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

作者： Sheng, Hao Liu, Yebin Yu, Jingyi Wu, Gaochang Xiong, Wei Guo, Longzhao Xie, Yanlin Zhang, Shuo Chang, Song Lin, Youfang Chao, Wentao Wang, Xuechun Wang, Guanghui Duan, Fuqing Wang, Tun Yang, Da Cui, Zhenglong Wang, Sizhe Zhao, Mingyuan Wang, Qiong Chen, Qianyu Liang, Zhengyu Wang, Yingqian Yang, Jungang Yang, Xueting Deng, Junli Cong, Ruixuan Chen, Rongshan State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China Beihang Hangzhou Innovation Institute Yuhang China Faculty of Applied Sciences Macao Polytechnic University China Tsinghua University China Shanghaitech University China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University China Beijing Meet Yuan Co. Ltd China Beijing Key Laboratory of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University China Beijing Normal University China Toronto Metropolitan University Canada College of Computer Science and Technology Zhejiang University of Technology China National University of Defense Technology China School of Information and Communication Engineering Communication University of China China

ISBN: (纸本)9798350302493

This paper reviews the 1st LFNAT challenge on light field depth estimation, which aims at predicting disparity information of central view image in a light field (i.e., pixel offset between central view image and adjacent view image). Compared to multi-view stereo matching, light field depth estimation emphasizes efficient utilization of the 2D angular information from multiple regularly varying views. This challenge specifies UrbanLF [20] light field dataset as the sole data source. There are two phases in total: submission phase and final evaluation phase, in which 75 registered participants successfully submit their predicted results in the first phase and 7 eligible teams compete in the second phase. The performance of all submissions is carefully reviewed and shown in this paper as a new standard for the current state-of-the-art in light field depth estimation. Moreover, the implementation details of these methods are also provided to stimulate related advanced research. © 2023 IEEE.

关键词： Stereo image processing

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Evaluation of Activated Sludge Settling Characteristics from Microscopy Images with Deep Convolutional Neural Networks and Transfer Learning

arXiv

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arXiv 2024年

作者： Borzooei, Sina Scabini, Leonardo Miranda, Gisele Daneshgar, Saba Deblieck, Lukas De Langhe, Piet Bruno, Odemir De Baets, Bernard Nopens, Ingmar Torfs, Elena IVL Swedish Environmental Research Institute P.O. Box 210 60 StockholmSE-100 31 Sweden BIOMATH Department of Data Analysis and Mathematical Modelling Faculty of Bioscience Engineering Ghent University Coupure links 653 Gent9000 Belgium São Carlos Institute of Physics University of São Paulo SP São Carlos13560-970 Brazil KERMIT Department of Data Analysis and Mathematical Modelling Ghent University Coupure links 653 Gent9000 Belgium Division of Computational Science and Technology KTH Royal Institute of Technology Stockholm10044 Sweden CAPTURE Centre for Advanced Process Technology for Urban Resource Recovery Frieda Saeysstraat 1 Gent9000 Belgium Pantarein Water Egide Walschaertsstraat 22L Mechelen2800 Belgium Université Laval 1065 avenue de la Médecine QuébecQCG1V 0A6 Canada

Microbial communities play a key role in biological wastewater treatment processes. Activated sludge settling characteristics, for example, are affected by microbial community composition, varying by changes in operating conditions and influent characteristics of wastewater treatment plants (WWTPs). Timely assessment and prediction of changes in microbial composition leading to settling problems, such as filamentous bulking (FB), can prevent operational challenges, reduction of treatment efficiency, and adverse environmental impacts. This study presents an innovative computer vision-based approach to assess activated sludge-settling characteristics based on the morphological properties of flocs and filaments in microscopy images. Implementing the transfer learning of deep convolutional neural network (CNN) models, this approach aims to overcome the limitations of existing quantitative image analysis techniques. The offline microscopy image dataset was collected over two years, with weekly sampling at a full-scale industrial WWTP in Belgium. Multiple data augmentation techniques were employed to enhance the generalizability of the CNN models. Various CNN architectures, including Inception v3, ResNet18, ResNet152, ConvNeXt-nano, and ConvNeXt-S, were tested to evaluate their performance in predicting sludge settling characteristics. The sludge volume index (SVI) was used as the final prediction variable, but the method can easily be adjusted to predict any other settling metric of choice. The results showed that the suggested CNN-based approach provides less labourintensive, objective, and consistent assessments while transfer learning notably minimises the training phase, resulting in a generalizable system that can be employed in real-time applications. The model's capability to signal early indicators of FB events makes it a valuable tool for effective monitoring and preemptive management of WWTPs facing activated sludge settling problems. © 2024, CC BY-NC-ND.

