Search

The present work emphasizes the relevance of low-field NMR relaxometry to investigate colloid-polymer hydrogels by probing water dynamics across a wide range of formulations between $\rm 10^{\circ}C$ and $\rm 80^{\circ}C$. By examining the temperature dependence of the transverse relaxation time $T_2$, we demonstrate a clear link between the NMR response and the rheological behavior of the hydrogels. In particular, we show that NMR relaxometry targeting the solvent provides reliable insights into the hydrogel microstructure and allows the detection of phase transitions and aging processes. Our findings suggest that this solvent-focused technique could greatly benefit the soft matter community, complementing other experimental methods in the study of gels., Comment: 6 pages, 4 figures

To maximize the amount of information extracted from cosmological datasets, simulations that accurately represent these observations are necessary. However, traditional simulations that evolve particles under gravity by estimating particle-particle interactions (N-body simulations) are computationally expensive and prohibitive to scale to the large volumes and resolutions necessary for the upcoming datasets. Moreover, modeling the distribution of galaxies typically involves identifying virialized dark matter halos, which is also a time- and memory-consuming process for large N-body simulations, further exacerbating the computational cost. In this study, we introduce CHARM, a novel method for creating mock halo catalogs by matching the spatial, mass, and velocity statistics of halos directly from the large-scale distribution of the dark matter density field. We develop multi-stage neural spline flow-based networks to learn this mapping at redshift z=0.5 directly with computationally cheaper low-resolution particle mesh simulations instead of relying on the high-resolution N-body simulations. We show that the mock halo catalogs and painted galaxy catalogs have the same statistical properties as obtained from $N$-body simulations in both real space and redshift space. Finally, we use these mock catalogs for cosmological inference using redshift-space galaxy power spectrum, bispectrum, and wavelet-based statistics using simulation-based inference, performing the first inference with accelerated forward model simulations and finding unbiased cosmological constraints with well-calibrated posteriors. The code was developed as part of the Simons Collaboration on Learning the Universe and is publicly available at \url{https://github.com/shivampcosmo/CHARM}., Comment: 12 pages and 8 figures. This is a Learning the Universe Publication

Tailoring the losses of optical systems within the frame of non-Hermitian physics has appeared very fruitful in the last few years. In particular, the description of exceptional points (EPs) with coupled resonators have become widespread. The on-chip realization of these functionalities is highly significant for integrated nanophotonics, but requires fine control techniques of the nanodevice properties. Here, we demonstrate pump-controlled directional emission of two coupled nanolasers that distantly interact via an integrated waveguide. This coupling scheme unusually enables both frequency- and loss-couplings between two cavities, which can be advantageously exploited to reach EPs by either detuning the cavities or controlling the gain of nanolasers. The system can be readily reconfigured from bidirectional to unidirectional emission by adjusting the pump power.

Mock halo catalogues are indispensable data products for developing and validating cosmological inference pipelines. A major challenge in generating mock catalogues is modelling the halo or galaxy bias, which is the mapping from matter density to dark matter halos or observable galaxies. To this end, N-body codes produce state-of-the-art catalogues. However, generating large numbers of these N-body simulations for big volumes, requires significant computational time. We introduce and benchmark a differentiable and physics-informed neural network that can generate mock halo catalogues of comparable quality to those obtained from full N-body codes. The model design is computationally efficient for the training procedure and the production of large mock suites. We present a neural network, relying only on 18 to 34 trainable parameters, that produces halo catalogues from dark matter overdensity fields. The reduction of network weights is realised through incorporating symmetries motivated by first principles into our model architecture. We train our model using dark matter only N-body simulations across different resolutions, redshifts, and mass bins. We validate the final mock catalogues by comparing them to N-body halo catalogues using different N-point correlation functions. Our model produces mock halo catalogues consistent with the reference simulations, showing that this novel network is a promising way to generate mock data for upcoming wide-field surveys due to its computational efficiency. Moreover, we find that the network can be trained on approximate overdensity fields to reduce the computational cost further. We also present how the trained network parameters can be interpreted to give insights into the physics of structure formation. Finally, we discuss the current limitations of our model as well as more general requirements and pitfalls for approximate halo mock generation., Comment: 17 pages, 17 figures, 3 tables. Abstract abridged for arXiv, layout corrections, added reference section 2, accepted for publication in A&A

