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Quantification and comparison of prediction uncertainty associated with different practices of crop modeling

Agricultural and Forest Meteorology 2025年 371卷

作者： Gao, Yujing Wallach, Daniel Gu, Baojing Tang, Liang Lin, Tao Chang, Xini Hasegawa, Toshi Asseng, Senthold Kahveci, Tamer Hoogenboom, Gerrit College of Environmental and Resource Sciences Zhejiang University Hangzhou China Agricultural & Biological Engineering Department University of Florida Gainesville United States College of Biosystems Engineering and Food Science Zhejiang University Hangzhou China Institute of Crop Science and Resource Conservation University of Bonn Germany National Engineering and Technology Center for Information Agriculture Key Laboratory for Crop System Analysis and Decision Making Ministry of Agriculture Jiangsu Key Laboratory for Information Agriculture Jiangsu Collaborative Innovation Center for Modern Crop Production Nanjing Agricultural University Jiangsu Nanjing China National Agriculture and Food Research Organization Ibaraki Tsukuba Japan Technical University of Munich Department of Life Science Engineering Freising Germany Department of Computer and Information Science and Engineering University of Florida Gainesville United States

Crop models are widely used as decision support tools in agriculture and natural resource management. However, the current practices in crop modeling and evaluation vary significantly, making it challenging to compare uncertainties across different models and studies. This study aims to quantify and compare the uncertainties associated with four different crop modeling practices using a standardized evaluation framework. The four modeling practices are: 1) using a single model, considering only model bias for uncertainty;2) using a single model, accounting for both model bias and parameter uncertainty;3) employing a multi-model ensemble to account for model bias, parameter uncertainty, and structural uncertainties;and 4) using a multi-model ensemble to consider uncertainties induced by model bias, parameters, structures, and inputs. We developed a framework that integrates Markov Chain Monte Carlo (MCMC) and Bayesian Model Averaging (BMA) to consistently quantify prediction uncertainty across these practices. The framework was applied to an Asian rice dataset. The results revealed that common model evaluation approach (Practice 1) tends to underestimate the uncertainty of model predictions. Relying on a single process-based model presents a substantial risk in critical decision-making situations. In contrast, the BMA ensemble predictor (e-BMA) demonstrated higher reliability, making it a preferable choice for future decision support. Our Bayesian framework provides a more robust and adaptable approach for project-specific decision-making, with promising applications in digital agriculture. © 2025 Elsevier B.V.

关键词： Bayesian model averaging Crop model Model structure uncertainty Multi-model ensemble Parameter uncertainty Uncertainty quantification

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Evaluating Hashing Algorithms in the Age of ASIC Resistance 3

Evaluating Hashing Algorithms in the Age of ASIC Resistance

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3rd International Workshop on Information Technologies: Theoretical and Applied Problems, ITTAP 2023

作者： Kuznetsov, Oleksandr Kuznetsova, Yelyzaveta Smirnov, Oleksii Kostenko, Oleksii Zvieriev, Volodymyr Department of Political Sciences Communication and International Relations University of Macerata Via Crescimbeni 30/32 Macerata62100 Italy Department of Information and Communication Systems Security School of Computer Sciences V. N. Karazin Kharkiv National University 4 Svobody Sq. Kharkiv61022 Ukraine Department of cyber security and software Central Ukrainian National Technical University 8 University Ave Kropyvnytskyi25006 Ukraine Scientific Laboratory of the Theory of Digital Transformation Law of the Scientific Center for Digital Transformation Law of the State Scientific Institution "Institute of Information Security Law of the National Academy of Legal Sciences of Ukraine" P.Orlyk str. 3 Kyiv01024 Ukraine Department of Software Engineering and Cybersecurity State University of Trade and Economics Kyoto 19 Kyiv02156 Ukraine

