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arXiv 2025年

作者： Yang, Xiaohong Liwang, Minghui Fu, Liqun Su, Yuhan Hosseinalipour, Seyyedali Wang, Xianbin Hong, Yiguang School of Informatics Xiamen University Fujian China Department of Control Science and Engineering The National Key Laboratory of Autonomous Intelligent Unmanned Systems China Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Tongji University Shanghai China School of Electronic Science and Engineering Xiamen University Xiamen China Department of Electrical Engineering University at Buffalo-SUNY BuffaloNY United States Department of Electrical and Computer Engineering Western University ON Canada

Hierarchical Federated Learning (HFL) introduces intermediate aggregation layers, addressing the limitations of conventional Federated Learning (FL) in geographically dispersed environments with limited communication infrastructure. An application of HFL is in smart IoT systems, such as remote monitoring, disaster response, and battlefield operations, where cellular connectivity is often unreliable or unavailable. In these scenarios, UAVs serve as mobile aggregators, providing connectivity to the terrestrial IoT devices. This paper studies an HFL architecture for energy-constrained UAVs in smart IoT systems, pioneering a solution to minimize global training cost increased caused by UAV disconnection. In light of this, we formulate a joint optimization problem involving learning configuration, bandwidth allocation, and device-to-UAV association, and perform global aggregation in time before UAV drops disconnect and redeployment of UAVs. The problem explicitly accounts for the dynamic nature of IoT devices and their interruptible communications and is unveiled to be NP-hard. To address this, we decompose it into three subproblems. First, we optimize the learning configuration and bandwidth allocation using an augmented Lagrangian function to reduce training costs. Second, we propose a device fitness score, integrating data heterogeneity (via Kullback-Leibler divergence), device-to-UAV distances, and IoT device resources, and develop a twin-delayed deep deterministic policy gradient (TD3)-based algorithm for dynamic device-to-UAV assignment. Third, We introduce a low-complexity two-stage greedy strategy for finding the location of UAVs redeployment and selecting the appropriate global aggregator UAV. Experiments on real-world datasets demonstrate significant cost reductions and robust performance under communication interruptions. © 2025, CC BY-NC-ND.

关键词： Federated learning

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A single-cell transcriptomic analysis of Neuroblastomas revealed a selective cGAS-STING pathway suppression in malignant cells

A single-cell transcriptomic analysis of Neuroblastomas reve...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Sibilio, Pasquale Di Giulio, Stefano Funari, Alessio Paci, Paola Veschi, Veronica Giannini, Giuseppe Sapienza University of Rome Department of Computer Control and Management Engineering Rome Italy National Research Council Institute for Systems Analysis and Computer Science 'Antonio Ruberti' Rome Italy Sapienza University of Rome Department of Molecular Medicine Rome Italy Sapienza University of Rome Department of Translational and Precision Medicine Rome Italy Karolinska Institutet Stockholm17177 Sweden San Gallicano Dermatological Institute Irccs Microbiology and Virology Rome Italy Istituto Pasteur-Fondazione Cenci Bolognetti Rome Italy

ISBN: (纸本)9798350337488

Neuroblastoma is a disease of disordered development accounting for 15% of childhood cancer deaths. The "cold"immunophenotype frequently occurring of these tumors is likely to contribute to its aggressiveness and refractoriness to treatments, including immune checkpoint blockade, observed in high risk neuroblastoma. The mechanisms that contribute to the cold immunostate have not been elucidated, yet. However, recent studies have reported the involvement of MYCN amplification in reducing the immune infiltrate in high-risk Neuroblastoma tumors. Its action mainly concerns suppressed interferon responses and pro-inflammatory pathways. An important pathway deputed to the activation of type I interferon responses and pro-inflammatory cytokines release is the cyclic GMP-AMP synthase (cGAS) - stimulator of interferon genes (STING) cytosolic DNA sensing pathway. Recent preclinical studies suggest a promising therapeutic effect of cGAS-STING pathway reactivation in Neuroblastomas. However, very little is known about the regulation of the cGAS-STING pathway in Neuroblastoma malignant cells, and its relationship with the MYCN amplification state. To this end, we performed a single-cell transcriptomic analysis of primary human Neuroblastoma cells compared with their normal fetal and embryonic progenitors. The results of the high-resolution analysis of cGAS-STING pathway showed that Neuroblastomas malignant cells have a much lower expression of this pathway compared to normal progenitors and other cell phenotypes populating the tumor microenvironment, possibly acquiring an evolutionary advantage. Moreover, the cGAS-STING pathway anticorrelated with MYCN expression suggesting a putative regulatory mechanism of the STING pathway. © 2023 IEEE.

