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Optical frequency combs and their spectra of evenly spaced discrete laser lines are essential to modern time and frequency metrology. Recent advances in integrated photonic waveguides enable efficient nonlinear broadening of an initially narrowband frequency comb to multi-octave bandwidth. Here, we study the nonlinear dynamics in the generation of such ultra-broadband spectra where different harmonics of the comb can overlap. We show that a set of interleaved combs with different offset frequencies extending across the entire spectrum can emerge, which transform into a single evenly spaced ultra-broadband frequency comb when the initial comb is offset-free.

We present a new approach to the conservative dynamics of binary systems, within the effective one-body (EOB) framework, based on the use of a Lagrange multiplier to impose the mass-shell constraint. When applied to the post-Minkowskian (PM) description of the two-body problem in Einsteinian gravity, this Lagrange-EOB (LEOB) approach allows for a new formulation of the conservative dynamics that avoids the drawbacks of the recursive definition of EOB-PM Hamiltonians. Using state-of-the-art values of the waveform and radiation reaction, based on factorized and resummed post-Newtonian (PN) expressions, we apply our new formalism to the construction of an aligned-spin, quasi-circular, inspiralling EOB waveform model, called LEOB-PM, that incorporates analytical information up to the 4PM level, completed by 4PN contributions up to the sixth order in eccentricity, in the orbital sector, and by 4.5PN contributions, in the spin-orbit sector. In the nonspinning case, we find, for the first time, that an uncalibrated LEOB-PM model delivers EOB/NR unfaithfulness (with the Advanced LIGO noise in the total mass range $10-200M_\odot$) of at most $1\%$ over all the nonspinning dataset of the Simulating eXtreme Spacetime (SXS) Numerical Relativity (NR) catalog up to mass ratio $q=15$, also yielding excellent phasing agreement with the $q=32$ configuration of the RIT catalog. When NR-informing the dynamics of the model (both orbital and spinning sectors) by using only 17 SXS dataset, we find that the EOB/NR unfaithfulness (compared to 530 spin-aligned SXS waveforms) has a median value of $5.39\times 10^{-4}$ reaching at most $\sim 1\%$ in some of the high-spin corners. Our finding shows that the LEOB approach is a promising route towards robustly incorporating both PM and PN information in the EOB description of the dynamics., Comment: 38 pages, 12 figures, submitted to Phys. Rev. D

As Large Langue Models have been shown to memorize real-world facts, the need to update this knowledge in a controlled and efficient manner arises. Designed with these constraints in mind, Knowledge Editing (KE) approaches propose to alter specific facts in pretrained models. However, they have been shown to suffer from several limitations, including their lack of contextual robustness and their failure to generalize to logical implications related to the fact. To overcome these issues, we propose SAKE, a steering activation method that models a fact to be edited as a distribution rather than a single prompt. Leveraging Optimal Transport, SAKE alters the LLM behavior over a whole fact-related distribution, defined as paraphrases and logical implications. Several numerical experiments demonstrate the effectiveness of this method: SAKE is thus able to perform more robust edits than its existing counterparts.

We develop a self-learning approach for conjecturing of induction predicates on a dataset of 16197 problems derived from the OEIS. These problems are hard for today's SMT and ATP systems because they require a combination of inductive and arithmetical reasoning. Starting from scratch, our approach consists of a feedback loop that iterates between (i) training a neural translator to learn the correspondence between the problems solved so far and the induction predicates useful for them, (ii) using the trained neural system to generate many new induction predicates for the problems, (iii) fast runs of the z3 prover attempting to prove the problems using the generated predicates, (iv) using heuristics such as predicate size and solution speed on the proved problems to choose the best predicates for the next iteration of training. The algorithm discovers on its own many interesting induction predicates, ultimately solving 5565 problems, compared to 2265 problems solved by CVC5, Vampire or Z3 in 60 seconds.

Traditional approaches to learning fair machine learning models often require rebuilding models from scratch, generally without accounting for potentially existing previous models. In a context where models need to be retrained frequently, this can lead to inconsistent model updates, as well as redundant and costly validation testing. To address this limitation, we introduce the notion of controlled model debiasing, a novel supervised learning task relying on two desiderata: that the differences between new fair model and the existing one should be (i) interpretable and (ii) minimal. After providing theoretical guarantees to this new problem, we introduce a novel algorithm for algorithmic fairness, COMMOD, that is both model-agnostic and does not require the sensitive attribute at test time. In addition, our algorithm is explicitly designed to enforce minimal and interpretable changes between biased and debiased predictions -a property that, while highly desirable in high-stakes applications, is rarely prioritized as an explicit objective in fairness literature. Our approach combines a concept-based architecture and adversarial learning and we demonstrate through empirical results that it achieves comparable performance to state-of-the-art debiasing methods while performing minimal and interpretable prediction changes.

