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arXiv 2024年

作者： Patil, Pratik Du, Jin-Hong Tibshirani, Ryan J. Department of Statistics University of California BerkeleyCA94720 United States Department of Statistics and Data Science Carnegie Mellon University PittsburghPA15213 United States Machine Learning Department Carnegie Mellon University PittsburghPA15213 United States

We study the behavior of optimal ridge regularization and optimal ridge risk for out-of-distribution prediction, where the test distribution deviates arbitrarily from the train distribution. We establish general conditions that determine the sign of the optimal regularization level under covariate and regression shifts. These conditions capture the alignment between the covariance and signal structures in the train and test data and reveal stark differences compared to the in-distribution setting. For example, a negative regularization level can be optimal under covariate shift or regression shift, even when the training features are isotropic or the design is underparameterized. Furthermore, we prove that the optimally tuned risk is monotonic in the data aspect ratio, even in the out-of-distribution setting and when optimizing over negative regularization levels. In general, our results do not make any modeling assumptions for the train or the test distributions, except for moment bounds, and allow for arbitrary shifts and the widest possible range of (negative) regularization levels. Copyright © 2024, The Authors. All rights reserved.

关键词： Aspect ratio

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Implicit Regularization Paths of Weighted Neural Representations

arXiv

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arXiv 2024年

作者： Du, Jin-Hong Patil, Pratik Department of Statistics and Data Science Carnegie Mellon University PittsburghPA15213 United States Machine Learning Department Carnegie Mellon University PittsburghPA15213 United States Department of Statistics University of California BerkeleyCA94720 United States

We study the implicit regularization effects induced by (observation) weighting of pretrained features. For weight and feature matrices of bounded operator norms that are infinitesimally free with respect to (normalized) trace functionals, we derive equivalence paths connecting different weighting matrices and ridge regularization levels. Specifically, we show that ridge estimators trained on weighted features along the same path are asymptotically equivalent when evaluated against test vectors of bounded norms. These paths can be interpreted as matching the effective degrees of freedom of ridge estimators fitted with weighted features. For the special case of subsampling without replacement, our results apply to independently sampled random features and kernel features and confirm recent conjectures (Conjectures 7 and 8) of the authors on the existence of such paths in [50]. We also present an additive risk decomposition for ensembles of weighted estimators and show that the risks are equivalent along the paths when the ensemble size goes to infinity. As a practical consequence of the path equivalences, we develop an efficient cross-validation method for tuning and apply it to subsampled pretrained representations across several models (e.g., ResNet-50) and datasets (e.g., CIFAR-100). Copyright © 2024, The Authors. All rights reserved.

关键词： Matrix algebra

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Estimating relative diffusion from 3D micro-CT images using CNNs

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Artificial Intelligence in Geosciences 2023年第1期4卷 199-208页

作者： Stephan Gättner Florian Frank Fabian Woller Andreas Meier Nadja Ray Friedrich-Alexander-Universitat Erlangen-Nürnberg Department MathematikCauerstraβe 11Erlangen91058Germany Math2 Market GmbH Richard-Wagner-Straβe 1Kaiserslautern67655Germany Mathematical Institute for Machine Learning and Data Science Goldknopfgasse 7Ingolstadt49085Germany

In recent years,convolutional neural networks(CNNs)have demonstrated their effectiveness in predicting bulk parameters,such as effective diffusion,directly from pore-space *** offer significant computational advantages over traditional methods,making them particularly ***,the current literature primarily focuses on fully saturated porous media,while the partially saturated case is also of high interest for various *** saturated conditions present more complex geometries for diffusive transport,making the prediction task more *** CNNs tend to lose robustness and accuracy with lower saturation *** this paper,we overcome this limitation by introducing a CNN,which conveniently fuses diffusion prediction and a well-established morphological model that describes phase distributions in partially saturated porous *** demonstrate the ability of our CNN to perform accurate predictions of relative diffusion directly from full pore-space ***,we compare our predictions with well-established relations such as the one by Millington–Quirk.

关键词： Digital rock Neural networks Deep learning Relative diffusion Partial saturation

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A Review on the Developments in the Field of AI-Based Gait Analysis 2

A Review on the Developments in the Field of AI-Based Gait A...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Wankhede, Vidhi Verma, Prateek Gahane, Shailesh Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Sciences and Technology Maharashtra Wardha442001 India

