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A Robust Detection Framework for Intelligent Growth Monitoring in Greenhouse Rose Cultivation

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A Robust Detection Framework for Intelligent Growth Monitori...

Advanced Algorithms and Control Engineering (ICAACE), International Conference on

作者： Danhua Liu Boheng Li Sicheng You Qingyang Wu Jiaqi Wang Zhiyan Ren Yongying Liu Jianghan Tao Zhijie Zhang Shan Sun Zhiben Yin Chenyu Zhang Feng Xue Yijia Chen Graduate School of Business and Finance Waseda University Tokyo Japan Department of Applied Informatics Hosei University Tokyo Japan Faculty of Data Science City University of Macau Macau China Department of Environmental Health Sciences University of California Los Angeles CA US Graduate School of Frontier Sciences The University of Tokyo Kashiwa Japan Graduate School of Global Environmental Studies Sophia University Tokyo Japan Department of Computer Science and Technology Xinjiang College of Science & Technology Xinjiang China Graduate School of Information Production and Systems Waseda University Kitakyushu Japan Department of Math and Applied Mathematics China University of Petroleum-Beijing Karamay China Graduate School of Frontier Science The University of Tokyo Kashiwa Japan

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

Accurately detecting roses in UAV-captured greenhouse imagery presents significant challenges due to occlusions, scale variability, and complex environmental conditions. This study introduces ROSE-MAMBA-YOLO, a hybrid detection framework that combines the efficiency of YOLOv11 with Mamba-inspired state-space modeling to enhance feature extraction, multi-scale fusion, and contextual representation. The model achieves a mAP@50 of 87.5%, precision of 90.4%, and recall of 83.1%, surpassing state-of-the-art object detection models. This framework offers a practical approach for precision floriculture and sets the stage for integrating advanced detection technologies into real-time crop monitoring systems, advancing intelligent, data-driven agriculture.

关键词： Deep learning Accuracy Green products Object detection Self-supervised learning Feature extraction Real-time systems Data models Monitoring Context modeling

Qudit Dynamical Decoupling on a Superconducting Quantum Processor

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Physical Review Letters 2025年第5期134卷 050601-050601页

作者： Vinay Tripathi Noah Goss Arian Vezvaee Long B. Nguyen Irfan Siddiqi Daniel A. Lidar Department of Physics and Astronomy University of Southern California Los Angeles California 90089 USA Center for Quantum Information Science and Technology University of Southern California Los Angeles California 90089 USA Quantum Nanoelectronics Laboratory Department of Physics University of California at Berkeley Berkeley California 94720 USA Applied Math and Computational Research Division Lawrence Berkeley National Lab Berkeley California 94720 USA Department of Electrical and Computer Engineering University of Southern California Los Angeles California 90089 USA Department of Chemistry University of Southern California Los Angeles California 90089 USA

Multilevel qudit systems are increasingly being explored as alternatives to traditional qubit systems due to their denser information storage and processing potential. However, qudits are more susceptible to decoherence than qubits due to increased loss channels, noise sensitivity, and crosstalk. To address these challenges, we develop protocols for dynamical decoupling (DD) of qudit systems based on the Heisenberg-Weyl group. We implement and experimentally verify these DD protocols on a superconducting transmon processor that supports qudit operation based on qutrits (d=3) and ququarts (d=4). Specifically, we demonstrate single-qudit DD sequences to decouple qutrits and ququarts from system-bath-induced decoherence. We also introduce two-qudit DD sequences designed to suppress the detrimental cross-Kerr couplings between coupled qudits. This allows us to demonstrate a significant improvement in the fidelity of time-evolved qutrit Bell states. Our results highlight the utility of leveraging DD to enable scalable qudit-based quantum computing.

关键词： Qubits

Good Quantum LDPC Codes with Linear Time Decoders

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arXiv 2022年

作者： Dinur, Irit Hsieh, Min-Hsiu Lin, Ting-Chun Vidick, Thomas Department of Applied Math and Computer Science The Weizmann Institute of Science Hong Hai Research Institute Taipei Taiwan Department of Physics University of California San DiegoCA United States Department of Computing and Mathematical Sciences California Institute of Technology CA United States

We construct a new explicit family of good quantum low-density parity-check codes which additionally have linear time decoders. Our codes are based on a three-term chain pFm2*mqV ÝÑ δ0 pFm2 qE ÝÑ δ1 FF2 where V (X-checks) are the vertices, E (qubits) are the edges, and F (Z-checks) are the squares of a left-right Cayley complex, and where the maps are defined based on a pair of constant-size random codes CA, CB: Fm2 Ñ F∆2 where ∆ is the regularity of the underlying Cayley graphs. One of the main ingredients in the analysis is a proof of an essentially-optimal robustness property for the tensor product of two random codes. © 2022, CC BY.

