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Mid-infrared imaging of exoplanets and disks is now possible with the coronagraphs of the MIRI on the JWST. This wavelength range unveils new features of young directly imaged systems and allows us to obtain new constraints for characterizing the atmosphere of young giant exoplanets and associated disks. These observations aim to characterize the atmosphere of the planet HD 95086 b by adding mid-infrared information so that the various hypotheses about its atmospheric parameters values can be unraveled. Improved images of circumstellar disks are provided. We present the MIRI coronagraphic imaging of the system HD 95086 obtained with the F1065C, F1140, and F2300C filters at central wavelengths of 10.575, 11.3, and 23 microns, respectively. We explored the method for subtracting the stellar diffraction pattern in the particular case when bright dust emitting at short separation is present. Furthermore, we compared different methods for extracting the photometry of the planet. Using the atmospheric models Exo-REM and ATMO, we measured the atmospheric parameters of HD 95086 b. The planet HD 95086 b and the contribution from the inner disk are detected at the two shortest MIRI wavelengths F1065C and F1140C. The outer colder belt is imaged at 23 microns. The mid-infrared photometry provides better constraints on the atmospheric parameters. We evaluate a temperature of 800-1050 K, consistent with one previous hypothesis that only used NIR data. The radius measurement of 1.0-1.14 RJup is better aligned with evolutionary models, but still smaller than predicted. These observations allow us to refute the hypothesis of a warm circumplanetary disk. HD 95086 is one of the first exoplanetary systems to be revealed at mid-infrared wavelengths. This highlights the interests and challenges of observations at these wavelengths., Comment: version published in A&A

The problem of conditioning a continuous-state branching process with quadratic competition (logistic CB process) on non-extinction is investigated. We first establish that non-extinction is equivalent to the total progeny of the population being infinite. The conditioning we propose is then designed by requiring the total progeny to exceed arbitrarily large exponential random variables. This is related to a Doob's $h$-transform with an explicit excessive function $h$. The $h$-transformed process, i.e. the conditioned process, is shown to have a finite lifetime almost surely (it is either killed or it explodes continuously). When starting from positive values, the conditioned process is furthermore characterized, up to its lifetime, as the solution to a certain stochastic equation with jumps. The latter superposes the dynamics of the initial logistic CB process with an additional density-dependent immigration term. Last, it is established that the conditioned process can be starting from zero. Key tools employed are a representation of the logistic CB process through a time-changed generalized Ornstein-Uhlenbeck process, as well as Laplace and Siegmund duality relationships with auxiliary diffusion processes.

Emergence of universal collective behaviour from interactions in a sufficiently large group of elementary constituents is a fundamental scientific paradigm. In physics, correlations in fluctuating microscopic observables can provide key information about collective states of matter such as deconfined quark-gluon plasma in heavy-ion collisions or expanding quantum degenerate gases. Two-particle correlations in mesoscopic colliders have provided smoking-gun evidence on the nature of exotic electronic excitations such as fractional charges, levitons and anyon statistics. Yet the gap between two-particle collisions and the emergence of collectivity as the number of interacting particles grows is hard to address at the microscopic level. Here, we demonstrate all-body correlations in the partitioning of up to $N = 5$ electron droplets driven by a moving potential well through a Y-junction in a semiconductor. We show that the measured multivariate cumulants (of order $k = 2$ to $N$) of the electron droplet are accurately described by $k$-spin correlation functions of an effective Ising model below the N\'eel temperature and can be interpreted as a Coulomb liquid in the thermodynamic limit. Finite size scaling of high-order correlation functions provides otherwise inaccessible fingerprints of emerging order. Our demonstration of emergence in a simple correlated electron collider opens a new way to study engineered states of matter.

Biclustering is an unsupervised machine-learning approach aiming to cluster rows and columns simultaneously in a data matrix. Several biclustering algorithms have been proposed for handling numeric datasets. However, real-world data mining problems often involve heterogeneous datasets with mixed attributes. To address this challenge, we introduce a biclustering approach called HBIC, capable of discovering meaningful biclusters in complex heterogeneous data, including numeric, binary, and categorical data. The approach comprises two stages: bicluster generation and bicluster model selection. In the initial stage, several candidate biclusters are generated iteratively by adding and removing rows and columns based on the frequency of values in the original matrix. In the second stage, we introduce two approaches for selecting the most suitable biclusters by considering their size and homogeneity. Through a series of experiments, we investigated the suitability of our approach on a synthetic benchmark and in a biomedical application involving clinical data of systemic sclerosis patients. The evaluation comparing our method to existing approaches demonstrates its ability to discover high-quality biclusters from heterogeneous data. Our biclustering approach is a starting point for heterogeneous bicluster discovery, leading to a better understanding of complex underlying data structures., Comment: 11 pages, 5 figures

We develop the theory of ``virtual morphisms'' in logarithmic algebraic geometry, introduced by Howell. It allows one to give algebro-geometric meaning to various useful maps of topological spaces that do not correspond to morphisms of (log) schemes in the classical sense, while retaining functoriality of key constructions. In particular, we explain how virtual morphisms provide a natural categorical home for Deligne's theory of tangential basepoints: the latter are simply the virtual morphisms from a point. We also extend Howell's results on the functoriality of Betti and de Rham cohomology. Using this framework, we lift the topological operad of little $2$-disks to an operad in log schemes over the integers, whose virtual points are isomorphism classes of stable marked curves of genus zero equipped with a tangential basepoint. The gluing of such curves along marked points is performed using virtual morphisms that transport tangential basepoints around the curves. This builds on Vaintrob's analogous construction for framed little disks, for which the classical notion of morphism in logarithmic geometry sufficed. In this way, we obtain a direct algebro-geometric proof of the formality of the little disks operad, following the strategy envisioned by Beilinson. Furthermore, Bar-Natan's parenthesized braids naturally appear as the fundamental groupoids of our moduli spaces, with all virtual basepoints defined over the integers., Comment: 55 pages, 4 figures, comments welcome