ISBN: (纸本)9798331528713

The examination of human gait is essential for comprehending motor function, the evolution of diseases, and the effectiveness of rehabilitation in different clinical and research environments. Artificial Intelligence (AI) advancements have recently improved gait analysis methodologies, resulting in increased accuracy, efficiency, and therapeutic effectiveness. This review article compiles the existing literature on AI-based gait analysis. The review emphasizes the role of AI in gait analysis, namely via the use of vision-based approaches, wearable sensor technologies, markerless motion capture systems, and inertial sensor-based methodologies. AI has shown potential in identifying musculoskeletal problems including osteopenia and sarcopenia, forecasting the start of Parkinson's disease, assessing the effects of post-stroke rehabilitation, and diagnosing neurological conditions. In addition, the integration of AI with new technologies such as augmented reality (AR), smart sensors, and wearable devices presents intriguing opportunities to improve gait rehabilitation, enable individualized therapies, and facilitate remote patient monitoring. Although there have been significant breakthroughs, there are still obstacles in creating AI models that can be easily understood for clinical decision-making, establishing standardized procedures for collecting data, and verifying AI algorithms across various populations. This study underlines the transformational potential of AI-based gait analysis in customized healthcare, rehabilitation techniques, and clinical results. In order to fully harness the potential of AI in enhancing human health and well-being, it is imperative to do more research and foster multidisciplinary cooperation. © 2024 IEEE.

关键词： Clinical research

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Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews 41

Monitoring AI-Modified Content at Scale: A Case Study on the...

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41st International Conference on machine learning, ICML 2024

作者： Liang, Weixin Izzo, Zachary Zhang, Yaohui Lepp, Haley Cao, Hancheng Zhao, Xuandong Chen, Lingjiao Ye, Haotian Liu, Sheng Huang, Zhi McFarland, Daniel A. Zou, James Y. Department of Computer Science Stanford University United States Machine Learning Department NEC Labs America United States Department of Electrical Engineering Stanford University United States Graduate School of Education Stanford University United States Department of Management Science and Engineering Stanford University United States Department of Computer Science UC Santa Barbara United States Department of Biomedical Data Science Stanford University United States Department of Sociology Stanford University United States Graduate School of Business Stanford University United States

We present an approach for estimating the fraction of text in a large corpus which is likely to be substantially modified or produced by a large language model (LLM). Our maximum likelihood model leverages expert-written and AI-generated reference texts to accurately and efficiently examine real-world LLM-use at the corpus level. We apply this approach to a case study of scientific peer review in AI conferences that took place after the release of ChatGPT: ICLR 2024, NeurIPS 2023, CoRL 2023 and EMNLP 2023. Our results suggest that between 6.5% and 16.9% of text submitted as peer reviews to these conferences could have been substantially modified by LLMs, i.e. beyond spell-checking or minor writing updates. The circumstances in which generated text occurs offer insight into user behavior: the estimated fraction of LLM-generated text is higher in reviews which report lower confidence, were submitted close to the deadline, and from reviewers who are less likely to respond to author rebuttals. We also observe corpus-level trends in generated text which may be too subtle to detect at the individual level, and discuss the implications of such trends on peer review. We call for future interdisciplinary work to examine how LLM use is changing our information and knowledge practices. Copyright 2024 by the author(s)

关键词： Maximum likelihood

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Giant Trevally Optimizer (GTO): Enhancing HVDC Transmission Capacity with Optimized Fault Current Limiters and HVDC Circuit Breaker Parameters 3

Giant Trevally Optimizer (GTO): Enhancing HVDC Transmission ...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Ramkumar, M. Siva Saranya, N. Gopan, Gokul Baig, Rahmath Ulla Josha Daniel, S. Babu, M. Sns College of Technology Department of Ece India Karpagam College of Engineering Department of Artificial Intelligence and Data Science India Department of Mechanical Engineering Chennai602105 India Cambridge Institute of Technology Department of Artificial Intelligence and Machine Learning K R Puram Bangalore560086 India Hindustan College of Engineering and Technology Department of Eee India Hindusthan College of Engineering and Technology Master of Engineering in Embedded Systems India

ISBN: (纸本)9798331509675

The multi-terminal HVDC system relies heavily on circuit breakers (CBs) and fault current limiters (FCLs) for protection and performance reliability. This research describes a new Giant Trevally optimizer-based strategy for improving the performance of 4-terminal HVDC systems by optimizing crucial circuit breakers and fault current limiters. The proposed method uses time domain estimation to correctly capture fault dynamics, such as traveling waves and reflection coefficients and finds the worst-case fault locations. The multi-objective optimization seeks to optimize the overcurrent, overvoltage handling, fault clearance time, and energy absorption in arresters. This technology provides an effective, dependable protection solution that reduces fault recovery time and increases system stability. The output pulse of the HVDC system shows very low THD results 1.98% for the minimum load condition, 0.87% for median load, and 0.04% for peak load conditions. The results established the simulation of the model in a MATLAB environment. © 2025 IEEE.

