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5th ACm/IEEE Workshop on machine Learning for CAD, mLCAD 2023

作者： Lin, Yishuang Li, Yaguang madhusudan, meghna Sapatnekar, Sachin S. Harjani, Ramesh Hu, Jiang University of Minnesota Dept. of Electrical and Computer Engineering United States Texas A&m University Dept. of Electrical and Computer Engineering United States Texas A&m University Dept. of Computer Science and Engineering United States

ISBN: (纸本)9798350309553

Performance modeling is a key bottleneck for analog design automation. Although machine learning-based models have advanced the state-of-the-art, they have so far suffered from huge data preparation cost, very limited reusability, and inadequate accuracy for large circuits. We introduce mL-based macro-modeling techniques to mitigate these problems for linear analog ICs and ADC/DACs. On representative testcases, our method achieves more than 1700× speedup for data preparation and remarkably smaller model errors compared to recent mL approaches. It also attains 3600× acceleration over SPICE simulation with very small errors and reduces data preparation time for an ADC design from 40 days to 9.6 hours. © 2023 IEEE.

关键词： Timing circuits

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Field Demonstration of Blockchain-based Security for a Solar Farm

Field Demonstration of Blockchain-based Security for a Solar...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Ahn, BoHyun Nakka, Kalyan Handke, Nathanial R. Reyna, Trevor J. Kim, Taesic Texas A&m University-Kingsville Dept. of Electrical Engineering and Computer Science KingsvilleTX78363 United States Texas A&m University Dept. of Computer Science and Engineering College StationTX77843 United States University of Missouri-Columbia Dept. of Electrical Engineering and Computer Science ColumbiaMO65211 United States

ISBN: (纸本)9798350376067

This paper introduces a Blockchain (BC)-based security model for a solar farm, providing security functions such as firmware patching management, role-based access control, public key infrastructure, and man-in-the-middle attacks detection (mITm), malware file detection. In particular, this paper provides detailed field-testing methods at a solar fam to demonstrate the feasibility and effectiveness of cyber-attack detection methods. Practical cyberattacks targeting solar farms are designed and conducted. It is demonstrated that the proposed BC-based system proactively detects mITm attacks, firmware modification attack, and malware attack, while ensuring the continuous operation of the solar farm during attack events. © 2024 IEEE.

关键词： Cyber attacks

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C/C++ Vulnerability Data Set Generation Framework using a Parser and LLm 4

C/C++ Vulnerability Data Set Generation Framework using a Pa...

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4th IEEE International Conference on Artificial Intelligence in Cybersecurity, ICAIC 2025

作者： Nguyen, minh Dao Carlisle, martin Texas A&M University College Station Dept. of Computer Science TX United States

ISBN: (纸本)9798331518882

Open-source software can reduce the cost of code use and development;however, it can also introduce vulnerabilities. machine learning methods show promise for finding and fixing vulnerabilities in source code, but require a large amount of labeled data for training. We provide a data set generation framework for C C++ code that will identify code commits that patch vulnerabilities and provide the patched and unpatched function. The framework uses a large language model to determine if the commit patches a vulnerability, then a custom parser to extract the function before and after the patch. Our data set provides over 18,000 functions from 12 repositories. Random sampling and human verification estimate the accuracy at 8 9%. © 2025 IEEE.

关键词： Adversarial machine learning

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Electroencephalography machine Learning Features and methods for Early Diagnosis and Classification of Parkinson’s Disease (2013–2023): A Review

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IEEE Sensors Journal 2025年第12期25卷 21017-21032页

作者： Emad-Ud-Din, muhammad Almuteb, Ibrahim Wang, Ya Hubbard, James E. Texas A&M University Department of Computer Science College StationTX77843 United States Texas A&M University Department of Biomedical Engineering College StationTX77843 United States Department of Electrical and Computer Engineering College Station77843 United States Texas A&M University Department of Mechanical Engineering College StationTX77843 United States

Exploring the machine Learning (mL) features and methods based on electroencephalography (EEG) and quantitative electroencephalographic (qEEG) holds the potential to early diagnosis and classification of Parkinson’s disease (PD) and can bring convenience to the decision-making process for healthcare professionals. PD is clinically heterogeneous, with high variability between patients in terms of motor and non-motor symptoms. This nature makes it extremely challenging for early diagnosis, and severity classification using traditionally questionnaire-based standards. EEG and qEEG provide objective signals with rich features that can potentially provide a translational alternative. In the past decade, a host of mL-based features and algorithms have been developed to detect and classify PD in patients using a range of EEG and qEEG datasets. Various methodologies, including graph-CNNs, deep RNNs, and hybrid models, are discussed within the context of early diagnosis and classification, each offering unique insights and performance levels. This review highlights the multifaceted nature of PD diagnosis and classification research. It addresses the challenges and limitations encountered in existing techniques, emphasizing the need for further improvements in accuracy, sensitivity, and specificity. This review provides a standardized insight into the evolving landscape of PD diagnosis and classification. It showcases the remarkable progress that has been made in utilizing EEG and qEEG data while emphasizing the ongoing quest for effective and convenient mL based methods for early diagnosis and classification. © 2001-2012 IEEE.

