检索结果-内蒙古大学图书馆

Quarterly Publications of the American Statistical Association 2025年第549期120卷

作者： James Leiner Boyan Duan Larry Wasserman Aaditya Ramdas a Department of Statistics and Data Science Carnegie Mellon University Pittsburgh PA c Google Mountain View CA a Department of Statistics and Data Science Carnegie Mellon University Pittsburgh PAb Machine Learning Department Carnegie Mellon University Pittsburgh PA

来源：评论

学校读者我要写书评

暂无评论

From Pixels to Histopathology: A Graph-Based Framework for Interpretable Whole Slide Image Analysis

arXiv

引用

arXiv 2025年

作者： Weers, Alexander Berger, Alexander H. Lux, Laurin Schüffler, Peter Rueckert, Daniel Paetzold, Johannes C. School of Computation Information and Technology Technical University of Munich Germany Department of Computing Imperial College London United Kingdom Munich Center of Machine Learning Germany Munich Data Science Institute Technical University of Munich Munich Germany Institute of Pathology TUM School of Medicine and Health Technical University of Munich Munich Germany Weill Cornell Medicine Cornell University New York CityNY United States

The histopathological classification of whole-slide images (WSIs) is a fundamental task in digital pathology;yet it requires extensive time and expertise from specialists. While deep learning methods show promising results, they typically process WSIs by dividing them into artificial patches, which inherently prevents a network from learning from the entire image context, disregards natural tissue structures and compromises interpretability. Our method overcomes this limitation through a novel graph-based framework that constructs WSI graph representations. The WSI-graph efficiently captures essential histopathological information in a compact form. We build tissue representations (nodes) that follow biological boundaries rather than arbitrary patches all while providing interpretable features for explainability. Through adaptive graph coarsening guided by learned embeddings, we progressively merge regions while maintaining discriminative local features and enabling efficient global information exchange. In our method’s final step, we solve the diagnostic task through a graph attention network. We empirically demonstrate strong performance on multiple challenging tasks such as cancer stage classification and survival prediction, while also identifying predictive factors using Integrated Gradients. Our implementation is publicly available at https://***/HistoGraph31/pix2pathology © 2025, CC BY.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

Condition Monitoring and Fault Diagnosis of BLDC Motor in Electric Vehicles Using Artificial Intelligence

Condition Monitoring and Fault Diagnosis of BLDC Motor in El...

引用

Artificial Intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Swapnil Gundewar Meher Langote Swapna Kamble Prashant Kamble Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Maharashtra India Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Maharashtra India Department of Information Technology Yeshwantrao Chavan College of Engineering Nagpur Maharashtra India Department of Mechanical Technology Yeshwantrao Chavan College of Engineering Nagpur Maharashtra India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

Due to their high benefits in terms of environment and standards of battery advancement, Electric Vehicles (EVs) are extremely important in the shift towards transportation. Here the Brushless DC (BLDC) motor is an integrated part of the EVs, and it has become the most popular choice due to better efficiency, reliability, and reduced maintenance requirements than the others. The major difference from traditional motors is that BLDC motors make use of electronic commutation, so brushes are not employed; this results in lower amounts of wear. This not only helps the motor to have a longer working life but also to work more efficiently, or to maintain their efficiency. However, one crucial drawback to using BLDC motors is the fact that they tend to perform and have great durability if the condition monitoring and fault diagnosis systems when utilized top-notch. These systems are important for the identification of abnormalities and possible defects that if not attended to can lead to major failures and long periods of non-productive time. The ability to track the state of BLDC motors in real time enables one to have preventive measures that cut the chances of the motor breaking down and consequently the costs attached to this eventuality. Condition monitoring involves continuous assessment of motor attributes such as; vibration, temperature, and electrical patterns in a bid to detect changes in healthy motor status. Whereas the existing literature on the level of faults goes beyond it by identifying the specific nature and location of the faults. Altogether, the procedures composed play the basic role of a predictive maintenance program and check BLDC motors’ performance and reliability throughout their usage. Given the importance of BLDC motors in EVs, improvements in the reliability of condition monitoring and fault diagnosis are unpreventable for the enhancement and sustainability of electric mobility.

关键词： Fault diagnosis Temperature measurement Condition monitoring Temperature sensors Vibrations machine learning Motors Electric vehicles Maintenance Monitoring

来源：评论

学校读者我要写书评

暂无评论

An Enhanced Traffic Incident Detection using Factor Analysis and Weighted Random Forest Algorithm

An Enhanced Traffic Incident Detection using Factor Analysis...