In the intricate matrix of cryptographic hashing, two contrasting paradigms vie for precedence: computational swiftness and resilience against specialized hardware, or ASICs. This study undertakes a meticulous exploration into these dueling priorities, juxtaposing conventional stalwarts like SHA-2 and KECCAK against the newer, ASIC-resistant X series. Leveraging detailed performance metrics and visual analytics, we discern the manifest advantage of SHA-2 and KECCAK in terms of computational alacrity. However, as the paper delves deeper, a compelling narrative unfolds. The deliberate design intricacies of the X series, despite their computational latency, emerge as robust bulwarks against potential centralization threats posed by ASIC miners. These algorithms embody a strategic deceleration, ensuring that the cryptocurrency terrain remains hospitable to a broad diaspora of miners. Through this multifaceted lens, we argue that in the cryptographic domain, raw speed is but one facet of a larger, more nuanced picture. The underlying ethos of decentralization and democratic access must guide our trajectory, even if it comes at the expense of pure efficiency. In essence, this paper endeavors to navigate the delicate balance between rapid computation and the overarching principles of an inclusive digital realm. © 2023 CEUR-WS. All rights reserved.

关键词： Computational efficiency

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Dual Bidirectional Copy-Paste with Shape Constraint for Semi-Supervised Image Segmentation

Dual Bidirectional Copy-Paste with Shape Constraint for Semi...

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IEEE International Symposium on Biomedical Imaging

作者： Bowei Shen Xiaoquan Huang Yonghong Shi Shiyao Chen School of Health Science and Engineering University of Shanghai for Science and Technology Shanghai China Department of Gastroenterology and Hepatology Zhongshan Hospital Fudan University Shanghai China Digital Medical Research Center School of Basic Medical Sciences Fudan University Shanghai China Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention Shanghai China

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Semi-supervised learning utilizes unlabeled data and a small amount of pixel-level labeled data to achieve image semantic segmentation. However, its performance largely depends on the semantic learning of the boundary details of the object in the image, as the uncertainty of the boundary region can reduce the segmentation accuracy. To this end, this paper proposes a novel semi-supervised learning method based on the student-teacher model, which is constrained by the dual bidirectional copy-paste and the signed distance map of the object shape. The method is carried out through two stages: fully supervised pre-training and semi-supervised re-training. The former uses labeled data to train and learn the initial student model, and its parameters are used to initialize and update the latter’s teacher model, which is used to segment unlabeled data to obtain its pseudo labels. At each stage, the dual bidirectional copy-paste between ‘labeled data’ enhances the data, while the similar copy-paste performed on the corresponding ‘label’ supervises the student model. Copy-paste allows the network to focus on the entire object, while the signed distance map on the label data helps to learn the complex shape changes of the object boundary. Especially in the re-training stage, the above calculation mainly occurs between labeled data and unlabeled data with 'pseudo label', which effectively bridges the empirical distribution mismatch between labeled data and unlabeled data, enabling the network to learn a more generalized data distribution. Our method was validated on two datasets, LA Heart and ACDC. Compared with other semi-supervised learning methods, our method outperforms previous works at 5% and 10% labeled data volumes, achieving the SOTA results.

关键词： Bridges Heart Uncertainty Shape Biological system modeling Semantic segmentation Semantics

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Communication Interruption Analysis for Air-water Wireless Optical Communication

Communication Interruption Analysis for Air-water Wireless O...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Chi Yang Fang Yang Department of Electronic Engineering Beijing National Research Center for Information Science and Technology (BNRist) Tsinghua University Beijing P. R. China Key Laboratory of Digital TV System of Guangdong Province and Shenzhen City Research Institute of Tsinghua University in Shenzhen Shenzhen P. R. China

In air-water wireless optical communication, the optical signal may be completely reflected at the sea surface, which will cause signal interruption and seriously affect communication quality. Therefore, this paper analyzes the signal interruption probability and duration caused by total reflection at different wind speeds and transmitter inclination angles. The bit error rate (BER) of L-ary pulse position modulation (L-PPM) across sea surface under different L values is studied in an attempt to find a suitable modulation method to optimize the BER of the system. The BER optimization effect is apparent when the wind speed is low and L=6. However, the optimization effect of L-PPM on BER decreases with the increase in wind speed.

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Enhancing security in OpenID connect Single Sign-On (SSO) systems: a comprehensive vulnerability analysis and mitigation approach

Enhancing security in OpenID connect Single Sign-On (SSO) sy...

