检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Sapienza, Facundo Bolibar, Jordi Schäfer, Frank Groenke, Brian Pal, Avik Boussange, Victor Heimbach, Patrick Hooker, Giles Pérez, Fernando Persson, Per-Olof Rackauckas, Christopher Department of Statistics University of California Berkeley United States Univ. Grenoble Alpes CNRS IRD G-INP Institut des Géosciences de l’Environnement Grenoble France TU Delft Department of Geosciences and Civil Engineering Delft Netherlands CSAIL Massachusetts Institute of Technology Cambridge United States TU Berlin Department of Electrical and Computer Engineering Berlin Germany Helmholtz Centre for Environmental Research Leipzig Germany Swiss Federal Research Institute WSL Birmensdorf Switzerland Oden Institute for Computational Engineering and Sciences University of Texas Austin United States Jackson School of Geosciences University of Texas Austin United States Department of Statistics and Data Science University of Pennsylvania United States Department of Mathematics University of California Berkeley United States Massachusetts Institute of Technology Cambridge United States JuliaHub Cambridge United States

The differentiable programming paradigm is a cornerstone of modern scientific computing. It refers to numerical methods for computing the gradient of a numerical model’s output. Many scientific models are based on differential equations, where differentiable programming plays a crucial role in calculating model sensitivities, inverting model parameters, and training hybrid models that combine differential equations with data-driven approaches. Furthermore, recognizing the strong synergies between inverse methods and machine learning offers the opportunity to establish a coherent framework applicable to both fields. Differentiating functions based on the numerical solution of differential equations is nontrivial. Numerous methods based on a wide variety of paradigms have been proposed in the literature, each with pros and cons specific to the type of problem investigated. Here, we provide a comprehensive review of existing techniques to compute derivatives of numerical solutions of differential equations. We first discuss the importance of gradients of solutions of differential equations in a variety of scientific domains. Second, we lay out the mathematical foundations of the various approaches and compare them with each other. Third, we cover the computational considerations and explore the solutions available in modern scientific software. Last but not least, we provide best-practices and recommendations for practitioners. We hope that this work accelerates the fusion of scientific models and data, and fosters a modern approach to scientific *** Codes 34-04, 49K40, 65D25, 65L09, 65M32, 86A22, 90C31 © 2024, CC BY.

关键词： Differential equations

来源：评论

学校读者我要写书评

暂无评论

Polarimetric SAR image semantic segmentation with 3D discrete wavelet transform and markov random field

arXiv

引用

arXiv 2020年

作者： Bi, Haixia Xu, Lin Cao, Xiangyong Xue, Yong Xu, Zongben Faculty of Engineering University of Bristol BristolBS8 1UB United Kingdom Institute of Artificial Intelligence Shanghai Em-Data Technology Co. Ltd. Shanghai200000 China School of Environment Science and Spatial Informatics China University of Mining and Technology XuzhouJiangsu221116 China School of Electronics Computing and Mathematics University of Derby DerbyDE22 1GB United Kingdom Institute for Information and System Sciences Xi’an Jiaotong University Xi’an China

Polarimetric synthetic aperture radar (PolSAR) image segmentation is currently of great importance in image processing for remote sensing applications. However, it is a challenging task due to two main reasons. Firstly, the label information is difficult to acquire due to high annotation costs. Secondly, the speckle effect embedded in the PolSAR imaging process remarkably degrades the segmentation performance. To address these two issues, we present a contextual PolSAR image semantic segmentation method in this paper. With a newly defined channel-wise consistent feature set as input, the three-dimensional discrete wavelet transform (3D-DWT) technique is employed to extract discriminative multi-scale features that are robust to speckle noise. Then Markov random field (MRF) is further applied to enforce label smoothness spatially during segmentation. By simultaneously utilizing 3D-DWT features and MRF priors for the first time, contextual information is fully integrated during the segmentation to ensure accurate and smooth segmentation. To demonstrate the effectiveness of the proposed method, we conduct extensive experiments on three real benchmark PolSAR image data sets. Experimental results indicate that the proposed method achieves promising segmentation accuracy and preferable spatial consistency using a minimal number of labeled pixels. Copyright © 2020, The Authors. All rights reserved.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Revolutionizing Genomics with Reinforcement Learning Techniques

arXiv

引用

arXiv 2023年

作者： Karami, Mohsen Jahanian, Khadijeh Alizadehsani, Roohallah Argha, Ahmadreza Dehzangi, Iman Gorriz, Juan M. Zhang, Yu-Dong Hajati, Farshid Yang, Min Alinejad-Rokny, Hamid Amirkabir University of Technology Tehran Iran Faculty of Engineering and IT University of Technology Sydney Sydney Australia Deakin University VIC Australia School of Biomedical Engineering UNSW SYDNEY SydneyNSW2052 Australia Department of Computer Science Rutgers University CamdenNJ08102 United States Center for Computational and Integrative Biology Rutgers University CamdenNJ08102 United States Department of Signal Theory Networking and Communications Universidad de Granada Spain School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom School of Science and Technology Faculty of Science Agriculture Business and Law University of New England ArmidaleNSW2350 Australia Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China School of Biomedical Engineering UNSW SYDNEY SydneyNSW2052 Australia

