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Job Corps is the largest and most comprehensive education and job training program in the United States for young people ages 16 to 24 who are not in school or working. Most students live at and take classes at its approximately 120 residential centers. To explore ways to improve student outcomes and reduce per-student costs, Job Corps pilot tested two nonresidential models where students received education and training at colleges, supported by intensive personal and career counseling, instead of at a center. The two pilot programs were: (1) The Job Corps Scholars (JCS) pilot program consisted of 26 colleges each delivering its own version of the Job Corps program to eligible young people; and (2) In the Idaho Job Corps (IJC) pilot program, Idaho paired its Boise Job Corps center with a nearby community college and opened three addition fully nonresidential programs at other colleges. The two pilot programs together offered 30 diverse examples of college-partnered Job Corps programs to study. The implementation and outcomes study focused on: (1) The types of students who enrolled in these programs; (2) The ability of colleges to serve young people eligible for Job Corps, who typically face more barriers than other students; and (3) The outcomes achieved by students in the pilot programs.

Context. Transient sky astronomy is entering a new era with the advent of the SVOM mission (Space Variable Objects Monitor), which was successfully launched on the 26th of June, 2024. The primary goal of SVOM is to monitor the hard X-ray sky searching for gamma-ray bursts (GRBs). On top of its on-board follow-up capabilities, SVOM will be backed by its ground segment composed of several facilities, of which the near-infrared imager CAGIRE. Mounted on the robotic telescope COLIBRI, it will be a unique instrument, able to perform fast follow-up of GRB afterglows in J and H bands, an ideal combination to catch high-redshift (z>6) and/or obscured GRBs. Aims. This paper aims at estimating the performances of CAGIRE for GRB near-infrared afterglow detection based on the characteristics of the detector and the specificities of the COLIBRI telescope. Quickly fading GRB afterglows pose challenges that should be addressed by adapting observing strategies to the capabilities of CAGIRE. Methods. We use an end-to-end image simulator to produce realistic CAGIRE images, taking into account results from the characterization of the ALFA detector used by CAGIRE. We implemented a GRB afterglow generator that simulates infrared lightcurves and spectra based on published observation of distant GRBs (z>6). Results. We retrieved the photometry of 9 GRB afterglows in various scenarios covered by CAGIRE. Catching afterglows as early as two minutes after burst allows the identification of a nIR counterpart in the brightest 4 events. When artificially redshifted even further away, these events remain detectable by CAGIRE up to z=9.6 in J band, and z=13.3 in H band, indicating the potential of CAGIRE to be a pioneer in the identification of the most distant GRBs to date., Comment: 8 pages, 5 figures, 3 tables, accepted for publication in A&A

In the context of the GRAVITY+ upgrade, the adaptive optics (AO) systems of the GRAVITY interferometer are undergoing a major lifting. The current CILAS deformable mirrors (DM, 90 actuators) will be replaced by ALPAO kilo-DMs (43x43, 1432 actuators). On top of the already existing 9x9 Shack-Hartmann wavefront sensors (SH-WFS) for infrared (IR) natural guide star (NGS), new 40x40 SH-WFSs for visible (VIS) NGS will be deployed. Lasers will also be installed on the four units of the Very Large Telescope to provide a laser guide star (LGS) option with 30x30 SH-WFSs and with the choice to either use the 9x9 IR-WFSs or 2x2 VIS-WFSs for low order sensing. Thus, four modes will be available for the GRAVITY+ AO system (GPAO): IR-NGS, IR-LGS, VIS-NGS and VIS-LGS. To prepare the instrument commissioning and help the observers to plan their observations, a tool is needed to predict the performances of the different modes and for different observing conditions (NGS magnitude, science object magnitude, turbulence conditions...) We developed models based on a Mar{\'e}chal approximation to predict the Strehl ratio of the four GPAO modes in order to feed the already existing tool that simulates the GRAVITY performances. Waiting for commissioning data, our model was validated and calibrated using the TIPTOP toolbox, a Point Spread Function simulator based on the computation of Power Spectrum Densities. In this work, we present our models of the NGS modes of GPAO and their calibration with TIPTOP.

In this paper, we introduce a novel method for safe navigation in agricultural robotics. As global environmental challenges intensify, robotics offers a powerful solution to reduce chemical usage while meeting the increasing demands for food production. However, significant challenges remain in ensuring the autonomy and resilience of robots operating in unstructured agricultural environments. Obstacles such as crops and tall grass, which are deformable, must be identified as safely traversable, compared to rigid obstacles. To address this, we propose a new traversability analysis method based on a 3D spectral map reconstructed using a LIDAR and a multispectral camera. This approach enables the robot to distinguish between safe and unsafe collisions with deformable obstacles. We perform a comprehensive evaluation of multispectral metrics for vegetation detection and incorporate these metrics into an augmented environmental map. Utilizing this map, we compute a physics-based traversability metric that accounts for the robot's weight and size, ensuring safe navigation over deformable obstacles., Comment: This work has been submitted to the IEEE for possible publication

A performance model of CVA6 RISC-V processor is built to evaluate performance related modifications before implementing them in RTL. Its accuracy is 99.2% on CoreMark. This model is used to evaluate a superscalar feature for CVA6. During design phase, the model helped detecting and fixing performance bugs. The superscalar feature resulted in a CVA6 performance improvement of 40% on CoreMark.