Squeezed states of light are essential for emerging quantum technology in metrology and information processing. Chip-integrated photonics offers a route to scalable and efficient squeezed light generation, however, parasitic nonlinear processes and optical losses remain significant challenges. Here, we demonstrate single-mode quadrature squeezing in a photonic crystal microresonator via degenerate dual-pump spontaneous four-wave mixing. Implemented in a scalable, low-loss silicon-nitride photonic-chip platform, the microresonator features a tailored nano-corrugation that modifies its resonances to suppress parasitic nonlinear processes. In this way, we achieve an estimated 7.8 dB of on-chip squeezing in the bus waveguide, with potential for further improvement. These results open a promising pathway toward integrated squeezed light sources for quantum-enhanced interferometry, Gaussian boson sampling, coherent Ising machines, and universal quantum computing., Comment: 9 pages, 3 figures

Many-body-localization (MBL) transitions are studied in a family of single-spin-flip spin-$\frac12$ models, including the one-dimensional (1D) chain with nearest-neighbor interactions, the quantum dot (QD) model with all-to-all pair interactions, and the quantum random energy model (QREM). We investigate the generalized imbalance that characterizes propagation in Fock space out of an initial basis state and, at the same time, can be efficiently probed by real-space measurements. For all models considered, the average imbalance and its quantum and mesoscopic fluctuations provide excellent indicators for the position of the MBL transition $W_c(n)$, where $n$ is the number of spins. Combining these findings with earlier results on level statistics, we determine phase diagrams of the MBL transitions in the $n$-$W$ plane. Our results provide evidence for a direct transition between the ergodic and MBL phases for each of the models, without any intermediate phase. For QREM and QD model, $W_c(n)$ grows as a power law of $n$ (with logarithmic corrections), in agreement with analytical predictions $W_c^{\rm QREM}(n) \sim n^{1/2} \ln n$ and $W_c^{\rm QD}(n) \gtrsim n^{3/4} \ln^{1/2} n$. This growth is in stark contrast to the 1D model, where $W_c(n)$ is essentially independent of $n$, consistent with the analytic expectation $W_c^{\rm 1D}(n\to \infty)= {\rm const}$. We also determine the scaling of the transition width $\Delta W (n) / W_c(n)$ and estimate the system size $n$ needed to study the asymptotic scaling behavior. While these values of $n$ are not accessible to exact simulations on a classical computer, they are within the reach of quantum simulators. Our results indicate feasibility of experimental studies of $n$-$W$ phase diagrams and scaling properties of MBL transitions in models of 1D and QD type and in their extensions to other spatial geometry or distance-dependent interactions., Comment: 33 pages, 14 figures

Unlike biological active matter that constantly adapt to their environment, the motors of synthetic active particles are typically agnostic to their surroundings and merely operate at constant force. Here, we design colloidal active rods capable of modulating their inner activity in response to crowding, thereby enforcing a primitive form of quorum sensing interactions. Through experiments, simulations and theory we elucidate the impact of these interactions on the phase behavior of isotropic active matter. We demonstrate that, when conditioned to density, motility regulation can either lead to an absorbing phase transition, where all particles freeze their dynamics, or to atypical phase separation, where flat interfaces supporting a net pressure drop are in mechanical equilibrium. Fully active and fully arrested particles can then form heterogeneous patterns ruled by the competition between quorum sensing and mechanical interactions. Beyond the specifics of motile colloids, we expect our findings to apply broadly to adaptive active matter assembled from living or synthetic units., Comment: 12 pages, 6 figures

This paper investigates the integration of technical vocabulary in merged language models. We explore the knowledge transfer mechanisms involved when combining a general-purpose language-specific model with a domain-specific model, focusing on the resulting model's comprehension of technical jargon. Our experiments analyze the impact of this merging process on the target model's proficiency in handling specialized terminology. We present a quantitative evaluation of the performance of the merged model, comparing it with that of the individual constituent models. The findings offer insights into the effectiveness of different model merging methods for enhancing domain-specific knowledge and highlight potential challenges and future directions in leveraging these methods for cross-lingual knowledge transfer in Natural Language Processing., Comment: Presented at the 263rd IPSJ-NL Workshop

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

Stochastic resetting breaks detailed balance and drives the formation of nonequilibrium steady states . Here, we consider a chain of diffusive processes $x_i(t)$ that interact unilaterally: at random time intervals, the process $x_n$ stochastically resets to the instantaneous value of $x_{n-1}$. We derive analytically the steady-state statistics of these nested stochastic resetting processes including the stationary distribution for each process as well as its moments. We are also able to calculate exactly the steady-state two-point correlations $\langle x_n x_{n+j}\rangle$ between processes by mapping the problem to one of the ordering statistics of random counting processes. Understanding statistics and correlations in many-particle nonequilibrium systems remains a formidable challenge and our results provide an example of such tractable correlations. We expect this framework will both help build a model-independent framework for random processes with unilateral interactions and find immediate applications, e.g. in the modelling of lossy information propagation., Comment: 6 pages (3 figures) of main text + 9 pages (4 figures) of supplementary information

Circular and non-flat data distributions are prevalent across diverse domains of data science, yet their specific geometric structures often remain underutilized in machine learning frameworks. A principled approach to accounting for the underlying geometry of such data is pivotal, particularly when extending statistical models, like the pervasive Gaussian distribution. In this work, we tackle those issue by focusing on the manifold of symmetric positive definite matrices, a key focus in information geometry. We introduced a non-isotropic wrapped Gaussian by leveraging the exponential map, we derive theoretical properties of this distribution and propose a maximum likelihood framework for parameter estimation. Furthermore, we reinterpret established classifiers on SPD through a probabilistic lens and introduce new classifiers based on the wrapped Gaussian model. Experiments on synthetic and real-world datasets demonstrate the robustness and flexibility of this geometry-aware distribution, underscoring its potential to advance manifold-based data analysis. This work lays the groundwork for extending classical machine learning and statistical methods to more complex and structured data.