Electronic flying qubits offer an interesting alternative to photonic qubits: electrons propagate slower, hence easier to control in real time, and Coulomb interaction enables direct entanglement between different qubits. While their coherence time is limited, picosecond-scale control would make them competitive in terms of number of possible coherent operations. The key challenge lies in achieving the dynamical regime, where the injected plasmonic pulse width is shorter than the quantum device dimensions. Here we reach this new regime in a quantum nanoelectronic system by injecting ultrashort single electron plasmonic pulses into a 14-micrometer-long Mach-Zehnder interferometer. Our findings reveal that quantum coherence is preserved for ultrashort plasmonic pulses, exhibiting enhanced contrast of coherent oscillations compared to the DC regime. Moreover, this coherence remains robust even under large bias voltages. This milestone demonstrates the feasibility of flying qubits as a promising alternative to localized qubit architectures, offering reduced hardware footprint, increased connectivity, and potential for scalable quantum information processing.

We view voting rules as classifiers that assign a winner (a class) to a profile of voters' preferences (an instance). We propose to apply techniques from formal explainability, most notably abductive and contrastive explanations, to identify minimal subsets of a preference profile that either imply the current winner or explain why a different candidate was not elected. Formal explanations turn out to have strong connections with classical problems studied in computational social choice such as bribery, possible and necessary winner identification, and preference learning. We design algorithms for computing abductive and contrastive explanations for scoring rules. For the Borda rule, we find a lower bound on the size of the smallest abductive explanations, and we conduct simulations to identify correlations between properties of preference profiles and the size of their smallest abductive explanations., Comment: 10 pages, 2 figures Extended version of a paper in proceedings of ECAI 2024

We investigate the dynamics of multipartite entanglement after quenches from initial states which generate multiplets of quasiparticle excitations beyond the usual pair structure. We focus on the dynamics of tripartite entanglement through the lens of the Markov gap -- a computable quantity that signals irreducible tripartite entanglement when positive. In the XX spin chain, we show that the Markov gap is positive at intermediate times, implying the presence of tripartite entanglement. After a time delay, the Markov gap increases and then decays at longer times, thus exhibiting an entanglement barrier. We argue that those qualitative features are consistent with an interpretation of the spreading of tripartite entanglement by triplets of tripartite-entangled quasiparticles., Comment: 3+3 pages, 3+2 figures

Particle-particle and particle-gas processes significantly impact planetary precursors such as dust aggregates and planetesimals. We investigate gas permeability ($\kappa$) in 12 granular samples, mimicking planetesimal dust regoliths. Using parabolic flights, this study assesses how gravitational compression -- and lack thereof -- influences gas permeation, impacting the equilibrium state of low-gravity objects. Transitioning between micro- and hyper-gravity induces granular sedimentation dynamics, revealing collective dust-grain aerodynamics. Our experiments measure $\kappa$ across Knudsen number (Kn) ranges, reflecting transitional flow. Using mass and momentum conservation, we derive $\kappa$ and calculate pressure gradients within the granular matrix. Key findings: 1. As confinement pressure increases with gravitational load and mass flow, $\kappa$ and average pore space decrease. This implies that a planetesimal's unique dust-compaction history limits sub-surface volatile outflows. 2. The derived pressure gradient enables tensile strength determination for asteroid regolith simulants with cohesion. This offers a unique approach to studying dust-layer properties when suspended in confinement pressures comparable to the equilibrium state on planetesimals surfaces, which will be valuable for modelling their collisional evolution. 3. We observe a dynamical flow symmetry breaking when granular material moves against the pressure gradient. This occurs even at low Reynolds numbers, suggesting that Stokes numbers for drifting dust aggregates near the Stokes-Epstein transition require a drag force modification based on permeability.

Building on previous works to construct a homogeneous sample of brown dwarfs in binary systems, we investigate microlensing events detected by the Korea Microlensing Telescope Network (KMTNet) survey during the 2022 and 2023 seasons. Given the difficulty in distinguishing brown-dwarf events from those produced by binary lenses with nearly equal-mass components, we analyze all lensing events detected during the seasons that exhibit anomalies characteristic of binary-lens systems. Using the same criteria consistently applied in previous studies, we identify six additional brown dwarf candidates through the analysis of lensing events KMT-2022-BLG-0412, KMT-2022-BLG-2286, KMT-2023-BLG-0201, KMT-2023-BLG-0601, KMT-2023-BLG-1684, and KMT-2023-BLG-1743. An examination of the mass posteriors shows that the median mass of the lens companions ranges from 0.02 $M_\odot$ to 0.05 $M_\odot$, indicating that these companions fall within the brown-dwarf mass range. The mass of the primary lenses ranges from 0.11 $M_\odot$ to 0.68 $M_\odot$, indicating that they are low-mass stars with substantially lower masses compared to the Sun., Comment: 13 pages, 17 figures, 12 tables

In this paper, we consider the problem of seriation of a permuted structured matrix based on noisy observations. The entries of the matrix relate to an expected quantification of interaction between two objects: the higher the value, the closer the objects. A popular structured class for modelling such matrices is the permuted Robinson class, namely the set of matrices whose coefficients are decreasing away from its diagonal, up to a permutation of its lines and columns. We consider in this paper two submodels of Robinson matrices: the T{\oe}plitz model, and the latent position model. We provide a computational lower bound based on the low-degree paradigm, which hints that there is a statistical-computational gap for seriation when measuring the error based on the Frobenius norm. We also provide a simple and polynomial-time algorithm that achives this lower bound. Along the way, we also characterize the information-theory optimal risk thereby giving evidence for the extent of the computation/information gap for this problem., Comment: 39 pages, 2 figures

