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Convection in planets and stars is predicted to occur in the "ultimate regime'' of diffusivity-free, rapidly rotating turbulence, in which flows are characteristically unaffected by viscous and thermal diffusion. Boundary layer diffusion, however, has historically hindered experimental study of this regime. Here, we utilize the boundary-independent oscillatory thermal-inertial mode of rotating convection to realize the diffusivity-free scaling in liquid metal laboratory experiments. This oscillatory style of convection arises in rotating liquid metals (low Prandtl number fluids) and is driven by the temperature gradient in the fluid bulk, thus remaining independent of diffusive boundary dynamics. We triply verify the existence of the diffusivity-free regime via measurements of heat transfer efficiency $Nu$, dimensionless flow velocities $Re$, and internal temperature anomalies $\theta$, all of which are in quantitative agreement with planar asymptotically-reduced models. Achieving the theoretical diffusivity-free scalings in desktop-sized laboratory experiments provides the validation necessary to extrapolate and predict the convective flows in remote geophysical and astrophysical systems.

Longitudinal analyses are increasingly used in clinical studies as they allow the study of subtle changes over time within the same subjects. In most of these studies, it is necessary to align all the images studied to a common reference by registering them to a template. In the study of white matter using the recently developed fixel-based analysis (FBA) method, this registration is important, in particular because the fiber bundle cross-section metric is a direct measure of this registration. In the vast majority of longitudinal FBA studies described in the literature, sessions acquired for a same subject are directly independently registered to the template. However, it has been shown in T1-based morphometry that a 2-step registration through an intra-subject average can be advantageous in longitudinal analyses. In this work, we propose an implementation of this 2-step registration method in a typical longitudinal FBA aimed at investigating the evolution of white matter changes in Alzheimer's disease (AD). We compared at the fixel level the mean absolute effect and standard deviation yielded by this registration method and by a direct registration, as well as the results obtained with each registration method for the study of AD in both fixelwise and tract-based analyses. We found that the 2-step method reduced the variability of the measurements and thus enhanced statistical power in both types of analyses., Comment: 4 pages, 6 figures, submitted to IEEE International Symposium on Biomedical Imaging (ISBI 2025)

We present here a real-time analysis of diffraction images acquired at high frame-rate (925 Hz) and its application to macromolecular serial crystallography. The software uses a new signal separation algorithm, able to distinguish the amorphous (or powder diffraction) component from the diffraction signal originating from single crystals. It relies on the ability to work efficiently in azimuthal space and derives from the work performed on pyFAI, the fast azimuthal integration library. Two applications are built upon this separation algorithm: a lossy compression algorithm and a peak-picking algorithm; the performances of both is assessed by comparing data quality after reduction with XDS and CrystFEL., Comment: 43 pages, 12 figures

Consider a graph $G$ with a path $P$ of order $n$. What conditions force $G$ to also have a long induced path? As complete bipartite graphs have long paths but no long induced paths, a natural restriction is to forbid some fixed complete bipartite graph $K_{t,t}$ as a subgraph. In this case we show that $G$ has an induced path of order $(\log \log n)^{1/5-o(1)}$. This is an exponential improvement over a result of Galvin, Rival, and Sands (1982) and comes close to a recent upper bound of order $O((\log \log n)^2)$. Another way to approach this problem is by viewing $G$ as an ordered graph (where the vertices are ordered according to their position on the path $P$). From this point of view it is most natural to consider which ordered subgraphs need to be forbidden in order to force the existence of a long induced path. Focusing on the exclusion of ordered matchings, we improve or recover a number of existing results with much simpler proofs, in a unified way. We also show that if some forbidden ordered subgraph forces the existence of a long induced path in $G$, then this induced path has size at least $\Omega((\log \log \log n)^{1/3})$, and can be chosen to be increasing with respect to $P$., Comment: 26 pages, 8 figures. Comments welcome !

The Cosmological Principle is a cornerstone of the standard model of cosmology and shapes how we view the Universe and our place within it. It is imperative, then, to devise multiple observational tests which can identify and quantify possible violations of this foundational principle. One possible method of probing large-scale anisotropies involves the use of weak gravitational lensing. We revisit this approach in order to analyse the imprint of late-time anisotropic expansion on cosmic shear. We show that the cross-correlation of shear $E$- and $B$-modes on large scales can be used to constrain the magnitude (and possibly direction) of anisotropic expansion. We estimate the signal to noise for multipoles $10\lesssim \ell\lesssim 100$ that is achievable by a Euclid-like survey. Our findings suggest that such a survey could detect the $E$-$B$ signal for reasonable values of the late-time anisotropy parameter.

Count data analysis is essential across diverse fields, from ecology and accident analysis to single-cell RNA sequencing (scRNA-seq) and metagenomics. While log transformations are computationally efficient, model-based approaches such as the Poisson-Log-Normal (PLN) model provide robust statistical foundations and are more amenable to extensions. The PLN model, with its latent Gaussian structure, not only captures overdispersion but also enables correlation between variables and inclusion of covariates, making it suitable for multivariate count data analysis. Variational approximations are a golden standard to estimate parameters of complex latent variable models such as PLN, maximizing a surrogate likelihood. However, variational estimators lack theoretical statistical properties such as consistency and asymptotic normality. In this paper, we investigate the consistency and variance estimation of PLN parameters using M-estimation theory. We derive the Sandwich estimator, previously studied in Westling and McCormick (2019), specifically for the PLN model. We compare this approach to the variational Fisher Information method, demonstrating the Sandwich estimator's effectiveness in terms of coverage through simulation studies. Finally, we validate our method on a scRNA-seq dataset.