Retrieval-augmented generation improves various aspects of large language models (LLMs) generation, but suffers from computational overhead caused by long contexts as well as the propagation of irrelevant retrieved information into generated responses. Context pruning deals with both aspects, by removing irrelevant parts of retrieved contexts before LLM generation. Existing context pruning approaches are however limited, and do not provide a universal model that would be both efficient and robust in a wide range of scenarios, e.g., when contexts contain a variable amount of relevant information or vary in length, or when evaluated on various domains. In this work, we close this gap and introduce Provence (Pruning and Reranking Of retrieVEd relevaNt ContExts), an efficient and robust context pruner for Question Answering, which dynamically detects the needed amount of pruning for a given context and can be used out-of-the-box for various domains. The three key ingredients of Provence are formulating the context pruning task as sequence labeling, unifying context pruning capabilities with context reranking, and training on diverse data. Our experimental results show that Provence enables context pruning with negligible to no drop in performance, in various domains and settings, at almost no cost in a standard RAG pipeline. We also conduct a deeper analysis alongside various ablations to provide insights into training context pruners for future work., Comment: Accepted to ICLR 2025

We study the number of propagating degrees of freedom, at non-linear order, in torsion gravity theories, a class of modified theories of gravity that include a propagating torsion in addition to the metric. We focus on a three-parameter subfamily of theories (``torsion bigravity") that contains, at linear order, only two physical excitations: a massless spin-2 one (with two degrees of freedom) and a massive spin-2 one (with five degrees of freedom). We study the dynamics of the massive spin-2 field in the limit where the torsion field decouples from the metric. The number of degrees of freedom of the torsion field is found to {\it change, at non-linear order, from five to nine}., Comment: 13 Pages

Accurate thermal emission models of neutron stars are essential for constraining the dense matter equation of state. However, incorporating realistic magnetic field structures is computationally prohibitive, severely constraining feasible parameter space exploration. In this work, we develop a NN emulator to generate model thermal bolometric X-ray light curves of millisecond pulsars with multipolar magnetic fields. We assess the NN's predictive and computational performance across a broad parameter space. We find that for a SVF model, the NN provides a >400 times speedup. We integrate this NN emulator into a MCMC framework to replace the computationally expensive physical model during parameter exploration. Applied to PSR J0030+0451, this approach allows the MCMC to reach equilibrium in ~1 day on 4000 cores, where with the original physical model alone it would have taken more than a year on the same hardware. We compare posterior distributions by running equivalent MCMC iterations with both the NN and the physical model, evaluate differences in distributions when continuing the physical model MCMC from the NN MCMC equilibrium state, and assess variations in posterior distributions resulting from NNs trained on datasets of different sizes. Our NN architecture is agnostic to the underlying physics of the physical model and can be trained for any other physical model, opening many avenues of analysis that were previously intractable. The NN speed remains the same regardless of the complexity of the physical model it was trained to emulate, allowing even greater speedups for physical models that are more complex than the SVF model.

1) Objectives: This technical report presents a synthetic summary and the principal outcomes of the project R-Vessel-X ("Robust vascular network extraction and understanding within hepatic biomedical images") funded by the French Agence Nationale de la Recherche, and developed between 2019 and 2023. 2) Material and methods: We used datasets and tools publicly available such as IRCAD, Bullitt or VascuSynth toobtain real or synthetic angiographic images. The main contributions lie in the field of 3D angiographic image analysis: filtering, segmentation, modeling and simulation, with a specific focus on the liver. 3) Results: We paid a particular attention to open-source software diffusion of the developed methods, by means of 3D Slicer plugins for the liver anatomy segmentation (SlicerRVXLiverSegmentation) and vesselness filtering (Slicer-RVXVesselnessFilters), and an online demo for the generation of synthetic and realistic vessels in 2D and 3D (OpenCCO). 4) Conclusion: The R-Vessel-X project provided extensive research outcomes, covering various topics related to 3D angiographic image analysis, such as filtering, segmentation, modeling and simulation. We also developed open-source and free softwares so that the research communities in biomedical engineering can use these results in their future research., Comment: Innovation and Research in BioMedical engineering, In press

Given a pair of nilpotent orbits in a simple Lie algebra, one can associate a pair of vertex algebras called affine W-algebras. Under some compatibility conditions on these orbits, we prove that one of these W-algebras can be obtained as the quantum Hamiltonian reduction of the other. This property is called reduction by stages. We provide several examples in classical and exceptional types. To prove reduction by stages for affine W-algebras, we use our previous work on reduction by stages for the Slodowy slices associated with these nilpotent orbits, these slices being the associated varieties of the W-algebras. We also prove and use the fact that each W-algebra can be defined using several equivalent BRST cohomology constructions: choosing the right BRST complexes allows us to connect the two W-algebras in a natural way., Comment: 47 pages, 1 figure, 1 table. Comments are welcome!