Fluctuations of observables provide unique insights into the nature of physical systems, and their study stands as a cornerstone of both theoretical and experimental science. Generalized fluctuations, or cumulants, provide information beyond the mean and variance of an observable. In this letter, we develop a systematic method to determine the asymptotic behavior of cumulants of local observables as the region becomes large. Our analysis reveals that the expansion is closely tied to the geometric characteristics of the region and its boundary, with coefficients given by convex moments of the connected correlation function: the latter is integrated against intrinsic volumes of convex polytopes built from the coordinates, which can be interpreted as averaged shadows. A particular application of our method shows that, in two dimensions, the leading behavior of odd cumulants of conserved quantities is topological, specifically depending on the Euler characteristic of the region. We illustrate these results with the paradigmatic strongly-interacting system of two-dimensional quantum Hall state at filling fraction $1/2$, by performing Monte-Carlo calculations of the skewness (third cumulant) of particle number in the Laughlin state., Comment: 4+8 pages, 3+3 figures

We introduce Imagen 3, a latent diffusion model that generates high quality images from text prompts. We describe our quality and responsibility evaluations. Imagen 3 is preferred over other state-of-the-art (SOTA) models at the time of evaluation. In addition, we discuss issues around safety and representation, as well as methods we used to minimize the potential harm of our models.

We leverage the interplay between gapped phases and dualities of symmetric one-dimensional quantum lattice models to demonstrate that every phase is efficiently characterised by the maximal breaking of the dual (genereralised) symmetry whose structure encodes the quasiparticle excitations. This result has strong implications for the complexity of simulating many-body systems using variational tensor network methods. For every phase in the phase diagram, the dual representation of the ground state that breaks all symmetries minimises both the entanglement entropy and the required number of variational parameters. We demonstrate the applicability of this idea by developing a generalised density matrix renormalisation group algorithm that works on (dual) constrained Hilbert spaces, and quantify the computational gains obtained over traditional DMRG methods in a perturbed Heisenberg model. Our work testifies to the usefulness of generalised non-invertible symmetries and their formal category theoretic description for the nuts and bolts simulation of strongly correlated systems., Comment: 7+6 pages

We investigate the ability of the Euclid telescope to detect galaxy-scale gravitational lenses. To do so, we perform a systematic visual inspection of the $0.7\,\rm{deg}^2$ Euclid ERO data towards the Perseus cluster using both the high-resolution VIS $I_{\scriptscriptstyle\rm E}$ band, and the lower resolution NISP bands. We inspect every extended source brighter than magnitude $23$ in $I_{\scriptscriptstyle\rm E}$ with $41$ expert human classifiers. This amounts to $12\,086$ stamps of $10^{\prime\prime}\,\times\,10^{\prime\prime}$. We find $3$ grade A and $13$ grade B candidates. We assess the validity of these $16$ candidates by modelling them and checking that they are consistent with a single source lensed by a plausible mass distribution. Five of the candidates pass this check, five others are rejected by the modelling and six are inconclusive. Extrapolating from the five successfully modelled candidates, we infer that the full $14\,000\,{\rm deg}^2$ of the Euclid Wide Survey should contain $100\,000^{+70\,000}_{-30\,000}$ galaxy-galaxy lenses that are both discoverable through visual inspection and have valid lens models. This is consistent with theoretical forecasts of $170\,000$ discoverable galaxy-galaxy lenses in Euclid. Our five modelled lenses have Einstein radii in the range $0.\!\!^{\prime\prime}68\,<\,\theta_\mathrm{E}\,<1.\!\!^{\prime\prime}24$, but their Einstein radius distribution is on the higher side when compared to theoretical forecasts. This suggests that our methodology is likely missing small Einstein radius systems. Whilst it is implausible to visually inspect the full Euclid data set, our results corroborate the promise that Euclid will ultimately deliver a sample of around $10^5$ galaxy-scale lenses., Comment: 21 pages, 20 figures, submitted to A&A

Med42-v2 introduces a suite of clinical large language models (LLMs) designed to address the limitations of generic models in healthcare settings. These models are built on Llama3 architecture and fine-tuned using specialized clinical data. They underwent multi-stage preference alignment to effectively respond to natural prompts. While generic models are often preference-aligned to avoid answering clinical queries as a precaution, Med42-v2 is specifically trained to overcome this limitation, enabling its use in clinical settings. Med42-v2 models demonstrate superior performance compared to the original Llama3 models in both 8B and 70B parameter configurations and GPT-4 across various medical benchmarks. These LLMs are developed to understand clinical queries, perform reasoning tasks, and provide valuable assistance in clinical environments. The models are now publicly available at \href{https://huggingface.co/m42-health}{https://huggingface.co/m42-health}.

For a class of two-dimensional Euclidean lattice field theories admitting topological lines encoded into a spherical fusion category, we explore aspects of their realisations as boundary theories of a three-dimensional topological quantum field theory. After providing a general framework for explicitly constructing such realisations, we specialise to non-abelian generalisations of the Ising model and consider two operations: gauging an arbitrary subsymmetry and performing Fourier transforms of the local weights encoding the dynamics of the theory. These are carried out both in a traditional way and in terms of the three-dimensional topological quantum field theory. Whenever the whole symmetry is gauged, combining both operations recovers the non-abelian Kramers-Wannier duals \`a la Freed and Teleman of the generalised Ising models. Moreover, we discuss the interplay between renormalisation group fixed points of gapped symmetric phases and these generalised Kramers-Wannier dualities.

