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Interacting Effects of Water and N-P-K Fertilizers on the Resource Use Efficiencies and Fruit Yield of Drip-Fertigated Chinese Wolfberry (Lycium Barbarum L.)

SSRN

引用

SSRN 2023年

作者： Deng, Zhen Eeswaran, Rasu Yin, Juan Abhiram, Gunaratnam Gong, Siyuan College of Information Engineering Ningxia University Yinchuan750021 China College of Civil and Hydraulic Engineering Ningxia University Yinchuan750021 China Department of Plant Soil and Microbial Sciences Michigan State University East LansingMI48824 United States Department of Agronomy Faculty of Agriculture University of Jaffna Ariviyal Nagar Kilinochchi44000 Sri Lanka Ministry of Education Engineering Research Center for Modern Agricultural Water Resources Efficient Utilization in Dry Areas Yinchuan750021 China Ningxia Water-saving Irrigation and Water Resources Control Engineering Technology Research Center Yinchuan750021 China School of Agriculture and Environment Massey University Palmerston North4442 New Zealand Department of Export Agriculture Uva Wellassa University Badulla90000 Sri Lanka School of Computer Science and Software Engineering Nanjing University of Information Science & Technology Nanjing210032 China

Chinese wolfberry (Lycium barbarum L.) is an important cash crop in the Ningxia region of China. However, low water and fertilizer use efficiencies have limited the growth of the Chinese wolfberry industry. Field experiments were conducted in the central Ningxia of Northwest China during 2018-2019 to investigate the interaction effects of different irrigation and fertilizer levels on agronomic performances of Chinese wolfberry, water use efficiency, fertilizer partial productivity and economic benefit. The optimal range of irrigation and fertilizer inputs for a multi-objective optimization problem was determined using multiple regression, entropy weight method and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) coupling comprehensive evaluation method. Three drip irrigation levels were designated based on the percentage of reference crop evapotranspiration (ETo) as low (65% ET0: W1), medium (85% ET0: W2) and high (105% ET0: W3) and three N-P2O5-K2O combined fertilization levels (kg ha-1) were designated as low (135-45-90: F1), medium (180-60-120: F2) and high (225-75-150: F3). The results showed that agronomic performances, water use efficiency (WUE), fertilizer partial productivity (PFP) and economic benefits increased initially and then decreased with increasing irrigation levels under the same fertilization level. Water use efficiency (WUE) was low under high irrigation level and high under low irrigation level. The PFP decreased with increasing fertilization levels and the lowest PFP was observed at high level of fertilizer (F3) application. The above parameters reached the maximum value under the medium irrigation level (85% ET0). Moreover, the multi-objective optimization highlighted that 252-262 mm of irrigation and 185-62-123～200-67-133 kg ha-1 of N-P2O5-K2O fertilization level offer more than 90% of yield, WUE, PFP and economic benefits simultaneously. These results provide a scientific reference for resource optimization under drip-

关键词： Multiobjective optimization

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Deep learning-based variational autoencoder for classification of quantum and classical states of light

arXiv

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arXiv 2024年

作者： Bhupati, Mahesh Mall, Abhishek Kumar, Anshuman Jha, Pankaj K. Department of Physics Indian Institute of Technology Bombay Powai Mumbai400076 India Max Planck Institute for the Structure and Dynamics of Matter Hamburg22761 Germany Center for Free Electron Laser Science Hamburg22761 Germany Centre of Excellence in Quantum Information Computation Science and Technology Indian Institute of Technology Bombay Powai Mumbai400076 India Quantum Technology Laboratory 〈Q|T|L〉 Department of Electrical Engineering and Computer Science Syracuse University SyracuseNY United States