The Gemini Planet Imager (GPI) is a high contrast imaging instrument that aims to detect and characterize extrasolar planets. GPI is being upgraded to GPI 2.0, with several subsystems receiving a re-design to improve its contrast. To enable observations on fainter targets and increase performance on brighter ones, one of the upgrades is to the adaptive optics system. The current Shack-Hartmann wavefront sensor (WFS) is being replaced by a pyramid WFS with an low-noise electron multiplying CCD (EMCCD). EMCCDs are detectors capable of counting single photon events at high speed and high sensitivity. In this work, we characterize the performance of the HN\"u 240 EMCCD from N\"uv\"u Cameras, which was custom-built for GPI 2.0. Through our performance evaluation we found that the operating mode of the camera had to be changed from inverted-mode (IMO) to non-inverted mode (NIMO) in order to improve charge diffusion features found in the detector's images. Here, we characterize the EMCCD's noise contributors (readout noise, clock-induced charges, dark current) and linearity tests (EM gain, exposure time) before and after the switch to NIMO., Comment: Proceeding of the SPIE Astronomical Telescopes+Instrumentation. 14 pages, 15 figures

Context: Over the last decade, the forestry sector has undergone substantial changes, evolving from a post-2008 financial crisis landscape to incorporating policies favoring sustainable and green alternatives, especially after the 2015 Paris agreement. This evolution was drastically disrupted with the advent of the COVID-19 pandemic in 2020, causing unprecedented interruptions in supply chains, product markets, and data collection. Grasping the aftermath of the COVID-19, regional instances of the forest supply chain sector need synthetic pictures of their present state and future opportunities for emerging wood products and better regional-scale carbon balance. But given the impact of COVID-19 lock-down on data collection, the production of such synthetic pictures has become more complex, yet essential. This was the case for the regional supply chain of the Grand-Est region in France that we studied. Aims: For this study, our aim was to demonstrate that an integrated methodology could provide such synthetic picture even though we sued heterogenous sources of data and different analytical objectives: i.e. (1) retrospectively evaluate the aftermath of COVID-19 pandemic on the supply chain outcomes within the forestry sector; and then (2) retrospectively explore possible options of structural change of regional supply chain that would be required to simultaneously recover from COVID-19 and transit to new objectives in line with the extraction of new bio-molecules from wood biomass, and with the reduction of the regional scale carbon footprint (in line with the IPCC Paris Agreement) Methods: To achieve this, our methodological approach was decomposed into three steps. We first used a Material Flow Analysis (MFA) recently conducted on the forestry sector in the Grand Est region to establish a Sankey diagram (i.e. a schematic representation of industrial sectors and biomass flows along the supply chain) for the pre-Covid-19 period (2014-2018). Then we compared pre-Covid-19 Sankey diagram to the only source of data we could access from the post-Covid-19 period (2020-2021) in order to estimate the impact of Covid-19. Finally, we used as input the reconciled supply chain model into a consequential Wood Product Model (WPMs), called CAT (carbon accounting tool) in order to compare three prospective scenarios: (1) a scenario that projected 2020-2021 Covid-19 conditions and assumed pre-Covid-19 business as usual practices, (2) a scenario illustrating the consequence or rerouting some of the biomass to satisfy the expected increase in pulp and paper production to satisfy the needs of the industry after Covid-19, and (3) a scenario that explored new opportunities in term of extraction of novel bio-molecules by the emerging biochemical wood industry. For every scenario we also evaluated the regional carbon gains and losses that these changes implied. Results: Our study conducted a detailed analysis of the impacts of the COVID-19 pandemic on the forestry sector's supply chain in the Grand Est region, using a dynamic and integrated Wood Product Model. We found significant disruptions during the pandemic period, with notable declines in industrial wood chips and timber hardwood production by 41.8% and 40%, respectively. Conversely, there were substantial increases in fuelwood, timber sawdust, and timber softwood, rising by 14.15%, 44.23%, and 15.29% respectively. These fluctuations underscore the resilience and vulnerabilities within the regional wood supply chain. Our findings also emphasize the potential for strategic rerouting of biomass flows to meet changing industry demands, which could play a crucial role in supporting the sector's recovery and adaptation to post-pandemic conditions. Discussion and conclusion: In addition, our study recognizes the limitations of the current approach combining MFA and WPM and suggests potential areas of enhancement. Ultimately, our findings shed light on the need to develop more integrated analytical methods to provide useful synthetic pictures of regional scale supply chains, when there is a need to adapt it to evolving situations and complex data landscapes.

