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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.

Summary: VTX is a molecular visualization software capable to handle most molecular structures and dynamics trajectories file formats. It features a real-time high-performance molecular graphics engine, based on modern OpenGL, optimized for the visualization of massive molecular systems and molecular dynamics trajectories. VTX includes multiple interactive camera and user interaction features, notably free-fly navigation and a fully modular graphical user interface designed for increased usability. It allows the production of high-resolution images for presentations and posters with custom background. VTX design is focused on performance and usability for research, teaching and educative purposes. Availability and implementation: VTX is open source and free for non commercial use. Builds for Windows and Ubuntu Linux are available at http://vtx.drugdesign.fr. The source code is available at https://github.com/VTX-Molecular-Visualization, Comment: 5 pages, 1 figure, download VTX at http://vtx.drugdesign.fr, follow development at https://github.com/VTX-Molecular-Visualization

We present an all-silicon source of near-infrared linearly-polarized single photons, fabricated by nanoscale positioning of a color center in a silicon-on-insulator microcavity. The color center consists of a single W center, created at a well-defined position by Si$^{+}$ ion implantation through a 150 nm-diameter nanohole in a mask. A circular Bragg grating cavity resonant with the W's zero-phonon line at 1217 nm is fabricated at the same location as the nanohole. Under above-gap continuous-wave excitation, a very clean photon antibunching behavior ($g{^2} \leq 0.06$) is observed over the entire power range, which highlights the absence of parasitic emitters. Purcell-enhancement of W's zero-phonon emission provides both a record-high photoluminescence count rate among Si color centers (ca $1.2 \times 10^{6}$ counts/s) and apparent Debye-Waller factor around 99%. We also demonstrate the triggered emission of single photons with 93% purity under weak pulsed laser excitation. At high pulsed laser power, we reveal a detrimental effect of repumping processes, that could be mitigated using selective pumping schemes in the future. These results represent a major step towards on-demand sources of indistinguishable near-infrared single photons within silicon photonics chips.

Data augmentation is a crucial step in the development of robust supervised learning models, especially when dealing with limited datasets. This study explores interpolation techniques for the augmentation of geo-referenced data, with the aim of predicting the presence of Commelina benghalensis L. in sugarcane plots in La R{\'e}union. Given the spatial nature of the data and the high cost of data collection, we evaluated two interpolation approaches: Gaussian processes (GPs) with different kernels and kriging with various variograms. The objectives of this work are threefold: (i) to identify which interpolation methods offer the best predictive performance for various regression algorithms, (ii) to analyze the evolution of performance as a function of the number of observations added, and (iii) to assess the spatial consistency of augmented datasets. The results show that GP-based methods, in particular with combined kernels (GP-COMB), significantly improve the performance of regression algorithms while requiring less additional data. Although kriging shows slightly lower performance, it is distinguished by a more homogeneous spatial coverage, a potential advantage in certain contexts.

Data augmentation is a crucial step in the development of robust supervised learning models, especially when dealing with limited datasets. This study explores interpolation techniques for the augmentation of geo-referenced data, with the aim of predicting the presence of Commelina benghalensis L. in sugarcane plots in La R\'eunion. Given the spatial nature of the data and the high cost of data collection, we evaluated two interpolation approaches: Gaussian processes (GPs) with different kernels and kriging with various variograms. The objectives of this work are threefold: (i) to identify which interpolation methods offer the best predictive performance for various regression algorithms, (ii) to analyze the evolution of performance as a function of the number of observations added, and (iii) to assess the spatial consistency of augmented datasets. The results show that GP-based methods, in particular with combined kernels (GP-COMB), significantly improve the performance of regression algorithms while requiring less additional data. Although kriging shows slightly lower performance, it is distinguished by a more homogeneous spatial coverage, a potential advantage in certain contexts., Comment: Preprint. in French language

Singular Spectrum Analysis (SSA) occupies a prominent place in the real signal analysis toolkit alongside Fourier and Wavelet analysis. In addition to the two aforementioned analyses, SSA allows the separation of patterns directly from the data space into the data space, with data that need not be strictly stationary, continuous, or even normally sampled. In most cases, SSA relies on a combination of Hankel or Toeplitz matrices and Singular Value Decomposition (SVD). Like Fourier and Wavelet analysis, SSA has its limitations. The main bottleneck of the method can be summarized in three points. The first is the diagonalization of the Hankel/Toeplitz matrix, which can become a major problem from a memory and/or computational point of view if the time series to be analyzed is very long or heavily sampled. The second point concerns the size of the analysis window, typically denoted as 'L', which will affect the detection of patterns in the time series as well as the dimensions of the Hankel/Toeplitz matrix. Finally, the third point concerns pattern reconstruction: how to easily identify in the eigenvector/eigenvalue space which patterns should be grouped. We propose to address each of these issues by describing a hopefully effective approach that we have been developing for over 10 years and that has yielded good results in our research work., Comment: 28 pages, 11 figures