In recent years, Reinforcement Learning (RL) has emerged as a powerful tool for solving a wide range of problems, including decision-making and genomics. The exponential growth of raw genomic data over the past two decades has exceeded the capacity of manual analysis, leading to a growing interest in automatic data analysis and processing. RL algorithms are capable of learning from experience with minimal human supervision, making them well-suited for genomic data analysis and interpretation. One of the key benefits of using RL is the reduced cost associated with collecting labeled training data, which is required for supervised learning. While there have been numerous studies examining the applications of Machine Learning (ML) in genomics, this survey focuses exclusively on the use of RL in various genomics research fields, including gene regulatory networks (GRNs), genome assembly, and sequence alignment. We present a comprehensive technical overview of existing studies on the application of RL in genomics, highlighting the strengths and limitations of these approaches. We then discuss potential research directions that are worthy of future exploration, including the development of more sophisticated reward functions as RL heavily depends on the accuracy of the reward function, the integration of RL with other machine learning techniques, and the application of RL to new and emerging areas in genomics research. Finally, we present our findings and conclude by summarizing the current state of the field and the future outlook for RL in genomics. © 2023, CC BY-SA.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

AstroMLab 1: Who Wins Astronomy Jeopardy!?

arXiv

引用

arXiv 2024年

作者： Ting, Yuan-Sen Nguyen, Tuan Dung Ghosal, Tirthankar Pan, Rui Arora, Hardik Sun, Zechang de Haan, Tijmen Ramachandra, Nesar Wells, Azton Madireddy, Sandeep Accomazzi, Alberto Research School of Astronomy & Astrophysics Australian National University Cotter Rd. WestonACT2611 Australia School of Computing Australian National University ActonACT2601 Australia Department of Astronomy The Ohio State University ColumbusOH43210 United States The Ohio State University ColumbusOH43210 United States Department of Computer and Information Science University of Pennsylvania PhiladelphiaPA19104 United States National Center for Computational Sciences Oak Ridge National Laboratory Oak RidgeTN37831 United States Department of Computer Science and Engineering Hong Kong University of Science and Technology Kowloon Hong Kong Indian Institute of Technology Patna Bihar Bihta801106 India Department of Astronomy MongManWai Building Tsinghua University Beijing100084 China Institute of Particle and Nuclear Studies High Energy Accelerator Research Organization Ibaraki Tsukuba305-0801 Japan Ibaraki Tsukuba305-0801 Japan Computational Science Division Argonne National Laboratory LemontIL60439 United States Mathematics and Computer Science Division Argonne National Laboratory LemontIL60439 United States Center for Astrophysics Harvard & Smithsonian CambridgeMA02138 United States

We present a comprehensive evaluation of proprietary and open-weights large language models using the first astronomy-specific benchmarking dataset. This dataset comprises 4,425 multiple-choice questions curated from the Annual Review of Astronomy and Astrophysics, covering a broad range of astrophysical topics.a) Our analysis examines model performance across various astronomical subfields and assesses response calibration, crucial for potential deployment in research environments. Claude-3.5-Sonnet outperforms competitors by up to 4.6 percentage points, achieving 85.0% accuracy. For proprietary models, we observed a universal reduction in cost every 3-to-12 months to achieve similar score in this particular astronomy benchmark. open-weights models have rapidly improved, with LLaMA-3-70b (80.6%) and Qwen-2-72b (77.7%) now competing with some of the best proprietary models. We identify performance variations across topics, with non-English-focused models generally struggling more in exoplanet-related fields, stellar astrophysics, and instrumentation related questions. These challenges likely stem from less abundant training data, limited historical context, and rapid recent developments in these areas. This pattern is observed across both open-weights and proprietary models, with regional dependencies evident, highlighting the impact of training data diversity on model performance in specialized scientific domains. Top-performing models demonstrate well-calibrated confidence, with correlations above 0.9 between confidence and correctness, though they tend to be slightly underconfident. The development for fast, low-cost inference of open-weights models presents new opportunities for affordable deployment in astronomy. The rapid progress observed suggests that LLM-driven research in astronomy may become feasible in the near future. © 2024, CC BY.

