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Design, Automation and Test in Europe Conference and Exhibition

作者： Guilherme Korol Michael Guilherme Jordan Mateus Beck Rutzig Jeronimo Castrillon Antonio Carlos Schneider Beck Institute of Informatics Universidade Federal do Rio Grande do Sul (UFRGS) Porto Alegre Brazil Electronics and Computing Department Universidade Federal de Santa Maria (UFSM) Santa Maria Brazil Center for Advancing Electronics Dresden TU Dresden Dresden Germany Center for Scalable Data Analytics and Artificial Intelligence Dresden Germany

The challenge of processing heavy-load ML tasks, particularly CNN-based ones at resource-constrained IoT devices, has encouraged the use of edge servers. The edge offers performance levels higher than the end devices and better latency and security levels than the Cloud. On top of that, the rising complexity of ML applications, the ever-increasing number of connected devices, and the current demands for energy efficiency require optimizing such CNN models. Pruning and early-exit are notable optimizations that have been successfully used to alleviate the computational cost of inference. However, these optimizations have not yet been exploited simultaneously: while pruning is usually applied at design time, which involves retraining the CNN before deployment, early-exit is inherently dynamic. In this work, we propose AdaPEx, a framework that exploits the intrinsic reconfigurable FPGA capabilities so both can be cooperatively employed. AdaPEx first explores the trade-off between pruning and early-exit at design-time, creating a design space never exploited in the state-of-the-art. Then, AdaPEx applies FPGA reconfiguration as a means to enable the combined use of pruning and early-exit dynamically. At runtime, this allows matching the inference processing to the current edge conditions and a user-configurable accuracy threshold. In a smart IoT application, AdaPEx processes up to 1.32× more inferences and improves EDP by up to 2.55× over the state-of-the-art FPGA-based FINN accelerator.

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Design Space Exploration for CNN Offloading to FPGAs at the Edge

Design Space Exploration for CNN Offloading to FPGAs at the ...

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IEEE Computer Society Annual Symposium on VLSI

AI-based IoT applications relying on heavy-load deep learning algorithms like CNNs challenge IoT devices that are restricted in energy or processing capabilities. Edge computing offers an alternative by allowing the data to get offloaded to so-called edge servers with hardware more powerful than IoT devices and physically closer than the cloud. However, the increasing complexity of data and algorithms and diverse conditions make even powerful devices, such as those equipped with FPGAs, insufficient to cope with the current demands. In this case, optimizations in the algorithms, like pruning and early-exit, are mandatory to reduce the CNNs computational burden and speed up inference processing. With that in mind, we propose ExpOL, which combines the pruning and early-exit CNN optimizations in a system-level FPGA-based IoT-Edge design space exploration. Based on a user-defined multi-target optimization, ExpOL delivers designs tailored to specific application environments and user needs. When evaluated against state-of-the-art FPGA-based accelerators (either local or offloaded), designs produced by ExpOL are more power-efficient (by up to 2$\times$) and process inferences at higher user quality of experience (by up to 12.5%).

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Advancing delirium classification: A clinical notes-based natural language processing-supported machine learning model

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Intelligence-Based Medicine 2024年 9卷 100140-100140页

作者： Amjad, Sobia Holmes, Natasha E. Kishore, Kartik Young, Marcus Bailey, James Bellomo, Rinaldo Verspoor, Karin School of Computing and Information System The University of Melbourne Parkville Melbourne VIC Australia Data Analytics Research and Evaluation (DARE) Centre Austin Health and the University of Melbourne Heidelberg Melbourne Australia Department of Critical Care The University of Melbourne Parkville Melbourne Australia Australian and New Zealand Intensive Care Research Center Monash University Melbourne Australia Department of Intensive Care Austin Hospital Melbourne Australia School of Computing Technologies RMIT University Melbourne Australia

