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Lens-to-Lens Bokeh Effect Transformation. NTIRE 2023 Challenge Report

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Lens-to-Lens Bokeh Effect Transformation. NTIRE 2023 Challen...

2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Conde, Marcos V. Kolmet, Manuel Seizinger, Tim Bishop, Tom E. Timofte, Radu Kong, Xiangyu Zhang, Dafeng Wu, Jinlong Wang, Fan Peng, Juewen Pan, Zhiyu Liu, Chengxin Luo, Xianrui Sun, Huiqiang Shen, Liao Cao, Zhiguo Xian, Ke Liu, Chaowei Chen, Zigeng Yang, Xingyi Liu, Songhua Jing, Yongcheng Mi, Michael Bi Wang, Xinchao Yang, Zhihao Lian, Wenyi Lai, Siyuan Zhang, Haichuan Hoang, Trung Yazdani, Amirsaeed Monga, Vishal Luo, Ziwei Gustafsson, Fredrik K. Zhao, Zheng Sjölund, Jens Schön, Thomas B. Zhao, Yuxuan Chen, Baoliang Xu, Yiqing Niu, Jixiang Computer Vision Lab CAIDAS IFI University of Würzburg Germany Glass Imaging Inc. China Huazhong University of Science and Technology China Nanyang Technological University Singapore National University of Singapore Singapore University of Sydney Australia Huawei Uppsala University Sweden Department of Electrical Engineering Pennsylvania State University United States Department of Information Technology Uppsala University Sweden Key Laboratory of Intelligent Perception and Image Understanding of the Ministry of Education Xidian University Xi'an China North China University of Technology China

ISBN: (纸本)9798350302493

We present the new Bokeh Effect Transformation Dataset (BETD), and review the proposed solutions for this novel task at the NTIRE 2023 Bokeh Effect Transformation Challenge. Recent advancements of mobile photography aim to reach the visual quality of full-frame cameras. Now, a goal in computational photography is to optimize the Bokeh effect itself, which is the aesthetic quality of the blur in out-of-focus areas of an image. Photographers create this aesthetic effect by benefiting from the lens optical *** aim of this work is to design a neural network capable of converting the the Bokeh effect of one lens to the effect of another lens without harming the sharp foreground regions in the image. For a given input image, knowing the target lens type, we render or transform the Bokeh effect accordingly to the lens properties. We build the BETD using two full-frame Sony cameras, and diverse lens *** the best of our knowledge, we are the first attempt to solve this novel task, and we provide the first BETD dataset and benchmark for it. The challenge had 99 registered participants. The submitted methods gauge the state-of-the-art in Bokeh effect rendering and transformation. © 2023 IEEE.

关键词： Color photography

AMLP: Adaptive Masking Lesion Patches for Self-supervised Medical Image Segmentation

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arXiv 2023年

作者： Wang, Xiangtao Wang, Ruizhi Zhou, Jie Lukasiewicz, Thomas Xu, Zhenghua State Key Laboratory of Reliability and Intelligence of Electrical Equipment School of Health Sciences and Biomedical Engineering Hebei University of Technology Tianjin China Institute of Logic and Computation Vienna University of Technology Vienna Austria Department of Computer Science University of Oxford Oxford United Kingdom

Self-supervised masked image modeling has shown promising results on natural images. However, directly applying such methods to medical images remains challenging. This difficulty stems from the complexity and distinct characteristics of lesions compared to natural images, which impedes effective representation learning. Additionally, conventional high fixed masking ratios restrict reconstructing fine lesion details, limiting the scope of learnable information. To tackle these limitations, we propose a novel self-supervised medical image segmentation framework, Adaptive Masking Lesion Patches (AMLP). Specifically, we design a Masked Patch Selection (MPS) strategy to identify and focus learning on patches containing lesions. Lesion regions are scarce yet critical, making their precise reconstruction vital. To reduce misclassification of lesion and background patches caused by unsupervised clustering in MPS, we introduce an Attention Reconstruction Loss (ARL) to focus on hard-to-reconstruct patches likely depicting lesions. We further propose a Category Consistency Loss (CCL) to refine patch categorization based on reconstruction difficulty, strengthening distinction between lesions and background. Moreover, we develop an Adaptive Masking Ratio (AMR) strategy that gradually increases the masking ratio to expand reconstructible information and improve learning. Extensive experiments on two medical segmentation datasets demonstrate AMLP's superior performance compared to existing self-supervised approaches. The proposed strategies effectively address limitations in applying masked modeling to medical images, tailored to capturing fine lesion details vital for segmentation tasks. Copyright © 2023, The Authors. All rights reserved.

