检索结果-内蒙古大学图书馆

Quantitative Phase Imaging X 2024

作者： Filan, Caroline E. Bharadwaj, Srinidhi Casteleiro Costa, Paloma Davarzani, Amin Cappabianca, Dan Tommasi, Anna Sarko, Lauren La Vonne Denne, Nina Mao, Leidong Saha, Krishanu Karumbaiah, Lohitash Robles, Francisco E. George W. Woodruff School of Mechanical Engineering Georgia Institute of Technology 801 Ferst Dr. NW AtlantaGA United States Wallace H. Coulter Dept. of Biomedical Engineering Georgia Institute of Technology and Emory University 313 Ferst Dr. NW AtlantaGA United States School of Electrical and Computer Engineering Georgia Institute of Technology 266 Ferst Drive AtlantaGA United States School of Electrical and Computer Engineering College of Engineering University of Georgia AthensGA United States Regenerative Bioscience Center University of Georgia AthensGA United States Department of Biomedical Engineering University of Wisconsin-Madison MadisonWI United States Wisconsin Institute for Discovery University of Wisconsin-Madison MadisonWI United States Division of Neuroscience Biomedical and Health Sciences Institute University of Georgia AthensGA United States Edgar L. Rhodes Center for Animal and Dairy Science College of Agriculture and Environmental Science University of Georgia AthensGA United States

ISBN: (纸本)9781510669635

Quantitative oblique back-illumination microscopy (qOBM) is a label-free imaging technique that enables tomographic phase imaging of thick scattering samples with epi-illumination. Here, we propose the use of two forms of functional imaging with qOBM to study tissue and cell cultures. In doing so, we obtain the spatiotemporal and quantitative functional information associated with the phase values extrapolated from qOBM imaging. We have applied this process to study the efficacy of individual immune T cells to kill glioblastoma spheroid cultures in 3D spheroids. Data show that we can effectively distinguish between cell phenotypes and characterize the dynamic motion of these cells in 3D cultures. This work offers a distinct advantage in tracking 3D cellular dynamics in thick tissue as many function imaging modalities are limited to 2D samples. Further, this technology can be expanded to analyze a wide variety of cellular and subcellular dynamics non-invasively in thick tissue. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Tissue

来源：评论

学校读者我要写书评

暂无评论

Research on Adaptive Cache Mechanism Based on TTL 2

Research on Adaptive Cache Mechanism Based on TTL

引用

2nd International Conference on Networking, Communications and Information Technology, NetCIT 2022

作者： Liu, Junping Wan, Xinrun Zhu, Qiang Peng, Tao Hu, Xinrong School of Computer Science and Artificial Intelligence Wuhan Textile University Wuhan China Hubei Provincial Engineering Research Center for Intelligent Textile and Fashion Wuhan Textile University Wuhan China Engineering Research Center of Hubei Province for Clothing Information Wuhan Textile University Wuhan China

ISBN: (纸本)9781665492737

This paper analyzes the impact of time to live (TTL) on system performance. If the cache content hits within the effective time of TTL, it will reduce the access pressure on the source server. If the TTL setting is too long, the consistency of cache content cannot be guaranteed. In order to solve the balance between cache hit rate and data consistency, a new adaptive cache mechanism based on TTL is proposed, which can dynamically adjust the TTL according to the dynamic changes of user visits and the updated characteristics of different web page data. When the TTL expires, only the first user is allowed to send a request to the source server to update the cache, Reject requests from other users until the update is complete. Experiments show that this mechanism can significantly improve the performance of the system. © 2022 IEEE.

关键词： Websites

来源：评论

学校读者我要写书评

暂无评论

Research on the Application of the Radiative Transfer Model Based on Deep Neural Network in One-dimensional Variational Algorithm

引用

Journal of Tropical Meteorology 2022年第3期28卷 326-342页

作者： HE Qiu-rui ZHANG Rui-ling LI Jiao-yang WANG Zhen-zhan School of Information Technology Luoyang Normal UniversityLuoyangHenan 471934 China Key Laboratory of Microwave Remote Sensing National Space Science CenterChinese Academy of SciencesBeijing 100190 China Department of Electrical and Computer Engineering Michigan State UniversityEast LansingMI 48824 USA