Network reconstruction consists in retrieving the hidden interaction structure of a system from observations. Many reconstruction algorithms have been proposed, although less research has been devoted to describe their theoretical limitations. In this work, we adopt an information-theoretic perspective and define the reconstructability: The fraction of structural information recoverable from data. The reconstructability depends on the true data generating (TDG) model which is shown to set the reconstruction limit: any algorithm can perform, on average, at best like the TDG model. We show that the reconstructability is related to various performance measures, such as the probability of error and the Jaccard similarity. In an empirical context where the TDG model is unknown, we introduce the reconstruction index as an approximation of the reconstructability. We find that performing model selection is crucial for the validity of the reconstruction index as a proxy of the reconstructability of empirical time series and networks.

The o1 model series is trained with large-scale reinforcement learning to reason using chain of thought. These advanced reasoning capabilities provide new avenues for improving the safety and robustness of our models. In particular, our models can reason about our safety policies in context when responding to potentially unsafe prompts, through deliberative alignment. This leads to state-of-the-art performance on certain benchmarks for risks such as generating illicit advice, choosing stereotyped responses, and succumbing to known jailbreaks. Training models to incorporate a chain of thought before answering has the potential to unlock substantial benefits, while also increasing potential risks that stem from heightened intelligence. Our results underscore the need for building robust alignment methods, extensively stress-testing their efficacy, and maintaining meticulous risk management protocols. This report outlines the safety work carried out for the OpenAI o1 and OpenAI o1-mini models, including safety evaluations, external red teaming, and Preparedness Framework evaluations.

In arXiv:2211.04917, it was shown that, over an algebraically closed field of characteristic zero, every fusion 2-category is Morita equivalent to a connected fusion 2-category, that is, one arising from a braided fusion 1-category. We extend this result to compact semisimple tensor 2-categories over an arbitrary field of characteristic zero. In order to do so, we generalize to an arbitrary field of characteristic zero many well-known results about braided fusion 1-categories over an algebraically closed field of characteristic zero. Most notably, we prove that the Picard group of any braided fusion 1-category is indfinite, generalizing the classical fact that the Brauer group of a field is torsion. As an application of our main result, we derive the existence of braided fusion 1-categories indexed by the fourth Galois cohomology group of the absolute Galois group that represent interesting classes in the appropriate Witt groups., Comment: Comments welcome!

Pattern images are everywhere in the digital and physical worlds, and tools to edit them are valuable. But editing pattern images is tricky: desired edits are often programmatic: structure-aware edits that alter the underlying program which generates the pattern. One could attempt to infer this underlying program, but current methods for doing so struggle with complex images and produce unorganized programs that make editing tedious. In this work, we introduce a novel approach to perform programmatic edits on pattern images. By using a pattern analogy -- a pair of simple patterns to demonstrate the intended edit -- and a learning-based generative model to execute these edits, our method allows users to intuitively edit patterns. To enable this paradigm, we introduce SplitWeave, a domain-specific language that, combined with a framework for sampling synthetic pattern analogies, enables the creation of a large, high-quality synthetic training dataset. We also present TriFuser, a Latent Diffusion Model (LDM) designed to overcome critical issues that arise when naively deploying LDMs to this task. Extensive experiments on real-world, artist-sourced patterns reveals that our method faithfully performs the demonstrated edit while also generalizing to related pattern styles beyond its training distribution., Comment: Website: https://bardofcodes.github.io/patterns/

A standard assumption in the study of logarithmic structures is "fineness", but this assumption is not preserved by intersections, fiber products, and more general limits. We explain how a coherent logarithmic scheme $X$ has a natural decomposition into "fine faces" -- a collection of fine logarithmic subschemes. This allows for importation of the techniques of tropical geometry into the study of more general logarithmic schemes. We explain the relevance of the construction in enumerative geometry and moduli via a range of examples. Techniques developed in the study of binomial schemes lead to effective algorithms for computing the fine faces., Comment: 24 pages including bibliography. All comments are welcome

Modelling information from complex systems such as humans social interaction or words co-occurrences in our languages can help to understand how these systems are organized and function. Such systems can be modelled by networks, and network theory provides a useful set of methods to analyze them. Among these methods, graph embedding is a powerful tool to summarize the interactions and topology of a network in a vectorized feature space. When used in input of machine learning algorithms, embedding vectors help with common graph problems such as link prediction, graph matching, etc. Word embedding has the goal of representing the sense of words, extracting it from large text corpora. Despite differences in the structure of information in input of embedding algorithms, many graph embedding approaches are adapted and inspired from methods in NLP. Limits of these methods are observed in both domains. Most of these methods require long and resource greedy training. Another downside to most methods is that they are black-box, from which understanding how the information is structured is rather complex. Interpretability of a model allows understanding how the vector space is structured without the need for external information, and thus can be audited more easily. With both these limitations in mind, we propose a novel framework to efficiently embed network vertices in an interpretable vector space. Our Lower Dimension Bipartite Framework (LDBGF) leverages the bipartite projection of a network using cliques to reduce dimensionality. Along with LDBGF, we introduce two implementations of this framework that rely on communities instead of cliques: SINr-NR and SINr-MF. We show that SINr-MF can perform well on classical graphs and SINr-NR can produce high-quality graph and word embeddings that are interpretable and stable across runs.

