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Between Generating Noise and Generating Images: Noise in the Correct Frequency Improves the Quality of Synthetic Histopathology Images for Digital Pathology

Between Generating Noise and Generating Images: Noise in the...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Nati Daniel Eliel Aknin Ariel Larey Yoni Peretz Guy Sela Yael Fisher Yonatan Savir Department of Physiology Biophysics and Systems Biology Faculty of Medicine Haifa Israel Faculty of Industrial Engineering Haifa Israel Faculty of Computer Science Haifa Israel Faculty of Electrical Engineering Haifa Israel Division of Pathology Rambam Health Care Campus Haifa Israel

Artificial intelligence and machine learning techniques have the promise to revolutionize the field of digital pathology. However, these models demand considerable amounts of data, while the availability of unbiased training data is limited. Synthetic images can augment existing datasets, to improve and validate AI algorithms. Yet, controlling the exact distribution of cellular features within them is still challenging. One of the solutions is harnessing conditional generative adversarial networks that take a semantic mask as an input rather than a random noise. Unlike other domains, outlining the exact cellular structure of tissues is hard, and most of the input masks depict regions of cell types. This is also the case for non-small cell lung cancer, the most common type of lung cancer. Deciding whether a patient would receive immunotherapy depends on quantifying regions of stained cells. However, using polygon-based masks introduce inherent artifacts within the synthetic images – due to the mismatch between the polygon size and the single-cell size. In this work, we show that introducing random single-pixel noise with the appropriate spatial frequency into a polygon semantic mask can dramatically improve the quality of the synthetic images. We used our platform to generate synthetic images of immunohistochemistry-treated lung biopsies. We test the quality of the images using a three-fold validation procedure. First, we show that adding the appropriate noise frequency yields 87% of the similarity metrics improvement that is obtained by adding the actual single-cell features. Second, we show that the synthetic images pass the Turing test. Finally, we show that adding these synthetic images to the train set improves AI performance in terms of PD-L1 semantic segmentation performances. Our work suggests a simple and powerful approach for generating synthetic data on demand to unbias limited datasets to improve the algorithms’ accuracy and validate their robustness.

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Ion irradiation induced crystalline disorder accelerates interfacial phonon conversion and reduces thermal boundary resistance

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Physical Review B 2024年第16期109卷 165421-165421页

作者： Thomas W. Pfeifer Henry Aller Eric R. Hoglund Ethan A. Scott John A. Tomko Habib Ahmad Alan Doolittle Ashutosh Giri Khalid Hattar Alan J. H. McGaughey Patrick E. Hopkins Department of Mechanical and Aerospace Engineering University of Virginia Charlottesville Virginia 22904 USA Department of Mechanical Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 USA Department of Materials Science and Engineering University of Virginia Charlottesville Virginia 22904 USA Sandia National Laboratories Albuquerque New Mexico 87185 USA School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta Georgia 30332 USA Center for Advanced Electronics & Photovoltaic Engineering IIUI Islamabad Department of Mechanical Industrial and Systems Engineering University of Rhode Island Kingston Rhode Island 02881 USA Department of Nuclear Engineering University of Tennessee Knoxville Tennessee 37996 USA Department of Physics University of Virginia Charlottesville Virginia 22904 USA

Traditional understanding of the thermal boundary resistance (TBR) across solid-solid interfaces posits that the vibrational densities of states overlap between materials dictates interfacial energy transport, with phonon scattering occurring at the interface. Using atomistic simulations, we show a mechanism for control of TBR; point defects near an interface can lead to both short- and midrange disorder, accelerating the conversion of vibrational energy between bulk and interfacial modes, ultimately reducing the TBR. We experimentally demonstrate this reduction through ion irradiation of gallium nitride and subsequently measuring the TBR across Al/GaN interfaces.

关键词： Crystal defects Defects Irradiation effects Lattice dynamics Phonons Thermal boundary conductance Vibrational states Interfaces Semiconductor compounds Transducers Ion implantation Molecular dynamics Thermoreflectance

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Eco-driving Incentive Mechanisms for Mitigating Emissions in Urban Transportation

arXiv

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arXiv 2024年

作者： Umar Niazi, M.B. Cho, Jung-Hoon Dahleh, Munther A. Dong, Roy Wu, Cathy Laboratory for Information and Decision Systems Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Division of Decision and Control Systems Digital Futures KTH Royal Institute of Technology StockholmSE-100 44 Sweden Laboratory for Information and Decision Systems Department of Civil and Environmental Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Department of Industrial and Enterprise Systems Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States

