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A next-generation medium-energy gamma-ray telescope targeting the MeV range would address open questions in astrophysics regarding how extreme conditions accelerate cosmic-ray particles, produce relativistic jet outflows, and more. One concept, AMEGO-X, relies upon the mission-enabling CMOS Monolithic Active Pixel Sensor silicon chip AstroPix. AstroPix is designed for space-based use, featuring low noise, low power consumption, and high scalability. Desired performance of the device include an energy resolution of 5 keV (or 10% FWHM) at 122 keV and a dynamic range per-pixel of 25-700 keV, enabled by the addition of a high-voltage bias to each pixel which supports a depletion depth of 500 um. This work reports on the status of the AstroPix development process with emphasis on the current version under test, version three (v3), and highlights of version two (v2). Version 3 achieves energy resolution of 10.4 +\- 3.2 % at 59.5 keV and 94 +\- 6 um depletion in a low-resistivity test silicon substrate., Comment: 20 pages, 13 figures

The kinetic stability of collisionless, sloshing beam-ion (45{\deg} pitch angle) plasma is studied in a 3D simple magnetic mirror, mimicking the Wisconsin High-temperature superconductor Axisymmetric Mirror (WHAM) experiment. The collisional Fokker-Planck code CQL3D-m provides a slowing-down beam-ion distribution to initialize the kinetic-ion/fluid-electron code Hybrid-VPIC, which then simulates free plasma decay without external heating or fueling. Over 1-10 $\mu$s, drift-cyclotron loss-cone (DCLC) modes grow and saturate in amplitude. DCLC scatters ions to a marginally-stable distribution with gas-dynamic rather than classical-mirror confinement. Sloshing ions can trap cool (low-energy) ions in an electrostatic potential well to stabilize DCLC, but DCLC itself does not scatter sloshing beam-ions into said well. Instead, cool ions must come from external sources such as charge-exchange collisions with a low-density neutral population. Manually adding cool ~1 keV ions improves beam-ion confinement ~2-5x in Hybrid-VPIC simulations, which qualitatively corroborates measurements from real mirror devices with sloshing ions., Comment: Submitted; 35 pages, 14 figures

Plastic flow is conventionally treated as continuous in finite element (FE) codes, whether in isotropic, anisotropic plasticity, or crystal plasticity. This approach, derived from continuum mechanics, contradicts the intermittent nature of plasticity at the elementary scale. Understanding crystal plasticity at micro-scale opens the door to new engineering applications, such as microscale machining. In this work, a new approach is proposed to account for the intermittence of plastic deformation while remaining within the framework of continuum mechanics. We introduce a material parameter, the plastic deformation threshold, denoted as $\Delta p_{min}$, corresponding to the plastic deformation carried by the minimal plastic deformation burst within the material. The incremental model is based on the traditional predictor-corrector algorithm to calculate the elastoplastic behavior of a material subjected to any external loading. The model is presented within the framework of small deformations for von Mises plasticity. To highlight the main features of the approach, the plastic strain increment is calculated using normality rule and consistency conditions, and is accepted only if it exceeds $\Delta p_{min}$. To achieve this, a time-discontinuous generalization of the Karush-Kuhn-Tucker (KKT) conditions is proposed. The simulations show that the introduction of the plastic threshold allows for the reproduction of the spatiotemporal intermittence of plastic flow, capturing the self-organization of plastic flow in complex loading scenarios within an FE model.

Various real-world challenges require planning algorithms that can adapt to a broad range of domains. Traditionally, the creation of planning domains has relied heavily on human implementation, which limits the scale and diversity of available domains. While recent advancements have leveraged generative AI technologies such as large language models (LLMs) for domain creation, these efforts have predominantly focused on translating existing domains from natural language descriptions rather than generating novel ones. In contrast, the concept of domain randomization, which has been highly effective in reinforcement learning, enhances performance and generalizability by training on a diverse array of randomized new domains. Inspired by this success, our tool, PDDLFuse, aims to bridge this gap in Planning Domain Definition Language (PDDL). PDDLFuse is designed to generate new, diverse planning domains that can be used to validate new planners or test foundational planning models. We have developed methods to adjust the domain generators parameters to modulate the difficulty of the domains it generates. This adaptability is crucial as existing domain-independent planners often struggle with more complex problems. Initial tests indicate that PDDLFuse efficiently creates intricate and varied domains, representing a significant advancement over traditional domain generation methods and making a contribution towards planning research., Comment: 218 Tables, 3 Figures, 4 Algorithms