Arm Confidential Computing Architecture (CCA) executes sensitive computation in an abstraction called realm VMs and protects it from the hypervisor, host OS, and other co-resident VMs. However, CCA does not allow integrated devices on the platform to access realm VMs and doing so requires intrusive changes to software and is simply not possible to achieve securely for some devices. In this paper, we present Devlore which allows realm VMs to directly access integrated peripherals. Devlore memory isolation re-purposes CCA hardware primitives (granule protection and stage-two page tables), while our interrupt isolation adapts a delegate-but-check strategy. Our choice of offloading interrupt management to the hypervisor but adding correctness checks in the trusted software allows Devlore to preserve compatibility and performance. We evaluate Devlore on Arm FVP to demonstrate 5 diverse peripherals attached to realm VMs.

Frequency estimation, a.k.a. histograms, is a workhorse of data analysis, and as such has been thoroughly studied under differentially privacy. In particular, computing histograms in the \emph{local} model of privacy has been the focus of a fruitful recent line of work, and various algorithms have been proposed, achieving the order-optimal $\ell_\infty$ error in the high-privacy (small $\varepsilon$) regime while balancing other considerations such as time- and communication-efficiency. However, to the best of our knowledge, the picture is much less clear when it comes to the medium- or low-privacy regime (large $\varepsilon$), despite its increased relevance in practice. In this paper, we investigate locally private histograms, and the very related distribution learning task, in this medium-to-low privacy regime, and establish near-tight (and somewhat unexpected) bounds on the $\ell_\infty$ error achievable. As a direct corollary of our results, we obtain a protocol for histograms in the \emph{shuffle} model of differential privacy, with accuracy matching previous algorithms but significantly better message and communication complexity. Our theoretical findings emerge from a novel analysis, which appears to improve bounds across the board for the locally private histogram problem. We back our theoretical findings by an empirical comparison of existing algorithms in all privacy regimes, to assess their typical performance and behaviour beyond the worst-case setting.

In recent years, state-of-the-art image and video denoising networks have become increasingly large, requiring millions of trainable parameters to achieve best-in-class performance. Improved denoising quality has come at the cost of denoising speed, where modern transformer networks are far slower to run than smaller denoising networks such as FastDVDnet and classic Bayesian denoisers such as the Wiener filter. In this paper, we implement a hybrid Wiener filter which leverages small ancillary networks to increase the original denoiser performance, while retaining fast denoising speeds. These networks are used to refine the Wiener coring estimate, optimise windowing functions and estimate the unknown noise profile. Using these methods, we outperform several popular denoisers and remain within 0.2 dB, on average, of the popular VRT transformer. Our method was found to be over x10 faster than the transformer method, with a far lower parameter cost., Comment: Paper accepted to ICME 2024

This paper investigates predictive probability inference for classification tasks using random forests in the context of imbalanced data. In this setting, we analyze the asymptotic properties of simplified versions of the original Breiman's algorithm, namely subsampling and under-sampling Infinite Random Forests (IRFs), and establish the asymptotic normality of these two models. The under-sampling IRFs, that tackle the challenge of the predicting the minority class by a downsampling strategy to under-represent the majority class show asymptotic bias. To address this problem, we introduce a new estimator based on an Importance Sampling debiasing procedure built upon on odds ratios. We apply our results considering 1-Nearest Neighbors (1-NN) as individual trees of the IRFs. The resulting bagged 1-NN estimator achieves the same asymptotic rate in the three configurations but with a different asymptotic variance. Finally, we conduct simulations to validate the empirical relevance of our theoretical findings., Comment: 55 pages, 6 figures

We consider the parametric estimation of the volatility and jump activity in a stable Cox-Ingersoll-Ross ($\alpha$-stable CIR) model driven by a standard Brownian Motion and a non-symmetric stable L\'evy process with jump activity $\alpha \in (1,2)$. The main difficulties to obtain rate efficiency in estimating these quantities arise from the superposition of the diffusion component with jumps of infinite variation. Extending the approach proposed in Mies (2020), we address the joint estimation of the volatility, scaling and jump activity parameters from high-frequency observations of the process and prove that the proposed estimators are rate optimal up to a logarithmic factor.

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.

This study evaluates the efficiency of code generation by Large Language Models (LLMs) and measures their performance against human-crafted solutions using a dataset from Leetcode. We compare 18 LLMs, considering factors such as model temperature and success rate, and their impact on code performance. This research introduces a novel method for measuring and comparing the speed of LLM-generated code, revealing that LLMs produce code with comparable performance, irrespective of the adopted LLM. We also find that LLMs are capable of generating code that is, on average, more efficient than the code written by humans. The paper further discusses the use of Leetcode as a benchmarking dataset, the limitations imposed by potential data contamination, and the platform's measurement reliability. We believe that our findings contribute to a better understanding of LLM capabilities in code generation and set the stage for future optimizations in the field.

