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To date, galaxy image simulations for weak lensing surveys usually approximate the light profiles of all galaxies as a single or double S\'ersic profile, neglecting the influence of galaxy substructures and morphologies deviating from such a simplified parametric characterization. While this approximation may be sufficient for previous data sets, the stringent cosmic shear calibration requirements and the high quality of the data in the upcoming Euclid survey demand a consideration of the effects that realistic galaxy substructures have on shear measurement biases. Here we present a novel deep learning-based method to create such simulated galaxies directly from HST data. We first build and validate a convolutional neural network based on the wavelet scattering transform to learn noise-free representations independent of the point-spread function of HST galaxy images that can be injected into simulations of images from Euclid's optical instrument VIS without introducing noise correlations during PSF convolution or shearing. Then, we demonstrate the generation of new galaxy images by sampling from the model randomly and conditionally. Next, we quantify the cosmic shear bias from complex galaxy shapes in Euclid-like simulations by comparing the shear measurement biases between a sample of model objects and their best-fit double-S\'ersic counterparts. Using the KSB shape measurement algorithm, we find a multiplicative bias difference between these branches with realistic morphologies and parametric profiles on the order of $6.9\times 10^{-3}$ for a realistic magnitude-S\'ersic index distribution. Moreover, we find clear detection bias differences between full image scenes simulated with parametric and realistic galaxies, leading to a bias difference of $4.0\times 10^{-3}$ independent of the shape measurement method. This makes it relevant for stage IV weak lensing surveys such as Euclid., Comment: Submitted to A&A. 29 pages, 20 figures, 2 tables

Small-sized exoplanets in tight orbits around young stars (10-1000 Myr) give us the opportunity to investigate the mechanisms that led to their formation, the evolution of their physical and orbital properties and, especially, of their atmospheres. Thanks to the all-sky survey carried out by TESS, many of these exoplanets have been discovered and have subsequently been characterized with dedicated follow-up observations. In the context of a collaboration among the GAPS, TKS and CPS teams, we measured with a high level of precision the mass and the radius of TOI-1430 b, a young (~700 Myr) exoplanet with an escaping He atmosphere orbiting the K-dwarf star HD 235088 (TOI-1430). By adopting appropriate stellar parameters, which were measured in this work, we were able to simultaneously model the signals due to strong stellar activity and the transiting planet TOI-1430 b in both photometric and spectroscopic series. This allowed us to measure the density of the planet with high precision, and reconstruct the evolution of its atmosphere. TOI-1430 is an active K-dwarf star born 700+/-150 Myr ago and rotates in ~12 d. It hosts a mini-Neptune whose orbital period is Pb=7.434133+/-0.000004 d. Thanks to long-term monitoring of this target performed with TESS, HARPS-N, HIRES, and APF, we estimated a radius Rb=1.98+/-0.07 $R_{\oplus}$, a mass Mb=4.2+/-0.8 $M_{\oplus}$, and thus a planetary density $\rho$b=0.5+/-0.1 $\rho_{\oplus}$. TOI-1430 b is hence a low-density mini-Neptune with an extended atmosphere, at the edge of the radius gap. Because this planet is known to have an evaporating atmosphere of He, we reconstructed its atmospheric history. Our analysis supports the scenario in which, shortly after its birth, TOI-1430 b may have been super-puffy, with a radius 5x-13x and a mass 1.5x-2x that of today; in ~200 Myr from now, TOI-1430 b should lose its envelope, showing its Earth-size core., Comment: 30 pages, 19 figures, 8 tables. Accepted for publication in Astronomy & Astrophysics on 16 November 2024. Electronic material (tables G1-G3) will soon be available on the CDS or upon request to the first author. Abstract shortened

We examined in depth the star BD+00 444 (GJ 105.5, TOI-2443; V = 9.5 mag; d = 23.9 pc), with the aim of characterizing and confirming the planetary nature of its small companion, the planet candidate TOI-2443.01, which was discovered by TESS. We monitored BD+00 444 with the HARPS-N spectrograph for 1.5 years to search for planet-induced radial-velocity (RV) variations, and then analyzed the RV measurements jointly with TESS and ground-based photometry. We determined that the host is a quiet K5 V, and we revealed that the sub-Neptune BD+00 444 b has a radius of $R_b=2.36\pm0.05 R_{\oplus}$, a mass of $M_b=4.8\pm1.1 M_{\oplus}$ and, consequently, a rather low-density value of $\rho_b=2.00+0.49-0.45$ g cm-3, which makes it compatible with both an Earth-like rocky interior with a thin H-He atmosphere and a half-rocky, half-water composition with a small amount of H-He. Having an orbital period of about 15.67 days and an equilibrium temperature of about 519 K, BD+00 444 b has an estimated transmission spectroscopy metric of about 159, which makes it ideal for atmospheric follow-up with the JWST. Notably, it is the second most eccentric inner transiting planet, $e=0.302+0.051-0.035$, with a mass below 20 $M_{\oplus}$, among those with well-determined eccentricities. We estimated that tidal forces from the host star affect both planet b's rotation and eccentricity, and strong tidal dissipation may signal intense volcanic activity. Furthermore, our analysis suggests the presence of a sub-Neptune-mass planet candidate, BD+00 444 c, having an orbital period of $P=96.6\pm1.4$ days, and a minimum mass $M\sin{i}=9.3+1.8-2.0 M_{\oplus}$. With an equilibrium temperature of about 283 K, BD+00 444 c is right inside the habitable zone; however, this candidate necessitates further observations and stronger statistical evidence to be confirmed. [...], Comment: Accepted for publication in A&A on November 13 2024. 20 pages, 11 figures

Speech impairments in Parkinson's disease (PD) provide significant early indicators for diagnosis. While models for speech-based PD detection have shown strong performance, their interpretability remains underexplored. This study systematically evaluates several explainability methods to identify PD-specific speech features, aiming to support the development of accurate, interpretable models for clinical decision-making in PD diagnosis and monitoring. Our methodology involves (i) obtaining attributions and saliency maps using mainstream interpretability techniques, (ii) quantitatively evaluating the faithfulness of these maps and their combinations obtained via union and intersection through a range of established metrics, and (iii) assessing the information conveyed by the saliency maps for PD detection from an auxiliary classifier. Our results reveal that, while explanations are aligned with the classifier, they often fail to provide valuable information for domain experts., Comment: The first two authors contributed equally to this research: author order is alphabetical