Advancements in optical quantum technologies have been enabled by the generation, manipulation, and characterization of light, with identification based on its photon statistics. However, characterizing light and its sources through single photon measurements often requires efficient detectors and longer measurement times to obtain high-quality photon statistics. Here we introduce a deep learning-based variational autoencoder (VAE) method for classifying single photon added coherent state (SPACS), single photon added thermal state (SPACS), mixed states between coherent/SPACS and thermal/SPATS of light. Our semi-supervised learning-based VAE efficiently maps the photon statistics features of light to a lower dimension, enabling quasi-instantaneous classification with low average photon counts. The proposed VAE method is robust and maintains classification accuracy in the presence of losses inherent in an experiment, such as finite collection efficiency, non-unity quantum efficiency, finite number of detectors, etc. Additionally, leveraging the transfer learning capabilities of VAE enables successful classification of data of any quality using a single trained model. We envision that such a deep learning methodology will enable better classification of quantum light and light sources even in the presence of poor detection quality. © 2024, CC BY.

关键词： Photons

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RECASNET: IMPROVING CONSISTENCY WITHIN THE TWO-STAGE MITOSIS DETECTION FRAMEWORK

arXiv

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arXiv 2022年

作者： Piansaddhayanon, Chawan Santisukwongchote, Sakun Shuangshoti, Shanop Tao, Qingyi Sriswasdi, Sira Chuangsuwanich, Ekapol Department of Computer Engineering Faculty of Engineering Chulalongkorn University Thailand Chula Intelligent and Complex Systems Faculty of Science Chulalongkorn University Thailand Department of Pathology King Chulalongkorn Memorial Hospital Faculty of Medicine Chulalongkorn University Thailand NVIDIA AI Technology Center Singapore Computational Molecular Biology Group Faculty of Medicine Chulalongkorn University Thailand Center of Excellence in Computational Molecular Biology Faculty of Medicine Chulalongkorn University Thailand

Mitotic count (MC) is an important histological parameter for cancer diagnosis and grading, but the manual process for obtaining MC from whole-slide histopathological images is very time-consuming and prone to error. Therefore, deep learning models have been proposed to facilitate this process. Existing approaches utilize a two-stage pipeline: the detection stage for identifying the locations of potential mitotic cells and the classification stage for refining prediction confidences. However, this pipeline formulation can lead to inconsistencies in the classification stage due to the poor prediction quality of the detection stage and the mismatches in training data distributions between the two stages. In this study, we propose a Refine Cascade Network (ReCasNet), an enhanced deep learning pipeline that mitigates the aforementioned problems with three improvements. First, window relocation was used to reduce the number of poor quality false positives generated during the detection stage. Second, object re-cropping was performed with another deep learning model to adjust poorly centered objects. Third, improved data selection strategies were introduced during the classification stage to reduce the mismatches in training data distributions. ReCasNet was evaluated on two large-scale mitotic figure recognition datasets, canine cutaneous mast cell tumor (CCMCT) and canine mammary carcinoma (CMC), which resulted in up to 4.8% percentage point improvements in the F1 scores for mitotic cell detection and 44.1% reductions in mean absolute percentage error (MAPE) for MC prediction. Techniques that underlie ReCasNet can be generalized to other two-stage object detection networks and should contribute to improving the performances of deep learning models in broad digital pathology applications. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

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UAV communications for sustainable federated learning

arXiv

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arXiv 2021年

作者： Pham, Quoc-Viet Zeng, Ming Ruby, Rukhsana Huynh-The, Thien Hwang, Won-Joo Korean Southeast Center for The 4th Industrial Revolution Leader Education Pusan National University Korea Republic of Department of Electrical Engineering and Computer Engineering Laval University Canada College of Computer Science and Software Engineering Shenzhen University China ICT Convergence Research Center Kumoh National Institute of Technology Korea Republic of Department of Biomedical Convergence Engineering Pusan National University Korea Republic of