In resonance fluorescence excitation experiments, light emitted from solid-state quantum emitters is typically filtered to eliminate the laser photons, ensuring that only red-shifted Stokes photons are detected. Theoretical analyses of the fluorescence intensity correlation often model emitters as two-level systems, focusing on light emitted exclusively from the purely electronic transition (the Zero-Phonon Line), or rely on statistical approaches based on conditional probabilities that do not account for quantum coherences. Here, we propose a general model to characterize the correlation of either Zero-Phonon Line photons or Stokes-shifted photons. This model successfully reproduces the experimental correlation of Stokes-shifted photons emitted from two interacting molecules and predicts that this correlation is affected by quantum coherence. Besides, we analyze the role of quantum coherence in light emission from two uncorrelated emitters, which helps to clarify the discrepancy between theory and experiments regarding the value of the correlation of photons emitted from this system at zero delay time., Comment: Main text: 6 pages, 3 figures. Supplemental Material: 9 pages, 5 figures

In this paper, we explore a taxonomy of connectivity for space-like structures. It is inspired by isolating posets of connected pieces of a space and examining its embedding in the ambient space. The taxonomy includes in its scope all standard notions of connectivity in point-set and point-free contexts, such as connectivity in graphs and hypergraphs (as well as k-connectivity in graphs), connectivity and path-connectivity in topology, and connectivity of elements in a frame., Comment: 22 pages, 13 figures

A longstanding open question in sub-Riemannian geometry is the following: are sub-Riemannian length minimizers smooth? We give a negative answer to this question, exhibiting an example of a $C^2$ but not $C^3$ length-minimizer of a real-analytic (even polynomial) sub-Riemannian structure.

We consider a teacher-student model of supervised learning with a fully-trained 2-layer neural network whose width $k$ and input dimension $d$ are large and proportional. We compute the Bayes-optimal generalisation error of the network for any activation function in the regime where the number of training data $n$ scales quadratically with the input dimension, i.e., around the interpolation threshold where the number of trainable parameters $kd+k$ and of data points $n$ are comparable. Our analysis tackles generic weight distributions. Focusing on binary weights, we uncover a discontinuous phase transition separating a "universal" phase from a "specialisation" phase. In the first, the generalisation error is independent of the weight distribution and decays slowly with the sampling rate $n/d^2$, with the student learning only some non-linear combinations of the teacher weights. In the latter, the error is weight distribution-dependent and decays faster due to the alignment of the student towards the teacher network. We thus unveil the existence of a highly predictive solution near interpolation, which is however potentially hard to find., Comment: 8 pages + appendix, 3 figures

Vortex states in thin film disks host spin wave modes that are geometrically quantized according to their radial and azimuthal indices. Previous studies have shown that hybridization between these modes and the vortex core results in a sizeable frequency nonreciprocity between low-order clockwise and counterclockwise propagating azimuthal modes. Here, we present a computational study of these spin wave modes in submicron disks in which the spatial extension of the vortex core becomes comparable to the wavelength of certain modes. In such cases, we find that the frequency nonreciprocity can be large even for higher order radial and azimuthal indices, reaching several GHz and comparable to the mode frequencies themselves.

Probabilistic neurosymbolic learning seeks to integrate neural networks with symbolic programming. Many state-of-the-art systems rely on a reduction to the Probabilistic Weighted Model Counting Problem (PWMC), which requires computing a Boolean formula called the logical provenance.However, PWMC is \\#P-hard, and the number of clauses in the logical provenance formula can grow exponentially, creating a major bottleneck that significantly limits the applicability of PNL solutions in practice.We propose a new approach centered around an exact algorithm DPNL, that enables bypassing the computation of the logical provenance.The DPNL approach relies on the principles of an oracle and a recursive DPLL-like decomposition in order to guide and speed up logical inference.Furthermore, we show that this approach can be adapted for approximate reasoning with $\epsilon$ or $(\epsilon, \delta)$ guarantees, called ApproxDPNL.Experiments show significant performance gains.DPNL enables scaling exact inference further, resulting in more accurate models.Further, ApproxDPNL shows potential for advancing the scalability of neurosymbolic programming by incorporating approximations even further, while simultaneously ensuring guarantees for the reasoning process.

This manuscript develops a framework for the strong approximation of Sobolev maps with values in compact manifolds, emphasizing the interplay between local and global topological properties. Building on topological concepts adapted to VMO maps, such as homotopy and the degree of continuous maps, it introduces and analyzes extendability properties, focusing on the notions of $\ell$-extendability and its generalization, $(\ell, e)$-extendability. We rely on Fuglede maps, providing a robust setting for handling compositions with Sobolev maps. Several constructions -- including opening, thickening, adaptive smoothing, and shrinking -- are carefully integrated into a unified approach that combines homotopical techniques with precise quantitative estimates. Our main results establish that a Sobolev map $u \in W^{k, p}$ defined on a compact manifold of dimension $m > kp$ can be approximated by smooth maps if and only if $u$ is $(\lfloor kp \rfloor, e)$-extendable with $e = m$. When $e < m$, the approximation can still be carried out using maps that are smooth except on structured singular sets of rank $m - e - 1$., Comment: 214 pages

The ability to generate quantum emitters with reproducible properties in solid-state matrices is crucial for quantum technologies. Here, we show that a high density of close-to-identical single-photon emitters can be created in commercial hexagonal boron nitride using annealing under oxygen atmosphere. This simple procedure yields a uniform in-plane distribution of color centers consistently emitting around 539.4 nm, with a wavelength spread smaller than 1 nm. We present an extensive characterization of their photophysical properties, showing that the emitters are bright and stable, and exhibit narrow lines at low temperatures with minimal spectral diffusion. These characteristics make this family of quantum emitters highly appealing for applications to quantum information science.

