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Modelling Growth, Remodelling and Damage of a Thick-Walled Fibre-Reinforced Artery with Active Response: Application to Cerebral Vasospasm and Treatment

SSRN

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SSRN 2024年

作者： Pederzani, Giulia Grytsan, Andrii Hoekstra, Alfons G. Robertson, Anne M. Watton, Paul N. Computational Science Lab Instituut voor Informatica Universiteit van Amsterdam Netherlands Department of Mechanical Engineering University of Sheffield United Kingdom Department of Computer Science University of Sheffield United Kingdom INSIGNEO Institute of in silico Medicine University of Sheffield United Kingdom Department of Mechanical Engineering and Materials Science University of Pittsburgh United States

Background: Cerebral vasospasm, a prolonged constriction of cerebral arteries, is the first cause of morbidity and mortality for patients who survive hospitalisation after aneurysmal subarachnoid haemorrhage. Recently, it has been observed that (low pressure) stent-retrievers can be used to successfully treat the disease which has challenged a widely held viewpoint that damage to the extracellular matrix is necessary to mechanically resolve the ***: We apply a 3D finite element rate-based constrained mixture model (rb-CMM) approach to simulate the artery, vasospasm, remodelling and treatment with stents. The artery is modelled as a thick-walled fibre-reinforced constrained mixture subject to physiological pressure and axial stretch. The model accounts for distributions of collagen fibre homeostatic stretches, VSMC active response, remodelling and damage. After simulating vasospasm and subsequent remodelling of the artery to a new homeostatic state, we simulate treatment with commonly available stent-retrievers. Subsequently, we perform a parameter study to examine how arterial diameter and thickness affect the success of stent ***: The model predictions on the amount of pressure required to mechanically resolve the constriction are consistent with those applied by stent-retrievers. In agreement with reported clinical observations, our model predicts that stent-retrievers tend to be effective in arteries of up to about 3mm original diameter, but usually fail in larger ones. In addition, we observe that accounting for (physiological) variations in arterial wall thickness, the stent pressure requirement may need to double to successfully treat ***: We have developed a novel rb-CMM that accounts for VSMC active response, remodelling and damage. Consistent with clinical observations, simulations predict that stent-retrievers can mechanically resolve vasospasm. Moreover, accounting for a patient’s arterial properties is imp

关键词： Stents

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ONE-SHOT SENSITIVITY-AWARE MIXED SPARSITY PRUNING FOR LARGE LANGUAGE MODELS

arXiv

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arXiv 2023年

作者： Shao, Hang Liu, Bei Xiao, Bo Zeng, Ke Wan, Guanglu Qian, Yanmin Auditory Cognition and Computational Acoustics Lab MoE Key Lab of Artificial Intelligence AI Institute Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Meituan Beijing China

Various Large Language Models (LLMs) from the Generative Pretrained Transformer (GPT) family have achieved outstanding performances in a wide range of text generation tasks. However, the enormous model sizes have hindered their practical use in real-world applications due to high inference latency. Therefore, improving the efficiencies of LLMs through quantization, pruning, and other means has been a key issue in LLM studies. In this work, we propose a method based on Hessian sensitivity-aware mixed sparsity pruning to prune LLMs to at least 50% sparsity without the need of any retraining. It allocates sparsity adaptively based on sensitivity, allowing us to reduce pruning-induced error while maintaining the overall sparsity level. The advantages of the proposed method exhibit even more when the sparsity is extremely high. Furthermore, our method is compatible with quantization, enabling further compression of LLMs. We have released the available code1 © 2023, CC BY.

关键词： computational linguistics

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Direct Shooting Method for Numerical Optimal Control: A Modified Transcription Approach

Direct Shooting Method for Numerical Optimal Control: A Modi...

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European Control Conference (ECC)

作者： Jiawei Tang Yuxing Zhong Pengyu Wang Xingzhou Chen Shuang Wu Ling Shi Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Water Bay Hong Kong SAR Department Of Electronic And Electrical Engineering Shenzhen Key Laboratory of Robotics Perception and Intelligence Southern University of Science and Technology Shenzhen China Noah's Ark Lab Huawei

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

Direct shooting is an efficient method to solve numerical optimal control. It utilizes the Runge-Kutta scheme to discretize a continuous-time optimal control problem making the problem solvable by nonlinear programming solvers. However, conventional direct shooting raises a contradictory dynamics issue when using an augmented state to handle high-order systems. This paper fills the research gap by considering the direct shooting method for high-order systems. We derive the modified Euler and Runge-Kutta-4 methods to transcribe the system dynamics constraint directly. Additionally, we provide the global error upper bounds of our proposed methods. A set of benchmark optimal control problems shows that our methods provide more accurate solutions than existing approaches.

