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arXiv 2023年

作者： Mohseni, Peiman Duffield, Nick Mallick, Bani Hasanzadeh, Arman Computer Science and Engineering Department Texas A&M University United States Electrical and Computer Engineering Department Texas A&M University United States Statistics Department Texas A&M University United States Google Cloud United States

Neural processes are a family of probabilistic models that inherit the flexibility of neural networks to parameterize stochastic processes. Despite providing well-calibrated predictions, especially in regression problems, and quick adaptation to new tasks, the Gaussian assumption that is commonly used to represent the predictive likelihood fails to capture more complicated distributions such as multimodal ones. To overcome this limitation, we propose Conditional Quantile Neural Processes (CQNPs), a new member of the neural processes family, which exploits the attractive properties of quantile regression in modeling the distributions irrespective of their form. By introducing an extension of quantile regression where the model learns to focus on estimating informative quantiles, we show that the sampling efficiency and prediction accuracy can be further enhanced. Our experiments with real and synthetic datasets demonstrate substantial improvements in predictive performance compared to the baselines, and better modeling of heterogeneous distributions’ characteristics such as multimodality. Copyright © 2023, The Authors. All rights reserved.

关键词： Random processes

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engineering synthetic gauge fields through the coupling phases in cavity magnonics

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Physical Review Applied 2024年第6期21卷 064033-064033页

作者： Alan Gardin Guillaume Bourcin Jeremy Bourhill Vincent Vlaminck Christian Person Christophe Fumeaux Giuseppe C. Tettamanzi Vincent Castel School of Physics Lab-STICC UMR CNRS 6285 ARC Centre of Excellence for Engineered Quantum Systems and ARC Centre of Excellence for Dark Matter Particle Physics Department of Physics School of Electrical Engineering and Computer Science School of Chemical Engineering and Advanced Materials

Cavity magnonics, which studies the interaction of light with magnetic systems in a cavity, is a promising platform for quantum transducers and quantum memories. At microwave frequencies, the coupling between a cavity photon and a magnon, the quasiparticle of a spin-wave excitation, is a consequence of the Zeeman interaction between the cavity’s magnetic field and the magnet’s macroscopic spin. For each photon-magnon interaction, a coupling phase factor exists, and this is often neglected in simple systems; however, in “loop-coupled” systems, where there are at least as many couplings as modes, the coupling phases become relevant for the physics and lead to synthetic gauge fields. We present experimental evidence of the existence of such coupling phases by considering two spheres made of yttrium-iron-garnet and two different re-entrant cavities. We predict numerically the values of the coupling phases, and we find good agreement between the theory and the experimental data. These results show that in cavity magnonics, one can engineer synthetic gauge fields, which can be useful for cavity-mediated coupling and engineering dark-mode physics.

关键词： Magnetic coupling Magnons Optical & microwave phenomena Ferrimagnets Ferromagnetic resonance Gauge symmetries Holstein-Primakoff method Microwave techniques

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ELMap: Area-Driven LUT Mapping with k-LUT Network Exact Synthesis

ELMap: Area-Driven LUT Mapping with k-LUT Network Exact Synt...

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2025 Design, Automation and Test in Europe Conference, DATE 2025

作者： Pan, Hongyang Zhu, Keren Yang, Fan Chu, Zhufei Zeng, Xuan School of Microelectronics Fudan University State Key Lab of Integrated Circuits and Systems Shanghai China Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China

ISBN: (纸本)9783982674100

Mapping to k-input lookup tables (k-LUTs) is a critical process in field-programmable gate array (FPGA) synthesis. However, the structure of the subject graph can introduce structural bias, which refers to the dependency of mapping results on the inherent graph structure, often leading to suboptimal results. To address this, we present ELMap, an area-driven LUT mapping framework. It incorporates structural choice during the collapsing phase. This enables dynamic decomposition, maximizing local-to-global optimization transfer. To ensure seamless integration between the optimization and mapping processes, ELMap leverages exact k-LUT synthesis to generate area-optimal sub-LUT networks. Experiments on the EPFL benchmark suite demonstrate that ELMap significantly outperforms state-of-the-art methods. Specifically, in 6-LUT mapping, ELMap reduces the average LUT area by 8.5% and improves the area-depth-product (ADP) by 5.8%. In 4-LUT remapping, it reduces the average LUT area by 17.6% and improves the ADP by 2.4%. © 2025 EDAA.