We resolve the multiple images of the binary-lens microlensing event ASASSN-22av using the GRAVITY instrument of the Very Large Telescope Interferometer (VLTI). The light curves show weak binary perturbations, complicating the analysis, but the joint modeling with the VLTI data breaks several degeneracies, arriving at a strongly favored solution. Thanks to precise measurements of angular Einstein radius \theta_E = 0.724 +/- 0.002 mas and microlens parallax, we determine that the lens system consists of two M dwarfs with masses of M_1 = 0.258 +/- 0.008 M_sun and M_2 = 0.130 +/- 0.007 M_sun, a projected separation of r_\perp = 6.83 +/- 0.31 AU and a distance of D_L = 2.29 +/- 0.08 kpc. The successful VLTI observations of ASASSN-22av open up a new path for studying intermediate-separation (i.e., a few AUs) stellar-mass binaries, including those containing dark compact objects such as neutron stars and stellar-mass black holes., Comment: Accepted by ApJ. See the ancillary file for animation associated with Fig. 8

Studying the orbital motion of stars around Sagittarius A* in the Galactic Center provides a unique opportunity to probe the gravitational potential near the supermassive black hole at the heart of our Galaxy. Interferometric data obtained with the GRAVITY instrument at the Very Large Telescope Interferometer (VLTI) since 2016 has allowed us to achieve unprecedented precision in tracking the orbits of these stars. GRAVITY data have been key to detecting the in-plane, prograde Schwarzschild precession of the orbit of the star S2, as predicted by General Relativity. By combining astrometric and spectroscopic data from multiple stars, including S2, S29, S38, and S55 - for which we have data around their time of pericenter passage with GRAVITY - we can now strengthen the significance of this detection to an approximately $10 \sigma$ confidence level. The prograde precession of S2's orbit provides valuable insights into the potential presence of an extended mass distribution surrounding Sagittarius A*, which could consist of a dynamically relaxed stellar cusp comprised of old stars and stellar remnants, along with a possible dark matter spike. Our analysis, based on two plausible density profiles - a power-law and a Plummer profile - constrains the enclosed mass within the orbit of S2 to be consistent with zero, establishing an upper limit of approximately $1200 \, M_\odot$ with a $1 \sigma$ confidence level. This significantly improves our constraints on the mass distribution in the Galactic Center. Our upper limit is very close to the expected value from numerical simulations for a stellar cusp in the Galactic Center, leaving little room for a significant enhancement of dark matter density near Sagittarius A*., Comment: Submitted to A&A on September 17, 2024

What is certain is that surface temperatures around the globe vary considerably, regardless of the time scales or underlying causes. Since 1850, we have observed an average increase in global surface temperature anomalies of 1.2$^{\circ}$ and a median increase of 0.7$^{\circ}$: this overall difference masks significant regional differences. Nearly 60\% of the world's population now lives in urban areas, where vegetation cover has been significantly reduced, despite the paradoxical fact that vegetation plays an important role in regulating the thermal environment (\textit{eg} through the shading provided by tree canopies). Continuous electrical and thermal measurements of trees in a Parisian grove (France) show and quantify that canopies are not the only protectors against heat waves; we must also consider the role of tree trunks. It is clear that these trunks probably regulate themselves, possibly by modulating the uptake of groundwater, whose geothermal stability is well established at a depth of just one meter. This quantitative observation should not be overlooked in the urban planning of our cities., Comment: 15 pages, 6 figures

Hexagonal boron nitride encapsulation is the method of choice for protecting graphene from environmental doping and impurity scattering. It was previously demonstrated that metal-organic vapor phase epitaxy (MOVPE) grows epitaxially ordered, uniform BN layers on epigraphene (graphene grown on SiC). Due to graphene non-wetting properties, h-BN growth starts preferentially from the graphene ledges. We use this fact here to selectively promote growth of high-quality flat h-BN on epigraphene by patterning epigraphene microstructures prior to BN growth. Thin h-BN films (down to 6 nm) grown by MOVPE show smooth and pleated surface morphology on epigraphene, while crumpled BN is observed on the SiC. Cross-sectional high-resolution transmission electron microscopy images and fluorescence imaging confirm the higher BN quality grown on the epigraphene. Transport measurements reveal p-doping as expected from hydrogen intercalation of epigraphene and regions of high and low mobility. This method can be used to produce structurally uniform high-quality h-BN/epigraphene micro/nano scale heterostructure.

With the expanding integration of infrared instruments in astronomical missions, accurate per-pixel flux estimation for near-infrared hybrid detectors has become critical to the success of these missions. Based on CPPM's involvement in both SVOM/Colibri and Euclid missions, this study introduces universally applicable methods and framework for characterizing IR hybrid detectors and decorrelating their intrinsic properties. The characterization framework, applied to the ALFA detector and \Euclid's H2RG, not only validates the proposed methods but also points out subtle behaviors inherent to each detector.

