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Ornstein-Uhlenbeck process and generalizations: Particle dynamics under comb constraints and stochastic resetting

Physical Review E 2023年第5期107卷 054129-054129页

作者： Pece Trajanovski Petar Jolakoski Kiril Zelenkovski Alexander Iomin Ljupco Kocarev Trifce Sandev Research Center for Computer Science and Information Technologies Macedonian Academy of Sciences and Arts Bul. Krste Misirkov 2 1000 Skopje Macedonia Department of Physics Technion Haifa 32000 Israel Max Planck Institute for the Physics of Complex Systems D-01187 Dresden Germany Faculty of Computer Science and Engineering Ss. Cyril and Methodius University PO Box 393 1000 Skopje Macedonia Institute of Physics & Astronomy University of Potsdam D-14776 Potsdam-Golm Germany Institute of Physics Faculty of Natural Sciences and Mathematics Ss. Cyril and Methodius University Arhimedova 3 1000 Skopje Macedonia

The Ornstein-Uhlenbeck process is interpreted as Brownian motion in a harmonic potential. This Gaussian Markov process has a bounded variance and admits a stationary probability distribution, in contrast to the standard Brownian motion. It also tends to a drift towards its mean function, and such a process is called mean reverting. Two examples of the generalized Ornstein-Uhlenbeck process are considered. In the first one, we study the Ornstein-Uhlenbeck process on a comb model, as an example of the harmonically bounded random motion in the topologically constrained geometry. The main dynamical characteristics (as the first and the second moments) and the probability density function are studied in the framework of both the Langevin stochastic equation and the Fokker-Planck equation. The second example is devoted to the study of the effects of stochastic resetting on the Ornstein-Uhlenbeck process, including stochastic resetting in the comb geometry. Here the nonequilibrium stationary state is the main question in task, where the two divergent forces, namely, the resetting and the drift towards the mean, lead to compelling results in the cases of both the Ornstein-Uhlenbeck process with resetting and its generalization on the two-dimensional comb structure.

关键词： Brownian motion Diffusion Fluctuations & noise Noise Random walks Stochastic processes

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Deep Predictive Coding with Bi-directional Propagation for Classification and Reconstruction

arXiv

引用

arXiv 2023年

作者： Qiu, Senhui Bhattacharyya, Saugat Coyle, Damien Dora, Shirin The Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University LondonderryBT48 7JL United Kingdom The Bath Institute for the Augmented Human University of Bath BathBA2 7AY United Kingdom The Department of Computer Science Loughborough University LoughboroughLE11 3TU United Kingdom

This paper presents a new learning algorithm, termed Deep Bi-directional Predictive Coding (DBPC) that allows developing networks to simultaneously perform classification and reconstruction tasks using the same weights. Predictive Coding (PC) has emerged as a prominent theory underlying information processing in the brain. The general concept for learning in PC is that each layer learns to predict the activities of neurons in the previous layer which enables local computation of error and in-parallel learning across layers. In this paper, we extend existing PC approaches by developing a network which supports both feedforward and feedback propagation of information. Each layer in the networks trained using DBPC learn to predict the activities of neurons in the previous and next layer which allows the network to simultaneously perform classification and reconstruction tasks using feedforward and feedback propagation, respectively. DBPC also relies on locally available information for learning, thus enabling in-parallel learning across all layers in the network. The proposed approach has been developed for training both, fully connected networks and convolutional neural networks. The performance of DBPC has been evaluated on both, classification and reconstruction tasks using the MNIST and FashionMNIST datasets. The classification and the reconstruction performance of networks trained using DBPC is similar to other approaches used for comparison but DBPC uses a significantly smaller network. Further, the significant benefit of DBPC is its ability to achieve this performance using locally available information and in-parallel learning mechanisms which results in an efficient training protocol. This results clearly indicate that DBPC is a much more efficient approach for developing networks that can simultaneously perform both classification and reconstruction. © 2023, CC BY.

关键词： Classification (of information)

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Evaluation of Robustness Metrics for Defense of Machine Learning systems

Evaluation of Robustness Metrics for Defense of Machine Lear...