关键词： Electroencephalography

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Equivariance via minimal Frame Averaging for more Symmetries and Efficiency 41

Equivariance via Minimal Frame Averaging for More Symmetries...

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41st International Conference on machine Learning, ICmL 2024

作者： Lin, Yuchao Helwig, Jacob Gui, Shurui Ji, Shuiwang Department of Computer Science and Engineering Texas A&M University Texas United States

We consider achieving equivariance in machine learning systems via frame averaging. Current frame averaging methods involve a costly sum over large frames or rely on sampling-based approaches that only yield approximate equivariance. Here, we propose minimal Frame Averaging (mFA), a mathematical framework for constructing provably minimal frames that are exactly equivariant. The general foundations of mFA also allow us to extend frame averaging to more groups than previously considered, including the Lorentz group for describing symmetries in spacetime, and the unitary group for complex-valued domains. Results demonstrate the efficiency and effectiveness of encoding symmetries via mFA across a diverse range of tasks, including n-body simulation, top tagging in collider physics, and relaxed energy prediction. Our code is available at https://***/divelab/mFA. Copyright 2024 by the author(s)

关键词： Colliding beam accelerators

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Graph Structure Extrapolation for Out-of-Distribution Generalization 41

Graph Structure Extrapolation for Out-of-Distribution Genera...

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41st International Conference on machine Learning, ICmL 2024

作者： Li, Xiner Gui, Shurui Luo, Youzhi Ji, Shuiwang Department of Computer Science and Engineering Texas A&M University Texas United States

Out-of-distribution (OOD) generalization deals with the prevalent learning scenario where test distribution shifts from training distribution. With rising application demands and inherent complexity, graph OOD problems call for specialized solutions. While data-centric methods exhibit performance enhancements on many generic machine learning tasks, there is a notable absence of data augmentation methods tailored for graph OOD generalization. In this work, we propose to achieve graph OOD generalization with the novel design of non-Euclidean-space linear extrapolation. The proposed augmentation strategy extrapolates structure spaces to generate OOD graph data. Our design tailors OOD samples for specific shifts without corrupting underlying causal mechanisms. Theoretical analysis and empirical results evidence the effectiveness of our method in solving target shifts, showing substantial and constant improvements across various graph OOD tasks. Copyright 2024 by the author(s)

关键词： Extrapolation

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Communication-Efficient Federated Group Distributionally Robust Optimization 38

Communication-Efficient Federated Group Distributionally Rob...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Guo, Zhishuai Yang, Tianbao Department of Computer Science and Engineering Texas A&M University United States

Federated learning faces challenges due to the heterogeneity in data volumes and distributions at different clients, which can compromise model generalization ability to various distributions. Existing approaches to address this issue based on group distributionally robust optimization (GDRO) often lead to high communication and sample complexity. To this end, this work introduces algorithms tailored for communication-efficient Federated Group Distributionally Robust Optimization (FGDRO). Our contributions are threefold: Firstly, we introduce the FGDRO-CVaR algorithm, which optimizes the average top-K losses while reducing communication complexity to O(1/ϵ4), where ϵ denotes the desired precision level. Secondly, our FGDRO-KL algorithm is crafted to optimize KL regularized FGDRO, cutting communication complexity to O(1/ϵ3). Lastly, we propose FGDRO-KL-Adam to to utilize Adam-type local updates in FGDRO-KL, which not only maintains a communication cost of O(1/ϵ3) but also shows potential to surpass SGD-type local steps in practical applications. The effectiveness of our algorithms has been demonstrated on a variety of real-world tasks, including natural language processing and computer vision. © 2024 Neural information processing systems foundation. All rights reserved.