Federated learning (FL), invented by Google in 2016, has become a hot research trend. However, enabling FL in wireless networks has to overcome the limited battery challenge of mobile users. In this regard, we propose to apply unmanned aerial vehicle (UAV)-empowered wireless power transfer to enable sustainable FL-based wireless networks. The objective is to maximize the UAV transmit power efficiency, via a joint optimization of transmission time and bandwidth allocation, power control, and the UAV placement. Directly solving the formulated problem is challenging, due to the coupling of variables. Hence, we leverage the decomposition technique and a successive convex approximation approach to develop an efficient algorithm, namely UAV for sustainable FL (UAV-SFL). Finally, simulations illustrate the potential of our proposed UAV-SFL approach in providing a sustainable solution for FL-based wireless networks, and in reducing the UAV transmit power by 32.95%, 63.18%, and 78.81% compared with the benchmarks. Copyright © 2021, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Lab-in-a-Tube: A portable imaging spectrophotometer for cost-effective, high-throughput, and label-free analysis of centrifugation processes

arXiv

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arXiv 2023年

作者： Wei, Yuanyuan Hu, Dehua Bai, Bijie Meng, Chenqi Chan, Tsz Kin Zhao, Xing Wang, Yuye Ho, Yi-Ping Yuan, Wu Ho, Ho-Pui Department of Biomedical Engineering The Chinese University of Hong Kong Shatin Hong Kong Electrical and Computer Engineering Department University of California Los AngelesCA90095 United States Department of Physics The Chinese University of Hong Kong Shatin Hong Kong Department of Orthopaedics and Traumatology Li Ka Shing Institute of Health Sciences The Chinese University of Hong Kong Hong Kong Bionic Sensing and Intelligence Center Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Centre for Biomaterials The Chinese University of Hong Kong Hong Kong Hong Kong Branch of CAS Center for Excellence in Animal Evolution and Genetics Hong Kong The Ministry of Education Key Laboratory of Regeneration Medicine Hong Kong

Centrifuges serve as essential instruments in modern experimental sciences, facilitating a wide range of routine sample-processing tasks that necessitate material sedimentation. However, the study for real-time observation of the dynamical process during centrifugation has remained elusive. In this study, we developed an innovative Lab-in-a-Tube (LIAT) imaging spectrophotometer that incorporates capabilities of real-time image analysis and programmable interruption. This portable LIAT device measures 25.0 mm × 105.0 mm in size and weighs 52.8 g, fitting in common 50mL lab tubes. The cost is less than $30. Based on our knowledge, it is the first Wi-Fi camera built-in in common lab centrifuges with active closed-loop control. We tested our LIAT imaging spectrophotometer with solute-solvent interaction investigation obtained from lab centrifuges with quantitative data plotting in a real-time manner. Single re-circulating flow with a maximum fluid rotational velocity of up to 0.98 mm/s was real-time observed, forming the ring-shaped pattern during centrifugation. To the best of our knowledge, this is the very first observation of similar phenomena. Motivated by this observed phenomenon, we developed theoretical simulations for the single particle in a rotating reference frame, which correlated well with experimental results. We also demonstrated the field portability and on-site operation of our LIAT imaging spectrophotometer by performing an automated hematocrit test (Hct) of human whole blood from finger-prick volumes (20 μL). To the best of our knowledge, this marks the first demonstration to visualize the blood sedimentation process in clinical lab centrifuges. This remarkable cost-effectiveness opens up exciting opportunities for centrifugation microbiology research and paves the way for the creation of a network of computational imaging spectrometers at an affordable price for large-scale and continuous monitoring of centrifugal processes in general. Copyright © 2

关键词： Spectrophotometers

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The Numerical Results of Binary Coherent-State Signal’s Quantum Detection in the Presence of Noise 6th

The Numerical Results of Binary Coherent-State Signal’s Qua...

引用

6th International Conference on Artificial Intelligence and Security, ICAIS 2020

作者： Yu, Wenbin Xiong, Zijia Dong, Zangqiang Xu, Yinsong Liu, Wenjie Qu, Zhiguo Liu, Alex X. Jiangsu Engineering Center of Network Monitoring School of Computer and Software Nanjing University of Information Science and Technology NanjingJiangsu210044 China The Department of Computer Science and Application Zhengzhou Institute of Aeronautical Industry Management ZhengzhouHenan450015 China Department of Computer Science and Engineering Michigan State University East LansingMI48824-1226 United States

ISBN: (纸本)9783030578831

In the optical frequency level communication, quantum effect becomes the source of significant communication errors. One alternative to classical detection is orthogonal projection measurement. In this paper, based on the coherent state’s quantum measurement and discrimination, the quantum optimal detection of binary coherent state signal in the presence of thermal noise was studied. Our numerical results showed the error probabilities of quantum detection under the OOK and BPSK modulation, and verified the performance of these two modulation methods in the thermal noise field background. © 2020, Springer Nature Switzerland AG.

