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This paper presents the design and implementation of the Scintillation Filter by Kalman-colored algorithm (SciFi), which is used to remove tropospheric scintillation from Q/V bands total attenuation data series. In contrast to the classical methods using low-pass filters, the SciFi algorithm allows to estimate both the attenuation, its slope and a confidence interval. Moreover, the linear observer structure of the Kalman filter allows it to operate in real time. Therefore, the states and uncertainties estimated by SciFi can be used as input for Fade Mitigation Techniques (FMT) such as Adaptive Coding and Modulation (ACM) or Site Diversity (SD). In this article, we propose a method to tune the noise level based on recommendations approved by the ITU-R. Finally, some results of filtering on Alphasat experimental data are discussed., Comment: Ka and Broadband Communications Conference, Seattle, WA, USA, Sept 2024

Return words are a classical tool for studying shift spaces with low factor complexity. In recent years, their projection inside groups have attracted some attention, for instance in the context of dendric shift spaces, of generation of pseudorandom numbers (through the welldoc property), and of profinite invariants of shift spaces. Aiming at unifying disparate works, we introduce a notion of stability for subgroups generated by return words. Within this framework, we revisit several existing results and generalize some of them. We also study general aspects of stability, such as decidability or closure under certain operations.

This report summarizes what has happened in the mini-workshops entitled ''My Earth in 180 minutes'' organized during the lunch break at the SF2A 2024 conference in Marseille. The project showcased an innovative serious game designed to raise awareness of greenhouse gas (GHG) emissions in astronomical research laboratories. Participants, organized into teams, simulate strategies to reduce their carbon footprints by 50\%, focusing on key astronomical activities such as space instrumentation, data analysis, and laboratory work. The sessions highlight the challenges of achieving significant emissions reductions without disrupting core research activities, such as telescope observations. While the serious game facilitates important discussions on sustainable practices, the results point to the need for broader engagement, adaptation to different cultural contexts, and institutional support. The project highlights the importance of integrating climate action into the academic environment and suggests potential future directions for expanding its impact., Comment: SF2A 2024, Jun 2024, Marseille, France

We present significant upgrades to the VAMPIRES instrument, a visible-light (600 nm to 800 nm) high-contrast imaging polarimeter integrated within SCExAO on the Subaru telescope. Key enhancements include new qCMOS detectors, coronagraphs, polarization optics, and a multiband imaging mode, improving sensitivity, resolution, and efficiency. These upgrades position VAMPIRES as a powerful tool for studying sub-stellar companions, accreting protoplanets, circumstellar disks, stellar jets, stellar mass-loss shells, and solar system objects. The instrument achieves angular resolutions from 17 mas to 21 mas and Strehl ratios up to 60\%, with 5$\sigma$ contrast limits of $10^{\text{-}4}$ at 0.1'' to $10^{\text{-}6}$ beyond 0.5''. We demonstrate these capabilities through spectro-polarimetric coronagraphic imaging of the HD 169142 circumstellar disk, ADI+SDI imaging of the sub-stellar companion HD 1160B, narrowband H$\alpha$ imaging of the R Aqr emission nebula, and spectro-polarimetric imaging of Neptune., Comment: 36 pages, 33 figures, accepted to PASP

This work is motivated by the following problem: Can we identify the disease-causing gene in a patient affected by a monogenic disorder? This problem is an instance of root cause discovery. In particular, we aim to identify the intervened variable in one interventional sample using a set of observational samples as reference. We consider a linear structural equation model where the causal ordering is unknown. We begin by examining a simple method that uses squared z-scores and characterize the conditions under which this method succeeds and fails, showing that it generally cannot identify the root cause. We then prove, without additional assumptions, that the root cause is identifiable even if the causal ordering is not. Two key ingredients of this identifiability result are the use of permutations and the Cholesky decomposition, which allow us to exploit an invariant property across different permutations to discover the root cause. Furthermore, we characterize permutations that yield the correct root cause and, based on this, propose a valid method for root cause discovery. We also adapt this approach to high-dimensional settings. Finally, we evaluate the performance of our methods through simulations and apply the high-dimensional method to discover disease-causing genes in the gene expression dataset that motivates this work.

We analyze near-infrared integral field spectropolarimetry of the AB Aurigae protoplanetary disk and protoplanet (AB Aur b), obtained with SCExAO/CHARIS in 22 wavelength channels covering the J, H, and K passbands ($\lambda_{\rm o}$ = 1.1--2.4 $\mu m$) over angular separations of $\rho$ $\approx$ 0.13" to 1.1" ($\sim$20--175 au). Our images resolve spiral structures in the disk in each CHARIS channel. At the longest wavelengths, the data may reveal an extension of the western spiral seen in previous polarimetric data at $\rho$ $<$ 0.3" out to larger distances clockwise from the protoplanet AB Aur b, coincident with the ALMA-detected $CO$ gas spiral. While AB Aur b is detectable in complementary total intensity data, it is a non-detection in polarized light at $\lambda$ $>$ 1.3 $\mu $m. While the observed disk color is extremely red across $JHK$, the disk has a blue intrinsic scattering color consistent with small dust grains. The disk's polarization spectrum is redder than AB Aur b's total intensity spectrum. The polarization fraction peaks at $\sim$ 0.6 along the major disk axis. Radiative transfer modeling of the CHARIS data shows that small, porous dust grains with a porosity of $p$ = 0.6--0.8 better reproduce the scattered-light appearance of the disk than more compact spheres ($p$ = 0.3), especially the polarization fraction. This work demonstrates the utility of integral field spectropolarimetry to characterize structures in protoplanetary disks and elucidate the properties of the disks' dust., Comment: 23 pages, 17 Figures, accepted to AAS Journals

We tackle the problem of highly-accurate, holistic performance capture for the face, body and hands simultaneously. Motion-capture technologies used in film and game production typically focus only on face, body or hand capture independently, involve complex and expensive hardware and a high degree of manual intervention from skilled operators. While machine-learning-based approaches exist to overcome these problems, they usually only support a single camera, often operate on a single part of the body, do not produce precise world-space results, and rarely generalize outside specific contexts. In this work, we introduce the first technique for marker-free, high-quality reconstruction of the complete human body, including eyes and tongue, without requiring any calibration, manual intervention or custom hardware. Our approach produces stable world-space results from arbitrary camera rigs as well as supporting varied capture environments and clothing. We achieve this through a hybrid approach that leverages machine learning models trained exclusively on synthetic data and powerful parametric models of human shape and motion. We evaluate our method on a number of body, face and hand reconstruction benchmarks and demonstrate state-of-the-art results that generalize on diverse datasets.

