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arXiv 2024年

作者： Liu, Chen-Yu Chen, Samuel Yen-Chi Chen, Kuan-Cheng Huang, Wei-Jia Chang, Yen-Jui Graduate Institute of Applied Physics National Taiwan University Taipei Taiwan Research Institute Taipei Taiwan Wells Fargo New YorkNY United States Department of Electrical and Electronic Engineering Imperial College London London United Kingdom Imperial College London London United Kingdom Quantum Information Center Chung Yuan Christian University Taoyuan City Taiwan Master Program in Intelligent Computing and Big Data Chung Yuan Christian University Taoyuan City Taiwan

In this study, the Quantum-Train Quantum Fast Weight programmer (QT-QFWP) framework is proposed, which facilitates the efficient and scalable programming of variational quantum circuits (VQCs) by leveraging quantum-driven parameter updates for the classical slow programmer that controls the fast programmer VQC model. This approach offers a significant advantage over conventional hybrid quantum-classical models by optimizing both quantum and classical parameter management. The framework has been benchmarked across several time-series prediction tasks, including Damped Simple Harmonic Motion (SHM), NARMA5, and Simulated Gravitational Waves (GW), demonstrating its ability to reduce parameters by roughly 70-90% compared to Quantum Long Short-term Memory (QLSTM) and Quantum Fast Weight programmer (QFWP) without compromising accuracy. The results show that QT-QFWP outperforms related models in both efficiency and predictive accuracy, providing a pathway toward more practical and cost-effective quantum machine learning applications. This innovation is particularly promising for near-term quantum systems, where limited qubit resources and gate fidelities pose significant constraints on model complexity. QT-QFWP enhances the feasibility of deploying VQCs in time-sensitive applications and broadens the scope of quantum computing in machine learning domains. Copyright © 2024, The Authors. All rights reserved.

关键词： Long short-term memory

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Application of Deep Artificial Neural Network to Model Characteristic Fluctuation of Multi-Channel Gate-All-Around Silicon Nanosheet and Nanofin MOSFETs Induced by Random Nanosized Metal Grains

Application of Deep Artificial Neural Network to Model Chara...

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IEEE Electron Devices Technology and Manufacturing Conference (EDTM)

作者： Sagarika Dash Yiming Li Wen-Li Sung Parallel and Scientific Computing Laboratory National Yang-Ming Chiao Tung University Hsinchu Taiwan Electrical Engineering and Computer Science International Graduate Program National Yang-Ming Chiao Tung University Hsinchu Taiwan Institute of Communications Engineering National Yang-Ming Chiao Tung University Hsinchu Taiwan Department of Electronics and Electrical Engineering National Yang-Ming Chiao Tung University Hsinchu Taiwan Institute of Biomedical Engineering National Yang-Ming Chiao Tung University Hsinchu Taiwan Center for mm Wave Smart Radar System and Technologies National Yang-Ming Chiao Tung University Hsinchu Taiwan

In this work, we propose a deep artificial neural network (D-ANN) to estimate the work function fluctuation (WKF) on 4-channel stacked gate-all-around (GAA) silicon (Si) nanosheet (NS) and nanofin (NF) MOSFET devices for the first time. The 2-layered simple deep model can well predict the transfer characteristics for both NS/NF FET with a large number of (128) input features, utilizing considerably lesser (1100 samples) data uniformly. The resultant model is evaluated by the $\mathrm{R}^{2}$ score and RMSE to witness its competency and the average error is $< 4\%$ . We do also discuss the circuit simulation possibility by applying the ANN approach.

关键词： Semiconductor device modeling MOSFET Gallium arsenide Artificial neural networks Predictive models Logic gates Silicon

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Detecting AI-Generated Text in Educational Content: Leveraging Machine Learning and Explainable AI for Academic Integrity

arXiv

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arXiv 2025年

作者： Najjar, Ayat A. Ashqar, Huthaifa I. Darwish, Omar A. Hammad, Eman Faculty of Modern Science Arab American University 13 Zababdeh P.O Box 240 Jenin Palestine Civil Engineering Department Arab American University 13 Zababdeh P.O Box 240 Jenin Palestine Artificial Intelligence Program Fu Foundation School of Engineering and Applied Science Columbia University 500 W 120th St New YorkNY10027 United States Information Security and Applied Computing Eastern Michigan University 900 Oakwood St YpsilantiMI48197 United States iSTAR Lab Engineering Technology & Industrial Distribution Texas A&M University 400 Bizzell St College StationTX77840 United States