关键词： Optical communication

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FishEye8K: A Benchmark and Dataset for Fisheye Camera Object Detection

arXiv

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arXiv 2023年

作者： Gochoo, Munkhjargal Otgonbold, Munkh-Erdene Ganbold, Erkhembayar Hsieh, Jun-Wei Chang, Ming-Ching Chen, Ping-Yang Dorj, Byambaa Al Jassmi, Hamad Batnasan, Ganzorig Alnajjar, Fady Abduljabbar, Mohammed Lin, Fang-Pang Department of Computer Science and Software Engineering United Arab Emirates University United Arab Emirates Emirates Center for Mobility Research United Arab Emirates University United Arab Emirates College of AI and Green Energy National Yang Ming Chiao Tung University Taiwan University at Albany State University of New York NY United States Department of Computer Science National Yang Ming Chiao Tung University Taiwan Mongolian University of Science and Technology Mongolia National Center for High-Performance Computing Taiwan

With the advance of AI, road object detection has been a prominent topic in computer vision, mostly using perspective cameras. Fisheye lens provides omnidirectional wide coverage for using fewer cameras to monitor road intersections, however with view distortions. To our knowledge, there is no existing open dataset prepared for traffic surveillance on fisheye cameras. This paper introduces an open FishEye8K benchmark dataset for road object detection tasks, which comprises 157K bounding boxes across five classes (Pedestrian, Bike, Car, Bus, and Truck). In addition, we present benchmark results of State-of-The-Art (SoTA) models, including variations of YOLOv5, YOLOR, YOLO7, and YOLOv8. The dataset comprises 8,000 images recorded in 22 videos using 18 fisheye cameras for traffic monitoring in Hsinchu, Taiwan, at resolutions of 1080×1080 and 1280×1280. The data annotation and validation process were arduous and time-consuming, due to the ultra-wide panoramic and hemispherical fisheye camera images with large distortion and numerous road participants, particularly people riding scooters. To avoid bias, frames from a particular camera were assigned to either the training or test sets, maintaining a ratio of about 70:30 for both the number of images and bounding boxes in each class. Experimental results show that YOLOv8 and YOLOR outperform on input sizes 640×640 and 1280×1280, respectively. The dataset will be available on the GitHub link with PASCAL VOC, MS COCO, and YOLO annotation formats. The Fish-Eye8K benchmark will provide significant contributions to the fisheye video analytics and smart city applications. Copyright © 2023, The Authors. All rights reserved.

关键词： Object detection

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Early Coronary Heart Disease Deciphered via Support Vector Machines: Insights from Experiments

Early Coronary Heart Disease Deciphered via Support Vector M...

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International Conference on Wavelet Active Media Technology and Information Processing (ICWAMTIP)

作者： Faijan Akhtar Md Belal Bin Heyat Saba Parveen Priya Singh Muhammad Faiz Ul Hassan Saima Parveen Mohd Ammar Bin Hayat Eram Sayeed Asad Ali Jian Ping Li Mohamad Sawan School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China CenBRAIN Neurotech Center of Excellence School of Engineering Westlake University Hangzhou Zhejiang China College of Electronics and Information Engineering Shenzhen University Shenzhen China Meerut International Institute of Technology Abdul Kalam Technical University UP India School of Electronic Science and Engineering University of Electronic Science and Technology of China Chengdu China Department of Mathematics Shah Abdul Latif University Khairpur Sindh Pakistan M.A.H. Inter College Deoria UP India Government Co-ed Sarvodaya Vidyalaya New Delhi India