In this paper we investigate the tractability of robust Markov Decision Processes (RMDPs) under various structural assumptions on the uncertainty set. Surprisingly, we show that in all generality (i.e. without any assumption on the instantaneous rewards), s-rectangular and sa-rectangular uncertainty sets are the only models of uncertainty that are tractable. Our analysis also shows that existing non-rectangular models, including r-rectangular uncertainty and new generalizations, are only weakly tractable in that they require an additional structural assumption that the instantaneous rewards do not depend on the next state, and in this case they are equivalent to rectangular models, which severely undermines their significance and usefulness. Interestingly, our proof techniques rely on identifying a novel simultaneous solvability property, which we show is at the heart of several important properties of RMDPs, including the existence of stationary optimal policies and dynamic programming-based formulations. The simultaneous solvability property enables a unified approach to studying the tractability of all existing models of uncertainty, rectangular and non-rectangular alike.

Dark matter is one of the pillars of the current standard model of structure formation: it is assumed to constitute most of the matter in the Universe. However, it can so far only be probed indirectly through its gravitational effects, and its nature remains elusive. In this focus meeting, we discussed different methods used to estimate galaxies' visible and dark matter masses in the nearby and distant Universe. We reviewed successes of the standard model relying on cold dark matter, confronted observations with simulations, and highlighted inconsistencies between the two. We discussed how robust mass measurements can help plan, perform, and refine particle dark matter searches. We further exchanged about alternatives to cold dark matter, such as warm, self-interacting, and fuzzy dark matter, as well as modified gravity. Finally, we discussed prospects and strategies that could be implemented to reveal the nature of this crucial component of the Universe., Comment: Rationale and highlights of the Focus Meeting 9 "Measures of luminous and dark matter in galaxies across time" at the XXXIInd IAU General Assembly in Cape Town, August 2024. 12 pages

In this paper, we introduce a novel framework to model the exchange rate dynamics between two intrinsically linked cryptoassets, such as stablecoins pegged to the same fiat currency or a liquid staking token and its associated native token. Our approach employs multi-level nested Ornstein-Uhlenbeck (OU) processes, for which we derive key properties and develop calibration and filtering techniques. Then, we design an automated market maker (AMM) model specifically tailored for the swapping of closely related cryptoassets. Distinct from existing models, our AMM leverages the unique exchange rate dynamics provided by the multi-level nested OU processes, enabling more precise risk management and enhanced liquidity provision. We validate the model through numerical simulations using real-world data for the USDC/USDT and wstETH/WETH pairs, demonstrating that it consistently yields efficient quotes. This approach offers significant potential to improve liquidity in markets for pegged assets.

This paper revisits the well-known wood-furniture cluster of Jepara (Central Java, Indonesia) with new parameters inspired by the theories of industrial districts and clusters. So far, literature on this production centre, mostly focused on value chain analysis, has failed to explain how it could survive external shocks and rising pressure of global competition, without upgrading. Based on a qualitative analysis encompassing the spatial organization and social structures supporting the productive system, this contribution of geography, sociology, and anthropology to development economics unveils a singular combination of ultraliberal business practices together with conservative social values, expressed in a unique arbourlike morphology. These qualitative results tend to show that, beyond the concept of flexible specialization identified by literature as the core driving force of the cluster, Jepara may be considered as a new avatar of Marshallian district in a context of subaltern globalization. Such findings question the opportunities offered to subaltern towns in emerging economies enrolled in globalization to capitalize their own local social and environmental assets to develop labour-intensive activities capable of adapting an ever-changing deal imposed by external price-driven competition.

Computing photo-z for AGN is challenging, primarily due to the interplay of relative emissions associated with the SMBH and its host galaxy. SED fitting methods, effective in pencil-beam surveys, face limitations in all-sky surveys with fewer bands available, lacking the ability to capture the AGN contribution to the SED accurately. This limitation affects the many 10s of millions of AGN clearly singled out and identified by SRG/eROSITA. Our goal is to significantly enhance photometric redshift performance for AGN in all-sky surveys while avoiding the need to merge multiple data sets. Instead, we employ readily available data products from the 10th Data Release of the Imaging Legacy Survey for DESI, covering > 20,000 deg$^{2}$ with deep images and catalog-based photometry in the grizW1-W4 bands. We introduce PICZL, a machine-learning algorithm leveraging an ensemble of CNNs. Utilizing a cross-channel approach, the algorithm integrates distinct SED features from images with those obtained from catalog-level data. Full probability distributions are achieved via the integration of Gaussian mixture models. On a validation sample of 8098 AGN, PICZL achieves a variance $\sigma_{\textrm{NMAD}}$ of 4.5% with an outlier fraction $\eta$ of 5.6%, outperforming previous attempts to compute accurate photo-z for AGN using ML. We highlight that the model's performance depends on many variables, predominantly the depth of the data. A thorough evaluation of these dependencies is presented in the paper. Our streamlined methodology maintains consistent performance across the entire survey area when accounting for differing data quality. The same approach can be adopted for future deep photometric surveys such as LSST and Euclid, showcasing its potential for wide-scale realisation. With this paper, we release updated photo-z (including errors) for the XMM-SERVS W-CDF-S, ELAIS-S1 and LSS fields., Comment: Accepted for publication in Astronomy & Astrophysics. 24 pages, 21 figures