引用

IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： P. Rajesh Kanna S. Vanithamani P. Karunakaran P. Pandiaraja N. Tamilarasi P. Nithin Department of Computer Science and Engineering Bannari Amman Institute of Technology Erode Tamil Nadu India Department of Master of Computer Applications M. Kumarasamy College of Engineering Karur Tamil Nadu India Department of Artificial Intelligence and Data Science Nandha Engineering College Erode Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Nehru Institute of Technology Coimbatore Tamil Nadu India Department of Artificial Intelligence and Machine Learning Bannari Amman Institute of Technology Erode Tamil Nadu India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Efficient and precise traffic incident detection is essential to reduce casualties and property damage. To address the issue of unbalanced event data, this work offers a novel methodology known as FA-WRF (Factor Analysis and Weighted Random Forest). This approach combines dimensionality reduction through factor analysis with classification using weighted random forests, data preparation through the Synthetic Minority Over-Sampling Technique (SMOTE), and data preparation with SMOTE. The included feature of severity detection is highlighted, as are the performance metrics of the FA-WRF model using well-established metrics such as detection level rate, number of false count alarm rate, classification type rate, and area specification under the receiver operating characteristic curve (AUC). The superiority of the FA-WRF model is illustrated using real-world expressway traffic data represented by imbalanced incidents through thorough comparisons with various machine learning-based algorithms such as Support Vector machine, k-nearest neighbours algorithms, Logistic Regression algorithms, and decision-making trees. In addition to advancing incident detection, this technique has encouraging prospects for enhancing traffic management procedures and well-informed decision-making procedures in transportation networks.

关键词： Measurement Support vector machines Logistic regression machine learning algorithms Decision making Transportation Receivers data models Classification algorithms Random forests

来源：评论

学校读者我要写书评

暂无评论

Recalibrating probabilistic forecasts of epidemics

arXiv

引用

arXiv 2021年

作者： Rumack, Aaron Tibshirani, Ryan J. Rosenfeld, Roni Machine Learning Department Carnegie Mellon University PittsburghPA United States Department of Statistics & Data Science Carnegie Mellon University PittsburghPA United States

Distributional forecasts are important for a wide variety of applications, including forecasting epidemics. Often, forecasts are miscalibrated, or unreliable in assigning uncertainty to future events. We present a recalibration method that can be applied to a black-box forecaster given retrospective forecasts and observations, as well as an extension to make this method more effective in recalibrating epidemic forecasts. This method is guaranteed to improve calibration and log score performance when trained and measured in-sample. We also prove that the increase in expected log score of a recalibrated forecaster is equal to the entropy of the PIT distribution. We apply this recalibration method to the 27 influenza forecasters in the FluSight Network and show that recalibration reliably improves forecast accuracy and calibration. This method is effective, robust, and easy to use as a post-processing tool to improve epidemic forecasts. © 2021, CC BY-SA.

关键词： Epidemiology

来源：评论

学校读者我要写书评

暂无评论

PLUGIN ESTIMATION OF SMOOTH OPTIMAL TRANSPORT MAPS

arXiv

引用

arXiv 2021年

作者： Manole, Tudor Balakrishnan, Sivaraman Niles-Weed, Jonathan Wasserman, Larry Department of Statistics and Data Science Carnegie Mellon University United States Machine Learning Department Carnegie Mellon University United States Courant Institute of Mathematical Sciences Center for Data Science New York University United States

We analyze a number of natural estimators for the optimal transport map between two distributions and show that they are minimax optimal. We adopt the plugin approach: our estimators are simply optimal couplings between measures derived from our observations, appropriately extended so that they define functions on Rd. When the underlying map is assumed to be Lipschitz, we show that computing the optimal coupling between the empirical measures, and extending it using linear smoothers, already gives a minimax optimal estimator. When the underlying map enjoys higher regularity, we show that the optimal coupling between appropriate nonparametric density estimates yields faster rates. Our work also provides new bounds on the risk of corresponding plugin estimators for the quadratic Wasserstein distance, and we show how this problem relates to that of estimating optimal transport maps using stability arguments for smooth and strongly convex Brenier potentials. As an application of our results, we derive central limit theorems for plugin estimators of the squared Wasserstein distance, which are centered at their population counterpart when the underlying distributions have sufficiently smooth densities. In contrast to known central limit theorems for empirical estimators, this result easily lends itself to statistical inference for the quadratic Wasserstein distance. Copyright © 2021, The Authors. All rights reserved.

关键词： Risk perception

来源：评论

学校读者我要写书评

暂无评论

Best arm identification under additive transfer bandits

arXiv

引用

arXiv 2021年

作者： Neopane, Ojash Ramdas, Aaditya Singh, Aarti Machine Learning Department Carnegie Mellon University PittsburghPA United States Department of Statistics and Data Science Carnegie Mellon University PittsburghPA United States

We consider a variant of the best arm identification (BAI) problem in multi-armed bandits (MAB) in which there are two sets of arms (source and target), and the objective is to determine the best target arm while only pulling source arms. In this paper, we study the setting when, despite the means being unknown, there is a known additive relationship between the source and target MAB instances. We show how our framework covers a range of previously studied pure exploration problems and additionally captures new problems. We propose and theoretically analyze an LUCB-style algorithm to identify an ǫ-optimal target arm with high probability. Our theoretical analysis highlights aspects of this transfer learning problem that do not arise in the typical BAI setup, and yet recover the LUCB algorithm for single domain BAI as a special case. Copyright © 2021, The Authors. All rights reserved.