MicroLED displays are the result of a complex manufacturing chain. Each stage of this process, if optimized, contributes to achieving the highest levels of final efficiencies. Common works carried out by Pollen Metrology, Aledia, and Universit{\'e} Clermont-Auvergne led to a generic process optimization workflow. This software solution offers a holistic approach where stages are chained together for gaining a complete optimal solution. This paper highlights key corners of the methodology, validated by the experiments and process experts: data cleaning and multi-objective optimization., Comment: Eurodisplay 2024, Sep 2024, Grenoble, France

The extreme environments of transiting close-in exoplanets in highly-eccentric orbits are ideal for testing exo-climate physics. Spectroscopically resolved phase curves not only allow for the characterization of their thermal response to irradiation changes but also unveil phase-dependent atmospheric chemistry and dynamics. We observed a partial phase curve of the highly-eccentric close-in giant planet HAT-P-2b ($e=0.51,M=9M_{\rm{Jup}}$) with the Wide Field Camera 3 aboard the Hubble Space Telescope. Using these data, we updated the planet's orbital parameters and radius, and retrieved high-frequency pulsations consistent with the planet-induced pulsations reported in Spitzer data. We found that the peak in planetary flux occurred at $6.7\pm0.6$ hr after periastron, with a heating and cooling timescales of $9.0^{+3.5}_{-2.1}$ hr, and $3.6^{+0.7}_{-0.6}$ hr, respectively. We compare the light-curve to various 1-dimensional and 3-dimensional forward models, varying the planet's chemical composition. The strong contrast in flux increase and decrease timescales before and after periapse indicates an opacity term that emerges during the planet's heating phase, potentially due to more H$^{-}$ than expected from chemical equilibrium models. The phase-resolved spectra are largely featureless, that we interpret as indicative an inhomogeneous dayside. However, we identified an anomalously high flux in the spectroscopic bin coinciding with the hydrogen Paschen $\beta$ line and that is likely connected to the planet's orbit. We interpret this as due to shock heating of the upper atmosphere given the short timescale involved, or evidence for other star-planet interactions., Comment: In review in AJ

We provide a conformal field theory (CFT) description of the probabilistic model of boundary effects in the wired uniform spanning tree (UST) and its algebraic content, concerning the entire first row of the Kac table with central charge $c=-2$. Namely, we prove that all boundary-to-boundary connection probabilities for (potentially fused) branches in the wired UST converge in the scaling limit to explicit CFT quantities, expressed in terms of determinants, which can also be viewed as conformal blocks of degenerate primary fields in a boundary CFT with central charge $c=-2$. Moreover, we verify that the Belavin-Polyakov-Zamolodchikov (BPZ) PDEs (i.e., Virasoro degeneracies) of arbitrary orders hold, and we also reveal an underlying valenced Temperley-Lieb algebra action on the space of boundary correlation functions of primary fields in this model. To prove these results, we combine probabilistic techniques with representation theory., Comment: 49 pages, 7 figures

We introduce a class of polynomials that we call fused Specht polynomials and use them to characterize irreducible representations of the fused Hecke algebra with parameter $q=-1$ in the space of polynomials. We apply the fused Specht polynomials to construct a basis for a space of holomorphic (chiral) conformal blocks with central charge $c=1$ which are degenerate at each point. In conformal field theory, this corresponds to all primary fields having conformal weight in the Kac table. The associated correlation functions are expected to give rise to conformally invariant boundary conditions for the Gaussian free field, which has also been verified in special cases., Comment: 48 pages

This paper introduces Mamba4Cast, a zero-shot foundation model for time series forecasting. Based on the Mamba architecture and inspired by Prior-data Fitted Networks (PFNs), Mamba4Cast generalizes robustly across diverse time series tasks without the need for dataset specific fine-tuning. Mamba4Cast's key innovation lies in its ability to achieve strong zero-shot performance on real-world datasets while having much lower inference times than time series foundation models based on the transformer architecture. Trained solely on synthetic data, the model generates forecasts for entire horizons in a single pass, outpacing traditional auto-regressive approaches. Our experiments show that Mamba4Cast performs competitively against other state-of-the-art foundation models in various data sets while scaling significantly better with the prediction length. The source code can be accessed at https://github.com/automl/Mamba4Cast.

This work establishes a linear response formula for sequential intermittent dynamical systems, consisting of Liverani-Saussol-Vaienti maps with varying parameters. As a consequence, we establish quenched (trajectory-wise) linear response for random compositions of these maps, complementing recent annealed (averaged) results in this setting. As an intermediate step, we show existence, uniqueness and statistical stability of the sequential equivariant densities for such non-uniformly expanding systems. Our arguments rely on the cone technique introduced by Baladi and Todd and further developed by Lepp{\"a}nen.