关键词： Astrophysics

来源：评论

学校读者我要写书评

暂无评论

Modality completion via gaussian process prior variational autoencoders for multi-modal glioma segmentation

arXiv

引用

arXiv 2021年

作者： Hamghalam, Mohammad Frangi, Alejandro F. Lei, Baiying Simpson, Amber L. School of Computing Queen's University KingstonON Canada Department of Electrical Biomedical and Mechatronics Engineering Qazvin Branch Azad University Qazvin34116846-13114 Iran Department of Biomedical and Molecular Sciences Queen's University KingstonON Canada CISTIB Centre for Computational Imaging & Simulation Technologies in Biomedicine School of Computing University of Leeds LeedsLS2 9LU United Kingdom LICAMM Leeds Institute of Cardiovascular and Metabolic Medicine School of Medicine LeedsLS2 9LU United Kingdom University Hospital Gasthuisberg KU Leuven Herestraat 49 Leuven3000 Belgium National-Regional Key Technology Engineering Laboratory for Medical Ultrasound Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen China

In large studies involving multi protocol Magnetic Resonance Imaging (MRI), it can occur to miss one or more sub-modalities for a given patient owing to poor quality (e.g. imaging artifacts), failed acquisitions, or hallway interrupted imaging examinations. In some cases, certain protocols are unavailable due to limited scan time or to retrospectively harmonise the imaging protocols of two independent studies. Missing image modalities pose a challenge to segmentation frameworks as complementary information contributed by the missing scans is then lost. In this paper, we propose a novel model, Multi-modal Gaussian Process Prior Variational Autoencoder (MGP-VAE), to impute one or more missing sub-modalities for a patient scan. MGP-VAE can leverage the Gaussian Process (GP) prior on the Variational Autoencoder (VAE) to utilize the subjects/patients and sub-modalities correlations. Instead of designing one network for each possible subset of present sub-modalities or using frameworks to mix feature maps, missing data can be generated from a single model based on all the available samples. We show the applicability of MGP-VAE on brain tumor segmentation where either, two, or three of four sub-modalities may be missing. Our experiments against competitive segmentation baselines with missing sub-modality on BraTS'19 dataset indicate the effectiveness of the MGP-VAE model for segmentation tasks. © 2021, CC BY.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

The aspect of bipartite coherence in quantum discord to semi-device-independent nonlocality and its implication for quantum information processing

arXiv

引用

arXiv 2024年

作者： Jebarathinam, Chellasamy Ku, Huan-Yu Cheng, Hao-Chung Goan, Hsi-Sheng Physics Division National Center for Theoretical Sciences National Taiwan University Taipei106319 Taiwan Department of Physics National Taiwan Normal University Taipei116059 Taiwan Department of Electrical Engineering Graduate Institute of Communication Engineering National Taiwan University Taipei10617 Taiwan Department of Mathematics National Taiwan University Taipei10617 Taiwan Center for Quantum Science and Engineering National Taiwan University Taipei10617 Taiwan Physics Division National Center for Theoretical Sciences Taipei10617 Taiwan Quantum Computing Center New Taipei City236 Taiwan Department of Physics Center for Theoretical Physics National Taiwan University Taipei106319 Taiwan Center for Quantum Science and Engineering National Taiwan University Taipei106319 Taiwan

Quantum discord can demonstrate quantum nonlocality in the context of a semi-device-independent Bell or steering scenario, i.e., by assuming only the Hilbert-space dimension. This work addresses which aspect of bipartite coherence is essential to such semi-device-independent quantum information tasks going beyond standard Bell nonlocality or quantum steering. It has been shown that the global coherence of a single system can be transformed into bipartite entanglement. However, global coherence can also be present in quantum discord. At the same time, discord can display bipartite coherence locally, i.e., only in a subsystem or both subsystems. Thus, global coherence of bipartite separable states is defined here as a form of bipartite coherence that is not reducible to local coherence in any of the subsystems or both subsystems. To operationally identify such a notion of global coherence in discord, we demonstrate that global coherence is necessary to demonstrate semi-device-independent nonlocality of quantum discord in Bell or steering scenarios. From this result, it follows that any local operations of the form ΦA ⊗ ΦB that may create coherence locally are free operations in the resource theory of semi-device-independent nonlocality of discord. Applying this resource theory, it is identified that the quantum nonlocality beyond quantum steering acts as a resource for the quantum communication task of remote state preparation using two-qubit separable states. © 2024, CC BY.