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

Cellulose ethers represent a class of water-soluble polymers widely utilized across diverse sectors, spanning from healthcare to the construction industry. This experimental study specifically delves into aqueous suspensions of carboxymethylcellulose (CMC), a polymer that undergoes gel formation in acidic environments due to attractive interactions between hydrophobic patches along its molecular chain. We use rheometry to determine the linear viscoelastic properties of both CMC suspensions and acid-induced gels at various temperatures. Then, applying the time-temperature superposition principle, we construct master curves for the viscoelastic spectra, effectively described by fractional models. The horizontal shift factors exhibit an Arrhenius-like temperature dependence, allowing us to extract activation energies compatible with hydrophobic interactions. Furthermore, we show that acid-induced CMC gels are physical gels that display a reversible yielding transition under external shear. In particular, we discuss the influence of pH on the non-linear viscoelastic response under large-amplitude oscillatory shear. Overall, our results offer a comprehensive description of the linear and non-linear rheological properties of a compelling case of physical hydrogel involving hydrophobic interactions., Comment: 23 pages, 9 figures

We present a novel approach based on Bayesian field-level inference capable of resolving individual galaxies within the Local Group (LG), enabling detailed studies of its structure and formation via posterior simulations. We extend the Bayesian Origin Reconstruction from Galaxies (BORG) algorithm with a multi-resolution approach, allowing us to reach smaller mass scales and apply observational constraints based on LG galaxies. Our updated data model simultaneously accounts for observations of mass tracers within the dark haloes of the Milky Way (MW) and M31, their observed separation and relative velocity, and the quiet surrounding Hubble flow represented through the positions and velocities of galaxies at distances from one to four Mpc. Our approach delivers representative posterior samples of $\Lambda$CDM realisations that are statistically and simultaneously consistent with all these observations, leading to significantly tighter mass constraints than found if the individual datasets are considered separately. In particular, we estimate the virial masses of the MW and M31 to be $\log_{10}(M_{200c}/M_\odot) = 12.07\pm0.08$ and $12.33\pm0.10$, respectively, their sum to be $\log_{10}(\Sigma M_{200c}/M_\odot)= 12.52\pm0.07$, and the enclosed mass within spheres of radius $R$ to be $\log_{10}(M(R)/M_\odot)= 12.71\pm0.06$ and $12.96\pm0.08$ for $R=1$ Mpc and 3 Mpc, respectively. The M31-MW orbit is nearly radial for most of our $\Lambda$CDM LG's, and most lie in a dark matter sheet that aligns approximately with the Supergalactic Plane, even though the surrounding density field was not used explicitly as a constraint. The approximate simulations employed in our inference are accurately reproduced by high-fidelity structure formation simulations, demonstrating the potential for future high-resolution, full-physics $\Lambda$CDM posterior simulations of LG look-alikes., Comment: 23 pages, 17 figures, 4 tables, submitted to A&A. Comments welcome!

The correlated Erd\"os-R\'enyi random graph ensemble is a probability law on pairs of graphs with $n$ vertices, parametrized by their average degree $\lambda$ and their correlation coefficient $s$. It can be used as a benchmark for the graph alignment problem, in which the labels of the vertices of one of the graphs are reshuffled by an unknown permutation; the goal is to infer this permutation and thus properly match the pairs of vertices in both graphs. A series of recent works has unveiled the role of Otter's constant $\alpha$ (that controls the exponential rate of growth of the number of unlabeled rooted trees as a function of their sizes) in this problem: for $s>\sqrt{\alpha}$ and $\lambda$ large enough it is possible to recover in a time polynomial in $n$ a positive fraction of the hidden permutation. The exponent of this polynomial growth is however quite large and depends on the other parameters, which limits the range of applications of the algorithm. In this work we present a family of faster algorithms for this task, show through numerical simulations that their accuracy is only slightly reduced with respect to the original one, and conjecture that they undergo, in the large $\lambda$ limit, phase transitions at modified Otter's thresholds $\sqrt{\widehat{\alpha}}>\sqrt{\alpha}$, with $\widehat{\alpha}$ related to the enumeration of a restricted family of trees., Comment: 34 pages, v2 : clarification of some points, and addition of a comparison with other algorithms

