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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Wang, Yanzhou Al-Zogbi, Lidia Liu, Jiawei Shepard, Lauren Ghazi, Ahmed Tokuda, Junichi Leonard, Simon Krieger, Axel Iordachita, Iulian Johns Hopkins University Department of Mechanical Engineering The Laboratory for Computational Sensing and Robotics BaltimoreMD United States Johns Hopkins University Laboratory of Computational Sensing and Robotics BaltimoreMD United States Johns Hopkins University Brady Urological Institute BaltimoreMD United States Harvard Medical School Department of Radiology Brigham and Women's Hospital BostonMA United States Johns Hopkins University Laboratory for Computational Sensing and Robotics Department of Computer Science BaltimoreMD United States

ISBN: (纸本)9798350371499

Prostate cancer diagnosis continues to encounter challenges, often due to imprecise needle placement in standard biopsies. Several control strategies have been developed to compensate for needle tip prediction inaccuracies, however none were compared against each other, and it is unclear whether any of them can be safely and universally applied in clinical settings. This paper compares the performance of two resolved-rate controllers, derived from a mechanics-based and a data-driven approach, for bevel-tip needle control using needle shape manipulation through a template. We demonstrate for a simulated 12-core biopsy procedure under model parameter uncertainty that the mechanics-based controller can better reach desired targets when only the final goal configuration is presented even with uncertainty on model parameters estimation, and that providing a feasible needle path is crucial in ensuring safe surgical outcomes when either controller is used for needle shape manipulation. © 2024 IEEE.

关键词： Biopsy

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Fast exact leverage score sampling from khatri-rao products with applications to tensor decomposition 23

Fast exact leverage score sampling from khatri-rao products ...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Vivek Bharadwaj Osman Asif Malik Riley Murray Laura Grigori Aydin Buluç James Demmel Electrical Engineering and Computer Science Department UC Berkeley and Computational Research Division Lawrence Berkeley National Lab Computational Research Division Lawrence Berkeley National Lab International Computer Science Institute and Computational Research Division Lawrence Berkeley National Lab and Electrical Engineering and Computer Science Department UC Berkeley Institute of Mathematics EPFL & Lab for Simulation and Modelling Paul Scherrer Institute Computational Research Division Lawrence Berkeley National Lab and Electrical Engineering and Computer Science Department UC Berkeley Electrical Engineering and Computer Science Department UC Berkeley

We present a data structure to randomly sample rows from the Khatri-Rao product of several matrices according to the exact distribution of its leverage scores. Our proposed sampler draws each row in time logarithmic in the height of the Khatri-Rao product and quadratic in its column count, with persistent space overhead at most the size of the input matrices. As a result, it tractably draws samples even when the matrices forming the Khatri-Rao product have tens of millions of rows each. When used to sketch the linear least squares problems arising in CANDECOMP / PARAFAC tensor decomposition, our method achieves lower asymptotic complexity per solve than recent state-of-the-art methods. Experiments on billion-scale sparse tensors validate our claims, with our algorithm achieving higher accuracy than competing methods as the decomposition rank grows.

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Learning Causal Representations from General Environments: Identifiability and Intrinsic Ambiguity

arXiv

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arXiv 2023年

作者： Jin, Jikai Syrgkanis, Vasilis Institute for Computational & Mathematical Engineering Stanford University CA United States Department of Management Science & Engineering Stanford University CA United States

We study causal representation learning, the task of recovering high-level latent variables and their causal relationships in the form of a causal graph from low-level observed data (such as text and images), assuming access to observations generated from multiple environments. Prior results on the identifiability of causal representations typically assume access to single-node interventions which is rather unrealistic in practice, since the latent variables are unknown in the first place. In this work, we provide the first identifiability results based on data that stem from general environments. We show that for linear causal models, while the causal graph can be fully recovered, the latent variables are only identified up to the surrounded-node ambiguity (SNA) (Varici et al. 2023). We provide a counterpart of our guarantee, showing that SNA is basically unavoidable in our setting. We also propose an algorithm, LiNGCReL which provably recovers the ground-truth model up to SNA, and we demonstrate its effectiveness via numerical experiments. Finally, we consider general non-parametric causal models and show that the same identification barrier holds when assuming access to groups of soft single-node interventions. © 2023, CC BY-SA.