关键词： MATLAB

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Correcting for heterogeneity in real-time epidemiological indicators

arXiv

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arXiv 2023年

作者： Rumack, Aaron Rosenfeld, Roni Townes, F. William Machine Learning Department Carnegie Mellon University PittsburghPA United States Department of Statistics & Data Science Carnegie Mellon University PittsburghPA United States

Auxiliary data sources have become increasingly important in epidemiological surveillance, as they are often available at a finer spatial and temporal resolution, larger coverage, and lower latency than traditional surveillance signals. We describe the problem of spatial and temporal heterogeneity in these signals derived from these data sources, where spatial and/or temporal biases are present. We present a method to use a "guiding" signal to correct for these biases and produce a more reliable signal that can be used for modeling and forecasting. The method assumes that the heterogeneity can be approximated by a low-rank matrix and that the temporal heterogeneity is smooth over time. We also present a hyperparameter selection algorithm to choose the parameters representing the matrix rank and degree of temporal smoothness of the corrections. In the absence of ground truth, we use maps and plots to argue that this method does indeed reduce heterogeneity. Reducing heterogeneity from auxiliary data sources greatly increases their utility in modeling and forecasting epidemics. © 2023, CC BY-NC-SA.

关键词： Matrix algebra

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Revisiting the Nowosiólka skull with RMaCzek

arXiv

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arXiv 2024年

作者： Bartoszek, Krzysztof Linköping University Department of Computer and Information Science The Division of Statistics and Machine Learning Linköping581 83 Sweden

One of the first fully quantitative distance matrix visualization methods was proposed by Jan Czekanowski at the beginning of the previous century. Recently, a software package, RMaCzek, was made available that allows for producing such diagrams in R. Here we reanalyze the original data that Czekanowski used for introducing his method, and in the accompanying code show how the user can specify their own custom distance functions in the *** Codes 62H99, 62-04, 92B10 Copyright © 2024, The Authors. All rights reserved.

关键词： Software packages

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A Review on the Applications of machine learning Algorithms for Mental Stress Detection 2

A Review on the Applications of Machine Learning Algorithms ...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Motghare, Renuka Bhoyar, Shivam Verma, Prateek Tale, Abhay Barhate, Aditya Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Sawangi Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Sawangi Maharashtra Wardha442001 India

ISBN: (纸本)9798331528713

Stress, a widespread issue affecting both individual well-being and societal productivity, has attracted significant research interest. Defined as a state of mental or emotional strain, stress arises from various factors such as work, relationships, and health concerns, manifesting physically, emotionally, and behaviorally. This paper provides a review of machine learning (ML) techniques to detect mental stress using physiological signals like ECG, GSR, and EEG. Through a review of existing studies, the paper highlights the progress made in this area and the challenges that remain, particularly concerning data quality, algorithm accuracy, and the practical deployment of stress monitoring devices. The study underscores the potential for real-time monitoring and personalized interventions, especially in daily activities. Future research should focus on enhancing model robustness, integrating multiple data sources, and addressing real-world applicability. This paper emphasizes the importance of early stress identification and personalized treatment to improve mental health outcomes © 2024 IEEE.

关键词： Adversarial machine learning

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Prevent data in Embedded Based Customized Wireless Message Transmitting System Using AES Algorithm with Artificial Bee Colony Optimisation Techniques 1

Prevent Data in Embedded Based Customized Wireless Message T...

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1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Sathia Priya, M. Bhagavathi Priya, M. Manojkumar, B. Vijayakumar, M. Karpagam Institute of Technology Department of Electronics and Communication Engineering Coimbatore India Dr.Mahalingam College of Engineering & Technology Department of Artificial Intelligence & Machine Learning Pollachi India Dr. Mahalingam College of Engineering & Technology Department of Artificial Intelligence & Data Science Pollachi India

ISBN: (纸本)9798350359756

The sender encrypts a message before transferring it to the recipient over a channel in a unidirectional, linear process, according to the transmission model of communication. Hardware and software components known as "message transmission systems"are in charge of managing the transmission path and the communication process. It is possible to study transmission data with the traditional information theory, which is based on "emitters,""messages,"and "receivers". The execution of control tasks such data collecting, control algorithm execution, system identification, and actuation frequently takes place in several system nodes. The effectiveness of a status monitoring system where a sensor delivers sporadic status updates to a remote monitor across a network is observed. Additionally, wireless protocol is utilized in embedded devices to convey messages because it is made for lightweight devices and unstable network conditions. The core concept is to develop a new class of applications through direct interaction amongst intelligent techniques, without the requirement for human involvement. Because it embeds user personal data and activates the aims of information, data prevention is crucial for information sharing. This article has covered the issues of using wireless data, messages, or information with an encryption system, as well as a summary of previous studies in the field. A suggested plan of action has also been offered. To protect user data and privacy safer than any other existing approach, the proposed system also uses Artificial Bee Colony Optimisation (ABC) to optimise the key used for encrypting the sensor data with the aid of the AES Encryption algorithm. Even so, the suggested solution uses less time to encrypt and decode patent data before saving it to the server. © 2023 IEEE.

关键词： Information theory

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