A Statistical Study of Local Dust Storm Occurrences on Mars Using the 2.77 μm CO 2 Band Observed by OMEGA/Mars Express

International audienceLocal Dust Storms (LDS) are defined as dust storm phenomena that cover an area smaller than 1.6 × 10 6 km 2 or persist for less than three sols. The study of LDS is critical for understanding dust transport processes in both horizontal and vertical directions and the evolution of large‐scale dust storms on Mars. However, the relatively small scale and short lifetime make it difficult to detect with previous studies. OMEGA onboard Mars Express (MEx) has conducted spectroscopic measurements with high spatial resolution (up to ∼400 m/pixel). Here, we present a method to retrieve dust optical depth and detect LDS using the 2.77 μm CO 2 absorption band. At this wavelength, photons are absorbed before reaching the surface, and the photons collected by OMEGA have been scattered around 20–30 km altitude by dust. We have detected 146 LDS events from the retrieved dust optical depth in MY27‐29. The LDS were generally observed in the southern summer season, while frequent occurrences of LDS were observed during the northern summer (Ls = 130°–150°) in MY27. The remarkable increase in LDS is also identified just before the global dust storm in MY28. We found a peak in the probability of LDS around noon in both seasons, Ls = 0°–180° and Ls = 180°–360°. In Ls = 0°–180°, high probability areas are found only in specific regions, such as Chryse Planitia. The probability areas expands over a wide range, except high‐latitude north of 40°N in Ls = 180°–360°. These findings highlight the spatiotemporal roles LDS play in dust transport, providing insights into the dust cycle (245/250 words)

Expressing general constitutive models in FEniCSx using external operators and algorithmic automatic differentiation

International audienceMany problems in solid mechanics involve general and non-trivial constitutive models that are difficult to express in variational form. Consequently, it can be challenging to define these problems in automated finite element solvers, such as the FEniCS Project, that use domain-specific languages specifically designed for writing variational forms. In this article, we describe a methodology and software framework for FEniCSx / DOLFINx that enables the expression of constitutive models in nearly any general programming language. We demonstrate our approach on two solid mechanics problems; the first is a simple von Mises elastoplastic model with isotropic hardening implemented with Numba, and the second a Mohr-Coulomb elastoplastic model with apex smoothing implemented with JAX. In the latter case we show that by leveraging JAX's algorithmic automatic differentiation transformations we can avoid error-prone manual differentiation of the terms necessary to resolve the constitutive model. We show extensive numerical results, including Taylor remainder testing, that verify the correctness of our implementation. The software framework and fully documented examples are available as supplementary material under the LGPLv3 or later license

Contributions on complexity bounds for Deterministic Partially Observed Markov Decision Process

International audienceMarkov Decision Processes (Mdps) form a versatile framework used to model a wide range of optimization problems. The Mdp model consists of sets of states, actions, time steps, rewards, and probability transitions. When in a given state and at a given time, the decision maker's action generates a reward and determines the state at the next time step according to the probability transition function. However, Mdps assume that the decision maker knows the state of the controlled dynamical system. Hence, when one needs to optimize controlled dynamical systems under partial observation, one often turns toward the formalism of Partially Observed Markov Decision Processes (Pomdp). Pomdps are often untractable in the general case as Dynamic Programming suffers from the curse of dimensionality. Instead of focusing on the general Pomdps, we present a subclass where transitions and observations mappings are deterministic: Deterministic Partially Observed Markov Decision Processes (Det-Pomdp). That subclass of problems has been studied by (Littman, 1996) and (Bonet, 2009). It was first considered as a limit case of Pomdps by Littman, mainly used to illustrate the complexity of Pomdps when considering as few sources of uncertainties as possible. In this paper, we improve on Littman's complexity bounds. We then introduce and study an even simpler class: Separated Det-Pomdps and give some new complexity bounds for this class. This new class of problems uses a property of the dynamics and observation to push back the curse of dimensionality

What are Capra-Convex Sets?