引用

International Conference on Military Communications and Information systems (ICMCIS)

作者： J. DeMarchi R. Rijken J. Melrose B. Madahar G. Fumera F. Roli E. Ledda M. Aktaş F. Kurth P. Baggenstoss B. Pelzer L. Kanestad Collaborative Engineering Systems & Aerospace Systems Information Supremacy Royal Netherlands Aerospace Centre NLR Amsterdam NLD Cyber & Information Systems Division Defence Science and Technology Laboratory Portondown GBR Department of Electrical and Electronic Engineering University of Cagliari Cagliari ITA Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Genoa ITA Department of Computer Control and Management Engineering Sapienza University of Rome Rome ITA Defence Systems Technologies Division ASELSAN Ankara TUR Fraunhofer Institute for Communication Information Processing and Ergonomics Bonn DEU Swedish Defence Research Agency Cyber Defence and C2 Technology Division Stockholm SWE

In this paper we explore some of the potential applications of robustness criteria for machine learning (ML) systems by way of tangible “demonstrator” scenarios. In each demonstrator, ML robustness metrics are applied to real-world scenarios with military relevance, indicating how they might be used to help detect and handle possible adversarial attacks on ML systems. We conclude by sketching promising future avenues of research in order to: (1) help establish useful verification methodologies to facilitate ML robustness compliance assessment; (2) support development of ML accountability mechanisms; and (3) reliably detect, repel, and mitigate adversarial attack.

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A Generalizable 3D Diffusion Framework for Low-Dose and Few-View Cardiac SPECT

arXiv

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arXiv 2024年

作者： Xie, Huidong Gan, Weijie Ji, Wei Chen, Xiongchao Alashi, Alaa Thorn, Stephanie L. Zhou, Bo Liu, Qiong Xia, Menghua Guo, Xueqi Liu, Yi-Hwa An, Hongyu Kamilov, Ulugbek S. Wang, Ge Sinusas, Albert J. Liu, Chi 1Department of Biomedical Engineering Yale University USA. 2Department of Computer Science Washington University in St. Louis USA. 3Department of Radiology and Biomedical Imaging Yale University USA. 5Department of Radiology Washington University in St. Louis USA. 6Department of Electrical & Systems Engineering Washington University in St. Louis USA. 9Department of Biomedical Engineering Rensselaer Polytechnic Institute USA.

Myocardial perfusion imaging using SPECT is widely utilized to diagnose coronary artery diseases, but image quality can be negatively affected in low-dose and few-view acquisition settings. Although various deep learning methods have been introduced to improve image quality from low-dose or few-view SPECT data, previous approaches often fail to generalize across different acquisition settings, limiting their applicability in reality. This work introduced DiffSPECT-3D, a diffusion framework for 3D cardiac SPECT imaging that effectively adapts to different acquisition settings without requiring further network re-training or fine-tuning. Using both image and projection data, a consistency strategy is proposed to ensure that diffusion sampling at each step aligns with the low-dose/few-view projection measurements, the image data, and the scanner geometry, thus enabling generalization to different low-dose/few-view settings. Incorporating anatomical spatial information from CT and total variation constraint, we proposed a 2.5D conditional strategy to allow the DiffSPECT-3D to observe 3D contextual information from the entire image volume, addressing the 3D memory/computational issues in diffusion model. We extensively evaluated the proposed method on 1,325 clinical 99mTc tetrofosmin stress/rest studies from 795 patients. Each study was reconstructed into 5 different low-count levels and 5 different projection few-view levels for model evaluations, ranging from 1% to 50% and from 1 view to 9 view, respectively. Validated against cardiac catheterization results and diagnostic comments from nuclear cardiologists, the presented results show the potential to achieve low-dose and few-view SPECT imaging without compromising clinical performance. Additionally, DiffSPECT-3D could be directly applied to full-dose SPECT images to further improve image quality, especially in a low-dose stress-first cardiac SPECT imaging protocol. © 2024, CC BY.

关键词： Heart

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Risk-Averse Self-Scheduling of Storage in Decentralized Markets

arXiv

引用

arXiv 2022年

作者： Yurdakul, Ogun Billimoria, Farhad Energy Systems and Infrastructure Analysis Division Argonne National Laboratory LemontIL60439 United States Department of Electrical Engineering and Computer Science Technical University of Berlin Berlin10623 Germany Department of Engineering Science University of Oxford OxfordOX1 2JD United Kingdom

Storage is expected to be a critical source of firming in low-carbon grids. A common concern raised from ex-post assessments is that storage resources can fail to respond to strong price signals during times of scarcity. While commonly attributed to forecast error or failures in operations, we posit that this behavior can be explained from the perspective of risk-averse scheduling. Using a stochastic self-scheduling framework and real-world data harvested from the Australian National Electricity Market, we demonstrate that risk-averse storage resources tend to have a myopic operational perspective, that is, they typically engage in near-term price arbitrage and chase only few extreme price spikes and troughs, thus remaining idle in several time periods with markedly high and low prices. This has important policy implications given the non-transparency of unit risk aversion and apparent risk in intervention decision-making. © 2022, CC BY.

