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SSRN 2024年

作者： Gu, Yunqi Zhou, Tao Zhang, Yizhe Zhou, Yi He, Kelei Gong, Chen Zhu, Huafu PCA Lab The Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education School of Computer Science and Engineering Nanjing University of Information Science and Technology Nanjing210094 China School of Computer Science and Engineering Southeast University Nanjing211189 China Medical School Nanjing University National Institute of Healthcare Data Science Nanjing210023 China Institute of High Performance Computing A*STAR Singapore

Medical image segmentation plays a crucial role in computer-aided diagnosis. However, existing methods heavily rely on fully supervised training, which requires a large amount of labeled data with time-consuming pixel-wise annotations. Moreover, accurately segmenting lesions poses challenges due to variations in shape, size, and location. To address these issues, we propose a novel Dual-scale Enhanced and Cross-generative consistency learning framework for semi-supervised medical image Segmentation (DEC-Seg). First, we propose a Cross-level Feature Aggregation (CFA) module that integrates cross-level adjacent layers to enhance the feature representation ability across different resolutions. To address scale variation, we present a scale-enhanced consistency constraint, which ensures consistency in the segmentation maps generated from the same input image at different scales. This constraint helps handle variations in lesion sizes and improves the robustness of the model. Furthermore, we propose a cross-generative consistency scheme, in which the original and perturbed images can be reconstructed using cross-segmentation maps. This consistency constraint allows us to mine effective feature representations and boost the segmentation performance. To produce more accurate segmentation maps, we propose a Dual-scale Complementary Fusion (DCF) module that integrates features from two scale-specific decoders operating at different scales. Extensive experimental results on polyp segmentation from colonoscopy images, skin lesion segmentation from dermoscopic images, and Brain glioma segmentation from MRI images demonstrate the effectiveness of our DEC-Seg against other state-of-the-art semi-supervised segmentation approaches. © 2024, The Authors. All rights reserved.

关键词： computer aided diagnosis

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Hollow mesoporous polyaniline nanoparticles with high drug payload and robust photothermal capability for cancer combination therapy

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Chinese Journal of Chemical engineering 2021年第10期34卷 221-228页

作者： Yinfeng Zhang Fang Fang Yongjie Chen Min Li Li Li Wenyue Li Jinfeng Zhang International Medical Center Beijing Friendship HospitalCapital Medical UniversityBeijing100050China Key Laboratory of Molecular Medicine and Biotherapy School of Life SciencesBeijing Institute of TechnologyBeijing 100081China College of Pharmacy Chongqing Medical UniversityChongqing 400016China School of Electrical Computer and Energy EngineeringArizona State UniversityTempeArizonaUSA

In recent years,synergistic chemo-photothermal therapy has revealed promising potential in treatments against various kinds of ***,the development of superb photothermal agents with high drug loading capacity is still highly *** this work,a hollow mesoporous polyaniline nanoparticle(HPANI NP)has been developed for encapsulating chemotherapeutic drug doxorubicin(DOX)with an remarkable drug loading content as high as 37.5%.Additional PEG modification endowed the drugloaded HPANI NPs with improved water-dispersibility and *** PEG-HPANI-DOX NPs exhibited strong NIR absorbance and robust photothermal conversion capacity,exhibiting highly efficient synergistic cancer *** interestingly,the responsively released DOX molecules could emit strong red fluorescence,which could be employed to monitor the cellular endocytosis and drug release profile of PEG-HPANI-DOX ***,the as-fabricated NPs showed good biocompatibility and low toxicity,serving as a promising nanoagent for highly efficient drug delivery and cancer combination therapy.

关键词： Hollow mesoporous polyaniline nanoparticles Drug delivery Nanocarriers Chemo-photothermal therapy Cancer combination therapy

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Planarizing Spalled GaAs(100) Surfaces by MOVPE Growth

Planarizing Spalled GaAs(100) Surfaces by MOVPE Growth

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IEEE Conference Photovoltaic Specialists (PVSC)

作者： Gavin P. Forcade William E. McMahon Nicholas Yoo Anica N. Neumann Michelle Young John Goldsmith Karin Hinzer Corinne E. Packard Myles A. Steiner Department of Physics University of Ottawa Ottawa Ontario Canada National Renewable Energy Laboratory Golden CO USA Colorado School of Mines Golden CO USA SUNLAB School of Electrical Engineering and Computer Sciences University of Ottawa Ottawa Ontario Canada