In this paper, we introduce PASTA (Perceptual Assessment System for explanaTion of Artificial intelligence), a novel framework for a human-centric evaluation of XAI techniques in computer vision. Our first key contribution is a human evaluation of XAI explanations on four diverse datasets (COCO, Pascal Parts, Cats Dogs Cars, and MonumAI) which constitutes the first large-scale benchmark dataset for XAI, with annotations at both the image and concept levels. This dataset allows for robust evaluation and comparison across various XAI methods. Our second major contribution is a data-based metric for assessing the interpretability of explanations. It mimics human preferences, based on a database of human evaluations of explanations in the PASTA-dataset. With its dataset and metric, the PASTA framework provides consistent and reliable comparisons between XAI techniques, in a way that is scalable but still aligned with human evaluations. Additionally, our benchmark allows for comparisons between explanations across different modalities, an aspect previously unaddressed. Our findings indicate that humans tend to prefer saliency maps over other explanation types. Moreover, we provide evidence that human assessments show a low correlation with existing XAI metrics that are numerically simulated by probing the model., Comment: https://github.com/ENSTA-U2IS-AI/Dataset_XAI

Vision-Language Models (VLMs) have shown significant promise in Visual Question Answering (VQA) tasks by leveraging web-scale multimodal datasets. However, these models often struggle with continual learning due to catastrophic forgetting when adapting to new tasks. As an effective remedy to mitigate catastrophic forgetting, rehearsal strategy uses the data of past tasks upon learning new task. However, such strategy incurs the need of storing past data, which might not be feasible due to hardware constraints or privacy concerns. In this work, we propose the first data-free method that leverages the language generation capability of a VLM, instead of relying on external models, to produce pseudo-rehearsal data for addressing continual VQA. Our proposal, named as GaB, generates pseudo-rehearsal data by posing previous task questions on new task data. Yet, despite being effective, the distribution of generated questions skews towards the most frequently posed questions due to the limited and task-specific training data. To mitigate this issue, we introduce a pseudo-rehearsal balancing module that aligns the generated data towards the ground-truth data distribution using either the question meta-statistics or an unsupervised clustering method. We evaluate our proposed method on two recent benchmarks, \ie VQACL-VQAv2 and CLOVE-function benchmarks. GaB outperforms all the data-free baselines with substantial improvement in maintaining VQA performance across evolving tasks, while being on-par with methods with access to the past data.

This paper presents a novel framework for full-waveform seismic source inversion using simulation-based inference (SBI). Traditional probabilistic approaches often rely on simplifying assumptions about data errors, which we show can lead to inaccurate uncertainty quantification. SBI addresses this limitation by building an empirical probabilistic model of the data errors using machine learning models, known as neural density estimators, which can then be integrated into the Bayesian inference framework. We apply the SBI framework to point-source moment tensor inversions as well as joint moment tensor and time-location inversions. We construct a range of synthetic examples to explore the quality of the SBI solutions, as well as to compare the SBI results with standard Gaussian likelihood-based Bayesian inversions. We then demonstrate that under real seismic noise, common Gaussian likelihood assumptions for treating full-waveform data yield overconfident posterior distributions that underestimate the moment tensor component uncertainties by up to a factor of 3. We contrast this with SBI, which produces well-calibrated posteriors that generally agree with the true seismic source parameters, and offers an order-of-magnitude reduction in the number of simulations required to perform inference compared to standard Monte Carlo techniques. Finally, we apply our methodology to a pair of moderate magnitude earthquakes in the North Atlantic. We utilise seismic waveforms recorded by the recent UPFLOW ocean bottom seismometer array as well as by regional land stations in the Azores, comparing full moment tensor and source-time location posteriors between SBI and a Gaussian likelihood approach. We find that our adaptation of SBI can be directly applied to real earthquake sources to efficiently produce high quality posterior distributions that significantly improve upon Gaussian likelihood approaches., Comment: 22 + 11 pages, 11 + 11 figures

In this paper, we present the Federated Upper Confidence Bound Value Iteration algorithm ($\texttt{Fed-UCBVI}$), a novel extension of the $\texttt{UCBVI}$ algorithm (Azar et al., 2017) tailored for the federated learning framework. We prove that the regret of $\texttt{Fed-UCBVI}$ scales as $\tilde{\mathcal{O}}(\sqrt{H^3 |\mathcal{S}| |\mathcal{A}| T / M})$, with a small additional term due to heterogeneity, where $|\mathcal{S}|$ is the number of states, $|\mathcal{A}|$ is the number of actions, $H$ is the episode length, $M$ is the number of agents, and $T$ is the number of episodes. Notably, in the single-agent setting, this upper bound matches the minimax lower bound up to polylogarithmic factors, while in the multi-agent scenario, $\texttt{Fed-UCBVI}$ has linear speed-up. To conduct our analysis, we introduce a new measure of heterogeneity, which may hold independent theoretical interest. Furthermore, we show that, unlike existing federated reinforcement learning approaches, $\texttt{Fed-UCBVI}$'s communication complexity only marginally increases with the number of agents.

Unmanned vehicles able to conduct advanced operations without human intervention are being developed at a fast pace for many purposes. Not surprisingly, they are also expected to significantly change how military operations can be conducted. To leverage the potential of this new technology in a physically and logically contested environment, security risks are to be assessed and managed accordingly. Research on this topic points to autonomous cyber defence as one of the capabilities that may be needed to accelerate the adoption of these vehicles for military purposes. Here, we pursue this line of investigation by exploring reinforcement learning to train an agent that can autonomously respond to cyber attacks on unmanned vehicles in the context of a military operation. We first developed a simple simulation environment to quickly prototype and test some proof-of-concept agents for an initial evaluation. This agent was then applied to a more realistic simulation environment and finally deployed on an actual unmanned ground vehicle for even more realism. A key contribution of our work is demonstrating that reinforcement learning is a viable approach to train an agent that can be used for autonomous cyber defence on a real unmanned ground vehicle, even when trained in a simple simulation environment., Comment: DIGILIENCE 2024

Atmospheric characterization is key to understanding exoplanetary systems, offering insights into the planets current and past conditions. By analyzing key lines like H alpha and the He I triplet, we can trace the evolution of planets through atmospheric photo-evaporation. While ultra-hot Jupiters have been the focus for years, attention is shifting toward smaller, colder planets, which are more challenging to study due to weaker signals, requiring more precise techniques. This study aims to characterize the atmosphere of TOI-5398 b, a warm Saturn with a 10.59-day orbit around a young (650 Myr) G-type star. The system also hosts a smaller inner planet, TOI-5398 c, with a 4.77-day orbit. Both planets are ideal for atmospheric studies due to their proximity to the host star, which drives strong photo-evaporation, especially in planet b, whose high transmission spectroscopy metric (288) makes it a prime target. We analyzed data from a transit observed with the HARPS-N and GIANO-B high-resolution spectrographs, using cross-correlation and single-line analysis to search for atomic species. During this observation, planet c was also transiting, so we investigated the source of the signals. Based on photo-evaporation models, we attribute the signal mainly to planet b, which is expected to lose more mass. We detected H alpha and He I triplets, key markers of photo-evaporation, corresponding to atmospheric heights of 2.33 Rp and 1.65 Rp, respectively. The ATES models supported our observations, predicting a similar He I absorption for planet b and suggesting an He/H ratio of 1/99. Additionally, we detected an Na I doublet via single-line analysis, though cross-correlation did not reveal other atomic species., Comment: Accepted to A&A

