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Event cameras are an interesting visual exteroceptive sensor that reacts to brightness changes rather than integrating absolute image intensities. Owing to this design, the sensor exhibits strong performance in situations of challenging dynamics and illumination conditions. While event-based simultaneous tracking and mapping remains a challenging problem, a number of recent works have pointed out the sensor's suitability for prior map-based tracking. By making use of cross-modal registration paradigms, the camera's ego-motion can be tracked across a large spectrum of illumination and dynamics conditions on top of accurate maps that have been created a priori by more traditional sensors. The present paper follows up on a recently introduced event-based geometric semi-dense tracking paradigm, and proposes the addition of inertial signals in order to robustify the estimation. More specifically, the added signals provide strong cues for pose initialization as well as regularization during windowed, multi-frame tracking. As a result, the proposed framework achieves increased performance under challenging illumination conditions as well as a reduction of the rate at which intermediate event representations need to be registered in order to maintain stable tracking across highly dynamic sequences. Our evaluation focuses on a diverse set of real world sequences and comprises a comparison of our proposed method against a purely event-based alternative running at different rates., Comment: 8 pages, 5 figures, 3 tables, International Conference on Intelligent Robots and Systems 2024

This paper presents an easy-to-use open-source software library for continuous-variable quantum key distribution (CV-QKD) systems. The library, written in C++, simplifies the crucial task of information reconciliation, ensuring that both communicating parties share the same secret key despite the noise. It offers a comprehensive set of tools, including modules for multidimensional reconciliation, error correction, and data integrity checks. Designed with user-friendliness in mind, the library hides the complexity of error correction, making it accessible even to users without knowledge of error-correcting codes., Comment: to be presented as a poster at QCrypt 2024

We present the identification of changing-look active galactic nuclei (CL-AGNs) from the Dark Energy Spectroscopic Instrument First Data Release and Sloan Digital Sky Survey Data Release 16 at z \leq 0.9. To confirm the CL-AGNs, we utilize spectral flux calibration assessment via an [O\,{\sc iii}]-based calibration, pseudo-photometry examination, and visual inspection. This rigorous selection process allows us to compile a statistical catalog of 561 CL-AGNs, encompassing 527 $\rm H\beta$, 149$\rm H\alpha$, and 129 Mg II CL behaviors. In this sample, we find 1) a 283:278 ratio of turn-on to turn-off CL-AGNs. 2) the critical value for CL events is confirmed around Eddington ratio \sim 0.01. 3) a strong correlation between the change in the luminosity of the broad emission lines (BEL) and variation in the continuum luminosity, with Mg II and $\rm H\beta$ displaying similar responses during CL phases. 4) the Baldwin-Phillips-Terlevich diagram for CL-AGNs shows no statistically difference from the general AGN catalog. 5) five CL-AGNs are associated with asymmetrical mid-infrared flares, possibly linked to tidal disruption events. Given the large CL-AGNs and the stochastic sampling of spectra, we propose that some CL events are inherently due to typical AGN variability during low accretion rates, particularly for CL events of the singular BEL. Finally, we introduce a Peculiar CL phase, characterized by a gradual decline over decades in the light curve and the complete disappearance of entire BEL in faint spectra, indicative of a real transition in the accretion disk., Comment: Submitted to ApJS, comments welcome

Volume-phase holographic gratings (VPHGs) are widely used in astronomical spectrographs due to their adaptability and high diffraction efficiency. Most VPHGs in operation use dichromated gelatin as a recording material, whose performance is sensitive to the coating and development process, especially in the near-UV. In this letter, we present the characterization of two UV-blue VPHG prototypes for the BlueMUSE integral field spectrograph on the VLT, based on dichromated gelatin and the Bayfol$\circledR$HX photopolymer film as recording materials. Our measurements show that both prototypes meet the required diffraction efficiency and exhibit similar performance with a wavelength-average exceeding 70% in the 350-580 nm range. Deviations from theoretical models increase towards 350 nm, consistently with previous studies on similar gratings. We also report similar performances in terms spatial uniformity and grating-to-grating consistency. Likewise, no significant differences in wavefront error or scattered light are observed between the prototypes., Comment: 7 pages, 5 figures, 1 table. Presented at SPIE "Astronomical Telescopes and Instrumentation", June 2024, in Yokohama, as part of "Advances in Optical and Mechanical Technologies for Telescopes and Instrumentation VI"