This paper presents the Hammir tandem mirror design based on Realta Fusion's first-of-a-kind model for axisymmetric magnetic mirror fusion performance. This model uses an integrated end plug simulation model including, heating, equilibrium, and transport combined with a new formulation of the plasma operation contours (POPCONs) technique for the tandem mirror central cell. Using this model, it is shown that an end plug utilizing high temperature superconducting magnets and modern neutral beams enables a classical tandem mirror pilot plant producing a fusion gain Q > 5. The approach here represents an important advance in tandem mirror design. The high fidelity end plug model enables calculations of heating and transport in the highly non-Maxwellian end plug to be made more accurately and the central cell POPCON technique allows consideration of a wide range of parameters in the relatively simple near-Maxwellian central cell, facilitating the selection of more optimal central cell plasmas. These advances make it possible to find more conservative classical tandem mirror fusion pilot plant operating points with lower $\beta$, temperatures, neutral beam energies, and end plug performance than designs in the literature. Despite being more conservative, it is shown that these operating points can still form the basis of a viable fusion pilot plant.

In this work, we investigate an effective method for showing that functors between categories are left adjoints. The method applies to a large class of categories, namely locally finitely presentable categories, which are ubiquitous in practice and include standard examples like Set, Grp, etc. Our method relies on a known description of these categories as orthogonal sub-classes of presheaf categories. The functors on which our method applies are the ones that can be presented as particular profunctors, called Kan models in this context. The method for left-adjointness then relies on computing that a particular criterion is satisfied. From this method, we also derive another method for showing that a category is cartesian closed. As proofs of concept and effectivity, we give a concrete implementation of the structures and of the left-adjointness criterion in OCaml and apply it on several examples.

Achieving the EU's climate neutrality goal requires retrofitting existing buildings to reduce energy use and emissions. A critical step in this process is the precise assessment of geometric building envelope characteristics to inform retrofitting decisions. Previous methods for estimating building characteristics, such as window-to-wall ratio, building footprint area, and the location of architectural elements, have primarily relied on applying deep-learning-based detection or segmentation techniques on 2D images. However, these approaches tend to focus on planar facade properties, limiting their accuracy and comprehensiveness when analyzing complete building envelopes in 3D. While neural scene representations have shown exceptional performance in indoor scene reconstruction, they remain under-explored for external building envelope analysis. This work addresses this gap by leveraging cutting-edge neural surface reconstruction techniques based on signed distance function (SDF) representations for 3D building analysis. We propose BuildNet3D, a novel framework to estimate geometric building characteristics from 2D image inputs. By integrating SDF-based representation with semantic modality, BuildNet3D recovers fine-grained 3D geometry and semantics of building envelopes, which are then used to automatically extract building characteristics. Our framework is evaluated on a range of complex building structures, demonstrating high accuracy and generalizability in estimating window-to-wall ratio and building footprint. The results underscore the effectiveness of BuildNet3D for practical applications in building analysis and retrofitting.

We consider the problem of finding the ``best'' approximation of an $n$-dimensional probability measure $\rho$ using a measure $\nu$ whose support is parametrized by $f : \mathbb{R}^m \to \mathbb{R}^n$ where $m < n$. We quantify the performance of the approximation with the Monge-Kantorovich $p$-cost (also called the Wasserstein $p$-cost) $\mathbb{W}_p^p(\rho, \nu)$, and constrain the complexity of the approximation by bounding the $W^{k,q}$ Sobolev norm of $f$, which acts as a ``budget.'' We may then reformulate the problem as minimizing a functional $\mathscr{J}_p(f)$ under a constraint on the Sobolev budget. We treat general $k \geq 1$ for the Sobolev differentiability order (though $q, m$ are chosen to restrict $W^{k,q}$ to the supercritical regime $k q > m$ to guarantee existence of optimizers). The problem is closely related to (but distinct from) principal curves with length constraints when $m=1, k = 1$ and smoothing splines when $k > 1$. New aspects and challenges arise from the higher order differentiability condition. We study the gradient of $\mathscr{J}_p$, which is given by a vector field along $f$ we call the barycenter field. We use it to construct improvements to a given $f$, which gives a nontrivial (almost) strict monotonicty relation between the functional $\mathscr{J}_p$ and the Sobolev budget. We also provide a natural discretization scheme and establish its consistency. We use this scheme to model a generative learning task; in particular, we demonstrate that adding a constraint like ours as a soft penalty yields substantial improvement in training a GAN to produce images of handwritten digits, with performance competitive with weight-decay., Comment: 68 pages, 23 figures, 50 pages without figures

