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In 2008, America's largest government-sponsored enterprises (GSEs)--the Federal National Mortgage Association (Fannie Mae) and the Federal Home Loan Mortgage Corporation (Freddie Mac)--recorded combined net losses of $109 billion. This figure surpassed the GSEs' cumulative net income over the prior 40 years, and the federal government placed both GSEs in conservatorship on September 6, 2008. Although Congress clearly intended such conservatorships to be temporary, unlike the receivership process that it designed for closing an insolvent GSE, both GSEs remain insolvent and in conservatorship. Throughout the 16-year conservatorship, many people have proposed plans to rework the Preferred Stock Purchase Agreements (PSPAs) that specify the terms of the support the US Treasury provided to the GSEs. Although renegotiating the PSPAs may be necessary to end the GSEs' conservatorships, any new deal should be structured to facilitate receivership and liquidation, accelerating an end to America's failed GSE experiment. The federal government's dominance in the housing finance market--both before and throughout the GSE conservatorships--has stifled private competition and worsened Americans' financial condition, but it has done virtually nothing to increase the homeownership rate. It is time to wind down the affairs of the GSEs through receivership, a process that the federal government should have undertaken in 2008.

The Athena mission entered a redefinition phase in July 2022, driven by the imperative to reduce the mission cost at completion for the European Space Agency below an acceptable target, while maintaining the flagship nature of its science return. This notably called for a complete redesign of the X-ray Integral Field Unit (X-IFU) cryogenic architecture towards a simpler active cooling chain. Passive cooling via successive radiative panels at spacecraft level is now used to provide a 50 K thermal environment to an X-IFU owned cryostat. 4.5 K cooling is achieved via a single remote active cryocooler unit, while a multi-stage Adiabatic Demagnetization Refrigerator ensures heat lift down to the 50 mK required by the detectors. Amidst these changes, the core concept of the readout chain remains robust, employing Transition Edge Sensor microcalorimeters and a SQUID-based Time-Division Multiplexing scheme. Noteworthy is the introduction of a slower pixel. This enables an increase in the multiplexing factor (from 34 to 48) without compromising the instrument energy resolution, hence keeping significant system margins to the new 4 eV resolution requirement. This allows reducing the number of channels by more than a factor two, and thus the resource demands on the system, while keeping a 4' field of view (compared to 5' before). In this article, we will give an overview of this new architecture, before detailing its anticipated performances. Finally, we will present the new X-IFU schedule, with its short term focus on demonstration activities towards a mission adoption in early 2027., Comment: 44 pages, 14 figures, accepted for publication in Experimental Astronomy

In this work, we present a preliminary study combining two approaches in the context of solving PDEs: the classical finite element method (FEM) and more recent techniques based on neural networks. Indeed, in recent years, physics-informed neural networks (PINNs) have become particularly interesting for rapidly solving such problems, especially in high dimensions. However, their lack of accuracy is a significant drawback in this context, hence the interest in combining them with FEM, for which error estimators are already known. The complete pipeline proposed here, therefore, consists of modifying classical FEM approximation spaces by taking information from a prior, chosen here as the prediction of a neural network. On the one hand, this combination improves and certifies the prediction of neural networks to obtain a fast and accurate solution. On the other hand, error estimates are proven, showing that such strategies outperform classical ones by a factor that depends only on the quality of the prior. We validate our approach with numerical results obtained for this preliminary work on parametric problems with one- and two-dimensional geometries. They demonstrate that to achieve a fixed error target, a coarser mesh can be used with our enhanced FEM compared to the standard one, leading to reduced computation time, particularly for parametric problems.

Due to the trade-off between the temporal and spatial resolution of thermal spaceborne sensors, super-resolution methods have been developed to provide fine-scale Land SurfaceTemperature (LST) maps. Most of them are trained at low resolution but applied at fine resolution, and so they require a scale-invariance hypothesis that is not always adapted. Themain contribution of this work is the introduction of a Scale-Invariance-Free approach for training Neural Network (NN) models, and the implementation of two NN models, calledScale-Invariance-Free Convolutional Neural Network for Super-Resolution (SIF-CNN-SR) for the super-resolution of MODIS LST products. The Scale-Invariance-Free approach consists ontraining the models in order to provide LST maps at high spatial resolution that recover the initial LST when they are degraded at low resolution and that contain fine-scale texturesinformed by the high resolution NDVI. The second contribution of this work is the release of a test database with ASTER LST images concomitant with MODIS ones that can be usedfor evaluation of super-resolution algorithms. We compare the two proposed models, SIF-CNN-SR1 and SIF-CNN-SR2, with four state-of-the-art methods, Bicubic, DMS, ATPRK, Tsharp,and a CNN sharing the same architecture as SIF-CNN-SR but trained under the scale-invariance hypothesis. We show that SIF-CNN-SR1 outperforms the state-of-the-art methods and the other two CNN models as evaluated with LPIPS and Fourier space metrics focusing on the analysis of textures. These results and the available ASTER-MODIS database for evaluation are promising for future studies on super-resolution of LST.

While the United States leads the world in Artificial Intelligence (AI) innovation, the schools lag behind in preparing teachers and students for the Fourth Industrial Revolution. The extraordinary pace of technological change, and the potential for both opportunity and risk, may be unprecedented. What leaders in education and education policy do next matters. To help accelerate action in U.S. public education and develop a short-term roadmap for districts and other education leaders, the Center on Reinventing Public Education (CRPE) brought together over 60 state and federal policymakers, edtech innovators, school system leaders, and advocates in Albuquerque, New Mexico, in April 2024 to participate in the Think Forward: Learning with AI forum. This report reflects key learnings and conversations that emerged from CRPE's Think Forward convening, including: (1) how AI can enable needed changes in our schools; (2) how current conditions in the edtech market act as barriers to closing equity gaps; and (3) how policy and practice must adapt for lasting system change. It concludes with a short-term action plan developed by forum participants that provides an immediate path forward and outlines the roles wide-ranging stakeholders must play to address our shared challenges and opportunities.