PLATO (PLAnetary Transits and Oscillations of stars) is ESA's M3 mission designed to detect and characterise extrasolar planets and perform asteroseismic monitoring of a large number of stars. PLATO will detect small planets (down to <2 R_(Earth)) around bright stars (<11 mag), including terrestrial planets in the habitable zone of solar-like stars. With the complement of radial velocity observations from the ground, planets will be characterised for their radius, mass, and age with high accuracy (5 %, 10 %, 10 % for an Earth-Sun combination respectively). PLATO will provide us with a large-scale catalogue of well-characterised small planets up to intermediate orbital periods, relevant for a meaningful comparison to planet formation theories and to better understand planet evolution. It will make possible comparative exoplanetology to place our Solar System planets in a broader context. In parallel, PLATO will study (host) stars using asteroseismology, allowing us to determine the stellar properties with high accuracy, substantially enhancing our knowledge of stellar structure and evolution. The payload instrument consists of 26 cameras with 12cm aperture each. For at least four years, the mission will perform high-precision photometric measurements. Here we review the science objectives, present PLATO's target samples and fields, provide an overview of expected core science performance as well as a description of the instrument and the mission profile at the beginning of the serial production of the flight cameras. PLATO is scheduled for a launch date end 2026. This overview therefore provides a summary of the mission to the community in preparation of the upcoming operational phases.

Photovoltaic (PV) energy grows rapidly and is crucial for the decarbonization of electric systems. However, centralized registries recording the technical characteristifs of rooftop PV systems are often missing, making it difficult to accurately monitor this growth. The lack of monitoring could threaten the integration of PV energy into the grid. To avoid this situation, the remote sensing of rooftop PV systems using deep learning emerged as a promising solution. However, existing techniques are not reliable enough to be used by public authorities or transmission system operators (TSOs) to construct up-to-date statistics on the rooftop PV fleet. The lack of reliability comes from the fact that deep learning models are sensitive to distribution shifts. This work proposes a comprehensive evaluation of the effects of distribution shifts on the classification accuracy of deep learning models trained to detect rooftop PV panels on overhead imagery. We construct a benchmark to isolate the sources of distribution shift and introduce a novel methodology that leverages explainable artificial intelligence (XAI) and decomposition of the input image and model's decision in terms of scales to understand how distribution shifts affect deep learning models. Finally, based on our analysis, we introduce a data augmentation technique meant to improve the robustness of deep learning classifiers to varying acquisition conditions. We show that our proposed approach outperforms competing methods. We discuss some practical recommendations for mapping PV systems using overhead imagery and deep learning models., Comment: 24 pages, 13 figures, 5 tables, manuscript submitted to Environmental Data Science

As part of its Resilience Strategy, the City of Paris' OASIS program aims to contribute ot its adaptation to heatwaves and climate change by transforming schoolyards into climate shelters, namely via desealing and greening. In this context, a variety of alternative pavement materials have been proposed to replace the initial schoolyard pavement, composed of an asphalt sidewalk structure. In the context of the EU-funded ERDF UIA OASIS project, the thermal performance of these alternative materials and their impact in terms of urban cooling was explored. To this aim, five samples of reference and innovative schoolyard pavements were studied in the lab under heat-wave conditions. Alternative green, biosourced, recycled and reflective pavement solutions were compared to standard fine-aggregate asphalt concrete. Their performance was evaluated with regards to their contribution to the urban heat island phenomenon and to pedestrian heat stress, account for the typical use schedule of schoolyards. Green and biosourced materials were found to perform well for both indicators, while the standard and recycled solutions had poor UHI performance but had limited negative effects on daytime heat stress. The reflective pavement had better UHI performance but had high radiosity during daytime which can negatively affect pedestrian heat stress.

In this work, we introduce Gemma 2, a new addition to the Gemma family of lightweight, state-of-the-art open models, ranging in scale from 2 billion to 27 billion parameters. In this new version, we apply several known technical modifications to the Transformer architecture, such as interleaving local-global attentions (Beltagy et al., 2020a) and group-query attention (Ainslie et al., 2023). We also train the 2B and 9B models with knowledge distillation (Hinton et al., 2015) instead of next token prediction. The resulting models deliver the best performance for their size, and even offer competitive alternatives to models that are 2-3 times bigger. We release all our models to the community.