Contemporary global health is increasingly shaped by Coronary Heart Disease (CHD), a swiftly growing concern. Precisely predicting CHD remains crucial for optimal patient care. This study analyzes various classifiers to sort through a complex dataset related to CHD, meticulously curated and computationally characterized with 919 patient cases. Narrowing the focus to a subset of 12 attributes, each annotated, a support vector machine (SVM) classifier is applied across different models—Linear, Polynomial, Radial Basis Function (RBF), and Sigmoid. Results show the SVM Linear model achieving 87.7% accuracy, while Polynomial, RBF, and Sigmoid models achieve 81.6%, 83.3%, and 87.8% respectively. Metrics like Area Under the Curve (AUC), sensitivity, and specificity add quantitative depth. Feature reduction's potential impact on precision and classifier efficiency is acknowledged, emphasizing ML's crucial role in refining diagnostic precision for cardiac conditions. The study offers a comprehensive view of cardiovascular ailments across age groups, empowering clinicians with data-driven medical insights via advanced machine learning.

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Exploring explainability: A definition, a model, and a knowledge catalogue

arXiv

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arXiv 2021年

作者： Chazette, Larissa Brunotte, Wasja Speith, Timo Leibniz University Hannover Software Engineering Group Hannover Germany Leibniz University Hannover Cluster of Excellence PhoenixD Hannover Germany Saarland University Institute of Philosophy Department of Computer Science Saarbrücken Germany

The growing complexity of software systems and the influence of software-supported decisions in our society awoke the need for software that is transparent, accountable, and trustworthy. Explainability has been identified as a means to achieve these qualities. It is recognized as an emerging non-functional requirement (NFR) that has a significant impact on system quality. However, in order to incorporate this NFR into systems, we need to understand what explainability means from a software engineering perspective and how it impacts other quality aspects in a system. This allows for an early analysis of the benefits and possible design issues that arise from interrelationships between different quality aspects. Nevertheless, explainability is currently under-researched in the domain of requirements engineering and there is a lack of conceptual models and knowledge catalogues that support the requirements engineering process and system design. In this work, we bridge this gap by proposing a definition, a model, and a catalogue for explainability. They illustrate how explainability interacts with other quality aspects and how it may impact various quality dimensions of a system. To this end, we conducted an interdisciplinary Systematic Literature Review and validated our findings with experts in workshops. © 2021, CC BY-NC-ND.

关键词： Requirements engineering

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Independent and Identically Distributed (IID) Data Assessment in Federated Learning

Independent and Identically Distributed (IID) Data Assessmen...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Mohamad Arafeh Ahmad Hammoud Hadi Otrok Azzam Mourad Chamseddine Talhi Zbigniew Dziong Department of Software Engineering Ecole de Technologie Superieure (ETS) Montreal QC Canada Department of Electrical Engineering Ecole de Technologie Superieure (ETS) Montreal Canada Department of EECS Center of Cyber-Physical Systems (C2PS) Khalifa University Abu Dhabi UAE Department of Computer science and Mathematics Lebanese American University Beirut Lebanon Division of Science New York University Abu Dhabi UAE

ISBN: (纸本)9781665435413

Federated learning extends the centralized machine learning architecture by enabling data privacy for its providers. The distributed structure of the emerged federated architecture imposes a problem of the data being not independent and identically distributed (non-IID), which drastically affects the performance of the learning process. While the majority of the recent works in the federated learning domain have accepted this limitation, only a few scholars addressed the non-IID problem straightforwardly. Nevertheless, these works lack the fundamental analysis of the data’ IIDness, and/or contradict the privacy feature of the federated learning paradigm. In this paper, we focus on evaluating the harmony of the participants by studying their data distribution and calculating their level of compatibility. The devised tool, in this work, is an assessment technique integrated within the federated learning framework to analyze the data distribution among the trainers. Our proposed method is proven by experimenting with several scenarios, and results show that our utility can fairly assess the selected participants before initiating the learning process.

关键词： Data privacy Federated learning Distributed databases Coherence Global communication Convergence

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