This study seeks to enhance academic integrity by providing tools to detect AI-generated content in student work using advanced technologies. The findings promote transparency and accountability, helping educators maintain ethical standards and supporting the responsible integration of AI in education. A key contribution of this work is the generation of the CyberHumanAI dataset, which has 1000 observations, 500 of which are written by humans and the other 500 produced by ChatGPT. We evaluate various machine learning (ML) and deep learning (DL) algorithms on the CyberHumanAI dataset comparing human-written and AI-generated content from Large Language Models (LLMs) (i.e., ChatGPT). Results demonstrate that traditional ML algorithms, specifically XGBoost and Random Forest, achieve high performance (83% and 81% accuracies respectively). Results also show that classifying shorter content seems to be more challenging than classifying longer content. Further, using Explainable Artificial Intelligence (XAI) we identify discriminative features influencing the ML model's predictions, where human-written content tends to use a practical language (e.g., use and allow). Meanwhile AI-generated text is characterized by more abstract and formal terms (e.g., realm and employ). Finally, a comparative analysis with GPTZero show that our narrowly focused, simple, and fine-tuned model can outperform generalized systems like GPTZero. The proposed model achieved approximately 77.5% accuracy compared to GPTZero's 48.5% accuracy when tasked to classify Pure AI, Pure Human, and mixed class. GPTZero showed a tendency to classify challenging and small-content cases as either mixed or unrecognized while our proposed model showed a more balanced performance across the three classes. © 2025, CC BY.

关键词： Adversarial machine learning

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Frequency-dependent dielectric constant prediction of polymers using machine learning

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npj Computational Materials 2020年第1期6卷 1147-1155页

作者： Lihua Chen Chiho Kim Rohit Batra Jordan P.Lightstone Chao Wu Zongze Li Ajinkya A.Deshmukh Yifei Wang Huan D.Tran Priya Vashishta Gregory A.Sotzing Yang Cao Rampi Ramprasad School of Materials Science and Engineering Georgia Institute of Technology771 Ferst Drive NWAtlantaGA 30332USA Electrical and Computer Engineering University of Connecticut371 Fairfield WayStorrsCT 06269USA Polymer Program Institute of Material ScienceUniversity of Connecticut97 North Eagleville RoadStorrsCT 06269USA Collaboratory for Advanced Computing and Simulations University of Southern CaliforniaLos AngelesCA 90089-0242USA

The dielectric constant(ϵ)is a critical parameter utilized in the design of polymeric dielectrics for energy storage capacitors,microelectronic devices,and high-voltage ***,agile discovery of polymer dielectrics with desirableϵremains a challenge,especially for high-energy,high-temperature *** aid accelerated polymer dielectrics discovery,we have developed a machine-learning(ML)-based model to instantly and accurately predict the frequency-dependentϵof polymers with the frequency range spanning 15 orders of *** model is trained using a dataset of 1210 experimentally measuredϵvalues at different frequencies,an advanced polymer fingerprinting scheme and the Gaussian process regression algorithm.

关键词： dielectric constant polymer

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Improving dynamic collision frequencies: impacts on dynamic structure factors and stopping powers in warm dense matter

arXiv

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arXiv 2023年

作者： Hentschel, Thomas W. Kononov, Alina Olmstead, Alexandra Cangi, Attila Baczewski, Andrew D. Hansen, Stephanie B. School of Applied & Engineering Physics Cornell University IthacaNY United States Center for Computing Research Sandia National Laboratories AlbuquerqueNM United States Nanoscience and Microsystems Engineering Program University of New Mexico AlbuquerqueNM United States Center for Advanced Systems Understanding Helmholtz-Zentrum Dresden-Rossendorf Görlitz Germany Department of Physics and Astronomy University of New Mexico AlbuquerqueNM United States Pulsed Power Sciences Center Sandia National Laboratories AlbuquerqueNM United States

Simulations and diagnostics of high-energy-density plasmas and warm dense matter rely on models of material response properties, both static and dynamic (frequency-dependent). Here, we systematically investigate variations in dynamic electron-ion collision frequencies ν(ω) in warm dense matter using data from a self-consistent-field average-atom model. We show that including the full quantum density of states, strong collisions, and inelastic collisions lead to significant changes in ν(ω). These changes result in red shifts and broadening of the plasmon peak in the dynamic structure factor, an effect observable in x-ray Thomson scattering spectra, and modify stopping powers around the Bragg peak. These changes improve the agreement of computationally efficient average-atom models with first-principles time-dependent density functional theory in warm dense aluminum, carbon, and deuterium. © 2023, CC BY.