关键词： Decoding

Hamiltonian Learning using Machine Learning Models Trained with Continuous Measurements

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arXiv 2024年

作者： Tucker, Kris Rege, Amit Kiran Smith, Conor Monteleoni, Claire Albash, Tameem Department of Applied Math University of Colorado Boulder United States Department of Computer Science University of Colorado Boulder United States Center for Quantum Information and Control University of New Mexico University of New Mexico AlbuquerqueNM United States Department of Electrical and Computer Engineering University of New Mexico AlbuquerqueNM United States INRIA Centre de Recherche de Paris France Center for Computing Research Sandia National Laboratories AlbuquerqueNM87185 United States

We build upon recent work on using Machine Learning models to estimate Hamiltonian parameters using continuous weak measurement of qubits as input. We consider two settings for the training of our model: (1) supervised learning where the weak measurement training record can be labeled with known Hamiltonian parameters, and (2) unsupervised learning where no labels are available. The first has the advantage of not requiring an explicit representation of the quantum state, thus potentially scaling very favorably to a larger number of qubits. The second requires the implementation of a physical model to map the Hamiltonian parameters to a measurement record, which we implement using an integrator of the physical model with a recurrent neural network to provide a model-free correction at every time step to account for small effects not captured by the physical model. We test our construction on a system of two qubits and demonstrate accurate prediction of multiple physical parameters in both the supervised and unsupervised contexts. We demonstrate that the model benefits from larger training sets establishing that it is in fact "learning," and we show robustness to errors in the assumed physical model by achieving accurate parameter estimation in the presence of unanticipated single particle relaxation. © 2024, CC BY.

关键词： Qubits

A well-posedness result for an extended KdV equation

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Partial Differential Equations in applied mathematics 2024年 10卷

作者： Berjawi, M. El Arwadi, T. Israwi, S. Department of mathematics and Computer science Faculty of science Beirut Arab University Beirut Lebanon Laboratoire de mathématiques-EDST Faculté des sciences et EDST Université Libanaise Hadat Lebanon Arts Sciences and Technology University in Lebanon CRAMS: Center for Research in Applied Mathematics and Statistics Faculty of sciences Beirut Lebanon

Among the most interesting things Russell discovered was there is a mathematical relation between the height of the wave, the depth of the wave when water at rest and the speed at which the wave travels. KdV equation was the first mathematical unidimensional model describing the phenomenon observed by Scott-Russell. Despite its nonlinearity and dispersivity terms, KdV equation has being interesting because of its integrability, and specially being a soliton equation. In this paper, we aim to analyze an extended KdV equation. The construction of approximate solution of this equation will be done in H^s(R), for s>[Formula presented], using the modified energy method, its uniqueness and continuity with respect to the initial data is presented referring to a Bona–Smith technique. © 2024

关键词： KdV Model Well posedness

Neural FIM for learning Fisher information metrics from point cloud data

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arXiv 2023年

作者： Fasina, Oluwadamilola Huguet, Guillaume Tong, Alexander Zhang, Yanlei Wolf, Guy Nickel, Maximilian Adelstein, Ian Krishnaswamy, Smita Applied Math Program Yale University New HavenCT United States Mila - Quebec AI Institute MontrealQC Canada Department of Mathematics and Statistics Université de Montréal MontrealQC Canada Department of Computer Science and Operations Research Université de Montréal MontrealQC Canada FAIR Meta AI United States Department of Math Yale University New HavenCT United States Department of Computer Science Yale University New HavenCT United States Department of Genetics Yale University New HavenCT United States

Although data diffusion embeddings are ubiquitous in unsupervised learning and have proven to be a viable technique for uncovering the underlying intrinsic geometry of data, diffusion embeddings are inherently limited due to their discrete nature. To this end, we propose neural FIM, a method for computing the Fisher information metric (FIM) from point cloud data - allowing for a continuous manifold model for the data. Neural FIM creates an extensible metric space from discrete point cloud data such that information from the metric can inform us of manifold characteristics such as volume and geodesics. We demonstrate Neural FIM's utility in selecting parameters for the PHATE visualization method as well as its ability to obtain information pertaining to local volume illuminating branching points and cluster centers embeddings of a toy dataset and two single-cell datasets of IPSC reprogramming and PBMCs (immune cells). Copyright © 2023, The Authors. All rights reserved.

关键词： Embeddings

Enhancing Industrial Productivity through AI-Driven Systematic Literature Reviews 19

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Enhancing Industrial Productivity through AI-Driven Systemat...