Ultra-hot Jupiters (UHJs) are gas giant planets orbiting close to their host star, with equilibrium temperatures exceeding 2000 K, and among the most studied planets in terms of their atmospheric composition. Thanks to a new generation of ultra-stable high-resolution spectrographs, it is possible to detect the signal from the individual lines of the species in the exoplanetary atmospheres. We employed two techniques in this study. First, we used transmission spectroscopy, which involved examining the spectra around single lines of FeII. Then we carried out a set of cross-correlation studies for two UHJs: KELT-9b and KELT-20b. Both planets orbit fast-rotating stars, which resulted in the detection of the strong Rossiter-McLaughlin (RM) effect and center-to-limb variations in the transmission spectrum. These effects had to be corrected to ensure a precise analysis. Using the transmission spectroscopy method, we detected 21 single lines of FeII in the atmosphere of KELT-9b. All of the detected lines are blue-shifted, suggesting strong day-to-night side atmospheric winds. The cross-correlation method leads to the detection of the blue-shifted signal with a signal-to-noise ratio (S/N) of 13.46. Our results are in agreement with models based on non-local thermodynamical equilibrium (NLTE) effects, with a mean micro-turbulence of nu_mic = 2.73 +/- 1.5 km/s and macro-turbulence of nu_mac = 8.22 +/- 3.85 km/s. In the atmosphere of KELT-20b, we detected 17 single lines of FeII. Considering different measurements of the systemic velocity of the system, we conclude that the existence of winds in the atmosphere of KELT-20b cannot be determined conclusively. The detected signal with the cross-correlation method presents a S/N of 11.51. The results are consistent with NLTE effects, including means of nu_mic = 3.04 +/- 0.35 km/s and nu_mac = 6.76 +/- 1.17 km/s., Comment: 38 pages, 19 Figures (39 figures in Appendix), 6 tables, Accepted for publication in A&A

We present a framework that for the first time allows Bayesian model comparison to be performed for field-level inference of cosmological models. We achieve this by taking a simulation-based inference (SBI) approach using neural likelihood estimation, which we couple with the learned harmonic mean estimator in order to compute the Bayesian evidence for model comparison. We apply our framework to mock Stage IV cosmic shear observations to assess its effectiveness at distinguishing between various models of dark energy. If the recent DESI results that provided exciting hints of dynamical dark energy were indeed the true underlying model, our analysis shows Stage IV cosmic shear surveys could definitively detect dynamical dark energy. We also perform traditional power spectrum likelihood-based inference for comparison, which we find is not able to distinguish between dark energy models, highlighting the enhanced constraining power for model comparison of our field-level SBI approach., Comment: 12 pages, 6 figures. COSMOPOWER available at https://github.com/alessiospuriomancini/cosmopower, COSMOPOWER-JAX available at https://github.com/dpiras/cosmopower-jax, HARMONIC available at https://github.com/astro-informatics/harmonic

We employ a novel framework for accelerated cosmological inference, based on neural emulators and gradient-based sampling methods, to forecast constraints on dark energy models from Stage IV cosmic shear surveys. We focus on dark scattering (DS), an interacting dark energy model with pure momentum exchange in the dark sector, and train COSMOPOWER emulators to accurately and efficiently model the DS non-linear matter power spectrum produced by the halo model reaction framework, including the effects of baryon feedback and massive neutrinos. We embed the emulators within a fully-differentiable pipeline for gradient-based cosmological inference for which the batch likelihood call is up to $O(10^5)$ times faster than with traditional approaches, producing parameter constraints from simulated Stage IV cosmic shear data running on a single graphics processing unit (GPU). We also perform model comparison on the output chains from the inference process, employing the learnt harmonic mean estimator implemented in the software HARMONIC. We investigate degeneracies between dark energy and systematics parameters and assess the impact of scale cuts on the final constraints. Assuming a DS model for the mock data vector, we find that a Stage IV survey cosmic shear analysis can constrain the DS amplitude parameter $A_{\mathrm{ds}}$ with an uncertainty roughly an order of magnitude smaller than current constraints from Stage III surveys, even after marginalising over baryonic feedback, intrinsic alignments and redshift distribution uncertainties. These results show great promise for constraining DS with Stage IV data; furthermore, our methodology can be straightforwardly extended to a wide range of dark energy and modified gravity models., Comment: 9 pages, 6 figures. COSMOPOWER available at https://github.com/alessiospuriomancini/cosmopower, COSMOPOWER-JAX available at https://github.com/dpiras/cosmopower-jax, emulators for Dark Scattering available at https://github.com/karimpsi22/DS-emulators

Vehicle-to-everything (V2X) communication technology is revolutionizing transportation by enabling interactions between vehicles, devices, and infrastructures. This connectivity enhances road safety, transportation efficiency, and driver assistance systems. V2X benefits from Machine Learning, enabling real-time data analysis, better decision-making, and improved traffic predictions, making transportation safer and more efficient. In this paper, we study the problem of joint channel selection, where vehicles with different technologies choose one or more Access Points (APs) to transmit messages in a network. In this problem, vehicles must learn a strategy for channel selection, based on observations that incorporate vehicles' information (position and speed), network and communication data (Signal-to-Interference-plus-Noise Ratio from past communications), and environmental data (road type). We propose an approach based on Federated Deep Reinforcement Learning (FedDRL), which enables each vehicle to benefit from other vehicles' experiences. Specifically, we apply the federated Proximal Policy Optimization (FedPPO) algorithm to this task. We show that this method improves communication reliability while minimizing transmission costs and channel switches. The efficiency of the proposed solution is assessed via realistic simulations, highlighting the potential of FedDRL to advance V2X technology.

