检索结果-内蒙古大学图书馆

Development of a compact mouse retinal imaging system using spectral domain optical coherence tomography (SD-OCT) with a full field stimulation channel for recording fast optoretinograms (ORGs) 34

Development of a compact mouse retinal imaging system using ...

引用

Ophthalmic Technologies XXXIV 2024

作者： Jo, Hang Chan Pijewska, Ewelina Meleppat, Ratheesh K. Jonnal, Ravi S. Kim, Dae Yu Zawadzki, Robert J. Department of Electrical and Computer Engineering Inha University 100 Inha-ro Michuhol-gu Incheon22212 Korea Republic of Dept. of Ophthalmology and Vision Science UC Davis Eye Center SacramentoCA95817 United States UC Davis Eye-Pod Small Animal Ocular Imaging Laboratory Department of Cell Biology and Human Anatomy University of California Davis 4320 Tupper Hall DavisCA95616 United States

ISBN: (纸本)9781510669079

While the a-wave of mouse electroretinogram (ERG) occurs within 50 milliseconds after exposure to light, the optoretinogram (ORG) slower than a 20Hz sampling rate could face limitations in observing immediate morphological changes from the retina. In this study, we develop a compact custom-built mouse ORG system based on spectral domain optical coherence tomography (SD-OCT) for 100Hz~1KHz B-scan rates comprised of 100 kHz A-scans. All the optics of the developed ORG system are designed on a 24 x 24 inches optical breadboard to move easily as well as to combine with the ERG system in a dark room. Without using a fundus camera, the OCT system provides en-face images from high-pass filtering and square of the OCT spectral signal for mouse retinal positioning in-vivo before acquiring ORG data. The 490nm LED for light stimulus is generated to make uniform illumination at the mouse retina using the Maxwellian view method. The common path of the OCT scanning light and the visible LED is built with achromatic doublet lens combinations based on optical simulation with Opticstudio® . The developed compact ORG system can not only observe light-evoked responses with 1~10 milliseconds but also be used for the studies of correlations between ORG and ERG in the mouse retina. © 2024 SPIE.

关键词： Optical tomography

来源：评论

学校读者我要写书评

暂无评论

Corner cutting connects chiral colorimetry to net electric flux in lossless all-dielectric metasurfaces

arXiv

引用

arXiv 2024年

作者： Haddadin, Zaid Nguyen, Anna M.Y. Poulikakos, Lisa V. Department of Electrical & Computer Engineering UC San Diego La Jolla CA92093-0021 United States Department of Mechanical & Aerospace Engineering UC San Diego La Jolla CA92093-0021 United States Program of Materials Science & Engineering UC San Diego La Jolla CA92093-0021 United States

All-dielectric metasurfaces can produce structural colors, but the most advantageous design criteria are still being investigated. This work numerically studies how the two-dimensional shape of nanoparticles affects the colorimetric response under circularly polarized light (CPL) to develop a sensor distinguishing CPL orientations. Using lossless dielectric materials (silicon nitride on silicon dioxide), we achieve far-field dichroism by modifying oblong nanoparticles into L-shaped structures through corner cuts. This design suppresses one electric dipole under CPL illumination, leading to differential colorimetric responses. We link these responses to a decoupling effect in the near-field net electric flux. Our findings provide design guidelines for all-dielectric, lossless colorimetric sensors of chiral light. Copyright © 2024, The Authors. All rights reserved.

关键词： Silicon nitride

来源：评论

学校读者我要写书评

暂无评论

Embodiment-agnostic Action Planning via Object-Part Scene Flow

arXiv

引用

arXiv 2024年

作者： Tang, Weiliang Pan, Jia-Hui Zhan, Wei Zhou, Jianshu Yao, Huaxiu Liu, Yun-Hui Tomizuka, Masayoshi Ding, Mingyu Fu, Chi-Wing The Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong The Department of Mechanical Engineering UC Berkeley United States The Department of Computer Science UNC-Chapel Hill United States The Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong

Observing that the key for robotic action planning is to understand the target-object motion when its associated part is manipulated by the end effector, we propose to generate the 3D object-part scene flow and extract its transformations to solve the action trajectories for diverse embodiments. The advantage of our approach is that it derives the robot action explicitly from object motion prediction, yielding a more robust policy by understanding the object motions. Also, beyond policies trained on embodiment-centric data, our method is embodiment-agnostic, generalizable across diverse embodiments, and being able to learn from human demonstrations. Our method comprises three components: an object-part predictor to locate the part for the end effector to manipulate, an RGBD video generator to predict future RGBD videos, and a trajectory planner to extract embodiment-agnostic transformation sequences and solve the trajectory for diverse embodiments. Trained on videos even without trajectory data, our method still outperforms existing works significantly by 27.7% and 26.2% on the prevailing virtual environments MetaWorld and Franka-Kitchen, respectively. Furthermore, we conducted real-world experiments, showing that our policy, trained only with human demonstration, can be deployed to various embodiments. Copyright © 2024, The Authors. All rights reserved.