Artificial neural networks trained on large, expert-labelled datasets are considered state-of-the-art for a range of medical image recognition tasks. However, categorically labelled datasets are time-consuming to generate and constrain classification to a pre-defined, fixed set of classes. For neuroradiological applications in particular, this represents a barrier to clinical adoption. To address these challenges, we present a self-supervised text-vision framework that learns to detect clinically relevant abnormalities in brain MRI scans by directly leveraging the rich information contained in accompanying free-text neuroradiology reports. Our training approach consisted of two-steps. First, a dedicated neuroradiological language model - NeuroBERT - was trained to generate fixed-dimensional vector representations of neuroradiology reports (N = 50,523) via domain-specific self-supervised learning tasks. Next, convolutional neural networks (one per MRI sequence) learnt to map individual brain scans to their corresponding text vector representations by optimising a mean square error loss. Once trained, our text-vision framework can be used to detect abnormalities in unreported brain MRI examinations by scoring scans against suitable query sentences (e.g., 'there is an acute stroke', 'there is hydrocephalus' etc.), enabling a range of classification-based applications including automated triage. Potentially, our framework could also serve as a clinical decision support tool, not only by suggesting findings to radiologists and detecting errors in provisional reports, but also by retrieving and displaying examples of pathologies from historical examinations that could be relevant to the current case based on textual descriptors., Comment: Under Review

The fundamental understanding of friction of liquids on solid surfaces remains one of the key knowledge gaps in the transport of fluids. While the standard perspective emphasizes the role of wettability and commensurability, recent works have unveiled the crucial role of the solid's internal excitations, whether electronic or phononic, on liquid-solid dissipation. In this work, we take advantage of the considerable variation of the molecular timescales of supercooled glycerol under mild change of temperature, in order to explore how friction depends on the liquid's molecular dynamics. Using a dedicated tuning-fork-based AFM to measure the hydrodynamic slippage of glycerol on mica, we report a 2-order of magnitude increase of the slip length with decreasing temperature by only 30{\deg}C. However the solid-liquid friction coefficient is found to be a non monotonous function of the fluid molecular relaxation rate, f{\alpha}, at odd with an expected Arrhenius behavior. In particular, the linear increase of friction with the liquid molecular rate measured at high temperature cannot be accounted for by existing modelling. We show that this unconventional and non-arrhenian friction is consistent with a contribution of the solid's phonons to the liquid-solid friction. This dynamical friction opens new perspectives to control hydrodynamic flows by properly engineering phononic and electronic excitation spectra in channel walls., Comment: 17 pages, 4 figures

Ultraviolet nanosecond laser annealing (UV-NLA) proves to be an important technique, particularly when tightly controlled heating and melting are necessary. In the realm of semiconductor technologies, the significance of nanosecond laser annealing (NLA) grows in tandem with the escalating intricacy of integration schemes in nano-scaled devices. Silicon-germanium alloys have been studied for decades for their compatibility with silicon devices. Indeed, they enable the manipulation of properties like strain, carrier mobilities and bandgap. In this framework, they can for instance boost the performances of p-type MOSFETs but also enable near infra-red absorption and emission for applications in photo-detection and photonics. Laser melting on such type of layers, however results, up to now, in the development of extended defects and poor control over layer morphology and homogeneity. In our study, we investigate the laser melting of ~700 nm thick relaxed silicon-germanium samples coated with SiO2 nano-arrays, observing the resulting material to maintain an unaltered lattice. We found the geometrical parameters of the silicon oxide having an impact on the thermal budget samples see, influencing melt threshold, melt depth and germanium distribution.

Following a previous work evidencing that short poly-propylene glycol (PPG) chains incorporated to crude SBR-silica nanocomposites act as filler-network softeners without changing their structure, we propose in the present letter to examine more operative cross-linked materials. We first evidence that the adsorption of PPG onto silica deactivates progressively the particle's catalytic effect on vulcanization, without perturbing however the cross-link density distribution that we investigate through multiple-quantum NMR. In addition, electron microscopy confirms that silica structure is conserved after vulcanization and that it does not depend on the PPG content either. Composites containing various amount of PPG can thus be seen as structurally identical, both from a matrix and filler point of view - which is confirmed by small and medium amplitude oscillation shear rheology showing strikingly identical viscoelastic properties. PPG signature only appears above 100% in tensile deformation where it is seen to soften dramatically the filler network. Our discovery makes it consequently possible to decorrelate the mechanical behavior of reinforced rubbers under normal conditions of use and urgent needs of energy dissipation.