Die studies are fundamental to quantifying ancient monetary production, providing insights into the relationship between coinage, politics, and history. The process requires tedious manual work, which limits the size of the corpora that can be studied. Few works have attempted to automate this task, and none have been properly released and evaluated from a computer vision perspective. We propose a fully automatic approach that introduces several innovations compared to previous methods. We rely on fast and robust local descriptors matching that is set automatically. Second, the core of our proposal is a clustering-based approach that uses an intrinsic metric (that does not need the ground truth labels) to determine its critical hyper-parameters. We validate the approach on two corpora of Greek coins, propose an automatic implementation and evaluation of previous baselines, and show that our approach significantly outperforms them., Comment: code: https://cea-list-lasti.github.io/projects/studies/studies.html

We report a new scanning nanomechanical noise microscopy platform enabling to both heat and acquire the fluctuations of mechanical nanostructures with nanometric resolution. We use this platform to image the thermally activated nanomechanical dynamics of a model system consisting of a $40\,\mathrm{nm}$ diameter single-defect nanowire, while scanning a localized heat source across its surface. We develop a thermal backaction model, which we use to demonstrate a close connection between the structure of the nanowire, its thermal response, its dissipation and its fluctuations. We notably show that the defect behaves as a single fluctuation hub, whose e-beam excitation yields a far off-equilibrium vibrational state, largely dominated by the quantum fluctuations of the heating source. Our platform is of interest for future quantitative investigation of fundamental nanoscale dynamical phenomena, and appears as a new playground for investigating quantum thermodynamics in the strongly dissipative regime and at room temperature., Comment: 10 pages, 4 figures

Recent research in explainability has given rise to numerous post-hoc attribution methods aimed at enhancing our comprehension of the outputs of black-box machine learning models. However, evaluating the quality of explanations lacks a cohesive approach and a consensus on the methodology for deriving quantitative metrics that gauge the efficacy of explainability post-hoc attribution methods. Furthermore, with the development of increasingly complex deep learning models for diverse data applications, the need for a reliable way of measuring the quality and correctness of explanations is becoming critical. We address this by proposing BEExAI, a benchmark tool that allows large-scale comparison of different post-hoc XAI methods, employing a set of selected evaluation metrics.

As large language models (LLMs) continue to evolve, the need for robust and standardized evaluation benchmarks becomes paramount. Evaluating the performance of these models is a complex challenge that requires careful consideration of various linguistic tasks, model architectures, and benchmarking methodologies. In recent years, various frameworks have emerged as noteworthy contributions to the field, offering comprehensive evaluation tests and benchmarks for assessing the capabilities of LLMs across diverse domains. This paper provides an exploration and critical analysis of some of these evaluation methodologies, shedding light on their strengths, limitations, and impact on advancing the state-of-the-art in natural language processing., Comment: 15 pages, 3 figures

We show that, in long time, quantum trajectories select an invariant subspace of the Hilbert space of the system being indirectly measured. This selection is shown to be exponentially fast in an almost sure sense and in average. This result generalizes a known result for non demolition measurements to arbitrary repeated indirect measurements. Our proofs are based on the introduction of a deformation of the original instrument to an equivalent one with a unique invariant state.

We study a mechanical oscillator coupled to a two-level system driven by a blue-detuned coherent source in the resolved sideband regime. For weak mechanical damping, we find dynamical instabilities leading to limit cycles. They are signaled by strong fluctuations in the number of emitted photons, with a large Fano factor. The phonon-number fluctuations exhibit a strikingly similar behavior. When the coupling strength becomes comparable to the mechanical frequency, non-classical mechanical states appear. We discuss the relation with cavity optomechanical systems. Candidates for observing these effects include superconducting qubits, NV centers, and single molecules coupled to oscillators.

Over the years, the use of superpixel segmentation has become very popular in various applications, serving as a preprocessing step to reduce data size by adapting to the content of the image, regardless of its semantic content. While the superpixel segmentation of standard planar images, captured with a 90{\deg} field of view, has been extensively studied, there has been limited focus on dedicated methods to omnidirectional or spherical images, captured with a 360{\deg} field of view. In this study, we introduce the first deep learning-based superpixel segmentation approach tailored for omnidirectional images called DSS (for Deep Spherical Superpixels). Our methodology leverages on spherical CNN architectures and the differentiable K-means clustering paradigm for superpixels, to generate superpixels that follow the spherical geometry. Additionally, we propose to use data augmentation techniques specifically designed for 360{\deg} images, enabling our model to efficiently learn from a limited set of annotated omnidirectional data. Our extensive validation across two datasets demonstrates that taking into account the inherent circular geometry of such images into our framework improves the segmentation performance over traditional and deep learning-based superpixel methods. Our code is available online.

Over the past few years, ionic conductors have gained a lot of attention given the possibility to implement them in various applications such as supercapacitors, batteries or fuel cells as well as for resistive memories. Especially, layered two-dimensional (2D) crystals such as h-BN, graphene oxide and MoSe2 have shown to provide unique properties originating from the specific 2D confinement of moving ions. Two important parameters are the ion conductivity and the chemical stability over a wide range of operating conditions. In this vein, Rb2Ti2O5 has been recently found displaying remarkable properties such as superionic conduction and colossal equivalent dielectric constant. Here, a first approach to the study of the electrical properties of layered Rb2Ti2O5 at the 100-nanometer scale is presented. Characterizations by means of micro-Raman spectroscopy and atomic force microscope (AFM) measurements of mechanically exfoliated RTO nanocrystals via the so-called adhesive-tape technique are reported. Finally, the results of electrical measurements performed on an exfoliated RTO nanocrystals are presented, and are found to be consistent with the results obtained on macroscopic crystals. 4, Comment: 4 figures

We propose and study a one-dimensional model which consists of two cross-diffusion systems coupled via a moving interface. The motivation stems from the modelling of complex diffusion processes in the context of the vapor deposition of thin films. In our model, cross-diffusion of the various chemical species can be respectively modelled by a size-exclusion system for the solid phase and the Stefan-Maxwell system for the gaseous phase. The coupling between the two phases is modelled by linear phase transition laws of Butler-Volmer type, resulting in an interface evolution. The continuous properties of the model are investigated, in particular its entropy variational structure and stationary states. We introduce a two-point flux approximation finite volume scheme. The moving interface is addressed with a moving-mesh approach, where the mesh is locally deformed around the interface. The resulting discrete nonlinear system is shown to admit a solution that preserves the main properties of the continuous system, namely: mass conservation, nonnegativity, volume-filling constraints, decay of the free energy and asymptotics. In particular, the moving-mesh approach is compatible with the entropy structure of the continuous model. Numerical results illustrate these properties and the dynamics of the model.