关键词： Convolutional neural networks

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A Novel Augmented Reality-Based Simulator for Enhancing Orthopedic Surgical Training

SSRN

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SSRN 2024年

作者： Wu, Luohong Seibold, Matthias Cavalcanti, Nicola A. Hein, Jonas Gerth, Tatiana Lekar, Roni Hoch, Armando Vlachopoulos, Lazaros Grabner, Helmut Zingg, Patrick Farshad, Mazda Fürnstahl, Philipp Research in Orthopedic Computer Science Balgrist University Hospital University of Zurich Lengghalde 5 Zurich Zurich8008 Switzerland Department of Orthopaedics Balgrist University Hospital University of Zurich Forchstrasse 340 Zurich Zurich8008 Switzerland Computer Vision and Geometry Group ETH Zurich Ramistrasse 101 Zurich Zurich8092 Switzerland Institute of Computer Science School of Engineering Zurich University of Applied Sciences Obere Kirchgasse 2 Winterthur Zurich8401 Switzerland Institute of Data Analysis and Process Design School of Engineering Zurich University of Applied Sciences Technikumstrasse 81 Winterthur Zurich8400 Switzerland

Background: Total Hip Arthroplasty (THA) is a well-established and common orthopedic surgery. Due to the complexity involved in THA, orthopedic surgeons require rigorous training. However, the current gold standard, the tutor-guided and -evaluated apprenticeship model is time-consuming, costly, and poses risks to patients. There is a pressing need for additional training resources to enhance the efficiency and safety of the training process. In this work, we present a novel Augmented Reality (AR)-based simulator designed for THA that helps enable a new self-paced training and learning paradigm without the need for ***: The simulator reduces the need for instructors by integrating an AR guidance module and an automated performance evaluation module. Three types of AR guidance were developed: Overlay, Virtual Twin, and Sectional Views. A feasibility study was conducted with five resident surgeons and two senior surgeons to compare these guidance methods quantitatively and qualitatively. The automated performance evaluation module was assessed against manual performance evaluation using Bland-Altman analysis with limits of agreement (LoA) and Mann-Whitney U ***: The quantitative feasibility results indicate the efficacy of the developed AR guidance, characterized by mean transitional and rotational deviation errors below 3 mm and 3 degrees. Based on the qualitative results, we provide recommendations for efficient AR guidance designs. The Bland-Altman analysis results (0.22 ± 1.32 mm with LoA -2.37 to 2.81 mm for distance deviation, 0.94 ± 2.41 degrees with LoA -3.78 to 5.66 degrees for yaw deviation, -0.34 ± 1.30 degrees with LoA -2.90 to 2.22 degrees for pitch deviation) and p-values of Mann-Whitney U tests (0.64 for distance deviation, 0.12 for yaw deviation, 0.11 for pitch deviation) indicate no statistically significant differences between the automated and manual performance evaluation at a significance level of ***: This wor

关键词： Augmented reality

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Artificial neural networks to impute rounded zeros in compositional data

arXiv

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arXiv 2020年

作者： Templ, Matthias Institute of Data Analysis and Process Design Zurich University of Applied Sciences Rosenstrasse 3 WinterthurCH-8401 Switzerland

Methods of deep learning have become increasingly popular in recent years, but they have not arrived in compositional data analysis. Imputation methods for compositional data are typically applied on additive, centered or isometric log-ratio representations of the data. Generally, methods for compositional data analysis can only be applied to observed positive entries in a data matrix. Therefore one tries to impute missing values or measurements that were below a detection limit. In this paper, a new method for imputing rounded zeros based on artificial neural networks is shown and compared with conventional methods. We are also interested in the question whether for ANNs, a representation of the data in log-ratios for imputation purposes, is relevant. It can be shown, that ANNs are competitive or even performing better when imputing rounded zeros of data sets with moderate size. They deliver better results when data sets are big. Also, we can see that log-ratio transformations within the artificial neural network imputation procedure nevertheless help to improve the results. This proves that the theory of compositional data analysis and the fulfillment of all properties of compositional data analysis is still very important in the age of deep learning. © 2020, CC BY-NC-ND.