Fully actuated aerial robot proved their superiority for Aerial Physical Interaction (APhI) over the past years. This work proposes a minimal setup for aerial telemanipulation, enhancing accessibility of these technologies. The design and the control of a 6-DoF joystick with 4-DoF haptic feedback is detailed. It is the first haptic device with standard Remote Controller (RC) form factor for APhI. By miniaturizing haptic device, it enhances RC with the sense of touch, increasing physical awareness. The goal is to give operators an extra sense, other than vision and sound, to help to perform safe APhI. To the best of the authors knowledge, this is the first teleoperation system able to decouple each single axis input command. On the omnidirectional quadrotor, by reducing the number of components with a new design, we aim a simplified maintenance, and improved force and thrust to weight ratio. Open-sourced physic based simulation and successful preliminary flight tests highlighted the tool as promising for future APhI applications., Comment: 6 pages, 6 figures

We address the online unconstrained submodular maximization problem (Online USM), in a setting with stochastic bandit feedback. In this framework, a decision-maker receives noisy rewards from a nonmonotone submodular function, taking values in a known bounded interval. This paper proposes Double-Greedy - Explore-then-Commit (DG-ETC), adapting the Double-Greedy approach from the offline and online full-information settings. DG-ETC satisfies a O(d log(dT)) problemdependent upper bound for the 1/2-approximate pseudo-regret, as well as a O(dT^{2/3}log(dT)^{1/3}) problem-free one at the same time, outperforming existing approaches. To that end, we introduce a notion of hardness for submodular functions, characterizing how difficult it is to maximize them with this type of strategy.

We study the defect CFT associated with the half-BPS Wilson line in $\mathcal{N}=4$ Super Yang-Mills theory in four dimensions. Using a perturbative bootstrap approach, we derive new analytical results for multipoint correlators of protected defect operators at large $N$ and weak coupling. At next-to-next-to-leading order, we demonstrate that the simplest five- and six-point functions are fully determined by non-perturbative constraints -- which include superconformal symmetry, crossing symmetry, and the pinching of operators to lower-point functions -- as well as by a single integral, known as the train track integral. Additionally, we present new analytical results for the four-point functions $\langle 1122 \rangle$ and $\langle 1212 \rangle$., Comment: 51 pages, 3 figures, 3 tables, 1 ancillary notebook. v2 includes updated references and fixed typos. Submitted to JHEP

We study the correlators of bulk and defect half-BPS operators in $\mathcal{N}=4$ super Yang-Mills theory with a Maldacena-Wilson line defect, focusing on the case involving one bulk and two defect local operators. We analyze the non-perturbative constraints on these correlators, which include a topological sector, pinching and splitting limits, as well as a compatibility with expanding in superconformal blocks. Using these constraints, we compute a variety of bulk-defect-defect correlators up to next-to-leading order at weak coupling, and observe that transcendental terms cancel. Additionally, we study the two leading terms in the strong-coupling regime, and present partial results for the next-to-next-to-leading order., Comment: 40 pages, 8 figures, 2 tables. v2 includes new references, fixed typos and clear credits to individuals who shared partial results. Submitted to JHEP

We apply the Effective Field Theory of Large Scale Structure (EFTofLSS) to non-standard models of dark matter with suppressed small-scale structure imprinted by early-time physics, here exemplified by interacting dark matter (IDM) coupled to standard model neutrinos, and cross-check that the EFTofLSS has no trouble replicating the real-space halo-halo power spectrum from N-body simulations. We perform forecasts for a DESI ELG-like experiment using the redshift-space power spectrum and find that, under very conservative priors on these parameters, the EFTofLSS is not expected to yield strong constraints on dark matter interactions. However, with a better understanding of the evolution of counterterms and stochastic terms with redshift, realistic IDM models could in principle be detected using the full-shape power spectrum analysis of such a spectroscopic galaxy survey., Comment: 23 pages, 7 figures

Carcinogenesis is a proteiform phenomenon, with tumors emerging in various locations and displaying complex, diverse shapes. At the crucial intersection of research and clinical practice, it demands precise and flexible assessment. However, current biomarkers, such as RECIST 1.1's long and short axis measurements, fall short of capturing this complexity, offering an approximate estimate of tumor burden and a simplistic representation of a more intricate process. Additionally, existing supervised AI models face challenges in addressing the variability in tumor presentations, limiting their clinical utility. These limitations arise from the scarcity of annotations and the models' focus on narrowly defined tasks. To address these challenges, we developed ONCOPILOT, an interactive radiological foundation model trained on approximately 7,500 CT scans covering the whole body, from both normal anatomy and a wide range of oncological cases. ONCOPILOT performs 3D tumor segmentation using visual prompts like point-click and bounding boxes, outperforming state-of-the-art models (e.g., nnUnet) and achieving radiologist-level accuracy in RECIST 1.1 measurements. The key advantage of this foundation model is its ability to surpass state-of-the-art performance while keeping the radiologist in the loop, a capability that previous models could not achieve. When radiologists interactively refine the segmentations, accuracy improves further. ONCOPILOT also accelerates measurement processes and reduces inter-reader variability, facilitating volumetric analysis and unlocking new biomarkers for deeper insights. This AI assistant is expected to enhance the precision of RECIST 1.1 measurements, unlock the potential of volumetric biomarkers, and improve patient stratification and clinical care, while seamlessly integrating into the radiological workflow.

Empirically, it has been observed that adding momentum to Stochastic Gradient Descent (SGD) accelerates the convergence of the algorithm. However, the literature has been rather pessimistic, even in the case of convex functions, about the possibility of theoretically proving this observation. We investigate the possibility of obtaining accelerated convergence of the Stochastic Nesterov Accelerated Gradient (SNAG), a momentum-based version of SGD, when minimizing a sum of functions in a convex setting. We demonstrate that the average correlation between gradients allows to verify the strong growth condition, which is the key ingredient to obtain acceleration with SNAG. Numerical experiments, both in linear regression and deep neural network optimization, confirm in practice our theoretical results.

