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This article addresses the overlooked but crucial role of the Haar measure in solid mechanics, a concept well-established in mathematical literature but frequently misunderstood by mechanicians. The aim is to provide practical insights and methodologies for the application of the Haar measure in various mechanical scenarios. The article begins with an introduction to the Haar measure, underlying its significance and a theoretical foundation for its definition and formulas. Moving beyond mathematical abstraction, the core of the article relies on practical applications, including the computation of the Haar measure for the orthogonal transformations of the 2D and 3D space under several commonly used parametrisations, an application about invariant theory in mechanics and sampling on a group orbit. These applications are presented with practical examples, enabling mechanicians to integrate the Haar measure into their research seamlessly. The article caters to a broad audience, with sections designed for both introductory comprehension and in-depth exploration. Our goal is to equip mechanicians with a valuable tool, bridging the gap between mathematical theory and practical applications in the field of mechanics.

We propose a fully discrete finite volume scheme for the standard Fokker-Planck equation. The space discretization relies on the well-known square-root approximation, which falls into the framework of two-point flux approximations. Our time discretization is novel and relies on a tailored nonlinear mid-point rule, designed to accurately capture the dissipative structure of the model. We establish well-posedness for the scheme, positivity of the solutions, as well as a fully discrete energy-dissipation inequality mimicking the continuous one. We then prove the rigorous convergence of the scheme under mildly restrictive conditions on the unstructured grids, which can be easily satisfied in practice. Numerical simulations show that our scheme is second order accurate both in time and space, and that one can solve the discrete nonlinear systems arising at each time step using Newton's method with low computational cost.

To obtain the highest confidence on the correction of numerical simulation programs for the resolution of Partial Differential Equations (PDEs), one has to formalize the mathematical notions and results that allow to establish the soundness of the approach. The finite element method is one of the popular tools for the numerical resolution of a wide range of PDEs. The purpose of this document is to provide the formal proof community with very detailed pen-and-paper proofs for the construction of the Lagrange finite elements of any degree on simplices in positive dimension.

Spin-based semiconductor qubits hold promise for scalable quantum computing, yet they require reliable autonomous calibration procedures. This study presents an experimental demonstration of online single-dot charge autotuning using a convolutional neural network integrated into a closed-loop calibration system. The autotuning algorithm explores the gates' voltage space to localize charge transition lines, thereby isolating the one-electron regime without human intervention. In 20 experimental runs on a device cooled to 25mK, the method achieved a success rate of 95% in locating the target electron regime, highlighting the robustness of this method against noise and distribution shifts from the offline training set. Each tuning run lasted an average of 2 hours and 9 minutes, primarily due to the limited speed of the current measurement. This work validates the feasibility of machine learning-driven real-time charge autotuning for quantum dot devices, advancing the development toward the control of large qubit arrays., Comment: 6 pages (main) + 5 pages (supplementary)

Ultra-short-period exoplanets like K2-141 b likely have magma oceans on their dayside, which play a critical role in redistributing heat within the planet. This could lead to a warm nightside surface, measurable by the James Webb Space Telescope, offering insights into the planet's structure. Accurate models of properties like viscosity, which can vary by orders of magnitude, are essential for such studies. We present a new model for predicting molten magma viscosity, applicable in diverse scenarios, including magma oceans on lava planets. Using a database of 28,898 viscosity measurements on phospho-alumino-silicate melts, spanning superliquidus to undercooled temperatures and pressures up to 30 GPa, we trained a greybox artificial neural network, refined by a Gaussian process. This model achieves high predictive accuracy (RMSE $\approx 0.4 \log_{10}$ Pa$\cdot$s) and can handle compositions from SiO$_2$ to multicomponent magmatic and industrial glasses, accounting for pressure effects up to 30 GPa for compositions such as peridotite. Applying this model, we calculated the viscosity of K2-141 b's magma ocean under different compositions. Phase diagram calculations suggest that the dayside is fully molten, with extreme temperatures primarily controlling viscosity. A tenuous atmosphere (0.1 bar) might exist around a 40{\deg} radius from the substellar point. At higher longitudes, atmospheric pressure drops, and by 90{\deg}, magma viscosity rapidly increases as solidification occurs. The nightside surface is likely solid, but previously estimated surface temperatures above 400 K imply a partly molten mantle, feeding geothermal flux through vertical convection., Comment: 21 pages, 9 figures, 2 tables