关键词： Neural networks

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Differential recurrent autoencoder for early fault detection of nonstationary industrial processes

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Engineering Applications of Artificial Intelligence 2025年 156卷

作者： Ji, Cheng Tao, Tingting Ma, Fangyuan Wang, Jingde Sun, Wei College of Chemical Engineering Beijing University of Chemical Technology North Third Ring Road 15 Chaoyang District Beijing100029 China Center of Process Monitoring and Data Analysis Wuxi Research Institute of Applied Technologies Tsinghua University Wuxi214072 China

Artificial Intelligence (AI) has emerged as a prominent technique for industrial fault detection, where deep learning demonstrates unique advantages in modeling nonlinear and dynamic process systems. Nevertheless, the presence of stochastic factors and permissible external disturbances frequently induce nonstationary characteristic during industrial process operation, which poses significant obstacles to early fault detection, particularly in capturing time-varying statistical patterns that precede critical failures. To bridge this gap, we propose a differential recurrent autoencoder (DRAE) for early fault detection of nonstationary industrial processes. DRAE based on differential operation, aiming to optimize the update mechanism of the forget gate to enhances its capacity to capture short-term nonstationary features. To facilitate the transition of time-variant nonstationary information in memory states, differential operations are embedded into the latent layer of Long Short-Term Memory (LSTM) neural network. The integration of the differential gradient layer with LSTM units directly models transient nonstationary dynamics while retaining original sequential dependencies, thereby overcoming the stationarity assumption inherent in conventional process monitoring approaches. Combined with an unsupervised encoder-decoder structure, this framework enables automatic feature extraction through fault-discriminative reconstruction to perform early fault detection. The proposed method is validated on a nonstationary numerical process, the Tennessee Eastman (TE) process, and a real-world chemical industrial process. The experimental results demonstrate significant improvement in fault detection rate and reduced detection delays compared to baseline methods, showcasing the potential of AI in enhancing process safety management within industrial systems. © 2025 Elsevier Ltd

关键词： Deep neural networks

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Bicycle as a traffic mode: From microscopic cycling behavior to macroscopic bicycle flow

Journal of Cycling and Micromobility Research

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Journal of Cycling and Micromobility Research 2024年 2卷

作者： Ying-Chuan Ni Michail A. Makridis Anastasios Kouvelas Traffic Engineering group Institute for Transport Planning and Systems ETH Zurich Stefano-Franscini-Platz 5 Zurich 8093 Switzerland Transport and Traffic Engineering group Institute of Data Analysis and Process Design Zurich University of Applied Sciences Technikumstrasse 81 Winterthur 8400 Switzerland

Cities allocate dedicated road space to bicycles in favor of active-mode road users. For urban environments with a mass bicycle volume, bicycle traffic congestion is likely to occur. Hence, a thorough understanding of bicycle traffic flow is necessary for the assessment of cycling infrastructure and the development of traffic management strategies considering cycling efficiency. This study aims to investigate bicycle flow characteristics using microscopic traffic simulation. As bicycle flow performance is subject to the non-lane-based movement strategy and the behavioral heterogeneity among cyclists, various scenarios with different microsimulation settings are evaluated. Ultimately, we derive the functional form fundamental diagrams and macroscopic fundamental diagrams using a curve-fitting approach and an analytical method, respectively. Important macroscopic traffic flow parameters, such as capacity, critical speed, critical density, backward wave speed, etc., are estimated. The results show that lane width, overtaking incentive, and desired speed distribution are factors that affect bicycle flow performance. The distinct features of bicycle flow under different traffic states are identified by discussing the simulation outcome and comparing the estimated flow parameters. The findings can be utilized by future research regarding large-scale bicycle traffic flow modeling and control.