The visibility of the Lyman-$\alpha$ (Ly$\alpha$) emission from reionization-epoch galaxies depends sensitively on the extent of the intrinsic \lya emission redwards of 1215.67~\AA. The prominent red peak resulting from resonant radiative transfer in the interstellar medium is often modelled as a single Gaussian. We use the \textsc{Azahar} simulation suite of a massive-reionization epoch galaxy to show that a significantly larger fraction of the \lya emission extends to $400$-$800$~km~s$^{-1}$, and thus significantly further to the red than predicted by a Gaussian line profile. A cycle of frequent galaxy mergers strongly modulates the \lya luminosity, the red peak velocity and its extended red wing emerging from the galaxy, which all also strongly vary with viewing angle. The \lya emission also depends sensitively on the implemented feedback, dust and star formation physics. Our simulations including cosmic rays reproduce the observed spectral properties of reionization epoch \lya emitters (LAEs) well if we assume that the \lya emission is affected by very little dust. The visibility of LAEs can be strongly underestimated if the extended red wings of the intrinsic \lya emission are not accounted for. We discuss implications for using the visibility of LAEs to constrain the evolution of the volume-averaged neutral fraction during reionization.

The BrowserGym ecosystem addresses the growing need for efficient evaluation and benchmarking of web agents, particularly those leveraging automation and Large Language Models (LLMs). Many existing benchmarks suffer from fragmentation and inconsistent evaluation methodologies, making it challenging to achieve reliable comparisons and reproducible results. In an earlier work, Drouin et al. (2024) introduced BrowserGym which aims to solve this by providing a unified, gym-like environment with well-defined observation and action spaces, facilitating standardized evaluation across diverse benchmarks. We propose an extended BrowserGym-based ecosystem for web agent research, which unifies existing benchmarks from the literature and includes AgentLab, a complementary framework that aids in agent creation, testing, and analysis. Our proposed ecosystem offers flexibility for integrating new benchmarks while ensuring consistent evaluation and comprehensive experiment management. As a supporting evidence, we conduct the first large-scale, multi-benchmark web agent experiment and compare the performance of 6 state-of-the-art LLMs across 6 popular web agent benchmarks made available in BrowserGym. Among other findings, our results highlight a large discrepancy between OpenAI and Anthropic's latests models, with Claude-3.5-Sonnet leading the way on almost all benchmarks, except on vision-related tasks where GPT-4o is superior. Despite these advancements, our results emphasize that building robust and efficient web agents remains a significant challenge, due to the inherent complexity of real-world web environments and the limitations of current models.

Quantum information science is a burgeoning research field attracting vast public and private investment in the last decade. This quick rise has led to a talent gap, where there are more open positions than new graduates who can fill these roles. To meet this critical need, higher education has been challenged to react accordingly by assuring a flow of highly skilled individuals who must be trained quickly. We thus present how Universit\'e de Sherbrooke, in Quebec, Canada, responded by creating and launching an innovative undergraduate degree in quantum information science, aiming to address this gap by training quantum software developers in three and a half years. At the end of this program, they will be ready to join the quantum workforce. We detail the creative process leading to a coherent curriculum, as well as why the local ecosystem led to these choices. The guiding principles and lessons learned during the development of this interdepartmental and interfaculty degree are shared to inspire other quantum education institutions.

We propose a generative technique to edit 3D shapes, represented as meshes, NeRFs, or Gaussian Splats, in approximately 3 seconds, without the need for running an SDS type of optimization. Our key insight is to cast 3D editing as a multiview image inpainting problem, as this representation is generic and can be mapped back to any 3D representation using the bank of available Large Reconstruction Models. We explore different fine-tuning strategies to obtain both multiview generation and inpainting capabilities within the same diffusion model. In particular, the design of the inpainting mask is an important factor of training an inpainting model, and we propose several masking strategies to mimic the types of edits a user would perform on a 3D shape. Our approach takes 3D generative editing from hours to seconds and produces higher-quality results compared to previous works., Comment: project page: https://amirbarda.github.io/Instant3dit.github.io/