A new generation of gravitational wave detectors is currently being designed with the likely use of a different laser wavelength compared to current instruments. The estimation of the optical losses for this new wavelength is particularly relevant to derive the detector sensitivity and also to anticipate the optical performances of future instruments. In this article, we measured the absorption and angle-resolved scattering of several mirror samples in order to compare optical losses at a wavelength of 1064 and 1550\ nm. In addition, we have carried out simulations of the Einstein Telescope arm cavities at 1064 and 1550\ nm taking into account losses due to surface low-spatial frequency flatness. Our results suggest that optical losses as measured at 1064\ nm are about twice as large as those at 1550\ nm as predicted with a simple model., Comment: 12 pages, 3 figures

Brownian thermal noise of thin-film coatings is a fundamental limit for high-precision experiments based on optical resonators such as gravitational-wave interferometers. Here we present the results of a research activity aiming to develop lower-noise ion-beam sputtered silicon nitride thin films compliant with the very stringent requirements on optical loss of gravitational-wave interferometers. In order to test the hypothesis of a correlation between the synthesis conditions of the films and their elemental composition and optical and mechanical properties, we varied the voltage, current intensity and composition of the sputtering ion beam, and we performed a broad campaign of characterizations. While the refractive index was found to monotonically depend on the beam voltage and linearly vary with the N/Si ratio, the optical absorption appeared to be strongly sensitive to other factors, as yet unidentified. However, by systematically varying the deposition parameters, an optimal working point was found. Thus we show that the loss angle and extinction coefficient of our thin films can be as low as $(1.0 \pm 0.1) \times 10^{-4}$ rad at $\sim$2.8 kHz and $(6.4 \pm 0.2) \times 10^{-6}$ at 1064 nm, respectively, after thermal treatment at 900 $^{\circ}$C. To the best of our knowledge, such loss angle value is the lowest ever measured on this class of thin films. We then used our silicon nitride thin films to design and produce a multi-material mirror coating showing a thermal noise amplitude of $(10.3 \pm 0.2) \times 10^{-18}$ m Hz$^{-1/2}$ at 100 Hz, which is 25\% lower than in current mirror coatings of the Advanced LIGO and Advanced Virgo interferometers, and an optical absorption as low as $(1.6 \pm 0.5)$ parts per million at 1064 nm.

This paper focuses on source-free domain adaptation for object detection in computer vision. This task is challenging and of great practical interest, due to the cost of obtaining annotated data sets for every new domain. Recent research has proposed various solutions for Source-Free Object Detection (SFOD), most being variations of teacher-student architectures with diverse feature alignment, regularization and pseudo-label selection strategies. Our work investigates simpler approaches and their performance compared to more complex SFOD methods in several adaptation scenarios. We highlight the importance of batch normalization layers in the detector backbone, and show that adapting only the batch statistics is a strong baseline for SFOD. We propose a simple extension of a Mean Teacher with strong-weak augmentation in the source-free setting, Source-Free Unbiased Teacher (SF-UT), and show that it actually outperforms most of the previous SFOD methods. Additionally, we showcase that an even simpler strategy consisting in training on a fixed set of pseudo-labels can achieve similar performance to the more complex teacher-student mutual learning, while being computationally efficient and mitigating the major issue of teacher-student collapse. We conduct experiments on several adaptation tasks using benchmark driving datasets including (Foggy)Cityscapes, Sim10k and KITTI, and achieve a notable improvement of 4.7\% AP50 on Cityscapes$\rightarrow$Foggy-Cityscapes compared with the latest state-of-the-art in SFOD. Source code is available at https://github.com/EPFL-IMOS/simple-SFOD., Comment: Accepted at ECCV 2024. 19 pages

Zn-Al-Mg coatings are characterized by a complex microstructure with dendritic and eutectic phases. This heterogeneous phase distribution contributes to multiple deformation and damage mechanisms. The presence of brittle phases promotes crack initiation and propagation. This study reveals a new deformation and damage mechanism of a Zn-Al-Mg coating, where twinning can induce crack initiation in the eutectic region. The chronology of different events leading to crack initiation and propagation is clearly established by in-situ tensile testing in a scanning electron microscope, which helps to establish a detailed characterization of the mechanical behavior of the coating.

