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Microscopy Histopathology and Analytics, Microscopy 2024 - Part of Optica Biophotonics Congress: Biomedical Optics

作者： Gorecki, J. Cao, J. Redwood-Sawyyer, C. Lindeque, R. Polizzi, K. Kontoravdi, C. Dehghani, H. Rowlands, C. Department of Bioengineering Imperial College London SW7 2BX United Kingdom School of Computer Science University of Birmingham B15 2TT United Kingdom Department of Chemical Engineering Imperial College London SW7 2BX United Kingdom Centre for Synthetic Biology and Innovation Imperial College London SW7 2BX United Kingdom

Here we propose to use fluorescent tags which are genetically modified to respond to various cell stimuli, such as lactate concentration, in combination with diffuse optical tomography to reconstruct 3D maps of in-sit... 详细信息

ISBN: (纸本)9781957171340

关键词： Absorption coefficient Diffuse optical tomography Laser beams Optical properties Optical systems Three dimensional reconstruction

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Observation of velocity and axle spacings of moving truck using geophone sensor 11

Observation of velocity and axle spacings of moving truck us...

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11th International Conference on Structural Health Monitoring of Intelligent Infrastructure, SHMII 2022

作者： Maedeh, Mirzaei Douglas, Thomson Karim, Helmi Aftab, Mufti Department of Civil Engineering SIMTReC University of Manitoba WinnipegR3T2N2 Canada Department of Electrical and Computer Engineering University of Manitoba WinnipegMBR3T2N2 Canada Building and Construction Engineering Department Arab Academy for Science Technology and Maritime Transport Alexandria Egypt

Heavy truckloads on a bridge cause vibration, which leads to bridge decks cracking and steel girders to fatigue. Understanding the Gross Vehicle Weight (GVW of a truck) and Axle weights determines the strength and the load-carrying capacity of the bridge decks and girders. The GVW is determined using the bridge Weigh in Motion (BWIM) system. This paper explores a new methodology to determine if geophones can be used to estimate the truck velocity, number of axles and axle spacings, and axle weights. Based on the test data set, the geophone could accurately determine the truck velocity and axle spacing. The truck, velocity, axle spacing and GVW will define the complete configuration of the truck. © 2022 International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII. All rights reserved.

关键词： Bridge decks

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Information-Theoretic State Variable Selection for Reinforcement Learning

arXiv

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arXiv 2024年

作者： Westphal, Charles Hailes, Stephen Musolesi, Mirco Department of Computer Science University College London Gower Street LondonWC1E 6BT United Kingdom Department of Computer Science and Engineering University of Bologna Viale del Risorgimento 2 Bologna40136 Italy

Identifying the most suitable variables to represent the state is a fundamental challenge in Reinforcement Learning (RL). These variables must efficiently capture the information necessary for making optimal decisions. In order to address this problem, in this paper, we introduce the Transfer Entropy Redundancy Criterion (TERC), an information-theoretic criterion, which determines if there is entropy transferred from state variables to actions during training. We define an algorithm based on TERC that provably excludes variables from the state that have no effect on the final performance of the agent, resulting in more sample efficient learning. Experimental results show that this speed-up is present across three different algorithm classes (represented by tabular Q-learning, Actor-Critic, and Proximal Policy Optimization (PPO)) in a variety of environments. Furthermore, to highlight the differences between the proposed methodology and the current state-of-the-art feature selection approaches, we present a series of controlled experiments on synthetic data, before generalizing to real-world decision-making tasks. We also introduce a representation of the problem that compactly captures the transfer of information from state variables to actions as Bayesian networks. © 2024, CC BY-NC-ND.

关键词： Reinforcement learning

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Tracking learners' knowledge and skills development 17

Tracking learners' knowledge and skills development

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Doctoral Consortium of the 17th European Conference on Technology Enhanced Learning, DCECTEL 2022

作者： Zanellati, Andrea Gabbrielli, Maurizio Levrini, Olivia Department of Computer Science and Engineering University of Bologna Mura Anteo Zamboni 7 Bologna Italy Department of Physics and Astronomy University of Bologna Viale Berti Pichat 6/2 Bologna Italy

This Ph.D. research proposal aims to investigate how exploiting educational data to track the learners' development of knowledge and skills, thus embedding this information in automated tools designed to enhance teaching and learning. The encoding of learners' knowledge and skills is a crucial issue which can be exploited in addressing several tasks, such as underachievement prediction and personalized learning. However, some challenges characterized how to design the encoding and include it in automated tools: dealing with several formats of data (among which also text, video, images, and audio recording), tackling the strong dependence of educational data from the context where they are collected, and consider ethical issues related to explainability and fairness. With this position paper, we introduce the research questions which lead the project, a brief state of the art about techniques used to tackle the students' knowledge and skills encoding, the methodology and the expected results. Specifically, we aim to investigate which data can be used to fulfill our main purpose, test our encoding solutions in two case studies (underachievement prediction and knowledge tracing), and assess the contribution of our encoding to tackle them. As for the methodology, we want to explore strategies of Informed Machine Learning, that is to say incorporating an external knowledge source in the machine learning pipeline, which can improve the explainability and fairness of the models and handle the influence of the external context on the educational data. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Machine learning