In order to increase rail freight transportation in Italy, Rete Ferroviaria Italiana (RFI) the Italian railway infrastructure manager, is carrying out several investment plans to enhance the Transshipment Yards, that act as an interface between the rail and road networks. The need is to increase their practical capacity, i.e. the maximum number of train services that can be inserted without altering the current timetable while respecting all relevant constraints. Several factors influence the practical capacity of a transshipment yard: physical resources (such as tracks and vehicles for loading/unloading); constraints on the possible time slots of individual operations; constraints on the length of time a train must stay in the yard, that follow from both timetable requirements that are settled by the (prevalent) main line and from administrative and organisational issues in the yard. In this paper, we propose a MILP-based optimization model that is based on the solution of a suitable saturation problem, that deals with all these constraints and that can be used for evaluating the practical capacity of a transshipment yard both in its current configuration and in any plausible future configuration. The model provides operational details, such as routes and schedules, for each train service, and allows to impose periodic timetables and schedules that keep the daily management of the yard easier. Both the model and its solutions are validated on a real Italian transshipment yard, located at Marzaglia, on different scenarios corresponding to different investment plans of RFI. The results show that proper investments allow to get a feasible timetable with a period of 24 hours with doubles the number of current train services.

Measurements of galaxy clustering are affected by RSD. Peculiar velocities, gravitational lensing, and other light-cone projection effects modify the observed redshifts, fluxes, and sky positions of distant light sources. We determine which of these effects leave a detectable imprint on several 2-point clustering statistics extracted from the EWSS on large scales. We generate 140 mock galaxy catalogues with the survey geometry and selection function of the EWSS and make use of the LIGER method to account for a variable number of relativistic RSD to linear order in the cosmological perturbations. We estimate different 2-point clustering statistics from the mocks and use the likelihood-ratio test to calculate the statistical significance with which the EWSS could reject the null hypothesis that certain relativistic projection effects can be neglected in the theoretical models. We find that the combined effects of lensing magnification and convergence imprint characteristic signatures on several clustering observables. Their S/N ranges between 2.5 and 6 (depending on the adopted summary statistic) for the highest-redshift galaxies in the EWSS. The corresponding feature due to the peculiar velocity of the Sun is measured with a S/N of order one or two. The $P_{\ell}(k)$ from the catalogues that include all relativistic effects reject the null hypothesis that RSD are only generated by the variation of the peculiar velocity along the line of sight with a significance of 2.9 standard deviations. As a byproduct of our study, we demonstrate that the mixing-matrix formalism to model finite-volume effects in the $P_{\ell}(k)$ can be robustly applied to surveys made of several disconnected patches. Our results indicate that relativistic RSD, the contribution from weak gravitational lensing in particular, cannot be disregarded when modelling 2-point clustering statistics extracted from the EWSS., Comment: 23 pages, 14 figures

Scientific imaging of the Moon, Mars, and other celestial bodies is often accomplished with pushbroom cameras. Craters with elliptical rims are common objects of interest within the images produced by such sensors. This work provides a framework to analyze the appearance of crater rims in pushbroom images. With knowledge of only common ellipse parameters describing the crater rim, explicit formulations are developed and shown to be convenient for drawing the apparent crater in pushbroom images. Implicit forms are also developed and indicate the orbital conditions under which craters form conics in images. Several numerical examples are provided which demonstrate how different forms of crater rim projections can be interpreted and used in practice.

Graph Neural Networks (GNNs) have proven effective in various medical imaging applications, such as automated disease diagnosis. However, due to the local neighborhood aggregation paradigm in message passing which characterizes these models, they inherently suffer from two fundamental limitations: first, indistinguishable node embeddings due to heterophilic node aggregation (known as over-smoothing), and second, impaired message passing due to aggregation through graph bottlenecks (known as over-squashing). These challenges hinder the model expressiveness and prevent us from using deeper models to capture long-range node dependencies within the graph. Popular solutions in the literature are either too expensive to process large graphs due to high time complexity or do not generalize across all graph topologies. To address these limitations, we propose DuoGNN, a scalable and generalizable architecture which leverages topology to decouple homophilic and heterophilic edges and capture both short-range and long-range interactions. Our three core contributions introduce (i) a topological edge-filtering algorithm which extracts homophilic interactions and enables the model to generalize well for any graph topology, (ii) a heterophilic graph condensation technique which extracts heterophilic interactions and ensures scalability, and (iii) a dual homophilic and heterophilic aggregation pipeline which prevents over-smoothing and over-squashing during the message passing. We benchmark our model on medical and non-medical node classification datasets and compare it with its variants, showing consistent improvements across all tasks. Our DuoGNN code is available at https://github.com/basiralab/DuoGNN.

Machine learning has brought significant advances in cybersecurity, particularly in the development of Intrusion Detection Systems (IDS). These improvements are mainly attributed to the ability of machine learning algorithms to identify complex relationships between features and effectively generalize to unseen data. Deep neural networks, in particular, contributed to this progress by enabling the analysis of large amounts of training data, significantly enhancing detection performance. However, machine learning models remain vulnerable to adversarial attacks, where carefully crafted input data can mislead the model into making incorrect predictions. While adversarial threats in unstructured data, such as images and text, have been extensively studied, their impact on structured data like network traffic is less explored. This survey aims to address this gap by providing a comprehensive review of machine learning-based Network Intrusion Detection Systems (NIDS) and thoroughly analyzing their susceptibility to adversarial attacks. We critically examine existing research in NIDS, highlighting key trends, strengths, and limitations, while identifying areas that require further exploration. Additionally, we discuss emerging challenges in the field and offer insights for the development of more robust and resilient NIDS. In summary, this paper enhances the understanding of adversarial attacks and defenses in NIDS and guide future research in improving the robustness of machine learning models in cybersecurity applications., Comment: 35 pages

For the first time at the Beam Test Facility of the DA{\Phi}NE accelerator complex at the Laboratori Nazionali di Frascati of INFN, 450 MeV positrons have been deflected with high efficiency, using the Planar Channeling process in a bent silicon crystal. The deflection angle obtained is beyond 1 mrad. This interesting result finds several applications for manipulation of this kind of beams, in particular for slow extraction from leptons circular accelerators like DA{\Phi}NE. In this work the experimental apparatus, the measurement procedure and the experimental results are reported., Comment: Author list corrected. Title modified. Changed text of abstract and in sections 2 and 5 Added references. Typos corrected

The optimization of geometries for aerodynamic design often relies on a large number of expensive simulations to evaluate and iteratively improve the geometries. It is possible to reduce the number of simulations by providing a starting geometry that has properties close to the desired requirements, often in terms of lift and drag, aerodynamic moments and surface areas. We show that generative models have the potential to provide such starting geometries by generalizing geometries over a large dataset of simulations. In particular, we leverage diffusion probabilistic models trained on XFOIL simulations to synthesize two-dimensional airfoil geometries conditioned on given aerodynamic features and constraints. The airfoils are parameterized with Bernstein polynomials, ensuring smoothness of the generated designs. We show that the models are able to generate diverse candidate designs for identical requirements and constraints, effectively exploring the design space to provide multiple starting points to optimization procedures. However, the quality of the candidate designs depends on the distribution of the simulated designs in the dataset. Importantly, the geometries in this dataset must satisfy other requirements and constraints that are not used in conditioning of the diffusion model, to ensure that the generated geometries are physical., Comment: 10 pages, 11 figures, DLRK 2024