19th International Conference on Web Information Systems and Technologies, WEBIST 2023

作者： Coelho, Jaqueline Gutierri Bispo, Guilherme Dantas Vergara, Guilherme Fay Saiki, Gabriela Mayumi Serrano, André Luiz Marques Weigang, Li Neumann, Clovis Martins, Patricia Helena de Oliveira, Welber Santos Albarello, Angela Brigida Casonatto, Ricardo Accorsi Missel, Patrícia de Medeiros Junior, Roberto de Oliveira Gomes, Jefferson Rosano-Peña, Carlos da Costa, Caroline Cabral F. Latitude Decision Making Technologies Lab University of Brasilia Campus Darcy Ribeiro Brasilia Brazil Projectum Research Group for Math. Methodologies Applied to Management University of Brasilia Brasilia Brazil SENAI National Service of Industrial Learning Federal District Brazil Department of Production Engineering Faculty of Technology University of Brasília Federal District Brazil Department of Computer Science University of Brasilia Federal District Brazil Department of Economics University of Brasília Federal District Brazil UNITEC Innovation and Technology Unit National Service of Industrial Learning Federal District Brazil Department of Electrical University of Brasilia Federal District Brazil Department of Administration University of Brasilia Federal District Brazil

ISBN: (纸本)9789897586729

The advent of Artificial Intelligence (AI) has opened up new possibilities for improving productivity in various industry sectors. In this paper, we propose a novel framework aimed at optimizing systematic literature reviews (SLRs) for industrial productivity. By combining traditional keyword selection methods with AI-driven classification techniques, we streamline the review process, making it more efficient. Leveraging advanced natural language processing (NLP) approaches, we identify six key sectors for optimization, thereby reducing workload in less relevant areas and enhancing the efficiency of SLRs. This approach helps conserve valuable time and resources in scientific research. Additionally, we implemented four machine learning models for category classification, achieving an impressive accuracy rate of over 75%. The results of our analyses demonstrate a promising pathway for future automation and refinements to boost productivity in the industry. Copyright © 2023 by SCITEPRESS - science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

关键词： Natural language processing systems

Supervised multiple kernel learning approaches for multi-omics data integration

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arXiv 2024年

作者： Briscik, Mitja Tazza, Gabriele Vidács, László Dillies, Marie-Agnès Déjean, Sébastien Institut de Mathématiques de Toulouse UMR5219 CNRS UPS Université de Toulouse Cedex 9 Toulouse31062 France Department of Computer Science Applied Artificial Intelligence Group University of Szeged Szeged6720 Hungary Institut Pasteur Université Paris Cité Bioinformatics and Biostatistics Hub ParisF‑75015 France

Background: Advances in high‑throughput technologies have originated an ever‑ increasing availability of omics datasets. The integration of multiple heterogeneous data sources is currently an issue for biology and bioinformatics. Multiple kernel learn‑ ing (MKL) has shown to be a flexible and valid approach to consider the diverse nature of multi‑omics inputs, despite being an underused tool in genomic data mining. Results: We provide novel MKL approaches based on different kernel fusion strategies To learn from the meta‑kernel of input kernels, we adapted unsupervised integration algorithms for supervised tasks with support vector machines. We also tested deep learning architectures for kernel fusion and classification. The results show that MKL‑ based models can outperform more complex, state‑of‑the‑art, supervised multi‑omics integrative approaches. Conclusion: Multiple kernel learning offers a natural framework for predictive models in multi‑omics data. It proved to provide a fast and reliable solution that can compete with and outperform more complex architectures. Our results offer a direction for bio‑ data mining research, biomarker discovery and further development of methods for heterogeneous data integration. © 2024, CC BY.

关键词： Self-supervised learning

Radial kinks in a Schwarzschild-like geometry

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arXiv 2024年

作者： Caputo, Jean-Guy Dobrowolski, Tomasz Gatlik, Jacek Kevrekidis, Panayotis G. Laboratoire de Mathématiques INSA de Rouen Avenue de l’Université Saint-Etienne du Rouvray76801 France Department of Computer Physics and Quantum Computing University of the National Education Commission in Krakow Podchoŗ azych 2 Cracow30-084 Poland AGH University of Krakow Faculty of Physics and Applied Computer Science Krakow30-059 Poland Department of Mathematics and Statistics University of Massachusetts AmherstMA01003-4515 United States

We study the propagation of a domain wall (kink) of the 4 model in a radially symmetric environment defined by a gravity source. This source introduces a Schwarzschild-like geometry. We introduce an effective model that accurately describes the dynamics of the kink center. This description works well even outside the perturbation region, i.e., even for large masses of the gravitating object. We observed that such a spherical domain wall surrounding a star-type object inevitably "collapses", i.e., shrinks in radius towards the origin and offer an understanding of the latter phenomenology. The relevant analysis is presented for a circular domain wall and a spherical one. © 2024, CC BY.

关键词： Spheres