关键词： Upper bound Accuracy System dynamics Optimal control Europe Programming Benchmark testing

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What can real information content tell us about compressing climate model data? 8

What can real information content tell us about compressing ...

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8th IEEE/ACM International Workshop on Data Analysis and Reduction for Big Scientific Data, DRBSD-8 2022

作者： Sather, Hayden Pinard, Alexander Baker, Allison H. Hammerling, Dorit M. Colorado School of Mines Department of Computer Science GoldenCO United States Applied Mathematics and Statistics Colorado School of Mines GoldenCO United States Computational and Information Systems Lab National Center for Atmospheric Research BoulderCO United States

ISBN: (纸本)9781665463379

The massive data volumes produced by climate simulation models create an urgent need for data reduction. Lossy compression in particular is a solution that can significantly reduce storage requirements, however, a tradeoff must be made between the amount of compression applied and the scientific integrity of the data. Determining how much compression can be applied is therefore vital for applying lossy compression. One particular metric for gauging the quality of compression is the percentage of real information present in the original data that is preserved in the compressed data. We compute bitwise real information content for several climate variables from the popular Community Earth System Model, and we investigate the amount of compression that can be applied to each of these climate variables using two popular compression algorithms designed for floating-point data while preserving 99% of the real information content. The analysis of the real information content of data after lossy compression has been applied shows a helpful visualization of how compression artifacts have been introduced to the data. Finally, we demonstrate how this real information content can be used in a straightforward manner to determine compressor settings for our data. © 2022 IEEE.

关键词： Climate models

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Deep Learning Model for computer Vision: Sustainable Image Classification Using Machine Learning 1st

Deep Learning Model for Computer Vision: Sustainable Image C...

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1st International Conference on Sustainable Development through Machine Learning, Artificial Intelligence, and IoT, ICSD 2023

作者： Alankar, Bhavya Chauhan, Ritu Kaur, Harleen Ebadati, E. Omid Mahdi Department of Computer Science and Engineering School of Engineering Sciences and Technology Jamia Hamdard New Delhi India Artificial Intelligence and IoT Lab Center for Computational Biology and Bioinformatics Amity University UP Noida India Department of Mathematics and Computer Science Kharazmi University Tehran Iran

ISBN: (纸本)9783031470547

The core challenge within computer vision is image classification, which involves assigning an image a label and categorizing it using the features that have been extracted from it. Even though this work appears rudimentary, it requires the application involving artificial intelligence, specifically Deep Learning, a subfield of Machine Learning. In this paper, we have discussed how Convolutional Neural Networks (CNN), the most modern technique for image classification, are used in Deep Learning to classify images. We have discussed the applications of image classification and drawn the conclusion that using deep learning is preferable to using machine learning. Additionally, we have also presented a comparative study of various CNN models to observe how they could potentially be employed as pre-trained models for the image classification job. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolution

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Fast Exact Leverage Score Sampling from Khatri-Rao Products with Applications to Tensor Decomposition

arXiv

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arXiv 2023年

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

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. © 2023, CC BY.

关键词： Matrix algebra

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Advanced Long-Content Speech Recognition with Factorized Neural Transducer

arXiv

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arXiv 2024年

作者： Gong, Xun Wu, Yu Li, Jinyu Liu, Shujie Zhao, Rui Chen, Xie Qian, Yanmin Auditory Cognition and Computational Acoustics Lab The Department of Computer Science and Engineering The MoE Key Laboratory of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai200240 China Microsoft Corporation United States

Long-content automatic speech recognition (ASR) has obtained increasing interest in recent years, as it captures the relationship among consecutive historical utterances while decoding the current utterance. In this paper, we propose two novel approaches, which integrate long-content information into the factorized neural transducer (FNT) based architecture in both non-streaming (referred to as LongFNT) and streaming (referred to as SLongFNT) scenarios. We first investigate whether long-content transcriptions can improve the vanilla conformer transducer (C-T) models. Our experiments indicate that the vanilla C-T models do not exhibit improved performance when utilizing long-content transcriptions, possibly due to the predictor network of C-T models not functioning as a pure language model. Instead, FNT shows its potential in utilizing long-content information, where we propose the LongFNT model and explore the impact of long-content information in both text (LongFNT-Text) and speech (LongFNT-Speech). The proposed LongFNT-Text and LongFNT-Speech models further complement each other to achieve better performance, with transcription history proving more valuable to the model. The effectiveness of our LongFNT approach is evaluated on LibriSpeech and GigaSpeech corpora, and obtains relative 19% and 12% word error rate reduction, respectively. Furthermore, we extend the LongFNT model to the streaming scenario, which is named SLongFNT, consisting of SLongFNT-Text and SLongFNT-Speech approaches to utilize long-content text and speech information. Experiments show that the proposed SLongFNT model achieves relative 26% and 17% WER reduction on LibriSpeech and GigaSpeech respectively while keeping a good latency, compared to the FNT baseline. Overall, our proposed LongFNT and SLongFNT highlight the significance of considering long-content speech and transcription knowledge for improving both non-streaming and streaming speech recognition systems. Copyright © 2024, The Authors.