Plasma instabilities are a major concern in plasma science, for applications ranging from particle accelerators to nuclear fusion reactors. In this work, we consider the possibility of controlling such instabilities by adding an external electric field to the Vlasov--Poisson equations. Our approach to determining the external electric field is based on conducting a linear analysis of the resulting equations. We show that it is possible to select external electric fields that completely suppress the plasma instabilities present in the system when the equilibrium distribution and the perturbation are known. In fact, the proposed strategy returns the plasma to its equilibrium with a rate that is faster than exponential in time. We further perform numerical simulations of the nonlinear two-stream and bump-on-tail instabilities to verify our theory and to compare the different strategies that we propose in this work.

In the pursuit of understanding the population of stellar remnants within the Milky Way, we analyze the sample of $\sim 950$ microlensing events observed by the Spitzer Space Telescope between 2014 and 2019. In this study we focus on a sub-sample of nine microlensing events, selected based on their long timescales, small microlensing parallaxes and joint observations by the Gaia mission, to increase the probability that the chosen lenses are massive and the mass is measurable. Among the selected events we identify lensing black holes and neutron star candidates, with potential confirmation through forthcoming release of the Gaia time-series astrometry in 2026. Utilizing Bayesian analysis and Galactic models, along with the Gaia Data Release 3 proper motion data, four good candidates for dark remnants were identified: OGLE-2016-BLG-0293, OGLE-2018-BLG-0483, OGLE-2018-BLG-0662, and OGLE-2015-BLG-0149, with lens masses of $2.98^{+1.75}_{-1.28}~M_{\odot}$, $4.65^{+3.12}_{-2.08}~M_{\odot}$, $3.15^{+0.66}_{-0.64}~M_{\odot}$ and $1.4^{+0.75}_{-0.55}~M_{\odot}$, respectively. Notably, the first two candidates are expected to exhibit astrometric microlensing signals detectable by Gaia, offering the prospect of validating the lens masses. The methodologies developed in this work will be applied to the full Spitzer microlensing sample, populating and analyzing the time-scale ($t_{\rm E}$) vs. parallax ($\pi_{\rm E}$) diagram to derive constraints on the population of lenses in general and massive remnants in particular., Comment: submitted to ApJ

Dynamic link prediction is a critical task in the analysis of evolving networks, with applications ranging from recommender systems to economic exchanges. However, the concept of the temporal receptive field, which refers to the temporal context that models use for making predictions, has been largely overlooked and insufficiently analyzed in existing research. In this study, we present a comprehensive analysis of the temporal receptive field in dynamic graph learning. By examining multiple datasets and models, we formalize the role of temporal receptive field and highlight their crucial influence on predictive accuracy. Our results demonstrate that appropriately chosen temporal receptive field can significantly enhance model performance, while for some models, overly large windows may introduce noise and reduce accuracy. We conduct extensive benchmarking to validate our findings, ensuring that all experiments are fully reproducible. Code is available at https://github.com/ykrmm/BenchmarkTW .

Low discrepancy point sets have been widely used as a tool to approximate continuous objects by discrete ones in numerical processes, for example in numerical integration. Following a century of research on the topic, it is still unclear how low the discrepancy of point sets can go; in other words, how regularly distributed can points be in a given space. Recent insights using optimization and machine learning techniques have led to substantial improvements in the construction of low-discrepancy point sets, resulting in configurations of much lower discrepancy values than previously known. Building on the optimal constructions, we present a simple way to obtain $L_{\infty}$-optimized placement of points that follow the same relative order as an (arbitrary) input set. Applying this approach to point sets in dimensions 2 and 3 for up to 400 and 50 points, respectively, we obtain point sets whose $L_{\infty}$ star discrepancies are up to 25% smaller than those of the current-best sets, and around 50% better than classical constructions such as the Fibonacci set.

Cloud workloads have dominated generative AI based on large language models (LLM). Specialized hardware accelerators, such as GPUs, NPUs, and TPUs, play a key role in AI adoption due to their superior performance over general-purpose CPUs. The AI models and the data are often highly sensitive and come from mutually distrusting parties. Existing CPU-based TEEs such as Intel SGX or AMD SEV do not provide sufficient protection. Device-centric TEEs like Nvidia-CC only address tightly coupled CPU-GPU systems with a proprietary solution requiring TEE on the host CPU side. On the other hand, existing academic proposals are tailored toward specific CPU-TEE platforms. To address this gap, we propose Ascend-CC, a confidential computing architecture based on discrete NPU devices that requires no trust in the host system. Ascend-CC provides strong security by ensuring data and model encryption that protects not only the data but also the model parameters and operator binaries. Ascend-CC uses delegation-based memory semantics to ensure isolation from the host software stack, and task attestation provides strong model integrity guarantees. Our Ascend-CC implementation and evaluation with state-of-the-art LLMs such as Llama2 and Llama3 shows that Ascend-CC introduces minimal overhead with no changes in the AI software stack.