Decomposing 3D assets into material parts is a common task for artists and creators, yet remains a highly manual process. In this work, we introduce Select Any Material (SAMa), a material selection approach for various 3D representations. Building on the recently introduced SAM2 video selection model, we extend its capabilities to the material domain. We leverage the model's cross-view consistency to create a 3D-consistent intermediate material-similarity representation in the form of a point cloud from a sparse set of views. Nearest-neighbour lookups in this similarity cloud allow us to efficiently reconstruct accurate continuous selection masks over objects' surfaces that can be inspected from any view. Our method is multiview-consistent by design, alleviating the need for contrastive learning or feature-field pre-processing, and performs optimization-free selection in seconds. Our approach works on arbitrary 3D representations and outperforms several strong baselines in terms of selection accuracy and multiview consistency. It enables several compelling applications, such as replacing the diffuse-textured materials on a text-to-3D output, or selecting and editing materials on NeRFs and 3D-Gaussians., Comment: Project Page: https://mfischer-ucl.github.io/sama

We demonstrate a robust signal extraction method for X-ray speckle-based tensor tomography. We validate the effectiveness of the method for several carbon fiber composites, highlighting its potential for industrial applications. The proposed method can be adapted to various acquisition schemes and wavefront-marking optical elements, making it a versatile and robust tool for X-ray scattering tensor tomography., Comment: under review in Applied Physics Letters

We present a combined experimental and theoretical investigation of the formation and decay kinetics of vortices in two dimensional, compressible quantum turbulence. We follow the temporal evolution of a quantum fluid of exciton polaritons, hybrid light matter quasiparticles, and measure both phase and modulus of the order parameter in the turbulent regime. Fundamental topological conservation laws require that the formation and annihilation of vortices also involve critical points of the velocity field, namely nodes and saddles. Identifying the simplest mechanisms underlying these processes enables us to develop an effective kinetic model that closely aligns with the experimental observations, and shows that different processes are responsible for vortex number growth and decay. These findings underscore the crucial role played by topological constraints in shaping nonlinear, turbulent evolution of two dimensional quantum fluids.

We present a novel, open-access dataset designed for semantic layout analysis, built to support document recreation workflows through mapping with the Text Encoding Initiative (TEI) standard. This dataset includes 7,254 annotated pages spanning a large temporal range (1600-2024) of digitised and born-digital materials across diverse document types (magazines, papers from sciences and humanities, PhD theses, monographs, plays, administrative reports, etc.) sorted into modular subsets. By incorporating content from different periods and genres, it addresses varying layout complexities and historical changes in document structure. The modular design allows domain-specific configurations. We evaluate object detection models on this dataset, examining the impact of input size and subset-based training. Results show that a 1280-pixel input size for YOLO is optimal and that training on subsets generally benefits from incorporating them into a generic model rather than fine-tuning pre-trained weights.

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 classify (multi)fusion 2-categories in terms of braided fusion categories and group cohomological data. This classification is homotopy coherent -- we provide an equivalence between the 3-groupoid of (multi)fusion 2-categories up to monoidal equivalences and a certain 3-groupoid of commuting squares of $\mathrm{B}\mathbb{Z}/2$-equivariant spaces. Rank finiteness and Ocneanu rigidity for fusion 2-categories are immediate corollaries of our classification., Comment: 50 pages

Misinformation is a complex societal issue, and mitigating solutions are difficult to create due to data deficiencies. To address this problem, we have curated the largest collection of (mis)information datasets in the literature, totaling 75. From these, we evaluated the quality of all of the 36 datasets that consist of statements or claims. We assess these datasets to identify those with solid foundations for empirical work and those with flaws that could result in misleading and non-generalizable results, such as insufficient label quality, spurious correlations, or political bias. We further provide state-of-the-art baselines on all these datasets, but show that regardless of label quality, categorical labels may no longer give an accurate evaluation of detection model performance. We discuss alternatives to mitigate this problem. Overall, this guide aims to provide a roadmap for obtaining higher quality data and conducting more effective evaluations, ultimately improving research in misinformation detection. All datasets and other artifacts are available at https://misinfo-datasets.complexdatalab.com/.

With the demonstrated ability to perform calculations in seconds that would take classical supercomputers thousands of years, quantum computers namely hold the promise of radically advancing sustainable IT. However, quantum computers face challenges due to the inherent noise in physical qubits, necessitating error correction for reliable operation in solving industrial-scale problems, which will require more computation time, energy, and electronic components than initial laboratory-scale experiments. Yet, while researchers have modeled and analyzed the environmental impacts of classical computers using Life Cycle Assessment (LCA), the environmental performance of quantum computing remains unknown to date. This study contributes to filling this critical gap in two ways: (1) by establishing an environmental profile for quantum computers based on superconducting qubits; and (2) by comparing it to a functionally equivalent profile of a state-of-the-art supercomputer. With the comparison based on the problem size, the paper shows how the usage time can drive an environmental advantage for quantum computers under specific scaling conditions and quantum error correcting codes. The results emphasize that quantum error correction hardware has a substantial environmental impact due to the numerous electronic components needed to achieve 100 logical qubits. This paper can serve as a basis for designing more environmentally friendly quantum computers and for establishing their environmental profiles, as well as those of the human activities that will use them., Comment: 26 pages, 9 figures, 3 tables