关键词： Image segmentation

Ring Formation Maneuvering with Double Integrator Dynamics

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Ring Formation Maneuvering with Double Integrator Dynamics

2021 International Conference on Unmanned Aircraft Systems, ICUAS 2021

作者： Tran, Dzung Casbeer, David Garcia, Eloy Weintraub, Isaac E. Milutinovic, Dejan Control Science Center of Excellence at the Air Force Research Laboratory Wright-Patterson Air Force Base Ohio45433 United States University of California Santa Cruz Department of Electrical and Computer Engineering Santa CruzCA95064 United States

ISBN: (纸本)9780738131153

Conventional leader-follower formations restrict the follower to a single desired position relative to the leader. To give the follower more flexibility in motion and to replicate typical human pilot operations, in this paper we propose a control architecture allowing the follower to converge to a ring, which is a set of desired points, relative to the leader. The follower is considered subject to a point-mass aircraft model, which can be transformed into the double integrator kinematics. For that reason, the nonlinear backstepping method is first utilized to design the controller for the double integrator kinematics with input saturation constraints being taken into account. The controller is then converted into control variables for the point-mass model. The stability of the proposed architecture is analyzed. Finally, a numerical example is presented to illustrate the efficacy of the proposed controller. © 2021 IEEE.

关键词： Controllers

Clinically Interpretable Machine Learning Models for Early Prediction of Mortality in Older Patients with Multiple Organ Dysfunction Syndrome: An International Multicenter Retrospective Study

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Journals of Gerontology - Series A Biological sciences and Medical sciences 2023年第4期78卷 718-726页

作者： Liu, Xiaoli DuMontier, Clark Hu, Pan Liu, Chao Yeung, Wesley Mao, Zhi Ho, Vanda Thoral, Patrick J. Kuo, Po-Chih Hu, Jie Li, Deyu Cao, Desen Mark, Roger G. Zhou, FeiHu Zhang, Zhengbo Celi, Leo Anthony Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education Beijing Advanced Innovation Center for Biomedical Engineering School of Biological Science and Medical Engineering Beihang University Beijing China Laboratory for Computational Physiology Institute for Medical Engineering and Science Massachusetts Institute of Technology Cambridge MA United States Center for Artificial Intelligence in Medicine General Hospital of PLA Beijing China New England Geriatric Research Education and Clinical Center VA Boston Healthcare System Boston MA United States Division of Aging Brigham and Women's Hospital Boston MA United States Department of Anesthesiology 920 Hospital of Joint Logistic Support Force of Chinese PLA Kunming Yunnan China Department of Critical Care Medicine First Medical Center General Hospital of PLA Beijing China Department of Medicine National University Hospital Singapore Division of Geriatric Medicine Department of Medicine National University Hospital Singapore Department of Intensive Care Medicine Amsterdam Netherlands Department of Computer Science National Tsing Hua University Hsinchu Taiwan Department of Biomedical Engineering General Hospital of PLA Beijing China Elderly Center General Hospital of PLA Beijing China Department of Medicine Beth Israel Deaconess Medical Center Boston MA United States Department of Biostatistics Harvard T.H. Chan School of Public Health Boston MA United States

BACKGROUND: Multiple organ dysfunction syndrome (MODS) is associated with a high risk of mortality among older patients. Current severity scores are limited in their ability to assist clinicians with triage and management decisions. We aim to develop mortality prediction models for older patients with MODS admitted to the ICU. METHODS: The study analyzed older patients from 197 hospitals in the United States and 1 hospital in the Netherlands. The cohort was divided into the young-old (65-80 years) and old-old (≥80 years), which were separately used to develop and evaluate models including internal, external, and temporal validation. Demographic characteristics, comorbidities, vital signs, laboratory measurements, and treatments were used as predictors. We used the XGBoost algorithm to train models, and the SHapley Additive exPlanations (SHAP) method to interpret predictions. RESULTS: Thirty-four thousand four hundred and ninety-seven young-old (11.3% mortality) and 21 330 old-old (15.7% mortality) patients were analyzed. Discrimination AUROC of internal validation models in 9 046 U.S. patients was as follows: 0.87 and 0.82, respectively;discrimination of external validation models in 1 905 EUR patients was as follows: 0.86 and 0.85, respectively;and discrimination of temporal validation models in 8 690 U.S. patients: 0.85 and 0.78, respectively. These models outperformed standard clinical scores like Sequential Organ Failure Assessment and Acute Physiology Score III. The Glasgow Coma Scale, Charlson Comorbidity Index, and Code Status emerged as top predictors of mortality. CONCLUSIONS: Our models integrate data spanning physiologic and geriatric-relevant variables that outperform existing scores used in older adults with MODS, which represents a proof of concept of how machine learning can streamline data analysis for busy ICU clinicians to potentially optimize prognostication and decision making. © The Author(s) 2022. Published by Oxford University Press on behalf