Climate change and other anthropogenic pressures are likely to induce tipping points in marine ecosystems, potentially leading to declines in primary productivity and fisheries. Despite increasing attention to nature-related financial risks and opportunities within the ocean economy, the extent to which these tipping points could affect investors has remained largely unexplored. Here we used satellite data to track fishing vessels operating in areas prone to marine regime shifts, as identified by their loss of resilience and vulnerability to marine heatwaves, and uncovered their corporate beneficial owners and shareholders. Despite some data gaps, we identified key countries, companies, and shareholders exposed to tipping risk. We also outline the potential challenges and opportunities that these actors may face if marine ecosystems shift to less productive states., Comment: 10 pages, 5 figures

Efficient deployment of resource-intensive transformers on edge devices necessitates cross-stack optimization. We thus study the interrelation between structured pruning and systolic acceleration, matching the size of pruned blocks with the systolic array dimensions. In this setting, computations of pruned weight blocks can be skipped, reducing run-time and energy consumption, but potentially impacting quality of service (QoS). To evaluate the trade-offs between systolic array size and sparsity opportunities, we present a novel co-design framework that integrates algorithmic optimization, system simulation, and hardware design. Targeting speech recognition using transformers as a case study, we analyze how configuration choices across the stack affect performance metrics. Results demonstrate that structured pruning on systems featuring systolic array acceleration can effectively increase performance, while maintaining high QoS levels. Up to 26% system-wide speedups due to structured pruning were measured, with only 1.4% word error rate degradation on the standard Librispeech dataset., Comment: 7 pages, 10 figures

Future quantum networks will be composed of both terrestrial links for metropolitan and continent-scale connections and space-based links for global coverage and infrastructure resilience. However, the propagation of quantum signals through the atmosphere is severely impacted by the effects of turbulence. This is even more the case for entanglement-based quantum communication protocols requiring two free-space channels to be considered simultaneously. In this work, we assess the advantage of turbulence mitigation by adaptive optics, in particular during daytime link operation, so as to increase the coupling of the received signal into an optical fiber. We show in particular that this improves the performance of entanglement-based quantum key distribution by up to a few hundred bits per second when compared with the uncorrected scenario

In this paper, we introduce the concepts of quasi-centroid and quasi-derivation for Zinbiel algebras. Utilizing the classification results of Zinbiel algebras established previously, we describe the quasi-centroids and quasi-derivations of low-dimensional Zinbiel algebras. Additionally, we explore certain properties of quasi-centroids in the context of Zinbiel algebras and employ these properties to classify algebras with so-called small quasi-centroids. This description of quasi-derivations allows us to identify a significant subclass of Zinbiel algebras characterized as quasi-characteristically nilpotent.

Accurately tracking neuronal activity in behaving animals presents significant challenges due to complex motions and background noise. The lack of annotated datasets limits the evaluation and improvement of such tracking algorithms. To address this, we developed SINETRA, a versatile simulator that generates synthetic tracking data for particles on a deformable background, closely mimicking live animal recordings. This simulator produces annotated 2D and 3D videos that reflect the intricate movements seen in behaving animals like Hydra Vulgaris. We evaluated four state-of-the-art tracking algorithms highlighting the current limitations of these methods in challenging scenarios and paving the way for improved cell tracking techniques in dynamic biological systems., Comment: 5 pages, 3 figures, submitted at 2025 IEEE International Symposium on Biomedical Imaging (ISBI)

We introduce DAHL, a benchmark dataset and automated evaluation system designed to assess hallucination in long-form text generation, specifically within the biomedical domain. Our benchmark dataset, meticulously curated from biomedical research papers, consists of 8,573 questions across 29 categories. DAHL evaluates fact-conflicting hallucinations in Large Language Models (LLMs) by deconstructing responses into atomic units, each representing a single piece of information. The accuracy of these responses is averaged to produce the DAHL Score, offering a more in-depth evaluation of hallucinations compared to previous methods that rely on multiple-choice tasks. We conduct experiments with 8 different models, finding that larger models tend to hallucinate less; however, beyond a model size of 7 to 8 billion parameters, further scaling does not significantly improve factual accuracy. The DAHL Score holds potential as an efficient alternative to human-annotated preference labels, being able to be expanded to other specialized domains. We release the dataset and code in public., Comment: EMNLP2024/FEVER

Left Ventricular Hypertrophy (LVH) is a major cardiovascular risk factor, linked to heart failure, arrhythmia, and sudden cardiac death, often resulting from chronic stress like hypertension. Electrocardiography (ECG), while varying in sensitivity, is widely accessible and cost-effective for detecting LVH-related morphological changes. This work introduces a bilateral signal warping (BSW) approach to improve ECG-based LVH diagnosis. Our method creates a library of heartbeat prototypes from patients with consistent ECG patterns. After preprocessing to eliminate baseline wander and detect R peaks, we apply BSW to cluster heartbeats, generating prototypes for both normal and LVH classes. We compare each new record to these references to support diagnosis. Experimental results show promising potential for practical application in clinical settings., Comment: 9 pages, 5 figures