One key challenge in Artificial Life is designing systems that display an emergence of complex behaviors. Many such systems depend on a high-dimensional parameter space, only a small subset of which displays interesting dynamics. Focusing on the case of continuous systems, we introduce the 'Phase Transition Finder'(PTF) algorithm, which can be used to efficiently generate parameters lying at the border between two phases. We argue that such points are more likely to display complex behaviors, and confirm this by applying PTF to Lenia showing it can increase the frequency of interesting behaviors more than two-fold, while remaining efficient enough for large-scale searches., Comment: 5 pages

We consider the additive version of the matrix denoising problem, where a random symmetric matrix $S$ of size $n$ has to be inferred from the observation of $Y=S+Z$, with $Z$ an independent random matrix modeling a noise. For prior distributions of $S$ and $Z$ that are invariant under conjugation by orthogonal matrices we determine, using results from first and second order free probability theory, the Bayes-optimal (in terms of the mean square error) polynomial estimators of degree at most $D$, asymptotically in $n$, and show that as $D$ increases they converge towards the estimator introduced by Bun, Allez, Bouchaud and Potters in [IEEE Transactions on Information Theory 62, 7475 (2016)]. We conjecture that this optimality holds beyond strictly orthogonally invariant priors, and provide partial evidences of this universality phenomenon when $S$ is an arbitrary Wishart matrix and $Z$ is drawn from the Gaussian Orthogonal Ensemble, a case motivated by the related extensive rank matrix factorization problem., Comment: 44 pages, v2 : updated references, minor clarifications

Lagrangian stochastic methods are widely used to model turbulent flows. Scarce consideration has, however, been devoted to the treatment of the near-wall region and to the formulation of a proper wall-boundary condition. With respect to this issue, the main purpose of this paper is to present an in-depth analysis of such flows when relying on particle/mesh formulations of the probability density function (PDF) model. This is translated into three objectives. The first objective is to assess the existing an-elastic wall-boundary condition and present new validation results. The second objective is to analyse the impact of the interpolation of the mean fields at particle positions on their dynamics. The third objective is to investigate the spatial error affecting covariance estimators when they are extracted on coarse volumes. All these developments allow to ascertain that the key dynamical statistics of wall-bounded flows are properly captured even for coarse spatial resolutions.

SrTiO$_3$-based conducting interfaces, which exhibit coexistence of gate-tunable 2D superconductivity and strong Rashba spin-orbit coupling (RSOC), are candidates to host topological superconductive phases. Yet, superconductivity is usually in the dirty limit, which tends to suppress nonconventional pairing and therefore challenges these expectations. Here we report on LaAlO$_3$/SrTiO$_3$ (LAO/STO) interfaces with remarkably large mobility and mean free paths comparable to the superconducting coherence length, approaching the clean limit for superconductivity. We further show that the carrier density, mobility, and formation of the superconducting condensate are controlled by the fine-tuning of La/Al chemical ratio in the LAO film. Interestingly, we find a region in the superconducting phase diagram where the critical temperature is not suppressed below the Lifshitz transition, at odds with previous experimental investigations. These findings point out the relevance of achieving a clean-limit regime to enhance the observation of unconventional pairing mechanisms in these systems

We propose a projection-based model order reduction procedure for the ageing of large prestressed concrete structures. Our work is motivated by applications in the nuclear industry, particularly in the simulation of containment buildings. Such numerical simulations involve a multi-modeling approach: a three-dimensional nonlinear thermo-hydro-visco-elastic rheological model is used for concrete; and prestressing cables are described by a one-dimensional linear thermo-elastic behavior. A kinematic linkage is performed in order to connect the concrete nodes and the steel nodes: coincident points in each material are assumed to have the same displacement. We develop an adaptive algorithm based on a Proper Orthogonal Decomposition (POD) in time and greedy in parameter to build a reduced order model (ROM). The nonlinearity of the operator entails that the computational cost of the ROM assembly scales with the size of the high-fidelity model. We develop an hyper-reduction strategy based on empirical quadrature to bypass this computational bottleneck: our approach relies on the construction of a reduced mesh to speed up online assembly costs of the ROM. We provide numerical results for a standard section of a double-walled containment building using a qualified and broadly-used industrial grade finite element solver for structural mechanics (code$\_$aster).