This paper explores the integration of Cybertwin technology within 6G networks to revolutionize healthcare delivery. It aims to enhance real-time monitoring, decision-making, and resource management through the Service-based Hierarchical Framework for Cybertwins in sixth-generation networks. The paper addresses the deployment challenges and proposes system theory as a comprehensive framework for designing complex interactions among healthcare-assigned Cybertwins. The article highlights the role of Cybertwin technology in advancing healthcare solutions, promising improved patient care and operational efficiency. It examines the current network setups and the potential of 6G infrastructure, discussing network topology optimization, theoretical modelling, and future directions. The paper underlines the transformative impact of combining 6G and Cybertwin technologies on healthcare, from high-definition telemedicine to large-scale patient monitoring. The paper further acknowledges the implementation challenges, such as technical complexity, security, and interoperability., Comment: 6 pages

Change point detection (CPD) has proved to be an effective tool for detecting drifts in data and its use over the years has become more pronounced due to the vast amount of data and IoT devices readily available. This study analyzes the effectiveness of Cumulative Sum (CUSUM) and Shewhart Control Charts for identifying the occurrence of abrupt pressure changes for pipe burst localization in Water Distribution Network (WDN). Change point detection algorithms could be useful for identifying the nodes that register the earliest and most drastic pressure changes with the aim of detecting pipe bursts in real-time. TSNet, a Python package, is employed in order to simulate pipe bursts in a WDN. The pressure readings are served to the pipe burst localization algorithm the moment they are available for real-time pie burst localization. The performance of the pipe burst localization algorithm is evaluated using a key metric such as localization accuracy under different settings to compare its performance when paired with either CUSUM or Shewhart. Results show that the pipe burst localization algorithm has an overall better performance when paired with CUSUM. Although, it does show great accuracy for both CPD algorithms when pressure readings are being continuously made available without a big gap between time steps. The proposed approach however still needs further experiments on different WDNs to assess the performance and accuracy of the algorithm on real-world WDN models., Comment: 8 pages

Semiconductor nanowires are believed to play a crucial role for future applications in electronics, spintronics and quantum technologies. A potential candidate is HgTe but its sensitivity to nanofabrication processes restrain its development. A way to circumvent this obstacle is the selective area growth technique. Here, in-plane HgTe nanostructures are grown thanks to selective area molecular beam epitaxy on a semi-insulating CdTe substrate covered with a patterned SiO$_{\mathrm{2}}$ mask. The shape of these nanostructures is defined by the in-plane orientation of the mask aperture along the <$110$>, <$1\bar{\mathrm{1}}0$>, or <$100$> direction, the deposited thickness, and the growth temperature. Several micron long in-plane nanowires can be achieved as well as more complex nanostructures such as networks, diamond structures or rings. A good selectivity is achieved with very little parasitic growth on the mask even for a growth temperature as low as $140${\deg}C and growth rate up to $0.5$ ML/s. For <$110$> oriented nanowires, the center of the nanostructure exhibits a trapezoidal shape with {$111$}B facets and two grains on the sides, while <$1\bar{\mathrm{1}}0$> oriented nanowires show {$111$}A facets with adatoms accumulation on the sides of the top surface. Transmission electron microscopy observations reveal a continuous epitaxial relation between the CdTe substrate and the HgTe nanowire. Measurements of the resistance with fourpoint scanning tunneling microscopy indicates a good electrical homogeneity along the main NW axis and a thermally activated transport. This growth method paves the way toward the fabrication of complex HgTe-based nanostructures for electronic transport measurements., Comment: 18 pages and 8 figures. Submitted to Nanotechnology

Recent advances in atom interferometry have led to the development of quantum inertial sensors with outstanding performance in terms of sensitivity, accuracy, and long-term stability. For ground-based implementations, these sensors are ultimately limited by the free-fall height of atomic fountains required to interrogate the atoms over extended timescales. This limitation can be overcome in Space and in unique ``microgravity'' facilities such as drop towers or free-falling aircraft. These facilities require large investments, long development times, and place stringent constraints on instruments that further limit their widespread use. The available ``up time'' for experiments is also quite low, making extended studies challenging. In this work, we present a new approach in which atom interferometry is performed in a laboratory-scale Einstein Elevator. Our experiment is mounted to a moving platform that mimics the vertical free-fall trajectory every 13.5 seconds. With a total interrogation time of $2T = 200$ ms, we demonstrate an acceleration sensitivity of $6 \times 10^{-7}$ m/s$^{2}$ per shot, limited primarily by the temperature of our atomic samples. We further demonstrate the capability to perform long-term statistical studies by operating the Einstein Elevator over several days with high reproducibility. These represent state-of-the-art results achieved in microgravity and further demonstrates the potential of quantum inertial sensors in Space. Our microgravity platform is both an alternative to large atomic fountains and a versatile facility to prepare future Space missions.

We review some recent development in the theory of spatial extremes related to Pareto Processes and modeling of threshold exceedances. We provide theoretical background, methodology for modeling, simulation and inference as well as an illustration to wave height modelling. This preprint is an author version of a chapter to appear in a collaborative book.

Human culture relies on collective innovation: our ability to continuously explore how existing elements in our environment can be combined to create new ones. Language is hypothesized to play a key role in human culture, driving individual cognitive capacities and shaping communication. Yet the majority of models of collective innovation assign no cognitive capacities or language abilities to agents. Here, we contribute a computational study of collective innovation where agents are Large Language Models (LLMs) that play Little Alchemy 2, a creative video game originally developed for humans that, as we argue, captures useful aspects of innovation landscapes not present in previous test-beds. We, first, study an LLM in isolation and discover that it exhibits both useful skills and crucial limitations. We, then, study groups of LLMs that share information related to their behaviour and focus on the effect of social connectivity on collective performance. In agreement with previous human and computational studies, we observe that groups with dynamic connectivity out-compete fully-connected groups. Our work reveals opportunities and challenges for future studies of collective innovation that are becoming increasingly relevant as Generative Artificial Intelligence algorithms and humans innovate alongside each other.