This study investigates the evolution in conceptual understanding of cloud physics among learners from diverse academic backgrounds, using the mathematical framework of graph theory. We provide a diagnosis of the changes occurring along a learner's journey in cloud physics, and of the structure of the discipline and its inherent conceptual complexities. Overall, our results both qualify and quantify the epistemological shift in the description of the life-cycle of a cloud (from the general physics of the water cycle to detailed description of cloud microphysical processes) as learners mature in their understanding of the discipline. A triangulation of data sources with a panel of experts in the discipline complements and confirms our findings. These can be used by lecturers to tailor their teaching towards the identified expert-like concepts, and by students to anticipate the main complexities in the field during their learning process., Comment: 30 pages, 10 figures, 4 supplementary files available on request

Brown dwarfs with measured dynamical masses and spectra from direct imaging are benchmarks that anchor substellar atmosphere cooling and evolution models. We present Subaru SCExAO/CHARIS infrared spectroscopy of HIP 93398 B, a brown dwarf companion recently discovered by Li et al. 2023 as part of an informed survey using the Hipparcos-Gaia Catalog of Accelerations. This object was previously classified as a T6 dwarf based on its luminosity, with its independently-derived age and dynamical mass in tension with existing models of brown dwarf evolution. Spectral typing via empirical standard spectra, temperatures derived by fitting substellar atmosphere models, and J-H, J-K and H-L' colors all suggest that this object has a substantially higher temperature and luminosity, consistent with classification as a late-L dwarf near the L/T transition (T = 1200$^{+140}_{-119}$ K) with moderate to thick clouds possibly present in its atmosphere. When compared with the latest generation of evolution models that account for clouds with our revised luminosity and temperature for the object, the tension between the model-independent mass/age and model predictions is resolved., Comment: Accepted to AJ, 19 pages

The GPU-based High-order adaptive OpticS Testbench (GHOST) at the European Southern Observatory (ESO) is a new 2-stage extreme adaptive optics (XAO) testbench at ESO. The GHOST is designed to investigate and evaluate new control methods (machine learning, predictive control) for XAO which will be required for instruments such as the Planetary Camera and Spectrograph of ESOs Extremely Large Telescope. The first stage corrections are performed in simulation, with the residual wavefront error at each iteration saved. The residual wavefront errors from the first stage are then injected into the GHOST using a spatial light modulator. The second stage correction is made with a Boston Michromachines Corporation 492 actuator deformable mirror and a pyramid wavefront sensor. The flexibility of the bench also opens it up to other applications, one such application is investigating the flip-flop modulation method for the pyramid wavefront sensor., Comment: spie astronomical telescopes + instrumentation 2022

Much of the research on the interpretability of deep neural networks has focused on studying the visual features that maximally activate individual neurons. However, recent work has cast doubts on the usefulness of such local representations for understanding the behavior of deep neural networks because individual neurons tend to respond to multiple unrelated visual patterns, a phenomenon referred to as "superposition". A promising alternative to disentangle these complex patterns is learning sparsely distributed vector representations from entire network layers, as the resulting basis vectors seemingly encode single identifiable visual patterns consistently. Thus, one would expect the resulting code to align better with human perceivable visual patterns, but supporting evidence remains, at best, anecdotal. To fill this gap, we conducted three large-scale psychophysics experiments collected from a pool of 560 participants. Our findings provide (i) strong evidence that features obtained from sparse distributed representations are easier to interpret by human observers and (ii) that this effect is more pronounced in the deepest layers of a neural network. Complementary analyses also reveal that (iii) features derived from sparse distributed representations contribute more to the model's decision. Overall, our results highlight that distributed representations constitute a superior basis for interpretability, underscoring a need for the field to move beyond the interpretation of local neural codes in favor of sparsely distributed ones.

The Kitaev model defined on the trivalent heptagonal hyperbolic tiling is shown to be a singular case which realizes a non-Abelian gapped chiral spin liquid at the isotropic point in a regular tiling., Comment: 6 pages, 2 figures, 1 table, and Supplemental Material

This paper presents a family of Fourier eigenfunctions indexed by the space dimension d. These eigenfunctions are radial and built upon some generalized exponential integral function. For d = 1, 2, 3, they are integrable or square integrable and give new explicit examples of Fourier eigenfunctions in the usual or Fourier-Plancherel sense. For d 4, the functions are examples of non standard eigenfunctions, i.e. eigenfunctions in the sense of distribution. The discovery of these eigenfunctions stems from research in thermal lens spectroscopy, at the intersection of thermodynamics and optics. Their use could simplify the analysis of thermo-optical systems, paving the way for applications in optical computing, material studies and thermodynamic.

Generalization outside the scope of one's training data requires leveraging prior knowledge about the effects that transfer, and the effects that don't, between different data sources. Bayesian transfer learning is a principled paradigm for specifying this knowledge, and refining it on the basis of data from the source (training) and target (prediction) tasks. We address the challenging transfer learning setting where the learner (i) cannot fine-tune in the target task, and (ii) does not know which source data points correspond to the same task (i.e., the data sources are unknown). We propose a proxy-informed robust method for probabilistic transfer learning (PROMPT), which provides a posterior predictive estimate tailored to the structure of the target task, without requiring the learner have access to any outcome information from the target task. Instead, PROMPT relies on the availability of proxy information. PROMPT uses the same proxy information for two purposes: (i) estimation of effects specific to the target task, and (ii) construction of a robust reweighting of the source data for estimation of effects that transfer between tasks. We provide theoretical results on the effect of this reweighting on the risk of negative transfer, and demonstrate application of PROMPT in two synthetic settings.