ISBN: (数字)9781665464260

ISBN: (纸本)9781665475822

III-V photovoltaic devices have demonstrated remarkable performance in many applications, and spalling is a promising technique for reducing device costs by recovering the substrate for reuse. In this study, we investigate the in situ planarization of A-directionally spalled GaAs(100) substrates using metal-organic vapor phase epitaxy (MOVPE) grown $\mathbf{C}:\mathbf{GaAs}$ with $\mathbf{CCl}_{\mathbf{4}}$ as the carbon source. We have characterized the (100)-oriented growth for various $\mathbf{CCl}_{\mathbf{4}}$ flow rates and observed that the $\mathbf{CCl}_{\mathbf{4}}$ or its by-products promote material diffusion from the facet tops to the underlying valleys. For facets with a height of 5 $\boldsymbol{\mu}\mathbf{m}$ on a substrate with an A-spall and $\mathbf{6}^{\circ}\mathbf{B}$ -offcut, it took 7 $\boldsymbol{\mu}\mathbf{m}$ of $\mathbf{C}:\mathbf{GaAs}$ to planarize the substrate. For a similar sample, with a $\mathbf{6}^{\circ}\mathbf{A}$ -offcut, it required 2 $\boldsymbol{\mu}\mathbf{m}$ of growth to fill the valleys but there were remnant facets.

关键词： Photovoltaic systems Performance evaluation Planarization Smoothing methods Epitaxial layers Fluid flow Epitaxial growth Carbon III-V semiconductor materials Substrates

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CLISC: Bridging Clip and SAM by Enhanced CAM for Unsupervised Brain Tumor Segmentation

CLISC: Bridging Clip and SAM by Enhanced CAM for Unsupervise...

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IEEE International Symposium on Biomedical Imaging

作者： Xiaochuan Ma Jia Fu Wenjun Liao Shichuan Zhang Guotai Wang School of Computer Science and Engineering School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China Department of Radiation Oncology Sichuan Cancer Hospital & Institute Sichuan Cancer Center School of Medicine University of Electronic Science and Technology of China Chengdu China Shanghai Artificial Intelligence Laboratory Shanghai China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Brain tumor segmentation is important for diagnosis of the tumor, and current deep-learning methods rely on a large set of annotated images for training, with high annotation costs. Unsupervised segmentation is promising to avoid human annotations while the performance is often limited. In this study, we present a novel unsupervised segmentation approach that leverages the capabilities of foundation models, and it consists of three main steps: (1) A vision-language model (i.e., CLIP) is employed to obtain image-level pseudo-labels for training a classification network. Class Activation Mapping (CAM) is then employed to extract Regions of Interest (ROIs), where an adaptive masking-based data augmentation is used to enhance ROI identification. (2) The ROIs are used to generate bounding box and point prompts for the Segment Anything Model (SAM) to obtain segmentation pseudo-labels. (3) A 3D segmentation network is trained with the SAM-derived pseudo-labels, where low-quality pseudo-labels are filtered out in a self-learning process based on the similarity between the SAM's output and the network's prediction. Evaluation on the BraTS2020 dataset demonstrates that our approach obtained an average Dice Similarity Score (DSC) of 85.60%, outperforming five state-of-the-art unsupervised segmentation methods by more than 10 percentage points. Besides, our approach outperforms directly using SAM for zero-shot inference, and its performance is close to fully supervised learning.

关键词： Training Image segmentation Adaptation models Three-dimensional displays Adaptive systems Foundation models Annotations Supervised learning Brain tumors Data augmentation

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Game Theory for Autonomy: From Min-Max Optimization to Equilibrium and Bounded Rationality Learning

Game Theory for Autonomy: From Min-Max Optimization to Equil...

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American Control Conference (ACC)

作者： Kyriakos G. Vamvoudakis Filippos Fotiadis João P. Hespanha Raphael Chinchilla Guosong Yang Mushuang Liu Jeff S. Shamma Lacra Pavel Daniel Guggenheim School of Aerospace Engineering Georgia Institute of Technology Atlanta GA USA Center for Control Dynamical Systems and Computation University of California Santa Barbara CA Department of Electrical and Computer Engineering Rutgers University Piscataway NJ USA Department of Mechanical and Aerospace Engineering University of Missouri Columbia MO USA Department of Industrial and Enterprise Systems Engineering University of Illinois at Urbana-Champaign IL USA Department of Electrical and Computer Engineering University of Toronto Canada