Neural networks are typically black-boxes that remain opaque with regards to their decision mechanisms. Several works in the literature have proposed post-hoc explanation methods to alleviate this issue. This paper proposes LMAC-TD, a post-hoc explanation method that trains a decoder to produce explanations directly in the time domain. This methodology builds upon the foundation of L-MAC, Listenable Maps for Audio Classifiers, a method that produces faithful and listenable explanations. We incorporate SepFormer, a popular transformer-based time-domain source separation architecture. We show through a user study that LMAC-TD significantly improves the audio quality of the produced explanations while not sacrificing from faithfulness., Comment: The first two authors contributed equally to this research. Author order is alphabetical

Let $G$ be a simple algebraic group over an algebraically closed field $\Bbbk$ of positive characteristic. We consider the questions of when the tensor product of two simple $G$-modules is multiplicity free or completely reducible. We develop tools for answering these questions in general, and we use them to provide complete answers for the groups $G = \mathrm{SL}_3(\Bbbk)$ and $G = \mathrm{Sp}_4(\Bbbk)$., Comment: references added, 49 pages, comments welcome

Let $k$ be an algebraically closed field of characteristic $p>0$. In this master thesis, we classify multiplicity-free tensor products of simple modules for the groups $SL_2(k)$ and $SL_3(k)$. We also provide a classification for $SL_n(k)$ when $p=2$ and give partial results in the case of $Sp_4(k)$., Comment: Master's thesis

We study the constraint on $f(R)$ gravity that can be obtained by photometric primary probes of the Euclid mission. Our focus is the dependence of the constraint on the theoretical modelling of the nonlinear matter power spectrum. In the Hu-Sawicki $f(R)$ gravity model, we consider four different predictions for the ratio between the power spectrum in $f(R)$ and that in $\Lambda$CDM: a fitting formula, the halo model reaction approach, ReACT and two emulators based on dark matter only $N$-body simulations, FORGE and e-Mantis. These predictions are added to the MontePython implementation to predict the angular power spectra for weak lensing (WL), photometric galaxy clustering and their cross-correlation. By running Markov Chain Monte Carlo, we compare constraints on parameters and investigate the bias of the recovered $f(R)$ parameter if the data are created by a different model. For the pessimistic setting of WL, one dimensional bias for the $f(R)$ parameter, $\log_{10}|f_{R0}|$, is found to be $0.5 \sigma$ when FORGE is used to create the synthetic data with $\log_{10}|f_{R0}| =-5.301$ and fitted by e-Mantis. The impact of baryonic physics on WL is studied by using a baryonification emulator BCemu. For the optimistic setting, the $f(R)$ parameter and two main baryon parameters are well constrained despite the degeneracies among these parameters. However, the difference in the nonlinear dark matter prediction can be compensated by the adjustment of baryon parameters, and the one-dimensional marginalised constraint on $\log_{10}|f_{R0}|$ is biased. This bias can be avoided in the pessimistic setting at the expense of weaker constraints. For the pessimistic setting, using the $\Lambda$CDM synthetic data for WL, we obtain the prior-independent upper limit of $\log_{10}|f_{R0}|< -5.6$. Finally, we implement a method to include theoretical errors to avoid the bias., Comment: 24 pages, 16 figures, submitted on behalf of the Euclid Collaboration

The Euclid mission will measure cosmological parameters with unprecedented precision. To distinguish between cosmological models, it is essential to generate realistic mock observables from cosmological simulations that were run in both the standard $\Lambda$-cold-dark-matter ($\Lambda$CDM) paradigm and in many non-standard models beyond $\Lambda$CDM. We present the scientific results from a suite of cosmological N-body simulations using non-standard models including dynamical dark energy, k-essence, interacting dark energy, modified gravity, massive neutrinos, and primordial non-Gaussianities. We investigate how these models affect the large-scale-structure formation and evolution in addition to providing synthetic observables that can be used to test and constrain these models with Euclid data. We developed a custom pipeline based on the Rockstar halo finder and the nbodykit large-scale structure toolkit to analyse the particle output of non-standard simulations and generate mock observables such as halo and void catalogues, mass density fields, and power spectra in a consistent way. We compare these observables with those from the standard $\Lambda$CDM model and quantify the deviations. We find that non-standard cosmological models can leave significant imprints on the synthetic observables that we have generated. Our results demonstrate that non-standard cosmological N-body simulations provide valuable insights into the physics of dark energy and dark matter, which is essential to maximising the scientific return of Euclid., Comment: 22 pages, 7 figures

To constrain models beyond $\Lambda$CDM, the development of the Euclid analysis pipeline requires simulations that capture the nonlinear phenomenology of such models. We present an overview of numerical methods and $N$-body simulation codes developed to study the nonlinear regime of structure formation in alternative dark energy and modified gravity theories. We review a variety of numerical techniques and approximations employed in cosmological $N$-body simulations to model the complex phenomenology of scenarios beyond $\Lambda$CDM. This includes discussions on solving nonlinear field equations, accounting for fifth forces, and implementing screening mechanisms. Furthermore, we conduct a code comparison exercise to assess the reliability and convergence of different simulation codes across a range of models. Our analysis demonstrates a high degree of agreement among the outputs of different simulation codes, providing confidence in current numerical methods for modelling cosmic structure formation beyond $\Lambda$CDM. We highlight recent advances made in simulating the nonlinear scales of structure formation, which are essential for leveraging the full scientific potential of the forthcoming observational data from the Euclid mission., Comment: 20 pages, 7 figures, 1 appendix; submitted on behalf of the Euclid Collaboration

We investigate the level of accuracy and precision of cluster weak-lensing (WL) masses measured with the \Euclid data processing pipeline. We use the DEMNUni-Cov $N$-body simulations to assess how well the WL mass probes the true halo mass, and, then, how well WL masses can be recovered in the presence of measurement uncertainties. We consider different halo mass density models, priors, and mass point estimates. WL mass differs from true mass due to, e.g., the intrinsic ellipticity of sources, correlated or uncorrelated matter and large-scale structure, halo triaxiality and orientation, and merging or irregular morphology. In an ideal scenario without observational or measurement errors, the maximum likelihood estimator is the most accurate, with WL masses biased low by $\langle b_M \rangle = -14.6 \pm 1.7 \, \%$ on average over the full range $M_\text{200c} > 5 \times 10^{13} \, M_\odot$ and $z < 1$. Due to the stabilising effect of the prior, the biweight, mean, and median estimates are more precise. The scatter decreases with increasing mass and informative priors significantly reduce the scatter. Halo mass density profiles with a truncation provide better fits to the lensing signal, while the accuracy and precision are not significantly affected. We further investigate the impact of additional sources of systematic uncertainty on the WL mass, namely the impact of photometric redshift uncertainties and source selection, the expected performance of \Euclid cluster detection algorithms, and the presence of masks. Taken in isolation, we find that the largest effect is induced by non-conservative source selection. This effect can be mostly removed with a robust selection. As a final \Euclid-like test, we combine systematic effects in a realistic observational setting and find results similar to the ideal case, $\langle b_M \rangle = - 15.5 \pm 2.4 \, \%$, under a robust selection.