Observations of the Jovian upper atmosphere at high latitudes in the UV, IR and mm/sub-mm all indicate that the chemical distributions and thermal structure are broadly influenced by auroral particle precipitations. Mid-IR and UV observations have shown that several light hydrocarbons (up to 6 carbon atoms) have altered abundances near Jupiter's main auroral ovals. Ion-neutral reactions influence the hydrocarbon chemistry, with light hydrocarbons produced in the upper stratosphere, and heavier hydrocarbons as well as aerosols produced in the lower stratosphere. One consequence of the magnetosphere-ionosphere coupling is the existence of ionospheric jets that propagate into the neutral middle stratosphere, likely acting as a dynamical barrier to the aurora-produced species. As the ionospheric jets and the background atmosphere do not co-rotate at the same rate, this creates a complex system where chemistry and dynamics are intertwined. The ion-neutral reactions produce species with a spatial distribution following the SIII longitude system in the upper stratosphere. As these species sediment down to the lower stratosphere, and because of the progressive dynamical decoupling between the ionospheric flows and the background atmosphere, the spatial distribution of the auroral-related species progressively follows a zonal distribution with increasing pressures that ultimately produces a system of polar and subpolar hazes that extends down to the bottom of the stratosphere. This paper reviews the most recent work addressing different aspects of this environment., Comment: Accepted for publication in Space Science Reviews. 65 pages, 20 figures (including appendix)

The rise of electron spin qubit architectures for quantum computing processors has led to a strong interest in designing and integrating ferromagnets to induce stray magnetic fields for electron dipole spin resonance (EDSR). The integration of nanomagnets imposes however strict layout and processing constraints, challenging the arrangement of different gating layers and the control of neighboring qubit frequencies. This work reports a successful integration of nano-sized cobalt control gates into a multi-gate FD-SOI nanowire with nanometer-scale dot-to-magnet pitch, simultaneously exploiting electrical and ferromagnetic properties of the gate stack at nanoscale. The electrical characterization of the multi-gate nanowire exhibits full field effect functionality of all ferromagnetic gates from room temperature to 10 mK, proving quantum dot formation when ferromagnets are operated as barrier gates. The front-end-of-line (FEOL) compatible integration of cobalt is examined by energy dispersive X-ray spectroscopy and high/low frequency capacitance characterization, confirming the quality of interfaces and control over material diffusion. Insights into the magnetic properties of thin films and patterned control-gates are provided by vibrating sample magnetometry and electron holography measurements. Micromagnetic simulations anticipate that this structure fulfills the requirements for EDSR driving for magnetic fields higher than 1 T, where a homogeneous magnetization along the hard magnetic axis of the Co gates is expected. The FDSOI architecture showcased in this study provides a scalable alternative to micromagnets deposited in the back-end-of-line (BEOL) and middle-of-line (MOL) processes, while bringing technological insights for the FEOL-compatible integration of Co nanostructures in spin qubit devices., Comment: 15 pages, 7 figures

Receptor occupancy (RO) studies using PET neuroimaging play a critical role in the development of drugs targeting the central nervous system (CNS). The conventional approach to estimate drug receptor occupancy consists in estimation of binding potential changes between two PET scans (baseline and post-drug injection). This estimation is typically performed separately for each scan by first reconstructing dynamic PET scan data before fitting a kinetic model to time activity curves. This approach fails to properly model the noise in PET measurements, resulting in poor RO estimates, especially in low receptor density regions. Objective: In this work, we evaluate a novel joint direct parametric reconstruction framework to directly estimate distributions of RO and other kinetic parameters in the brain from a pair of baseline and post-drug injection dynamic PET scans. Methods: The proposed method combines the use of regularization on RO maps with alternating optimization to enable estimation of occupancy even in low binding regions. Results: Simulation results demonstrate the quantitative improvement of this method over conventional approaches in terms of accuracy and precision of occupancy. The proposed method is also evaluated in preclinical in-vivo experiments using 11C-MK-6884 and a muscarinic acetylcholine receptor 4 positive allosteric modulator drug, showing improved estimation of receptor occupancy as compared to traditional estimators. Conclusion: The proposed joint direct estimation framework improves RO estimation compared to conventional methods, especially in intermediate to low-binding regions. Significance: This work could potentially facilitate the evaluation of new drug candidates targeting the CNS.

The advancement of concrete 3D printing (C3DP) technology has revolutionized the construction industry, offering unique opportunities for innovation and efficiency. At the heart of this process lies a comprehensive digital chain that integrates various stages, from initial design to post-processing. This article provides an overview of this digital chain, explaining each crucial step. The chain begins with design, utilizing Design for Additive Manufacturing (DFAM) concept and parametric modeling to create optimized structures. Path generation follows, determining the precise toolpath for extruding concrete layers. Simulations, both numerical and analytical, ensure the design's integrity and feasibility. Several articles have addressed parametric modeling, process and numerical simulation, and the post-processing phase. However, none has proposed an updated methodology for the workflow. This study aims to propose a robust digital chain for C3DP technology, using one platform (3Dexperience) and seamless data transfer between applications. These steps provide insights into the structural performance of printed components, enabling necessary adjustments and optimizations. In essence, the digital chain coordinates a seamless workflow that transforms digital designs into concrete structures, unlocking the full potential of C3DP and paving the way for innovative and efficient construction., Comment: Digital Concrete 2024 - Supplementary Proceedings, Sep 2024, Munich, France