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4th International Conference on Distributed Sensing and Intelligent systems (ICDSIS 2023)

作者： M. Al-Shabi A. Bennour M. Al-Sarem O. I. Khalaf R. Sikder S. Algburi Department of management Information system Taibah University Medina KSA ``LAMIS" Laboratory Echahid Chikh Larbi Tebessi University Tebessa Algeria College of Computer Science and Engineering Taibah University Medina KSA Al-Nahrain University Jadriya Baghdad Iraq Information Technology University of the Cumberlands USA Al-Kitab University College of Engineering Techniques kirkuk Iraq

ISBN: (数字)9781837240258

Single Sign-On (SSO) systems have become popular for simplifying the login process by allowing users to authenticate with a single identity provider (IDP). However, the widespread use of these systems raises concerns about user privacy, as IDPs like Google or Facebook can gather extensive information about user behavior on the web. This poses a significant challenge for privacy-conscious users who wish to limit the disclosure of their web access behavior to third-party organizations. In this paper, we present a comprehensive and large-scale practical study focused on the OpenID Connect protocol, a widely used SSO protocol. Our analysis delves into the various stages of the protocol's operation, highlighting its security flaws and vulnerabilities. Additionally, we conduct a systematic examination of the privacy implications associated with user access to SSO systems. We provide a detailed account of how easily user information can be accessed, shedding light on the potential risks.

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Broadband and repetition rate tunable frequency comb based on electro-optic time lens with AlGaAsOI waveguide

Broadband and repetition rate tunable frequency comb based o...

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49th European Conference on Optical Communications (ECOC 2023)

作者： C. Ma C. Wang Y. Pi L. Wang Z. He J. E. Bowers L. Chang Z. Cao S. Yu Research Center of Circuits and Systems Peng Cheng Laboratory Shenzhen 518055 Guangdong People's Republic of China Department of Electrical and Computer Engineering University of California Santa Barbara CA 93106 USA State Key Laboratory of Advanced Optical Communications System and Networks Peking University Beijing 100871 People's Republic of China

We present a tunable repetition rate frequency comb realized by cascaded electro-optical modulators with a high nonlinear AlGaAs-on-isolator waveguide. 18-26.5 GHz comb spacing tunability with broadband spectra was achieved. All the devices can be integrated onto a single chip, which makes this work useful for many applications.

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K-Space-Aware Cross-Modality Score for Quality Assessment of Synthesized Neuroimages

arXiv

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arXiv 2023年

作者： Xie, Guoyang Wang, Jinbao Huang, Yawen Lyu, Jiayi Zheng, Feng Zheng, Yefeng Jin, Yaochu Southern University of Science and Technology Department of Computer Science Shenzhen518055 China CATL Department of Intelligent Manufacturing Ningde352000 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China Tencent Youtu Lab Jarvis Research Center Shenzhen510310 China University of Chinese Academy of Sciences the School of Engineering Science Beijing100049 China Westlake University the School of Engineering Hangzhou310030 China

The problem of how to assess cross-modality medical image synthesis has been largely unexplored. The most used measures like PSNR and SSIM focus on analyzing the structural features but neglect the crucial lesion location and fundamental k-space speciality of medical images. To overcome this problem, we propose a new metric K-CROSS to spur progress on this challenging problem. Specifically, K-CROSS uses a pre-trained multi-modality segmentation network to predict the lesion location, together with a tumor encoder for representing features, such as texture details and brightness intensities. To further reflect the frequency-specific information from the magnetic resonance imaging principles, both k-space features and vision features are obtained and employed in our comprehensive encoders with a frequency reconstruction penalty. The structure-shared encoders are designed and constrained with a similarity loss to capture the intrinsic common structural information for both modalities. As a consequence, the features learned from lesion regions, k-space, and anatomical structures are all captured, which serve as our quality evaluators. We evaluate the performance by constructing a large-scale cross-modality neuroimaging perceptual similarity (NIRPS) dataset with 6,000 radiologist judgments. Extensive experiments demonstrate that the proposed method outperforms other metrics, especially in comparison with the radiologists on NIRPS. To forster reproducibility and accessibility, the source codeis uploaded to the website: https://***/M-3LAB/K-CROSS © 2023, CC BY.