This paper proposes incentive mechanisms that promote eco-driving in transportation networks with the overarching objective of minimizing emissions. The transportation system operator provides the drivers with energy-efficient driving guidance throughout their trips, and their eco-driving levels are measured by how closely they follow this guidance via vehicle telematics. Drivers choose their eco-driving levels to optimize a combination of their travel times and their emissions. To obtain optimal budget allocation and recommendations for the incentive mechanism, the system operator gathers drivers’ preferences, or types, to assess each driver’s trip urgency and natural willingness to eco-drive. In a setting where drivers truthfully report their types, we introduce the first-best incentive mechanism and show that the obedience condition holds (i.e., drivers find it optimal to comply with the system operator’s recommendations) when the recommended eco-driving profile constitutes a Nash equilibrium. Moreover, in a setting where drivers can strategically report their types, we introduce the second-best incentive mechanism and show that the proposed mechanism is incentive-compatible (i.e., drivers find it optimal to be truthful). Under this mechanism, we also show that all equilibrium outcomes are at least as good as the recommended eco-driving profile in terms of the system operator’s objective. Overall, this work offers a framework for designing eco-driving incentive mechanisms while considering both the strategic behavior of individual drivers and the network effects of collective decision-making. © 2024, CC BY.

关键词： Budget control

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Statistical Mechanics of Dynamical System Identification

arXiv

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arXiv 2024年

作者： Klishin, Andrei A. Bakarji, Joseph Kutz, J. Nathan Manohar, Krithika AI Institute in Dynamic Systems University of Washington SeattleWA98195 United States Department of Mechanical Engineering University of Washington SeattleWA98195 United States Department of Mechanical Engineering Artificial Intelligence Hub American University of Beirut Beirut1107 2020 Lebanon Departments of Applied Mathematics and Electrical and Computer Engineering University of Washington SeattleWA98195 United States

Recovering dynamical equations from observed noisy data is the central challenge of system identification. We develop a statistical mechanical approach to analyze sparse equation discovery algorithms, which typically balance data fit and parsimony through a trial-and-error selection of hyperparameters. In this framework, statistical mechanics offers tools to analyze the interplay between complexity and fitness, in analogy to that done between entropy and energy. To establish this analogy, we define the optimization procedure as a two-level Bayesian inference problem that separates variable selection from coefficient values and enables the computation of the posterior parameter distribution in closed form. A key advantage of employing statistical mechanical concepts, such as free energy and the partition function, is in the quantification of uncertainty, especially in in the low-data limit;frequently encountered in real-world applications. As the data volume increases, our approach mirrors the thermodynamic limit, leading to distinct sparsity- and noise-induced phase transitions that delineate correct from incorrect identification. This perspective of sparse equation discovery, is versatile and can be adapted to various other equation discovery algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Statistical mechanics

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Blockchain and artificial intelligence enabled autonomous smart manufacturing consortium

Blockchain and artificial intelligence enabled autonomous sm...

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IISE Annual Conference and Expo 2021

作者： Hasan, Mahmud Mullick, Tahsin Edward P. Fitts Department of Industrial and Systems Engineering North Carolina State University RaleighNC United States Charles L. Brown Department of Electrical and Computer Engineering University of Virginia CharlottesvilleVA United States

ISBN: (纸本)9781713838470

The emergence of blockchain technology has revolutionized the finance industry through decentralized, cryptographically secure transactions. Similarly, the evolution of artificial intelligence has drastically transformed a plethora of activities in human lives. In this paper, the authors present the application of blockchains and artificial intelligence into the construction of a server based autonomous cloud manufacturing consortium. Appropriate data models required to automate manufacturing contracts and negotiations through blockchain smart contracts is proposed. The proposed models are subsequently deployed on a consortium of actual five axis manufacturing CNC machine nodes typically found on shop floors. Through the seamless inter-communication of the nodes via communication protocols, the nodes are allowed to autonomously interact with each other and clients, negotiate prices and initiate manufacturing protocols through computer coded logic in blockchain smart contracts. Additionally, through the application of machine learning models that can predict seasonal prices of products based on historical time series data of parts made on the machines, the machines are allowed to autonomously decide on price and profit margins. The upshot of the investigation is the design and implementation of a server based cyber-manufacturing infrastructure that exploits blockchains and artificial intelligence to make intelligent decisions. The eventual outcome is to propose a system that can act as a guideline to automate high data throughput scenarios in typical mass cloud manufacturing platforms. © 2021 IISE Annual Conference and Expo 2021. All rights reserved.