We present an example of low-carbon research activity carried out by astrophysicists and focused on ecology and environmental protection, with direct impacts on territories and society. This project serves as an illustration of action research for an astrophysics lab in the context of the current ecological crisis., Comment: 4 pages, 3 figures, proceeding of Societe Francaise d'Astronomie et d'Astrophysique (SF2A) 2024

The use of small amplitude electric currents delivered through the scalp, termed transcranial current stimulation (tCS, tDCS / tACS when using DC / AC currents) holds considerable promise for developing safe and effective treatments for central nervous system disorders. Initially welcomed with skepticism due to significant gaps of knowledge in terms of neurophysiology and biophysical mechanisms, tCS is maturing as a technology while its mechanisms of action are gradually being elucidated. However, there remain open questions about the mechanisms of action that warrant clarification to bring tCS to its full potential. In this review focused on tACS, we make an attempt at providing an overview of the converging experimental evidence, from results obtained in various species, regarding the mechanisms of action. We also highlight the remaining points of uncertainty regarding potential confounds, and propose possible experimentally testable solutions to address those issues. Finally, we outline how a continued focus on deepening our understanding of tACS mechanisms might provide significant insights into fundamental, long-standing questions in neuroscience.

Given $K_n$ whose edges are coloured red and blue, and a forest $F$ of order $n$, we seek embeddings of $F$ with small imbalance, that is, difference between the numbers of red and blue edges. We show that if the $2$-colouring of the edges of $K_n$ is balanced, meaning that the numbers of red and blue edges are equal, and $F$ has maximum degree $\Delta$, then one can find an embedding of $F$ into $K_n$ whose imbalance is at most $\Delta/2 + 18$, which is essentially best possible and resolves a conjecture of Mohr, Pardey, and Rautenbach. Furthermore, we give a tighter bound for the imbalance for small values of $\Delta$. In particular, we prove that the imbalance can be taken to be constant in the case where $\Delta0$.

Abstraction-based control design is a promising approach for ensuring safety-critical control of complex cyber-physical systems. A key aspect of this methodology is the relation between the original and abstract systems, which ensures that the abstract controller can be transformed into a valid controller for the original system through a concretization procedure. In this paper, we provide a comprehensive and systematic framework that characterizes various simulation relations, through their associated concretization procedures. We introduce the concept of augmented system, which universally enables a feedback refinement relation with the abstract system. This augmented system encapsulates the specific characteristics of each simulation relation within an interface, enabling a plug-and-play control architecture. Our results demonstrate that the existence of a particular simulation relation between the concrete and abstract systems is equivalent to the implementability of a specific control architecture, which depends on the considered simulation relation. This allows us to introduce new types of relations, and to establish the advantages and drawbacks of different relations, which we exhibit through detailed examples., Comment: 14 pages, 6 figures

Quantum entanglement is a key resource for quantum technologies, including emerging ground-to-satellite quantum communication. In such a scenario, an important challenge to be overcome is to consider entanglement between two or more quantum particles in different inertial frames, potentially experiencing relativistic effects affecting quantum correlations. In this paper, we present a consistent framework that overcomes this challenge. To this end, we establish the notion of factorizable and entangled multi-time trajectories and derive a class of Euler--Lagrange equations under the constraint of a non-entangling behavior. Comparing this restricted evolution to the solutions of the unrestricted equations of motion allows one to investigate the trajectory-based entanglement of general systems. We solve our equations for interacting particles in a Klein--Gordon-type setting, thereby quantifying the dynamic and relativistic impact of entanglement in a self-consistent manner.

Learning skills that interact with objects is of major importance for robotic manipulation. These skills can indeed serve as an efficient prior for solving various manipulation tasks. We propose a novel Skill Learning approach that discovers composable behaviors by solving a large and diverse number of autonomously generated tasks. Our method learns skills allowing the robot to consistently and robustly interact with objects in its environment. The discovered behaviors are embedded in primitives which can be composed with Hierarchical Reinforcement Learning to solve unseen manipulation tasks. In particular, we leverage Asymmetric Self-Play to discover behaviors and Multiplicative Compositional Policies to embed them. We compare our method to Skill Learning baselines and find that our skills are more interactive. Furthermore, the learned skills can be used to solve a set of unseen manipulation tasks, in simulation as well as on a real robotic platform., Comment: Accepted at the 2024 IEEE-RAS International Conference on Humanoid Robots

EK Draconis, a nearby young solar-type star (G1.5V, 50-120 Myr), is known as one of the best proxies for inferring the environmental conditions of the young Sun. The star frequently produces superflares and Paper I presented the first evidence of an associated gigantic prominence eruption observed as a blueshifted H$\alpha$ Balmer line emission. In this paper, we present the results of dynamical modeling of the stellar eruption and examine its relationship to the surface starspots and large-scale magnetic fields observed concurrently with the event. By performing a one-dimensional free-fall dynamical model and a one dimensional hydrodynamic simulation of the flow along the expanding magnetic loop, we found that the prominence eruption likely occurred near the stellar limb (12$^{+5}_{-5}$-16$^{+7}_{-7}$ degrees from the limb) and was ejected at an angle of 15$^{+6}_{-5}$-24$^{+6}_{-6}$ degrees relative to the line of sight, and the magnetic structures can expand into a coronal mass ejection (CME). The observed prominence displayed a terminal velocity of $\sim$0 km s$^{-1}$ prior to disappearance, complicating the interpretation of its dynamics in Paper I. The models in this paper suggest that prominence's H$\alpha$ intensity diminishes at around or before its expected maximum height, explaining the puzzling time evolution in observations. The TESS light curve modeling and (Zeeman) Doppler Imaging revealed large mid-latitude spots with polarity inversion lines and one polar spot with dominant single polarity, all near the stellar limb during the eruption. This suggests that mid-latitude spots could be the source of the pre-existing gigantic prominence we reported in Paper I. These results provide valuable insights into the dynamic processes that likely influenced the environments of early Earth, Mars, Venus, and young exoplanets., Comment: 25 pages, 14 figures, 5 tables. Accepted for publication in The Astrophysical Journal

This study introduces a novel frequency-based data-driven controller for adaptive optics, using power spectral density for optimization while ensuring stability criteria. It addresses disturbance rejection, command amplitude constraints and system transfer functions through convex optimization to obtain an optimal control in an infinite input response filter form. Evaluated within the SAXO+ project, it demonstrates efficacy under diverse atmospheric conditions and operational scenarios. The proposed controller is tested in both standard and disentangled adaptive optics schemes, showcasing its adaptability and performance. Experimental validation is conducted using the COMPASS simulation tool, affirming the controller's promise for enhancing adaptive optics systems in real-world applications.