关键词： Digital storage

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DALL-M: Context-aware clinical data augmentation with large language models

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computers in Biology and Medicine 2025年 190卷 110022-110022页

作者： Hsieh, Chihcheng Moreira, Catarina Nobre, Isabel Blanco Sousa, Sandra Costa Ouyang, Chun Brereton, Margot Jorge, Joaquim Nascimento, Jacinto C. School of Information Systems Queensland University of Technology 2 George Street BrisbaneQLD4000 Australia School of Computer Science Queensland University of Technology 2 George Street BrisbaneQLD4000 Australia Data Science Institute University of Technology Sydney 61 Broadway Ultimo SydneyNSW2007 Australia Imagiology Department Grupo Lusiadas Rua Abílio Mendes 12 Lisboa1500-458 Portugal Instituto Superior Tecnico Universidade de Lisboa Av. Rovisco Pais 1 Lisboa1049-001 Portugal INESC-ID Av. Rovisco Pais 1 Lisboa1049-001 Portugal

X-ray images are vital in medical diagnostics, but their effectiveness is limited without clinical context. Radiologists often find chest X-rays insufficient for diagnosing underlying diseases, necessitating the integration of structured clinical features with radiology reports. To address this, we introduce DALL-M, a novel framework that enhances clinical datasets by generating contextual synthetic data. DALL-M augments structured patient data, including vital signs (e.g., heart rate, oxygen saturation), radiology findings (e.g., lesion presence), and demographic factors. It integrates this tabular data with contextual knowledge extracted from radiology reports and domain-specific resources (e.g., Radiopaedia, Wikipedia), ensuring clinical consistency and reliability. DALL-M follows a three-phase process: (i) clinical context storage, (ii) expert query generation, and (iii) context-aware feature augmentation. Using large language models (LLMs), it generates both contextual synthetic values for existing clinical features and entirely new, clinically relevant features. Applied to 799 cases from the MIMIC-IV dataset, DALL-M expanded the original 9 clinical features to 91. Empirical validation with machine learning models – including Decision Trees, Random Forests, XGBoost, and TabNET – demonstrated a 16.5% improvement in F1 score and a 25% increase in Precision and Recall. DALL-M bridges an important gap in clinical data augmentation by preserving data integrity while enhancing predictive modeling in healthcare. Our results show that integrating LLM-generated synthetic features significantly improves model performance, making DALL-M a scalable and practical approach for AI-driven medical diagnostics. © 2025 The Authors

关键词： Radiology

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Assessing quality of decision reducts 24

Assessing quality of decision reducts

引用

24th KES International Conference on Knowledge-Based and Intelligent Information and Engineering systems, KES 2020

作者： Stanczyk, Urszula Zielosko, Beata Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A Gliwice44-100 Poland Institute of Computer Science University of Silesia in Katowice Bedzinska 39 Sosnowiec41-200 Poland

The paper presents research focused on decision reducts, a feature reduction mechanism inherent to rough sets theory. As a reduct enables to protect the discriminative properties of attributes with respect to described concepts, from the point of data representation, a reduct length is considered to be the most important measure of its quality. However, such approach is insufficient while taking into account the performance of a reduct-based rule classifier applied to test samples. When many reducts of the same length are available, they can lead to vastly different predictions. The paper provides a description for the proposed procedure for iterative reduct generation, which results in decrease of diversity in the observed levels of accuracy, supporting reduct selection. The procedure was applied for binary classification with balanced classes, for the stylometric task of authorship attribution. © 2020 The Authors. Published by Elsevier B.V.

关键词： Rough set theory

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Quantification of Honey Adulteration Using a Planar Microstrip Metamaterial-Motivated Sensor and Software-Defined Radio

引用

IEEE Sensors Journal 2025年

作者： Hasar, Ugur C. Hasar, Hafize Kaya, Yunus Ozturk, Hamdullah Korkmaz, Huseyin Kosunalp, Selahattin Iliev, Teodor Islam, Mohammad Tariqul Nayyeri, Vahid Ramahi, Omar Mustafa Gaziantep University Department of Electrical and Electronics Engineering Gaziantep27310 Turkey Directorate of Provincial Agriculture and Forestry Ministry of Agriculture and Forestry of the Republic of Turkey Gaziantep Turkey Bayburt University Department of Electronics and Automation Bayburt69010 Turkey Gaziantep Islam Science and Technology University Department of Electrical and Electronics Engineering Gaziantep27010 Turkey Bandırma Onyedi Eylül University Department of Computer Technologies Bandırma10200 Turkey University of Ruse "Angel Kanchev" Department of Telecommunication Ruse7017 Bulgaria Faculty of Engineering and Built Environment Department of Electrical Electronic and Systems Engineering Bangi43600 Malaysia Iran University of Science and Technology School of Advanced Technologies Tehran*** Iran University of Waterloo Department of Electrical and Computer Engineering ON WaterlooN2L 3G1 Canada