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Imitating and exploring the human brain's resting and task-performing states via brain computing: scaling and architecture

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National science Review 2024年第5期11卷 17-28页

作者： Wenlian Lu Longbin Zeng Jiexiang Wang Shitong Xiang Yang Qi Qibao Zheng Ningsheng Xu Jianfeng Feng DTB Consortium Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence(Fudan University) Ministry of Education Fudan University Shanghai Center for Mathematical Sciences Fudan University Department of Computer Science University of Warwick Zhangjiang Fudan International Innovation Center Fudan University

A computational human brain model with the voxel-wise assimilation method was established based on individual structural and functional imaging data. We found that the more similar the brain model is to the biological counterpart in both scale and architecture, the more similarity was found between the assimilated model and the biological brain both in resting states and during tasks by quantitative metrics. The hypothesis that resting state activity reflects internal body states was validated by the interoceptive circuit's capability to enhance the similarity between the simulation model and the biological brain. We identified that the removal of connections from the primary visual cortex(V1) to downstream visual pathways significantly decreased the similarity at the hippocampus between the model and its biological counterpart,despite a slight influence on the whole brain. In conclusion, the model and methodology present a solid quantitative framework for a digital twin brain for discovering the relationship between brain architecture and functions, and for digitally trying and testing diverse cognitive, medical and lesioning approaches that would otherwise be unfeasible in real subjects.

关键词： digital twin brain computational cortico-subcortical model fMRI data assimilation interoceptive circuit

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Large-scale group decision-making method based on knowledge differentiation and the grouping of alternatives into modifiable scenarios 9th

Large-scale group decision-making method based on knowledge ...

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9th International Conference on Information Technology and Quantitative Management, ITQM 2022

作者： Trillo, José Ramón Herrera-Viedma, Enrique Higueras-Ruiz, María José Alonso, Sergio Morente-Molinera, Juan Antonio Cabrerizo, Francisco Javier Department of Computer Science and Artificial Intelligence Andalusian Research Institute of Data Science and Computational Intelligence DaSCI University of Granada Granada18071 Spain Department of Information and Communication University of Granada Granada18071 Spain Department of Software Engineering Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada Granada18071 Spain

Nowadays, group decision-making is an everyday event, since we usually live in a community. Nevertheless, when the number of participants is large, it starts to generate a lot of information that is complex to manage, and unlike group decision-making, where the participants are usually experts, when the number of participants increases, not all of them are, which generates a conflict of interest because the value of the vote of an expert and a (non-expert) user is worth the same. Moreover, it may happen that not all participants who start a process will finish it and not all of them will be able to make all the assessments, since they do not know all the options. Therefore, to solve all the drawbacks, this paper presents a large-scale group decision-making system in a modifiable scenario environment that groups participants according to their preferred alternative and also performs knowledge differentiation, clustering them into experts and users. This allows for optimising the consensus process, as the consensus is made by comparing the clusters and the participants with each other. © 2022 The Authors. Published by Elsevier B.V.

关键词： Decision making

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Investigating The Role of Deep Learning Technology in Health Care System 3

Investigating The Role of Deep Learning Technology in Health...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Karetla, Girija Rani Sriram, Tarigoppula V. S. Joshi, Kapil Gor, Mehul Bothe, Shivaji Bhatia, Shruti Western Sydney University School of Computer Data and Mathematical Sciences Australia Nsrit Department of Cse A.P. Visakhapatnam India Uttaranchal University Uttaranchal Institute of Technology India Parul University Department of Piet India India Graphic Era Hill University Department of Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350399264

A major obstacle to the transformation of health care is the ability to extract information and useful insights from complex, high-dimensional, and varied biological data. Modern biomedical research has seen the emergence of a number of new data sources, some of which are complicated, diverse, poorly documented, and typically unstructured, such as digital health records, neuroimaging, -omics, sensor data, and text. Prior to building predictive or classification models on top of the features, conventional data mining and computational science, complex data and insufficient domain expertise, there are several problems on both phases. New efficient paradigms for creating end-to-end learning approaches from complex information are provided by the most recent deep learning technology advancements. © 2023 IEEE.