Studies conducted on the experimental site of Lavalette (IRSTEA Montpellier) have shown variability in the observed agricultural yield, either attributable to spatial or temporal heterogeneities in water and nitrogen supply or to gradients of soil properties. The latter is addressed by performing a multi-depthg geophysical prospection that delivers maps of apparent electrical resistivity., Comment: in French language, GEOFCAN, UMR GEOPS, 2014, Orsay (Universit{\'e} Paris-Sud 11), France

Spectroastrometry, which measures wavelength-dependent shifts in the center of light, is well-suited for studying objects whose morphology changes with wavelength at very high angular resolutions. Photonic lantern (PL)-fed spectrometers have potential to enable measurement of spectroastrometric signals because the relative intensities between the PL output SMFs contain spatial information on the input scene. In order to use PL output spectra for spectroastrometric measurements, it is important to understand the wavelength-dependent behaviors of PL outputs and develop methods to calibrate the effects of time-varying wavefront errors in ground-based observations. We present experimental characterizations of the 3-port PL on the SCExAO testbed at the Subaru Telescope. We develop spectral response models of the PL and verify the behaviors with lab experiments. We find sinusoidal behavior of astrometric sensitivity of the 3-port PL as a function of wavelength, as expected from numerical simulations. Furthermore, we compare experimental and numerically simulated coupling maps and discuss their potential use for offsetting pointing errors. We then present a method of building PL spectral response models (solving for the transfer matrices as a function of wavelength) using coupling maps, which can be used for further calibration strategies., Comment: Accepted for publication in the Journal of Astronomical Telescopes, Instruments, and Systems (JATIS)

The randomized power method has gained significant interest due to its simplicity and efficient handling of large-scale spectral analysis and recommendation tasks. As modern datasets contain sensitive private information, we need to give formal guarantees on the possible privacy leaks caused by this method. This paper focuses on enhancing privacy preserving variants of the method. We propose a strategy to reduce the variance of the noise introduced to achieve Differential Privacy (DP). We also adapt the method to a decentralized framework with a low computational and communication overhead, while preserving the accuracy. We leverage Secure Aggregation (a form of Multi-Party Computation) to allow the algorithm to perform computations using data distributed among multiple users or devices, without revealing individual data. We show that it is possible to use a noise scale in the decentralized setting that is similar to the one in the centralized setting. We improve upon existing convergence bounds for both the centralized and decentralized versions. The proposed method is especially relevant for decentralized applications such as distributed recommender systems, where privacy concerns are paramount.

Although spin and charge stripes in high-Tc cuprates have been extensively studied, the exact range of carrier concentration over which they form a static order remains uncertain, complicating efforts to understand their significance. In La2-xSrxCuO4 (LSCO) and in zero external magnetic field, static spin stripes are confined to a doping range well below p*, the pseudogap boundary at zero temperature. However, when high fields suppress the competing effect of superconductivity, spin stripe order is found to extend up to p*. Here, we investigated La1.8-xEu0.2SrxCuO4 (Eu-LSCO) using 139La nuclear magnetic resonance and observe field-dependent spin fluctuations suggesting a similar competition between superconductivity and spin order as in LSCO. Nevertheless, we find that static spin stripes are present practically up to p* irrespective of field strength: the stronger stripe order in Eu-LSCO prevents superconductivity from enforcing a non-magnetic ground state, except very close to p*. Thus, spin-stripe order is consistently bounded by p* in both LSCO and Eu-LSCO, despite their differing balances between stripe order and superconductivity. This indicates that the canonical stripe order, where spins and charges are intertwined in a static pattern, is fundamentally tied to the pseudogap phase. Any stripe order beyond the pseudogap endpoint must then be of a different nature: either spin and charge orders remain intertwined, but both fluctuating, or only spin order fluctuates while charge order remains static. The presence of spin-stripe order up to p*, the pervasive, slow, and field-dependent spin-stripe fluctuations, as well as the electronic inhomogeneity documented in this work, must all be carefully considered in discussions of Fermi surface transformations, quantum criticality, and strange metal behavior.

The LHCb Upgrade I introduced a triggerless data acquisition system, crucial for readout across sub-detectors. Upgrade II aims for fivefold throughput enhancement and requires precise clock distribution. The PCIe400 development significantly boosts performance with 400Gbps bandwidth and advanced FPGA capabilities. Key design considerations included thermal dissipation, power distribution, and signal integrity, addressing power consumption exceeding 220 watts. Additionally, advancements in striplines were achieved through improved simulation methods, reducing processing times while optimizing geometry for performance., Comment: 7 pages (5 pages excluding abstract, title and references), 9 figures, TWEPP2024 conference proceeding

We propose a numerical method to estimate one-point functions and the free-energy density of conformal field theories at finite temperature by solving the Kubo-Martin-Schwinger condition for the two-point functions of identical scalars. We apply the method for the critical O(N) model for N = 1,2,3 in 3 $\leq$ d $\leq$ 4. We find agreement with known results from Monte Carlo simulations and previous results for the 3d Ising model, and we provide new predictions for N = 2,3., Comment: 5 pages plus supplemental material

Dye attenuation, or photometric imaging, is an optical technique commonly used in fluid dynamics to measure tracer concentration fields and fluid thicknesses under the assumption that the motion of the dye is representative of the fluid motion and that its presence does not affect the behaviour of the system. However, in some systems, particularly living biological systems or those with strong chemical interactions and reactions, the addition of dye may non-trivially influence the system and may not follow the fluid containing it. To overcome this, we demonstrate how short-wave infrared imaging can be used to measure concentration and height profiles of water and other liquids without the introduction of dye for heights down to 0.2mm with spatial and temporal resolutions of the order of 50 microns per pixel and 120 fps respectively. We showcase the utility of this technique by demonstrating its ability to accurately track the temporal evolution of the total water content of two model systems, namely a water drop spreading on a glass slide and spreading within a hydrogel sheet, validating both against an analytical mass balance. Finally, we discuss how the spectral resolution of the present setup could be increased to the point that concentrations within a multi-component system containing more than one type of liquid could be quantified.