This work considers an Inertial version of Bregman Proximal Gradient algorithm (IBPG) for minimizing the sum of two single-valued functions in finite dimension. We suppose that one of the functions is proper, closed, and convex but non-necessarily smooth whilst the second is a smooth enough function but not necessarily convex. For the latter, we ask the smooth adaptable property (smad) with respect to some kernel or entropy which allows to remove the very popular global Lipschitz continuity requirement on the gradient of the smooth part. We consider the IBPG under the framework of the triangle scaling property (TSP) which is a geometrical property for which one can provably ensure acceleration for a certain subset of kernel/entropy functions in the convex setting. Based on this property, we provide global convergence guarantees when the entropy is strongly convex under the framework of the Kurdyka-\L{}ojasiewicz (KL) property. Turning to the local convergence properties, we show that when the nonsmooth part is partly smooth relative to a smooth submanifold, IBPG has a finite activity identification property before entering a local linear convergence regime for which we establish a sharp estimate of the convergence rate. We report numerical simulations to illustrate our theoretical results on low complexity regularized phase retrieval.

The distribution of the first positive position reached by a random walker starting from the origin is fundamental for understanding the statistics of extremes and records in one-dimensional random walks. We present a comprehensive study of this distribution, focusing particularly on its moments and asymptotic tail behaviour, in the case where the step distribution is continuous and symmetric, encompassing both diffusive random walks and L\'evy flights., Comment: 61 pages, 14 figures

The traces of gauge-covariant Sobolev spaces on a Riemannian vector bundle for some connection are characterised as some gauge-covariant fractional Sobolev spaces when the curvature of the connection is bounded. The constants in the trace and extension theorems only depend on this curvature. When the connection is abelian, one recovers known results for magnetic Sobolev spaces., Comment: 31 pages

In population genetics and other application fields, models with intractable likelihood are common. Approximate Bayesian Computation (ABC) or more generally Simulation-Based Inference (SBI) methods work by simulating instrumental data sets from the models under study and comparing them with the observed data set, using advanced machine learning tools for tasks such as model selection and parameter inference. The present work focuses on model criticism, and more specifically on Goodness of fit (GoF) tests, for intractable likelihood models. We introduce two new GoF tests: the pre-inference \gof tests whether the observed dataset is distributed from the prior predictive distribution, while the post-inference GoF tests whether there is a parameter value such that the observed dataset is distributed from the likelihood with that value. The pre-inference test can be used to prune a large set of models using a limited amount of simulations, while the post-inference test is used to assess the fit of a selected model. Both tests are based on the Local Outlier Factor (LOF, Breunig et al., 2000). This indicator was initially defined for outlier and novelty detection. It is able to quantify local density deviations, capturing subtleties that a more traditional k-NN-based approach may miss. We evaluated the performance of our two GoF tests on simulated datasets from three different model settings of varying complexity. We then illustrate the utility of these approaches on a dataset of single nucleotide polymorphism (SNP) markers for the evaluation of complex evolutionary scenarios of modern human populations. Our dual-test GoF approach highlights the flexibility of our method: the pre-inference \gof test provides insight into model validity from a Bayesian perspective, while the post-inference test provides a more general and traditional view of assessing goodness of fit

Let W be a planar 3-web defined on a neighborhood of a point M. We call "symmetry of W around M" any local diffeomorphism which fixes M and maps each foliation of W to a (not necessarily the same) foliation of W. We say that it is a simple symmetry if it respects each foliation, a mirror symmetry if its respects one foliation and permutes the two other and a circular symmetry if it permutes circularly the three foliations. Hexagonal (i.e. flat) planar 3-webs have always the three types of symmetry. We study here the non-flat case. We give a classification of 3-webs which admits simple or mirror symmetries. We give a method to build all the 3-webs with a circular symmetry and we exhibit a precise non-flat example.

We present and release MIDI-GPT, a generative system based on the Transformer architecture that is designed for computer-assisted music composition workflows. MIDI-GPT supports the infilling of musical material at the track and bar level, and can condition generation on attributes including: instrument type, musical style, note density, polyphony level, and note duration. In order to integrate these features, we employ an alternative representation for musical material, creating a time-ordered sequence of musical events for each track and concatenating several tracks into a single sequence, rather than using a single time-ordered sequence where the musical events corresponding to different tracks are interleaved. We also propose a variation of our representation allowing for expressiveness. We present experimental results that demonstrate that MIDI-GPT is able to consistently avoid duplicating the musical material it was trained on, generate music that is stylistically similar to the training dataset, and that attribute controls allow enforcing various constraints on the generated material. We also outline several real-world applications of MIDI-GPT, including collaborations with industry partners that explore the integration and evaluation of MIDI-GPT into commercial products, as well as several artistic works produced using it., Comment: AAAI 25

