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arXiv 2024年

作者： Shi, Lu Haseli, Masih Mamakoukas, Giorgos Bruder, Daniel Abraham, Ian Murphey, Todd Cortes, Jorge Karydis, Konstantinos Dept. of Electrical and Computer Engineering University of California Riverside United States Dept. of Mechanical and Aerospace Engineering University of California San Diego United States Dept. of Planning and Control Zoox Inc. United States Dept. of Mechanical Engineering University of Michigan United States Dept. of Mechanical Engineering Yale University United States Dept. of Mechanical Engineering Northwestern University United States Tsinghua University China

Koopman operator theory offers a rigorous treatment of dynamics and has been emerging as a powerful modeling and learning-based control method enabling significant advancements across various domains of robotics. Due to its ability to represent nonlinear dynamics as a linear operator, Koopman theory offers a fresh lens through which to understand and tackle the modeling and control of complex robotic systems. Moreover, it enables incremental updates and is computationally inexpensive making it particularly appealing for real-time applications and online active learning. This review comprehensively presents recent research results on advancing Koopman operator theory across diverse domains of robotics, encompassing aerial, legged, wheeled, underwater, soft, and manipulator robotics. Furthermore, it offers practical tutorials to help new users get started as well as a treatise of more advanced topics leading to an outlook on future directions and open research questions. Taken together, these provide insights into the potential evolution of Koopman theory as applied to the field of robotics. © 2024, CC BY.

关键词： Adversarial machine learning

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MIRROR: miRNA regulation-level differential network to study sex and ethnic disparities in cancer

MIRROR: miRNA regulation-level differential network to study...

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

作者： Caterina Alfano Marco Filetti Lorenzo Farina Manuela Petti Dept. of Experimental Medicine Sapienza University of Rome Rome Italy Phase 1 Unit Fondazione Policlinico Universitario A. Gemelli IRCCS Rome Italy Dept. of Computer Control and Management Engineering PhD Program in Network Oncology and Precision Medicine Sapienza University of Rome Rome Italy Dept. of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Cancer disparities are adverse differences in cancer measures that exist among certain population groups. Given that the role they play not only in the disease prognosis but also in therapy response, there is an urgent need to understand what causes them. Most studies investigate these disparities by analyzing transcriptomic data and in particular miRNAs for their regulatory role, but only focusing on expression levels. To face this challenge we propose MIRROR, a new method which analyzes a differential co-expression network of miRNAs between patients’ cohorts, to study the role they play at the target genes’ level. Doing so, we can study the altered molecular mechanism that are linked to cancer disparities. The application of MIRROR to two different cases of cancer disparities has demonstrated its efficacy in identifying molecular players involved in the considered disparity, presenting itself as a viable option to approach this challenge.

关键词： Proteins Technological innovation Transcriptomics Medical treatment Focusing Mirrors Prostate cancer Prognostics and health management Faces Cancer

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Energy Consumption Model for LoRaWAN via Field Experiments for Smart Agriculture

Energy Consumption Model for LoRaWAN via Field Experiments f...

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IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Satoshi Yamazaki Taiki Ogura Dept. of Control and Computer Engineering National Institute of Technology Numazu College Numazu Japan Dept. of Electrical Electronics and Information Engineering Nagaoka University of Technology Niigata Japan

This paper proposes a scheme to model the energy consumption of LoRaWAN, which is a popular example of low-power wide-area networks (LPWANs), nodes via the results of outdoor field experiments by assuming regional smart agriculture as a use case for internet of things (IoT). Specifically, we derive an experimental approximation formula to estimate the battery lifetime by introducing parameters such as spread factor and payload length. The validity of the proposed scheme is demonstrated by confirming that the results obtained by the approximate formula, the experimental and theoretical results generally agree, regardless of the node state and spread factor. Furthermore, we show that the obtained approximate formula can be used to identify the current consumption value in the sleep state that should be achieved to achieve the desired battery lifetime.

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The Goldilocks Framework: Towards Selecting the Optimal Approach to Conducting AI Projects 22

The Goldilocks Framework: Towards Selecting the Optimal Appr...