关键词： Quantum communication

来源：评论

学校读者我要写书评

暂无评论

A Novel Smartphone Recommendation System Using Ensemble Machine Learning

A Novel Smartphone Recommendation System Using Ensemble Mach...

引用

Computer science and Data engineering (CSDE), IEEE Asia-Pacific Conference on

作者： Ahmad Almadhor Sidra Abbas Gabriel Avelino Sampedro Mideth Abisado Thippa Reddy Gadekallu College of Computer and Information Sciences Jouf University Sakaka Saudi Arabia Department of Computer Science COMSATS University Islamabad Islamabad Pakistan Faculty of Information and Communication Studies University of the Philippines Open University Los Baños Philippines Center for Computational Imaging and Visual Innovations De La Salle University Manila Philippines College of Computing and Information Technologies National University Manila Philippines Zhongda Group Jiaxing City Zhejiang Province China Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu India College of Information Science and Engineering Jiaxing University Jiaxing China Division of Research and Development Lovely Professional University Phagwara India

Due to the proliferation of internet evaluations brought on by the rising demand for smartphones, consumers find it challenging to make accurate selections when purchasing. In this paper, we offer ensemble voting methods based on TF-IDF (Term Frequency-Inverse Document Frequency) features for clas-sifying mobile phone ratings. We use a recently assembled dataset comprising over 13,000 smartphone reviews from the Flipkart website. The suggested approach includes feature extraction using the TF-IDF, data cleaning, balancing, and voting-based model prediction. To identify the recently created Flipkart dataset, the suggested method created an ensemble voting mechanism based on machine learning techniques. According to the experimental findings, the suggested method performs more accurately and efficiently than conventional machine learning techniques. At 98.0 %, the model achieved the greatest accuracy. The suggested method can be expanded to additional e-commerce platforms with sizable datasets of online evaluations and assist customers in making updated purchase preferences.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Hypo-elasticity, Cauchy-elasticity, corotational stability and monotonicity in the logarithmic strain

arXiv

引用

arXiv 2024年

作者： Neff, Patrizio Holthausen, Sebastian d'Agostino, Marco Valerio Bernardini, Davide Sky, Adam Ghiba, Ionel-Dumitrel Martin, Robert J. University of Duisburg-Essen Faculty of Mathematics Thea-Leymann-Straße 9 EssenD-45127 Germany GEOMAS INSA-Lyon Université de Lyon 20 avenue Albert Einstein Villeurbanne69621 cedex France Department of Structural and Geotechnical Engineering Sapienza University of Rome Rome Italy Institute of Computational Engineering and Sciences Department of Engineering Faculty of Science Technology and Medicine University of Luxembourg 6 Avenue de la Fonte Esch-sur-AlzetteL-4362 Luxembourg Alexandru Ioan Cuza University of Iaşi Department of Mathematics Blvd. Carol I no. 11 Iaşi700506 Romania Octav Mayer Institute of Mathematics of the Romanian Academy Iaşi Branch Iaşi700505 Romania Robert J. Martin Lehrstuhl für Nichtlineare Analysis und Modellierung Fakultät für Mathematik Universität Duisburg-Essen Thea-Leymann Str. 9 Essen45127 Germany

MSC Codes 15A24, 73G05, 73G99, 74B20We combine the rate-formulation for the objective, corotational Zaremba-Jaumann rate (Equation presented) operating on the Cauchy stress σ, the Eulerian strain rate D and the spatial velocity v with the novel "corotational stability postulate" (Equation presented) to show that for a given isotropic Cauchy-elastic constitutive law B ↦ σ(B) in terms of the left Cauchy-Green tensor B = FFT, the induced fourth-order tangent stiffness tensor ℍZJ(σ) is positive definite if and only if for (Equation presented)(log B):= σ(B), the strong monotonicity condition ((TSTS-M++)) in the logarithmic strain is satisfied: (Equation presented) Thus (CSP) implies (TSTS-M++) and vice-versa, and both imply the invertibility of the hypo-elastic material law between the stress and strain rates given by the tensor ℍZJ(σ), since ℍZJ(σ) is accordingly positive definite. Notably, (TSTS-M++) is one way to characterize the fundamental notion of "stress increases with strain". The same characterization remains true for the corotational Green-Naghdi rate as well as the corotational logarithmic rate, conferring the corotational stability postulate (CSP) together with the monotonicity in the logarithmic strain tensor (TSTS-M++) a far reaching generality. It is conjectured that this characterization of (CSP) holds for a large class of reasonable corotational rates. The result for the logarithmic rate is based on a novel chain rule for corotational derivatives of isotropic tensor functions. © 2024, CC BY.