关键词： International multicenter Interpretable models Machine learning Mortality Multiple organ dysfunction syndrome

Fast projection onto convex smooth constraints

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arXiv 2021年

作者： Usmanova, Ilnura Kamgarpour, Maryam Krause, Andreas Levy, Kfir Yehuda Automatic Control Laboratory D-ITET ETH Zürich Switzerland Department of Electrical and Computer Engineering University of British Columbia Canada Department of Computer Science ETH Zürich Switzerland Viterby Department of Electrical & Computer Engineering Technion - Israel Institute of Technology

The Euclidean projection onto a convex set is an important problem that arises in numerous constrained optimization tasks. Unfortunately, in many cases, computing projections is computationally demanding. In this work, we focus on projection problems where the constraints are smooth and the number of constraints is significantly smaller than the dimension. The runtime of existing approaches to solving such problems is either cubic in the dimension or polynomial in the inverse of the target accuracy. Conversely, we propose a simple and efficient primal-dual approach, with a runtime that scales only linearly with the dimension, and only logarithmically in the inverse of the target accuracy. We empirically demonstrate its performance, and compare it with standard baselines. © 2021, CC BY.

关键词： Constrained optimization

Endoscopic Fourier-transform infrared spectroscopy through a fiber microprobe

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arXiv 2024年

作者： Kim, Jaehyeon Tian, Yue Qiao, Guanhua Villarta, Julinna Abulencia Zhao, Fujia He, Andrew Ho, Ruo-Jing Liu, Haoran Bhargava, Rohit Zhang, Yingjie Department of Materials Science and Engineering University of Illinois UrbanaIL61801 United States Materials Research Laboratory University of Illinois UrbanaIL61801 United States Beckman Institute for Advanced Science and Technology University of Illinois UrbanaIL61801 United States Department of Bioengineering University of Illinois UrbanaIL61801 United States Department of Electrical and Computer Engineering University of Illinois UrbanaIL61801 United States Cancer Center at Illinois University of Illinois UrbanaIL61801 United States Department of Mechanical Science and Engineering University of Illinois UrbanaIL61801 United States Department of Chemical and Biomolecular Engineering University of Illinois UrbanaIL61801 United States Department of Chemistry University of Illinois UrbanaIL61801 United States

Fourier-transform infrared spectroscopy (FTIR) is a powerful analytical method for not only the chemical identification of solid, liquid, and gas species, but also the quantification of their concentration. However, the chemical quantification capability of FTIR is significantly hindered when the analyte is surrounded by a strong IR absorbing medium, such as liquid solutions. To overcome this limit, here we develop an IR fiber microprobe that can be inserted into liquid medium, and obtain full FTIR spectra at points of interest. To benchmark this endoscopic FTIR method, we insert the microprobe into bulk water covering a ZnSe substrate and measure the IR transmittance of water as a function of the probe–substrate distance. The obtained vibrational modes, overall transmittance vs z profiles, quantitative absorption coefficients, and micro z-section IR transmittance spectra are all consistent with the standard IR absorption properties of water. The results pave the way for endoscopic chemical profiling inside bulk liquid solutions, promising for applications in many biological, chemical, and electrochemical systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Fourier transform infrared spectroscopy

Classical and Quantum Distributed Algorithms for the Survivable Network Design Problem

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arXiv 2024年

作者： Kerger, Phillip A. Bernal Neira, David E. Izquierdo, Zoe Gonzalez Rieffel, Eleanor G. Department of Applied Mathematics and Statistics Johns Hopkins University BaltimoreMD United States Research Institute of Advanced Computer Science USRA Mountain ViewCA United States Quantum Artificial Intelligence Laboratory NASA Ames Research Center Mountain ViewCA United States Department of Chemical Engineering Purdue University West LafayetteIN United States