We study the growth rate of weak coloring numbers of graphs excluding a fixed graph as a minor. Van den Heuvel et al. (European J. of Combinatorics, 2017) showed that for a fixed graph $X$, the maximum $r$-th weak coloring number of $X$-minor-free graphs is polynomial in $r$. We determine this polynomial up to a factor of $\mathcal{O}(r \log r)$. Moreover, we tie the exponent of the polynomial to a structural property of $X$, namely, $2$-treedepth. As a result, for a fixed graph $X$ and an $X$-minor-free graph $G$, we show that $\mathrm{wcol}_r(G)= \mathcal{O}(r^{\mathrm{td}(X)-1}\mathrm{log}\ r)$, which improves on the bound $\mathrm{wcol}_r(G) = \mathcal{O}(r^{g(\mathrm{td}(X))})$ given by Dujmovi\'c et al. (SODA, 2024), where $g$ is an exponential function. In the case of planar graphs of bounded treewidth, we show that the maximum $r$-th weak coloring number is in $\mathcal{O}(r^2\mathrm{log}\ r$), which is best possible., Comment: 52 pages, 17 figures

As speech processing systems in mobile and edge devices become more commonplace, the demand for unintrusive speech quality monitoring increases. Deep learning methods provide high-quality estimates of objective and subjective speech quality metrics. However, their significant computational requirements are often prohibitive on resource-constrained devices. To address this issue, we investigated binary activation maps (BAMs) for speech quality prediction on a convolutional architecture based on DNSMOS. We show that the binary activation model with quantization aware training matches the predictive performance of the baseline model. It further allows using other compression techniques. Combined with 8-bit weight quantization, our approach results in a 25-fold memory reduction during inference, while replacing almost all dot products with summations. Our findings show a path toward substantial resource savings by supporting mixed-precision binary multiplication in hard- and software., Comment: Accepted for Interspeech 2024

As large language models (LLMs) start interacting with each other and generating an increasing amount of text online, it becomes crucial to better understand how information is transformed as it passes from one LLM to the next. While significant research has examined individual LLM behaviors, existing studies have largely overlooked the collective behaviors and information distortions arising from iterated LLM interactions. Small biases, negligible at the single output level, risk being amplified in iterated interactions, potentially leading the content to evolve towards attractor states. In a series of telephone game experiments, we apply a transmission chain design borrowed from the human cultural evolution literature: LLM agents iteratively receive, produce, and transmit texts from the previous to the next agent in the chain. By tracking the evolution of text toxicity, positivity, difficulty, and length across transmission chains, we uncover the existence of biases and attractors, and study their dependence on the initial text, the instructions, language model, and model size. For instance, we find that more open-ended instructions lead to stronger attraction effects compared to more constrained tasks. We also find that different text properties display different sensitivity to attraction effects, with toxicity leading to stronger attractors than length. These findings highlight the importance of accounting for multi-step transmission dynamics and represent a first step towards a more comprehensive understanding of LLM cultural dynamics., Comment: Code available at https://github.com/jeremyperez2/TelephoneGameLLM. Companion website with a Data Explorer tool at https://sites.google.com/view/telephone-game-llm

This paper focuses on robust stability and $H_\infty$ performance analyses of hybrid continuous/discrete time linear multi-rate control systems in the presence of parametric uncertainties. These affect the continuous-time plant in a rational way which is then modeled as a Linear Fractional Transformation (LFT). Based on a zero-order-hold (ZOH) LFT discretization process at the cost of bounded quantifiable approximations, and then using LFT-preserving down-sampling operations, a single-rate discrete-time closed-loop LFT model is derived. Interestingly, for any step inputs, and any admissible values of the uncertain parameters, the outputs of this model cover those of the initial hybrid multi-rate closed-loop system at every sampling time of the slowest control loop. Such an LFT model, which also captures the discretization errors, can then be used to evaluate both robust stability and guaranteed $H_\infty$ performance with a $\mu$-based approach. The proposed methodology is illustrated on a realistic and easily reproducible example inspired by the validation of multi-rate attitude control systems., Comment: 7 pages, 8 figures

Circular Rydberg atoms (CRAs), i.e., Rydberg atoms with maximal orbital momentum, ideally combine long coherence times and strong interactions, a key property of quantum systems, in particular for the development of quantum technologies. However, the dipole-dipole interaction between CRAs has not been observed so far. We report the measurement and characterization of the resonant dipole-dipole interaction between two CRAs, individually trapped in optical tweezers, and find excellent agreement with theoretical predictions. We demonstrate a dynamic control over the strength of the interaction by tuning the orientation of an electric field. We use the interaction between the CRAs as a meter for the interatomic distance, and record the relative motion between two atoms in their traps. This motion, that we induce through the interaction between Rydberg levels with permanent electric dipoles, transiently populated during the preparation of the circular states, is a signature of spin-motion coupling.

The aim of this research work is to analyse the large scale response of a vehicle wake to on-road perturbations by using an instrumented vehicle and a combination of scale one wind tunnel tests, track trials and on road experiments. More precisely, in all these tests, we focus on the analysis of the asymmetry of the pressure distribution at the base. Proper Orthogonal Decomposition (POD) is used. For all cases considered, POD analysis reveals two dominant modes, respectively associated with vertical and horizontal wake large scale reorganisation. More than 50\% of the total energy is carried by these two modes and this value increases significantly for on-road tests. Noteworthy, the low-frequency energy content of the temporal coefficients of these modes is significantly higher on-road. Low frequencies (even very low ones) then play a major role, corresponding to a quasi-static perturbation domain of the velocity seen by the vehicle. We show that a quasi-steady exploration of the on-road yaw angle statistical distribution during a wind tunnel test captures phenomena similar to those observed on the road and is therefore interesting to evaluate on-road aerodynamic performances. This also opens perspectives for developing closed loop control strategies aiming to maintain a prescribed wake balance in order to reduce drag experienced on the road.