This study investigates the influence of Visual Guidance (VG) on user performance and human factors within Augmented Reality (AR) via a between-subjects experiment. VG is a crucial component in AR applications, serving as a bridge between digital information and real-world interactions. Unlike prior research, which often produced inconsistent outcomes, our study focuses on varying types of supportive visualisations rather than interaction methods. Our findings reveal a 31% reduction in task completion time, offset by a significant rise in errors, highlighting a compelling trade-off between speed and accuracy. Furthermore, we assess the detrimental effects of occlusion as part of our experimental design. In addition to examining other variables such as cognitive load, motivation, and usability, we identify specific directions and offer actionable insights for future research. Overall, our results underscore the promise of VG for enhancing user performance in AR, while emphasizing the importance of further investigating the underlying human factors.

Several space missions are proposed or planned for the coming two decades dedicated or including mid- to high-resolution spectropolarimetry on a wide UV band. This includes the European instrument Pollux for the NASA HWO flagship mission, the NASA SMEX candidate Polstar, and the French nanosatellite demonstrator CASSTOR. We are developing UV polarimeters for these missions thanks to a R&D program funded by CNES. For the mid- and near-UV, i.e. above 120 nm, birefringent material (MgF2) can be used to produce a polarimeter. This is the baseline for Polstar, CASSTOR, and the MUV and NUV channels of Pollux. Prototypes have been built and tested with excellent results, and further tests are ongoing to fully characterize them. For the FUV channel of Pollux however, it is not possible to use this technology and we have instead studied a design based on mirrors only. We will present the various missions and instruments, their technical challenges, as well as the R&D work performed on UV polarimeters and the proposed design solutions., Comment: 4 pages, ICSO 2024

We propose a general approach for quantitative convergence analysis of non-reversible Markov processes, based on the concept of second-order lifts and a variational approach to hypocoercivity. To this end, we introduce the flow Poincar{\'e} inequality, a space-time Poincar{\'e} inequality along trajectories of the semigroup, and a general divergence lemma based only on the Dirichlet form of an underlying reversible diffusion. We demonstrate the versatility of our approach by applying it to a pair of run-and-tumble particles with jamming, a model from non-equilibrium statistical mechanics, and several piecewise deterministic Markov processes used in sampling applications, in particular including general stochastic jump kernels.

This paper deals with the characterization, in terms of closedness of certain sets regarding other sets, of Farkas lemmas determining when the upperlevel set of a given convex function contains the intersection, say F, of a convex set of a locally convex space X with the inverse image by a continuous linear operator from X to another locally convex space Y of certain convex subset of Y. More in detail, each of the mentioned characterizations of Farkas type lemmas consists in the closedness of certain subset of either one of the "primal" spaces XxYxR and YxR, or of the "dual" space X'xR, regarding some singleton set of the corresponding space. Moreover, the paper also provides an existence theorem for the feasible set F in terms of the closedness of certain subset of the dual space X'xR regarding the singleton set formed by the null element. These results are illustrated with significant applications., Comment: 24 pages

Inelastic neutron scattering measurements on the hexagonal Zn67Mg33S semiconductor alloy reveal a bimodal pattern of the optical modes across the Brillouin zone, confirmed by first-principles simulations. Such modes are sensitive to the local fluctuations in the composition inherent to random Zn/Mg alloying, distinguishing homo from hetero environments of a given bond (1-bond/2-mode), as is formalized for cubic alloys by the percolation model. The latter model thus emerges as a generic framework for systematizing the optical modes of semiconductor alloys in various crystal structures., Comment: 30 pages, 14 figures

Hydrogen is an energy vector, and one possible way to reduce CO 2 emissions. This paper focuses on a hydrogen transport problem where mobile storage units are moved by trucks between sources to be refilled and destinations to meet demands, involving swap operations upon arrival. This contrasts with existing literature where inventories remain stationary. The objective is to optimize daily routing and refilling schedules of the mobile storages. We model the problem as a flow problem on a time-expanded graph, where each node of the graph is indexed by a time-interval and a location and then, we give an equivalent Mixed Integer Linear Programming (MILP) formulation of the problem. For small to medium-sized instances, this formulation can be efficiently solved using standard MILP solvers. However, for larger instances, the computational complexity increases significantly due to the highly combinatorial nature of the refilling process at the sources. To address this challenge, we propose a two-step heuristic that enhances.

Objective: ascending aortic aneurysm growth prediction is still challenging in clinics. In this study, we evaluate and compare the ability of local and global shape features to predict ascending aortic aneurysm growth. Material and methods: 70 patients with aneurysm, for which two 3D acquisitions were available, are included. Following segmentation, three local shape features are computed: (1) the ratio between maximum diameter and length of the ascending aorta centerline, (2) the ratio between the length of external and internal lines on the ascending aorta and (3) the tortuosity of the ascending tract. By exploiting longitudinal data, the aneurysm growth rate is derived. Using radial basis function mesh morphing, iso-topological surface meshes are created. Statistical shape analysis is performed through unsupervised principal component analysis (PCA) and supervised partial least squares (PLS). Two types of global shape features are identified: three PCA-derived and three PLS-based shape modes. Three regression models are set for growth prediction: two based on gaussian support vector machine using local and PCA-derived global shape features; the third is a PLS linear regression model based on the related global shape features. The prediction results are assessed and the aortic shapes most prone to growth are identified. Results: the prediction root mean square error from leave-one-out cross-validation is: 0.112 mm/month, 0.083 mm/month and 0.066 mm/month for local, PCA-based and PLS-derived shape features, respectively. Aneurysms close to the root with a large initial diameter report faster growth. Conclusion: global shape features might provide an important contribution for predicting the aneurysm growth.