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1st International Conference on AI engineering - Software engineering for AI, CAIN 2022

作者： Dzhusupova, Rimman Bosch, Jan Olsson, Helena Holmstrom Control & Safety Systems McDermott Dept. of Electrical & Instrumentation The Hague Netherlands Chalmers University of Technology Department of Computer Science and Engineering Gothenburg Sweden Malmö University Department of Computer Science and Media Technology Malmö Sweden

ISBN: (纸本)9781450392754

Artificial intelligence is increasingly becoming important to businesses since many companies have realized the benefits of applying Machine Learning (ML) and Deep Learning (DL) into their operations. Nevertheless, ML/DL technologies' industrial development and deployment examples are still rare and generally confined within a small cluster of large international companies who are struggling to apply ML more broadly and deploy their use cases at a large scale. Meanwhile, current AI market has started offering various solutions and services. Thus, organizations must understand how to acquire AI technology based on their business strategy and available resources. This paper discusses the industrial experience of developing and deploying ML/DL use cases to support organizations in their transformation towards AI. We identify how various factors, like cost, schedule, and intellectual property, can be affected by the choice of approach towards ML/DL project development and deployment within large international engineering corporations. As a research result, we present a framework that covers the trade-offs between those various factors and can support engineering companies to choose the best approach based on their long-term business strategies and, therefore, would help to accomplish their ML/DL project deployment successfully. © 2022 ACM.

关键词： Commerce

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Physics-Informed Neural Networks: A Step Towards Data-Driven Optimization of Additive Manufacturing

Physics-Informed Neural Networks: A Step Towards Data-Driven...

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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Fabio Depaoli Stefano Felicioni Francesco Ponzio Alessandro Aliberti Enrico Macii Federica Bondioli Elisa Padovano Santa Di Cataldo Dept. of Control and Computer Engineering Politecnico di Torino Italy Dept. of Applied Science and Technology Politecnico di Torino Italy Interuniv. Dept. of Reg. and Urban Studies and Planning Politecnico di Torino Italy

ISBN: (数字)9798350361230

ISBN: (纸本)9798350361247

Laser powder bed fusion (L-PBF) is the most popular Additive Manufacturing (AM) process for metals. It builds a 3D object layer-by-layer, by spreading metal powder on top of the previous layer and selectively melting it with a laser. Despite its many advantages, large-scale production may be hampered by the large number of process parameters and the challenges associated with their optimization. We propose an automated parameter selection approach based on process signatures extracted from a parameterized simulation of the process. Specifically, we outline a rapid data-driven simulation method based on Physics-Informed Neural Network (PINN). This approach involves training a neural network to solve the partial differential equation describing the process at varying values of a parameter of interest (for example, the laser power), thus eliminating the need for repeated Finite Elements Method (FEM) simulations. Our preliminary experiments demonstrate the feasibility of our approach.

关键词： Training Powders Three-dimensional displays Neural networks Metals Production Three-dimensional printing Finite element analysis Laser fusion Optimization

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Targeting different defect-oriented fault models in IC testing: an experimental approach

Targeting different defect-oriented fault models in IC testi...

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Euromicro Symposium on Digital System Design

作者： N. Mirabella A. Floridia R. Cantoro M. Grosso M. Sonza Reorda STMicroelearonics s.r.l. Italy Dept. of Control and Computer Engineering Politecnico di Torino Italy

In the field of integrated circuit (IC) testing, the detection of defects is crucial to ensure the reliability and functionality of the final product. Among the variety of fault models that can be used to target the many possible defects in a circuit, delay faults (transition and path delay) have been used for many years. Lately, cell-aware testing (CAT) has been introduced as a different approach that aims to improve the detection of internal defects of standard cells: it involves using specific patterns to detect faults that could not be detected by common fault models (e.g., stuck-at and transition delay fault models). Both delay and cell-aware faults can be caused by several factors, such as manufacturing defects, environmental conditions, and aging effects. In this paper, we investigate the application of test patterns generated with the transition and path delay fault models in comparison with others developed with the cell-aware approach, in terms of fault coverage, pattern count and test generation time. Overall, the study shows that the combination of the path delay fault model and cell-aware testing can lead to improved fault coverage and lower test. The experimental results are presented over a wide range of open-source benchmarks and on a RISC-V design using a proprietary industrial technology library.