As a typical physical retrieval algorithm for retrieving atmospheric parameters,one-dimensional variational(1 DVAR)algorithm is widely used in various climate and meteorological communities and enjoys an important position in the field of microwave remote *** algorithm parameters affecting the performance of the 1 DVAR algorithm,the accuracy of the microwave radiative transfer model for calculating the simulated brightness temperature is the fundamental constraint on the retrieval accuracies of the 1 DVAR algorithm for retrieving atmospheric *** this study,a deep neural network(DNN)is used to describe the nonlinear relationship between atmospheric parameters and satellite-based microwave radiometer observations,and a DNN-based radiative transfer model is developed and applied to the 1 DVAR algorithm to carry out retrieval experiments of the atmospheric temperature and humidity *** retrieval results of the temperature and humidity profiles from the Microwave Humidity and Temperature Sounder(MWHTS)onboard the Feng-Yun-3(FY-3)satellite show that the DNN-based radiative transfer model can obtain higher accuracy for simulating MWHTS observations than that of the operational radiative transfer model RTTOV,and also enables the 1 DVAR algorithm to obtain higher retrieval accuracies of the temperature and humidity *** this study,the DNN-based radiative transfer model applied to the 1 DVAR algorithm can fundamentally improve the retrieval accuracies of atmospheric parameters,which may provide important reference for various applied studies in atmospheric sciences.

关键词： one-dimensional variational algorithm radiative transfer model deep neural network FY-3 MWHTS temperature and humidity profiles

来源：评论

学校读者我要写书评

暂无评论

Spin-UP: Spin Light for Natural Light Uncalibrated Photometric Stereo

Spin-UP: Spin Light for Natural Light Uncalibrated Photometr...

引用

Conference on computer Vision and Pattern Recognition (CVPR)

作者： Zongrui Li Zhan Lu Haojie Yan Boxin Shi Gang Pan Qian Zheng Xudong Jiang Rapid-Rich Object Search (ROSE) Lab Interdisciplinary Graduate Programme Nanyang Technological University School of Electrical and Electronic Engineering Nanyang Technological University The State Key Lab of Brain-Machine Intelligence Zhejiang University College of Computer Science and Technology Zhejiang University National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University National Engineering Research Center of Visual Technology School of Computer Science Peking University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Natural Light Uncalibrated Photometric Stereo (NaUPS) relieves the strict environment and light assumptions in classical Uncalibrated Photometric Stereo (UPS) methods. However, due to the intrinsic ill-posedness and high-dimensional ambiguities, addressing NaUPS is still an open question. Existing works impose strong assumptions on the environment lights and objects' material, restricting the effectiveness in more general scenarios. Alternatively, some methods leverage supervised learning with intricate models while lacking interpretability, resulting in a biased estimation. In this work, we propose Spin Light Uncalibrated Photometric Stereo (Spin-UP), an unsupervised method to tackle NaUPS in various environment lights and objects. The proposed method uses a novel setup that captures the object's images on a rotatable platform, which mitigates NaUPS's ill-posedness by reducing unknowns and provides reliable priors to alleviate NaUPS's ambiguities. Leveraging neural inverse rendering and the proposed training strategies, Spin-UP recovers surface normals, environment light, and isotropic reflectance under complex natural light with low computational cost. Experiments have shown that Spin-UP outperforms other supervised / unsupervised NaUPS meth-ods and achieves state-of-the-art performance on synthetic and real-world datasets. Codes and data are available at https://***/LMozart/CVPR2024-SpinUP.

关键词： Reflectivity Training Shape Supervised learning Anisotropic Rendering (computer graphics) Structural engineering

来源：评论

学校读者我要写书评

暂无评论

MechTac: A Multifunctional Tendon-Linked Optical Tactile Sensor for In/Out-the-Field-of-View Perception with Deep Learning

MechTac: A Multifunctional Tendon-Linked Optical Tactile Sen...