Unstable Periodic Orbits (UPOs) were used to identify regimes, and transitions between regimes, in a reduced-order coupled atmosphere-land spectral model. In this paper we describe how the chaotic attractor of this model was clustered using the numerically derived set of UPOs. Using continuation software, the origin of these clusters were also investigated. The flow of model trajectories can be approximated using UPOs, a concept known as shadowing. Here we extend that idea to look at the number of times a UPO shadows a model trajectory over a fixed time period, which we call cumulative shadowing. This concept was used to identify sets of UPOs that describe different life cycles of each cluster. The different regions of the attractor that were identified in the current work, and the transitions between these regions, are linked to specific atmospheric features known as atmospheric blocks.

An $\mathbb{F}_q$-linear code of minimum distance $d$ is called complete if it is not contained in a larger $\mathbb{F}_q$-linear code of minimum distance $d$. In this paper, we classify $\mathbb{F}_q$-linear complete symmetric rank-distance (CSRD) codes in $M_{3\times 3}(\mathbb{F}_q)$ up to equivalence. This includes the classification of $\mathbb{F}_q$-linear maximum symmetric rank-distance (MSRD) codes in $M_{3\times 3}(\mathbb{F}_q)$. Our approach is mainly geometric, and our results contribute towards the classification of nets of conics in $\mathrm{PG}(2, q)$.

Objective: we propose a procedure for calibrating 4 parameters governing the mechanical boundary conditions (BCs) of a thoracic aorta (TA) model derived from one patient with ascending aortic aneurysm. The BCs reproduce the visco-elastic structural support provided by the soft tissue and the spine and allow for the inclusion of the heart motion effect. Methods: we first segment the TA from magnetic resonance imaging (MRI) angiography and derive the heart motion by tracking the aortic annulus from cine-MRI. A rigid-wall fluid-dynamic simulation is performed to derive the time-varying wall pressure field. We build the finite element model considering patient-specific material properties and imposing the derived pressure field and the motion at the annulus boundary. The calibration, which involves the zero-pressure state computation, is based on purely structural simulations. After obtaining the vessel boundaries from the cine-MRI sequences, an iterative procedure is performed to minimize the distance between them and the corresponding boundaries derived from the deformed structural model. A strongly-coupled fluid-structure interaction (FSI) analysis is finally performed with the tuned parameters and compared to the purely structural simulation. Results and Conclusion: the calibration with structural simulations allows to reduce maximum and mean distances between image-derived and simulation-derived boundaries from 8.64 mm to 6.37 mm and from 2.24 mm to 1.83 mm, respectively. The maximum root mean square error between the deformed structural and FSI surface meshes is 0.19 mm. This procedure may prove crucial for increasing the model fidelity in replicating the real aortic root kinematics.

We present a coherent-population trapping (CPT) microcell atomic clock using symmetric auto-balanced Ramsey (SABR) spectroscopy. The pulsed SABR sequence is applied through direct current-based power modulation of the vertical-cavity surface-emitting laser, eliminating the need for an external optical shutter and enabling compatibility with fully-integrated clocks. The sequence is controlled by a single FPGA-based digital electronics board. A key aspect of proper clock operation was the implementation of a real-time tracking of the atomic signal detection window. The clock frequency dependence on laser power, microwave power, laser frequency, and timing of the detection window has been measured, obtaining sensitivity coefficients lower than those obtained with Ramsey-CPT spectroscopy. The Allan deviation of the SABR-CPT clock, based on a microfabricated cell with low-permeation glass windows, is 1.1~$\times$~10$^{-9}$ at 1~s and averages down to the low 10$^{-12}$ range at 1 day integration time. These results pave the way towards the development of Ramsey-CPT chip-scale atomic clocks with enhanced timekeeping performances., Comment: 9 pages, 7 figures

This article explores the different formation scenarios of the Kronian moons system in the context of a highly dissipative Saturn, with the objective of identifying the most likely of these scenarios. First, we review the diversity of objects - moons and rings - orbiting solar system giant planets, and the diversity of their architectures, which formation scenarios must reproduce. We then identify in this broader context the specific features of the Saturn system, such as the particularly large spectrum of its moon masses, the uniqueness of Titan and the presence of both dense and tenuous rings, before discussing the applicability of the different giant planet moon formation scenarios to the Saturn case. We discuss each of the most relevant scenarios and their respective merits. Finally, we tentatively propose a "favorite" scenario and we identify the key observations to be made by future space missions and/or Earth-based telescopic observations to validate this scenario or possibly alternative ones., Comment: 44 pages, 13 figures, accepted for publication in Space Science Reviews, as a part of the collection "New Vision of the Saturnian System in the Context of a Highly Dissipative Saturn" presenting the results from the ISSI Workhop "New Vision of the Saturnian System in the Context of a Highly Dissipative Saturn"

In this article, we aim to approximate a solution to the bilevel equilibrium problem $\mathbf{(BEP})$ for short: find $\bar{x} \in \mathbf{S}_f$ such that $ g(\bar{x}, y) \geq 0, \,\, \forall y \in \mathbf{S}_f, $ where $ \mathbf{S}_f = \{ u \in \mathbf{K} : f(u, z) \geq 0, \forall z \in \mathbf{K} \}. $ Here, $\mathbf{K}$ is a closed convex subset of a real Hilbert space $\mathcal{H}$, and $f$ and $g$ are two real-valued bifunctions defined on $\mathbf{K} \times \mathbf{K}$. We propose an inertial version of the proximal splitting algorithm introduced by Z. Chbani and H. Riahi: \textit{Weak and strong convergence of prox-penalization and splitting algorithms for bilevel equilibrium problems}. \textit{Numer. Algebra Control Optim.}, 3 (2013), pp. 353-366. Under suitable conditions, we establish the weak and strong convergence of the sequence generated by the proposed iterative method. We also report a numerical example illustrating our theoretical result., Comment: 28 pages, 2 figures