We investigate distributed classical and quantum approaches for the survivable network design problem (SNDP), sometimes called the generalized Steiner problem. These problems generalize many complex graph problems of interest, such as the traveling salesperson problem, the Steiner tree problem, and the k-connected network problem. To our knowledge, no classical or quantum algorithms for the SNDP have been formulated in the distributed settings we consider. We describe algorithms that are heuristics for the general problem but give concrete approximation bounds under specific parameterizations of the SNDP, which in particular hold for the three aforementioned problems that SNDP generalizes. We use a classical, centralized algorithmic framework first studied in (Goemans & Bertsimas, 1993) and provide a distributed implementation thereof. Notably, we obtain asymptotic quantum speedups by leveraging quantum shortest path computations in this framework, generalizing recent work of (Kerger, Bernal Neira, Gonzalez Izquierdo, & Rieffel, 2023). These results raise the question of whether there is a separation between the classical and quantum models for application-scale instances of the problems considered. © 2024, CC BY.

关键词： Trees (mathematics)

Eco-driving Incentive Mechanisms for Mitigating Emissions in Urban Transportation

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arXiv 2024年

作者： Umar Niazi, M.B. Cho, Jung-Hoon Dahleh, Munther A. Dong, Roy Wu, Cathy Laboratory for Information and Decision Systems Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Division of Decision and Control Systems Digital Futures KTH Royal Institute of Technology StockholmSE-100 44 Sweden Laboratory for Information and Decision Systems Department of Civil and Environmental Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Department of Industrial and Enterprise Systems Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States

This paper proposes incentive mechanisms that promote eco-driving in transportation networks with the overarching objective of minimizing emissions. The transportation system operator provides the drivers with energy-efficient driving guidance throughout their trips, and their eco-driving levels are measured by how closely they follow this guidance via vehicle telematics. Drivers choose their eco-driving levels to optimize a combination of their travel times and their emissions. To obtain optimal budget allocation and recommendations for the incentive mechanism, the system operator gathers drivers’ preferences, or types, to assess each driver’s trip urgency and natural willingness to eco-drive. In a setting where drivers truthfully report their types, we introduce the first-best incentive mechanism and show that the obedience condition holds (i.e., drivers find it optimal to comply with the system operator’s recommendations) when the recommended eco-driving profile constitutes a Nash equilibrium. Moreover, in a setting where drivers can strategically report their types, we introduce the second-best incentive mechanism and show that the proposed mechanism is incentive-compatible (i.e., drivers find it optimal to be truthful). Under this mechanism, we also show that all equilibrium outcomes are at least as good as the recommended eco-driving profile in terms of the system operator’s objective. Overall, this work offers a framework for designing eco-driving incentive mechanisms while considering both the strategic behavior of individual drivers and the network effects of collective decision-making. © 2024, CC BY.

关键词： Budget control

Deep Surrogate Docking: Accelerating Automated Drug Discovery with Graph Neural Networks

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arXiv 2022年

作者： Hosseini, Ryien Simini, Filippo Clyde, Austin Ramanathan, Arvind Argonne Leadership Computing Facility Argonne National Laboratory LemontIL60439 United States Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States Department of Computer Science University of Chicago ChicagoIL60657 United States

The process of screening molecules for desirable properties is a key step in several applications, ranging from drug discovery to material design. During the process of drug discovery specifically, protein-ligand docking, or chemical docking, is a standard in-silico scoring technique that estimates the binding affinity of molecules with a specific protein target. Recently, however, as the number of virtual molecules available to test has rapidly grown, these classical docking algorithms have created a significant computational bottleneck. We address this problem by introducing Deep Surrogate Docking (DSD), a framework that applies deep learning-based surrogate modeling to accelerate the docking process substantially. DSD can be interpreted as a formalism of several earlier surrogate prefiltering techniques, adding novel metrics and practical training practices. Specifically, we show that graph neural networks (GNNs) can serve as fast and accurate estimators of classical docking algorithms. Additionally, we introduce FiLMv2, a novel GNN architecture which we show outperforms existing state-of-the-art GNN architectures, attaining more accurate and stable performance by allowing the model to filter out irrelevant information from data more efficiently. Through extensive experimentation and analysis, we show that the DSD workflow combined with the FiLMv2 architecture provides a 9.496x speedup in molecule screening with a © 2022, CC BY.

关键词： Molecules