Finding Nash equilibria in non-cooperative games can be, in general, an exceptionally challenging task. This is owed to various factors, including but not limited to the cost functions of the game being nonconvex/nonconcave, the players of the game having limited information about one another, or even due to issues of computational complexity. The present tutorial draws motivation from this harsh reality and provides methods to approximate Nash or min-max equilibria in non-ideal settings using both optimization- and learning-based techniques. The tutorial acknowledges, however, that such techniques may not always converge, but instead lead to oscillations or even chaos. In that respect, tools from passivity and dissipativity theory are provided, which can offer explanations about these divergent behaviors. Finally, the tutorial highlights that, more frequently than often thought, the search for equilibrium policies is simply vain; instead, bounded rationality and non-equilibrium policies can be more realistic to employ owing to some players’ learning imperfectly or being relatively naive – "bounded rational." The efficacy of such plays is demonstrated in the context of autonomous driving systems, where it is explicitly shown that they can guarantee vehicle safety.

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A Data-Informed Analysis of Scalable Supervision for Safety in Autonomous Vehicle Fleets

arXiv

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arXiv 2024年

作者： Hickert, Cameron Yan, Zhongxia Wu, Cathy The Institute for Data Systems and Society Massachusetts Institute of Technology CambridgeMA02139 United States The Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States The Laboratory for Information & Decision Systems The Institute for Data Systems and Society The Department of Civil and Environmental Engineering Massachusetts Institute of Technology CambridgeMA02139 United States

Autonomous driving is a highly anticipated approach toward eliminating roadway fatalities. At the same time, the bar for safety is both high and costly to verify. This work considers the role of remotely-located human operators supervising a fleet of autonomous vehicles (AVs) for safety. Such a 'scalable supervision' concept was previously proposed to bridge the gap between still-maturing autonomy technology and the pressure to begin commercial offerings of autonomous driving. The present article proposes DISCES, a framework for Data-Informed Safety-Critical Event Simulation, to investigate the practicality of this concept from a dynamic network loading standpoint. With a focus on the safety-critical context of AVs merging into mixed-autonomy traffic, vehicular arrival processes at 1,097 highway merge points are modeled using microscopic traffic reconstruction with historical data from interstates across three California counties. Combined with a queuing theoretic model, these results characterize the dynamic supervision requirements and thereby scalability of the teleoperation approach. Across all scenarios we find reductions in operator requirements greater than 99% as compared to in-vehicle supervisors for the time period analyzed. The work also demonstrates two methods for reducing these empirical supervision requirements: (i) the use of cooperative connected AVs - which are shown to produce an average 3.67 orders-of-magnitude system reliability improvement across the scenarios studied - and (ii) aggregation across larger regions. Copyright © 2024, The Authors. All rights reserved.

关键词： Autonomous vehicles

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An effective Reinforcement Learning method for preventing the overfitting of Convolutional Neural Networks

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Advances in Computational Intelligence 2022年第5期2卷 1-15页

作者： Mahdavi-Hormat, Ali Menhaj, Mohammad Bagher Shakarami, Ashkan Department of Electrical Computer and Biomedical Engineering Islamic Azad University Qazvin Iran Department of Electrical Engineering Amirkabir University of Technology (Tehran Polytechnic) Tehran Iran Intelligent Autonomous Systems Laboratory (IAS-Lab) Department of Information Engineering University of Padova Padua Italy

Convolutional Neural Networks are machine learning models that have proven abilities in many variants of tasks. This powerful machine learning model sometimes suffers from overfitting. This paper proposes a method based on Reinforcement Learning for addressing this problem. In this research, the parameters of a target layer in the Convolutional Neural Network take as a state for the Agent of the Reinforcement Learning section. Then the Agent gives some actions as forming parameters of a hyperbolic secant function. This function’s form is changed gradually and implicitly by the proposed method. The inputs of the function are the weights of the layer, and its outputs multiply by the same weights to updating them. In this study, the proposed method is inspired by the Deep Deterministic Policy Gradient model because the actions of the Agent are into a continuous domain. To show the proposed method’s effectiveness, the classification task is considered using Convolutional Neural Networks. In this study, 7 datasets have been used for evaluating the model; MNIST, Extended MNIST, small-notMNIST, Fashion-MNIST, sign language MNIST, CIFAR-10, and CIFAR-100.

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Computing Flow Experience Based on Physiological Signals: A Systematic Literature Review

Computing Flow Experience Based on Physiological Signals: A ...