Light-induced mechanical deformations in single-domain (PbTiO$_{3}$)$_{n}$/(SrTiO$_{3}$)$_{m}$ superlattices were simulated using first-principle calculations. By varying the chemical fraction PbTiO$_{3}$, we discover that these heterostructures' photostrictive behavior can be tuned quantitatively and qualitatively. Additionally, we present simple analytical models to explain the calculated deformations and predict a critical PbTiO$_{3}$ fraction with no photostriction. In addition to the report in [1], our results present another way for tuning the photostrictive behavior of (PbTiO$_{3}$)$_{n}$/(SrTiO$_{3}$)$_{m}$ superlattices, which could be utilized for innovative optomechanical applications.

We report systematic first-principles investigation of light-induced mechanical deformations in monodomain (PbTiO$_{3}$)$_{n}$/(SrTiO$_{3}$)$_{n}$ superlattices ($n=1-5$). We reveal that photostriction in these heterostructures quantitatively and qualitatively depends on the chemical period $n$. Specifically, we show that by changing the chemical period, we can induce $\textit {positive}$ or $\textit {negative}$ photostriction. We also present a simple analytical model to account for the calculated deformations. Our findings indicate that superlattices architectures may be key to design novel optomechanical applications.

Electronic Health Records (EHRs) and Medical Data are classified as personal data in every privacy law, meaning that any related service that includes processing such data must come with full security, confidentiality, privacy and accountability. Solutions for health data management, as in storing it, sharing and processing it, are emerging quickly and were significantly boosted by the Covid-19 pandemic that created a need to move things online. EHRs makes a crucial part of digital identity data, and the same digital identity trends -- as in self sovereign identity powered by decentralized ledger technologies like Blockchain, are being researched or implemented in contexts managing digital interactions between health facilities, patients and health professionals. In this paper, we propose a blockchain-based solution enabling secure exchange of EHRs between different parties powered by a self-sovereign identity (SSI) wallet and decentralized identifiers. We also make use of a consortium IPFS network for off-chain storage and attribute-based encryption (ABE) to ensure data confidentiality and integrity. Through our solution, we grant users full control over their medical data, and enable them to securely share it in total confidentiality over secure communication channels between user wallets using encryption. We also use DIDs for better user privacy and limit any possible correlations or identification by using pairwise DIDs. Overall, combining this set of technologies guarantees secure exchange of EHRs, secure storage and management along with by-design features inherited from the technological stack., Comment: 13 pages, 11 figures

(abridged) We present an account of known cataclysmic variables (CVs) that were detected as X-ray sources in eROSITA X-ray surveys and have Gaia DR3 counterparts. We address standard CVs with main sequence donors and white dwarfs accreting via Roche-lobe overflow (RLOF) and related objects, the double degenerates (DDs), and the symbiotic stars (SySts). We discern between nonmagnetic (dwarf novae and nova-like objects) and magnetic CVs (polars and intermediate polars (IPs)). In the publically available eROSITA catalog from the recent DR1, typically 65\% of known cataloged and classified CVs are detected. This fraction rises to over 90\% if the stack of all \ero X-ray surveys (called S45 in this paper) is considered and the search volume is restricted to a radius of 500\,pc. We examine the various classes of CVs in various diagnostic diagrams relating X-ray and optical properties (luminosity, absolute magnitude, color, X-ray spectral hardness, and optical variability) and establish their average class properties. We derive spectral properties for the 22 brightest polars and confirm an increase in the ratio of soft to hard X-rays with increasing magnetic field in the accretion region. We report three new soft IPs and present a spectral analysis of all soft IPs. Their blackbody temperatures agree well with published values. The DDs represent the bluest and faintest subcategory but reach the same identification fraction as the standard CVs. The SySts are the most distant systems; only 20 (13\%) were detected as X-ray sources in S45, and 7 of those are first-time detections. We investigate their mean properties using an upper limit on the flux of the nondetected CVs. Their X-ray nondetection is indeed a distance effect., Comment: 21 pages, 12 figures, 10 tables, accepted for publication in A&A