Snake robots enable mobility through extreme terrains and confined environments in terrestrial and space applications. However, robust perception and localization for snake robots remain an open challenge due to the proximity of the sensor payload to the ground coupled with a limited field of view. To address this issue, we propose Blind-motion with Intermittently Scheduled Scans (BLISS) which combines proprioception-only mobility with intermittent scans to be resilient against both localization failures and collision risks. BLISS is formulated as an integrated Task and Motion Planning (TAMP) problem that leads to a Chance-Constrained Hybrid Partially Observable Markov Decision Process (CC-HPOMDP), known to be computationally intractable due to the curse of history. Our novelty lies in reformulating CC-HPOMDP as a tractable, convex Mixed Integer Linear Program. This allows us to solve BLISS-TAMP significantly faster and jointly derive optimal task-motion plans. Simulations and hardware experiments on the EELS snake robot show our method achieves over an order of magnitude computational improvement compared to state-of-the-art POMDP planners and $>$ 50\% better navigation time optimality versus classical two-stage planners., Comment: 8 pages, 9 figures. Accepted article with supplemental material for presentation at the 2025 IEEE International Conference on Robotics and Automation (ICRA)

The rotational distribution of asteroids as a function of their size is used {as a diagnostic of} their physical properties and evolution. Recent photometric surveys from the Gaia mission, allowing observation of asteroids with long spin periods (for example $\geq 24$h), found an excessive group of slow rotators and a gap separating them from faster rotators, which is unexplained by current theories. Here we developed an asteroid rotational evolution model capable of reproducing the observed distribution. {We suggest that this distribution is regulated by the competition between collisions and internal friction dampening of "tumblers" -asteroids with unstable rotation vectors, and that the slow rotator group is mainly populated by tumblers.} {We constrain the product of the rigidity and quality factor, which relates to the body's viscosity, to $\mu Q \sim 4 \times 10^9~$Pa. This number, two orders of magnitude smaller than the one assumed for monolithic boulders,} implies that {rubble pile} asteroids could have a porous structure or a thick regolith layer, and undergo stronger tidal effects., Comment: Published in Nature Astronomy

This work proposes a hybrid framework combining classical computers with quantum annealers for structural optimisation. At each optimisation iteration of an iterative process, two minimisation problems are formulated one for the underlying mechanical boundary value problem through the minimisation potential energy principle and one for the minimisation problem to update the design variables. Our hybrid approach leverages the strength of quantum computing to solve these two minimisation problems at each step, thanks to the developed quantum annealing-assisted sequential programming strategy introduced in [Nguyen, Wu, Remacle, and Noels. A quantum annealing-sequential quadratic programming assisted finite element simulation for non-linear and history-dependent mechanical problems. European Journal of Mechanics-A/Solids 105 (2024): 105254]. The applicability of the proposed framework is demonstrated through several case studies of truss optimisation, highlighting its capability to perform optimisation with quantum computers. The proposed framework offers a promising direction for future structural optimisation applications, particularly in scenarios where the quantum computer could resolve the size limitations of the classical computers due to problem complexities.

Continual Learning (CL) addresses the problem of learning from a data sequence where the distribution changes over time. Recently, efficient solutions leveraging Pre-Trained Models (PTM) have been widely explored in the offline CL (offCL) scenario, where the data corresponding to each incremental task is known beforehand and can be seen multiple times. However, such solutions often rely on 1) prior knowledge regarding task changes and 2) hyper-parameter search, particularly regarding the learning rate. Both assumptions remain unavailable in online CL (onCL) scenarios, where incoming data distribution is unknown and the model can observe each datum only once. Therefore, existing offCL strategies fall largely behind performance-wise in onCL, with some proving difficult or impossible to adapt to the online scenario. In this paper, we tackle both problems by leveraging Online Prototypes (OP) and Class-Wise Hypergradients (CWH). OP leverages stable output representations of PTM by updating its value on the fly to act as replay samples without requiring task boundaries or storing past data. CWH learns class-dependent gradient coefficients during training to improve over sub-optimal learning rates. We show through experiments that both introduced strategies allow for a consistent gain in accuracy when integrated with existing approaches. We will make the code fully available upon acceptance., Comment: Under review

Chat-based prompts respond with verbose linear-sequential texts, making it difficult to explore and refine ambiguous intents, back up and reinterpret, or shift directions in creative AI-assisted design work. AI-Instruments instead embody "prompts" as interface objects via three key principles: (1) Reification of user-intent as reusable direct-manipulation instruments; (2) Reflection of multiple interpretations of ambiguous user-intents (Reflection-in-intent) as well as the range of AI-model responses (Reflection-in-response) to inform design "moves" towards a desired result; and (3) Grounding to instantiate an instrument from an example, result, or extrapolation directly from another instrument. Further, AI-Instruments leverage LLM's to suggest, vary, and refine new instruments, enabling a system that goes beyond hard-coded functionality by generating its own instrumental controls from content. We demonstrate four technology probes, applied to image generation, and qualitative insights from twelve participants, showing how AI-Instruments address challenges of intent formulation, steering via direct manipulation, and non-linear iterative workflows to reflect and resolve ambiguous intents., Comment: 18 pages, 10 figures. To appear in the Proceedings of the 2025 ACM CHI Conference on Human Factors in Computing Systems, Yokohama, Japan. https://hugoromat.github.io/ai_instruments/