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2025 CHI Conference on Human Factors in Computing systems, CHI EA 2025

作者： Hong, Yichen Zhou, Chao Liu, Zelu Zifeng, Ni Liu, Juan Li, Xiangxian Yang, Chenglei Bian, Yulong Shandong University Weihai China Human Computer Interaction Technology and Intelligent Information Processing Laboratory Institute of Software Chinese Academy of Sciences Beijing China Shandong University School of Mechanical Electrical & Information Engineering Weihai China Shandong University Jinan China School of Software Shandong University Jinan China Shandong Key Laboratory of Intelligent Electronic Packaging Testing and Application Shandong University Shandong Weihai China Shandong Key Laboratory of Intelligent Electronic Packaging Testing and Application China

ISBN: (纸本)9798400713958

Flow experience is a key user experience in human-computer interaction (HCI). Compared with traditional subjective measurements, physiological signal-based methods (i.e. physiological computation of flow experience) can measure flow experience in a more objective and real-time way without interrupting the ongoing activity of the user. Despite scattered studies, a systematic review of research in this field is absent. Synthesizing key aspects of this field, including the conceptualization of flow experience, prevalent theoretical models, foundational research steps for experiment, computerized tasks for inducing flow, commonly used physiological signals, methods for analyzing flow state, and application in HCI. Finally, we discuss the potential of this field to propel HCI and user experience evaluation technologies, while projecting future research avenues. This paper is expected to help researchers grasp existing work in physiological signal-based flow computing and inspire subsequent explorations. © 2025 Copyright held by the owner/author(s).

关键词： Deep learning Flow experience Flow model Machine learning Physiological signals

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Co-Fix3D: Enhancing 3D Object Detection With Collaborative Refinement

arXiv

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arXiv 2024年

作者： Li, Wenxuan Zou, Qin Chen, Chi Du, Bo Chen, Long Zhou, Jian Yu, Hongkai The School of Computer Science Wuhan University Wuhan430072 China The State Key Laboratory of Surveying Mapping and Remote Sensing Information Engineering Wuhan University Wuhan430079 China The Institute of Automation Chinese Academy of Sciences Beijing130028 China The Department of Electrical and Computer Engineering Cleveland State University ClevelandOH44115 United States

3D object detection in driving scenarios faces the challenge of complex road environments, which can lead to the loss or incompleteness of key features, thereby affecting perception performance. To address this issue, we propose an advanced detection framework called Co-Fix3D. Co-Fix3D integrates Local and Global Enhancement (LGE) modules to refine Bird’s Eye View (BEV) features. The LGE module uses Discrete Wavelet Transform (DWT) for pixel-level local optimization and incorporates an attention mechanism for global optimization. To handle varying detection difficulties, we adopt multi-head LGE modules, enabling each module to focus on targets with different levels of detection complexity, thus further enhancing overall perception capability. Experimental results show that on the nuScenes dataset’s LiDAR benchmark, Co-Fix3D achieves 69.4% mAP and 73.5% NDS, while on the multimodal benchmark, it achieves 72.3% mAP and 74.7% NDS. The source code is publicly available at https://***/rubbish001/Co-Fix3d. © 2024, CC BY.

关键词： Discrete wavelet transforms

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Transpointflow: Learning Scene Flow from Point Clouds with Transformer

Transpointflow: Learning Scene Flow from Point Clouds with T...

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IEEE International Conference on Image Processing

作者： Rokia Abdein Xuezhi Xiang Abdulmotaleb El Saddik School of Information and Communication Engineering Harbin Engineering University Harbin China Key Laboratory of Advanced Marine Communication and Information Technology Ministry of Industry and Information Technology Harbin China School of Electrical Engineering and Computer Science University of Ottawa Ottawa Canada

Scene flow estimation is the task of obtaining 3D motion from a dynamic scene. Due to the sparseness of point clouds, extracting features for a local group of points separately may result in different features that may all belong to the same object. This difference makes global correlation prone to producing an unacceptable flow. Local correlation restricts the algorithm to capturing limited movements and fails when fast movement or large deformation of an object occurs. Therefore, we propose a transformer-based scene flow method that can perform global feature modeling through a self-attention layer. Moreover, we propose a cross-attention-based flow embedding layer for global feature matching. We further propose a learnable attention-based up-sampling layer to up-sample the estimated flow to higher resolution based on a single feature scale, eliminating the need to model global dependencies at all scales. Experimental results show that our model produces competitive results on Flyingthings3D and KITTI datasets with efficient performance.

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