关键词： Table lookup

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Discovering High Utility Episodes in Sequences

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第3期4卷 473-486页

作者： Gan, Wensheng Lin, Jerry Chun-Wei Chao, Han-Chieh Yu, Philip S. Jinan University College of Cyber Security Guangzhou510632 China Pazhou Lab Guangzhou510330 China Western Norway University of Applied Sciences Department of Computing Mathematics and Physics Bergen5063 Norway National Dong Hwa University Department of Electrical Engineering Hualien974 Taiwan University of Illinois at Chicago Department of Computer Science ChicagoIL60607 United States

Sequence data are more commonly seen than other types of data (e.g., transaction data) in real-world applications. For the mining task from sequence data, several problems have been formulated, such as sequential pattern mining, episode mining, and sequential rule mining. As one of the fundamental problems, episode mining has often been studied. The common wisdom is that discovering frequent episodes is not useful enough. In this article, we propose an efficient utility mining approach, namely, UMEpi: utility mining of high-utility episodes from complex event sequences. We propose the concept of remaining utility of episodes and achieve a tighter upper bound, namely, episode-weighted utilization (EWU), which will provide better pruning. Thus, the optimized EWU-based pruning strategies can achieve better improvements in mining efficiency. The search space of UMEpi w.r.t. a prefix-based lexicographic sequence tree is spanned and determined recursively for mining high-utility episodes, by prefix-spanning in a depth-first way. Finally, extensive experiments on four real-life datasets demonstrate that UMEpi can discover the complete high-utility episodes from complex event sequences. Furthermore, the improved variants of UMEpi significantly outperform the baseline in terms of execution time, memory consumption, and scalability. © 2020 IEEE.

关键词： Artificial intelligence

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Decentralized Differentially Private Without-Replacement Stochastic Gradient Descent 57

Decentralized Differentially Private Without-Replacement Sto...

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57th Annual Conference on Information sciences and Systems, CISS 2023

作者： Jin, Richeng He, Xiaofan Dai, Huaiyu Zhejiang-Singapore Innovation and Ai Joint Research Lab The College of Information Science and Electronic Engineering Zhejiang University Hangzhou310000 China Electronic Information School Wuhan University Wuhan430000 China Department of Electrical and Computer Engineering North Carolina State University RaleighNC27695 United States

ISBN: (纸本)9781665451819

While machine learning has achieved remarkable results in a wide variety of domains, the training of models often requires large datasets that may need to be collected from different individuals. As sensitive information may be contained in the individual's dataset, sharing training data may lead to severe privacy concerns. Therefore, there is a compelling need to develop privacy-aware machine learning methods, for which one effective approach is to leverage the generic framework of differential privacy. Considering that stochastic gradient descent (SGD) is one of the most commonly adopted methods for large-scale machine learning problems, a decentralized differentially private SGD algorithm is proposed in this work. Particularly, we focus on SGD without replacement due to its favorable structure for practical implementation. Both privacy and convergence analysis are provided for the proposed algorithm. Finally, extensive experiments are performed to demonstrate the effectiveness of the proposed method. © 2023 IEEE.

关键词： Large dataset

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A Machine Learning-based Approach to Model Highly-thermally Robust Metasurface Absorber

A Machine Learning-based Approach to Model Highly-thermally ...

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2022 Conference on Lasers and Electro-Optics Pacific Rim, CLEO/PR 2022

作者： Ijaz, Sumbel Noureen, Sadia Rehman, Bacha Zubair, Muhammad Mehmood, Muhammad Qasim Massoud, Yehia MicroNano Lab Electrical Engineering Department Information Technology University of the Punjab Lahore54600 Pakistan Department of Computer Science Namal University Punjab Mianwali Pakistan Thuwal23955 Saudi Arabia

For accelerating design procedure of compact and efficient on-chip nano-photonics and to aid computationally expensive, time-exhaustive state-of-the-art iterative simulation schemes, regression-based machine-learning models are demonstrated that predict the optical response and structural parameters of the meta-atoms. © IEEE 2022.

关键词： Machine learning

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A Machine Learning-based Approach to Model Highly-thermally Robust Metasurface Absorber

A Machine Learning-based Approach to Model Highly-thermally ...