The Euclid mission, designed to map the geometry of the dark Universe, presents an unprecedented opportunity for advancing our understanding of the cosmos through its photometric galaxy cluster survey. This paper focuses on enhancing the precision of halo bias (HB) predictions, which is crucial for deriving cosmological constraints from the clustering of galaxy clusters. Our study is based on the peak-background split (PBS) model linked to the halo mass function (HMF); it extends with a parametric correction to precisely align with results from an extended set of $N$-body simulations carried out with the OpenGADGET3 code. Employing simulations with fixed and paired initial conditions, we meticulously analyze the matter-halo cross-spectrum and model its covariance using a large number of mock catalogs generated with Lagrangian Perturbation Theory simulations with the PINOCCHIO code. This ensures a comprehensive understanding of the uncertainties in our HB calibration. Our findings indicate that the calibrated HB model is remarkably resilient against changes in cosmological parameters including those involving massive neutrinos. The robustness and adaptability of our calibrated HB model provide an important contribution to the cosmological exploitation of the cluster surveys to be provided by the Euclid mission. This study highlights the necessity of continuously refining the calibration of cosmological tools like the HB to match the advancing quality of observational data. As we project the impact of our model on cosmological constraints, we find that, given the sensitivity of the Euclid survey, a miscalibration of the HB could introduce biases in cluster cosmology analyses. Our work fills this critical gap, ensuring the HB calibration matches the expected precision of the Euclid survey. The implementation of our model is publicly available in https://github.com/TiagoBsCastro/CCToolkit., Comment: 20 pages; 12 figures; accepted for publication in A&A; abstract abridged for arXiv submission

The study of exoplanets at different evolutionary stages can shed light on their formation, migration, and evolution. The determination of exoplanet properties depends on the properties of their host stars. It is therefore important to characterise the host stars for accurate knowledge on their planets. Our final goal is to derive, in a homogeneous and accurate way, the stellar atmospheric parameters and elemental abundances of ten young TESS transiting planet-hosting GK stars followed up with the HARPS-N at TNG spectrograph within the GAPS programme. We derived stellar kinematic properties, atmospheric parameters, and abundances of 18 elements. Lithium line measurements were used as approximate age estimations. We exploited chemical abundances and their ratios to derive information on planetary composition. Elemental abundances and kinematic properties are consistent with the nearby Galactic thin disk. All targets show C/O<0.8 and 1.0

The Cosmic Dawn Survey (DAWN survey) provides multiwavelength (UV/optical to mid-IR) data across the combined 59 deg$^{2}$ of the Euclid Deep and Auxiliary fields (EDFs and EAFs). Here, the first public data release (DR1) from the DAWN survey is presented. DR1 catalogues are made available for a subset of the full DAWN survey that consists of two Euclid Deep fields: Euclid Deep Field North (EDF-N) and Euclid Deep Field Fornax (EDF-F). The DAWN survey DR1 catalogues do not include $Euclid$ data as they are not yet public for these fields. Nonetheless, each field has been covered by the ongoing Hawaii Twenty Square Degree Survey (H20), which includes imaging from CFHT MegaCam in the new $u$ filter and from Subaru Hyper Suprime-Cam (HSC) in the $griz$ filters. Each field is further covered by $Spitzer$/IRAC 3.6-4.5$\mu$m imaging spanning 10 deg$^{2}$ and reaching $\sim$25 mag AB (5$\sigma$). All present H20 imaging and all publicly available imaging from the aforementioned facilities are combined with the deep $Spitzer$/IRAC data to create source catalogues spanning a total area of 16.87 deg$^{2}$ in EDF-N and 2.85 deg$^{2}$ in EDF-F for this first release. Photometry is measured using The Farmer, a well-validated model-based photometry code. Photometric redshifts and stellar masses are computed using two independent codes for modeling spectral energy distributions: EAZY and LePhare. Photometric redshifts show good agreement with spectroscopic redshifts ($\sigma_{\rm NMAD} \sim 0.5, \eta < 8\%$ at $i < 25$). Number counts, photometric redshifts, and stellar masses are further validated in comparison to the COSMOS2020 catalogue. The DAWN survey DR1 catalogues are designed to be of immediate use in these two EDFs and will be continuously updated. Future data releases will provide catalogues of all EDFs and EAFs and include $Euclid$ data.

Euclid will provide deep NIR imaging to $\sim$26.5 AB magnitude over $\sim$59 deg$^2$ in its deep and auxiliary fields. The Cosmic DAWN survey complements the deep Euclid data with matched depth multiwavelength imaging and spectroscopy in the UV--IR to provide consistently processed Euclid selected photometric catalogs, accurate photometric redshifts, and measurements of galaxy properties to a redshift of $z\sim 10$. In this paper, we present an overview of the survey, including the footprints of the survey fields, the existing and planned observations, and the primary science goals for the combined data set., Comment: 16 pages, 10 figures, submitted to A&A; Updated references; Updated author list

Galaxy proto-clusters are receiving an increased interest since most of the processes shaping the structure of clusters of galaxies and their galaxy population are happening at early stages of their formation. The Euclid Survey will provide a unique opportunity to discover a large number of proto-clusters over a large fraction of the sky (14 500 square degrees). In this paper, we explore the expected observational properties of proto-clusters in the Euclid Wide Survey by means of theoretical models and simulations. We provide an overview of the predicted proto-cluster extent, galaxy density profiles, mass-richness relations, abundance, and sky-filling as a function of redshift. Useful analytical approximations for the functions of these properties are provided. The focus is on the redshift range z= 1.5 to 4. We discuss in particular the density contrast with which proto-clusters can be observed against the background in the galaxy distribution if photometric galaxy redshifts are used as supplied by the ESA Euclid mission together with the ground-based photometric surveys. We show that the obtainable detection significance is sufficient to find large numbers of interesting proto-cluster candidates. For quantitative studies, additional spectroscopic follow-up is required to confirm the proto-clusters and establish their richness., Comment: Submitted to Astronomy and Astrophysics, 24 pages, 28 figures

