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Contrastive learning and physics oriented evaluation for advanced segmentation in electron tomography
No full textInternational audienceDeep learning methods are now achieving strong results for segmentation tasks, and the standard metric for evaluating methods is the Intersection over Union (IOU). However, we show in this paper that IOU is not efficient in evaluating the quality of segmentation for electron tomography (ET) images of zeolites. We perform a physics-oriented evaluation to ensure that the segmentation results yield coherent physical measures. We also formalize Mixed Supervised / Self-Supervised Contrastive Learning Segmentation (M3S-CLS), a semi-supervised approach using a contrastive learning approach that uses expert annotations to train the neural network model. A detailed comparison of this method with a standard cross-entropy-based model is provided. In addition, we publish a database of five fully segmented ET volumes along with corresponding baseline results. The code and the database is available at http://gitlab.univ-st-etienne.fr/labhc-iscv/M3S-CLS
SV-GaSRelight: Single-View Gaussian Splatting for 3D Human Relighting
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Steady state radiation responses of graded-index germanosilicate multimode optical fibers
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Tailoring nanometric vanadium dioxide morphology to tune thermochromic optical properties
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Efficient Luminescent Coatings Constituted of Hybrid Sol–Gel Matrixes Embedding YVO 4 :Eu 3+ Nanocrystals
No full textInternational audiencehis study focuses on the development of luminescent coatings incorporating YVO4:Eu3+ nanocrystals (NCs) into a hybrid sol–gel matrix and investigates the influence of the NC size and loading rate on the optical properties. Two distinct hydrothermal protocols were employed to synthesize YVO4:Eu3+ NCs with average sizes of approximately 340 and 10 nm, respectively. Their structural, morphological and optical characterizations were performed using various techniques. Interestingly, the smaller YVO4:Eu3+ NCs exhibit the highest external quantum yield of about 44% without the necessity of additional thermal treatment. The hybrid matrix, composed of 3-glycidoxypropyltrimethoxysilane (GPTMS) and zirconium propoxide, demonstrates excellent transparency, mechanical stability, and uniform dispersion of the NPs. Spin-coating techniques were used to fabricate homogeneous coatings on glass substrates with NP loadings of 2 and 5 wt %. Upon UV excitation, the different samples exhibit strong red emission centered at 619 nm attributed to the 5D0 → 7F2 transition of Eu3+ ions. The angular distribution of the emission depends on the NC size. Larger NCs significantly reduce photon leakage at the edges due to optical scattering
Unsupervised horizontal attacks against public-key primitives with DCCA
No full textInternational audienceIn order to protect against side-channel attacks, masking countermeasure is widely considered. Its application on asymmetric cryptographic algorithms, such as RSA implementations, rendered multiple traces aggregation inefficient and led to the development of single trace horizontal attacks. Among these horizontal attacks proposed in the literature, many are based on the use of clustering techniques or statistical distinguishers to identify operand collisions. These attacks can be difficult to implement in practice, as they often require advanced trace pre-processing, including the selection of points of interest, a step that is particularly complex to perform in a non-profiling context. In recent years, numerous studies have shown the effectiveness of deep learning in security evaluation for conducting side-channel attacks. However, few attentions have been given to its application in asymmetric cryptography and horizontal attack scenarios. Additionally, the majority of deep learning attacks tend to focus on profiling attacks, which involve a supervised learning phase. In this paper, we propose a new non-profiling horizontal attack using an unsupervised deep learning method called Deep Canonical Correlation Analysis. In this approach, we propose to use a siamese neural network to maximize the correlation between pairs of modular operation traces through canonical correlation analysis, projecting them into a highly correlated latent space that is more suitable for identifying operand collisions. Several experimental results, on simulated traces and a protected RSA implementation with up-to-date countermeasures, show how our proposal outperformed state-of-the-art attacks despite being simpler to implement. This suggests that the use of deep learning can be impactful for security evaluators, even in a non-profiling context and in a fully unsupervised way
Détection non supervisée de changements radiométriques en imagerie radar à synthèse d'ouverture
