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Understanding patient acceptance of ai-assisted oncology treatment: a consumption values perspective
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The Role of Absolute Space in Newton's Physics
No full textInternational audienceIn this paper, we examine Newton’s concept of absolute space. We aim at clarifying: (1) the reasons that led Newton to introduce this notion; (2) the arguments he used to justify its existence; (3) the relationship between absolute space and the principle of inertia; and (4) the role of absolute space within Newtonian physics. We then analyse the criticisms raised by Leibniz and Mach, focusing on their specific arguments rather than their broader philosophical frameworks, due to the space limitations of this article. We conclude by discussing Einstein’s views, particularly how his theory of relativity ultimately ruled out the existence of absolute space and time. Nevertheless, we argue that in the context of Newtonian physics, the concept of absolute space was not at all absurd, even if it presented certain conceptual difficulties. The paper ends with an appendix highlighting noteworthy annotations from the editors of the Geneva Edition to Proposition XIX of the third book of Newton’s Principia
Compact segmented meta-liners for enhanced acoustic absorption with grazing flow
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0.53 mΩ.cm 2 /592 V High Breakdown Voltage GaN-on-Sapphire Schottky Diodes Without Edge Termination
No full textInternational audienceIn this letter, we report high-performance Pt/GaN quasi-vertical Schottky barrier diodes on sapphire substrate. With a 5 μm drift layer and a highly controlled fabrication process, the devices demonstrate an extremely low on-resistance of 0.53 mΩ.cm² while exhibiting a high breakdown voltage of 592 V. The devices achieve a state-of-the-art Baliga’s figure of merit of 0.66 GW.cm-2. The turn-on voltage is also notably low at 0.72 V. Remarkably, the high device performance reported in this work was achieved without the use of passivation or edge termination techniques, in clear contrast to other recent studies
Unsupervised local learning based on voltage-dependent synaptic plasticity for resistive and ferroelectric synapses
No full textInternational audienceThe deployment of AI on edge computing devices faces significant challenges related to energy consumption and functionality. These devices could greatly benefit from brain-inspired learning mechanisms, allowing for real-time adaptation while using low-power. In-memory computing with nanoscale resistive memories may play a crucial role in enabling the execution of AI workloads on these edge devices. In this study, we introduce voltage-dependent synaptic plasticity (VDSP) as an efficient approach for unsupervised and local learning in memristive synapses based on Hebbian principles. This method enables online learning without requiring complex pulse-shaping circuits typically necessary for spike-timing-dependent plasticity (STDP). We show how VDSP can be advantageously adapted to three types of memristive devices (TiO2, HfO2-based metal-oxide filamentary synapses, and HfZrO4-based ferroelectric tunnel junctions (FTJ)) with disctinctive switching characteristics. System-level simulations of spiking neural networks incorporating these devices were conducted to validate unsupervised learning on MNIST-based pattern recognition tasks, achieving state-of-the-art performance. The results demonstrated over 83% accuracy across all devices using 200 neurons. Additionally, we assessed the impact of device variability, such as switching thresholds and ratios between high and low resistance state levels, and proposed mitigation strategies to enhance robustness
Pick the Largest Margin for Robust Detection of Splicing
No full textInternational audienceDespite advancements in splicing detection, practitioners still struggle to fully leverage forensic tools from the literature due to a critical issue: deep learning-based detectors are extremely sensitive to their trained instances. Simple post-processing applied to evaluation images can easily decrease their performances, leading to a lack of confidence in splicing detectors for operational contexts. In this study, we show that a deep splicing detector behaves differently against unknown post-processes for different learned weights, even if it achieves similar performances on a test set from the same distribution as its training one. We connect this observation to the fact that different learnings create different latent spaces separating training samples differently. Our experiments reveal a strong correlation between the distributions of latent margins and the ability of the detector to generalize to post-processed images. We thus provide to the practitioner a way to build deep detectors that are more robust than others against post-processing operations, suggesting to train their architecture under different conditions and picking the one maximizing the latent space margin
What User Involvement in Sustainable Campuses? An Analysis of the Decision-Making Processes of University Smart Buildings in the Lille Region (France)
No full textInternational audienceThis chapter question the inclusion of users in universities’ ecological transitions. Toexplore this issue, the authors particularly focus on the decision to build andrenovate university buildings as ‘smart’ buildings. What vision of the ecologicaltransition do these technical choices reflect? And on what vision of users and theirrole in the ecological transition? Through a qualitative study of the decision-makingprocesses behind the construction and renovation of four smart university buildingsin the Lille region, the authors highlight both the difficulty and necessity ofincluding users to achieve energy and ecological objectives. This necessitatesrethinking the role of users in the ecological transition, as well as developingmethods and tools to achieve this technical inclusion