Blood-mimicking fluids (BMFs) play a critical role in ultrasonic imaging and Doppler flow studies by replicating the physical and acoustic properties of blood. This study introduces a novel soybean oil-in-water emulsion as a BMF with particle size and deformability akin to red blood cells. Using a millifluidic device, we cross-validated flow profiles through both Doppler velocimetry and optical particle tracking, demonstrating compatibility with theoretical Poiseuille flow models. The millifluidic chip, fabricated via stereolithography, provided an optimized platform for dual optical and ultrasonic assessments. Results showed strong agreement between the two methods across a range of flow rates, affirming the suitability of the emulsion for velocimetry applications. Furthermore, the acoustic properties of soybean oil droplets support their potential as an echogenic and stable alternative to conventional BMFs., Comment: 16 pages, 5 figures

While shared autonomy offers significant potential for assistive robotics, key questions remain about how to effectively map 2D control inputs to 6D robot motions. An intuitive framework should allow users to input commands effortlessly, with the robot responding as expected, without users needing to anticipate the impact of their inputs. In this article, we propose a dynamic input mapping framework that links joystick movements to motions on control frames defined along a trajectory encoded with canal surfaces. We evaluate our method in a user study with 20 participants, demonstrating that our input mapping framework reduces the workload and improves usability compared to a baseline mapping with similar motion encoding. To prepare for deployment in assistive scenarios, we built on the development from the accessible gaming community to select an accessible control interface. We then tested the system in an exploratory study, where three wheelchair users controlled the robot for both daily living activities and a creative painting task, demonstrating its feasibility for users closer to our target population.

In this paper, we introduce holiAtlas, a holistic, multimodal and high-resolution human brain atlas. This atlas covers different levels of details of the human brain anatomy, from the organ to the substructure level, using a new dense labelled protocol generated from the fusion of multiple local protocols at different scales. This atlas has been constructed averaging images and segmentations of 75 healthy subjects from the Human Connectome Project database. Specifically, MR images of T1, T2 and WMn (White Matter nulled) contrasts at 0.125 $mm^{3}$ resolution that were nonlinearly registered and averaged using symmetric group-wise normalisation to construct the atlas. At the finest level, the holiAtlas protocol has 350 different labels derived from 10 different delineation protocols. These labels were grouped at different scales to provide a holistic view of the brain at different levels in a coherent and consistent manner. This multiscale and multimodal atlas can be used for the development of new ultra-high resolution segmentation methods that can potentially leverage the early detection of neurological disorders., Comment: 22 pages

Behavior cloning has shown success in many sequential decision-making tasks by learning from expert demonstrations, yet they can be very sample inefficient and fail to generalize to unseen scenarios. One approach to these problems is to introduce general domain knowledge, such that the policy can focus on the essential features and may generalize to unseen states by applying that knowledge. Although this knowledge is easy to acquire from the experts, it is hard to be combined with learning from individual examples due to the lack of semantic structure in neural networks and the time-consuming nature of feature engineering. To enable learning from both general knowledge and specific demonstration trajectories, we use a large language model's coding capability to instantiate a policy structure based on expert domain knowledge expressed in natural language and tune the parameters in the policy with demonstrations. We name this approach the Knowledge Informed Model (KIM) as the structure reflects the semantics of expert knowledge. In our experiments with lunar lander and car racing tasks, our approach learns to solve the tasks with as few as 5 demonstrations and is robust to action noise, outperforming the baseline model without domain knowledge. This indicates that with the help of large language models, we can incorporate domain knowledge into the structure of the policy, increasing sample efficiency for behavior cloning.

Alignment in large language models (LLMs) is used to enforce guidelines such as safety. Yet, alignment fails in the face of jailbreak attacks that modify inputs to induce unsafe outputs. In this paper, we present and evaluate a method to assess the robustness of LLM alignment. We observe that alignment embeds a safety classifier in the target model that is responsible for deciding between refusal and compliance. We seek to extract an approximation of this classifier, called a surrogate classifier, from the LLM. We develop an algorithm for identifying candidate classifiers from subsets of the LLM model. We evaluate the degree to which the candidate classifiers approximate the model's embedded classifier in benign (F1 score) and adversarial (using surrogates in a white-box attack) settings. Our evaluation shows that the best candidates achieve accurate agreement (an F1 score above 80%) using as little as 20% of the model architecture. Further, we find attacks mounted on the surrogate models can be transferred with high accuracy. For example, a surrogate using only 50% of the Llama 2 model achieved an attack success rate (ASR) of 70%, a substantial improvement over attacking the LLM directly, where we only observed a 22% ASR. These results show that extracting surrogate classifiers is a viable (and highly effective) means for modeling (and therein addressing) the vulnerability of aligned models to jailbreaking attacks.