No full textInternational audienceSynthethic Aperture Radar (SAR) imaging is a key imaging technique for change detection in remote sensing. Thistask is difficult due to the speckle phenomenon, so a denoising step is helpful to be more robust to this phenomenon. However, it ismandatory to take into account the denoising uncertainties for a constant false alarm probability change detection because denoisinginstabilities must be distinguished from changes. Thus, we propose a neural network, trained in a self-supervised way, to predictdenoising uncertainties for a radiometric change detection whose performance is evaluated on TerraSAR-X satellite images.L'imagerie radar à synthèse d'ouverture est un mode d'imagerie clé pour la détection de changements en télédétection. Cette tâche est difficile à cause du phénomène de chatoiement, un phénomène qui nécessite de réaliser une étape de débruitage pour y être davantage robuste. Cependant, il est nécessaire de prendre en compte les incertitudes de débruitage pour contrôler la probabilité de fausse alarme des changements détectés car les instabilités de débruitage doivent être distinguées des changements. Nous proposons donc un réseau, entraîné de manière auto-supervisée, pour prédire les incertitudes de débruitage menant à une détection de changements radiométriques dont la performance est évaluée sur des images du satellite TerraSAR-X
Fast, accurate, and predictive method for atom detection in site-resolved images of microtrap arrays
No full textÀ paraître dans Physical Review AppliedWe introduce a new method, rooted in estimation theory, to detect individual atoms in site-resolved images of microtrap arrays, such as optical lattices or optical tweezers arrays. Using labelled test images, we demonstrate drastic improvement of the detection accuracy compared to the popular method based on Wiener deconvolution when the inter-site distance is comparable to the radius of the point spread function. The runtime of our method scales approximately linearly with the number of sites, and remains well below 100 ms for an array of 100 x 100 sites on a desktop computer. It is therefore fully compatible with a real-time usage. Finally, we propose a rigorous definition for the signal-to-noise ratio of the problem, and show that it can be used as a predictor for the detection error rate. Our work opens the prospect for future experiments with increased array sizes, or reduced inter-site distances
Wettability characterization of laser textured thin film metallic glasses: from macro- to micro- scale using Environmental SEM
No full textInternational audienceMetallic glasses (MGs) are known for their exceptional mechanical and chemical properties, resulting from their amorphous structure and absence of crystalline defects. While bulk metallic glasses (BMGs) have been studied since the 1960s, their size limitations and the complexity of synthesis, due to the high number of elements required, pose significant challenges. Recent advances have shown that physical vapor deposition (PVD) processes, which enable rapid cooling of deposited atoms, facilitate the formation of metastable amorphous metallic phases [1]. As a result, metallic glasses in thin film form are easier to produce via PVD than their bulk counterparts, offering greater flexibility in tailoring their chemical composition.Magnetron sputtering from pure metallic targets has demonstrated success in creating binary Zr-Cu thin film metallic glasses (TFMGs) with a wide range of Cu compositions (from 13 to 85 at.% [2]). These films exhibit low surface roughness and a lack of grain boundaries, making them well-suited for femtosecond laser treatment [3] aimed at further enhancing their properties. Laser irradiation allows for one-step, highly repeatable surface modifications, inducing localized changes in both topography and chemistry.This work investigates the formation of laser-induced periodic surface structures (LIPSS) on ternary magnetron-sputtered TFMG (ZrCuAg), which possess notable biological properties [4]. The TFMGs are treated with infrared ultrashort laser pulses, and various surface textures are created by adjusting laser parameters such as fluence or pulse overlap. The resulting surfaces are analyzed for topographic and chemical changes using scanning electron microscopy and atomic force microscopy. Additionally, the study focuses on the modification of surface wettability (hydrophilicity/hydrophobicity). Wettability is assessed macroscopically through water contact angle measurements, while microscale condensation behavior is examined via in situ environmental scanning electron microscopy. These complementary methods provide valuable insights into the interaction of small water droplets with the textured surfaces, and the wetting behavior is discussed in relation to the surface chemistry and texture.[1] C.-Y. Chuand, et al., “Mechanical properties study of a magnetron-sputtered Zr-based thin film metallic glass”, Surface and Coatings Technology, 2013[2] M. Apreutesei, et al., “Zr-Cu thin film metallic glasses: An assessment of the thermal stability and phases transformation mechanisms”, Journal of Alloys and Compounds, 2015[3] M. Prudent, et al., “Initial morphology and feedback effects on laser-induced periodic nano-structuring of thin-film metallic glasses”, Nanomaterials, 2021[4] N. Lebrun, et al., “Metallic glasses for biological applications and opportunities opened by laser surface texturing: A review”, Applied Surface Science, 202