This paper focuses on a specific form of abstract convexity known as Capra-convexity, where a constant along primal rays (Capra) coupling replaces the scalar product used in standard convex analysis to define generalized Fenchel conjugacies. A key motivating result is that the ℓ0 pseudonorm — which counts the number of nonzero components in a vector — is equal to its Capra-biconjugate. This implies that ℓ0 is a Capra-convex function, highlighting potential applications in statistics and machine learning, particularly for enforcing sparsity in models. Building on prior work characterizing the Capra-subdifferential of ℓ0 and the role of source norms in defining the Capra-coupling, the paper provides a characterization of Capra-convex sets

Climate Change, Natural Resources, and Conflict

This paper examines how climate change and natural resource dynamics contribute to conflict, with a focus on the implications of the green transition. It reviews empirical evidence showing that extreme weather events-such as droughts, floods, and heatwaves-are linked to increased violence, particularly through economic disruptions, reduced agricultural productivity, and displacement. The analysis also explores the mechanisms through which climate shocks influence conflict, including opportunity costs, resource competition, and behavioral responses to environmental stress. The discussion then turns to the role of natural resource exploitation, especially in the context of rising demand for minerals essential to low-carbon technologies. The paper highlights how resource price and availability shocks can trigger conflict, often depending on the type of resource, extraction method, and local governance. It also addresses the overlap between climate-and resource-driven conflict risks, emphasizing that their interaction may amplify instability. Throughout, the paper identifies open research questions related to prediction, the effects of long-run environmental changes, and the design of policy responses. These include insurance schemes, climate adaptation strategies, infrastructure investment, and regulatory frameworks for resource governance. The findings point to the need for research that integrates climate and conflict dynamics, with the goal of informing policies that can mitigate the risks associated with environmental change and resource pressures

H(curl)\boldsymbol {H} (\textbf{curl})-reconstruction of piecewise polynomial fields with application to hphp-a posteriori nonconforming error analysis for Maxwell's equations

We devise and analyse a novel $\boldsymbol{H}(\textbf{curl})$-reconstruction operator for piecewise polynomial fields on shape-regular simplicial meshes. The (non-polynomial) reconstruction is devised over the mesh vertex patches using the partition of unity induced by hat basis functions in combination with local Helmholtz decompositions. Our main focus is on homogeneous tangential boundary conditions. We prove that the difference between the reconstructed $\boldsymbol{H}_0(\textbf{curl})$-field and the original, piecewise polynomial field, measured in the broken curl norm and in the $\boldsymbol{L}^2$-norm, can be bounded in terms of suitable jump norms of the original field. The bounds are always $h$-optimal, and $p$-suboptimal by $\frac12$-order for the broken curl norm and by $\frac32$-order for the $\boldsymbol{L}^2$-norm. An auxiliary result of independent interest is a novel broken-curl, divergence-preserving Poincar\'{e} inequality on vertex patches. Moreover, the $\boldsymbol{L}^2$-norm estimate can be improved to $\frac12$-order suboptimality under a (reasonable) assumption on the uniform elliptic regularity pickup for a Poisson problem with Neumann conditions over the vertex patches. We also discuss extensions of the $\boldsymbol{H}_0(\textbf{curl})$-reconstruction operator to the prescription of mixed boundary conditions, to agglomerated polytopal meshes, and to convex domains. Finally, we showcase an important application of the $\boldsymbol{H}(\textbf{curl})$-reconstruction operator to the $hp$-a posteriori nonconforming error analysis of Maxwell's equations. We focus on the (symmetric) interior penalty discontinuous Galerkin (dG) approximation of some simplified forms of Maxwell's equations

Mitigation of tire wear particles in stormwater management practices

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Transport policy at an impasse: managing on-street delivery areas in Paris

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A note on the complexity of the picker routing problem in multi-block warehouses and related problems

International audienceThe Picker Routing Problem (PRP), which consists of finding a minimum-length tour between a set of storage locations in a warehouse, is one of the most important problems in the warehousing logistics literature. Despite its popularity, the tractability of the PRP in multi-block warehouses remains an open question. This technical note aims to fill this research gap by establishing that the problem is strongly NP-hard. As a corollary, the complexity status of other related problems is settled

Found in Translation: semantic approaches for enhancing AI interpretability in face verification

The increasing complexity of machine learning models in computer vision, particularly in face verification, requires the development of explainable artificial intelligence (XAI) to enhance interpretability and transparency. This study extends previous work by integrating semantic concepts derived from human cognitive processes into XAI frameworks to bridge the comprehension gap between model outputs and human understanding. We propose a novel approach combining global and local explanations, using semantic features defined by user-selected facial landmarks to generate similarity maps and textual explanations via large language models (LLMs). The methodology was validated through quantitative experiments and user feedback, demonstrating improved interpretability. Results indicate that our semantic-based approach, particularly the most detailed set, offers a more nuanced understanding of model decisions than traditional methods. User studies highlight a preference for our semantic explanations over traditional pixelbased heatmaps, emphasizing the benefits of human-centric interpretability in AI. This work contributes to the ongoing efforts to create XAI frameworks that align AI models behaviour with human cognitive processes, fostering trust and acceptance in critical applications