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2022 Conference on Lasers and Electro-Optics Pacific Rim, CLEO-PR 2022

ISBN: (纸本)9798350350012

关键词：

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ELMap: Area-Driven LUT Mapping with $k$-LUT Network Exact Synthesis

ELMap: Area-Driven LUT Mapping with $k$-LUT Network Exact Sy...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Hongyang Pan Keren Zhu Fan Yang Zhufei Chu Xuan Zeng State Key Lab of Integrated Circuits and Systems School of Microelectronics Fudan University Shanghai China Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

Mapping to $k$ -input lookup tables ( $k$ -LUTs) is a critical process in field-programmable gate array (FPGA) synthesis. However, the structure of the subject graph can introduce structural bias, which refers to the dependency of mapping results on the inherent graph structure, often leading to suboptimal results. To address this, we present ELMap, an area-driven LUT mapping framework. It incorporates structural choice during the collapsing phase. This enables dynamic decomposition, maximizing local-to-global optimization transfer. To ensure seamless integration between the optimization and mapping processes, ELMap leverages exact $k$ -LUT synthesis to generate area-optimal sub-LUT networks. Experiments on the EPFL benchmark suite demonstrate that ELMap significantly outperforms state-of-the-art methods. Specifically, in 6-LUT mapping, ELMap reduces the average LUT area by 8.5% and improves the area-depth-product (ADP) by 5.8%. In 4-LUT remapping, it reduces the average LUT area by 17.6% and improves the ADP by 2.4%.

关键词： Europe Benchmark testing Table lookup Logic Optimization Field programmable gate arrays

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Secure Solar Forecasting: Deep Learning Approaches for Cyber Attacks Detection and Mitigation

Secure Solar Forecasting: Deep Learning Approaches for Cyber...

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Smart Power & Internet Energy Systems (SPIES), International Conference on

作者： Ahmed Abughali Mohamad Alansari Ameena S. Al-Sumaiti Mohamed S. El Moursi APEC Electrical Engineering Department Smart OR Lab Khalifa University Abu Dhabi UAE Computer Science Department Khalifa University Abu Dhabi UAE APEC Electrical Engineering Department Khalifa University Abu Dhabi UAE

ISBN: (数字)9798350368864

ISBN: (纸本)9798350368871

Due to the increasing integration of solar energy into the energy infrastructure, solar power forecasting has become an attractive target for malicious attackers. However, existing studies in the literature primarily focus on detecting a single type of cyber attack, namely False Data Injection Attacks (FDIAs), and they lack recovery approaches. This paper proposes two deep learning-based security schemes: a two-stage detection approach and a mitigation approach. The proposed schemes are designed to accurately detect and recover stealthy FDIAs and Denial of Service (DoS) attacks. The first stage in the detection model determines whether the sample has been attacked, while the second stage categorizes the type of attack if an attack is present. After identifying the attack type, the proposed mitigation model recovers the corrupted measurements. For comparative analysis, the performance of three different deep learning models is evaluated for both tasks: detection and mitigation. The proposed models are tested utilizing a real Global Horizontal Irradiance (GHI) dataset collected from Abu Dhabi between 2017 and 2019. The two-stage detection approach yielded significantly better results than tackling the problem directly as a multi-class classification task. Specifically, the best-performing two-stage detection model, the Long Short Term Memory (LSTM) model, showed an average improvement of 6.52% across the Area Under the Curve (AUC). Additionally, the best-performing proposed mitigation method, LSTM, substantially recovered the corrupted measurements by 92.53% for FDIAs and 99.62% for DoS attacks.

关键词： Deep learning Prevention and mitigation Noise Solar energy Internet Security Noise measurement Forecasting Cyberattack Long short term memory

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Linear Discriminant Analysis in Credit Scoring: A Transparent Hybrid Model Approach

Linear Discriminant Analysis in Credit Scoring: A Transparen...

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International Conference on computer and Information Technology (ICCIT)

作者： Md Shihab Reza Monirul Islam Mahmud Ifti Azad Abeer Nova Ahmed Department of Electrical and Computer Engineering (ECE) Design Inclusion & Access Lab (DIAL) North South University Dhaka Bangladesh Department of Computer and Information Science Fordham University New York USA

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

The development of computing has made credit scoring approaches possible, with various machine learning (ML) and deep learning (DL) techniques becoming more and more valuable. While complex models yield more accurate predictions, their interpretability is often weakened, which is a concern for credit scoring that places importance on decision fairness. As features of the dataset are a crucial factor for the credit scoring system, we implement Linear Discriminant Analysis (LDA) as a feature reduction technique, which reduces the burden of the model’s complexity. We compared 6 different machine learning models, 1 deep learning model, and a hybrid model with and without using LDA. From the result, we have found our hybrid model, ‘XG-DNN,’ outperformed other models with the highest accuracy of 99.45% and a 99% F1 score with LDA. Lastly, to interpret model decisions, we have applied 2 different explainable AI techniques named LIME (local) and Morris Sensitivity Analysis (global). Through this research, we showed how feature reduction techniques can be used without affecting the performance and explainability of the model, which can be very useful in resource-constrained settings to optimize the computational workload.

关键词： Deep learning Analytical models Accuracy Sensitivity analysis Explainable AI Computational modeling Predictive models Linear discriminant analysis Complexity theory Information technology

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