Objective: The study of the epidemiology of delirium in hospitalized patients is challenging. We aimed to identify the presence or absence of delirium from clinical text notes using natural language processing (NLP) techniques and machine learning (ML) models. Materials and methods: We developed a delirium predictive model using 942 clinical notes from hospitalized patients with an ICD-10 delirium hospital discharge code. Moreover, we implemented ML models using a) delirium-suggestive words from an expert-defined dictionary or b) free text in clinical notes. Both strategies considered positive and negative delirium-associated words. Results: At the note level, for the dictionary method, the logistic regression model achieved an area under the receiver-operating curve (AUROC) of 0.917 for positive words and 0.914 for combined positive and negative words. The areas under the precision-recall curve (AUPR) were 0.893 and 0.897, respectively. For the free-text method, the model achieved an AUROC of 0.826 and 0.830 and AUPR of 0.852 and 0.856, respectively. Discussion: NLP-based ML models accurately identified the presence of delirium in clinical notes. The dictionary-based method was superior to the free-text method. The use of negative features improved performance in both methods. Conclusion: Our proposed NLP-based ML model identified delirium in clinical notes. This model could automatically screen millions of notes and facilitate the study of the epidemiology of in-hospital delirium. © 2024

关键词： Delirium Document classification Machine learning Medical dictionary Natural language processing

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The Effect of Prolonged Conditionally Automated Driving on Fatigue, Physiological Activity, and Takeover Performance: A Driving Simulator Experiment

SSRN

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SSRN 2025年

作者： Coyne, Rory Khan, Bilal Alam Shiel, Matthew Ur Rahman, Shams Vlad, Victor Lillis, Conor Usman, Muzna Kielty, Paul Parsi, Ashkan Lemley, Joseph Smeaton, Alan F. Corcoran, Peter Walsh, Jane C. School of Psychology University of Galway Ireland School of Psychology Trinity College Dublin Ireland FotoNation Ltd Block 5 Parkmore East Galway Ireland Insight Research Ireland Centre for Data Analytics Dublin City University Dublin Ireland School of Computer Science University of Galway Ireland Department of Electrical and Electronic Engineering University of Galway Ireland School of Computing Dublin City University Dublin Ireland

The transition from manual to conditionally automated driving involves the driver’s role changing from an active operator to a ‘fallback-ready’ user, who is free to disengage from driving, but must remain ready to take over when prompted. Driver fatigue, which can be distinguished from drowsiness and already accounts for as many as 20% of all road traffic accidents, is likely to remain a pervasive problem during conditionally automated driving, as reductions in attention and vigilance due to fatigue could imperil safe transitions of control between automated system and user. While considerable research exists concerning drivers’ responses under states of drowsiness or distraction, fewer studies have investigated the effect of automation on fatigue. This driving simulator experiment was conducted to examine the effect of prolonged automation on fatigue during conditionally automated driving. Drivers’ self-reported fatigue and workload were measured, in addition to a suite of physiological measures and takeover reaction time and quality. Findings show that fatigue was significantly higher following 50 minutes of automated driving than at baseline, or while participants performed a non-driving-related task. Strikingly, 80% of participants experienced signs of sleepiness, and almost half had to exercise effort to stay awake. Fatigue also resulted in decreases in heart rate and relative beta power derived from electroencephalography, and an increase in blink rate and heart rate variability. Overall, the findings advance knowledge in this area by supporting the idea of fatigue as failure to adequately self-regulate during automation. Several physiological measures have also been identified as possible markers of fatigue to inform emerging monitoring technology. © 2025, The Authors. All rights reserved.

关键词： Heart

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Random Features Hopfield Networks generalize retrieval to previously unseen examples

arXiv

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arXiv 2024年

作者： Kalaj, Silvio Lauditi, Clarissa Perugini, Gabriele Lucibello, Carlo Malatesta, Enrico M. Negri, Matteo University of Rome 'La Sapienza' Department of Physics Piazzale Aldo Moro 5 Roma00185 Italy Department of Applied Math John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Department of Computing Sciences Bocconi University Milano20136 Italy Institute for Data Science and Analytics Bocconi University Milano20136 Italy CNR-NANOTEC Institute of Nanotechnology Rome Unit Piazzale Aldo Moro Roma00185 Italy