This work presents the application of a planar microstrip metamaterial (MM)- motivated sensor in the form of a split-ring resonator (SRR) for the detection and quantification of water adulteration in honey samples. The proposed sensor utilizes inexpensive software-defined radio (SDR) measurements, offering a cost-effective solution. Unlike previous MM sensors employing SRR configuration, our proposed MM-motivated sensor is integrated with a microstrip feedline and an additional vertical bar, enhancing its sensing capability. To enable accurate measurements, a simple calibration procedure based on baseline normalization is implemented, allowing for amplitude-only transmission measurements (|S21|) using the SDR. A special sample holder was designed to increase repeatability of measurements. We separately employ two post-processing techniques, namely the rolling average (RA) approach and the Savitzky-Golay (SG) filter, to effectively eliminate ripples in the measured |S21| data obtained from the SDR. For validation and evaluation of the proposed sensor and post-processing techniques, measurements of flower honey samples with water adulteration levels (δ) up to 10% (mass-to-mass basis) were also performed using a vector network analyzer. The resonance frequency of |S21| is utilized as the basis for analysis. We establish a metric function (linear function) that correlates the shift in resonance frequency with the δ. It is observed that this function fits to measured resonance frequency values with an R2 value greater than 0.96 for both post-processing techniques with or without the sample holder. By inverting this function, we can predict δ with considerable accuracy for a given resonance frequency by using another linear function. It is observed that percentage variations between predicted and measured δ values are between 1.54% and 6.46% for testing samples with δ = 5% and δ = 7%. © 2001-2012 IEEE.

关键词： Adulteration honey metamaterials microwaves planar structures rolling average Savitzky-Golay filter sensors water

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Integration of Mixture of Experts and Multimodal Generative AI in Internet of Vehicles: A Survey

arXiv

引用

arXiv 2024年

作者： Xu, Minrui Niyato, Dusit Kang, Jiawen Xiong, Zehui Jamalipour, Abbas Fang, Yuguang Kim, Dong In Shen, Xuemin The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The School of Automation Guangdong University of Technology Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore The School of Electrical and Information Engineering University of Sydney SydneyNSW2006 Australia Department of Computer Science City University of Hong Kong Kowloon Hong Kong The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of The Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

Generative AI (GAI) can enhance the cognitive, reasoning, and planning capabilities of intelligent modules in the Internet of Vehicles (IoV) by synthesizing augmented datasets, completing sensor data, and making sequential decisions. In addition, the mixture of experts (MoE) can enable the distributed and collaborative execution of AI models without performance degradation between connected vehicles. In this survey, we explore the integration of MoE and GAI to enable Artificial General Intelligence in IoV, which can enable the realization of full autonomy for IoV with minimal human supervision and applicability in a wide range of mobility scenarios, including environment monitoring, traffic management, and autonomous driving. In particular, we present the fundamentals of GAI, MoE, and their interplay applications in IoV. Furthermore, we discuss the potential integration of MoE and GAI in IoV, including distributed perception and monitoring, collaborative decision-making and planning, and generative modeling and simulation. Finally, we present several potential research directions for facilitating the integration. © 2024, CC BY.

关键词： Integration

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Toward Mixture-of-Experts Enabled Trustworthy Semantic Communication for 6G Networks

arXiv

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arXiv 2024年

作者： He, Jiayi Luo, Xiaofeng Kang, Jiawen Du, Hongyang Xiong, Zehui Chen, Ci Niyato, Dusit Shen, Xuemin School of Automation Guangdong University of Technology Guangzhou510006 China The College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore The Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

Semantic Communication (SemCom) plays a pivotal role in 6G networks, offering a viable solution for future efficient communication. Deep Learning (DL)-based semantic codecs further enhance this efficiency. However, the vulnerability of DL models to security threats, such as adversarial attacks, poses significant challenges for practical applications of SemCom systems. These vulnerabilities enable attackers to tamper with messages and eavesdrop on private information, especially in wireless communication scenarios. Although existing defenses attempt to address specific threats, they often fail to simultaneously handle multiple heterogeneous attacks. To overcome this limitation, we introduce a novel Mixture-of-Experts (MoE)based SemCom system. This system comprises a gating network and multiple experts, each specializing in different security challenges. The gating network adaptively selects suitable experts to counter heterogeneous attacks based on user-defined security requirements. Multiple experts collaborate to accomplish semantic communication tasks while meeting the security requirements of users. A case study in vehicular networks demonstrates the efficacy of the MoE-based SemCom system. Simulation results show that the proposed MoE-based SemCom system effectively mitigates concurrent heterogeneous attacks, with minimal impact on downstream task accuracy. © 2024, CC BY.

关键词： Latent semantic analysis

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