关键词： immunotherapy Neuroblastoma single-cell transcriptomic

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Carbon-Aware Computing in a Network of Data Centers: A Hierarchical Game-Theoretic Approach

arXiv

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arXiv 2024年

作者： Breukelman, Enno Hall, Sophie Belgioioso, Giuseppe Dörfler, Florian The KTH Royal Institute of Technology School of Electrical Engineering and Computer Science Division of Decision and Control Systems Malvinas väg 10 StockholmSE-100 44 Sweden The Automatic Control Laboratory Department of Electrical Engineering and Information Technology ETH Zurich Physikstrasse 3 Zürich8092 Switzerland

Over the past decade, the continuous surge in cloud computing demand has intensified data center workloads, leading to significant carbon emissions and driving the need for improving their efficiency and sustainability. This paper focuses on the optimal allocation problem of batch compute loads with temporal and spatial flexibility across a global network of data centers. We propose a bilevel game-theoretic solution approach that captures the inherent hierarchical relationship between supervisory control objectives, such as carbon reduction and peak shaving, and operational objectives, such as priority-aware scheduling. Numerical simulations with real carbon intensity data demonstrate that the proposed approach successfully reduces carbon emissions while simultaneously ensuring operational reliability and priority-aware scheduling. © 2024, CC BY-NC-ND.

关键词： Carbon

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Resilient Set-based State Estimation for Linear Time-Invariant systems Using Zonotopes

arXiv

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arXiv 2022年

作者： Niazi, Muhammad Umar B. Alanwar, Amr Chong, Michelle S. Johansson, Karl Henrik Division of Decision and Control Systems Digital Futures School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Computer Science and Electrical Engineering Jacobs University Bremen Germany Control Systems Technology Section Department of Mechanical Engineering Eindhoven University of Technology Netherlands

This paper considers the problem of set-based state estimation for linear time-invariant (LTI) systems under time-varying sensor attacks. Provided that the LTI system is stable and observable via every single sensor and that at least one sensor is uncompromised, we guarantee that the true state is always contained in the estimated set. We use zonotopes to represent these sets for computational efficiency. However, we show that intelligently designed stealthy attacks may cause exponential growth in the algorithm’s worst-case complexity. We present several strategies to handle this complexity issue and illustrate our resilient zonotope-based state estimation algorithm on a rotating target system. Copyright © 2022, The Authors. All rights reserved.

关键词： Linear time-invariant system

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One solution of a Distributed System intended for communication between ADAS and IVI systems

One solution of a Distributed System intended for communicat...

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Zooming Innovation in Consumer Electronics International Conference (ZINC)

作者： Vuk Čabrilo Dušan Kenjić Radomir Zlatković Marija Antić Department of Computing and Control Engineering Faculty of Technical Sciences University of Novi Sad Novi Sad Serbia RT-RK Institute of Computer Based Systems Novi Sad Serbia

Current trends of digitalization and automation in the automotive industry have increased the complexity of computer clusters in different domains of vehicular ecosystems, which are required to support new features. This has led to redundancy in resource usage in specific use cases, resulting in increased production costs and manufacturer suggested retail price (MSRP) markups for the finalized products. The average retail price of a new vehicle in the United States increased by 13.4% between 2015 and 2020. Considering the global chip shortage of 2020 and current global inflation, the average price of a new vehicle in the United States reached $49,500 in 2023 [1]. With the electrification of current automotive platforms underway, as well as the move to centralized system architectures, new approaches to inter-domain communication are expected. This paper proposes a solution of a distributed AUTOSAR Adaptive system that can establish communication with the In-Vehicle Infotainment (IVI) domain. The proof of concept implemented in a controlled environment proves viable for inter-domain communication, as it provides good scalability and adaptability to different scenarios. The use of a distributed approach facilitates the applicability of distributed system principles, allowing for the implementation of a stable system.