We investigate two-dimensional crystallization phenomena, i.e. minimality of a lattice's patch for interaction energies, with pair potentials of type $(x,y)\mapsto V(\|x-y\|)$ where $\|\cdot\|$ is an arbitrary norm on $\mathbb{R}^2$ and $V:\mathbb{R}_+^*\to \mathbb{R}$ is a function. For the Heitmann-Radin sticky disk potential $V=V_{\text{HR}}$, we prove, using Brass' key result from [Computational Geometry, 6:195--214, 1996], that crystallization occurs for any fixed norm, with a classification of minimizers and minimal energies according to the kissing number associated to $\|\cdot\|$. The minimizer is proved to be, up to affine transform, a patch of the triangular or the square lattice, which shows how to easily get anisotropy in a crystallization phenomenon. We apply this result to the $p$-norms $\|\cdot\|_p$, $p\geq 1$, which allows us to construct an explicit family of norms for which crystallization holds on any given lattice. We also solve part of a crystallization problem studied in [Arch. Ration. Mech. Anal., 240:987--1053] where points are constrained to be on $\mathbb{Z}^2$. Moreover, we numerically investigate the minimization problem for the energy per point among lattices for the Lennard-Jones potential $V=V_{\text{LJ}}:r\mapsto r^{-12}-2r^{-6}$ as well as the Epstein zeta function associated to a $p$-norm $\|\cdot\|_p$, i.e. when $V=V_s:r\mapsto r^{-s}$, $s>2$. Our simulations show a new and unexpected phase transition for the minimizers with respect to $p$., Comment: 15 pages, 6 figures

HAL (Hyper Articles en Ligne) is the French national publication repository, used by most higher education and research organizations for their open science policy. As a digital library, it is a rich repository of scholarly documents, but its potential for advanced research has been underutilized. We present HALvest, a unique dataset that bridges the gap between citation networks and the full text of papers submitted on HAL. We craft our dataset by filtering HAL for scholarly publications, resulting in approximately 700,000 documents, spanning 34 languages across 13 identified domains, suitable for language model training, and yielding approximately 16.5 billion tokens (with 8 billion in French and 7 billion in English, the most represented languages). We transform the metadata of each paper into a citation network, producing a directed heterogeneous graph. This graph includes uniquely identified authors on HAL, as well as all open submitted papers, and their citations. We provide a baseline for authorship attribution using the dataset, implement a range of state-of-the-art models in graph representation learning for link prediction, and discuss the usefulness of our generated knowledge graph structure.

In the aftermath of the 2003 heatwave, and with growing concern over climate change, Paris City Hall has been implementing several heat mitigation strategies. One of these is the OASIS Schoolyard Strategy which aims to transform Parisian schoolyards into cool islands. Within this framework, the EU-funded ERDF UIA OASIS Project aims to study the transformation of ten schoolyards, including an evaluation of their microclimatic performance.The present article presents case study results from one schoolyard using GIS data and fixed and mobile microclimatic measurements. An analysis method for mobile measurement data is proposed, tested and discussed on the basis of this case study.

The dominant contribution to the energy relaxation of state-of-the-art superconducting qubits is often attributed to their coupling to an ensemble of material defects which behave as two-level systems. These defects have varying microscopic characteristics which result in a large range of observable defect properties such as resonant frequencies, coherence times and coupling rates to qubits $g$. Here, we investigate strategies to mitigate losses to the family of defects that strongly couple to qubits ($g/2\pi\ge$ 0.5 MHz). Such strongly coupled defects are particularly detrimental to the coherence of qubits and to the fidelities of operations relying on frequency excursions, such as flux-activated two-qubit gates. To assess their impact, we perform swap spectroscopy on 92 frequency-tunable qubits and quantify the spectral density of these strongly coupled modes. We show that the frequency configuration of the defects is rearranged by warming up the sample to room temperature, whereas the total number of defects on a processor tends to remain constant. We then explore methods for fabricating qubits with a reduced number of strongly coupled defect modes by systematically measuring their spectral density for decreasing Josephson junction dimensions and for various surface cleaning methods. Our results provide insights into the properties of strongly coupled defect modes and show the benefits of minimizing Josephson junction dimensions to improve qubit properties.