Generalized Additive Models (GAMs) are widely recognized for their ability to create fully interpretable machine learning models for tabular data. Traditionally, training GAMs involves iterative learning algorithms, such as splines, boosted trees, or neural networks, which refine the additive components through repeated error reduction. In this paper, we introduce GAMformer, the first method to leverage in-context learning to estimate shape functions of a GAM in a single forward pass, representing a significant departure from the conventional iterative approaches to GAM fitting. Building on previous research applying in-context learning to tabular data, we exclusively use complex, synthetic data to train GAMformer, yet find it extrapolates well to real-world data. Our experiments show that GAMformer performs on par with other leading GAMs across various classification benchmarks while generating highly interpretable shape functions., Comment: 20 pages, 12 figures

Epilepsy represents the most prevalent neurological disease in the world. One-third of people suffering from mesial temporal lobe epilepsy (MTLE) exhibit drug resistance, urging the need to develop new treatments. A key part in anti-seizure medication (ASM) development is the capability of detecting and quantifying epileptic seizures occurring in electroencephalogram (EEG) signals, which is crucial for treatment efficacy evaluation. In this study, we introduced a seizure detection pipeline based on deep learning models applied to raw EEG signals. This pipeline integrates: a new pre-processing technique which segments continuous raw EEG signals without prior distinction between seizure and seizure-free activities; a post-processing algorithm developed to reassemble EEG segments and allow the identification of seizures start/end; and finally, a new evaluation procedure based on a strict seizure events comparison between predicted and real labels. Models training have been performed using a data splitting strategy which addresses the potential for data leakage. We demonstrated the fundamental differences between a seizure classification and a seizure detection task and showed the differences in performance between the two tasks. Finally, we demonstrated the generalization capabilities across species of our best architecture, combining a Convolutional Neural Network and a Transformer encoder. The model was trained on animal EEGs and tested on human EEGs with a F1-score of 93% on a balanced Bonn dataset., Comment: 25 pages, 7 tables, 4 figures

In this thesis, we have investigated the spreading of quantum correlations in isolated lattice models with short- or long-range interactions driven far from equilibrium via sudden global quenches. A general theoretical approach relying on a quasiparticle theory is presented. The latter has permitted to unveil a generic expression for the equal-time connected correlation functions valid both for short-range and long-range interacting particle and spin lattice models on a hypercubic lattice. Relying on stationary phase arguments, we have shown that its causality cone displays a universal twofold structure consisting of a correlation edge and a series of local extrema defining the outer and inner structure of the space-time correlations. For short-range interactions, the motion of each structure is ballistic and the associated spreading velocities are related to the group and phase velocities of the quasiparticle dispersion relation of the post-quench Hamiltonian. For long-range interactions of the form $1/|R|^{\alpha}$, the correlation spreading is substantially different due to a possible divergence of group velocity when tuning the power-law exponent $\alpha$. For a divergent group velocity, i.e. the quasi-local regime, we have presented evidence of a universal algebraic structure for the causality cone. While, the correlation edge motion has been found to be always slower than ballistic, the local extrema propagate faster than ballistically and ballistically for gapless and gapped quantum systems respectively. For the local regime implying a well-defined group velocity, we have recovered similar scaling laws and spreading velocities than the short-range case for the causality cone of correlations., Comment: Doctoral thesis

Latent variable collaborative filtering methods have been a standard approach to modelling user-click interactions due to their simplicity and effectiveness. However, there is limited work on analyzing the mathematical properties of these methods in particular on preventing the overfitting towards the identity, and such methods typically utilize loss functions that overlook the geometry between items. In this work, we introduce a notion of generalization gap in collaborative filtering and analyze this with respect to latent collaborative filtering models. We present a geometric upper bound that gives rise to loss functions, and a way to meaningfully utilize the geometry of item-metadata to improve recommendations. We show how these losses can be minimized and gives the recipe to a new latent collaborative filtering algorithm, which we refer to as GeoCF, due to the geometric nature of our results. We then show experimentally that our proposed GeoCF algorithm can outperform other all existing methods on the Movielens20M and Netflix datasets, as well as two large-scale internal datasets. In summary, our work proposes a theoretically sound method which paves a way to better understand generalization of collaborative filtering at large., Comment: 13 pages, 1 figure, 3 tables

Astrophysical black holes (BHs) have two fundamental properties: mass and spin. While the mass-evolution of BHs has been extensively studied, much less work has been done on predicting the distribution of BH spins. In this paper we present the spin evolution for a sample of intermediate-mass and massive BHs from the newHorizon simulation, which evolved BH spin across cosmic time in a full cosmological context through gas accretion, BH-BH mergers and BH feedback including jet spindown. As BHs grow, their spin evolution alternates between being dominated by gas accretion and BH mergers. Massive BHs are generally highly spinning. Accounting for the spin energy extracted through the Blandford-Znajek mechanism increases the scatter in BH spins, especially in the mass range $10^{5-7} \rm \ M_\odot$, where BHs had previously been predicted to be almost universally maximally spinning. We find no evidence for spin-down through efficient chaotic accretion. As a result of their high spin values, massive BHs have an average radiative efficiency of $<\varepsilon_{\rm r}^{\rm thin}> \approx 0.19$. As BHs spend much of their time at low redshift with a radiatively inefficient thick disc, BHs in our sample remain hard to observe. Different observational methods probe different sub-populations of BHs, significantly influencing the observed distribution of spins. Generally, X-ray-based methods and higher luminosity cuts increase the average observed BH spin. When taking BH spin evolution into account, BHs inject on average between 3 times (in quasar mode) and 8 times (in radio mode) as much feedback energy into their host galaxy as previously assumed., Comment: 21 pages, accepted for publication in MNRAS