We propose a modular modeling framework designed to enhance the capture and validation of uncertainty in autonomous vehicle (AV) trajectory prediction. Departing from traditional deterministic methods, our approach employs a flexible, end-to-end differentiable probabilistic encoder-decoder architecture. This modular design allows the encoder and decoder to be trained independently, enabling seamless adaptation to diverse traffic scenarios without retraining the entire system. Our key contributions include: (1) a probabilistic heatmap predictor that generates context-aware occupancy grids for dynamic forecasting, (2) a modular training approach that supports independent component training and flexible adaptation, and (3) a structured validation scheme leveraging uncertainty metrics to evaluate robustness under high-risk conditions. To highlight the benefits of our framework, we benchmark it against an end-to-end baseline, demonstrating faster convergence, improved stability, and flexibility. Experimental results validate these advantages, showcasing the capacity of the framework to efficiently handle complex scenarios while ensuring reliable predictions and robust uncertainty representation. This modular design offers significant practical utility and scalability for real-world autonomous driving applications.

We present necessary and sufficient conditions on triples of weights $(u,v,w)$ for the boundedness of the dyadic weighted square function $S_w$ from $L^2(u)$ into $L^2(v)$. We use this characterization to obtain necessary and sufficient conditions for the boundedness of the $t$-Haar multipliers from $L^2(u)$ into $L^2(v)$ in terms of boundedness of the dyadic weighted square function.

A simple model of wave-particle interaction is studied in its self-consistent form, that is, where the particles are allowed to feedback on the waves dynamics. We focus on the configurations of locked solutions (equilibria) and how the energy-momentum exchange mechanism induces chaos in the model. As we explore the system, we analyse the mathematical structure that gives rise to locked states and how the model's non-linearity enables multiple equilibrium wave amplitudes for equilibrium. We also explain the predominance of regularity as we vary the control parameters and the mechanism behind the emergence of chaos under limited parameter choices., Comment: 10 pages, 8 figures

The spectral energy distribution (SED) of observed stars in wide-field images is crucial for chromatic point spread function (PSF) modelling methods, which use unresolved stars as integrated spectral samples of the PSF across the field of view. This is particularly important for weak gravitational lensing studies, where precise PSF modelling is essential to get accurate shear measurements. Previous research has demonstrated that the SED of stars can be inferred from low-resolution observations using machine-learning classification algorithms. However, a degeneracy exists between the PSF size, which can vary significantly across the field of view, and the spectral type of stars, leading to strong limitations of such methods. We propose a new SED classification method that incorporates stellar spectral information by using a preliminary PSF model, thereby breaking this degeneracy and enhancing the classification accuracy. Our method involves calculating a set of similarity features between an observed star and a preliminary PSF model at different wavelengths and applying a support vector machine to these similarity features to classify the observed star into a specific stellar class. The proposed approach achieves a 91\% top-two accuracy, surpassing machine-learning methods that do not consider the spectral variation of the PSF. Additionally, we examined the impact of PSF modelling errors on the spectral classification accuracy.

Under which condition is quantization optimal? We address this question in the context of the additive uniform noise channel under peak amplitude and power constraints. We compute analytically the capacity-achieving input distribution as a function of the noise level, the average power constraint and the exponent of the power constraint. We found that when the cost constraint is tight and the cost function is concave, the capacity-achieving input distribution is discrete, whereas when the cost function is convex, the support of the capacity-achieving input distribution spans the entire interval., Comment: 6 pages, 3 figures

We report an extensive rotational spectroscopic analysis of singly deuterated methyl mercaptan (CH$_{2}$DSH) using both millimeter and far-infrared synchrotron spectra to achieve a global torsional analysis of the three lowest torsional substates (e$_{0}$, e$_{1}$, and o$_{1}$) of this non-rigid species. A fit including 3419 millimeter wave transitions along with 43 infrared torsional subband centers was performed with root mean square deviations of 0.233 MHz and 0.270 cm$^{-1}$, respectively, resulting in 68 fit parameters. A spectroscopic catalogue built from this analysis for a temperature of 125 K has led to the first interstellar detection of CH$_{2}$DSH towards the Solar-like protostar IRAS 16293-2422 B. We report the identification of 46 transitions, including eight relatively unblended lines, resulting in a derived column density of (3.0$\pm$0.3)$\times$10$^{14}$ cm$^{-2}$. The column density ratio for HDCS/CH$_{2}$DSH compared to HDCO/CH$_{2}$DOH suggests a difference in the interstellar chemistry between the sulphur and oxygen complex organics, in particular a different link between H$_{2}$CO and CH$_{3}$OH and between H$_{2}$CS and CH$_{3}$SH. This is the first interstellar detection of a deuterated sulphur-bearing COM and therefore an important step into understanding the chemical origin of sulphur-based prebiotics., Comment: Accepted for publication in Astrophysical Journal Letters (ApJL)

We study recurrence and ergodicity of cocycles with values in R d , d $\ge$ 1, over rotations by badly approximable irrational numbers on T $\rho$ , $\rho$ \> 1. The discontinuities of the functions generating the cocycles also satisfy a Diophantine condition. For simplicity of notation we mainly consider the cases $\rho$ = 2, d = 1 and 2.