Practical implementation of many quantum algorithms known today is limited by the coherence time of the executing quantum hardware and quantum sampling noise. Here we present a machine learning algorithm, NISQRC, for qubit-based quantum systems that enables inference on temporal data over durations unconstrained by decoherence. NISQRC leverages mid-circuit measurements and deterministic reset operations to reduce circuit executions, while still maintaining an appropriate length persistent temporal memory in quantum system, confirmed through the proposed Volterra Series analysis. This enables NISQRC to overcome not only limitations imposed by finite coherence, but also information scrambling in monitored circuits and sampling noise, problems that persist even in hypothetical fault-tolerant quantum computers that have yet to be realized. To validate our approach, we consider the channel equalization task to recover test signal symbols that are subject to a distorting channel. Through simulations and experiments on a 7-qubit quantum processor we demonstrate that NISQRC can recover arbitrarily long test signals, not limited by coherence time., Comment: 15+17 pages, 4+7 figures

Analysing next-generation cosmological data requires balancing accurate modeling of non-linear gravitational structure formation and computational demands. We propose a solution by introducing a machine learning-based field-level emulator, within the Hamiltonian Monte Carlo-based Bayesian Origin Reconstruction from Galaxies (BORG) inference algorithm. Built on a V-net neural network architecture, the emulator enhances the predictions by first-order Lagrangian perturbation theory to be accurately aligned with full $N$-body simulations while significantly reducing evaluation time. We test its incorporation in BORG for sampling cosmic initial conditions using mock data based on non-linear large-scale structures from $N$-body simulations and Gaussian noise. The method efficiently and accurately explores the high-dimensional parameter space of initial conditions, fully extracting the cross-correlation information of the data field binned at a resolution of $1.95h^{-1}$ Mpc. Percent-level agreement with the ground truth in the power spectrum and bispectrum is achieved up to the Nyquist frequency $k_\mathrm{N} \approx 2.79h \; \mathrm{Mpc}^{-1}$. Posterior resimulations - using the inferred initial conditions for $N$-body simulations - show that the recovery of information in the initial conditions is sufficient to accurately reproduce halo properties. In particular, we show highly accurate $M_{200\mathrm{c}}$ halo mass function and stacked density profiles of haloes in different mass bins $[0.853,16]\times 10^{14}M_{\odot}h^{-1}$. As all available cross-correlation information is extracted, we acknowledge that limitations in recovering the initial conditions stem from the noise level and data grid resolution. This is promising as it underscores the significance of accurate non-linear modeling, indicating the potential for extracting additional information at smaller scales., Comment: 16 pages, 11 figures

Cu-Cr-based alloys with Cr content from 5 to 50 wt.% are widely used as electrical contacts for vacuum interrupters for medium voltage applications because of their excellent combination of mechanical, thermal, and electrical conductivity. Cu-Cr electrical contacts are usually processed by sintering or casting processes. For solid-state sintered Cu-Cr materials, the physical properties vary as a function of the Cr content, phase morphology and porosity volume fraction. Some studies have investigated the effect of the microstructural characteristics of Cu-Cr alloys with different Cr content and morphology on their properties. However, the porosity characterization and Cr spatial distribution and how they affect these alloys' physical properties are not as well documented. In this study, we report an in-depth 3D characterization of the porosity and Cr-phase of solid-state sintered Cu-25Cr alloys with three final relative densities using X-ray Computed Tomography (XCT). An image analysis algorithm assisted by a machine learning-based segmentation method has been specifically developed. Results show that for Cu-25Cr solid sintered alloys there are mainly two types of pores, pores located at the Cu/Cr interfaces, and pores within the Cu matrix. The interfacial porosity represents the larger volume fraction, over 75% of the total porosity for all cases, forming a large network of interconnected pores. With the increase of final density, the Cu-matrix becomes nearly fully dense while interfacial pores still represent the largest fraction decreases in size and volume. These interfacial pores networks are believed to be formed due to poor filling and packing of Cu around the percolated Cr-phase. These observations might be helpful to optimize the functional properties of Cu-Cr sintered alloys.