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Enhancing Precision Agriculture Through Human-in-the-Loop Planning and control

Enhancing Precision Agriculture Through Human-in-the-Loop Pl...

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IEEE International Conference on Automation Science and engineering (CASE)

作者： Shankar A. Deka Sujet Phodapol Andreu Matoses Gimenez Victor Nan Fernandez-Ayala Rufus Wong Pian Yu Xiao Tan Dimos V. Dimarogonas Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Espoo Finland Division of Decision and Control Systems School of EECS Royal Institute of Technology (KTH) Stockholm Sweden Department of Cognitive Robotics Faculty of Mechanical Engineering TU Delft Delft Netherlands Department of Computer Science University of Oxford Oxford UK

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

In this paper, we introduce a ROS based framework designed for the planning and control of robotic systems within the context of precision agriculture, with an emphasis on human-in-the-loop capabilities. Utilizing Linear Temporal Logic to articulate complex task specifications, our algorithm creates high-level robotic plans that are not only correct by design but also adaptable in real time by human operators. This dual-focus approach ensures that while humans have the flexibility to modify the high-level plan on-the-fly or even take over low-level control of the robots, the system inherently safeguards against any human actions that could potentially breach the predefined task specifications. We demonstrate our algorithm within the dynamic and challenging environment of a real vineyard, where the collaboration between human workers and robots is critical for tasks such as harvesting and pruning, and show the practical applicability and robustness of our software. This work marks a pioneering application of formal methods to complex, real-world agricultural environments.

关键词： Precision agriculture Heuristic algorithms Software algorithms Robustness Human in the loop Software Real-time systems Planning Robots Multi-agent systems

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On the Robustness of Adversarial Training Against Uncertainty Attacks

arXiv

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arXiv 2024年

作者： Ledda, Emanuele Scodeller, Giovanni Angioni, Daniele Piras, Giorgio Cinà, Antonio Emanuele Fumera, Giorgio Biggio, Battista Roli, Fabio Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Rome00185 Italy Department of Informatics Bioengineering Robotics and Systems Engineering University of Genova Via Dodecaneso 35 Genova16146 Italy Department of Electric and Electronic Engineering University of Cagliari Via Marengo 3 Cagliari09100 Italy

In learning problems, the noise inherent to the task at hand hinders the possibility to infer without a certain degree of uncertainty. Quantifying this uncertainty, regardless of its wide use, assumes high relevance for security-sensitive applications. Within these scenarios, it becomes fundamental to guarantee good (i.e., trustworthy) uncertainty measures, which downstream modules can securely employ to drive the final decision-making process. However, an attacker may be interested in forcing the system to produce either (i) highly uncertain outputs jeopardizing the system’s availability or (ii) low uncertainty estimates, making the system accept uncertain samples that would instead require a careful inspection (e.g., human intervention). Therefore, it becomes fundamental to understand how to obtain robust uncertainty estimates against these kinds of attacks. In this work, we reveal both empirically and theoretically that defending against adversarial examples, i.e., carefully perturbed samples that cause misclassification, additionally guarantees a more secure, trustworthy uncertainty estimate under common attack scenarios without the need for an ad-hoc defense strategy. To support our claims, we evaluate multiple adversarial-robust models from the publicly available benchmark RobustBench on the CIFAR-10 and ImageNet datasets. © 2024, CC BY.