Despite decades of research, demonstration of all-optical detection and control of free electron spins in silicon remains elusive. Here, we directly probe the electron spin properties in bulk silicon by measuring the polarization of luminescence following circularly polarized light excitation. The experiments performed for both direct and indirect gap excitation allow not only an experimental determination of the optical selection rules in silicon for the different phononassisted transitions but they also lead to the measurement of the spin relaxation of electrons in conditions which are not accessible using transport techniques. We also measure the spin properties of free excitons in bulk silicon, a very little explored field.

Exoplanet imaging uses coronagraphs to block out the bright light from a star, allowing astronomers to observe the much fainter light from planets orbiting the star. However, these instruments are heavily impacted by small wavefront aberrations and require the minimization of starlight residuals directly in the focal plane. State-of-the art wavefront control methods suffer from errors in the underlying physical models, and often require several iterations to minimize the intensity in the dark hole, limiting performance and reducing effective observation time. This study aims at developing a data-driven method to create a dark hole in post-coronagraphic images. For this purpose, we leverage the model-free capabilities of reinforcement learning to train an agent to learn a control strategy directly from phase diversity images acquired around the focal plane. Initial findings demonstrate successful aberration correction in non-coronagraphic simulations and promising results for dark hole creation in post-coronagraphic scenarios. These results highlight the potential of model-free reinforcement learning for dark-hole creation, justifying further investigation and eventually experimental validation on a dedicated testbed., Comment: 9 pages, 6 figures, to be published in SPIE Astronomical Telescopes + Instrumentation proceedings

We propose an algorithm to identify numerically periodic solutions of high-dimensional dynamical systems and their local stability properties. One of the most popular approaches is the Harmonic Balance Method (HBM), which expresses the cycle as a sum of Fourier modes and analyses its stability using the Hill's method. A drawback of Hill's method is that the relevant Floquet exponents have to be chosen from all the computed exponents. To overcome this problem the current work discusses the application of Chebyshev polynomials to the description of the time dependence of the periodic dynamics. The stability characteristics of the periodic orbit are directly extracted from the linearisation around the periodic orbit. The method is compared with the HBM with examples from Lorenz and Langford systems. The main advantage of the present method is that, unlike HBM, it allows for an unambiguous determination of the Floquet exponents. The method is applied to natural convection in a differentially heated cavity which demonstrates its potential for large scale problems arising from the discretisation of the incompressible Navier-Stokes equations.

SOXS (Son Of X-Shooter) is the new single object spectrograph for the ESO New Technology Telescope (NTT) at the La Silla Observatory, able to cover simultaneously both the UV-VIS and NIR bands (350-2000 nm). The instrument is currently in the integration and test phase, approaching the Preliminary Acceptance in Europe (PAE) before shipment to Chile for commissioning. After the assembly and preliminary test of the control electronics at INAF - Astronomical Observatory of Capodimonte (Napoli), the two main control cabinets of SOXS are now hosted in Padova, connected to the real hardware. This contribution describes the final electronic cabinets layout, the control strategy and the different integration phases, waiting for the Preliminary Acceptance in Europe and the installation of the instrument in Chile.

The Son Of X-Shooter (SOXS) will be the specialized facility to observe any transient event with a flexible scheduler at the ESO New Technology Telescope (NTT) at La Silla, Chile. SOXS is a single object spectrograph offering simultaneous spectral coverage in UV-VIS (350-850 nm) and NIR (800-2000 nm) wavelength regimes with an average of R~4500 for a 1arcsec slit. SOXS also has imaging capabilities in the visible wavelength regime. Currently, SOXS is being integrated at the INAF-Astronomical Observatory of Padova. Subsystem- and system-level tests and verification are ongoing to ensure and confirm that every requirement and performance are met. In this paper, we report on the integration and verification of SOXS as the team and the instrument prepare for the Preliminary Acceptance Europe (PAE).