We introduce the notion of a weak A2 space (or wA2-space), which generalises spaces satisfying Todor\v{c}evi\'c's axioms A1-A4 and countable vector spaces. We show that in any Polish weak A2 space, analytic sets are Kastanas Ramsey, and discuss the relationship between Kastanas Ramsey sets and sets in the projective hierarchy. We also show that in all spaces satisfying A1-A4, every subset of $\cal{R}$ is Kastanas Ramsey iff Ramsey, generalising the recent result by Cano and Di Prisco. Finally, we show that in the setting of Gowers wA2-spaces, Kastanas Ramsey sets and strategically Ramsey sets coincide, providing a connection between the recent studies on topological Ramsey spaces and countable vector spaces., Comment: 26 pages

We use a double-duality argument to give a new proof of Dieudonn\'e's theorem on spaces of singular matrices. The argument connects the situation to the structure of spaces of operators with rank at most $1$, and works best over algebraically closed fields., Comment: 12 pages

Quantum trajectories are Markov processes describing the evolution of a quantum system subject to indirect measurements. They can be viewed as place dependent iterated function systems or the result of products of dependent and non identically distributed random matrices. In this article, we establish a complete classification of their invariant measures. The classification is done in two steps. First, we prove a Markov process on some linear subspaces called dark subspaces, defined in (Maassen, K\"ummerer 2006), admits a unique invariant measure. Second, we study the process inside the dark subspaces. Using a notion of minimal family of isometries from a reference space to dark subspaces, we prove a set of measures indexed by orbits of a unitary group is the set of ergodic measures of quantum trajectories., Comment: 54 pages, 6 figures

Despite a significant proportion of the Earth being covered in water, exploration of what lies below has been limited due to the challenges and difficulties inherent in the process. Current state of the art robots such as Remotely Operated Vehicles (ROVs) and Autonomous Underwater Vehicles (AUVs) are bulky, rigid and unable to conform to their environment. Soft robotics offers solutions to this issue. Fluid-actuated eversion or growing robots, in particular, are a good example. While current eversion robots have found many applications on land, their inherent properties make them particularly well suited to underwater environments. An important factor when considering underwater eversion robots is the establishment of a suitable steering mechanism that can enable the robot to change direction as required. This project proposes a design for an eversion robot that is capable of steering while underwater, through the use of bending pouches, a design commonly seen in the literature on land-based eversion robots. These bending pouches contract to enable directional change. Similar to their land-based counterparts, the underwater eversion robot uses the same fluid in the medium it operates in to achieve extension and bending but also to additionally aid in neutral buoyancy. The actuation method of bending pouches meant that robots needed to fully extend before steering was possible. Three robots, with the same design and dimensions were constructed from polyethylene tubes and tested. Our research shows that although the soft eversion robot design in this paper was not capable of consistently generating the same amounts of bending for the inflation volume, it still achieved suitable bending at a range of inflation volumes and was observed to bend to a maximum angle of 68 degrees at 2000 ml, which is in line with the bending angles reported for land-based eversion robots in the literature.

The performance of software systems remains a persistent concern in the field of software engineering. While traditional metrics like binary size and execution time have long been focal points for developers, the power consumption concern has gained significant attention, adding a layer of complexity to performance evaluation. Configurable software systems, with their potential for numerous configurations, further complicate this evaluation process. In this experience paper, we examine the impact of configurations on performance, specifically focusing on the web stack generator JHipster. Our goal is to understand how configuration choices within JHipster influence the performance of the generated system. We undertake an exhaustive analysis of JHipster by examining its configurations and their effects on system performance. Additionally, we explore individual configuration options to gauge their specific influence on performance. Through this process, we develop a comprehensive performance model for JHipster, enabling us to automate the identification of configurations that optimize specific performance metrics. In particular, we identify configurations that demonstrate near-optimal performance across multiple indicators and report on significant correlations between configuration choices within JHipster and the performance of generated systems.