引用

Annual Conference of Industrial Electronics Society

作者： Zhenyu Lu Tianqi Yue Zhou Zhao Weiyong Si Ning Wang Chenguang Yang Bristol Robotics Laboratory University of the West of England Bristol UK Department of Engineering Mathematics University of Bristol Bristol UK School of Computer Science Central China Normal University Wuhan China

Tactile sensors can be used for motion detection and object perception in robot manipulation. The contact detection within the camera's visual inspection area has been well-developed, but perception outside the field of view of the camera is overlooked. In this paper, we present a new tendon-linked tactile sensor, MechTac, to achieve perceptions inside and outside the field of view. The MechTac is an evolution of the typical TacTip sensor with the following two advantages. 1) The ability to provide perception outside the field of view. This is achieved by using a network of braided tendons to transfer deformation from the blind perception regions (TacSide) to the visual areas (TacTip). 2) The tactility of the TacSide and TacTip is reflected by the movements of multiple papillae pins and visible markers on the pin tips on the inner surface of the TacTip. The pins and markers are differentially sensitive to various touch features, which is similar to the differentiated perceptual ability of humans. TacTip is more sensitive to small touches, corresponding to the fingertip, while the TacSide is less sensitive but has a larger perceptual area, corresponding to the middle part of the finger. Moreover, we propose a new deep learning method to decompose the mixed information affected by the TacSide and the TacTip. A modified DenseNet121 was specifically designed for object perception at the TacTip and localization at the TacSide. The experimental results show that prediction accuracy reaches about 98% for object perception or localization and over 99% for the case requiring two functions.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Improvement of Depth Mapping and Occlusion Removal Method Using Integral Imaging

Improvement of Depth Mapping and Occlusion Removal Method Us...

引用

International Conference on Control, Automation and Systems ( ICCAS)

作者： Haruka Sakanoue Hyun-Woo Kim Myungjin Cho Min-Chul Lee Department of Computer Science and Networks Kyushu Institute of Technology Iizuka Fukuoka Japan School of ICT Robotics and Mechanical Engineering IITC Hankyong National University Anseong Kyonggi-do South Korea

In this paper, we propose the depth mapping and occlusion removal method using integral imaging (InIm). InIm is a method to obtain 3D information by capturing and reconstructing an object from multiple viewpoints. InIm can remove occlusion using parallax obtained by photographing an object from multiple viewpoints. However, occlusion removal uses a unit matrix to calculate the number of overlaps, which results in an overall darkening of the image. To solve this problem, this paper aims to improve occlusion removal by using the matrix of the mask image to calculate the number of overlaps. InIm can provide depth mapping that shows the distance from an object as pixel values. However, when there is an occlusion in front of the object, it is difficult to provide depth mapping object due to the influence of the occlusion. To solve this problem, we propose a method of depth mapping and occlusion removal method using InIm. Our method is expected to make great progress in improving safety in the field of autonomous driving.

关键词：

来源：评论

学校读者我要写书评

暂无评论

SirLLM: Streaming Infinite Retentive LLM

arXiv

引用

arXiv 2024年

作者： Yao, Yao Li, Zuchao Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan430072 China

As Large Language Models (LLMs) become increasingly prevalent in various domains, their ability to process inputs of any length and maintain a degree of memory becomes essential. However, the one-off input of overly long texts is limited, as studies have shown that when input lengths exceed the LLMs' pre-trained text length, there is a dramatic decline in text generation capabilities. Moreover, simply extending the length of pre-training texts is impractical due to the difficulty in obtaining long text data and the substantial memory consumption costs this would entail for LLMs. Recent efforts have employed streaming inputs to alleviate the pressure of excessively long text inputs, but this approach can significantly impair the model's long-term memory capabilities. Motivated by this challenge, we introduce Streaming Infinite Retentive LLM (SirLLM), which allows LLMs to maintain longer memory during infinite-length dialogues without the need for fine-tuning. SirLLM utilizes the Token Entropy metric and a memory decay mechanism to filter key phrases, endowing LLMs with both long-lasting and flexible memory. We designed three distinct tasks and constructed three datasets to measure the effectiveness of SirLLM from various angles: (1) DailyDialog;(2) Grocery Shopping;(3) Rock-Paper-Scissors. Our experimental results robustly demonstrate that SirLLM can achieve stable and significant improvements across different LLMs and tasks, compellingly proving its effectiveness. When having a coversation, "A sir could forget himself," but SirLLM never does! Our code is publicly available at https://***/Zoeyyao27/SirLLM. © 2024, CC BY.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Parsing Natural Language into Propositional and First-Order Logic with Dual Reinforcement Learning 29

Parsing Natural Language into Propositional and First-Order ...