Despite Generative AI (GenAI) systems' potential for enhancing content creation, users often struggle to effectively integrate GenAI into their creative workflows. Core challenges include misalignment of AI-generated content with user intentions (intent elicitation and alignment), user uncertainty around how to best communicate their intents to the AI system (prompt formulation), and insufficient flexibility of AI systems to support diverse creative workflows (workflow flexibility). Motivated by these challenges, we created IntentTagger: a system for slide creation based on the notion of Intent Tags - small, atomic conceptual units that encapsulate user intent - for exploring granular and non-linear micro-prompting interactions for Human-GenAI co-creation workflows. Our user study with 12 participants provides insights into the value of flexibly expressing intent across varying levels of ambiguity, meta-intent elicitation, and the benefits and challenges of intent tag-driven workflows. We conclude by discussing the broader implications of our findings and design considerations for GenAI-supported content creation workflows., Comment: 31 pages, 30 figures, 3 tables. To appear in the Proceedings of the 2025 ACM CHI Conference on Human Factors in Computing Systems, Yokohama, Japan

In industrial practice, requirements are an indispensable element of any serious software project. In the academic study of software engineering, requirements are one of the heavily researched subjects. And yet requirements engineering, as practiced in industry, makes shockingly sparse use of the concepts propounded in the requirements literature. The present paper starts from an assumption about the causes for this situation and proposes a remedy to redress it. The posited explanation is that change is the major factor affecting the practical application of even the best-intentioned requirements techniques. No sooner has the ink dried on the specifications than the system environment and stakeholders' views of the system begin to evolve. The proposed solution is a requirements engineering method, called UOOR, which unifies many known requirements concepts and a few new ones in a framework entirely devised to accommodate and support seamless change throughout the project lifecycle. The method encompasses the commonly used requirements techniques, namely, scenarios, and integrates them into the seamless software development process. The work presented here introduces the notion of seamless requirements traceability, which relies on the propagation of traceability links, themselves based on formal properties of relations between project artifacts. As a proof of concept, the paper presents a traceability tool to be integrated into a general-purpose IDE that provides the ability to link requirements to other software project artifacts, display notifications of changes in requirements, and trace those changes to the related project elements. The UOOR approach is not just a theoretical proposal but has been designed for practical use and has been applied to a significant real-world case study: Roborace, a competition of autonomous racing cars.

We revisit the problem of fair representation learning by proposing Fair Partial Least Squares (PLS) components. PLS is widely used in statistics to efficiently reduce the dimension of the data by providing representation tailored for the prediction. We propose a novel method to incorporate fairness constraints in the construction of PLS components. This new algorithm provides a feasible way to construct such features both in the linear and the non linear case using kernel embeddings. The efficiency of our method is evaluated on different datasets, and we prove its superiority with respect to standard fair PCA method.

NASA's Double Asteroid Redirection Test (DART) spacecraft impacted Dimorphos, the natural satellite of (65803) Didymos, on 2022 September 26, as a first successful test of kinetic impactor technology for deflecting a potentially hazardous object in space. The experiment resulted in a small change to the dynamical state of the Didymos system consistent with expectations and Level 1 mission requirements. In the pre-encounter paper Richardson (2022), predictions were put forward regarding the pre- and post-impact dynamical state of the Didymos system. Here we assess these predictions, update preliminary findings published after the impact, report on new findings related to dynamics, and provide implications for ESA's Hera mission to Didymos, scheduled for launch in 2024 with arrival in late December 2026. Pre-encounter predictions tested to date are largely in line with observations, despite the unexpected, flattened appearance of Didymos compared to the radar model and the apparent pre-impact oblate shape of Dimorphos (with implications for the origin of the system that remain under investigation). New findings include that Dimorphos likely became prolate due to the impact and may have entered a tumbling rotation state. A possible detection of a post-impact transient secular decrease in the binary orbital period suggests possible dynamical coupling with persistent ejecta. Timescales for damping of any tumbling and clearing of any debris are uncertain. The largest uncertainty in the momentum transfer enhancement factor of the DART impact remains the mass of Dimorphos, which will be resolved by the Hera mission., Comment: 24 pages, 11 figures

We describe the vision, user interface, governing equations, and numerical methods that underpin new ocean modeling software called ``Oceananigans''. Oceananigans is being developed by the Climate Modeling Alliance as part of a larger project to build a trainable climate model with quantifiable uncertainty. We argue that Oceananigans status as a popular, capable modeling system realizes a vision for accelerating progress in Earth system modeling that balances demands for model accuracy and performance, needed for state-of-the-art science, against accessibility, which is needed to accelerate development. This vision combines three cooperative elements: (i) a relatively simple finite volume algorithm (ii) optimized for high-resolution simulations on GPUs which is (iii) exposed behind an expressive, high-level user interface (using the Julia programming language in our case). We offer evidence for the vision's potential by illustrating the creative potential of our user interface, showcasing Oceananigans physics with example simulations that range from simple classroom problems to a realistic global ocean simulation spanning all scales of oceanic fluid motion, and describing advances in parameterization, numerical methods, and computational efficiency.

Palms are ecologically and economically indicators of tropical forest health, biodiversity, and human impact that support local economies and global forest product supply chains. While palm detection in plantations is well-studied, efforts to map naturally occurring palms in dense forests remain limited by overlapping crowns, uneven shading, and heterogeneous landscapes. We develop PRISM (Processing, Inference, Segmentation, and Mapping), a flexible pipeline for detecting and localizing palms in dense tropical forests using large orthomosaic images. Orthomosaics are created from thousands of aerial images and spanning several to hundreds of gigabytes. Our contributions are threefold. First, we construct a large UAV-derived orthomosaic dataset collected across 21 ecologically diverse sites in western Ecuador, annotated with 8,830 bounding boxes and 5,026 palm center points. Second, we evaluate multiple state-of-the-art object detectors based on efficiency and performance, integrating zero-shot SAM 2 as the segmentation backbone, and refining the results for precise geographic mapping. Third, we apply calibration methods to align confidence scores with IoU and explore saliency maps for feature explainability. Though optimized for palms, PRISM is adaptable for identifying other natural objects, such as eastern white pines. Future work will explore transfer learning for lower-resolution datasets (0.5 to 1m)., Comment: 15 pages, 8 figures, 4 tables

Bonnet has characterized his surfaces by a geometric condition. What is done here is a characterization of the same surfaces by two analytic conditions: (i) the mean curvature $H$ of a surface in $\mathbb{R}^3$ should admit a reduction to an ordinary differential equation; (ii) this latter equation should possess the Painlev\'e property., Comment: 7 pages, to appear, Journal of geometry and physics

In this paper we consider a dynamic version of the Chung-Lu random graph in which the edges alternate between being present and absent. The main contribution concerns a technique by which one can estimate the underlying dynamics from partial information, in particular from snapshots of the total number of edges present. The efficacy of our inference method is demonstrated through a series of numerical experiments.