关键词： Blockchain

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Explainable Machine Learning-Based SpO2 Estimation Using Photoplethysmograms Measured from a Neck Wearable

SSRN

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SSRN 2023年

作者： Zhong, Yuhao Jatav, Ashish Afrin, Kahkashan Shivaram, Tejaswini Bukkapatnam, Satish T.S. Wm Michael Barnes’64 Department of Industrial & Systems Engineering Texas A&M University College StationTX77840 United States Department of Electrical & Computer Engineering Texas A&M University College StationTX77840 United States

Conventional transmittance-based photoplethysmogram (PPG) sensors for measuring blood oxygen saturation (SpO2) are limited by standard measuring sites like fingers. Reflectance-based sensors provide more flexible options of measuring sites, but they are mostly limited by accuracy, especially when applied to different subjects, due to the diverse human characteristics (skin colors, hair density, etc.) and usage conditions of different sensor settings. This study addresses the estimation of SpO2 at non-standard measuring sites employing reflectance-based sensors. It proposes an automated construction of subject inclusion-exclusion criteria for SpO2 measuring devices, using a combination of unsupervised clustering, supervised regression, and model explanations. This is perhaps the first adaptation of SHAP to explain the clusters gleaned from unsupervised learning methods. As a case study of wellness application, a patent pending pillow-based wearable device was developed to collect reflectance PPGs from both brachiocephalic and carotid arteries around the neck. The experiment was conducted on 33 subjects, each under totally 80 different sensor settings. The proposed approach tackled the human and the device variations as well as the heterogeneous mapping between signals and SpO2 values. It identified the effective device settings and the characteristics of their applicable subject groups (i.e., subject inclusion-exclusion criteria) that resulted in a maximum absolute error (MaAE) [[EQUATION]] 1.7% and a root mean squared error (RMSE) [[EQUATION]] 0.9% for estimating SpO2 over the range of 90% to 100%. Thus, the approach works to detect the propensity of SpO2 falling into an unhealthy level ( © 2023, The Authors. All rights reserved.

关键词： Reflection

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Advancements in UAV-based Integrated Sensing and Communication: A Comprehensive Survey

arXiv

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arXiv 2025年

作者： Ahmed, Manzoor Nasir, Ali Arshad Masood, Mudassir Memon, Kamran Ali Qureshi, Khurram Karim Khan, Feroz Khan, Wali Ullah Xu, Fang Han, Zhu School of Computer and Information Science Institute for AI Industrial Technology Research Hubei Engineering University Xiaogan432000 China Interdisciplinary Research Center for Communication Systems and Sensing King Fahd University of Petroleum and Minerals Dhahran31261 Saudi Arabia Department of Electrical Engineering King Fahd University of Petroleum and Minerals Dhahran31261 Saudi Arabia School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing China University of Luxembourg Luxembourg City1855 Luxembourg Department of Electrical and Computer Engineering The University of Houston HoustonTX77004 United States Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of

Unmanned aerial vehicle (UAV)-based integrated sensing and communication (ISAC) systems are poised to revolutionize next-generation wireless networks by enabling simultaneous sensing and communication (S&C). This survey comprehensively reviews UAV-ISAC systems, highlighting foundational concepts, key advancements, and future research directions. We explore recent advancements in UAV-based ISAC systems from various perspectives and objectives, including advanced channel estimation (CE), beam tracking, and system throughput optimization under joint sensing and communication S&C constraints. Additionally, we examine weighted sum rate (WSR) and sensing trade-offs, delay and age of information (AoI) minimization, energy efficiency (EE), and security enhancement. These applications highlight the potential of UAV-based ISAC systems to improve spectrum utilization, enhance communication reliability, reduce latency, and optimize energy consumption across diverse domains, including smart cities, disaster relief, and defense operations. The survey also features summary tables for comparative analysis of existing methodologies, emphasizing performance, limitations, and effectiveness in addressing various challenges. By synthesizing recent advancements and identifying open research challenges, this survey aims to be a valuable resource for developing efficient, adaptive, and secure UAV-based ISAC systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Group-equivariant autoencoder for identifying spontaneously broken symmetries

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Physical Review E 2023年第5期107卷 054104-054104页

作者： Devanshu Agrawal Adrian Del Maestro Steven Johnston James Ostrowski Department of Industrial and Systems Engineering University of Tennessee Knoxville Tennessee 37996 USA Department of Physics and Astronomy University of Tennessee Knoxville Tennessee 37996 USA Min H. Kao Department of Electrical Engineering and Computer Science University of Tennessee Knoxville Tennessee 37996 USA Institute for Advanced Materials and Manufacturing University of Tennessee Knoxville Tennessee 37996 USA