Electron spins coupled with optical transitions in solids stand out as a promising platform for developing spin-based quantum technologies. Recently, hexagonal boron nitride (hBN) - a layered Van der Waals (vdW) crystal, has emerged as a promising host for optically addressable spin systems. However, to date, on-demand generation of isolated single photon emitters with pre-determined spin transitions has remained elusive. Here, we report on a single step, thermal processing of hBN flakes that produces high density, narrowband, quantum emitters with optically active spin transitions. Remarkably, over 25% of the emitters exhibit a clear signature of an optical spin readout at room temperature, surpassing all previously reported results by an order of magnitude. The generated spin defect complexes exhibit both S = 1 and S = 1/2 transitions, which are explained by charge transfer from strongly to weakly coupled spin pairs. Our work advances the understanding of spin complexes in hBN and paves the way for single spin - photon interfaces in layered vdW materials with applications in quantum sensing and information processing.

Generative AI has recently propelled the decoding of images from brain activity. How do these approaches scale with the amount and type of neural recordings? Here, we systematically compare image decoding from four types of non-invasive devices: electroencephalography (EEG), magnetoencephalography (MEG), high-field functional Magnetic Resonance Imaging (3T fMRI) and ultra-high field (7T) fMRI. For this, we evaluate decoding models on the largest benchmark to date, encompassing 8 public datasets, 84 volunteers, 498 hours of brain recording and 2.3 million brain responses to natural images. Unlike previous work, we focus on single-trial decoding performance to simulate real-time settings. This systematic comparison reveals three main findings. First, the most precise neuroimaging devices tend to yield the best decoding performances, when the size of the training sets are similar. However, the gain enabled by deep learning - in comparison to linear models - is obtained with the noisiest devices. Second, we do not observe any plateau of decoding performance as the amount of training data increases. Rather, decoding performance scales log-linearly with the amount of brain recording. Third, this scaling law primarily depends on the amount of data per subject. However, little decoding gain is observed by increasing the number of subjects. Overall, these findings delineate the path most suitable to scale the decoding of images from non-invasive brain recordings., Comment: 29 pages, 14 figures, fixed typo in author list

The use of chatbots in language learning has evolved significantly since the 1960s, becoming more sophisticated platforms as generative AI emerged. These tools now simulate natural conversations, adapting to individual learners' needs, including those studying Chinese. Our study explores how learners can use specific prompts to engage Large Language Models (LLM) as personalized chatbots, aiming to target their language level based on the Common European Framework of Reference for Languages (CEFR) and the European Benchmarking Chinese Language (EBCL) project. Focusing on A1, A1+ and A2 levels, we examine the teaching of Chinese, which presents unique challenges due to its logographic writing system. Our goal is to develop prompts that integrate oral and written skills, using high-frequency character lists and controlling oral lexical productions. These tools, powered by generative AI, aim to enhance language practice by crossing lexical and sinographic recurrence. While generative AI shows potential as a personalized tutor, further evaluation is needed to assess its effectiveness. We conducted a systematic series of experiments using ChatGPT models to evaluate their adherence to constraints specified in the prompts. The results indicate that incorporating level A1 and A1+ characters, along with the associated reference list, significantly enhances compliance with the EBCL character set. Properly prompted, LLMs can increase exposure to the target language and offer interactive exchanges to develop language skills., Comment: 35 pages, 1 figure, 5 tables, 7 appendices

We employ the renormalization group optimized perturbation theory (RGOPT) resummation method to evaluate the equation of state (EoS) for strange ($N_f=2+1$) and non-strange ($N_f=2$) cold quark matter at NLO. This allows us to obtain the mass-radius relation for pure quark stars and compare the results with the predictions from perturbative QCD (pQCD) at NNLO. Choosing the renormalization scale to generate maximum star masses of order $M=2 - 2.6 M_\odot$, we show that the RGOPT can produce mass-radius curves compatible with the masses and radii of some recently observed pulsars, regardless of their strangeness content. The scale values required to produce the desired maximum masses are higher in the strange scenario since the EoS is softer in this case. The possible reasons for such behavior are discussed. Our results also show that, as expected, the RGOPT predictions for the relevant observables are less sensitive to scale variations than those furnished by pQCD., Comment: 15 pages, 5 figures, 1 table

Pressure is a key parameter for tuning or revealing superconductivity in materials and compounds. Many measurements of superconducting phase transition temperatures have been conducted using diamond anvil cells (DACs), which provide a wide pressure range and enable concomitant microscopic structural characterization of the sample. However, the inherently small sample volumes in DACs complicate the unambiguous detection of the Meissner effect, the hallmark of superconductivity. Recently, the Meissner effect in superconductors within a DAC was successfully demonstrated using diamond nitrogen-vacancy (N-V) widefield magnetometry, a non-invasive optical technique. In this work, we show that N-V magnetometry can also map superconductivity with micrometer resolution. We apply this technique to a microcrystal of HgBa$_2$Ca$_2$Cu$_3$O$_{8+\delta}$ (Hg-1223) mercury-based cuprate superconductor under 4 GPa of pressure. The method is capable to detect the magnetic field expulsion and heterogeneities in the sample, visible in a set of characteristic parameters as the local critical temperature $T_{c}$. Flux pinning zones are identified through flux trapping maps. This approach could enable detailed investigations of superconductivity of a broad range of materials under high-pressure conditions.