From microactuators to biological tissues, non-porous materials with the ability to strongly expand when in contact with a solvent are ubiquitous. Consequently, the swelling of polymer systems such as hydrogel has received recently much research attention. However, the related dehydration of these systems has received much less attention. Here, we present experiments investigating the rapid dehydration of a swollen hydrogel sheet whose surface exhibits a transient crumpling instability characterised by line segments of cusps patterning the surface of the gel into an array of bumps. We perform this dehydration through immersion in acetone, which is highly miscible in water, but poorly miscible in the hydrogel. We report the onset of a fascinating pattern formation where regions of the hydrogel sheet turn turbid. We find that the emerging pattern is independent of the overall extent of the hydrated swollen surface. The pattern wavelength only depends on the duration of hydration before immersion in acetone, growing temporally with a power law behaviour. We conclude through drawing comparisons between features of this dehydration induced pattern and the original crumpling instability in the water swollen hydrogel sheet.

Virtual Production (VP) technologies have continued to improve the flexibility of on-set filming and enhance the live concert experience. The core technology of VP relies on high-resolution, high-brightness LED panels to playback/render video content. There are a number of technical challenges to effective deployment e.g. image tile synchronisation across the panels, cross panel colour balancing and compensating for colour fluctuations due to changes in camera angles. Given the complexity and potential quality degradation, the industry prefers "pristine" or lossless compressed source material for displays, which requires significant storage and bandwidth. Modern lossy compression standards like AV1 or H.265 could maintain the same quality at significantly lower bitrates and resource demands. There is yet no agreed methodology for assessing the impact of these standards on quality when the VP scene is recorded in-camera. We present a methodology to assess this impact by comparing lossless and lossy compressed footage displayed through VP screens and recorded in-camera. We assess the quality impact of HAP/NotchLC/Daniel2 and AV1/HEVC/H.264 compression bitrates from 2 Mb/s to 2000 Mb/s with various GOP sizes. Several perceptual quality metrics are then used to automatically evaluate in-camera picture quality, referencing the original uncompressed source content through the LED wall. Our results show that we can achieve the same quality with hybrid codecs as with intermediate encoders at orders of magnitude less bitrate and storage requirements., Comment: SMPTE Media Summit Paper on use of Compression in Virtual Production from TCD and Disguise

As AI systems are becoming more and more popular and used in various critical domains (health, transport, energy, ...), the need to provide guarantees and trust of their safety is undeniable. To this end, we present PyRAT, a tool based on abstract interpretation to verify the safety and the robustness of neural networks. In this paper, we describe the different abstractions used by PyRAT to find the reachable states of a neural network starting from its input as well as the main features of the tool to provide fast and accurate analysis of neural networks. PyRAT has already been used in several collaborations to ensure safety guarantees, with its second place at the VNN-Comp 2024 showcasing its performance.

Many properties of soft contact interfaces are controlled by the contact area (e.g. friction, contact stiffness and surface charge generation). The contact area increases with the contact age at rest. In contrast, it usually reduces under unidirectional shear loading. Although the physical origin of such a reduction is still debated, it always happens in an anisotropic way because the reduction mainly occurs along the shearing direction. Whether such anisotropy is a necessary condition for shear-induced area reduction remains an open question. Here, we investigate the contact area evolution of elastomer-based sphere-plane contacts under an isotropic shear loading, i.e. torsional loading. We find that, when macroscopic sliding is reached, the contact area has undergone a net area reduction. However, the area evolves non-monotonically as the twisting angle increases, with an initial rise up to a maximum before dropping to the value during macroscopic sliding. The ratio of maximum to initial contact area is found weakly dependent on the normal load, angular velocity and dwell time (time interval between the instants when the normal load and twist motion are first applied) within the investigated ranges. We show that non-monotonic area evolution can also be found under unidirectional shear loading conditions under large normal force. These observations challenge the current descriptions of shear-induced contact area evolution and are expected to serve as a benchmark for future modelling attempts in the field.

We present the photometric performance of SPIRIT, a ground-based near-infrared InGaAs CMOS-based instrument (1280 by 1024 pixels, 12 micron pitch), using on-sky results from the SPECULOOS-Southern Observatory during 2022 - 2023. SPIRIT was specifically designed to optimise time-series photometric precision for observing late M and L type stars. To achieve this, a custom wide-pass filter (0.81 - 1.33 microns, zYJ ) was used, which was also designed to minimise the effects of atmospheric precipitable water vapour (PWV) variability on differential photometry. Additionally, SPIRIT was designed to be maintenance-free by eliminating the need for liquid nitrogen for cooling. We compared SPIRIT's performance with a deeply-depleted (2048 by 2048 pixels, 13.5 micron pitch) CCD-based instrument (using an I+z' filter, 0.7 - 1.1 microns) through simultaneous observations. For L type stars and cooler, SPIRIT exhibited better photometric noise performance compared to the CCD-based instrument. The custom filter also significantly minimised red noise in the observed light curves typically introduced by atmospheric PWV variability. In SPIRIT observations, the detector's read noise was the dominant limitation, although in some cases, we were limited by the lack of comparison stars., Comment: SPIE Astronomical Telescopes + Instrumentation 2024

We extend the Gibbs conditioning principle to an abstract setting combining infinitely many linear equality constraints and non-linear inequality constraints, which need not be convex. A conditional large large deviation principle (LDP) is proved in a Wassersteintype topology, and optimality conditions are written in this abstract setting. This setting encompasses versions of the Schr{\"o}dinger bridge problem with marginal non-linear inequality constraints at every time. In the case of convex constraints, stability results for perturbations both in the constraints and the reference measure are proved. We then specify our results when the reference measure is the path-law of a continuous diffusion process, whose law is constrained at each time. We obtain a complete description of the constrained process through an atypical mean-field PDE system involving a Lagrange multiplier.