关键词： Additives

来源：评论

学校读者我要写书评

暂无评论

Adaptive hybrid density functionals

arXiv

引用

arXiv 2024年

作者： Khan, Danish Price, Alastair James Arthur Ach, Maximilian L. von Lilienfeld, O. Anatole Trottier, Olivier Chemical Physics Theory Group Department of Chemistry University of Toronto St. George Campus TorontoON Canada Vector Institute for Artificial Intelligence TorontoON Canada Acceleration Consortium University of Toronto TorontoON Canada Department of Physics University of Toronto St. George Campus TorontoON Canada Department of Physics Ludwig-Maximilians-Universität Munich Germany Department of Materials Science and Engineering University of Toronto St. George Campus TorontoON Canada Machine Learning Group Technische Universität Berlin Berlin10587 Germany Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany

Exact exchange and correlation contributions are known to crucially affect electronic states, which in turn govern covalent bond formation and breaking in chemical species. Empirically averaging the exact exchange admixture and the amount of correlation over configurational and compositional degrees of freedom, hybrid density functional approximations (DFAs) have been widely successful, yet have fallen short to reach explicitly correlated high level quantum chemistry accuracy in general. Using quantum machine learning, we have adaptified hybrid DFAs by generating optimal admixture ratios of exact exchange or of correlation "on the fly", specifically for any chemical compound. Adaptive exchange in the PBE0 hybrid DFA yields atomization energies sufficiently accurate to effectively cure the infamous spin gap problem in DFT, and also improves electron densities, and HOMO-LUMO gaps in organic molecules. Using adaptive PBE0, we revised the entire QM9 data set presenting more accurate quantum properties that include, on average, stronger covalent binding, larger band-gaps, more localized electron densities, and slightly larger dipole-moments. Adaptive PBE correlation (in conjunction with 100% HF exchange) also affords much improved atomization energies, and even enables covalent bond dissociation. © 2024, CC BY.

关键词： Carrier concentration

来源：评论

学校读者我要写书评

暂无评论

Optical Character Recognition (OCR) in Handwritten Characters Using Convolutional Neural Networks to Assist in Exam Reader System

Optical Character Recognition (OCR) in Handwritten Character...

引用

Advancement in Computation & Computer Technologies (InCACCT), International Conference on

作者： P L Lekshmy S. Velmurugan Indra Kumari S. Kayalvili B. Teja Sree P. Karthik Kumar Department of Computer Science and Engineering LBS Institute of Technology for Women Kerala India Department of Electronics and Communication Engineering T.J.S. Engineering College Chennai Tamil Nadu India Department of Machine Learning Data Research Applied AI Korea National University of Science and Technology (UST) Daejeon South Korea Department of Artificial Intelligence Kongu Engineering College (Autonomous) Erode Tamilnadu India Department of Information Technology S.R.K.R. Engineering College Chinaamiram Bhimavaram Andhra Pradesh India Department of Computer Science and Engineering (Cyber Security) Karpagam College of Engineering Coimbatore India

ISBN: (数字)9798350371314

ISBN: (纸本)9798350371321

This work aimed to develop a character recognition method to facilitate the correction of answer cards in the Multiprova software through the development of a response card analysis flow that would culminate in the recognition of letters written by students and automatic correction of the tests. To isolate and identify the answers written on the answer cards, image segmentation techniques were used based on fixed marks printed on the cards. To recognize the letters and numbers written on the cards, trained three convolutional neural networks (for digits, letters and true or false). The results achieved (98.84% accuracy for digit CNN, 98.38% accuracy for letter CNN and 99.89% accuracy for true or false CNN) point out a great average success rate.

关键词： Training Image segmentation Image recognition Codes Optical character recognition Neural networks Streaming media

来源：评论

学校读者我要写书评

暂无评论

Universal inference meets random projections: a scalable test for log-concavity

arXiv

引用

arXiv 2021年

作者： Dunn, Robin Gangrade, Aditya Wasserman, Larry Ramdas, Aaditya Advanced Methodology and Data Science Novartis Pharmaceuticals Corporation Electrical Engineering and Computer Science University of Michigan Department of Electrical & Computer Engineering Boston University United States Department of Statistics & Data Science Machine Learning Department Carnegie Mellon University United States

Shape constraints yield flexible middle grounds between fully nonparametric and fully parametric approaches to modeling distributions of data. The specific assumption of log-concavity is motivated by applications across economics, survival modeling, and reliability theory. However, there do not currently exist valid tests for whether the underlying density of given data is log-concave. The recent universal inference methodology provides a valid test. The universal test relies on maximum likelihood estimation (MLE), and efficient methods already exist for finding the log-concave MLE. This yields the first test of log-concavity that is provably valid in finite samples in any dimension, for which we also establish asymptotic consistency results. Empirically, we find that a random projections approach that converts the d-dimensional testing problem into many one-dimensional problems can yield high power, leading to a simple procedure that is statistically and computationally efficient. Copyright © 2021, The Authors. All rights reserved.

关键词： Maximum likelihood estimation

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：