life, particularly through information retrieval and recommender systems, has necessitated advanced user modeling and profiling techniques to deliver personalized experiences. These techniques aim to construct accurate user representations based on the rich amounts of data generated through interactions with these systems. This paper presents a comprehensive survey of the current state, evolution, and future directions of user modeling and profiling research. We provide a historical overview, tracing the development from early stereotype models to the latest deep learning techniques, and propose a novel taxonomy that encompasses all active topics in this research area, including recent trends. Our survey highlights the paradigm shifts towards more sophisticated user profiling methods, emphasizing implicit data collection, multi-behavior modeling, and the integration of graph data structures. We also address the critical need for privacy-preserving techniques and the push towards explainability and fairness in user modeling approaches. By examining the definitions of core terminology, we aim to clarify ambiguities and foster a clearer understanding of the field by proposing two novel encyclopedic definitions of the main terms. Furthermore, we explore the application of user modeling in various domains, such as fake news detection, cybersecurity, and personalized education. This survey serves as a comprehensive resource for researchers and practitioners, offering insights into the evolution of user modeling and profiling and guiding the development of more personalized, ethical, and effective AI systems.","PeriodicalId":8425,"journal":{"name":"ArXiv","volume":"15 17","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139962669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

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