Convection is a ubiquitous process driving geophysical/astrophysical fluid flows, which are typically strongly constrained by planetary rotation on large scales. A celebrated model of such flows, rapidly rotating Rayleigh-B\'enard convection, has been extensively studied in direct numerical simulations (DNS) and laboratory experiments, but the parameter values attainable by state-of-the-art methods are limited to moderately rapid rotation (Ekman numbers $Ek\gtrsim10^{-8}$), while realistic geophysical/astrophysical $Ek$ are significantly smaller. Asymptotically reduced equations of motion, the nonhydrostatic quasi-geostrophic equations (NHQGE), describing the flow evolution in the limit $Ek\to 0$, do not apply at finite rotation rates. The geophysical/astrophysical regime of small but finite $Ek$ therefore remains currently inaccessible. Here, we introduce a new, numerically advantageous formulation of the Navier-Stokes-Boussinesq equations informed by the scalings valid for $Ek\to0$, the \textit{rescaled incompressible Navier-Stokes equations} (RiNSE). We solve the RiNSE using a spectral quasi-inverse method resulting in a sparse, fast algorithm to perform efficient DNS in this previously unattainable parameter regime. We validate our results against the literature across a range of $Ek$, and demonstrate that the algorithmic approaches taken remain accurate and numerically stable at $Ek$ as low as $10^{-15}$. Like the NHQGE, the RiNSE derive their efficiency from adequate conditioning, eliminating spurious growing modes that otherwise induce numerical instabilities at small $Ek$. We show that the time derivative of the mean temperature is inconsequential for accurately determining the Nusselt number in the stationary state, significantly reducing the required simulation time, and demonstrate that full DNS using RiNSE agree with the NHQGE at very small $Ek$.

A computationally efficient method for calculating the transport of neutrino flavor in simulations is to use angular moments of the neutrino one-body reduced density matrix, i.e., `quantum moments'. As with any moment-based radiation transport method, a closure is needed if the infinite tower of moment evolution equations is truncated. We derive a general parameterization of a quantum closure and the limits the parameters must satisfy in order for the closure to be physical. We then derive from multi-angle calculations the evolution of the closure parameters in two test cases which we then progressively insert into a moment evolution code and show how the parameters affect the moment results until the full multi-angle results are reproduced. This parameterization paves the way to setting prescriptions for genuine quantum closures adapted to neutrino transport in a range of situations., Comment: 20 pages, 8 figures

We show that strong interactions violating the conservation of lepton numbers in the neutrino sector could significantly alter the standard low entropy picture for the pre-supernova collapsing core of a massive star. A rapid neutrino-antineutrino equilibration leads to entropy generation and enhanced electron capture and, hence, a lower electron fraction than in the standard model. This would affect the downstream core evolution, the prospects for a supernova explosion, and the emergent neutrino signal. If realized by lepton-number-violating neutrino self-interactions (LNV $\nu$SI), the relevant mediator mass and coupling ranges can be probed by future accelerator-based experiments., Comment: 5+8 pages, 2+5 figures. Comments are welcome!

We consider kinetic SDEs with low regularity coefficients in the setting recently introduced in [6]. For the solutions to such equations, we first prove a Harnack inequality. Using the abstract approach of [5], this inequality then allows us to prove, under a Lyapunov condition, the existence and uniqueness (in a suitable class of measures) of a quasi-stationary distribution in cylindrical domains of the phase space. We finally exhibit two settings in which the Lyapunov condition holds: general kinetic SDEs in domains which are bounded in position, and Langevin processes with a non-conservative force and a suitable growth condition on the force.

In the last decade, EU became aware of its dependency on mining countries for mineral resources. In that context in 2022, France launched new airborne geophysical surveys in the Limousin region (northwestern French Massif Central) which is historically renown for its gold, uranium, tungsten and tin production. Thanks to these new data, it has become possible to both explore the Saint-Goussaud - Auriat zone, centered on the Puy-les-Vignes tungsten deposit and to investigate the numerous W occurrences. To do so, direct geophysical modelling using gravimetric, airborne magnetic and electromagnetic data has allowed to create a 3D regional model. It mainly illustrates locations where evolved granites deepen, possibly associated with hidden granitic cupolas. Besides, electromagnetic data have been analysed to highlight conductive anomalies. The lineament analysis has contributed to identify a conductive corridor, interpreted as a regional structure. Conductive vertical conduits, which are spatially close to mineralized occurrences and stream-sediment anomalies have also been spotted. This new method has thus allowed generating new elements which could be combined and could have implications for granite-related tungsten exploration in this region.