关键词： Quantum theory

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Heterogeneous Oblique Double Random Forest

arXiv

引用

arXiv 2023年

作者： Ganaie, M.A. Tanveer, M. Beheshti, I. Ahmad, N. Suganthan, P.N. Department of Robotics University of Michigan Ann ArborMI United States Department of Mathematics Indian Institute of Technology Indore Simrol Indore453552 India Department of Human Anatomy and Cell Science Rady Faculty of Health Sciences Max Rady College of Medicine University of Manitoba WinnipegMB Canada Department of International Program of Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan KINDI Center for Computing Research College of Engineering Qatar University Doha Qatar

The decision tree ensembles use a single data feature at each node for splitting the data. However, splitting in this manner may fail to capture the geometric properties of the data. Thus, oblique decision trees generate the oblique hyperplane for splitting the data at each non-leaf node. Oblique decision trees capture the geometric properties of the data and hence, show better generalization. The performance of the oblique decision trees depends on the way oblique hyperplanes are generate and the data used for the generation of those hyperplanes. Recently, multiple classifiers have been used in a heterogeneous random forest (RaF) classifier, however, it fails to generate the trees of proper depth. Moreover, double RaF studies highlighted that larger trees can be generated via bootstrapping the data at each non-leaf node and splitting the original data instead of the bootstrapped data recently. The study of heterogeneous RaF lacks the generation of larger trees while as the double RaF based model fails to take over the geometric characteristics of the data. To address these shortcomings, we propose heterogeneous oblique double RaF. The proposed model employs several linear classifiers at each non-leaf node on the bootstrapped data and splits the original data based on the optimal linear classifier. The optimal hyperplane corresponds to the models based on the optimized impurity criterion. The experimental analysis indicates that the performance of the introduced heterogeneous double random forest is comparatively better than the baseline models. To demonstrate the effectiveness of the proposed heterogeneous double random forest, we used it for the diagnosis of Schizophrenia disease. The proposed model predicted the disease more accurately compared to the baseline models. © 2023, CC BY-NC-ND.

关键词： Decision trees

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Software Effort Estimation with Use Case Points Using Ensemble Machine Learning Models

Software Effort Estimation with Use Case Points Using Ensemb...

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Electrical, Computer and Energy Technologies (ICECET), International Conference on

作者： Bhaskar Marapelli Anil Carie Sardar M. N. Islam College of Computing and Informatics Wolkite university Wolkite Ethiopia School of Computer Science and Engineering VIT-AP University Amaravathi India ISILC and Decision Sciences and Modelling Program Victoria University Melbourne Australia

ISBN: (纸本)9781665442329

Software development process includes estimating effort as a crucial task. The Use Case Point Analysis (UCPA) is a well know size metric that can be used to calculate effort. The software size is measured using use case diagrams in the UCPA method, using the calculated software size the effort required to complete the project is estimated. The traditional effort estimation with statistical methods is not accurate when compared to the real effort. In-accurate estimation of effort leads to problems with cost calculation and human resources calculation and it might lead to project failure. Machine learning techniques based on regression might help in estimating the effort with accuracy. In this study we proposed a method to identify best performing regression based machine learning model using two data sets Dataset1, Dataset2. Ensembles of different Machine learning methods is created and compared with individual methods and other ensemble methods to find an accurate estimation model. The results shows the individual model SVR and ensemble model GBR gives the best performance with a regression score above 98% with both data sets.

关键词： Support vector machines Statistical analysis Computational modeling Estimation Predictive models Boosting Software

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First Demonstration of Defect Elimination for Cryogenic Ge FinFET CMOS Inverter Showing Steep Subthreshold Slope by Using Ge-on-Insulator Structure

First Demonstration of Defect Elimination for Cryogenic Ge F...