关键词： Magnetic resonance imaging

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SI-YOLO: A Deep Learning Algorithm Suitable for Surgical Instrument Detection

SSRN

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SSRN 2022年

作者： Liu, Zhenzhong Zhou, Yifan Sun, Qiyuan Wang, Yan Polat, Kemal Nour, Majid Alenezi, Fayadh Alhudhaif, Adi Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China Tianjin300384 China Department of Electrical and Electronics Engineering Bolu Abant Izzet Baysal University Bolu14280 Turkey Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia Department of Electrical Engineering College of Engineering Jouf University 72238 Saudi Arabia Department of Computer Science College of Computer Engineering and Sciences in Al-kharj Prince Sattam bin Abdulaziz University P.O. Box 151 Al-Kharj 11942 Saudi Arabia

Surgical instrument detection and tracking is one of the core technologies of surgical robots, which can assist clinicians in completing operations. However, existing methods need further improvement in terms of real-time performance and accuracy. We propose a deep learning algorithm, SI-YOLO, for detection of surgical instruments. SimAM attention mechanism is used to improve the feature extraction ability of YOLOv5. Replacing PANet with BiFPN in the neck network improves the feature fusion strategy. The α-IoU loss function is used to improve the regression accuracy of detection boxes. The mAP and FPS of the SI-YOLO reach 99.2% and 120 frames/sec, respectively. Aiming at the problem of ID transformation in the disappearance and occlusion of surgical instruments, the Inception-ResNet-v2 structure is added on the basis of the appearance feature extraction network Cosine of the DeepSort tracking algorithm. Combining SI-YOLO with the improved DeepSort implements the Tracking by Detection (TBD) algorithm. In the case of device disappearance or occlusion, the frequency of ID transitions is reduced by approximately 30%. Finally, we built a simulated surgery experimental platform, established our own data set, and realized the preliminary application of the proposed algorithm. © 2022, The Authors. All rights reserved.

关键词： Object detection

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Classification and Assessment of Autism Based on Eye-movement Technology and Deep Learning

Classification and Assessment of Autism Based on Eye-movemen...

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第九届中国国际眼动大会（CICEM 2023）

作者： Wenhao Wang Ziting Xiao Wei Fan Yufeng Yuan Muyun Liao Jingyue Yan Lingmin Yao Xiaoyu Hu Hanxi Li School of Psychology Jiangxi Normal University School of Computer and Information Engineering Jiangxi Normal University Department of Child Health Care Jiangxi Provincial Children's Hospital Research Center of Jiangxi Social Psychological Service System Construction Jiangxi Key Laboratory of Psychology and Cognitive Science

The core symptoms of autism spectrum disorder(ASD) are impaired social communication interactions and repetitive stereotypical interests and *** studies have found differences in eye-movement behavior between children...

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Single Image Super-Resolution Based on Generative Adversarial Networks 15th

Single Image Super-Resolution Based on Generative Adversaria...

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15th Conference on Image and Graphics Technology and Applications, IGTA 2020

作者： Su, Kai Li, Xueming Li, Xuewei School of Digital Media and Design Arts Beijing University of Posts and Telecommunications Beijing100876 China Beijing Key Laboratory of Network System and Network Culture Beijing University of Posts and Telecommunications Beijing100876 China Department of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9789813360327

Traditional super-resolution algorithms are computationally intensive and the quality of generated images is not high. In recent years, due to the superiority of GAN in generating image details, it has begun to attract researchers’ attention. This paper proposes a new GAN-based image super-resolution algorithm, which solves the problems of the current GAN-based super-resolution algorithm that the generated image quality is not high and the multi-scale feature information is not processed enough. In the proposed algorithm, a densely connected dilated convolution network module with different dilation rate is added to enhance the network’s ability to generate image features at different scale levels;a channel attention mechanism is introduced in the network to adaptively select the generated image features, which improves the quality of the generated image on the network. After conducting experiments on classic test datasets, the proposed algorithm has improved PSNR and SSIM compared to ESRGAN. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Convolution

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