关键词： Trajectories

来源：评论

学校读者我要写书评

暂无评论

Safe and Balanced: A Framework for Constrained Multi-Objective Reinforcement Learning

arXiv

引用

arXiv 2024年

作者： Gu, Shangding Sel, Bilgehan Ding, Yuhao Wang, Lu Lin, Qingwei Knoll, Alois Jin, Ming Department of Computer Science Technical University of Munich Germany Electrical and Computer Engineering Department Virginia Tech United States Department of Industrial Engineering and Operations Research UC Berkeley United States Microsoft Research Asia China

In numerous reinforcement learning (RL) problems involving safety-critical systems, a key challenge lies in balancing multiple objectives while simultaneously meeting all stringent safety constraints. To tackle this issue, we propose a primal-based framework that orchestrates policy optimization between multi-objective learning and constraint adherence. Our method employs a novel natural policy gradient manipulation method to optimize multiple RL objectives and overcome conflicting gradients between different tasks, since the simple weighted average gradient direction may not be beneficial for specific tasks' performance due to misaligned gradients of different task objectives. When there is a violation of a hard constraint, our algorithm steps in to rectify the policy to minimize this violation. We establish theoretical convergence and constraint violation guarantees in a tabular setting. Empirically, our proposed method also outperforms prior state-of-the-art methods on challenging safe multi-objective reinforcement learning tasks. Copyright © 2024, The Authors. 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

Conformal Decision Theory: Safe Autonomous Decisions from Imperfect Predictions

arXiv

引用

arXiv 2023年

作者： Lekeufack, Jordan Angelopoulos, Anastasios N. Bajcsy, Andrea Jordan, Michael I. Malik, Jitendra Department of Statistics UC Berkeley United States Department of Electrical Engineering and Computer Science UC Berkeley United States Robotics Institute Carnegie Mellon University United States

We introduce Conformal Decision Theory, a framework for producing safe autonomous decisions despite imperfect machine learning predictions. Examples of such decisions are ubiquitous, from robot planning algorithms that rely on pedestrian predictions, to calibrating autonomous manufacturing to exhibit high throughput and low error, to the choice of trusting a nominal policy versus switching to a safe backup policy at run-time. The decisions produced by our algorithms are safe in the sense that they come with provable statistical guarantees of having low risk without any assumptions on the world model whatsoever;the observations need not be I.I.D. and can even be adversarial. The theory extends results from conformal prediction to calibrate decisions directly, without requiring the construction of prediction sets. Experiments demonstrate the utility of our approach in robot motion planning around humans, automated stock trading, and robot manufacturing. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

FastStamp: Accelerating Neural Steganography and Digital Watermarking of Images on FPGAs

arXiv

引用

arXiv 2022年

作者： Hussain, Shehzeen Sheybani, Nojan Neekhara, Paarth Zhang, Xinqiao Duarte, Javier Koushanfar, Farinaz Department of Electrical and Computer Engineering UC San Diego United States Department of Computer Science and Engineering UC San Diego United States Department of Physics UC San Diego United States

Steganography and digital watermarking are the tasks of hiding recoverable data in image pixels. Deep neural network (DNN) based image steganography and watermarking techniques are quickly replacing traditional hand-engineered pipelines. DNN based watermarking techniques have drastically improved the message capacity, imperceptibility and robustness of the embedded watermarks. However, this improvement comes at the cost of increased computational overhead of the watermark encoder neural network. In this work, we design the first accelerator platform FastStamp to perform DNN based steganography and digital watermarking of images on hardware. We first propose a parameter efficient DNN model for embedding recoverable bit-strings in image pixels. Our proposed model can match the success metrics of prior state-of-the-art DNN based watermarking methods while being significantly faster and lighter in terms of memory footprint. We then design an FPGA based accelerator framework to further improve the model throughput and power consumption by leveraging data parallelism and customized computation paths. FastStamp allows embedding hardware signatures into images to establish media authenticity and ownership of digital media. Our best design achieves 68× faster inference as compared to GPU implementations of prior DNN based watermark encoder while consuming less power. Copyright © 2022, The Authors. All rights reserved.

关键词： Steganography

来源：评论

学校读者我要写书评

暂无评论

Data Redaction from Pre-trained GANs

arXiv

引用

arXiv 2022年

作者： Kong, Zhifeng Chaudhuri, Kamalika Computer Science and Engineering UC San Diego La Jolla United States

Large pre-trained generative models are known to occasionally output undesirable samples, which undermines their trustworthiness. The common way to mitigate this is to re-train them differently from scratch using different data or different regularization – which uses a lot of computational resources and does not always fully address the problem. In this work, we take a different, more compute-friendly approach and investigate how to post-edit a model after training so that it "redacts", or refrains from outputting certain kinds of samples. We show that redaction is a fundamentally different task from data deletion, and data deletion may not always lead to redaction. We then consider Generative Adversarial Networks (GANs), and provide three different algorithms for data redaction that differ on how the samples to be redacted are described. Extensive evaluations on real-world image datasets show that our algorithms out-perform data deletion baselines, and are capable of redacting data while retaining high generation quality at a fraction of the cost of full retraining. Copyright © 2022, The Authors. All rights reserved.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Provably Efficient Reinforcement Learning via Surprise Bound

arXiv

引用

arXiv 2023年

作者： Zhu, Hanlin Wang, Ruosong Lee, Jason D. Department of Electrical Engineering and Computer Sciences Uc Berkeley United States Paul G. Allen School of Computer Science & Engineering University of Washington United States Electrical and Computer Engineering Princeton University United States