In this work, we present a new unsupervised anomaly (outlier) detection (AD) method using the sliced-Wasserstein metric. This filtering technique is conceptually interesting for integration in MLOps pipelines deploying trustworthy machine learning models in critical sectors like energy. Additionally, we open the first dataset showcasing localized critical peak rebate demand response in a northern climate. We demonstrate the capabilities of our method on synthetic datasets as well as standard AD datasets and use it in the making of a first benchmark for our open-source localized critical peak rebate dataset.

With the aim of progressing toward a practical implementation of an effective quantum-electrodynamics (QED) theory of atoms and molecules, which includes the effects of vacuum polarization through the creation of virtual electron-positron pairs but without the explicit photon degrees of freedom, we study a one-dimensional effective QED model of the hydrogen-like atom with delta-potential interactions. This model resembles the three-dimensional effective QED theory with Coulomb interactions while being substantially simpler. We provide some mathematical details about the definition of this model, calculate the vacuum-polarization density, and the Lamb-type shift of the bound-state energy, correcting and extending results of previous works. We also study the approximation of the model in a finite plane-wave basis, and in particular we discuss the basis convergence of the bound-state energy and eigenfunction, of the vacuum-polarization density, and of the Lamb-type shift of the bound-state energy. We highlight the difficulty of converging the vacuum-polarization density in a finite basis and we propose a way to improve it. The present work could give hints on how to perform similar calculations for the three-dimensional effective QED theory of atoms and molecules.

Despite its widespread adoption, Adam's advantage over Stochastic Gradient Descent (SGD) lacks a comprehensive theoretical explanation. This paper investigates Adam's sensitivity to rotations of the parameter space. We demonstrate that Adam's performance in training transformers degrades under random rotations of the parameter space, indicating a crucial sensitivity to the choice of basis. This reveals that conventional rotation-invariant assumptions are insufficient to capture Adam's advantages theoretically. To better understand the rotation-dependent properties that benefit Adam, we also identify structured rotations that preserve or even enhance its empirical performance. We then examine the rotation-dependent assumptions in the literature, evaluating their adequacy in explaining Adam's behavior across various rotation types. This work highlights the need for new, rotation-dependent theoretical frameworks to fully understand Adam's empirical success in modern machine learning tasks.

In drug discovery, highly automated high-throughput laboratories are used to screen a large number of compounds in search of effective drugs. These experiments are expensive, so we might hope to reduce their cost by experimenting on a subset of the compounds, and predicting the outcomes of the remaining experiments. In this work, we model this scenario as a sequential subset selection problem: we aim to select the smallest set of candidates in order to achieve some desired level of accuracy for the system as a whole. Our key observation is that, if there is heterogeneity in the difficulty of the prediction problem across the input space, selectively obtaining the labels for the hardest examples in the acquisition pool will leave only the relatively easy examples to remain in the inference set, leading to better overall system performance. We call this mechanism inference set design, and propose the use of an uncertainty-based active learning solution to prune out these challenging examples. Our algorithm includes an explicit stopping criterion that stops running the experiments when it is sufficiently confident that the system has reached the target performance. Our empirical studies on image and molecular datasets, as well as a real-world large-scale biological assay, show that deploying active learning for inference set design leads to significant reduction in experimental cost while obtaining high system performance.

We describe Hopf-Galois objects over bicrossed product Hopf algebras. More precisely, we show that any right Hopf-Galois object over a bicrossed product of Hopf algebras is obtained from Hopf-Galois objects over the two factors and a certain twisting map, while the unique Hopf algebra making it into a bi-Galois object is again a bicrossed product.

Preclinical diffusion MRI (dMRI) has proven value in methods development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages that facilitate high spatial resolution and high signal-to-noise ratio (SNR) images, cutting-edge diffusion contrasts, and direct comparison with histological data as a methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with many decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work concludes a 3-part series of recommendations and considerations for preclinical dMRI. Herein, we describe best practices for dMRI of ex vivo tissue, with a focus on image pre-processing, data processing and model fitting, and tractography. In each section, we attempt to provide guidelines and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should lie. We end by providing guidelines on code sharing and data sharing, and point towards open-source software and databases specific to small animal and ex vivo imaging., Comment: Part 3 of 3 in "Considerations and recommendations for preclinical diffusion MRI"

We report the observation of spin-dependent force induced by Nitrogen Vacancy (NV) centers embedded in a diamond crystal attached to a tethered oscillator. This result was obtained using a spin-dependent torque generated by a micro-diamond containing billions of NV centers, placed at the end of a commercially available silicon cantilever. We demonstrate that the fundamental flexural mode of the cantilever is excited by the spins of the NV centers. These spins are uniformly coupled to an externally applied homogeneous magnetic field, which enables the NV centers to exert a measurable spin-dependent torque on the cantilever, driving its center of mass motion. Our experiment highlights the intricate interplay between the spin properties of NV centers and the mechanical behavior of the oscillator in the presence of a pure torque, providing a set of tools for exploring quantum effects in state of the art mechanical systems.