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International Electron Devices Meeting (IEDM)

作者： X.-R. Yu C.-C. Hsieh M.-H. Chuang M.-Y. Chiu T.-C. Sun W.-Z. Geng W.-H. Chang Y.-J. Shih W.-H. Lu W.-C. Chang Y.-C. Lin Y.-C. Pai C.-Y. Lai Y. Dei C.-Y. Yang H.-Y. Lu N.-C. Lin C.-T. Wu K.-H. Kao W. C.-Y. Ma D. D. Lu Y.-J. Lee G.-L. Luo M.-H. Chiang T. Maeda W.-F. Wu Y.-M. Li T.-H. Hou Dept of Electrical Engineering National Cheng Kung University Tainan Taiwan Academy of Innovative Semiconductor and Sustainable Manufacturing National Cheng Kung University Tainan Taiwan Dept. of Electrical Engineering National. Sun Yat-Sen University Kaohsiung Taiwan National Institute of Advanced Industrial Science and Technology Ibaraki Japan Inst. of Artificial Intelligence Innovation National Yang Ming Chiao Tung University Hsinchu Taiwan Inst. of Biomedical Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Intelligent Computing Industrial Doctorate Program Miin Wu School of Computing National Cheng Kung University Taiwan Semiconductor Research Institute Inst. of Pioneer Semiconductor Innovation National Yang Ming Chiao Tung University Hsinchu Taiwan Inst. of Communications Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan

This work presents experimental electrical characteristics and circuit prediction at cryogenic temperatures (down to 10 K) for three different kinds of germanium (Ge)-based FETs with advanced Fin/GAA structures. Among them, the layer transferred Ge-on-Insulator (GeOI) FinFET significantly improves its I-V characteristic during cryogenic measurements, such as a steeper subthreshold swing at 10K and a better I on . The developed GeOI fabrication method provides an effective way to eliminate the defects originating from misfit dislocations at the Ge/Si substrate during epitaxial growth, which would be treated as the key to device performance enhancement under 10 K. According to the measured IV at 10 K and circuit prediction, GeOI FinFETs with high Ge crystallinity are strong candidates for High-Performance-computing (HPC) applications.

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Fair Patient Model: Mitigating Bias in the Patient Representation Learned from the Electronic Health Records

arXiv

引用

arXiv 2023年

作者： Sivarajkumar, Sonish Huang, Yufei Wang, Yanshan Intelligent Systems Program School of Computing and Information University of Pittsburgh PittsburghPA United States Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA United States Department of Health Information Management University of Pittsburgh PittsburghPA United States Department of Biomedical Informatics University of Pittsburgh PittsburghPA United States Clinical and Translational Science Institute University of Pittsburgh PittsburghPA United States

Objective: To pre-train fair and unbiased patient representations from Electronic Health Records (EHRs) using a novel weighted loss function that reduces bias and improves fairness in deep representation learning models. Methods: We defined a new loss function, called weighted loss function, in the deep representation learning model to balance the importance of different groups of patients and features. We applied the proposed model, called Fair Patient Model (FPM), to a sample of 34,739 patients from the MIMIC-III dataset and learned patient representations for four clinical outcome prediction tasks. Results: FPM outperformed the baseline models in terms of three fairness metrics: demographic parity, equality of opportunity difference, and equalized odds ratio. FPM also achieved comparable predictive performance with the baselines, with an average accuracy of 0.7912. Feature analysis revealed that FPM captured more information from clinical features than the baselines. Conclusion: FPM is a novel method to pre-train fair and unbiased patient representations from EHR data using a weighted loss function. The learned representations can be used for various downstream tasks in healthcare and can be extended to other domains where bias and fairness are important. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

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Data gap/outlier correction and treatment

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AIP Conference Proceedings 2024年第1期3034卷

作者： Juan Zuluaga Michael Castillo Divya Syal Andres Calle Navid Shaghaghi Ethical Pragmatic and Intelligent Computing (EPIC) Research Laboratory and the Healthcare Innovation and Design (HID) Program Departments of Computer Science and Engineering (CSEN) and Bioengineering (BIOE) of the School of Engineering (SoE) at Santa Clara University (SCU) in Santa Clara California USA

The prevalence of gaps and outliers within datasets presents substantial challenges, particularly in the realm of time series fore-casting and various other predictive machine learning (ML) tasks. This paper, introduces an effective technique for correcting gaps and outliers in data and validates the approach by applying it to datasets with outlier zones drawn from three diverse contexts. This innovative technique holds promising potential to enhance the performance of machine learning models by treating the data to alleviate the complications posed by these issues and in doing so contributes a valuable tool to the data science toolbox.

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