Complex microstructure evolution has been observed \rev{both bare and coated } Ni-based single crystal superalloys. Rafting and $\gamma'$ depletion are investigated in this study through a brief experimental analysis and a detailed phase field model to account for mechanical-diffusion coupling. The proposed model has been implemented in a finite element code. As a main result, it is shown that rafting, $\gamma'$ depletion close to free surface/oxide layer or $\gamma'$ coalescence close to coating layer, and mechanical behavior are strongly coupled. The local additional flux of Al explains this coupling to a large extent. Finally, a discussion of strain localization and local flux of Al paves the way for clarification of these cases that degrade the performance of superalloys.

Damage to road pavement can develop into cracks, potholes, spallings, and other issues posing significant challenges to the integrity, safety, and durability of the road structure. Detecting and monitoring the evolution of these damages is crucial for maintaining the condition and structural health of road infrastructure. In recent years, researchers have explored various data-driven methods for image-based damage detection in road monitoring applications. The field gained attention with the introduction of the Road Damage Detection Challenge (RDDC2018), encouraging competition in developing object detectors on street-view images from various countries. Leading teams have demonstrated the effectiveness of ensemble models, mostly based on the YOLO and Faster R-CNN series. Data augmentations have also shown benefits in object detection within the computer vision field, including transformations such as random flipping, cropping, cutting out patches, as well as cut-and-pasting object instances. Applying cut-and-paste augmentation to road damages appears to be a promising approach to increase data diversity. However, the standard cut-and-paste technique, which involves sampling an object instance from a random image and pasting it at a random location onto the target image, has demonstrated limited effectiveness for road damage detection. This method overlooks the location of the road and disregards the difference in perspective between the sampled damage and the target image, resulting in unrealistic augmented images. In this work, we propose an improved Cut-and-Paste augmentation technique that is both content-aware (i.e. considers the true location of the road in the image) and perspective-aware (i.e. takes into account the difference in perspective between the injected damage and the target image)., Comment: Extended abstract accepted at ESREL 2024. 2 pages

Pathfinding problems are found throughout robotics, computational science, and natural sciences. Traditional methods to solve these require training deep neural networks (DNNs) for each new problem domain, consuming substantial time and resources. This study introduces a novel foundation model, leveraging deep reinforcement learning to train heuristic functions that seamlessly adapt to new domains without further fine-tuning. Building upon DeepCubeA, we enhance the model by providing the heuristic function with the domain's state transition information, improving its adaptability. Utilizing a puzzle generator for the 15-puzzle action space variation domains, we demonstrate our model's ability to generalize and solve unseen domains. We achieve a strong correlation between learned and ground truth heuristic values across various domains, as evidenced by robust R-squared and Concordance Correlation Coefficient metrics. These results underscore the potential of foundation models to establish new standards in efficiency and adaptability for AI-driven solutions in complex pathfinding problems.

Deep learning approaches have recently been extensively explored for the prognostics of industrial assets. However, they still suffer from a lack of interpretability, which hinders their adoption in safety-critical applications. To improve their trustworthiness, explainable AI (XAI) techniques have been applied in prognostics, primarily to quantify the importance of input variables for predicting the remaining useful life (RUL) using post-hoc attribution methods. In this work, we propose the application of Concept Bottleneck Models (CBMs), a family of inherently interpretable neural network architectures based on concept explanations, to the task of RUL prediction. Unlike attribution methods, which explain decisions in terms of low-level input features, concepts represent high-level information that is easily understandable by users. Moreover, once verified in actual applications, CBMs enable domain experts to intervene on the concept activations at test-time. We propose using the different degradation modes of an asset as intermediate concepts. Our case studies on the New Commercial Modular AeroPropulsion System Simulation (N-CMAPSS) aircraft engine dataset for RUL prediction demonstrate that the performance of CBMs can be on par or superior to black-box models, while being more interpretable, even when the available labeled concepts are limited. Code available at \href{https://github.com/EPFL-IMOS/concept-prognostics/}{\url{github.com/EPFL-IMOS/concept-prognostics/}}., Comment: Under review