Consider a graph $G$ with a path $P$ of order $n$. What conditions force $G$ to also have a long induced path? As complete bipartite graphs have long paths but no long induced paths, a natural restriction is to forbid some fixed complete bipartite graph $K_{t,t}$ as a subgraph. In this case we show that $G$ has an induced path of order $(\log \log n)^{1/5-o(1)}$. This is an exponential improvement over a result of Galvin, Rival, and Sands (1982) and comes close to a recent upper bound of order $O((\log \log n)^2)$. Another way to approach this problem is by viewing $G$ as an ordered graph (where the vertices are ordered according to their position on the path $P$). From this point of view it is most natural to consider which ordered subgraphs need to be forbidden in order to force the existence of a long induced path. Focusing on the exclusion of ordered matchings, we improve or recover a number of existing results with much simpler proofs, in a unified way. We also show that if some forbidden ordered subgraph forces the existence of a long induced path in $G$, then this induced path has size at least $\Omega((\log \log \log n)^{1/3})$, and can be chosen to be increasing with respect to $P$., Comment: 26 pages, 8 figures. Comments welcome !

In this paper, we present the general theory of embedding independence tests on Hilbert spaces that generalizes the concepts of distance covariance, distance multivariance and HSIC. This is done by defining new types of kernel on an $n$ Cartesian product called positive definite independent of order $k$. An emphasis is given on the continuous case in order to obtain a version of the Kernel Mean Embedding for this new classes of kernels. We also provide $2$ explicit methods to construct examples for this new type of kernel on a general space by using Bernstein functions of several variables and completely monotone functions of higher order.

Interpretable Machine Learning faces a recurring challenge of explaining the predictions made by opaque classifiers such as ensemble models, kernel methods, or neural networks in terms that are understandable to humans. When the model is viewed as a black box, the objective is to identify a small set of features that jointly determine the black box response with minimal error. However, finding such model-agnostic explanations is computationally demanding, as the problem is intractable even for binary classifiers. In this paper, the task is framed as a Constraint Optimization Problem, where the constraint solver seeks an explanation of minimum error and bounded size for an input data instance and a set of samples generated by the black box. From a theoretical perspective, this constraint programming approach offers PAC-style guarantees for the output explanation. We evaluate the approach empirically on various datasets and show that it statistically outperforms the state-of-the-art heuristic Anchors method.

Pick $n$ independent and uniform random points $U_1,\ldots,U_n$ in a compact convex set $K$ of $\mathbb{R}^d$ with volume 1, and let $P^{(d)}_K(n)$ be the probability that these points are in convex position. The Sylvester conjecture in $\mathbb{R}^d$ is that $\min_K P^{(d)}_K(d+2)$ is achieved by the $d$-dimensional simplices $K$ (only). In this paper, we focus on a companion model, already studied in the $2d$ case, which we define in any dimension $d$: we say that $K$ has $F$ as a flat floor, if $F$ is a subset of $K$, contained in a hyperplan $P$, such that $K$ lies in one of the half-spaces defined by $P$. We define $Q_K^F(n)$ as the probability that $U_1,\cdots,U_n$ together with $F$ are in convex position (i.e., the $U_i$ are on the boundary of the convex hull ${\sf CH}(\{U_1,\cdots,U_n\}\cup F\})$). We prove that, for all fixed $F$, $K\mapsto Q_K^F(2)$ reaches its minimum on the "mountains" with floor $F$ (mountains are convex hull of $F$ union an additional vertex), while the maximum is not reached, but $K\mapsto Q_K^F(2)$ has values arbitrary close to 1. If the optimisation is done on the set of $K$ contained in $F\times[0,d]$ (the "subprism case"), then the minimum is also reached by the mountains, and the maximum by the "prism" $F\times[0,1]$. Since again, $Q_K^F{(2)}$ relies on the expected volume (of ${\sf CH}(\{V_1,V_2\}\cup F\})$), this result can be seen as a proof of the Sylvester problem in the floor case. In $2d$, where $F$ can essentially be the segment $[0,1],$ we give a general decomposition formula for $Q_K^F(n)$ so to compute several formulas and bounds for different $K$. In 3D, we give some bounds for $Q_K^F(n)$ for various floors $F$ and special cases of $K$., Comment: 28 pages, 8 figures

Thermal transport in nanostructures plays a critical role in modern technologies. As devices shrink, techniques that can measure thermal properties at nanometer and nanosecond scales are increasingly needed to capture transient, out-of-equilibrium phenomena. We present a novel pump-probe photon-electron method within a scanning transmission electron microscope (STEM) to map temperature dynamics with unprecedented spatial and temporal resolutions. By combining focused laser-induced heating and synchronized time-resolved monochromated electron energy loss spectroscopy (EELS), we track phonon, exciton and plasmon signals in various materials, including silicon nitride, aluminum thin film, and transition metal dichalcogenides. Our results demonstrate the technique's ability to follow temperature changes at the nanometer and nanosecond scales. The experimental data closely matched theoretical heat diffusion models, confirming the method's validity. This approach opens new opportunities to investigate transient thermal phenomena in nanoscale materials, offering valuable insights for applications in thermoelectric devices and nanoelectronics.