In the rapidly rotating limit, we derive a balanced set of reduced equations governing the strongly nonlinear development of the convective wall-mode instability in the interior of a general container. The model illustrates that wall-mode convection is a multiscale phenomenon where the dynamics of the bulk interior diagnostically determine the small-scale dynamics within Stewartson boundary layers at the sidewalls. The sidewall boundary layers feedback on the interior via a nonlinear lateral heat-flux boundary condition, providing a closed system. Outside the asymptotically thin boundary layer, the convective modes connect to a dynamical interior that maintains scales set by the domain geometry. In many ways, the final system of equations resembles boundary-forced planetary geostrophic baroclinic dynamics coupled with barotropic quasi-geostrophic vorticity. The reduced system contains the results from previous linear instability theory but captured in an elementary fashion, providing a new avenue for investigating wall-mode convection in the strongly nonlinear regime. We also derive the dominant Ekman-flux correction to the onset Rayleigh number for large Taylor number, Ra ~ 31.8 Ta^(1/2) - 4.43 Ta^(5/12) for no-slip boundaries. We demonstrate some of the reduced model's nonlinear dynamics with numerical simulations in a cylindrical container., Comment: 40 pages, 13 figures

Machine learning models are increasingly being deployed in real-world contexts. However, systematic studies on their transferability to specific and critical applications are underrepresented in the research literature. An important example is visual anomaly detection (VAD) for robotic power line inspection. While existing VAD methods perform well in controlled environments, real-world scenarios present diverse and unexpected anomalies that current datasets fail to capture. To address this gap, we introduce $\textit{CableInspect-AD}$, a high-quality, publicly available dataset created and annotated by domain experts from Hydro-Qu\'ebec, a Canadian public utility. This dataset includes high-resolution images with challenging real-world anomalies, covering defects with varying severity levels. To address the challenges of collecting diverse anomalous and nominal examples for setting a detection threshold, we propose an enhancement to the celebrated PatchCore algorithm. This enhancement enables its use in scenarios with limited labeled data. We also present a comprehensive evaluation protocol based on cross-validation to assess models' performances. We evaluate our $\textit{Enhanced-PatchCore}$ for few-shot and many-shot detection, and Vision-Language Models for zero-shot detection. While promising, these models struggle to detect all anomalies, highlighting the dataset's value as a challenging benchmark for the broader research community. Project page: https://mila-iqia.github.io/cableinspect-ad/., Comment: 35 pages, to appear at NeurIPS 2024

In this paper, we extend the work of Liesen et al. (2002), which analyzes how the condition number of an orthonormal matrix Q changes when a column is added ([Q, c]), particularly focusing on the perpendicularity of c to the span of Q. Their result, presented in Theorem 2.3 of Liesen et al. (2002), assumes exact arithmetic and orthonormality of Q, which is a strong assumption when applying these results to numerical methods such as QR factorization algorithms. In our work, we address this gap by deriving bounds on the condition number increase for a matrix B without assuming perfect orthonormality, even when a column is not perfectly orthogonal to the span of B. This framework allows us to analyze QR factorization methods where orthogonalization is imperfect and subject to Gaussian noise. We also provide results on the performance of orthogonal projection and least squares under Gaussian noise, further supporting the development of this theory.

This paper evaluates and compares the performance of model-free and model-based reinforcement learning for the attitude control of fixed-wing unmanned aerial vehicles using PID as a reference point. The comparison focuses on their ability to handle varying flight dynamics and wind disturbances in a simulated environment. Our results show that the Temporal Difference Model Predictive Control agent outperforms both the PID controller and other model-free reinforcement learning methods in terms of tracking accuracy and robustness over different reference difficulties, particularly in nonlinear flight regimes. Furthermore, we introduce actuation fluctuation as a key metric to assess energy efficiency and actuator wear, and we test two different approaches from the literature: action variation penalty and conditioning for action policy smoothness. We also evaluate all control methods when subject to stochastic turbulence and gusts separately, so as to measure their effects on tracking performance, observe their limitations and outline their implications on the Markov decision process formalism., Comment: Published at ICINCO 2024

Generative text-to-image models are disrupting the lives of creative professionals. Specifically, illustrators are threatened by models that claim to extract and reproduce their style. Yet, research on style transfer has rarely focused on their perspectives. We provided four illustrators with a model fine-tuned to their style and conducted semi-structured interviews about the model's successes, limitations, and potential uses. Evaluating their output, artists reported that style transfer successfully copies aesthetic fragments but is limited by content-style disentanglement and lacks the crucial emergent quality of their style. They also deemed the others' copies more successful. Understanding the results of style transfer as "boundary objects," we analyze how they can simultaneously be considered unsuccessful by artists and poised to replace their work by others. We connect our findings to critical HCI frameworks, demonstrating that style transfer, rather than merely a Creativity Support Tool, should also be understood as a supply chain optimization one.

In the field of explainable AI, a vibrant effort is dedicated to the design of self-explainable models, as a more principled alternative to post-hoc methods that attempt to explain the decisions after a model opaquely makes them. However, this productive line of research suffers from common downsides: lack of reproducibility, unfeasible comparison, diverging standards. In this paper, we propose CaBRNet, an open-source, modular, backward-compatible framework for Case-Based Reasoning Networks: https://github.com/aiser-team/cabrnet.

While previous years have seen great progress in the 3D reconstruction of humans from monocular videos, few of the state-of-the-art methods are able to handle loose garments that exhibit large non-rigid surface deformations during articulation. This limits the application of such methods to humans that are dressed in standard pants or T-shirts. Our method, ReLoo, overcomes this limitation and reconstructs high-quality 3D models of humans dressed in loose garments from monocular in-the-wild videos. To tackle this problem, we first establish a layered neural human representation that decomposes clothed humans into a neural inner body and outer clothing. On top of the layered neural representation, we further introduce a non-hierarchical virtual bone deformation module for the clothing layer that can freely move, which allows the accurate recovery of non-rigidly deforming loose clothing. A global optimization jointly optimizes the shape, appearance, and deformations of the human body and clothing via multi-layer differentiable volume rendering. To evaluate ReLoo, we record subjects with dynamically deforming garments in a multi-view capture studio. This evaluation, both on existing and our novel dataset, demonstrates ReLoo's clear superiority over prior art on both indoor datasets and in-the-wild videos., Comment: Project page: https://moygcc.github.io/ReLoo/