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Near-infrared optical thermometry of nickel color centers in diamond

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Optics Letters 2025年第3期50卷 968-971页

作者： Dharmasiri, A. Vincent, C. Rajib, T.I. Pochechuev, m. Liu, X. Verhoef, A.J. Hemmer, P.R. Zheltikov, A.m. Institute for Quantum Science and Engineering Texas A&M University College StationTX77843 United States Department of Physics and Astronomy Texas A&M University College StationTX77843 United States Department of Soil and Crop Sciences Texas A&M University College StationTX77843 United States Department of Electrical & Computer Engineering Texas A&M University College StationTX77843 United States

We present an experimental study of temperature-dependent near-infrared fluorescence spectra of nickel color centers in diamond. The amplitude, the central wavelength, and the linewidth of the zero-phonon line (ZPL) in the fluorescence spectrum of these centers exhibit a strong temperature dependence, enabling highly sensitive temperature measurements. Due to the ZPL wavelength, falling within the biological transparency window, combined with a high-temperature sensitivity and low noise floor, as demonstrated by our experiments in practical thermometry settings, nickel color centers in diamond are ideally suited for all-optical temperature measurements, including thermometry of biological systems. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDm), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Infrared devices

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A Domain Incremental Continual Learning Benchmark for ICU Time Series model Transportability

A Domain Incremental Continual Learning Benchmark for ICU Ti...

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2024 IEEE-EmBS International Conference on Biomedical and Health Informatics, BHI 2024

作者： King, Ryan Krueger, Conrad Veselka, Ethan Yang, Tianbao mortazavi, Bobak J. Texas A&m University Computer Science & Engineering College Station United States

ISBN: (纸本)9798350351552

In recent years, machine learning has made sig-nificant progress in clinical outcome prediction, demonstrating increasingly accurate results. However, the substantial resources required for hospitals to train these models, such as data collection, labeling, and computational power, limit the feasibility for smaller hospitals to develop their own models. An alternative approach involves transferring a machine learning model trained by a large hospital to smaller hospitals, allowing them to fine-tune the model on their specific patient data. However, these models are often trained and validated on data from a single hospital, raising concerns about their generalizability to new data. Our research shows that there are notable differences in measurement distributions and frequencies across various regions in the United States. To address this, we propose a benchmark that tests a machine learning model's ability to transfer from a source domain to different regions across the country. This benchmark assesses a model's capacity to learn meaningful information about each new domain while retaining key features from the original domain. Using this benchmark, we frame the transfer of a machine learning model from one region to another as a domain in-cremental learning problem. While the task of patient outcome prediction remains the same, the input data distribution varies, necessitating a model that can effectively manage these shifts. We evaluate two popular domain incremental learning methods: data replay, which stores examples from previous data sources for fine-tuning on the current source, and Elastic Weight Consolidation (EWC), a model parameter regularization method that maintains features important for both data sources. Finally, we propose a new domain incremental learning method that combines EWC and data replay with the ability to adjust the number of updates utilizing data from previous sources. Our results show that this proposed method outperforms EWC and data

关键词： Adversarial machine learning

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An Efficient Contrastive Unimodal Pretraining method for EHR Time Series Data

An Efficient Contrastive Unimodal Pretraining Method for EHR...

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2024 IEEE-EmBS International Conference on Biomedical and Health Informatics, BHI 2024

作者： King, Ryan Kodali, Shivesh Krueger, Conrad Yang, Tianbao mortazavi, Bobak J. Texas A&m University Computer Science & Engineering College Station United States

ISBN: (纸本)9798350351552

machine learning has revolutionized the modeling of clinical timeseries data. Using machine learning, a Deep Neural Network (DNN) can be automatically trained to learn a complex mapping of its input features for a desired task. This is particularly valuable in Electronic Health Record (EHR) databases, where patients often spend extended periods in intensive care units (ICUs). machine learning serves as an efficient method for extract meaningful information. However, many state-of-the-art (SOTA) methods for training DNNs demand substantial volumes of labeled data, posing significant challenges for clinics in terms of cost and time. Self-supervised learning offers an alternative by allowing practitioners to extract valuable insights from data without the need for costly labels. Yet, current SOTA methods often necessitate large data batches to achieve optimal performance, increasing computational demands. This presents a challenge when working with long clinical timeseries data. To address this, we propose an efficient method of contrastive pretraining tailored for long clinical timeseries data. Our approach utilizes an estimator for negative pair comparison, enabling effective feature extraction. We assess the efficacy of our pretraining using standard self-supervised tasks such as linear evaluation and semi-supervised learning. Additionally, our model demonstrates the ability to impute missing measurements, providing clinicians with deeper insights into patient conditions. We demonstrate that our pretraining is capable of achieving better performance as both the size of the model and the size of the measurement vocabulary scale. Finally, we externally validate our model, trained on the mImIC-III dataset, using the eICU dataset. We demonstrate that our model is capable of learning robust clinical information that is transferable to other clinics. © 2024 IEEE.

关键词： Self-supervised learning

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