We have extended {\tt MadGraph5\_aMC@NLO} capabilities by implementing computations for asymmetric hadron-hadron collisions, including proton-nucleus, pion-hadron or nucleus-nucleus collisions in order to obtain a tool for automated perturbative computations of cross sections (or ratios of cross sections) in asymmetric reactions at next-to-leading (NLO) order in $\alpha_S$ in collinear factorisation. This tool, like the original symmetric version of \texttt{MadGraph5\_aMC@NLO}, automatically computes cross sections for different factorisation and renormalisation scales and PDFs provided by the {\tt LHAPDF} library, thereby allowing for an easy assessment of the associated theoretical uncertainties. In this paper, we present the validation of our code using $W$ and $Z$ boson production in $p$Pb collisions and Drell-Yan-pair production in $\pi W$ collisions. We also illustrate the capabilities of the framework by providing cross section and nuclear modification factors with their uncertainties for the production of $W$ and $Z$ bosons, charm and bottom quarks as well as associated production of $b\bar b+H^0$ in $p$Pb collisions at the LHC., Comment: 17 pages, 6 figures

We investigate the effect of the microstructure on the mechanical response of lattice structures made of a defect tolerant Ti-14 Mo binary alloy fabricated by Electron Powder Bed Fusion. Some of the lattice structures were subjected to different heat treatments to compare different microstructures: a two-phase $\alpha$+$\beta$ microstructure inherited from the fabrication (as-built), a full $\beta$-metastable microstructure, and a $\beta$-metastable microstructure strengthened via nanoprecipitation of the $\omega$iso phase. We demonstrate that the macroscopic mechanical behavior of one of the most popular stretching-dominated lattice structure, namely the octet-truss, can be tailored through microstructural optimization by increasing the tolerance to plastic buckling of the constitutive struts. Defect-tolerant materials pave the way towards lattice structures fabricated by additive manufacturing with optimized energy absorption performances.

Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achieve over 90\% accuracy on popular benchmarks like MMLU, limiting informed measurement of state-of-the-art LLM capabilities. In response, we introduce Humanity's Last Exam (HLE), a multi-modal benchmark at the frontier of human knowledge, designed to be the final closed-ended academic benchmark of its kind with broad subject coverage. HLE consists of 3,000 questions across dozens of subjects, including mathematics, humanities, and the natural sciences. HLE is developed globally by subject-matter experts and consists of multiple-choice and short-answer questions suitable for automated grading. Each question has a known solution that is unambiguous and easily verifiable, but cannot be quickly answered via internet retrieval. State-of-the-art LLMs demonstrate low accuracy and calibration on HLE, highlighting a significant gap between current LLM capabilities and the expert human frontier on closed-ended academic questions. To inform research and policymaking upon a clear understanding of model capabilities, we publicly release HLE at https://lastexam.ai., Comment: 25 pages, 6 figures

The classical Moment-Sum Of Squares hierarchy allows to approximate a global minimum of a polynomial optimization problem through semidefinite relaxations of increasing size. However, for many optimization instances, solving higher order relaxations becomes impractical or even impossible due to the substantial computational demands they impose. To address this, existing methods often exploit intrinsic problem properties, such as symmetries or sparsity. Here, we present a complementary approach, which enhances the accuracy of computationally more efficient low-order relaxations by leveraging Christoffel-Darboux kernels. Such strengthened relaxations often yield significantly improved bounds or even facilitate minimizer extraction. We illustrate the efficiency of our approach on several classes of important quadratically constrained quadratic Programs., Comment: 25 pages, 4 algorithms, 5 figures, 5 tables

As opposed to conventional training methods tailored to minimize a given statistical metric or task-agnostic loss (e.g., mean squared error), Decision-Focused Learning (DFL) trains machine learning models for optimal performance in downstream decision-making tools. We argue that DFL can be leveraged to learn the parameters of system dynamics, expressed as constraint of the convex optimization control policy, while the system control signal is being optimized, thus creating an end-to-end learning framework. This is particularly relevant for systems in which behavior changes once the control policy is applied, hence rendering historical data less applicable. The proposed approach can perform system identification - i.e., determine appropriate parameters for the system analytical model - and control simultaneously to ensure that the model's accuracy is focused on areas most relevant to control. Furthermore, because black-box systems are non-differentiable, we design a loss function that requires solely to measure the system response. We propose pre-training on historical data and constraint relaxation to stabilize the DFL and deal with potential infeasibilities in learning. We demonstrate the usefulness of the method on a building Heating, Ventilation, and Air Conditioning day-ahead management system for a realistic 15-zone building located in Denver, US. The results show that the conventional RC building model, with the parameters obtained from historical data using supervised learning, underestimates HVAC electrical power consumption. For our case study, the ex-post cost is on average six times higher than the expected one. Meanwhile, the same RC model with parameters obtained via DFL underestimates the ex-post cost only by 3%., Comment: 12 pages, 9 figures, submitted to ACM e-energy 2025

Domain generalization aims to find ways for deep learning models to maintain their performance despite significant domain shifts between training and inference datasets. This is particularly important for models that need to be robust or are costly to train. LiDAR perception in autonomous driving is impacted by both of these concerns, leading to the emergence of various approaches. This work addresses the challenge by proposing a geometry-based approach, leveraging the sequential structure of LiDAR sensors, which sets it apart from the learning-based methods commonly found in the literature. The proposed method, called 3DLabelProp, is applied on the task of LiDAR Semantic Segmentation (LSS). Through extensive experimentation on seven datasets, it is demonstrated to be a state-of-the-art approach, outperforming both naive and other domain generalization methods.