关键词： Bicycle flow Cycling behavior Fundamental diagram Macroscopic fundamental diagram Microscopic traffic simulation Non-lane-based traffic

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Climatic Limitations on Grassland Photosynthesis Over the Tibetan Plateau Shifted from Temperature to Water

SSRN

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SSRN 2023年

作者： Xie, Jiangliu Yin, Gaofei Ma, Dujuan Chen, Rui Zhao, Wei Xie, Qiaoyun Wang, Cong Lin, Shangrong Yuan, Wenping Faculty of Geosciences and Environmental Engineering Southwest Jiaotong University Chengdu610031 China Institute of Mountain Hazards and Environment Chinese Academy of Sciences Chengdu610041 China School of Engineering The University of Western Australia PerthWA6009 Australia Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province School of Urban and Environmental Sciences Central China Normal University Wuhan430079 China School of Atmospheric Sciences Guangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon Cycle Sun Yat-sen University Zhuhai China

Plant photosynthesis plays an essential role in regulating the global carbon cycle. Therefore, it is essential to understand the limitations imposed by climate on plant photosynthesis to comprehend the impacts of climate change on land carbon dynamics. In this study, taking gross primary productivity as a direct representation of photosynthesis, we employed a light use efficiency paradigm (i.e., the revised EC-LUE) and factorial analysis method to quantify the spatiotemporal variation of temperature- and water-limitations on plant photosynthesis over the Tibetan Plateau (TP) grasslands during growing season (May to October) in 1983-2018. Results revealed a clear spatiotemporal pattern of the temperature- and water-limitations: temperature is the primary climatic limiting factor in the eastern TP, while water is the primary climatic limiting factor in the western TP;the water- and temperature-limitations prevail in summer and spring/autumn, respectively. The water- and temperature-limitations intensified and alleviated during 1983 through 2018. There also was a widespread shift from temperature-limitation to water-limitation in the TP, particularly in midsummer (August). Our findings demonstrated the shifting relative importance of climatic limitations on plant photosynthesis under changing climate, which is crucial for predicting future terrestrial carbon cycle dynamics. © 2023, The Authors. All rights reserved.

关键词： Photosynthesis

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Deep conditional transformation models for survival analysis

arXiv

引用

arXiv 2022年

作者： Campanella, Gabriele Kook, Lucas Häggström, Ida Hothorn, Torsten Fuchs, Thomas J. Department of AI and Human Health Icahn School of Medicine at Mount Sinai New York10029 United States Hasso Plattner Institute for Digital Health at Mount Sinai New York10029 United States Epidemiology Biostatistics & Prevention Institute University of Zurich CH-8001 Switzerland Institute for Data Analysis and Process Design Zurich University of Applied Sciences CH-8400 Switzerland Chalmers University of Technology Department of Electrical Engineering 41296 Sweden Memorial Sloan Kettering Cancer Center Department of Radiology New York10065 United States

An every increasing number of clinical trials features a time-to-event outcome and records non-tabular patient data, such as magnetic resonance imaging or text data in the form of electronic health records. Recently, several neural-network based solutions have been proposed, some of which are binary classifiers. Parametric, distribution-free approaches which make full use of survival time and censoring status have not received much attention. We present deep conditional transformation models (DCTMs) for survival outcomes as a unifying approach to parametric and semiparametric survival analysis. DCTMs allow the specification of non-linear and non-proportional hazards for both tabular and non-tabular data and extend to all types of censoring and truncation. On real and semi-synthetic data, we show that DCTMs compete with state-of-the-art DL approaches to survival analysis. © 2022, CC BY.

关键词： Magnetic resonance imaging

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Bias, awareness, and ignorance in deep-learning-based face recognition

引用

AI and Ethics 2021年第3期2卷 509-522页

作者： Wehrli, Samuel Hertweck, Corinna Amirian, Mohammadreza Glüge, Stefan Stadelmann, Thilo School of Social Work Zurich University of Applied Sciences Zurich Switzerland Institute of Data Analysis and Process Design Zurich University of Applied Sciences Winterthur Switzerland Department of Informatics University of Zurich Zurich Switzerland Centre for Artificial Intelligence Zurich University of Applied Sciences Winterthur Switzerland Institute of Neural Information Processing Ulm University Ulm Germany Institute for Applied Simulation Zurich University of Applied Sciences Wädenswil Switzerland ECLT European Centre for Living Technology Venice Italy