Value function approximation is important in modern reinforcement learning (RL) problems especially when the state space is (infinitely) large. Despite the importance and wide applicability of value function approximation, its theoretical understanding is still not as sophisticated as its empirical success, especially in the context of general function approximation. In this paper, we propose a provably efficient RL algorithm (both computationally and statistically) with general value function approximations. We show that if the value functions can be approximated by a function class F which satisfies the Bellman-completeness assumption, our algorithm achieves an Õ(poly(ιH) √T) regret bound where ι is the product of the surprise bound and log-covering numbers, H is the planning horizon, K is the number of episodes and T = HK is the total number of steps the agent interacts with the environment. Our algorithm achieves reasonable regret bounds when applied to both the linear setting and the sparse high-dimensional linear setting. Moreover, our algorithm only needs to solve O(H log K) empirical risk minimization (ERM) problems, which is far more efficient than previous algorithms that need to solve ERM problems for Ω(HK) times. Copyright © 2023, The Authors. 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

Integrated Convolutional and Recurrent Neural Networks for Health Risk Prediction using Patient Journey Data with Many Missing Values

Integrated Convolutional and Recurrent Neural Networks for H...

引用

2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Liu, Yuxi Qin, Shaowen Yepes, Antonio Jimeno Shao, Wei Zhang, Zhenhao Salim, Flora D. Flinders University College of Science and Engineering AdelaideSA Australia RMIT University School of Computing Technologies MelbourneVIC Australia UC Davis College of Electrical and Computer Engineering California DavisCA United States Northwest A&f University College of Life Sciences Shaanxi Yangling China UNSW School of Computer Science and Engineering SydneyNSW Australia

ISBN: (纸本)9781665468190

Predicting the health risks of patients using Electronic Health Records (EHR) has attracted considerable attention in recent years, especially with the development of deep learning techniques. Health risk refers to the probability of the occurrence of a specific health outcome for a specific patient. The predicted risks can be used to support decision-making by healthcare professionals. EHRs are structured patient journey data. Each patient journey contains a chronological set of clinical events, and within each clinical event, there is a set of clinical/medical activities. Due to variations of patient conditions and treatment needs, EHR patient journey data has an inherently high degree of missingness that contains important information affecting relationships among variables, including time. Existing deep learning-based models generate imputed values for missing values when learning the relationships. However, imputed data in EHR patient journey data may distort the clinical meaning of the original EHR patient journey data, resulting in classification bias. This paper proposes a novel end-to-end approach to modeling EHR patient journey data with Integrated Convolutional and Recurrent Neural Networks. Our model can capture both long- and short-term temporal patterns within each patient journey and effectively handle the high degree of missingness in EHR data without any imputation data generation. Extensive experimental results using the proposed model on two real-world datasets demonstrate robust performance as well as superior prediction accuracy compared to existing state-of-the-art imputation-based prediction methods. © 2022 IEEE.

关键词： Data mining

来源：评论

学校读者我要写书评

暂无评论

Correct–by–Construction Design of Contextual Robotic Missions Using Contracts

arXiv

引用

arXiv 2023年

作者： Mallozzi, Piergiuseppe Nuzzo, Pierluigi Piterman, Nir Schneider, Gerardo Pelliccione, Patrizio Department of Electrical Engineering and Computer Science UC Berkeley. Previously United States Department of Computer Science and Engineering Chalmers University of Technology Sweden Ming Hsieh Department of Electrical and Computer Engineering Univeristy of Southern California Los Angeles United States Department of Computer Science and Engineering University of Gothenburg Sweden L’Aquila Italy

Effectively specifying and implementing robotic missions poses a set of challenges to software engineering for robotic systems. These challenges stem from the need to formalize and execute a robot’s high-level tasks while considering various application scenarios and conditions—also known as contexts—in real-world operational environments. Writing correct mission specifications that explicitly account for multiple contexts can be tedious and error-prone. Furthermore, as the number of contexts—and consequently the complexity of the specification—increases, generating a correct-by-construction implementation (e.g., by using synthesis methods) can become intractable. A viable approach to address these issues is to decompose the mission specification into smaller, manageable sub-missions, with each sub-mission tailored to a specific context. Nevertheless, this compositional approach introduces its own set of challenges in ensuring the overall mission’s correctness. In this paper, we propose a novel compositional framework for specifying and implementing contextual robotic missions using assume-guarantee contracts. The mission specification is structured in a hierarchical and modular fashion, allowing for each sub-mission to be synthesized as an independent robot controller. We address the problem of dynamically switching between sub-mission controllers while ensuring correctness under predefined conditions. Copyright © 2023, The Authors. All rights reserved.

关键词： Formal verification

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：