We construct an anti-halo void catalogue of $150$ voids with radii $R > 10\,h^{-1}\mathrm{\,Mpc}$ in the Local Super-Volume ($<135\,h^{-1}\mathrm{\,Mpc}$ from the Milky Way), using posterior resimulation of initial conditions inferred by field-level inference with Bayesian Origin Reconstruction from Galaxies (\codefont{BORG}). We describe and make use of a new algorithm for creating a single, unified void catalogue by combining different samples from the posterior. The catalogue is complete out to $135\,h^{-1}\mathrm{\,Mpc}$, with void abundances matching theoretical predictions. Finally, we compute stacked density profiles of those voids which are reliably identified across posterior samples, and show that these are compatible with $\Lambda$CDM expectations once environmental selection (e.g., the estimated $\sim 4\%$ under-density of the Local Super-Volume) is accounted for., Comment: 11 pages, 4 figures. Updated to match version accepted by MNRAS. Minor clarifications and no changes to conclusion or catalogue

Two-dimensional electron gases(2DEGs)based on KTaO3 are emerging as a promising platform for spin-orbitronics due to their high Rashba spin-orbit coupling (SOC) and gate-voltage tunability. The recent discovery of a superconducting state in KTaO3 2DEGs now expands their potential towards topological superconductivity. Although the band structure of KTaO3 surfaces of various crystallographic orientations has already been mapped using angle-resolved photoemission spectroscopy(ARPES), this is not the case for superconducting KTaO3 2DEGs. Here, we reveal the electronic structure of superconducting 2DEGs based on KTaO3 (111) single crystals through ARPES measurements. We fit the data with a tight-binding model and compute the associated spin textures to bring insight into the SOC-driven physics of this fascinating system., Comment: 9 pages, 4 figures

The present work offers a comprehensive description of the acid-induced gelation of carboxymethylcellulose (CMC), a water-soluble derivative of cellulose broadly used in numerous applications ranging from food packaging to biomedical engineering. Linear viscoelastic properties measured at various pH and CMC contents allow us to build a sol-gel phase diagram, and show that CMC gels exhibit broad power-law viscoelastic spectra that can be rescaled onto a master curve following a time-composition superposition principle. These results demonstrate the microstructural self-similarity of CMC gels, and inspire a mean-field model based on hydrophobic inter-chain association that accounts for the sol-gel boundary over the entire range of CMC content under study. Neutron scattering experiments further confirm this picture and suggest that CMC gels comprise a fibrous network crosslinked by aggregates. Finally, low-field NMR measurements offer an original signature of acid-induced gelation from the solvent perspective. Altogether, these results open avenues for precise manipulation and control of CMC-based hydrogels., Comment: 7 pages, 4 figures and 4 figures as SI

Galaxies of different types are not equally distributed in the Local Universe. In particular, the supergalactic plane is prominent among the brightest ellipticals, but inconspicuous among the brightest disk galaxies. This striking difference provides a unique test for our understanding of galaxy and structure formation. Here we use the SIBELIUS DARK constrained simulation to confront the predictions of the standard Lambda Cold Dark Matter ($\Lambda$CDM) model and standard galaxy formation theory with these observations. We find that SIBELIUS DARK reproduces the spatial distributions of disks and ellipticals and, in particular, the observed excess of massive ellipticals near the supergalactic equator. We show that this follows directly from the local large-scale structure and from the standard galaxy formation paradigm, wherein disk galaxies evolve mostly in isolation, while giant ellipticals congregate in the massive clusters that define the supergalactic plane. Rather than being anomalous as earlier works have suggested, the distributions of giant ellipticals and disks in the Local Universe and in relation to the supergalactic plane are key predictions of the $\Lambda$CDM model., Comment: 21 pages, 9 figures, published in Nature Astronomy

Muscle volume is a useful quantitative biomarker in sports, but also for the follow-up of degenerative musculo-skelletal diseases. In addition to volume, other shape biomarkers can be extracted by segmenting the muscles of interest from medical images. Manual segmentation is still today the gold standard for such measurements despite being very time-consuming. We propose a method for automatic segmentation of 18 muscles of the lower limb on 3D Magnetic Resonance Images to assist such morphometric analysis. By their nature, the tissue of different muscles is undistinguishable when observed in MR Images. Thus, muscle segmentation algorithms cannot rely on appearance but only on contour cues. However, such contours are hard to detect and their thickness varies across subjects. To cope with the above challenges, we propose a segmentation approach based on a hybrid architecture, combining convolutional and visual transformer blocks. We investigate for the first time the behaviour of such hybrid architectures in the context of muscle segmentation for shape analysis. Considering the consistent anatomical muscle configuration, we rely on transformer blocks to capture the longrange relations between the muscles. To further exploit the anatomical priors, a second contribution of this work consists in adding a regularisation loss based on an adjacency matrix of plausible muscle neighbourhoods estimated from the training data. Our experimental results on a unique database of elite athletes show it is possible to train complex hybrid models from a relatively small database of large volumes, while the anatomical prior regularisation favours better predictions.