Direct imaging of exoplanets is particularly challenging due to the high contrast between the planet and the star luminosities, and their small angular separation. In addition to tailored instrumental facilities implementing adaptive optics and coronagraphy, post-processing methods combining several images recorded in pupil tracking mode are needed to attenuate the nuisances corrupting the signals of interest. Most of these post-processing methods build a model of the nuisances from the target observations themselves, resulting in strongly limited detection sensitivity at short angular separations due to the lack of angular diversity. To address this issue, we propose to build the nuisance model from an archive of multiple observations by leveraging supervised deep learning techniques. The proposed approach casts the detection problem as a reconstruction task and captures the structure of the nuisance from two complementary representations of the data. Unlike methods inspired by reference differential imaging, the proposed model is highly non-linear and does not resort to explicit image-to-image similarity measurements and subtractions. The proposed approach also encompasses statistical modeling of learnable spatial features. The latter is beneficial to improve both the detection sensitivity and the robustness against heterogeneous data. We apply the proposed algorithm to several datasets from the VLT/SPHERE instrument, and demonstrate a superior precision-recall trade-off compared to the PACO algorithm. Interestingly, the gain is especially important when the diversity induced by ADI is the most limited, thus supporting the ability of the proposed approach to learn information across multiple observations.

Consider a family of random masses $\mathbf{m}(v)$ indexed by vertices of the lattice $\mathbb Z^d$. In the case where the masses are i.i.d.\ and satisfy a certain moment condition, it is known that there exists a deterministic $A\ge 0$ such that the maximal mass $A_n$ of an animal containing $0$ with cardinal $n$ satisfies $A_n/n \rightarrow A$ when $n\to \infty$, almost surely. The same also goes for self-avoiding paths. We extend this result to the case where the family of masses is an ergodic marked point process, with a suitable definition for animals in this context. Special cases include the initial model with ergodic instead of i.i.d.\ masses and marked Poisson point processes. We also discuss some sufficient or necessary conditions for integrability.

In this paper, we propose the Reduced Order Hysteretic Magnetization (ROHM) model to describe the magnetization and instantaneous power loss of composite superconductors subject to time-varying magnetic fields. Once the parameters of the ROHM model are fixed based on reference simulations, it allows to directly compute the macroscopic response of composite superconductors without the need to solve the detailed current density distribution. It can then be used as part of a homogenization method in large-scale superconducting models to significantly reduce the computational effort compared to detailed simulations. In this contribution, we focus on the case of a strand with twisted superconducting filaments subject to a time-varying transverse magnetic field. We propose two levels of ROHM models: (i) a rate-independent model that reproduces hysteresis in the filaments, and (ii) a rate-dependent model that generalizes the first level by also reproducing dynamic effects due to coupling and eddy currents. We describe the implementation and inclusion of these models in a finite element framework, discuss how to deduce the model parameters, and finally demonstrate the capabilities of the approach in terms of accuracy and efficiency over a wide range of excitation frequencies and amplitudes., Comment: Updated preprint (improved figures and added explanations), 20 pages, 28 figures

James Webb Space Telescope opens a new window to directly probe luminous quasars powered by billion solar mass black holes in the epoch of reionization and their co-evolution with massive galaxies with unprecedented details. In this paper, we report the first results from the deep NIRSpec integral field spectroscopy study of a quasar at $z = 7.5$. We obtain a bolometric luminosity of $\sim$$1.8\times10^{47}$ erg s$^{-1}$ and a black hole mass of $\sim$0.7--2.5$\times10^{9}$ M$_{\odot}$ based on H$\beta$ emission line from the quasar spectrum. We discover $\sim$2 kpc scale, highly blueshifted ($\sim$$-$870 km/s) and broad ($\sim$1400 km/s) [O III] line emission after the quasar PSF has been subtracted. Such line emission most likely originates from a fast, quasar-driven outflow, the earliest one on galactic-scale known so far. The dynamical properties of this outflow fall within the typical ranges of quasar-driven outflows at lower redshift, and the outflow may be fast enough to reach the circumgalactic medium. Combining both the extended and nuclear outflow together, the mass outflow rate, $\sim$300 M$_{\odot}$yr, is $\sim$60%--380% of the star formation rate of the quasar host galaxy, suggesting that the outflow may expel a significant amount of gas from the inner region of the galaxy. The kinetic energy outflow rate, $\sim$3.6$\times10^{44}$ erg s$^{-1}$, is $\sim$0.2% of the quasar bolometric luminosity, which is comparable to the minimum value required for negative feedback based on simulation predictions. The dynamical timescale of the extended outflow is $\sim$1.7 Myr, consistent with the typical quasar lifetime in this era.

In direct imaging at high contrast, the bright glare produced by the host star makes the detection and the characterization of sub-stellar companions particularly challenging. In spite of the use of an extreme adaptive optics system combined with a coronagraphic mask to strongly attenuate the starlight contamination, dedicated post-processing methods combining several images recorded with the pupil tracking mode of the telescope are needed to reach the required contrast. In that context, we recently proposed to combine the statistics-based model of PACO with a deep learning approach in a three-step algorithm. First, the data are centered and whitened locally using the PACO framework to improve the stationarity and the contrast in a preprocessing step. Second, a convolutional neural network (CNN) is trained in a supervised fashion to detect the signature of synthetic sources in the preprocessed science data. Finally, the trained network is applied to the preprocessed observations and delivers a detection map. A second network is trained to infer locally the photometry of detected sources. Both deep models are trained from scratch with a custom data augmentation strategy allowing to generate a large training set from a single spatio-temporo-spectral dataset. This strategy can be applied to process jointly the images of observations conducted with angular, and eventually spectral, differential imaging (A(S)DI). In this proceeding, we present in a unified framework the key ingredients of the deep PACO algorithm both for ADI and ASDI. We apply our method on several datasets from the the IRDIS imager of the VLT/SPHERE instrument. Our method reaches, in average, a better trade-off between precision and recall than the comparative algorithms., Comment: AO4ELT 2023