The BICEP3 and BICEP Array polarimeters are small-aperture refracting telescopes located at the South Pole designed to measure primordial gravitational wave signatures in the Cosmic Microwave Background (CMB) polarization, predicted by inflation. Constraining the inflationary signal requires not only excellent sensitivity, but also careful control of instrumental systematics. Both instruments use antenna-coupled orthogonally polarized detector pairs, and the polarized sky signal is reconstructed by taking the difference in each detector pair. As a result, the differential response between detectors within a pair becomes an important systematic effect we must control. Additionally, mapping the intensity and polarization response in regions away from the main beam can inform how sidelobe levels affect CMB measurements. Extensive calibration measurements are taken in situ every austral summer for control of instrumental systematics and instrument characterisation. In this work, we detail the set of beam calibration measurements that we conduct on the BICEP receivers, from deep measurements of main beam response to polarized beam response and sidelobe mapping. We discuss the impact of these measurements for instrumental systematics studies and design choices for future CMB receivers., Comment: 13 pages, 7 figures, 1 table, Proceedings paper SPIE 2024

Memory disaggregation is an emerging data center architecture that improves resource utilization and scalability. Replication is key to ensure the fault tolerance of applications, but replicating shared data in disaggregated memory is hard. We propose SWARM (Swift WAit-free Replication in disaggregated Memory), the first replication scheme for in-disaggregated-memory shared objects to provide (1) single-roundtrip reads and writes in the common case, (2) strong consistency (linearizability), and (3) strong liveness (wait-freedom). SWARM makes two independent contributions. The first is Safe-Guess, a novel wait-free replication protocol with single-roundtrip operations. The second is In-n-Out, a novel technique to provide conditional atomic update and atomic retrieval of large buffers in disaggregated memory in one roundtrip. Using SWARM, we build SWARM-KV, a low-latency, strongly consistent and highly available disaggregated key-value store. We evaluate SWARM-KV and find that it has marginal latency overhead compared to an unreplicated key-value store, and that it offers much lower latency and better availability than FUSEE, a state-of-the-art replicated disaggregated key-value store., Comment: To appear in the proceedings of SOSP '24

We discuss in this article the creation and organization of a national core facility for bioimage analysis, based on a distributed team. F-BIAS federates bioimage analysts across France and relies on them to deliver services to the researchers of this territory. The main challenge in implementing this structure is to provide significant scientific value to its members, thereby encouraging their active participation and persuading their respective host teams to support their involvement. F-BIAS accomplished this by creating a professional network that mitigates the negative effects of isolation experienced by its members, who are often the sole bioimage analyst within their local teams, and fosters the development of their technical skills. In a second phase we capitalized on F-BIAS to create a virtual, remotely-operating core facility for bioimage analysis, offering consultations and collaborative project services to the scientific community of France. The services are organized so that they also contribute to the technical proficiency of the analysts. To promote the creation of similar structures, we present and discuss here the organization of this nationally distributed bioimage analysis service core, highlighting successes and challenges.

Large Language Models (LLMs) have demonstrated significant potential in transforming clinical applications. In this study, we investigate the efficacy of four techniques in adapting LLMs for clinical use-cases: continuous pretraining, instruct fine-tuning, NEFTune, and prompt engineering. We employ these methods on Mistral 7B and Mixtral 8x7B models, leveraging a large-scale clinical pretraining dataset of 50 billion tokens and an instruct fine-tuning dataset of 500 million tokens. Our evaluation across various clinical tasks reveals the impact of each technique. While continuous pretraining beyond 250 billion tokens yields marginal improvements on its own, it establishes a strong foundation for instruct fine-tuning. Notably, NEFTune, designed primarily to enhance generation quality, surprisingly demonstrates additional gains on our benchmark. Complex prompt engineering methods further enhance performance. These findings show the importance of tailoring fine-tuning strategies and exploring innovative techniques to optimize LLM performance in the clinical domain.