The persistence exponent, which characterises the long-time decay of the survival probability of stochastic processes in the presence of an absorbing target, plays a key role in quantifying the dynamics of fluctuating systems. Determining this exponent for non-Markovian processes is known to be a difficult task, and exact results remain scarce despite sustained efforts. In this Letter, we consider the fundamental class of self-interacting random walks (SIRWs), which display long-range memory effects that result from the interaction of the random walker at time $t$ with the territory already visited at earlier times $t'

Stellar limb darkening must be properly accounted for to accurately determine the radii of exoplanets at various wavelengths. The standard approach to address limb darkening involves either using laws with coefficients from modelled stellar spectra or determining the coefficients empirically during light curve fitting of the data. Here, we test how accurately three common laws -- quadratic, power, and a three-parameter law -- can reproduce stellar limb darkening at different wavelengths and across a broad range of stars. We show that using a quadratic limb darkening law, which is most frequently employed by the community, leads to wavelength-dependent offsets in retrieved transmission spectra. For planets with high impact parameters ($b$ larger than about 0.5) the amplitude of these offsets can reach 1\% of the transit depth which is some cases is comparable to and can even exceed the expected signals from the planetary atmosphere. Furthermore, the quadratic law causes an offset in the value of the impact parameter when it is determined by fitting the broadband transit light curves. In contrast, using the Kipping--Sing three-parameter law leads to robust retrievals. We advocate the use of this law in retrievals, especially for transits with large impact parameters., Comment: submitted to ApJ

Integrated circuit technology enables the scaling of circuit complexity and functionality while maintaining manufacturability and reliability. Integration is expected to play an important role in quantum information technologies, including in the highly demanding task of producing the classical signals to control and measure quantum circuits at scales needed for fault-tolerant quantum computation. Here we experimentally characterize the cryogenic performance of a miniaturized photonic integrated circuit fabricated by a commercial foundry that down-converts classical optical signals to microwave signals. The circuit consists of waveguide-integrated germanium PIN photodiodes packaged using a scalable photonic wire bonding approach to a multi-channel optical fiber array that provides the optical excitation. We find the peak optical-to-microwave conversion response to be $\sim 150 \pm 13$ mA/W in the O-band at 4.2 K, well below the temperature the circuit was designed for and tested at in the past, for two different diode designs. The second diode design operates to over 6 GHz of 3 dB bandwidth making it suitable for controlling quantum circuits, with improvements in bandwidth and response expected from improved packaging. The demonstrated miniaturization and integration offers new perspectives for wavelength-division multiplexed control of microwave quantum circuits and scalable processors using light delivered by optical fiber arrays., Comment: 8 pages, 6 figures, 5 Supplementary Figures, accepted for publication in APL Photonics

From the vasculature of animals to the porous media making up batteries, transport by fluid flow within complex networks is crucial to service all cells or media with resources. Yet, living flow networks have a key advantage over porous media: they are adaptive and can self-organize their geometry to achieve a homogeneous perfusion throughout the network. Here, we show that, through erosion, artificial flow networks self-organize to a geometry where perfusion is more homogeneous. Flowing a pulse of cleaving enzyme through a network patterned into an erodible hydrogel, with initial channels disparate in width, we observe a homogenization in channel resistances. Experimental observations are matched with numerical simulations of the diffusion-advection-sorption dynamics of an eroding enzyme within a network. Analyzing transport dynamics theoretically, we show that homogenization only occurs if the pulse of eroding enzyme lasts longer than the time it takes any channel to equilibrate to the pulse concentration. The equilibration time scale derived analytically is in agreement with simulations. Lastly, we show both numerically and experimentally that erosion leads to homogenization of complex networks containing loops. Erosion being an omnipresent reaction, our results pave the way for a very versatile self-organized increase in the performance of porous media.

We construct a set of $2^n$ points in $\mathbb{R}^n$ such that all pairwise Manhattan distances are odd integers, which improves the recent linear lower bound of Golovanov, Kupavskii and Sagdeev. In contrast to the Euclidean and maximum metrics, this shows that the odd-distance set problem behaves very differently to the equilateral set problem under the Manhattan metric. Moreover, all coordinates of the points in our construction are integers or half-integers, and we show that our construction is optimal under this additional restriction., Comment: 6 pages

Motivated by recent observations of rapid (interannual) signals in the geomagnetic data, and by advances in numerical simulations approaching the Earth's outer core conditions, we present a study on the dynamics of hydromagnetic waves evolving over a static base state. Under the assumption of timescales separation between the rapid waves and the slow convection, we linearise the classical magneto-hydrodynamics equations over a steady non-axisymmetric background magnetic field and a zero velocity field. The initial perturbation is a super-rotating pulse of the inner core, which sets the amplitude and length-scales of the waves in the system. The initial pulse triggers axisymmetric, outward propagating torsional Alfv\'en waves, with characteristic thickness scaling with the magnetic Ekman number as $Ek_M^{1/4}$. Because the background state is non-axisymmetric, the pulse also triggers non-axisymmetric, quasi-geostrophic Alfv\'en waves. As these latter waves propagate outwards, they turn into quasi-geostrophic, magneto-Coriolis waves (QG-MC) as the Coriolis force supersedes inertia in the force balance. The period of the initial wave packet is preserved across the shell but the QG-MC wave front disperses and a westward drift is observed after this transformation. Upon reaching the core surface, the westward drift of the QG-MC waves presents an estimated phase speed of about $1100\,km/y$. This analysis confirms the QG-MC nature of the rapid magnetic signals observed in geomagnetic field models near the equator., Comment: 18 pages, 7 figures