Euclid will collect an enormous amount of data during the mission's lifetime, observing billions of galaxies in the extragalactic sky. Along with traditional template-fitting methods, numerous machine learning algorithms have been presented for computing their photometric redshifts and physical parameters (PPs), requiring significantly less computing effort while producing equivalent performance measures. However, their performance is limited by the quality and amount of input information, to the point where the recovery of some well-established physical relationships between parameters might not be guaranteed. To forecast the reliability of Euclid photo-$z$s and PPs calculations, we produced two mock catalogs simulating Euclid photometry. We simulated the Euclid Wide Survey (EWS) and Euclid Deep Fields (EDF). We tested the performance of a template-fitting algorithm (Phosphoros) and four ML methods in recovering photo-$z$s, PPs (stellar masses and star formation rates), and the SFMS. To mimic the Euclid processing as closely as possible, the models were trained with Phosphoros-recovered labels. For the EWS, we found that the best results are achieved with a mixed labels approach, training the models with wide survey features and labels from the Phosphoros results on deeper photometry, that is, with the best possible set of labels for a given photometry. This imposes a prior, helping the models to better discern cases in degenerate regions of feature space, that is, when galaxies have similar magnitudes and colors but different redshifts and PPs, with performance metrics even better than those found with Phosphoros. We found no more than 3% performance degradation using a COSMOS-like reference sample or removing u band data, which will not be available until after data release DR1. The best results are obtained for the EDF, with appropriate recovery of photo-$z$, PPs, and the SFMS., Comment: 26 pages, 13 figures. Accepted for publication on A&A

We present the framework for measuring angular power spectra in the Euclid mission. The observables in galaxy surveys, such as galaxy clustering and cosmic shear, are not continuous fields, but discrete sets of data, obtained only at the positions of galaxies. We show how to compute the angular power spectra of such discrete data sets, without treating observations as maps of an underlying continuous field that is overlaid with a noise component. This formalism allows us to compute exact theoretical expectations for our measured spectra, under a number of assumptions that we track explicitly. In particular, we obtain exact expressions for the additive biases ("shot noise") in angular galaxy clustering and cosmic shear. For efficient practical computations, we introduce a spin-weighted spherical convolution with a well-defined convolution theorem, which allows us to apply exact theoretical predictions to finite-resolution maps, including HEALPix. When validating our methodology, we find that our measurements are biased by less than 1% of their statistical uncertainty in simulations of Euclid's first data release., Comment: 27 pages, 12 figures. Submitted to A&A. Code available at https://github.com/heracles-ec/heracles

Recent progress in Text-to-Image (T2I) generative models has enabled high-quality image generation. As performance and accessibility increase, these models are gaining significant attraction and popularity: ensuring their fairness and safety is a priority to prevent the dissemination and perpetuation of biases. However, existing studies in bias detection focus on closed sets of predefined biases (e.g., gender, ethnicity). In this paper, we propose a general framework to identify, quantify, and explain biases in an open set setting, i.e. without requiring a predefined set. This pipeline leverages a Large Language Model (LLM) to propose biases starting from a set of captions. Next, these captions are used by the target generative model for generating a set of images. Finally, Vision Question Answering (VQA) is leveraged for bias evaluation. We show two variations of this framework: OpenBias and GradBias. OpenBias detects and quantifies biases, while GradBias determines the contribution of individual prompt words on biases. OpenBias effectively detects both well-known and novel biases related to people, objects, and animals and highly aligns with existing closed-set bias detection methods and human judgment. GradBias shows that neutral words can significantly influence biases and it outperforms several baselines, including state-of-the-art foundation models. Code available here: https://github.com/Moreno98/GradBias., Comment: Under review. Code: https://github.com/Moreno98/GradBias

We collected more than 300 high-resolution spectra of the 300 Myr old star BD+40 2790 (TOI-2076) over ~3 years. This star hosts three transiting planets discovered by TESS, with orbital periods ~10, 21, and 35 days. BD+40 2790 shows an activity-induced scatter larger than 30 m/s in the radial velocities. We employed different methods to measure the stellar radial velocities and several models to filter out the dominant stellar activity signal, in order to bring to light the planet-induced signals which are expected to have semi-amplitudes one order of magnitude lower. We evaluated the mass loss rate of the planetary atmospheres using photoionization hydrodynamic modeling. The dynamical analysis confirms that the three sub-Neptune-sized companions (our radius measurements are $R_b$=2.54$\pm$0.04, $R_c$=3.35$\pm$0.05, and $R_d$=3.29$\pm$0.06 $R_{\rm Earth}$) have masses in the planetary regime. We derive 3$\sigma$ upper limits below or close to the mass of Neptune for all the planets: 11--12, 12--13.5, and 14--19 $M_{\rm Earth}$ for planet $b$, $c$, and $d$ respectively. In the case of planet $d$, we found promising clues that the mass could be between ~7 and 8 $M_{\rm Earth}$, with a significance level between 2.3--2.5$\sigma$ (at best). This result must be further investigated using other analysis methods or using high-precision near-IR spectrographs to collect new radial velocities, which could be less affected by stellar activity. Atmospheric photo-evaporation simulations predict that BD+40~2790 b is currently losing its H-He gaseous envelope, which will be completely lost at an age within 0.5--3 Gyr if its current mass is lower than 12 $M_{\rm Earth}$. BD+40 2790 c could have a lower bulk density than $b$, and it could retain its atmosphere up to an age of 5 Gyr. For the outermost planet $d$, we predict almost negligible evolution of its mass and radius induced by photo-evaporation., Comment: Accepted for publication on A&A. Abstract abridged

In this paper we consider the $N$-dimensional Euclidean Onofri inequality proved by del Pino and Dolbeault for smooth compactly supported functions in $\mathbb{R}^N$, $N \geq 2$. We extend the inequality to a suitable weighted Sobolev space, although no clear connection with standard Sobolev spaces on $\mathbb{S}^N$ through stereographic projection is present, except for the planar case. Moreover, in any dimension $N \geq 2$, we show that the Euclidean Onofri inequality is equivalent to the logarithmic Moser-Trudinger inequality with sharp constant proved by Carleson and Chang for balls in $\mathbb{R}^N$.