引用

29th International Conference on Computational Linguistics, COLING 2022

作者： Lu, Xuantao Liu, Jingping Gu, Zhouhong Tong, Hanwen Xie, Chenhao Huang, Junyang Xiao, Yanghua Wang, Wenguang Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China SenseDeal Intelligent Technology Co. Ltd. Beijing China School of Information Science and Engineering East China University of Science and Technology China Fudan-Aishu Cognitive Intelligence Joint Research Center China DataGrand Inc. Shanghai China

Semantic parsing converts natural language utterances into structured logical expressions. We consider two such formal representations: Propositional Logic (PL) and First-order Logic (FOL). The paucity of labeled data is a major challenge in this field. In previous works, dual reinforcement learning has been proposed as an approach to reduce dependence on labeled data. However, this method has the following limitations: 1) The reward needs to be set manually and is not applicable to all kinds of logical expressions. 2) The training process easily collapses when models are trained with only the reward from dual reinforcement learning. In this paper, we propose a scoring model to automatically learn a model-based reward, and an effective training strategy based on curriculum learning is further proposed to stabilize the training process. In addition to the technical contribution, a Chinese-PL/FOL dataset is constructed to compensate for the paucity of labeled data in this field. Experimental results show that the proposed method outperforms competitors on several datasets. Furthermore, by introducing PL/FOL generated by our model, the performance of existing Natural Language Inference (NLI) models is further enhanced. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Downstream-agnostic Adversarial Examples

Downstream-agnostic Adversarial Examples

引用

International Conference on computer Vision (ICCV)

作者： Ziqi Zhou Shengshan Hu Ruizhi Zhao Qian Wang Leo Yu Zhang Junhui Hou Hai Jin School of Cyber Science and Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan University School of Information and Communication Technology Griffith University Department of Computer Science City University of Hong Kong School of Computer Science and Technology Huazhong University of Science and Technology Cluster and Grid Computing Lab

Self-supervised learning usually uses a large amount of unlabeled data to pre-train an encoder which can be used as a general-purpose feature extractor, such that downstream users only need to perform fine-tuning operations to enjoy the benefit of "large model". Despite this promising prospect, the security of pre-trained encoder has not been thoroughly investigated yet, especially when the pre-trained encoder is publicly available for commercial *** this paper, we propose AdvEncoder, the first framework for generating downstream-agnostic universal adversarial examples based on the pre-trained encoder. AdvEncoder aims to construct a universal adversarial perturbation or patch for a set of natural images that can fool all the downstream tasks inheriting the victim pre-trained encoder. Unlike traditional adversarial example works, the pre-trained encoder only outputs feature vectors rather than classification labels. Therefore, we first exploit the high frequency component information of the image to guide the generation of adversarial examples. Then we design a generative attack framework to construct adversarial perturbations/patches by learning the distribution of the attack surrogate dataset to improve their attack success rates and transferability. Our results show that an attacker can successfully attack downstream tasks without knowing either the pre-training dataset or the downstream dataset. We also tailor four defenses for pre-trained encoders, the results of which further prove the attack ability of AdvEncoder. Our codes are available at: https://***/CGCL-codes/AdvEncoder.

关键词：

来源：评论

学校读者我要写书评

暂无评论

ASVFI: Audio-Driven Speaker Video Frame Interpolation

ASVFI: Audio-Driven Speaker Video Frame Interpolation

引用

IEEE International Conference on Image Processing

作者： Qianrui Wang Dengshi Li Liang Liao Hao Song Wei Li Jing Xiao School of Artificial Intelligence Jianghan University China School of Computer Science and Engineering Nanyang Technological University Singapore National Engineering Research Center for Multimedia Software Wuhan University China

Due to limited data transmission, the video frame rate is low during the online conference, severely affecting user experience. Video frame interpolation can solve the problem by interpolating intermediate frames to increase the video frame rate. Generally, most existing video frame interpolation methods are based on the linear motion assumption. However, the mouth motion is nonlinear, and these methods can not generate superior intermediate frames in speaker video. Considering the strong correlation between mouth shape and vocalization, a new method is proposed, named Audio-driven Speaker Video Frame Interpolation(ASVFI). First, we extract the audio feature from Audio Net(ANet). Second, we use Video Net(VNet) encoder to extract the video feature. Finally, we fuse the audio and video features by AVFusion and decode out the intermediate frame in the VNet decoder. The experimental results show that the PSNR is nearly 0.13dB higher than the baseline of interpolating one frame. When interpolating seven frames, the PSNR is 0.33dB higher than the baseline.

关键词：

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：