We present the R package MSTest, which implements hypothesis testing procedures to identify the number of regimes in Markov switching models. These models have wide-ranging applications in economics, finance, and numerous other fields. The MSTest package includes the Monte Carlo likelihood ratio test procedures proposed by Rodriguez-Rondon and Dufour (2024), the moment-based tests of Dufour and Luger (2017), the parameter stability tests of Carrasco, Hu, and Ploberger (2014), and the likelihood ratio test of Hansen (1992). Additionally, the package enables users to simulate and estimate univariate and multivariate Markov switching and hidden Markov processes, using the expectation-maximization (EM) algorithm or maximum likelihood estimation (MLE). We demonstrate the functionality of the MSTest package through both simulation experiments and an application to U.S. GNP growth data.

In this paper, we first propose a novel method for transferring material transformations across different scenes. Building on disentangled Neural Radiance Field (NeRF) representations, our approach learns to map Bidirectional Reflectance Distribution Functions (BRDF) from pairs of scenes observed in varying conditions, such as dry and wet. The learned transformations can then be applied to unseen scenes with similar materials, therefore effectively rendering the transformation learned with an arbitrary level of intensity. Extensive experiments on synthetic scenes and real-world objects validate the effectiveness of our approach, showing that it can learn various transformations such as wetness, painting, coating, etc. Our results highlight not only the versatility of our method but also its potential for practical applications in computer graphics. We publish our method implementation, along with our synthetic/real datasets on https://github.com/astra-vision/BRDFTransform

The MUltiplexed Survey Telescope (MUST) is a 6.5-meter telescope under development. Dedicated to highly-multiplexed, wide-field spectroscopic surveys, MUST observes over 20,000 targets simultaneously using 6.2-mm pitch positioning robots within a ~5 deg2 field of view. MUST aims to carry out the first Stage-V spectroscopic survey in the 2030s to map the 3D Universe with over 100 million galaxies and quasars, spanning from the nearby Universe to redshift z~5.5, corresponding to around 1 billion years after the Big Bang. To cover this extensive redshift range, we present an initial conceptual target selection algorithm for different types of galaxies, from local bright galaxies, luminous red galaxies, and emission line galaxies to high-redshift (2 < z < 5.5) Lyman-break galaxies. Using Fisher forecasts, we demonstrate that MUST can address fundamental questions in cosmology, including the nature of dark energy, test of gravity theories, and investigations into primordial physics. This is the first paper in the series of science white papers for MUST, with subsequent developments focusing on additional scientific cases such as galaxy and quasar evolution, Milky Way physics, and dynamic phenomena in the time-domain Universe., Comment: To be submitted to SCPMA

In the context of particle therapy monitoring, we are developing a gamma-ray detector to determine the ion range in vivo from the measurement of particle time-of-flight. For this application, a beam monitor capable to tag in time the incident ion with a time resolution below 235 ps FWHM (100 ps rms) is required to provide a start signal for the acquisition. We have therefore developed a dedicated detector based on a fast organic scintillator (EJ-204) of 25x25x1 mm3 coupled to four SiPM strips that allow measuring the particle incident position by scintillation light sharing. The prototype was characterised with single protons of energies between 63 and 225 MeV at the MEDICYC and ProteusONE facilities of the Antoine Lacassagne proton therapy centre in Nice. We obtained a time resolution of 120 ps FWHM at 63 MeV, and a spatial resolution of ~2 mm rms for single particles. Two identical detectors also allowed to measure the MEDICYC proton energy with 0.3% accuracy.

We extend the methodology introduced by Jahandar et al. (2024) to determine the effective temperature and chemical abundances of 31 slowly-rotating solar neighborhood M dwarfs (M1-M5) using high-resolution spectra from CFHT/SPIRou. This group includes 10 M dwarfs in binary systems with FGK primaries of known metallicity from optical measurements. By testing our $T_{\rm eff}$ method on various synthetic models, we find a consistent inherent synthetic uncertainty of $\sim$10 K at a signal-to-noise ratio greater than 100. Additionally, we find that our results align with interferometric measurements, showing a consistent residual of $-$29 $\pm$ 31 K. Taking the inherent uncertainties into account, we infer the $T_{\rm eff}$ values of our targets and find an excellent agreement with previous optical and NIR studies. Our high-resolution chemical analysis examines hundreds of absorption lines using $\chi^2$ minimization using PHOENIX-ACES stellar atmosphere models. We present elemental abundances for up to 10 different elements, including refractory elements such as Si, Mg, and Fe, which are important for modelling the interior structure of exoplanets. In binary systems, we find an average [Fe/H] of $-$0.15 $\pm$ 0.08 for M dwarfs, marginally lower than the reported metallicity of $-$0.06 $\pm$ 0.18 for the FGK primaries from Mann et al. (2013a). We also observe slightly sub-solar chemistry for various elements in our non-binary M dwarfs, most notably for O, C, and K abundances. In particular, we find an average metallicity of $-$0.11 $\pm$ 0.16 lower but still consistent with the typical solar metallicity of FGK stars (e.g. [Fe/H] = 0.04 $\pm$ 0.20 from Brewer et al. 2016). This study highlights significant discrepancies in various major M dwarf surveys likely related to differences in the methodologies employed., Comment: 24 pages, 9 figures, accepted for publication in ApJ