关键词： Neuroimaging

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An Analysis on Virtual Mouse Control using Human Eye

An Analysis on Virtual Mouse Control using Human Eye

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Image Processing and Capsule Networks (ICIPCN), International Conference on

作者： P. M. Naidu N. Muthukumaran S. Chandralekha K. Tejaswini Reddy K.Sri Vaishnavi Department of Computer Science and Engineering (AI&ML) Vignan Institute of Technology and Science Department of ECE Centre for Computational Imaging and Machine Vision Sri Eshwar College of Engineering Coimbatore India

ISBN: (数字)9798350367171

ISBN: (纸本)9798350367188

Technological advances have enabled hands-free computer interaction, especially for people with limited hand function, such as amputees and paraplegics. However, current solutions face limitations in terms of input methods, accuracy, functionality, and accessibility. The proposed model aims to solve these problems by developing a reliable, user-friendly, and accurate system by utilizing facial expressions, especially eye and mouth movements. The proposed system aims to improve reliability and expressiveness by combining two modalities. Using advanced image processing technology, errors caused by lighting changes are minimized to provide a smooth user experience. The virtual mouse interface provides comprehensive features such as scrolling, clicking, and typing, improving the experience of users with different needs. By providing a reliable, versatile, and convenient hands-free interaction system, this study aims to give users greater independence, improve communication, and increase their participation in the digital world.

关键词： Accuracy Image processing computer network reliability computational modeling Mouth Lighting User experience

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Automation Through Machine Learning: Reinvigorating Cyber Security 15

Automation Through Machine Learning: Reinvigorating Cyber Se...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Bagade, Jayashree V. Dari, Sukhvinder Singh Dhivrani, Rekha Chowhan, Santosh Ravivarman, G. Janakiraman, V. Vishwakarma Institute of Technology Department of Information Technology Pune India Symbiosis International Symbiosis Law School Nagpur Pune India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Department of Data Analytics and Mathematical Sciences Karnataka Bangalore India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore641021 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Chennai127 India

ISBN: (纸本)9798350370249

Cyber security is predicated heavily on manual efforts to fight malicious assaults, but such processes are increasingly turning into overwhelmed by means of the quantity of threats - a hassle worsened in the course of the global COVID-19 pandemic. This has highlighted the importance of leveraging device studying (ML) to automate cyber protection methods. In this article, we speak how ML may be used to optimize the detection, prevention, and remediations of malicious assaults in an extensive kind of applications and offerings. We discover the various procedures being evolved that may assist make the identity of threats greater accurate and greater effortlessly to be had. We additionally recollect the capacity challenges associated with the software of ML strategies, along with the potential for unfair bias in opposition to positive instructions of customers, and become aware of methods for addressing those issues. Our end emphasizes the significance of leveraging ML in order to enable automation in cyber safety, as well as to make sure the detection of state-of-the-art new threats. © 2024 IEEE.

关键词： Cyber Instructions Malicious Procedures Threats

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A Comprehensive WebScraping of IMDb’s Top 50 Movies using Beautiful Soup

A Comprehensive WebScraping of IMDb’s Top 50 Movies using B...