The in-vivo identification of the kidney stone types during an ureteroscopy would be a major medical advance in urology, as it could reduce the time of the tedious renal calculi extraction process, while diminishing infection risks. Furthermore, such an automated procedure would make possible to prescribe anti-recurrence treatments immediately. Nowadays, only few experienced urologists are able to recognize the kidney stone types in the images of the videos displayed on a screen during the endoscopy. Thus, several deep learning (DL) models have recently been proposed to automatically recognize the kidney stone types using ureteroscopic images. However, these DL models are of black box nature whicl limits their applicability in clinical settings. This contribution proposes a case-based reasoning DL model which uses prototypical parts (PPs) and generates local and global descriptors. The PPs encode for each class (i.e., kidney stone type) visual feature information (hue, saturation, intensity and textures) similar to that used by biologists. The PPs are optimally generated due a new loss function used during the model training. Moreover, the local and global descriptors of PPs allow to explain the decisions ("what" information, "where in the images") in an understandable way for biologists and urologists. The proposed DL model has been tested on a database including images of the six most widespread kidney stone types. The overall average classification accuracy was 90.37. When comparing this results with that of the eight other DL models of the kidney stone state-of-the-art, it can be seen that the valuable gain in explanability was not reached at the expense of accuracy which was even slightly increased with respect to that (88.2) of the best method of the literature. These promising and interpretable results also encourage urologists to put their trust in AI-based solutions., Comment: Paper submitted to Artificial Intelligence in Medicine. (AIIM), Elsevier

The study of interactions between biomimetic membranes and micron-sized particles serves as a fundamental cornerstone for understanding various biological processes. Here, we aim at controlling the engulfment of microparticles in absence of any externally applied force. We investigate systems involving particles and lipid vesicles, where interactions are primarily mediated by electrostatics. Our attention is directed towards dissipative phenomena at the micron and nano scales occurring between a vesicle membrane and a particle, highlighting the influence of the particle penetration depth and the impact of substructures on particle friction. Our work is complemented by hydrodynamic simulations considering the impacts of the shape of the membrane wrapping the particle and the water gap separating the lipid bilayer membrane and the particle on the translational particle drag. Lastly, we explore a system of aqueous microdroplets in oil, stabilized by a single lipid layer and compare the friction experienced by a particle in contact with this monolayer to the friction observed for particles wrapped by lipid bilayers. Surprisingly, the particle drag is higher on the lipid monolayer than the bilayer, which can be explained by introducing a drag contribution due to the partial wetting dynamics of the particle at the monolayer interface.

The realisation of an universal quantum computer will require the operation of thousands to millions of qubits. The possibility of using existing industrial semiconductor fabrication techniques and infrastructure for up-scaling and reproducibility makes silicon based spin qubits one of the most promising platforms to achieve this goal. The implementation of the up to now largest semiconductor based quantum processor was realized in a silicon/silicon-germanium heterostructure known for its low charge noise, long qubit coherence times and fast driving speeds, but the high structural complexity creates challenges for industrial implementations. Here we demonstrate quantum dots hosted in a natural Si/SiGe heterostructure fully fabricated by an industrial 300$\,$mm semiconductor wafer process line from heterostructure growth to Co micromagnet monolithic integration. We report charge noise values below 2$\,\mathrm{\mu eV/\sqrt{Hz}}$, spin relaxation times of over 1$\,$s and coherence times $T_2^*$ and $T_2^H$ of 1$\,\mathrm{\mu s}$ and 50$\,\mathrm{\mu s}$ respectively, for quantum wells grown using natural silicon. Further, we achieve Rabi frequencies up to 5$\,$MHz and single qubit gate fidelities above 99$\,\%$. In addition to scalability, the high reproducibility of the 300$\,$mm processes enables the deterministic study of qubit metric dependencies on process parameters, which is essential for optimising qubit quality.

We present the first measurement of the $^{47}$K($d,p\gamma$)$^{48}$K transfer reaction, performed in inverse kinematics using a reaccelerated beam of $^{47}$K. The level scheme of $^{48}$K has been greatly extended with nine new bound excited states identified and spectroscopic factors deduced. Detailed comparisons with SDPF-U and SDPF-MU shell-model calculations reveal a number of discrepancies with these results, and a preference for SDPF-MU is found. Intriguingly, an apparent systematic overestimation of spectroscopic factors and a poor reproduction of the energies for 1$^-$ states suggests that the mixing between the $\pi s^{\,\,\,1}_{1/2} d^{\,\,\,4}_{3/2}$ and $\pi s^{\,\,\,2}_{1/2} d^{\,\,\,3}_{3/2}$ proton configurations in $^{48}$K is not correctly described using current interactions, challenging our descriptions of light $N=28$ nuclei., Comment: 7 pages, 3 figures, 1 table