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Towards More Human-like AI Communication: A Review of Emergent Communication Research

arXiv

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arXiv 2023年

作者： Brandizzi, Nicolo' Dept. of Computer Control and Management Engineering Sapienza University of Rome via Ariosto 25 Rome00185 Italy

In the recent shift towards human-centric AI, the need for machines to accurately use natural language has become increasingly important. While a common approach to achieve this is to train large language models, this method presents a form of learning misalignment where the model may not capture the underlying structure and reasoning humans employ in using natural language, potentially leading to unexpected or unreliable behavior. Emergent communication (EmCom) is a field of research that has seen a growing number of publications in recent years, aiming to develop artificial agents capable of using natural language in a way that goes beyond simple discriminative tasks and can effectively communicate and learn new concepts. In this review, we present EmCom under two aspects. Firstly, we delineate all the common proprieties we find across the literature and how they relate to human interactions. Secondly, we identify two subcategories and highlight their characteristics and open challenges. We encourage researchers to work together by demonstrating that different methods can be viewed as diverse solutions to a common problem and emphasize the importance of including diverse perspectives and expertise in the field. We believe a deeper understanding of human communication is crucial to developing machines that can accurately use natural language in human-machine interactions. © 2023, CC BY.

关键词： Reinforcement learning

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Precise Orbit Determination on LEO Satellite using Pseudorange and Pseudorange-Rate Measurements

Precise Orbit Determination on LEO Satellite using Pseudoran...

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Mediterranean Conference on control and Automation (MED)

作者： Andrea Tantucci Andrea Wrona Antonio Pietrabissa Dept. of Computer Control and Management Engineering “Antonio Ruberti” - (DIAG) University of Rome “La Sapienza” Rome Italy

Nowadays, along with the trend of developing highly autonomous satellites, there is a strong motivation to improve real-time Precise Orbit Determination (POD), in particular for Low Earth Orbit (LEO) satellites. The development of Global Navigation Satellite System (GNSS) sensors allows to obtain low-noise measurements and provide a satellite with autonomous continuous tracking onboard. Following the deactivation of Selective Availability, a representative real-time positioning accuracy of 10 m is presently achieved by means of Global Positioning System (GPS) receivers on LEO satellites. The introduction of dynamical filtering methods has opened a new way to improve this accuracy by making use of measurements such as pseudorange or carrier-phase. This paper presents a Kalman filtering approach using pseudorange and pseudorange-rate measurements instead of pseudorange and carrier-phase ones, with advantages in terms of storage and processing requirements. An error of around 0.2 m and 1e-3 m/s for position and velocity is obtained, which is in line if not better w.r.t. other approaches.

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Lightweight Direct Memory Access on FPGA Using AXI Protocol

Lightweight Direct Memory Access on FPGA Using AXI Protocol

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computer Science, computer engineering, & Applied Computing (CSCE), Congress in

作者： Adam Kidwell Matthew P. Horvath Darrin Hanna Bryant Jones Dept. of Electrical and Computer Engineering Oakland University Rochester Hills MI Intrepid Control Systems Troy MI

This paper presents a lightweight AXI DMA controller architecture useful for embedded systems that do not require fully featured DMA controllers. Simulation is accomplished with VUnit, and implementation results are obtained on a Xilinx XC7Z010CLG400-1 FPGA. When compared with Xilinx's AXI DMA controller with the same configuration, the presented controller utilizes between 16 and 82% fewer resources with comparable speed.

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Dynamic Optimization of Provider-Based Scheduling for HPC Workloads

Dynamic Optimization of Provider-Based Scheduling for HPC Wo...

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International Conference on Software, Telecommunications and computer Networks (SoftCOM)

作者： Jacopo Marino Fulvio Risso Mauro Bighi Dept. of Control and Computer Engineering Politecnico di Torino Torino Italy Dept. of Information Technology PUNCH Torino S.p.A Torino Italy

The vast array of cloud providers present in today’s market proffer a suite of High-Performance Computing (HPC) services. However, these offerings are characterized by significant variations in execution times and cost structures. Consequently, selecting the optimal cloud provider and configuring the features of the chosen computing instance (e.g. virtual machines) proves to be a challenging task for users intending to execute HPC workloads. This paper introduces a novel component designed for effortless integration with existing HPC scheduling systems. This module’s primary function is to facilitate the selection of the most appropriate cloud provider for each distinct job, thereby empowering dynamic and adaptive cost-minimization strategies. Through the application of data augmentation techniques and the employment of Continuous Machine Learning, the system is endowed with the capability to operate efficiently with cloud providers that have not been previously utilized. Furthermore, it is capable of tracking the evolution of jobs over time. Our results show that this component can achieve consistent economic savings, based on the quality of the data used in the training phase.

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