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International Conference on Communication, Computing and Internet of Things (IC3IoT)

作者： Akhilan Anbu D Doreen Hephzibah Miriam C.R. Rene Robin Computer Science and Engineering (CORE) School of Computer Science and Engineering (SCOPE) Vellore Institute of Technology (VIT) Chennai Computational Intelligence Research Foundation(CIRF) Chennai Computational Intelligence Research Foundation(CIRF) Chennai India Department of CSE Sri Sairam Engineering College Chennai India

ISBN: (数字)9798350352689

ISBN: (纸本)9798350352696

Data encompasses all factual and measurable information that is accessible, quantifiable, or recordable. This information can take on a variety of forms, including numerical figures, written content, visual depictions, or symbolic annotations. The primary goal behind this undertaking is to extract such data from the official website of the Internet Movie Database, IMDb’s top 50 movies of all time using the BeautifulSoup package available in the Python package library which can be used for HTML parsing to create a parse tree for parsed pages. These parsed pages can in turn be utilised to extract data from the HTML code snippets used to code the official website’s data aiding in further analysis and research studies. For the main objective here, we have used IMDb’s official website as the foundation, with the language Python serving as a programming link to scrape data using the built-in functions in the Python package library such as Numpy, Pandas, Requests and *** this paper, we create an accurate Data Frame using a Python package called Pandas, this Data Frame can enable users to search for any required attribute from the variety of attributes available in the data frame such as release year, meta scores of each film, the type of genre they belong to and parental guidance suggestion. The objective of the paper mainly focuses on easing the user’s search process by providing them with different attributes they can select from. The example website utilisation presented herein serves as a fundamental illustration of our research efforts. Extrapolating this methodology across various websites spanning multiple domains opens up an immense amount of opportunities for insightful analysis and strategic data utilization, considering the transformative potential of employing such a thorough strategy across a range of digital platforms.

关键词： Visualization Codes Soft sensors Motion pictures Search problems Libraries Telecommunication computing

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Weakly supervised pathological differentiation of primary central nervous system lymphoma and glioblastoma on multi-site whole slide images

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Journal of Medical Imaging 2025年第1期12卷 017502页

作者： Wang, Liping Chen, Lin Wei, Kaixi Zhou, Huiyu Zwiggelaar, Reyer Fu, Weiwei Liu, Yingchao Shandong Normal University School of Information Science and Engineering Jinan China The Affiliated Hospital of Southwest Medical University Department of Neurosurgery Luzhou China Hejiang County Traditional Chinese Medicine Hospital Department of Neurosurgery Luzhou China University of Leicester School of Computing and Mathematical Sciences Leicester United Kingdom Aberystwyth University Department of Computer Science Aberystwyth United Kingdom The Affiliated Hospital of Qingdao University Department of Pathology Qingdao China Shandong Provincial Hospital Affiliated Shandong First Medical University Department of Neurosurgery Jinan China Shandong Provincial Hospital Affiliated Shandong First Medical University Shandong Institute of Brain Science and Brain-inspired Research Jinan China

Purpose: Differentiating primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM) is crucial because their prognosis and treatment differ substantially. Manual examination of their histological characteristics is considered the golden standard in clinical diagnosis. However, this process is tedious and time-consuming and might lead to misdiagnosis caused by morphological similarity between their histology and tumor heterogeneity. Existing research focuses on radiological differentiation, which mostly uses multi-parametric magnetic resonance imaging. By contrast, we investigate the pathological differentiation between the two types of tumors using whole slide images (WSIs) of postoperative formalin-fixed paraffin-embedded samples. Approach: To learn the specific and intrinsic histological feature representations from the WSI patches, a self-supervised feature extractor is trained. Then, the patch representations are fused by feeding into a weakly supervised multiple-instance learning model for the WSI classification. We validate our approach on 134 PCNSL and 526 GBM cases collected from three hospitals. We also investigate the effect of feature extraction on the final prediction by comparing the performance of applying the feature extractors trained on the PCNSL/GBM slides from specific institutions, multi-site PCNSL/GBM slides, and large-scale histopathological images. Results: Different feature extractors perform comparably with the overall area under the receiver operating characteristic curve value exceeding 85% for each dataset and close to 95% for the combined multi-site dataset. Using the institution-specific feature extractors generally obtains the best overall prediction with both of the PCNSL and GBM classification accuracies reaching 80% for each dataset. Conclusions: The excellent classification performance suggests that our approach can be used as an assistant tool to reduce the pathologists’ workload by providing an accurate and objective

关键词： Magnetic resonance imaging

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