The Son Of X-Shooter (SOXS) is a single object spectrograph, built by an international consortium for the 3.58-m ESO New Technology Telescope at the La Silla Observatory [1]. It offers a simultaneous spectral coverage over 350-2000 nm, with two separate spectrographs. In this paper we present the status of the Near InfraRed (NIR) cryogenic echelle cross-dispersed spectrograph [1], in the range 0.80-2.00 {\mu}m with 15 orders, equipped with an 2k x 2k Hawaii H2RG IR array from Teledyne, working at 40K, that is currently assembled and tested on the SOXS instrument, in the premises of INAF in Padova. We describe the different tests and results of the cryo, vacuum, opto-mechanics and detector subsystems that finally will be part of the PAE by ESO., Comment: 8 Pages, 7 Figures, Astronomical Telescopes and Instrumentation, SPIE Proceedings 2024

We present the advancements in the development of the scheduler for the Son Of X-shooter instrument at the ESO-NTT 3.58-m telescope in La Silla, Chile. SOXS is designed as a single-object spectroscopic facility and features a high-efficiency spectrograph with two arms covering the spectral range of 350-2000 nm and a mean resolving power of approximately R=4500. It will conduct UV-visible and near-infrared follow-up observations of astrophysical transients, drawing from a broad pool of targets accessible through the streaming services of wide-field telescopes, both current and future, as well as high-energy satellites. The instrument will cater to various scientific objectives within the astrophysical community, each entailing specific requirements for observation planning. SOXS will operate at the European Southern Observatory (ESO) in La Silla, without the presence of astronomers on the mountain. This poses a unique challenge for the scheduling process, demanding a fully automated algorithm that is autonomously interacting with the appropriate databases and the La Silla Weather API, and is capable of presenting the operator not only with an ordered list of optimal targets (in terms of observing constraints) but also with optimal backups in the event of changing weather conditions. This imposes the necessity for a scheduler with rapid-response capabilities without compromising the optimization process, ensuring the high quality of observations and best use of the time at the telescope. We thus developed a new highly available and scalable architecture, implementing API Restful applications like Docker Containers, API Gateway, and Python-based Flask frameworks. We provide an overview of the current state of the scheduler, which is now ready for the approaching on-site testing during Commissioning phase, along with insights into its web interface and preliminary performance tests.

The Instrument Control Software of SOXS (Son Of X-Shooter), the forthcoming spectrograph for the ESO New Technology Telescope at the La Silla Observatory, has reached a mature state of development and is approaching the crucial Preliminary Acceptance in Europe phase. Now that all the subsystems have been integrated in the laboratories of the Padova Astronomical Observatory, the team operates for testing purposes with the whole instrument at both engineering and scientific level. These activities will make use of a set of software peculiarities that will be discussed in this contribution. In particular, we focus on the synoptic panel, the co-rotator system special device, on the Active Flexure Compensation system which controls two separate piezo tip-tilt devices., Comment: 6 pages, 3 figures, SPIE conference

We consider certain analytic correspondences on a Riemann surface, and show that they admit a weak form of expansion. In terms of their algebraic encoding by bisets, this translates to contraction of group elements along sequences arising from iterated lifting. As an application, we show that for every non-exceptional rational map on $\mathbb{P}^1$ with $4$ post-critical points, there is a finite collection of isotopy classes of curves into which every curve eventually lands under iterated lifting., Comment: 36 pages, 3 figures

Accurately segmenting and individualizing cells in SEM images is a highly promising technique for elucidating tissue architecture in oncology. While current AI-based methods are effective, errors persist, necessitating time-consuming manual corrections, particularly in areas where the quality of cell contours in the image is poor and requires gap filling. This study presents a novel AI-driven approach for refining cell boundary delineation to improve instance-based cell segmentation in SEM images, also reducing the necessity for residual manual correction. A CNN COp-Net is introduced to address gaps in cell contours, effectively filling in regions with deficient or absent information. The network takes as input cell contour probability maps with potentially inadequate or missing information and outputs corrected cell contour delineations. The lack of training data was addressed by generating low integrity probability maps using a tailored PDE. We showcase the efficacy of our approach in augmenting cell boundary precision using both private SEM images from PDX hepatoblastoma tissues and publicly accessible images datasets. The proposed cell contour closing operator exhibits a notable improvement in tested datasets, achieving respectively close to 50% (private data) and 10% (public data) increase in the accurately-delineated cell proportion compared to state-of-the-art methods. Additionally, the need for manual corrections was significantly reduced, therefore facilitating the overall digitalization process. Our results demonstrate a notable enhancement in the accuracy of cell instance segmentation, particularly in highly challenging regions where image quality compromises the integrity of cell boundaries, necessitating gap filling. Therefore, our work should ultimately facilitate the study of tumour tissue bioarchitecture in onconanotomy field., Comment: 13 pages, 8 figures, 2 tables