It has been recently shown that a learning transition happens when a Hopfield Network stores examples generated as superpositions of random features, where new attractors corresponding to such features appear in the model. In this work we reveal that the network also develops attractors corresponding to previously unseen examples generated with the same set of features. We explain this surprising behaviour in terms of spurious states of the learned features: we argue that, increasing the number of stored examples beyond the learning transition, the model also learns to mix the features to represent both stored and previously unseen examples. We support this claim with the computation of the phase diagram of the model. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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The phase diagram of compressed sensing with 0-norm regularization

arXiv

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arXiv 2024年

作者： Barbier, Damien Lucibello, Carlo Saglietti, Luca Krzakala, Florent Zdeborová, Lenka Ecole Polytechnique Fédérale de Lausanne Information Learning and Physics lab Statistical Physics of Computation lab LausanneCH-1015 Switzerland Institute for Data Science and Analytics Bocconi University Milan Italy Department of Computing Sciences Bocconi University Milan Italy Ecole Polytechnique Fédérale de Lausanne Statistical Physics of Computation Laboratory LausanneCH-1015 Switzerland

Noiseless compressive sensing is a two-steps setting that allows for undersampling a sparse signal and then reconstructing it without loss of information. The LASSO algorithm, based on 1 regularization, provides an efficient and robust to address this problem, but it fails in the regime of very high compression rate. Here we present two algorithms based on 0-norm regularization instead that outperform the LASSO in terms of compression rate in the Gaussian design setting for measurement matrix. These algorithms are based on the Approximate Survey Propagation, an algorithmic family within the Approximate Message Passing class. In the large system limit, they can be rigorously tracked through State Evolution equations and it is possible to exactly predict the range compression rates for which perfect signal reconstruction is possible. We also provide a statistical physics analysis of the 0-norm noiseless compressive sensing model. We show the existence of both a replica symmetric state and a 1-step replica symmmetry broken (1RSB) state for sufficiently low 0-norm regularization. The recovery limits of our algorithms are linked to the behavior of the 1RSB solution. Copyright © 2024, The Authors. All rights reserved.

关键词： Compressed sensing

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A Quality-Aware Rendezvous Framework for Cognitive Radio Networks

A Quality-Aware Rendezvous Framework for Cognitive Radio Net...

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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Hai Liu Lu Yu Chung Keung Poon Zhiyong Lin Yiu- Wing Leung Xiaowen Chu Department of Computing The Hang Seng University of Hong Kong Hong Kong Department of Computer Science GuangDong Polytechnic Normal University Guangzhou China Department of Computer Science Hong Kong Baptist University Hong Kong Data Science and Analytics Thrust Hong Kong University of Science and Technology (Guangzhou)

In cognitive radio networks, rendezvous is a fundamental operation by which cognitive users establish communication links. Most of existing works were devoted to shortening the time-to-rendezvous (TTR) but paid little attention to qualities of the channels on which rendezvous is achieved. In fact, qualities of channels, such as resistance to primary users' activities, have a great effect on the rendezvous operation. If users achieve a rendezvous on a low-quality channel, the communication link is unstable and the communication performance is poor. In this case, re- rendezvous is required which results in considerable communication overhead and a large latency. In this paper, we first show that actual TTRs of existing rendezvous solutions increase by 65.40-104.38% if qualities of channels are not perfect. Then we propose a Quality-Aware Rendezvous Framework (QARF) that can be applied to any existing ren-dezvous algorithms to achieve rendezvous on high-quality channels. The basic idea of QARF is to expand the set of available channels by selectively duplicating high-quality channels. We prove that QARF can reduce the expected TTR of any rendezvous algorithm when the expanded ratio $\lambda$ is smaller than the threshold $(-3+\sqrt{1+4(\frac{\sigma}{\mu})^{2}}) / 2$ , where $\mu$ and $\sigma$ , respectively, are the mean and the standard deviation of qualities of channels. We further prove that QARF can always reduce the expected TTR of Random algorithm by a factor of $1+(\frac{\sigma}{\mu})^{2}$ . Extensive experiments are conducted and the results show that QARF can significantly reduce the TTRs of the existing rendezvous algorithms by 10.50-51.05 % when qualities of channels are taken into account.

关键词： Resistance Sensors Cognitive radio Standards

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Leveraging Digital Twins and AI for Enhanced Clinical Decision Support in Endometrial Cancer Treatment

Leveraging Digital Twins and AI for Enhanced Clinical Decisi...