We present a thorough study of spin-to-charge current interconversion in bulk and thin films of (0001) {\alpha}-Fe2O3 /Pt heterostructures by means of all-optical polarization-controlled microwave excitation at sub-Terahertz frequencies. Our results demonstrate that coherent spin pumping is generated through excitations of both the acoustic and optical modes of antiferromagnetic resonance, provided that the corresponding selection rules are met for the relative orientation between the microwave magnetic field h_ac and the magnetic moment m_0 of the Hematite. In particular, our results unanimously show that while a microwave field with h_ac perpendicular to m_0 pumps a net spin angular momentum from the acoustic mode, spin pumping from the optical mode is only enabled when h_ac parallel to m_0, as expected from the selection rules imposed by the Neel vector dynamics. Our results support the current understanding of spin mixing conductance in antiferromagnetic/non-magnetic interfaces, contrary to recent reports where the absence of spin pumping from the optical mode in Hematite was interpreted as a cancellation effect between the diagonal and off-diagonal components of the spin mixing conductance. We also provide an explanation for the previously reported observations and show how the optical spin pumping actually vanishes for thin films, which we speculate being either due to an increased level of inhomogeneities or to insufficient film thickness for the optical mode to fully realize., Comment: 14 pages, 4 figures

In this paper, we propose a way to solve partial differential equations (PDEs) by combining machine learning techniques and the finite element method called Phi-FEM. For that, we use the Fourier Neural Operator (FNO), a learning mapping operator. The purpose of this paper is to provide numerical evidence to show the effectiveness of this technique. We will focus here on the resolution of two equations: the Poisson-Dirichlet equation and the non-linear elasticity equations. The key idea of our method is to address the challenging scenario of varying domains, where each problem is solved on a different geometry. The considered domains are defined by level-set functions due to the use of the Phi-FEM approach. We will first recall the idea of $\varphi$-FEM and of the Fourier Neural Operator. Then, we will explain how to combine these two methods. We will finally illustrate the efficiency of this combination with some numerical results on three test cases. In addition, in the last test case, we propose a new numerical scheme for hyperelastic materials following the Phi-FEM paradigm.

We study sequences of partitions of the unit interval into subintervals, starting from the trivial partition, in which each partition is obtained from the one before by splitting its subintervals in two, according to a given rule, and then merging pairs of subintervals at the break points of the old partition. The $n$th partition then comprises $n+1$ subintervals with $n$ break points, and the empirical distribution of these points reveals a surprisingly rich structure, even when the splitting rule is completely deterministic. We consider both deterministic and randomized splitting rules and study the limiting behavior of the empirical distribution of the break points, from multiple angles., Comment: 19 pages, 1 figure

Air quality monitoring requires to produce accurate estimation of nitrogen dioxide or fine particulate matter concentration maps, at different moments. A typical strategy is to combine different types of data. On the one hand, concentration maps produced by deterministic physicochemical models at urban scale, and on the other hand, concentration measures made at different points, different moments, and by different devices. These measures are provided first by a small number of reference stations, which give reliable measurements of the concentration, and second by a larger number of micro-sensors, which give biased and noisier measurements. The proposed approach consists in modeling the bias of the physicochemical model and estimating the parameters of this bias using all the available concentration measures. Our model relies on a partition of the geographical space of interest into different zones within which the bias is assumed to be modeled by a single affine transformation of the actual concentration. Our approach allows to improve the concentration maps provided by the deterministic models but also to understand the behavior of micro-sensors and their contribution in improving air quality monitoring. We introduce the model, detail its implementation and experiment it through numerical results using datasets collected in Grenoble (France).

Cluster scaling relations are key ingredients in cluster abundance-based cosmological studies. In optical cluster cosmology, weak gravitational lensing has proven to be a powerful tool to constrain the cluster mass-richness relation. This work is conducted as part of the Dark Energy Science Collaboration (DESC), which aims to analyze the Legacy Survey of Space and Time (LSST) of Vera C. Rubin Observatory, starting in 2026. Weak lensing-inferred cluster properties, such as mass, suffer from several sources of bias. In this paper, we aim to test the impact of modeling choices and observational systematics in cluster lensing on the inference of the mass-richness relation. We constrain the mass-richness relation of 3,600 clusters detected by the redMaPPer algorithm in the cosmoDC2 extra-galactic mock catalog (covering $440$ deg$^2$) of the LSST DESC DC2 simulation, using number count measurements and stacked weak lensing profiles in several intervals of richness ($20 \leq \lambda \leq 200$) and redshift ($0.2 \leq z \leq 1$). By modeling the mean of the scaling relation as $\langle \ln \lambda|M_{\rm 200c}, z\rangle = \ln\lambda_0 + \mu_z\log[(1+z)/(1+0.5)] + \mu_m[\log_{10}(M_{\rm 200c}) - 14.3]$, our baseline constraints are $\ln\lambda_0 = 3.37\pm 0.03$, $\mu_z = 0.08\pm 0.07$ and $\mu_m = 2.18 \pm 0.07$. We have found that, for a LSST-like source galaxy density, our constraints are robust to a change in concentration-mass relation and dark matter density profile modeling choices, when source redshifts and shapes are perfectly known. We have found that photometric redshift uncertainties can introduce bias at the $1\sigma$ level, which can be mitigated by an overall correcting factor, fitted jointly with scaling parameters. We find that including positive shear-richness covariance in the fit shifts the results by up to 0.5$\sigma$., Comment: 26 pages, 15 figures, 3 tables, submitted to A&A (abstract shortened for arXiv)

We show that every expansive continuous monoid action on a compact Hausdorff space which has the shadowing property is topologically stable and that a subshift with finite alphabet over a monoid has the shadowing property if and only if it is of finite type. This extends results previously obtained by Walters, Chung and Lee, Meyerovitch, and Oprocha for continuous actions of countable groups on compact metric spaces.