We experimentally investigate a solid-state quantum emitter - a B center in hexagonal boron nitride (hBN) - that has lifetime-limited coherence at short times, and experiences inhomogeneous broadening due to spectral diffusion at longer times. By making use of power broadening in resonant laser excitation, we explore the crossover between the inhomogeneous and the homogeneous broadening regimes. With the support of numerical simulations, we show that the lineshape, count rate, second-order correlations and long-time photon statistics evolve from a regime where they are dictated by spectral diffusion to a regime where they are simply given by the homogeneous response of the emitter, yielding restored Lorentzian shape and Poissonian photon statistics. Saturation of the count rate and line broadening occur not at the onset of the Rabi oscillations, but when the power-broadened homogeneous response becomes comparable with the inhomogeneous linewidth. Moreover, we identify specific signatures in both the second-order correlations and long-time photon statistics that are well explained by a microscopic spectral diffusion model based on discrete jumps at timescales of micro- to milliseconds. Our work provides an extensive description of the photophysics of B-centers under resonant excitation, and can be readily extended to a wide variety of solid-state quantum emitters.

The transition matrix of a Markov chain $(X_k,k\geq 0)$ on a finite or infinite rooted tree is said to be almost upper-directed if, given $X_k$, the node $X_{k+1}$ is either a descendant of $X_k$ or the parent of $X_k$. It is said to be almost lower-directed if given $X_k$, $X_{k+1}$ is either an ancestor of $X_k$ or a child of $X_k$. These models include nearest neighbor Markov chains on trees. Under an irreducibility assumption, we show that every almost upper-directed transition matrix on infinite (locally finite) trees has some invariant measures. An invariant measure $\pi$ is expressed thanks to a determinantal formula. We give general explicit criteria for recurrence and positive recurrence. An efficient algorithm (the leaf addition algorithm) of independent interest allows $\pi$ to be computed on many trees, without resorting to linear algebra considerations. Flajolet, in a series of papers, provided some relations between continuous fractions, generating functions of weighted M\"otzkin paths, and used them in connection with the analysis of birth and death processes. These fruitful representations made it possible to establish many formulae for continuous fractions. Analogous considerations appear here: this type of study can be extended to weighted paths on trees, whose generating functions can also be expressed, this time in terms of multicontinuous fractions.

We demonstrate an end-to-end technique for observing and characterizing massive black hole binary signals before they merge with the LISA space-based gravitational-wave observatory. Our method uses a zero-latency whitening filter, originally designed for rapidly observing compact binary mergers in ground-based observatories, to be able to observe signals with no additional latency due to filter length. We show that with minimal computational cost, we are able to reliably observe signals as early as 14 days premerger as long as the signal has accrued a signal-to-noise ratio of at least 8 in the LISA data. We also demonstrate that this method can be used to characterize the source properties, providing early estimates of the source's merger time, chirp mass, and sky localization. Early observation and characterization of massive black holes is crucial to enable the possibility of rapid multimessenger observations, and to ensure that LISA can enter a protected operating period when the merger signal arrives., Comment: 16 pages, 11 figures

We analyze the timing of NGO campaigns to shed light on NGOs' objectives and how their strategies evolve over time. Using data from 2,500 campaigns, we find that NGOs are eight times more likely to launch campaigns on Annual General Meeting (AGM) dates. These campaigns increase media exposure and stakeholder scrutiny, raising the likelihood of consumer boycotts and related shareholder proposals at the following AGM of the targeted firm but have no impact on current AGM votes. As NGOs build reputational capital, they adjust their timing to influence AGM outcomes, revealing the trade-offs they face to drive corporate change.

We study a class of non-reversible, continuous-time random walks in random environments on $\mathbb{Z}^d$ that admit a cycle representation with finite cycle length. The law of the transition rates, taking values in $[0, \infty)$, is assumed to be stationary and ergodic with respect to space shifts. Moreover, the transition rate from $x$ to $y$, denoted by $c^\omega(x,y)$, is a superposition of non-negative random weights on oriented cycles that contain the edge $(x,y)$. We prove a quenched invariance principle under moment conditions that are comparable to the well-known p-q moment condition of Andres, Deuschel, and Slowik [2] for the random conductance model. A key ingredient in proving the sublinearity is an energy estimate for the non-symmetric generator. Our result extends that of Deuschel and K\"osters [12] beyond strong ellipticity and bounded cycle lengths.

In this communication, we deal with the question of automatic target detection, recognition and tracking (ATD/R/T) algorithms performance assessment. We propose a complete methodology of evaluation which approaches objective image datasets development and adapted metrics definition for the different tasks (detection, recognition and tracking). We present some performance results which are currently processed in a French-MoD program called 2ACI (``Acquisition Automatique de Cibles par Imagerie``).