Effective frequency control in power grids has become increasingly important with the increasing demand for renewable energy sources. Here, we propose a novel strategy for resolving this challenge using graph convolutional proximal policy optimization (GC-PPO). The GC-PPO method can optimally determine how much power individual buses dispatch to reduce frequency fluctuations across a power grid. We demonstrate its efficacy in controlling disturbances by applying the GC-PPO to the power grid of the UK. The performance of GC-PPO is outstanding compared to the classical methods. This result highlights the promising role of GC-PPO in enhancing the stability and reliability of power systems by switching lines or decentralizing grid topology., Comment: 11 pages, 6 figures

These lecture notes attempt to invite the reader towards the theory of singular foliations, both smooth and holomorphic. In addition to a systematic review of the foundations, and an attempt to put in order examples and several elementary constructions, we detail several recent tools developed for non-commutative geometry, in particular the holonomy groupoid of Androulidakis and Skandalis and various methods for resolving singularities. We also introduce various homotopic notions, and end with a series of open questions.

In this paper, we investigate the extrapolation capabilities of implicit deep learning models in handling unobserved data, where traditional deep neural networks may falter. Implicit models, distinguished by their adaptability in layer depth and incorporation of feedback within their computational graph, are put to the test across various extrapolation scenarios: out-of-distribution, geographical, and temporal shifts. Our experiments consistently demonstrate significant performance advantage with implicit models. Unlike their non-implicit counterparts, which often rely on meticulous architectural design for each task, implicit models demonstrate the ability to learn complex model structures without the need for task-specific design, highlighting their robustness in handling unseen data., Comment: Accepted at the Workshop on Explainable Artificial Intelligence (XAI) at IJCAI 2024

Convolutional Neural Networks are particularly suited for image analysis tasks, such as Image Classification, Object Recognition or Image Segmentation. Like all Artificial Neural Networks, however, they are "black box" models, and suffer from poor explainability. This work is concerned with the specific downstream task of Emotion Recognition from images, and proposes a framework that combines CAM-based techniques with Object Detection on a corpus level to better understand on which image cues a particular model, in our case EmoNet, relies to assign a specific emotion to an image. We demonstrate that the model mostly focuses on human characteristics, but also explore the pronounced effect of specific image modifications., Comment: 10 pages, 7 figures

We study the relation between the multigraded Castelnuovo-Mumford regularity of a multihomogeneous ideal $I$ and the multidegrees of a Gr\"obner basis of $I$ with respect to the degree reverse lexicographical monomial order in generic coordinates. For the single graded case, forty years ago, Bayer and Stillman unravelled all aspects of this relation, which in turn the use to complexity estimates for the computation with Gr\"obner bases. We build on their work to introduce a bounding region of the multidegrees of minimal generators of multigraded Gr\"obner bases for $I$. We also use this region to certify the presence of some minimal generators close to its boundary. Finally, we show that, up to a certain shift, this region is related to the multigraded Castelnuovo-Mumford regularity of $I$.

The certification of quantum resources is a critical tool in the development of quantum information processing. In particular, quantum state verification is a fundamental building block for communication and computation applications, determining whether the involved parties can trust the resources at hand or whether the application should be aborted. Self-testing methods have been used to tackle such verification tasks in a device-independent (DI) setting. However, these approaches commonly consider the limit of large (asymptotic), identically and independently distributed (IID) samples, which weakens the DI claim and poses serious challenges to their experimental implementation. Here we overcome these challenges by adopting a theoretical protocol enabling the certification of quantum states in the few-copies and non-IID regime and by leveraging a high-fidelity multipartite entangled photon source. This allows us to show the efficient and device-independent certification of a single copy of a four-qubit GHZ state that can readily be used for the robust and reliable implementation of quantum information tasks.