We present an atmospheric characterization and orbital analysis of HD 206893 B, an exceptionally red, L/T-transition substellar companion in a multiplanetary system, via Keck Planet Imager and Characterizer (KPIC) high-resolution (R $\sim$ 35,000) K-band spectroscopy. Using PHOENIX atmospheric models in a forward-model framework that fits the spectrum of the companion and diffracted starlight simultaneously, we detect HD 206893 B at $>8\sigma$ significance via cross-correlation in two epochs. We find an effective temperature for the companion of $1634^{+72}_{-38}$ K and a log(g) of $4.55^{+0.17}_{-0.22}$. Only accounting for statistical uncertainties, we measure the carbon-oxygen ratio (C/O) of this companion to be $0.57 \pm 0.02$, or near-solar while assuming solar metallicity. The C/O ratio we measure fits the tentative trend of $>4 M_{Jup}$ companions having near-solar C/O ratios while less massive companions have greater-than-solar C/O ratios. Using substellar evolution models, we find an age of $112^{+36}_{-22}$ Myr, a mass of $22.7^{+2.5}_{-1.7} M_{Jup}$, and a radius of $1.11 \pm 0.03 R_{Jup}$ for this companion. We also use KPIC radial velocity data to fit the orbit of HD 206893 B and analyze the orbital stability of this system. We find that the orbital stability is relatively independent of the mass of HD 206893 B, and favors an orbital configuration where B and its interior planetary companion, HD 206893 c, are co-planar. The measured C/O ratio coupled with the current architecture of the system cannot rule out a core accretion scenario, nor a disk fragmentation scenario regarding the formation pathway of HD 206893 B., Comment: 37 pages, 23 figures

The role of grain size in determining fracture toughness in metals is incompletely understood with apparently contradictory experimental observations. We study this grain-size dependence computationally by building a model that combines the phase-field formulation of fracture mechanics with dislocation density-based crystal plasticity. We apply the model to cleavage fracture of body-centered cubic materials in plane strain conditions, and find non-monotonic grain-size dependence of plastic-brittle transgranular fracture. We find two mechanisms at play. The first is the nucleation of failure due to cross-slip in critically located grains within transgranular band of localized deformation, and this follows the classical Hall-Petch law that predicts a higher failure stress for smaller grains. The second is the resistance to the propagation of a mode I crack, where grain boundaries can potentially pin a crack, and this follows an inverse Hall-Petch law with higher toughness for larger grains. The result of the competition between the two mechanisms gives rise to non-monotonic behavior and reconciles the apparently contradictory experimental observations.

We propose a two-step procedure to detect cointegration in high-dimensional settings, focusing on sparse relationships. First, we use the adaptive LASSO to identify the small subset of integrated covariates driving the equilibrium relationship with a target series, ensuring model-selection consistency. Second, we adopt an information-theoretic model choice criterion to distinguish between stationarity and nonstationarity in the resulting residuals, avoiding dependence on asymptotic distributional assumptions. Monte Carlo experiments confirm robust finite-sample performance, even under endogeneity and serial correlation.

High-dimensional time series appear in many scientific setups, demanding a nuanced approach to model and analyze the underlying dependence structure. However, theoretical advancements so far often rely on stringent assumptions regarding the sparsity of the underlying signals. In this contribution, we expand the scope by investigating a high-dimensional time series model wherein the number of features grows proportionally to the number of sampling points, without assuming sparsity in the signal. Specifically, we consider the stochastic regression model and derive a single-letter formula for the normalized mutual information between observations and the signal. We also empirically study the vector approximate message passing (VAMP) algorithm and show that, despite a lack of theoretical guarantees, its performance for inference in our time series model is robust and often statistically optimal.

Exploration of the Universe requires communication with Earth, either on a direct path or through a cascade of proximate celestial bodies. Microwaves have traditionally been used for space communication, but electromagnetic waves of higher frequencies, such as in the optical domain, will enable probing farther in space due to their considerably lower diffraction loss. At a given data rate, the ultimate limit to point-to-point optical communication is determined by the received signal power and the photon information efficiency. The latter measures the number of information bits extracted per photon incident on a detector. As distances across space and, consequently, path loss increases, the system that can achieve the highest photon information efficiency will determine the longest distance at which communication is possible. We report here an experimental demonstration of optical detection at a record photon information efficiency of 14.5 bits per incident photon, or 17.8 bits per detected photon, after 87.5 dB of attenuation. Expressed in terms of energy per bit, this corresponds to 8.84 zeptojoules per bit, or 0.069 photons per bit at 1550 nm. To our knowledge, this is the highest photon information efficiency or lowest energy per bit detection system ever demonstrated at optical frequencies. Such a sensitive detection system holds promise for a wide range of applications.