This study addresses the influence of internal liquid dynamics on liquid-solid friction. Taking advantage of the wide range of relaxation timescales in supercooled liquids, we use a tuning-fork-based AFM to measure the slippage of supercooled glycerol on mica at 30 kHz. We report a 2-order of magnitude increase of slippage with decreasing temperature by only 30{\deg}C. More importantly, as the bulk liquid dynamics are slowed with decreasing temperature, we report a sharp drop of the interfacial friction coefficient in contrast with the usual assumption of thermally activated interfacial dynamics. To rationalize this original behavior, we account for the contribution of solid fluctuations to liquid friction. We show that a minimalistic single phonon-branch model of the mica surface yields semi-quantitative agreement with our measurements. In this picture, the liquid's relaxation rate is the tuning knob between two friction regimes where the wall is seen either as a static corrugated potential or as a thermally fluctuating surface. Remarkably, this study bridges soft and hard condensed matter: hydrodynamic flow controlled by the solid's dynamical modes., Comment: Data analysis is modified

Coral reefs are among the most diverse ecosystems on our planet, and are depended on by hundreds of millions of people. Unfortunately, most coral reefs are existentially threatened by global climate change and local anthropogenic pressures. To better understand the dynamics underlying deterioration of reefs, monitoring at high spatial and temporal resolution is key. However, conventional monitoring methods for quantifying coral cover and species abundance are limited in scale due to the extensive manual labor required. Although computer vision tools have been employed to aid in this process, in particular SfM photogrammetry for 3D mapping and deep neural networks for image segmentation, analysis of the data products creates a bottleneck, effectively limiting their scalability. This paper presents a new paradigm for mapping underwater environments from ego-motion video, unifying 3D mapping systems that use machine learning to adapt to challenging conditions under water, combined with a modern approach for semantic segmentation of images. The method is exemplified on coral reefs in the northern Gulf of Aqaba, Red Sea, demonstrating high-precision 3D semantic mapping at unprecedented scale with significantly reduced required labor costs: a 100 m video transect acquired within 5 minutes of diving with a cheap consumer-grade camera can be fully automatically analyzed within 5 minutes. Our approach significantly scales up coral reef monitoring by taking a leap towards fully automatic analysis of video transects. The method democratizes coral reef transects by reducing the labor, equipment, logistics, and computing cost. This can help to inform conservation policies more efficiently. The underlying computational method of learning-based Structure-from-Motion has broad implications for fast low-cost mapping of underwater environments other than coral reefs.

Chip-scale multimode optomechanical systems have unique benefits for sensing, metrology and quantum technologies relative to their single-mode counterparts. Slot-mode optomechanical crystals enable sideband resolution and large optomechanical couplings of a single optical cavity to two microwave-frequency mechanical modes. Still, previous implementations have been limited to nanobeam geometries, whose effective quantum cooperativity at ultralow temperatures is limited by their low thermal conductance. In this work, we design and experimentally demonstrate a two-dimensional mechanical-optical-mechanical (MOM) platform that dispersively couples a slow-light slot-guided photonic-crystal waveguide mode and two slow-sound $\sim 7$ GHz phononic wire modes localized in physically distinct regions. We first demonstrate optomechanical interactions in long waveguide sections, unveiling acoustic group velocities below 800 m/s, and then move on to mode-gap adiabatic heterostructure cavities with a tailored mechanical frequency difference. Through optomechanical spectroscopy, we demonstrate optical quality factors $Q \sim 10^5$, vacuum optomechanical coupling rates, $g_o/2\pi$, of 1.5 MHz and dynamical backaction effects beyond the single-mode picture. At larger power and adequate laser-cavity detuning, we demonstrate regenerative optomechanical oscillations involving a single mechanical mode, extending to both mechanical modes through modulation of the input laser drive at their frequency difference. This work constitutes an important advance towards engineering MOM systems with nearly degenerate mechanical modes as part of hybrid multipartite quantum systems.