The Fibonacci cube $\Gamma_n$ is the subgraph of the hypercube $Q_n$ induced by vertices with no consecutive $1$s. Recently Jianxin Wei and Yujun Yang introduced a one parameter generalization, Fibonacci $p$-cubes $\Gamma_n^p$, which are subgraphs of hypercubes induced by strings where there is at least $p$ consecutive $0$s between two $1$s. In this paper we first prove the expression conjectured by the authors for the cube polynomial of $\Gamma_n^p$. By a totally different method we then determine a generalization, the distance cube polynomial. We also complete the invariants investigated in the original paper by two new ones, the Mostar index $\mathit{Mo}(\Gamma_n^p)$ and the Irregularity $\irr(\Gamma_n^p)$., Comment: arXiv admin note: text overlap with arXiv:2202.09068

Zero-Knowledge Proofs (ZKPs) are a cryptographic primitive that allows a prover to demonstrate knowledge of a secret value to a verifier without revealing anything about the secret itself. ZKPs have shown to be an extremely powerful tool, as evidenced in both industry and academic settings. In recent years, the utilization of user data in practical applications has necessitated the rapid development of privacy-preserving techniques, including ZKPs. This has led to the creation of several robust open-source ZKP frameworks. However, there remains a significant gap in understanding the capabilities and real-world applications of these frameworks. Furthermore, identifying the most suitable frameworks for the developers' specific applications and settings is a challenge, given the variety of options available. The primary goal of our work is to lower the barrier to entry for understanding and building applications with open-source ZKP frameworks. In this work, we survey and evaluate 25 general-purpose, prominent ZKP frameworks. Recognizing that ZKPs have various constructions and underlying arithmetic schemes, our survey aims to provide a comprehensive overview of the ZKP landscape. These systems are assessed based on their usability and performance in SHA-256 and matrix multiplication experiments. Acknowledging that setting up a functional development environment can be challenging for these frameworks, we offer a fully open-source collection of Docker containers. These containers include a working development environment and are accompanied by documented code from our experiments. We conclude our work with a thorough analysis of the practical applications of ZKPs, recommendations for ZKP settings in different application scenarios, and a discussion on the future development of ZKP frameworks., Comment: Preprint

Benjamini, Kalifa and Tzalik recently proved that there is an absolute constant $c>0$ such that any graph with at most $c\cdot2^d/d$ edges and no isolated vertices is a minor of the $d$-dimensional hypercube $Q_d$, while there is an absolute constant $K > 0$ such that $Q_d$ is not $(K\cdot2^d/\sqrt{d})$-minor-universal. We show that $Q_d$ does not contain 3-uniform expander graphs with $C\cdot2^d/d$ edges as minors. This matches the lower bound up to a constant factor and answers one of their questions., Comment: 7 pages, 1 figure

MIMOSIS is a CMOS Monolithic Active Pixel Sensor developed to equip the Micro Vertex Detector of the Compressed Baryonic Matter (CBM) experiment at FAIR/GSI. The sensor will combine an excellent spatial precision of $5~\mu m$ with a time resolution of $5~\mu s$ and provide a peak hit rate capability of $\mathrm{\sim 80~ MHz/cm^2}$. To fulfill its task, MIMOSIS will have to withstand ionising radiation doses of $\sim 5~ \mathrm{MRad}$ and fluences of $\sim 7 \times 10^{13}~\mathrm{n_{eq}/cm^2}$ per year of operation. This paper introduces the reticle size full feature sensor prototype MIMOSIS-2.1, which was improved with respect to earlier prototypes by adding on-chip grouping circuts and by improving the analog power grid. Moreover, it features for a first time a $50~\mu m$ epitaxial layer, which is found to improve the performances of the non-irradiated device significantly. We discuss the in beam sensor performances as measured during beam tests at the CERN-SPS., Comment: 6 pages, 7 figures

We consider dynamics of the empirical measure of vertex neighborhood states of Markov interacting jump processes on sparse random graphs, in a suitable asymptotic limit as the graph size goes to infinity. Under the assumption of a certain acyclic structure on single-particle transitions, we provide a tractable autonomous description of the evolution of this hydrodynamic limit in terms of a finite coupled system of ordinary differential equations. Key ingredients of the proof include a characterization of the hydrodynamic limit of the neighborhood empirical measure in terms of a certain local-field equation, well-posedness of its Markovian projection, and a Markov random field property of the time-marginals, which may be of independent interest. We also show how our results lead to principled approximations for classes of interacting jump processes and illustrate its efficacy via simulations on several examples, including an idealized model of seizure spread in the brain.