The rise of AI-driven manipulation poses significant risks to societal trust and democratic processes. Yet, studying these effects in real-world settings at scale is ethically and logistically impractical, highlighting a need for simulation tools that can model these dynamics in controlled settings to enable experimentation with possible defenses. We present a simulation environment designed to address this. We elaborate upon the Concordia framework that simulates offline, `real life' activity by adding online interactions to the simulation through social media with the integration of a Mastodon server. We improve simulation efficiency and information flow, and add a set of measurement tools, particularly longitudinal surveys. We demonstrate the simulator with a tailored example in which we track agents' political positions and show how partisan manipulation of agents can affect election results.

This paper is concerned with the geometry the moduli space $\mathscr{M}$ of torsion-free $G_2$-structures on a compact $G_2$-manifold $M$, equipped with the volume-normalised $L^2$-metric $\mathscr{G}$. When $b^1(M) = 0$, this metric is known to be of Hessian type and to admit a global potential. Here we give a new description of the geometry of $\mathscr{M}$, based on the observation that there is a natural way to immerse the moduli space into a homogeneous space $D$ diffeomorphic to $GL(n+1)/ (\{\pm 1\} \times O(n))$, where $n = b^3(M) - 1$. We point out that the formal properties of this immersion $\Phi : \mathscr{M} \rightarrow D$ are very similar to those of the period map defined on the moduli spaces of Calabi--Yau threefolds. With a view to understand the curvatures of $\mathscr{G}$, we also derive a new formula for the fourth derivative of the potential and relate it to the second fundamental form of $\Phi(\mathscr{M}) \subset D$., Comment: 31 pages

In many real-world applications of machine learning, we are interested to know if it is possible to train on the data that we have gathered so far, and obtain accurate predictions on a new test data subset that is qualitatively different in some respect (time period, geographic region, etc). Another question is whether data subsets are similar enough so that it is beneficial to combine subsets during model training. We propose SOAK, Same/Other/All K-fold cross-validation, a new method which can be used to answer both questions. SOAK systematically compares models which are trained on different subsets of data, and then used for prediction on a fixed test subset, to estimate the similarity of learnable/predictable patterns in data subsets. We show results of using SOAK on six new real data sets (with geographic/temporal subsets, to check if predictions are accurate on new subsets), 3 image pair data sets (subsets are different image types, to check that we get smaller prediction error on similar images), and 11 benchmark data sets with predefined train/test splits (to check similarity of predefined splits).

We compute the conservative scattering angle of two classical charged particles at the sixth order in electromagnetic coupling, and at the fourth order in velocity, thereby going beyond the current state of the art [fifth order in coupling, derived by Bern {\it et al.}, Phys. Rev. Lett. \textbf{132}, 251601 (2024)]. Our result is obtained by using the electromagnetic version of the Effective One-Body formalism to transfer information from the exact circular binary-charge solution of Schild [Phys. Rev. \textbf{131}, 2762 (1963)] to the post-Lorentzian expansion of the scattering angle., Comment: 15 pages, 1 figure

Many successful methods to learn dynamical systems from data have recently been introduced. Such methods often rely on the availability of the system's full state. However, this underlying hypothesis is rather restrictive as it is typically not confirmed in practice, leaving us with partially observed systems. Utilizing the Mori-Zwanzig (MZ) formalism from statistical physics, we demonstrate that Constant Lag Neural Delay Differential Equations (NDDEs) naturally serve as suitable models for partially observed states. In empirical evaluation, we show that such models outperform existing methods on both synthetic and experimental data.

We present an experimental study on the perturbed evolution of Korteweg-deVries soliton gases in a weakly dissipative nonlinear electrical transmission line. The system's dynamics reveal that an initially dense, fully randomized, soliton gas evolves into a coherent macroscopic state identified as a soliton condensate through nonlinear spectral analysis. The emergence of the soliton condensate is driven by the spatial rearrangement of the systems's eigenmodes and by the proliferation of new solitonic states due to nonadiabatic effects, a phenomenon not accounted for by the existing hydrodynamic theories.

The Onion Decomposition has recently been shown to provide principled models of complex graphs that better reproduce the sparse networks found in nature, but at the cost of complicated connection rules. We propose a k-edge swapping MCMC algorithm to efficiently obtain a uniform sample from the ensemble of simple graphs with a fixed Onion Decomposition and degree sequence. We prove the non-connectivity of the 2-edge swap algorithm for some small graphs, but then provide numerical experiments to show that this non-connectivity is not a problem for 2-edge swap in many practical cases, and likely irrelevant when using k-edge swaps with k>2. We finish by comparing our null model to other well-known models in the literature, and show that keeping constraints on the meso-scale structures of the Onion Decomposition greatly increases both the structural and functional realism of random graph null model.