A generative diffusion model is used to produce probabilistic ensembles of precipitation intensity maps at the 1-hour 5-km resolution. The generation is conditioned on infrared and microwave radiometric measurements from the GOES and DMSP satellites and is trained with merged ground radar and gauge data over southeastern United States. The generated precipitation maps reproduce the spatial autocovariance and other multiscale statistical properties of the gauge-radar reference fields on average. Conditioning the generation on the satellite measurements allows us to constrain the magnitude and location of each generated precipitation feature. The mean of the 128- member ensemble shows high spatial coherence with the reference fields with 0.82 linear correlation between the two. On average, the coherence between any two ensemble members is approximately the same as the coherence between any ensemble member and the ground reference, attesting that the ensemble dispersion is a proper measure of the estimation uncertainty. From the generated ensembles we can easily derive the probability of the precipitation intensity exceeding any given intensity threshold, at the 5-km resolution of the generation or at any desired aggregated resolution.

The recent development of Physics-Augmented Neural Networks (PANN) opens new opportunities for modeling material behaviors. These approaches have demonstrated their efficiency when trained on synthetic cases. This study aims to demonstrate the effectiveness of training PANN using real experimental data for modeling hyperelastic behavior. The approach involved two uni-axial experiments equipped with digital image correlation and force sensors. The tests achieved axial deformations exceeding 200% and presented non-linear responses. Twenty loading steps extracted from one experiment were used to train the PANN. The model architecture was optimized based on results from a validation dataset, utilizing equilibrium gap loss computed on six loading steps. Finally, 544 loading steps from the first experiment and 80 steps from a second independent experiment were used for testing purposes. The PANN model effectively captured the hyperelastic behavior across and beyond the training loads, showing superior performance compared to the standard Neo-Hookean model when assessed using various evaluation metrics. Training PANN with experimental mechanical data shows promising results, outperforming traditional modeling approaches., Comment: Experimental Mechanics, 2024

This work presents a novel rehabilitation framework designed for a therapist, wearing an inertial measurement unit (IMU) suit, to virtually interact with a lower-limb exoskeleton worn by a patient with motor impairments. This framework aims to harmonize the skills and knowledge of the therapist with the capabilities of the exoskeleton. The therapist can guide the patient's movements by moving their own joints and making real-time adjustments to meet the patient's needs, while reducing the physical effort of the therapist. This eliminates the need for a predefined trajectory for the patient to follow, as in conventional robotic gait training. For the virtual interaction medium between the therapist and patient, we propose an impedance profile that is stiff at low frequencies and less stiff at high frequencies, that can be tailored to individual patient needs and different stages of rehabilitation. The desired interaction torque from this medium is commanded to a whole-exoskeleton closed-loop compensation controller. The proposed virtual interaction framework was evaluated with a pair of unimpaired individuals in different teacher-student gait training exercises. Results show the proposed interaction control effectively transmits haptic cues, informing future applications in rehabilitation scenarios.

Ultrasound imaging, despite its widespread use in medicine, often suffers from various sources of noise and artifacts that impact the signal-to-noise ratio and overall image quality. Enhancing ultrasound images requires a delicate balance between contrast, resolution, and speckle preservation. This paper introduces a novel approach that integrates adaptive beamforming with denoising diffusion-based variance imaging to address this challenge. By applying Eigenspace-Based Minimum Variance (EBMV) beamforming and employing a denoising diffusion model fine-tuned on ultrasound data, our method computes the variance across multiple diffusion-denoised samples to produce high-quality despeckled images. This approach leverages both the inherent multiplicative noise of ultrasound and the stochastic nature of diffusion models. Experimental results on a publicly available dataset demonstrate the effectiveness of our method in achieving superior image reconstructions from single plane-wave acquisitions. The code is available at: https://github.com/Yuxin-Zhang-Jasmine/IUS2024_Diffusion., Comment: Accepted by the IEEE International Ultrasonics Symposium (IUS) 2024

Fueled by the ever-increasing need for statistics that guarantee the privacy of their training sets, this article studies the centrally-private estimation of Sobolev-smooth densities of probability over the hypercube in dimension d. The contributions of this article are two-fold : Firstly, it generalizes the one dimensional results of (Lalanne et al., 2023) to non-integer levels of smoothness and to a high-dimensional setting, which is important for two reasons : it is more suited for modern learning tasks, and it allows understanding the relations between privacy, dimensionality and smoothness, which is a central question with differential privacy. Secondly, this article presents a private strategy of estimation that is data-driven (usually referred to as adaptive in Statistics) in order to privately choose an estimator that achieves a good bias-variance trade-off among a finite family of private projection estimators without prior knowledge of the ground-truth smoothness $\beta$. This is achieved by adapting the Lepskii method for private selection, by adding a new penalization term that makes the estimation privacy-aware.