关键词： Transducers

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AirLine: Efficient Learnable Line Detection with Local Edge Voting

AirLine: Efficient Learnable Line Detection with Local Edge ...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Xiao Lin Chen Wang College of Computing Georgia Institute of Technology Atlanta GA USA Department of Computer Science and Engineering Spatial AI & Robotics Lab State University of New York at Buffalo NY USA

Line detection is widely used in many robotic tasks such as scene recognition, 3D reconstruction, and simultaneous localization and mapping (SLAM). Compared to points, lines can provide both low-level and high-level geometrical information for downstream tasks. In this paper, we propose a novel learnable edge-based line detection algorithm, AirLine, which can be applied to various tasks. In contrast to existing learnable endpoint-based methods, which are sensitive to the geometrical condition of environments, AirLine can extract line segments directly from edges, resulting in a better generalization ability for unseen environments. To balance efficiency and accuracy, we introduce a region-grow algorithm and a local edge voting scheme for line parameterization. To the best of our knowledge, AirLine is one of the first learnable edge-based line detection methods. Our extensive experiments have shown that it retains state-of-the-art-Ievel precision, yet with a $3-80\times$ runtime acceleration compared to other learning-based methods, which is critical for low-power robots.

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Prototype and Instance Contrastive Learning for Unsupervised Domain Adaptation in Speaker Verification

Prototype and Instance Contrastive Learning for Unsupervised...

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International Symposium on Chinese Spoken Language Processing

作者： Wen Huang Bing Han Zhengyang Chen Shuai Wang Yanmin Qian Department of Computer Science and Engineering Auditory Cognition and Computational Acoustics Lab MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen

ISBN: (数字)9798331516826

ISBN: (纸本)9798331516833

Speaker verification system trained on one domain usually suffers performance degradation when applied to another domain. To address this challenge, researchers commonly use feature distribution matching-based methods in unsupervised domain adaptation scenarios where some unlabeled target domain data is available. However, these methods often have limited performance improvement and lack generalization in various mismatch situations. In this paper, we propose Prototype and Instance Contrastive Learning (PICL), a novel method for unsupervised domain adaptation in speaker verification through dual-level contrastive learning. For prototype contrastive learning, we generate pseudo labels via clustering to create dynamically updated prototype representations, aligning instances with their corresponding class or cluster prototypes. For instance contrastive learning, we minimize the distance between different views or augmentations of the same instance, ensuring robust and invariant representations resilient to variations like noise. This dual-level approach provides both high-level and low-level supervision, leading to improved generalization and robustness of the speaker verification model. Unlike previous studies that only evaluated mismatches in one situation, we have conducted relevant explorations on various datasets and achieved state-of-the-art performance currently, which also proves the generalization of our method.

关键词： Degradation Adaptation models Noise Prototypes Contrastive learning Robustness

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Analog Magnetic Sensor-Robotic System for Steel Structure Inspection

Analog Magnetic Sensor-Robotic System for Steel Structure In...

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IEEE/SICE International Symposium on System Integration

作者： Anh Quyen Pham Duc La Hung Manh La AIR-VIET Corporation in Vietnam Kahlert School of Computing University of Utah Salt Lake City UT USA Department of Computer Science and Engineering Advanced Robotics and Automation (ARA) Lab University of Nevada Reno NV USA

More and more steel bridge collapse accidents occur worldwide due to broken steel structures, causing significant l oss o f l ife a nd p roperty t o m ankind. T his has spurred much research into robots that can climb steel surfaces and carry smart sensors with the desire to assist inspectors in inspecting steel defects. However, current non-destructive evaluation (NDE) sensors such as eddy-current and giant magnetoresistance (GMR) are not able to be integrated with the robotic platforms due to their large size, mass, and limited ability to interface. More importantly, these NDE sensors may fail to detect hidden and underlying cracks. Therefore, in this paper, we present a novel and compact analog magnetic sensor system to detect different types of cracks in steel. Validations are carried out on a steel test plate 600mm long, 140mm wide, and 6mm thick, and precision machined by CNCs to produce various man-made cracks including penetrating cracks, surface cracks, internal/hidden cracks, and underlying cracks. These cracks have a width varying from 0.6mm to 1.2mm with different depth levels. Combined with applying the Kalman filter t o t he n oise r eduction s ystem, t he r esults o btained are accurate, and the response speed is fast. The sensor is small enough and has firmware/software with an interface to make it possible to be integrated with a small drone or a climbing robot to perform an in-depth inspection of the steel bridge. A demonstration of the system can be seen in this video: https://***/OrdpLdnFlYk

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