Face Recognition (FR) is increasingly influencing our lives: we use it to unlock our phones; police uses it to identify suspects. Two main concerns are associated with this increase in facial recognition: (1) the fact that these systems are typically less accurate for marginalized groups, which can be described as “bias”, and (2) the increased surveillance through these systems. Our paper is concerned with the first issue. Specifically, we explore an intuitive technique for reducing this bias, namely “blinding” models to sensitive features, such as gender or race, and show why this cannot be equated with reducing bias. Even when not designed for this task, facial recognition models can deduce sensitive features, such as gender or race, from pictures of faces—simply because they are trained to determine the “similarity” of pictures. This means that people with similar skin tones, similar hair length, etc. will be seen as similar by facial recognition models. When confronted with biased decision-making by humans, one approach taken in job application screening is to “blind” the human decision-makers to sensitive attributes such as gender and race by not showing pictures of the applicants. Based on a similar idea, one might think that if facial recognition models were less aware of these sensitive features, the difference in accuracy between groups would decrease. We evaluate this assumption—which has already penetrated into the scientific literature as a valid de-biasing method—by measuring how “aware” models are of sensitive features and correlating this with differences in accuracy. In particular, we blind pre-trained models to make them less aware of sensitive attributes. We find that awareness and accuracy do not positively correlate, i.e., that bias $$\ne$$ awareness. In fact, blinding barely affects accuracy in our experiments. The seemingly simple solution of decreasing bias in facial recognition rates by reducing awareness of sensitive features does thus not w

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Ethical Aspects of Computational Modelling in Science, Decision Support and Communication 15th

Ethical Aspects of Computational Modelling in Science, Decis...

引用

15th International Workshop on Artificial Life and Evolutionary Computation, WIVACE 2021

作者： Füchslin, Rudolf Marcel Ambühl, Jacques Faggian, Alessia Fellermann, Harold M. Flumini, Dandolo Geller, Armando Hanczyc, Martin M. Klinkert, Andreas Krütli, Pius Matuttis, Hans-Georg Ott, Thomas Scheidegger, Stephan Schmid, Gary Bruno Smieszek, Timo Schneider, Johannes J. Steiner, Albert Weyland, Mathias S. Institute of Applied Mathematics and Physics Zurich University of Applied Sciences Zurich Technikumstrasse 9 Winterthur8400 Switzerland European Centre for Living Technology Ca’ Bottacin Dorsoduro 3911 Calle Crosera Venice30123 Italy World Bank Group Geneva Switzerland University of Trento Trento38123 Italy School of Computing Newcastle University Science Square 1 Newcastle Upon TyneNE4 5TG United Kingdom GmbH Fraumünsterstrasse 11 Zurich8001 Switzerland Institute of Data Analysis and Process Design Zurich University of Applied Sciences Rosenstrasse 3 Winterthur8400 Switzerland Transdisciplinarity Lab ETH Zurich Zurich8092 Switzerland Department of Mechanical Engineering and Intelligent Systems The University of Electrocommunications Tokyo182-8585 Japan Institute of Computational Life Sciences Zurich University of Applied Sciences Schloss 1 Wädenswil8820 Switzerland Swiss NeuroCreations GmbH Hambergersteig 25 Zurich8008 Switzerland Muesmattweg 70 Allschwil4123 Switzerland

ISBN: (纸本)9783031239281

The development of data science, the increase of computational power, the availability of the internet infrastructure for data exchange and the urgency for an understanding of complex systems require a responsible and ethical use of computational models in science, communication and decision-making. Starting with a discussion of the width of different purposes of computational models, we first investigate the process of model construction as an interplay of theory and experimentation. We emphasise the different aspects of the tension between model variables and experimentally measurable observables. The resolution of this tension is a prerequisite for the responsible use of models and an instrumental part of using models in the scientific processes. We then discuss the impact of models and the responsibility that results from the fact that models support and may also guide experimentation. Further, we investigate the difference between computational modelling in an interdisciplinary science project and computational models as tools in transdisciplinary decision support. We regard the communication of model structures and modelling results as essential;however, this communication cannot happen in a technical manner, but model structures and modelling results must be translated into a "narrative." We discuss the role of concepts from disciplines such as literary theory, communication science, and cultural studies and the potential gains that a broader approach can obtain. Considering concepts from the liberal arts, we conclude that there is, besides the responsibility of the model author, also a responsibility of the user/reader of the modelling results. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision making

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