Community-based fact-checking is a promising approach to verify social media content and correct misleading posts at scale. Yet, causal evidence regarding its effectiveness in reducing the spread of misinformation on social media is missing. Here, we performed a large-scale empirical study to analyze whether community notes reduce the spread of misleading posts on X. Using a Difference-in-Differences design and repost time series data for N=237,677 (community fact-checked) cascades that had been reposted more than 431 million times, we found that exposing users to community notes reduced the spread of misleading posts by, on average, 62.0%. Furthermore, community notes increased the odds that users delete their misleading posts by 103.4%. However, our findings also suggest that community notes might be too slow to intervene in the early (and most viral) stage of the diffusion. Our work offers important implications to enhance the effectiveness of community-based fact-checking approaches on social media.

The rapid development of Large Language Models (LLMs) for healthcare applications has spurred calls for holistic evaluation beyond frequently-cited benchmarks like USMLE, to better reflect real-world performance. While real-world assessments are valuable indicators of utility, they often lag behind the pace of LLM evolution, likely rendering findings obsolete upon deployment. This temporal disconnect necessitates a comprehensive upfront evaluation that can guide model selection for specific clinical applications. We introduce MEDIC, a framework assessing LLMs across five critical dimensions of clinical competence: medical reasoning, ethics and bias, data and language understanding, in-context learning, and clinical safety. MEDIC features a novel cross-examination framework quantifying LLM performance across areas like coverage and hallucination detection, without requiring reference outputs. We apply MEDIC to evaluate LLMs on medical question-answering, safety, summarization, note generation, and other tasks. Our results show performance disparities across model sizes, baseline vs medically finetuned models, and have implications on model selection for applications requiring specific model strengths, such as low hallucination or lower cost of inference. MEDIC's multifaceted evaluation reveals these performance trade-offs, bridging the gap between theoretical capabilities and practical implementation in healthcare settings, ensuring that the most promising models are identified and adapted for diverse healthcare applications., Comment: Technical report

Data-driven methods demonstrate considerable potential for accelerating the inherently expensive computational fluid dynamics (CFD) solvers. Nevertheless, pure machine-learning surrogate models face challenges in ensuring physical consistency and scaling up to address real-world problems. This study presents a versatile and scalable hybrid methodology, combining CFD and machine learning, to accelerate long-term incompressible fluid flow simulations without compromising accuracy. A neural network was trained offline using simulated data of various two-dimensional transient buoyant plume flows. The objective was to leverage local features to predict the temporal changes in the pressure field in comparable scenarios. Due to cell-level predictions, the methodology was successfully applied to diverse geometries without additional training. Pressure estimates were employed as initial values to accelerate the pressure-velocity coupling procedure. The results demonstrated an average improvement of 94% in the initial guess for solving the Poisson equation. The first pressure corrector acceleration reached a mean factor of 3, depending on the iterative solver employed. Our work reveals that machine learning estimates at the cell level can enhance the efficiency of CFD iterative linear solvers while maintaining accuracy. Although the scalability of the methodology to more complex cases has yet to be demonstrated, this study underscores the prospective value of domain-specific hybrid solvers for CFD., Comment: Twelfth International Conference on Computational Fluid Dynamics (ICCFD12), Kobe, Japan, July 14-19, 2024. 18 pages, 8 figures

A seminal result of Koml\'os, S\'ark\"ozy, and Szemer\'edi states that any n-vertex graph G with minimum degree at least (1/2 + {\alpha})n contains every n-vertex tree T of bounded degree. Recently, Pham, Sah, Sawhney, and Simkin extended this result to show that such graphs G in fact support an optimally spread distribution on copies of a given T, which implies, using the recent breakthroughs on the Kahn-Kalai conjecture, the robustness result that T is a subgraph of sparse random subgraphs of G as well. Pham, Sah, Sawhney, and Simkin construct their optimally spread distribution by following closely the original proof of the Koml\'os-S\'ark\"ozy-Szemer\'edi theorem which uses the blow-up lemma and the Szemer\'edi regularity lemma. We give an alternative, regularity-free construction that instead uses the Koml\'os-S\'ark\"ozy-Szemer\'edi theorem (which has a regularity-free proof due to Kathapurkar and Montgomery) as a black-box. Our proof is based on the simple and general insight that, if G has linear minimum degree, almost all constant sized subgraphs of G inherit the same minimum degree condition that G has., Comment: 12 pages