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Data Poisoning Attacks on EEG Signal-based Risk Assessment Systems

arXiv

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arXiv 2023年

作者： Zhang, Zhibo Umar, Sani Damiani, Ernesto Al Hammadi, Ahmed Y. Yeun, Chan Yeob Yoon, Sangyoung C2PS Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi United Arab Emirates Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi United Arab Emirates

Industrial insider risk assessment using electroencephalogram (EEG) signals has consistently attracted a lot of research attention. However, EEG signal-based risk assessment systems, which could evaluate the emotional states of humans, have shown several vulnerabilities to data poison attacks. In this paper, from the attackers’ perspective, data poison attacks involving label-flipping occurring in the training stages of different machine learning models intrude on the EEG signal-based risk assessment systems using these machine learning models. This paper aims to propose two categories of label-flipping methods to attack different machine learning classifiers including Adaptive Boosting (AdaBoost), Multilayer Perceptron (MLP), Random Forest, and K-Nearest Neighbors (KNN) dedicated to the classification of 4 different human emotions using EEG signals. This aims to degrade the performance of the aforementioned machine learning models concerning the classification task. The experimental results show that the proposed data poison attacks are model-agnostically effective whereas different models have different resilience to the data poison attacks. © 2023, CC BY-SA.

关键词： Risk assessment

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Reputation-Based Federated Learning Defense to Mitigate Threats in EEG Signal Classification

Reputation-Based Federated Learning Defense to Mitigate Thre...

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International Conference on computer and Automation engineering, ICCAE

作者： Zhibo Zhang Pengfei Li Ahmed Y. Al Hammadi Fusen Guo Ernesto Damiani Chan Yeob Yeun Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi United Arab Emirates Department of Electrical Engineering and Computer Science C2PS Khalifa University Abu Dhabi United Arab Emirates School of Information Technology Deakin University Geelong Australia

ISBN: (数字)9798350370058

ISBN: (纸本)9798350370164

This paper presents a reputation-based threat mitigation framework that defends potential security threats in electroencephalogram (EEG) signal classification during model aggregation of Federated Learning. While EEG signal analysis has attracted attention because of the emergence of brain-computer interface (BCI) technology, it is difficult to create efficient learning models for EEG analysis because of the distributed nature of EEG data and related privacy and security concerns. To address these challenges, the proposed defending framework leverages the Federated Learning paradigm to preserve privacy by collaborative model training with localized data from dispersed sources and introduces a reputation-based mechanism to mitigate the influence of data poisoning attacks and identify compromised participants. To assess the efficiency of the proposed reputation-based federated learning defense framework, data poisoning attacks based on the risk level of training data derived by Explainable Artificial Intelligence (XAI) techniques are conducted on both publicly available EEG signal datasets and the self-established EEG signal dataset. Experimental results on the poisoned datasets show that the proposed defense methodology performs well in EEG signal classification while reducing the risks associated with security threats.

关键词： Training Data privacy Federated learning Explainable AI Pattern classification Training data Brain modeling

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Quantum noise spectroscopy as an incoherent imaging problem

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Physical Review A 2023年第1期107卷 012611-012611页

作者： Mankei Tsang Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 Queenstown 117583 Singapore and Department of Physics National University of Singapore 2 Science Drive 3 Queenstown 117551 Singapore

I point out the mathematical correspondence between an incoherent imaging model proposed by my group in the study of quantum-inspired superresolution [M. Tsang, R. Nair, and X.-M. Lu, Phys. Rev. X 6, 031033 (2016)] and a noise spectroscopy model also proposed by us [M. Tsang and R. Nair, Phys. Rev. A 86, 042115 (2012); S. Ng et al., Phys. Rev. A 93, 042121 (2016)]. Both can be regarded as random displacement models, where the probability measure for the random displacement depends on unknown parameters. The spatial-mode demultiplexing (SPADE) method proposed for imaging is analogous to the spectral photon counting method proposed by Ng et al. for optical phase noise spectroscopy: Both methods are discrete-variable measurements that are superior to direct displacement measurements (direct imaging or homodyne detection) and can achieve the respective quantum limits. Inspired by SPADE, I propose a modification of spectral photon counting when the input field is squeezed: The output field is simply unsqueezed before spectral photon counting. I show that this method is quantum optimal and far superior to homodyne detection for both parameter estimation and detection, thus solving the open problems in the work of Tsang and Nair and Ng et al.