We introduce the group-equivariant autoencoder (GE autoencoder), a deep neural network (DNN) method that locates phase boundaries by determining which symmetries of the Hamiltonian have spontaneously broken at each temperature. We use group theory to deduce which symmetries of the system remain intact in all phases, and then use this information to constrain the parameters of the GE autoencoder such that the encoder learns an order parameter invariant to these “never-broken” symmetries. This procedure produces a dramatic reduction in the number of free parameters such that the GE-autoencoder size is independent of the system size. We include symmetry regularization terms in the loss function of the GE autoencoder so that the learned order parameter is also equivariant to the remaining symmetries of the system. By examining the group representation by which the learned order parameter transforms, we are then able to extract information about the associated spontaneous symmetry breaking. We test the GE autoencoder on the 2D classical ferromagnetic and antiferromagnetic Ising models, finding that the GE autoencoder (1) accurately determines which symmetries have spontaneously broken at each temperature; (2) estimates the critical temperature in the thermodynamic limit with greater accuracy, robustness, and time efficiency than a symmetry-agnostic baseline autoencoder; and (3) detects the presence of an external symmetry-breaking magnetic field with greater sensitivity than the baseline method. Finally, we describe various key implementation details, including a quadratic-programming-based method for extracting the critical temperature estimate from trained autoencoders and calculations of the DNN initialization and learning rate settings required for fair model comparisons.

关键词： Phase transitions Structural order parameter Data analysis

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Design and Performance Analysis of a Chemically-Etched Flexible NFC Tag Antenna

arXiv

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arXiv 2022年

作者： Rahman, Muhammad Enayetur Abdelatti, Marwan Sodhi, Manbir Singh Mankodiya, Kunal Wearable Biosensing Lab Dept of Electrical Computer and Biomedical Engineering University of Rhode Island United States Dept of Mechanical Industrial and Systems Engineering University of Rhode Island United States

Near Field Communication (NFC) is a perfect example of ubiquitous computing that is secured, short-ranged, low-powered contactless communication. High demand is predicted for NFC, especially with wearables, and the variety of applications may require that this technology be fabricated onto different materials. In this research, we first designed flexible NFC antennas based on mathematical models. A simulation of the NFC coil inductance was performed and verified using the predictive models. Later, two flexible NFC antennas, including 160x80 mm2 (rectangular) and 80x80 mm2 (square) were fabricated through a chemical etching process and verified at 13.56 MHz frequency. Two experiments 1) Read range detection and 2) User experience study on 22 participants validate longer read range and shorter connection time of our fabricated flexible NFC tags. Copyright © 2022, The Authors. All rights reserved.

关键词： Near field communication

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Bayesian Optimization Framework for Efficient Fleet Design in Autonomous Multi-Robot Exploration

arXiv

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arXiv 2024年

作者： Concha, David Molina Li, Jiping Yin, Haoran Park, Kyeonghyeon Lee, Hyun-Rok Lee, Taesik Sirohi, Dhruv Lee, Chi-Guhn The Dynamic Optimization and Reinforcement Learning Laboratory The Department of Mechanical & Industrial Engineering University of Toronto TorontoONM5S3G8 Canada The Department of Electrical & Computer Engineering University of Toronto TorontoONM5S3G8 Canada The Division of Engineering Science The Faculty of Applied Science and Engineering University of Toronto TorontoONM5S3G8 Canada The Complex System Design Laboratory The Department of Industrial and Systems Engineering Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of The Department of Industrial Engineering Inha University Incheon22212 Korea Republic of

This study addresses the challenge of fleet design optimization in the context of heterogeneous multi-robot fleets, aiming to obtain feasible designs that balance performance and costs. In the domain of autonomous multi-robot exploration, reinforcement learning agents play a central role, offering adaptability to complex terrains and facilitating collaboration among robots. However, modifying the fleet composition results in changes in the learned behavior, and training multi-robot systems using multi-agent reinforcement learning is expensive. Therefore, an exhaustive evaluation of each potential fleet design is infeasible. To tackle these hurdles, we introduce Bayesian Optimization for Fleet Design (BOFD), a framework leveraging multiobjective Bayesian Optimization to explore fleets on the Pareto front of performance and cost while accounting for uncertainty in the design space. Moreover, we establish a sub-linear bound for cumulative regret, supporting BOFD’s robustness and efficacy. Extensive benchmark experiments in synthetic and simulated environments demonstrate the superiority of our framework over state-of-the-art methods, achieving efficient fleet designs with minimal fleet evaluations. Copyright © 2024, The Authors. All rights reserved.

关键词： Multipurpose robots

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