We report on spin atomistic calculations used to model static and dynamic magnetic properties of inhomogeneous ultrathin iron films. Active magnetic layers in next-generation spintronic devices are becoming so thin that they exhibit some variable degree of roughness at the low-scale making them magnetically inhomogeneous. We propose a multiscale approach to progressively shift from a rough atomic-scale system to an ensemble of macrospins. By studying nanoscale islands of atoms in contact with each other, we demonstrate that ultrathin rough layers can be described by a set of macrospins coupled by a Heisenberg-like exchange interaction driven by the existence and shape of nanoconstrictions linking the islands. We show that the magnetization dynamics at 0 K is strongly impacted by this surface morphology since the resonant frequency of a typical ultrathin iron layer can drop by up to an order of magnitude due to inhomogeneities. Additionally, we used Monte Carlo simulations to determine the ferromagnetic-paramagnetic and spin reorientation transition temperatures for various morphology parameters and we show how nanoconstrictions and shapes of the atomic clusters can modify these transition temperatures. Our results demonstrate the possibility to account for the morphology of ultrathin structures with significant roughness. We believe that our approach makes it possible to model complete devices as close as possible to experimental reality.

We show that for a braided Hopf algebra in the category of comodules over a cosemisimple coquasitriangular Hopf algebra, the Hochschild cohomological dimension, the left and right global dimensions and the projective dimensions of the trivial left and right module all coincide. We also provide convenient criteria for smoothness and the twisted Calabi-Yau property for such braided Hopf algebras (without the cosemisimplicity assumption on $H$), in terms of properties of the trivial module.These generalize a well-known result in the case of ordinary Hopf algebras. As an illustration, we study the case of the coordinate algebra on the two-parameter braided quantum group $\textrm{SL}_{2}$.

We address several related problems on combinatorial discrepancy of trees in a setting introduced by Erd\H{o}s, F\"{u}redi, Loebl and S\'{o}s. Given a fixed tree $T$ on $n$ vertices and an edge-colouring of the complete graph $K_n$, for every colour, we find a copy of $T$ in $K_n$ where the number of edges in that colour significantly exceeds its expected count in a uniformly random embedding. This resolves a problem posed by Erd\H{o}s, F\"{u}redi, Loebl and S\'{o}s by generalising their work from two to many colours. Furthermore, if $T$ has maximum degree $\Delta\leq\epsilon n$ for sufficiently small $\epsilon > 0$ and the edge-colouring of $K_n$ is both balanced and ``not too close'' to one particular instance, we show that, for every colour, there is a copy of $T$ in $K_n$ where that colour appears on linearly more edges than any other colour. Several related examples are provided to demonstrate the necessity of the introduced structural restrictions. Our proofs combine saturation arguments for the existence of particular coloured substructures and analysis of conveniently defined local exchanges. Using similar methods, we investigate the existence of copies of a graph $H$ with prescribed number of edges in each colour in $2$-edge-coloured dense host graphs. In particular, for a graph $H$ with bounded maximum degree and balanced $2$-edge-colourings $\mathbf{c}$ of a host graph $G$ with minimum degree at least $(1-\epsilon)n$ for some $\epsilon > 0$, we show that, for any sufficiently large $n$ and sufficiently small $\epsilon$, there exists a copy of $H$ where the number of edges in the two colours differ by at most $2$. Moreover, we completely characterise the pairs $(H,\mathbf{c})$ for which the difference of $2$ cannot be improved, refuting a conjecture by Mohr, Pardey, and Rautenbach.

The paper introduces a novel framework for safe and autonomous aerial physical interaction in industrial settings. It comprises two main components: a neural network-based target detection system enhanced with edge computing for reduced onboard computational load, and a control barrier function (CBF)-based controller for safe and precise maneuvering. The target detection system is trained on a dataset under challenging visual conditions and evaluated for accuracy across various unseen data with changing lighting conditions. Depth features are utilized for target pose estimation, with the entire detection framework offloaded into low-latency edge computing. The CBF-based controller enables the UAV to converge safely to the target for precise contact. Simulated evaluations of both the controller and target detection are presented, alongside an analysis of real-world detection performance., Comment: 8 pages,14 figures, ICUAS 2024

This paper introduces a novel compliant mechanism combining lightweight and energy dissipation for aerial physical interaction. Weighting 400~g at take-off, the mechanism is actuated in the forward body direction, enabling precise position control for force interaction and various other aerial manipulation tasks. The robotic arm, structured as a closed-loop kinematic chain, employs two deported servomotors. Each joint is actuated with a single tendon for active motion control in compression of the arm at the end-effector. Its elasto-mechanical design reduces weight and provides flexibility, allowing passive-compliant interactions without impacting the motors' integrity. Notably, the arm's damping can be adjusted based on the proposed inner frictional bulges. Experimental applications showcase the aerial system performance in both free-flight and physical interaction. The presented work may open safer applications for \ac{MAV} in real environments subject to perturbations during interaction., Comment: 6 pages, 7 figures, ROBOSOFT 2024

We study two continuous and isotropic analogues of the model of greedy lattice animals introduced by Cox, Gandolfi, Griffin and Kesten in 1993. In our framework, animals collect masses scattered on a Poisson point process on $\mathbb R^d$, and are allowed to have vertices outside the process or not, depending on the model. The author recently proved in a more general setting that for all $u$ in the Euclidean open unit ball, the maximal mass of animals with length $\ell$, containing $0$ and $\ell u$ satisfies a law of large numbers. We prove some additional properties in the Poissonian case, including an extension of the functional law of large numbers to the closed unit ball, and study strict monotonicity of the limit function along a radius. Moreover, we prove that a third, penalized model is a suitable interpolation between the former two.