We compute the AdS Virasoro-Shapiro amplitude in the Regge limit, in terms of the CFT data of the exchanged operators in the leading Regge trajectory. To any order in the small curvature expansion, the result can be written in terms of derivatives of the flat space Virasoro-Shapiro amplitude in the Regge limit. The result also admits an integral representation involving single-valued logarithms, fully consistent with recent proposals for the full AdS Virasoro-Shapiro amplitude., Comment: 20 pages, 1 figure

Uranus and Neptune have atmospheres dominated by molecular hydrogen and helium. In the upper troposphere, methane is the third main molecule and condenses, yielding a vertical gradient in CH4. This condensable species being heavier than H2 and He, the resulting change in mean molecular weight due to condensation comes as a factor countering dry and moist convection. As observations also show latitudinal variations in methane abundance, one can expect different vertical gradients from one latitude to another. In this paper, we investigate the impact of this methane vertical gradient on the atmospheric regimes, especially on the formation and inhibition of moist convective storms in the troposphere of ice giants. We develop a 3D cloud-resolving model to simulate convective processes. Using our simulations, we conclude that typical velocities of dry convection in the deep atmosphere are rather low (of the order of 1 m/s) but sufficient to sustain upward methane transport, and that moist convection at methane condensation level is strongly inhibited. Previous studies derived an analytical criterion on the methane vapor amount above which moist convection should be inhibited. We first validate this analytical criterion numerically. We then show that the critical methane abundance governs the inhibition and formation of moist convective storms, and we conclude that the intensity and intermittency of these storms should depend on the methane abundance and saturation. In ice giants, dry convection is weak, and moist convection is strongly inhibited. However, when enough methane is transported upwards, through dry convection and turbulent diffusion, sporadic moist convective storms can form. These storms should be more frequent on Neptune than on Uranus, because of Neptune's internal heat flow. Our results can explain the observed sporadicity of clouds in ice giants.

Optimizing risk-averse objectives in discounted MDPs is challenging because most models do not admit direct dynamic programming equations and require complex history-dependent policies. In this paper, we show that the risk-averse {\em total reward criterion}, under the Entropic Risk Measure (ERM) and Entropic Value at Risk (EVaR) risk measures, can be optimized by a stationary policy, making it simple to analyze, interpret, and deploy. We propose exponential value iteration, policy iteration, and linear programming to compute optimal policies. In comparison with prior work, our results only require the relatively mild condition of transient MDPs and allow for {\em both} positive and negative rewards. Our results indicate that the total reward criterion may be preferable to the discounted criterion in a broad range of risk-averse reinforcement learning domains.

Deep Learning algorithms, such as those used in Reinforcement Learning, often require large quantities of data to train effectively. In most cases, the availability of data is not a significant issue. However, for some contexts, such as in autonomous cyber defence, we require data efficient methods. Recently, Quantum Machine Learning and Boltzmann Machines have been proposed as solutions to this challenge. In this work we build upon the pre-existing work to extend the use of Deep Boltzmann Machines to the cutting edge algorithm Proximal Policy Optimisation in a Reinforcement Learning cyber defence environment. We show that this approach, when solved using a D-WAVE quantum annealer, can lead to a two-fold increase in data efficiency. We therefore expect it to be used by the machine learning and quantum communities who are hoping to capitalise on data-efficient Reinforcement Learning methods.

In the strategic multi-armed bandit setting, when arms possess perfect information about the player's behavior, they can establish an equilibrium where: 1. they retain almost all of their value, 2. they leave the player with a substantial (linear) regret. This study illustrates that, even if complete information is not publicly available to all arms but is shared among them, it is possible to achieve a similar equilibrium. The primary challenge lies in designing a communication protocol that incentivizes the arms to communicate truthfully.

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 850-1020 K, consistent with one previous hypothesis that only used NIR data. The radius measurement of 1.0-1.13 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: Accepted for publication 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.