关键词： Tensors

来源：评论

学校读者我要写书评

暂无评论

Astromlab 1: Who Wins Astronomy Jeopardy!?

SSRN

引用

SSRN 2024年

作者： Ting, Yuan-Sen Nguyen, Tuan Dung Ghosal, Tirthankar Pan, Rui Arora, Hardik Sun, Zechang de Haan, Tijmen Ramachandra, Nesar Wells, Azton Madireddy, Sandeep Accomazzi, Alberto Research School of Astronomy & Astrophysics Australian National University Cotter Rd. WestonACT2611 Australia School of Computing Australian National University ActonACT2601 Australia Department of Astronomy The Ohio State University ColumbusOH43210 United States The Ohio State University ColumbusOH43210 United States Department of Computer and Information Science University of Pennsylvania PhiladelphiaPA19104 United States National Center for Computational Sciences Oak Ridge National Laboratory Oak RidgeTN37831 United States Department of Computer Science and Engineering Hong Kong University of Science and Technology Kowloon Hong Kong Indian Institute of Technology Patna Bihar Bihta801106 India Department of Astronomy Tsinghua University MongManWai Building Beijing100084 China Institute of Particle and Nuclear Studies High Energy Accelerator Research Organization Ibaraki Tsukuba305-0801 Japan Ibaraki Tsukuba305-0801 Japan Computational Science Division Argonne National Laboratory LemontIL60439 United States Mathematics and Computer Science Division Argonne National Laboratory LemontIL60439 United States Center for Astrophysics Harvard & Smithsonian CambridgeMA02138 United States

We present a comprehensive evaluation of proprietary and open-weights large language models using the first astronomy-specific benchmarking dataset. This dataset comprises 4,425 multiple-choice questions curated from the Annual Review of Astronomy and Astrophysics, covering a broad range of astrophysical topics. Our analysis examines model performance across various astronomical subfields and assesses response calibration, crucial for potential deployment in research environments. Claude-3.5-Sonnet outperforms competitors by up to 4.6 percentage points, achieving 85.0% accuracy. For proprietary models, we observed a universal reduction in cost every 3-to-12 months to achieve similar score in this particular astronomy benchmark. open-weights models have rapidly improved, with LLaMA-3-70b (80.6%) and Qwen-2-72b (77.7%) now competing with some of the best proprietary models. We identify performance variations across topics, with non-English-focused models generally struggling more in exoplanet-related fields, stellar astrophysics, and instrumentation related questions. These challenges likely stem from less abundant training data, limited historical context, and rapid recent developments in these areas. This pattern is observed across both open-weights and proprietary models, with regional dependencies evident, highlighting the impact of training data diversity on model performance in specialized scientific domains. Top-performing models demonstrate well-calibrated confidence, with correlations above 0.9 between confidence and correctness, though they tend to be slightly underconfident. The development for fast, low-cost inference of open-weights models presents new opportunities for affordable deployment in astronomy. The rapid progress observed suggests that LLM-driven research in astronomy may become feasible in the near future. © 2024, The Authors. All rights reserved.

关键词： Astrophysics

来源：评论

学校读者我要写书评

暂无评论

Quantum error correction decoheres noise

arXiv

引用

arXiv 2018年

作者： Beale, Stefanie J. Wallman, Joel J. Gutiérrez, Mauricio Brown, Kenneth R. Laflamme, Raymond Institute for Quantum Computing Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada Institute for Quantum Computing Department of Applied Mathematics University of Waterloo WaterlooONN2L 3G1 Canada Department of Physics College of Science Swansea University Singleton Park SwanseaSA2 8PP United Kingdom Escuela de Química Universidad de Costa Rica San José2060 Costa Rica Department of Electrical and Computer Engineering Chemistry and Physics Duke University DurhamNC27708 United States Perimeter Institute for Theoretical Physics WaterlooONN2L 2Y5 Canada

Typical studies of quantum error correction assume probabilistic Pauli noise, largely because it is relatively easy to analyze and simulate. Consequently, the effective logical noise due to physically realistic coherent errors is relatively unknown. Here, we prove that encoding a system in a stabilizer code and measuring error syndromes decoheres errors, that is, causes coherent errors to converge toward probabilistic Pauli errors, even when no recovery operations are applied. Two practical consequences are that the error rate in a logical circuit is well quantified by the average gate fidelity at the logical level and that essentially optimal recovery operators can be determined by independently optimizing the logical fidelity of the effective noise per syndrome. Copyright © 2018, The Authors. All rights reserved.

关键词： Error correction

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：