A quantum communication protocol between harmonic oscillator detectors, interacting with a quantum field, is developed in a cosmological expanding background. The aim is to see if the quantum effects arising in an expanding universe, such as the cosmological particle production, could facilitate the communication between two distant parts or if they provide an additive noisy effect. By considering a perfect cosmic fluid, the resulting expansion turns out to increase the classical capacity of the protocol. This increasing occurs for all the cosmological expansions unless the latter is sharpened just before the receiver's detector interacts with the field. Moreover, the classical capacity turns out to be sensible to the barotropic parameter $w$ of the perfect fluid and to the coupling between the field and the scalar curvature $\xi$. As a consequence, by performing this protocol, one can achieve information about the cosmological dynamics and its coupling with a background quantum field., Comment: 22 pages, 4 figures

Vision-Language Models (VLMs) combine visual and textual understanding, rendering them well-suited for diverse tasks like generating image captions and answering visual questions across various domains. However, these capabilities are built upon training on large amount of uncurated data crawled from the web. The latter may include sensitive information that VLMs could memorize and leak, raising significant privacy concerns. In this paper, we assess whether these vulnerabilities exist, focusing on identity leakage. Our study leads to three key findings: (i) VLMs leak identity information, even when the vision-language alignment and the fine-tuning use anonymized data; (ii) context has little influence on identity leakage; (iii) simple, widely used anonymization techniques, like blurring, are not sufficient to address the problem. These findings underscore the urgent need for robust privacy protection strategies when deploying VLMs. Ethical awareness and responsible development practices are essential to mitigate these risks.

Methyl cyanide is one of the simplest interstellar complex organic molecules, widely detected in young solar analogues, shocked regions, protoplanetary disks and comets. CH3CN can be considered a key species to explore the chemical connections between planet forming disks and comets. For such comparison to be meaningful kinetics data for the reactions leading to CH3CN formation and destruction must be updated. We focus on the destruction of methyl cyanide through collisions with He+. A combined experimental and theoretical methodology is employed to obtain cross sections (CSs) and branching ratios (BRs) as a function of collision energy, from which reaction rate coefficients $k(T)$ are calculated in the temperature range from 10 to 300 K. CSs and BRs are measured using a guided ion beam set-up. A theoretical treatment based on an analytical formulation of the potential energy surfaces (PESs) for the charge exchange process is developed. The method employs a Landau Zener model to obtain reaction probabilities at crossings between the entrance and exit PESs, and an adiabatic centrifugal sudden approximation to calculate CSs and k(T). Rates and BRs differ from those predicted from widely-used capture models. In particular, the rate coefficient at 10 K is estimated to be almost one order of magnitude smaller than what reported in the KIDA database. As for BRs, the charge exchange is completely dissociative and the most abundant fragments are HCCN+/CCNH+, HCNH+ and CH2+. Our results, combined with a revised chemical network for formation of CH3CN, support the hypothesis that methyl cyanide in protoplanetary disks could be mostly the product of gas-phase processes rather than grain chemistry, as currently proposed. These findings are expected to have implications in the comparison of the abundance ratios of N-bearing molecules observed in disks with cometary abundance ratios, Comment: 19 pages, 7 figures, full article

We study the possibility of discriminating between two bosonic dephasing quantum channels. We show that unambiguous discrimination is not realizable. We then consider discrimination with nonzero error probability and minimize this latter in the absence of input constraints. In the presence of an input energy constraint, we derive an upper bound on the error probability. Finally, we extend these results from single-shot to multi-shot discrimination, envisaging the asymptotic behavior.

Observing exoplanets through transmission spectroscopy supplies detailed information on their atmospheric composition, physics, and chemistry. Prior to JWST, these observations were limited to a narrow wavelength range across the near-ultraviolet to near-infrared, alongside broadband photometry at longer wavelengths. To understand more complex properties of exoplanet atmospheres, improved wavelength coverage and resolution are necessary to robustly quantify the influence of a broader range of absorbing molecular species. Here we present a combined analysis of JWST transmission spectroscopy across four different instrumental modes spanning 0.5-5.2 micron using Early Release Science observations of the Saturn-mass exoplanet WASP-39b. Our uniform analysis constrains the orbital and stellar parameters within sub-percent precision, including matching the precision obtained by the most precise asteroseismology measurements of stellar density to-date, and further confirms the presence of Na, K, H$_2$O, CO, CO$_2$, and SO$_2$ atmospheric absorbers. Through this process, we also improve the agreement between the transmission spectra of all modes, except for the NIRSpec PRISM, which is affected by partial saturation of the detector. This work provides strong evidence that uniform light curve analysis is an important aspect to ensuring reliability when comparing the high-precision transmission spectra provided by JWST., Comment: 34 pages, 11 figures, 3 tables. Nat Astron (2024)

We describe the design of a Thomson scattering (TS) diagnostic to be used on the SMall Aspect Ratio Tokamak (SMART). SMART is a spherical tokamak being commissioned in Spain that aims to explore positive triangularity (PT) and negative triangularity (NT) plasma scenarios at a low aspect ratio. The SMART TS diagnostic is designed to enable a wide range of electron temperature (1 eV to 1 keV) and density (0.5 - 10 $\times 10^{19} ~\rm{m^{ - 3}}$) measurements. A 2Joule laser operating at 1064 nm will be used to probe the electron temperature and density of the plasma. The laser is capable of operating in the burst mode at 1kHz, 2kHz, and 4kHz to investigate fast phenomena or at $30$ Hz to study 1 sec (or more) long discharges. The scattered light will be collected over an angular range of 60-120 degrees at 28 spatial points in the midplane covering the core region and edge plasma on both the low-field side (LFS) and the high-field side (HFS). Simulation data is used to determine the optimum location of Thomson scattering measurement points to effectively resolve the edge pedestal in the LFS and HFS regions under different triangularity conditions. Each scattering signal will be spectrally resolved on five wavelength channels of a polychromator to obtain the electron temperature measurement. We will also present a method to monitor in-situ laser alignment in the core during calibrations and plasma operations., Comment: Five pages and six figures

Machine unlearning (MU) aims to erase data from a model as if it never saw them during training. To this extent, existing MU approaches assume complete or partial access to the training data, which can be limited over time due to privacy regulations. However, no setting or benchmark exists to probe the effectiveness of MU methods in such scenarios, i.e. when training data is missing. To fill this gap, we propose a novel task we call One-Shot Unlearning of Personal Identities (O-UPI) that evaluates unlearning models when the training data is not accessible. Specifically, we focus on the identity unlearning case, which is relevant due to current regulations requiring data deletion after training. To cope with data absence, we expect users to provide a portraiting picture to perform unlearning. To evaluate methods in O-UPI, we benchmark the forgetting on CelebA and CelebA-HQ datasets with different unlearning set sizes. We test applicable methods on this challenging benchmark, proposing also an effective method that meta-learns to forget identities from a single image. Our findings indicate that existing approaches struggle when data availability is limited, with greater difficulty when there is dissimilarity between provided samples and data used at training time. We will release the code and benchmark upon acceptance., Comment: Preprint. Under review