The Micro Vertex Detector (MVD) is the most upstream detector of the fixed-target Compressed Baryonic Matter Experiment (CBM) at the future Facility for Antiproton and Ion Research (FAIR). It enables high-precision low-momentum tracking in direct proximity of the target. Reaching the stringent requirements for the MVD, a material budget of~$0.3\,-\,0.5\%\,X_0$ per layer, operating the dedicated CMOS MAPS~(`MIMOSIS') in the target vacuum, the strong magnetic dipole field, and a harsh radiation environment~(5\,Mrad, $7\times10^{13}\,n_{\text{eq}}/\text{cm}^2$ per CBM year), poses an unprecedented integration challenge. In this paper, the integration concept of the detector is be outlined, elaborating on the selection and preparation of materials, assembly procedures, and quality assessment steps in the ongoing preparation of pre-series production and detector commissioning in 2028., Comment: Titlepage + 5 pages, 9 figures, proceedings for Pixel 2024

This work focuses on feedback control strategies for applying the sterile insect technique (SIT) to eliminate pest populations. The presentation is centered on the case of mosquito populations, but most of the results can be extended to other species by adapting the model and selecting appropriate parameter values to describe the reproduction and movement dynamics of the species under consideration. In our study, we address the spatial distribution of the population in a two dimensional bounded domain by extending the temporal SIT model analyzed in [2], thereby obtaining a reaction-diffusion SIT model. After the analysis of the existence and the uniqueness of the solution of this problem, we construct a feedback law that globally asymptotically stabilizes the extinction equilibrium thus yielding a robust strategy to keep the pest population at very low levels in the long term.

We study mean-field descriptions for spatially-extended networks of linear (leaky) and quadratic integrate-and-fire neurons with stochastic spiking times. We consider large-population limits of continuous-time Galves-L\"ocherbach (GL) networks with linear and quadratic intrinsic dynamics. We prove that that the Poisson Hypothesis holds for the replica-mean-field limit of these networks, that is, in a suitably-defined limit, neurons are independent with interaction times replaced by independent time-inhomogeneous Poisson processes with intensities depending on the mean firing rates, extending known results to networks with quadratic intrinsic dynamics and resets. Proving that the Poisson Hypothesis holds opens up the possibility of studying the large-population limit in these networks. We prove this limit to be a well-posed neural field model, subject to stochastic resets., Comment: 13 pages, 1 figure

In this paper we consider a dynamic Erd\H{o}s-R\'{e}nyi random graph with independent identically distributed edge processes. Our aim is to describe the joint evolution of the entries of a subgraph count vector. The main result of this paper is a functional central limit theorem: we establish, under an appropriate centering and scaling, the joint functional convergence of the vector of subgraph counts to a specific multidimensional Gaussian process. The result holds under mild assumptions on the edge processes, most notably a Lipschitz-type condition., Comment: 39 pages

Estimating optimal transport maps between two distributions from respective samples is an important element for many machine learning methods. To do so, rather than extending discrete transport maps, it has been shown that estimating the Brenier potential of the transport problem and obtaining a transport map through its gradient is near minimax optimal for smooth problems. In this paper, we investigate the private estimation of such potentials and transport maps with respect to the distribution samples.We propose a differentially private transport map estimator achieving an $L^2$ error of at most $n^{-1} \vee n^{-\frac{2 \alpha}{2 \alpha - 2 + d}} \vee (n\epsilon)^{-\frac{2 \alpha}{2 \alpha + d}} $ up to poly-logarithmic terms where $n$ is the sample size, $\epsilon$ is the desired level of privacy, $\alpha$ is the smoothness of the true transport map, and $d$ is the dimension of the feature space. We also provide a lower bound for the problem.

In this work, we introduce a novel framework for privately optimizing objectives that rely on Wasserstein distances between data-dependent empirical measures. Our main theoretical contribution is, based on an explicit formulation of the Wasserstein gradient in a fully discrete setting, a control on the sensitivity of this gradient to individual data points, allowing strong privacy guarantees at minimal utility cost. Building on these insights, we develop a deep learning approach that incorporates gradient and activations clipping, originally designed for DP training of problems with a finite-sum structure. We further demonstrate that privacy accounting methods extend to Wasserstein-based objectives, facilitating large-scale private training. Empirical results confirm that our framework effectively balances accuracy and privacy, offering a theoretically sound solution for privacy-preserving machine learning tasks relying on optimal transport distances such as Wasserstein distance or sliced-Wasserstein distance.

Quantum computation with electron spin qubits requires coherent and efficient manipulation of these spins, typically accomplished through the application of alternating magnetic or electric fields for electron spin resonance (ESR). In particular, electrical driving allows us to apply localized fields on the electrons, which benefits scale-up architectures. However, we have found that Electric Dipole Spin Resonance (EDSR) is insufficient for modeling the Rabi behavior in recent experimental studies. Therefore, we propose that the electron spin is being driven by a new method of electric spin qubit control which generalizes the spin dynamics by taking into account a quadrupolar contribution of the quantum dot: electric quadrupole spin resonance (EQSR). In this work, we explore the electric quadrupole driving of a quantum dot in silicon, specifically examining the cases of 5 and 13 electron occupancies., Comment: Main: 5 pages, 4 figures Supp: 4 pages

Large Language Models are typically trained with next-turn rewards, limiting their ability to optimize for long-term interaction. As a result, they often respond passively to ambiguous or open-ended user requests, failing to help users reach their ultimate intents and leading to inefficient conversations. To address these limitations, we introduce CollabLLM, a novel and general training framework that enhances multiturn human-LLM collaboration. Its key innovation is a collaborative simulation that estimates the long-term contribution of responses using Multiturn-aware Rewards. By reinforcement fine-tuning these rewards, CollabLLM goes beyond responding to user requests, and actively uncovers user intent and offers insightful suggestions-a key step towards more human-centered AI. We also devise a multiturn interaction benchmark with three challenging tasks such as document creation. CollabLLM significantly outperforms our baselines with averages of 18.5% higher task performance and 46.3% improved interactivity by LLM judges. Finally, we conduct a large user study with 201 judges, where CollabLLM increases user satisfaction by 17.6% and reduces user spent time by 10.4%., Comment: 23 pages