关键词： Adversarial machine learning

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Collaborative learning technologies for project work 1

Collaborative learning technologies for project work

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1st International Conference on Technology Enhanced Learning in Higher Education, TELE 2021

作者： Galkin, Alexander Blyumin, Semen Saraev, Pavel Pimenov, Vladimir Lipetsk State Technical University Faculty of Automation and Computer Science Lipetsk Russia Lipetsk State Technical University Department of Applied Mathematics Lipetsk Russia Lipetsk State Technical University Department of Automated Control Systems Lipetsk Russia Novolipetsk Steel Corporation Lipetsk Russia

ISBN: (纸本)9781665443968

The paper presents an approach to learning based on the implementation of projects under the joint supervision and control of the educational organization and industrial partner. When students carry out projects, collaborative learning technologies are implemented. Periodic joint consultations of students by specialists of different profiles of educational organization and industrial partner help to bring the training closer to the real production process, and upon the completion of the project to get a ready-to-use product. Working in this mode allows students using modern technologies to work on the project, in particular, flexible methodologies of agile development. It helps not to formulate a completely ready technical task for the project, but to adjust it in the process of implementation. Tasks are formulated for a limited time interval of 1-2 weeks, after which the results are evaluated and tasks for the next stage are formed. The paper provides examples of works by students of Lipetsk State Technical University, performed in this format. © 2021 IEEE.

关键词： Students

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Risk-Sensitive RL Using Sampling-Based Expectation-Maximization

Risk-Sensitive RL Using Sampling-Based Expectation-Maximizat...

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IEEE Conference on Decision and control

作者： Erfaun Noorani John S. Baras Karl H. Johansson Department of Electrical and Computer Engineering Institute for Systems Research (ISR) at the University of Maryland College Park MD USA Automatic Control Lab Royal Institute of Technology Stockholm Sweden

There is a need for robust Reinforcement Learning (RL) algorithms that can cope with model misspecification, parameter uncertainty, disturbances, etc. Risk-sensitive methods offer an approach to developing robust RL algorithms by hedging against undesirable outcomes in a probabilistic manner. The Probabilistic Graphical Model (PGM) framework offers systematic exploration for risk-sensitive RL. In this paper, we bridge the Markov Decision Process (MDP) and the PGM frameworks. We exploit the equivalence of optimizing a certain risk-sensitive criterion in the MDP formalism with optimizing a log-likelihood objective in the PGM formalism. By utilizing this equivalence, we offer an approach for developing risk-sensitive algorithms by leveraging the PGM framework. We explore the Expectation-Maximization (EM) algorithm under the PGM formalism. We show that risk-sensitive policy gradient methods can be obtained by applying sampling-based approaches to the EM algorithm, e.g., Monte-Carlo EM, with the log-likelihood. We show that Monte-Carlo EM leads to a risk-sensitive Monte-Carlo policy gradient algorithm. Our simulations illustrate the risk-sensitive nature of the resulting algorithm.

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Vehicle type recognition based on audio data 58

Vehicle type recognition based on audio data

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58th Hawaii International Conference on System Sciences, HICSS 2025

作者： Kobiela, Dariusz Hajdasz, Michal Erezman, Mateusz Nurzyńska, Karolina Zaporowski, Szymon Kurowski, Adam Weichbroth, Pawel Department of Software Engineering Faculty of Electronics Telecommunication and Informatics Gdańsk University of Technology Gabriela Narutowicza 11/12 Gdańsk80-233 Poland Department of Multimedia Systems Faculty of Electronics Telecommunication and Informatics Gdańsk University of Technology Gabriela Narutowicza 11/12 Gdańsk80-233 Poland Department of Algorithmics and Software Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 16 Gliwice44-100 Poland

ISBN: (纸本)9780998133188

Identifying different vehicle types can help manage traffic more efficiently, reduce congestion, and improve public safety. This study aims to create a classification model that can recognize vehicle types based on the sound of passing vehicles. To achieve this, a database of raw audio files containing 1763 samples from two sources was assembled. The time-domain signals were converted to a time-frequency representation using the short-time Fourier transform to generate Mel Spectrograms. Mel-frequency Cepstral Coefficients (MFCCs) were also generated using the discrete cosine transform. In our experiments we compared these approaches. Since the data was imbalanced we applied online augmentation. Based on the literature review, we chose a Convolutional Neural Network (CNN) classifier because it is particularly well suited for analyzing large datasets due to its automatic feature extraction, parameter sharing and sparsity. The results showed that Mel Spectrograms were more effective for audio data preprocessing in this particular use case, achieving the highest accuracy of 0.875 and the highest f1-score of 0.877 compared to MFCCs. © 2025 IEEE computer Society. All rights reserved.

关键词： Image coding

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