关键词： Quantum fluctuations & noise Quantum metrology Quantum parameter estimation Gravitational wave detectors Super-resolution techniques

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A Unified Physics-Based Model for Analyzing Hysteresis in Organic Thin-Film Transistors

IEEE Journal on Flexible Electronics

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IEEE Journal on Flexible Electronics 2024年第11期3卷 502-507页

作者： Zaibi, Amer Abdelmoneam, Ahmed Mounir Golec, Patryk Estrada, Magali Cerdeira, Antonio Kadura, Lina Calvet, Laurie E. Iniguez, Benjamin Universitat Rovira I Virgili DEEEA Tarragona43007 Spain University of Surrey Department of Computer Science and Electronic Engineering GuildfordGU2 7XH United Kingdom CINVESTV Mexico City07360 Mexico Université Grenoble Alpes CEA-Liten Grenoble38000 France École Polytechnique Laboratoire de Physique et des Couches Minces CNRS Palaiseau91120 France

Hysteresis effects are often observed in voltage sweeps of organic thin-film transistors (OTFTs), resulting in threshold voltage shifts. While commonly associated with bias stress effects, the origins of hysteresis are diverse, with limited scientific studies providing a complete understanding of hysteresis in OFETs. Here, we use a physics-based model for organic thin film to investigate the hysteresis effects in OTFTS. Unlike many previous studies, our model allows for a more in-depth investigation of this phenomenon. Our method demonstrates a strong agreement between measurement and the model using parameter values obtained through the extraction process given by the unified modeling and extraction method (UMEM). In our quantitative analysis, we examined the hysteresis behavior in transistors of different channel lengths. For a transistor with a channel length of 200 μm, the threshold voltage shift (ΔVT) between backward and forward sweeps was 0.15 V. In contrast, for a transistor with a channel length of 5 μm, ΔVT was observed to be 0.3 V. In addition, we observed that the characteristics temperature ( T0 ) of the density of states (DOSs) increases, which impacts the trapping behavior. © 2024 IEEE. All rights reserved.

关键词： Organic field effect transistors

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Exploiting Machine Learning in Multiscale Modelling of Materials

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Journal of The Institution of Engineers (India): Series D 2023年第2期104卷 867-877页

作者： Anand, G. Ghosh, Swarnava Zhang, Liwei Anupam, Angesh Freeman, Colin L. Ortner, Christoph Eisenbach, Markus Kermode, James R. Department of Metallurgy and Materials Engineering Indian Institute of Engineering Science and Technology Shibpur Howrah711103 India National Centre for Computational Sciences Oak Ridge National Laboratory 1 Bethel Valley Rd Oak RidgeTN37831 United States Department of Mathematics University of British Columbia 1984 Mathematics Rd VancouverBCV6T1Z2 Canada Department of Computer Science Cardiff Metropolitan University Llandaff Campus CardiffCF5 2YB United Kingdom Department of Materials Science and Engineering University of Sheffield Mappin St SheffieldS1 3JD United Kingdom Warwick Centre for Predictive Modelling School of Engineering University of Warwick WarwickCV4 7AL United Kingdom

Recent developments in efficient machine learning algorithms have spurred significant interest in the materials community. The inherently complex and multiscale problems in Materials science and engineering pose a formidable challenge. The present scenario of machine learning research in Materials science has a clear lacunae, where efficient algorithms are being developed as a separate endeavour, while such methods are being applied as ‘black-box’ models by others. The present article aims to discuss pertinent issues related to the development and application of machine learning algorithms for various aspects of multiscale materials modelling. The authors present an overview of machine learning of equivariant properties, machine learning-aided statistical mechanics, the incorporation of ab initio approaches in multiscale models of materials processing and application of machine learning in uncertainty quantification. In addition to the above, the applicability of Bayesian approach for multiscale modelling will be discussed. Critical issues related to the multiscale materials modelling are also discussed. © 2022, The Institution of Engineers (India).

关键词： Statistical mechanics

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Flexible Link Adaptation in Fully-Decoupled RAN: A Machine Learning Approach

Flexible Link Adaptation in Fully-Decoupled RAN: A Machine L...

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Zou, Xianfeng Zhao, Jiwei Yu, Kai Chen, Jiacheng Zhou, Haibo Shen, Xuemin Sherman Nanjing University School of Electronic Science and Engineering Nanjing210023 China Peng Cheng Laboratory Department of Mathematics and Theories Shenzhen518000 China University of Waterloo Department of Electrical and Computer Engineering 200 University Avenue West WaterlooONN2L 3G1 Canada

ISBN: (纸本)9798350345384

Fully decoupled radio access network (FD-RAN), as an emerging radio access architecture through physical uplink-downlink decoupling and control-data decoupling, has potential in flexible spectrum utilization and network cooperation. However, real-time uplink feedback is challenging in FD-RAN due to the complete physical decoupling of control and data base stations. In this paper, we propose a flexible link adaptation mechanism that leverages outdated channel state information (CSI) to determine the appropriate Modulation and Coding Scheme (MCS) for the user in the FD-RAN downlink. Specifically, we first utilizes kernel recursive least squares to predict the CSI at the future moment. We then select the optimal modulation and coding scheme based on the predicted CSI and the frame error rate estimated by a neural network. Simulation results show that the proposed link adaptation mechanism has a significant throughput performance gain in various scenarios. © 2023 IEEE.

关键词： Radio access networks

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