In our demo, participants are invited to explore the Diff-MSTC prototype, which integrates the Diff-MST model into Steinberg's digital audio workstation (DAW), Cubase. Diff-MST, a deep learning model for mixing style transfer, forecasts mixing console parameters for tracks using a reference song. The system processes up to 20 raw tracks along with a reference song to predict mixing console parameters that can be used to create an initial mix. Users have the option to manually adjust these parameters further for greater control. In contrast to earlier deep learning systems that are limited to research ideas, Diff-MSTC is a first-of-its-kind prototype integrated into a DAW. This integration facilitates mixing decisions on multitracks and lets users input context through a reference song, followed by fine-tuning of audio effects in a traditional manner., Comment: Presented at 2024 International Society for Music Information Retrieval

Large language models are increasingly relied upon as sources of information, but their propensity for generating false or misleading statements with high confidence poses risks for users and society. In this paper, we confront the critical problem of epistemic miscalibration $\unicode{x2013}$ where a model's linguistic assertiveness fails to reflect its true internal certainty. We introduce a new human-labeled dataset and a novel method for measuring the linguistic assertiveness of Large Language Models (LLMs) which cuts error rates by over 50% relative to previous benchmarks. Validated across multiple datasets, our method reveals a stark misalignment between how confidently models linguistically present information and their actual accuracy. Further human evaluations confirm the severity of this miscalibration. This evidence underscores the urgent risk of the overstated certainty LLMs hold which may mislead users on a massive scale. Our framework provides a crucial step forward in diagnosing this miscalibration, offering a path towards correcting it and more trustworthy AI across domains.

We present 22 GHz imaging of regions of massive star formation within the Local Wolf-Rayet Galaxy Sample (LWRGS), a NSF's Karl G. Jansky Very Large Array (VLA) survey of 30 local galaxies showing spectral features of Wolf-Rayet (WR) stars. These spectral features are present in galaxies with young super star clusters (SSCs), and are an indicator of large concentrations of massive stars. We present a catalog of 92 individually-identified regions of likely free-free emission associated with potential young SSCs located in these WR galaxies. The free-free fluxes from these maps allow extinction-free estimates of the Lyman continuum rates, masses, and luminosities of the emission regions. 39 of these regions meet the minimum Lyman continuum rate to contain at least once SSC, and 29 of these regions could contain individual SSCs massive enough to test specific theories on star formation and feedback inhibition in SSCs, requiring follow-up observations at higher spatial resolution. The resulting catalog provides sources for future molecular line and infrared studies into the properties of super star cluster formation., Comment: 26 pages, 8 figures; accepted for publication in ApJ

Portfolio diversification, traditionally measured through asset correlations and volatilitybased metrics, is fundamental to managing financial risk. However, existing diversification metrics often overlook non-numerical relationships between assets that can impact portfolio stability, particularly during market stresses. This paper introduces the lexical ratio (LR), a novel metric that leverages textual data to capture diversification dimensions absent in standard approaches. By treating each asset as a unique document composed of sectorspecific and financial keywords, the LR evaluates portfolio diversification by distributing these terms across assets, incorporating entropy-based insights from information theory. We thoroughly analyze LR's properties, including scale invariance, concavity, and maximality, demonstrating its theoretical robustness and ability to enhance risk-adjusted portfolio returns. Using empirical tests on S&P 500 portfolios, we compare LR's performance to established metrics such as Markowitz's volatility-based measures and diversification ratios. Our tests reveal LR's superiority in optimizing portfolio returns, especially under varied market conditions. Our findings show that LR aligns with conventional metrics and captures unique diversification aspects, suggesting it is a viable tool for portfolio managers.

We present the production of quantum degenerate, superfluid gases of $^6$Li through direct evaporative cooling in a cavity-enhanced optical dipole trap. The entire evaporative cooling process is performed in a trap created by the TEM$_{00}$ mode of a Fabry-P\'erot cavity, simultaneously driven on several successive longitudinal modes. This leads to near-complete cancellation of the inherent lattice structure along the axial direction of the cavity, as evidenced by the observation of long-lived dipole oscillations of the atomic cloud. We demonstrate the production of molecular Bose-Einstein condensates upon adiabatic conversion of a unitary Fermi gas evaporatively cooled in this trap. The lifetime and heating in the cavity trap is similar to that of a running wave dipole trap. Our system enables the optical production of ultracold samples using a total trap-laser power below $1$ W, leveraging the benefits of optical resonators as dipole traps in quantum gas research while maintaining a simple resonator design and minimizing additional experimental complexity., Comment: Submission to SciPost

This paper presents a deep reinforcement learning (DRL) framework for active flow control (AFC) to reduce drag in aerodynamic bodies. Tested on a 3D cylinder at Re = 100, the DRL approach achieved a 9.32% drag reduction and a 78.4% decrease in lift oscillations by learning advanced actuation strategies. The methodology integrates a CFD solver with a DRL model using an in-memory database for efficient communication between, Comment: ECOMMAS 2024 conference proceeding paper