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IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： Suresh Palarimath Pyingkodi Maran Wilfred Blessing N. R Cibi. A S. J. Kavitha Steffi S College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Sultanate of Oman Department of Computer Application Kongu Engineering College Erode India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Sultanate of Oman Data & Analytics Visualizaation – Delivery Virtusa Consulting Service Private Limited Chennai India Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Digital twins are a relatively new concept that have lately garnered interest in the industrial sector. Digital twins are virtual models that mirror actual products. In this research, we suggest using the technology of digital twins to the field of healthcare in order to enhance clinical decision support and to provide more individualized treatment for patients. When artificial intelligence (AI) is combined with digital twins, healthcare professionals are able to more effectively analyze large volumes of varied data and increase their ability to make diagnostic and therapeutic decisions. In this research, we provide a conceptual framework for leveraging digital twins and AI to solve existing limits in cancer care, notably endometrial cancer therapy. This research study mainly focuses on cancer care, and more specifically on endometrial cancer treatment. In addition, we analyze the possible challenges and opportunities that may arise throughout the process of integrating this technology in healthcare settings. Our overarching objective is to improve the standard of treatment as well as the clinical results for people who have cancer.

关键词： Accuracy Cancer treatment Medical services Machine learning Digital twins Mirrors Artificial intelligence Wearable sensors Standards Cancer

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Storage and Learning phase transitions in the Random-Features Hopfield Model

arXiv

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arXiv 2023年

作者： Negri, Matteo Lauditi, Clarissa Perugini, Gabriele Lucibello, Carlo Malatesta, Enrico M. University of Rome ‘La Sapienza’ Department of Physics Piazzale Aldo Moro 5 Roma00185 Italy CNR-NANOTEC Institute of Nanotechnology Rome Unit Piazzale Aldo Moro Roma00185 Italy Department of Applied Science and Technology Politecnico di Torino Torino10129 Italy Department of Computing Sciences Bocconi University Milano20136 Italy Institute for Data Science and Analytics Bocconi University Italy

The Hopfield model is a paradigmatic model of neural networks that has been analyzed for many decades in the statistical physics, neuroscience, and machine learning communities. Inspired by the manifold hypothesis in machine learning, we propose and investigate a generalization of the standard setting that we name Random-Features Hopfield Model. Here P binary patterns of length N are generated by applying to Gaussian vectors sampled in a latent space of dimension D a random projection followed by a non-linearity. Using the replica method from statistical physics, we derive the phase diagram of the model in the limit P, N, D → ∞ with fixed ratios α = P/N and αD = D/N. Besides the usual retrieval phase, where the patterns can be dynamically recovered from some initial corruption, we uncover a new phase where the features characterizing the projection can be recovered instead. We call this phenomena the learning phase transition, as the features are not explicitly given to the model but rather are inferred from the patterns in an unsupervised fashion. Copyright © 2023, The Authors. All rights reserved.

关键词： Statistical Physics

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A Smart Air Pollutants Monitoring System Using IOT Technologies

A Smart Air Pollutants Monitoring System Using IOT Technolog...

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Intelligent Systems for Communication, IoT and Security (ICISCoIS), International Conference on

作者： M Pyingkodi N R Wilfred Blessing K Thenmozhi K Kanimozhi L Pavithira P K Sandhya Balakrishnan Department of Computer Applications Kongu Engineering College Erode India IT Department College of Computing and Information Sciences University of Technology and Applied Sciences-Ibri(Government) Oman Department of Computer Science Kristu Jayanti College Bengaluru India Department of Information Technology Sri Krishna Adithya College of Arts and Science Coimbatore Department of Computer Science with Data Analytics PSG college of Arts & Science College Coimbatore Department of Computer Science CMS College of Science and Commerce Coimbatore

On international basis, one in all our century's essential problems is air pollutants. Every year, the range of individuals killed via way of means of pollutants climbs, with air pollutants being the main cause. The Internet of Things (IoT) is used worldwide community of “inventive gadgets” which could sense, and join to, and have interaction with different humans and systems. The solution is furnished via way of means of a sensor-primarily based totally hardware module that can be deployed anywhere. These gadgets, which can be hooked up on mild poles, homes, and different structures, wirelessly speak air pleasant facts to a far flung server. The microprocessor collects facts from IoT sensing gadgets and sends it to a cloud server for processing. These facts can be used to expecting while the vicinity is maximum polluted. The machine additionally gives air pleasant statistics through a cell application, permitting clients to choose alternatives wherein the air pleasant is great. It is likewise a totally less expensive device.

关键词： Cloud computing Microprocessors Air pollution Hardware Sensors Servers Internet of Things

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