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The increasing quality of cosmological data has revealed some tensions that could be signalling the necessity of incorporating new physics into our cosmological model. One particularly intriguing possibility is the existence of elastic interactions between dark matter and dark energy. Not only do these interactions provide a natural mechanism to relief cosmological tensions, but there is also compelling observational evidence for them, to the extent that a detection could even be claimed. We review the potential of these scenarios in relation to the cosmological tensions and discuss distinctive signatures that can be probed with future data, thus providing a smoking gun for these interactions., Comment: 11 pages, 6 Figures

Current images of the supermassive black hole (SMBH) candidates at the center of our Galaxy and M87 have opened an unprecedented era for studying strong gravity and the nature of relativistic sources. Very-long-baseline interferometry (VLBI) data show images consistent with a central SMBH within General Relativity (GR). However, it is essential to consider whether other well-motivated dark compact objects within GR could produce similar images. Recent studies have shown that dark matter (DM) halos modeled as self-gravitating systems of neutral fermions can harbor very dense fermionic cores at their centers, which can mimic the spacetime features of a black hole (BH). Such dense, horizonless DM cores can satisfy the observational constraints: they can be supermassive and compact and lack a hard surface. We investigate whether such cores can produce similar observational signatures to those of BHs when illuminated by an accretion disk. We compute images and spectra of the fermion cores with a general-relativistic ray tracing technique, assuming the radiation originates from standard $\alpha$ disks, which are self-consistently solved within the current DM framework. Our simulated images possess a central brightness depression surrounded by a ring-like feature, resembling what is expected in the BH scenario. For Milky Way-like halos, the central brightness depressions have diameters down to $\sim 35\, \mu$as as measured from a distance of approximately $8\,$kpc. Finally, we show that the DM cores do not possess photon rings, a key difference from the BH paradigm, which could help discriminate between the models., Comment: 11 pages, 7 figures. Accepted for publication in MNRAS

Context. S26 is an extragalactic microquasar with the most powerful jets ever discovered. They have a kinetic luminosity of $L_{\rm j}\sim5\times 10^{40}\,{\rm erg\,s^{-1}}$. This implies that the accretion power to the black hole should be super-Eddington, of the order of $L_{\rm acc}\sim L_{\rm j}$. However, the observed X-ray flux of this system indicates an apparent very sub-Eddington accretion luminosity of $L_{\rm X}\approx 10^{37}\,{\rm erg\,s^{-1}}$. Aims. We aim to characterize the nature of S26, explain the system emission, and study the feasibility of super-Eddington microquasars as potential PeVatron sources. Methods. We first analyze X-ray observations of S26 obtained with XMM-Newton and model the super-Eddington disk and its wind. We then develop a jet model and study the particle acceleration and radiative processes that occur in shocks generated near the base of the jet and in its terminal region. Results. We find that the discrepancy between the jet and the apparent disk luminosities in S26 is caused by the complete absorption of the disk radiation by the wind ejected from the super-Eddington disk. The nonthermal X-rays are produced near the base of the jet, and the thermal X-rays are emitted in the terminal regions. The radio emission observed with the Australia Telescope Compact Array can be explained as synchrotron radiation produced at the reverse shock in the lobes. We also find that S26 can accelerate protons to PeV energies in both the inner jet and the lobes. The ultra-high energy protons accelerated in the lobes are injected into the ISM with a total power of $\sim 10^{36}\,{\rm erg\,s^{-1}}$. Conclusions. We conclude that S26 is a super-Eddington microquasar with a dense disk-driven wind that obscures the X-ray emission from the inner disk, and that the supercritical nature of the system allows the acceleration of cosmic rays to PeV energies., Comment: 14 pages, accepted for publication in A&A

Deep learning has shown tremendous progress in a wide range of digital pathology and medical image classification tasks. Its integration into safe clinical decision-making support requires robust and reliable models. However, real-world data comes with diversities that often lie outside the intended source distribution. Moreover, when test samples are dramatically different, clinical decision-making is greatly affected. Quantifying predictive uncertainty in models is crucial for well-calibrated predictions and determining when (or not) to trust a model. Unfortunately, many works have overlooked the importance of predictive uncertainty estimation. This paper evaluates whether predictive uncertainty estimation adds robustness to deep learning-based diagnostic decision-making systems. We investigate the effect of various carcinoma distribution shift scenarios on predictive performance and calibration. We first systematically investigate three popular methods for improving predictive uncertainty: Monte Carlo dropout, deep ensemble, and few-shot learning on lung adenocarcinoma classification as a primary disease in whole slide images. Secondly, we compare the effectiveness of the methods in terms of performance and calibration under clinically relevant distribution shifts such as in-distribution shifts comprising primary disease sub-types and other characterization analysis data; out-of-distribution shifts comprising well-differentiated cases, different organ origin, and imaging modality shifts. While studies on uncertainty estimation exist, to our best knowledge, no rigorous large-scale benchmark compares predictive uncertainty estimation including these dataset shifts for lung carcinoma classification., Comment: 17 pages, 2 figures, 5 tables

We demonstrate a novel approach to the reconstruction of scanning probe x-ray diffraction tomography data with anisotropic poly crystalline samples. The method involves reconstructing a voxel map containing an orientation distribution function in each voxel of an extended 3D sample. This method differs from existing approaches by not relying on a peak-finding and is therefore applicable to sample systems consisting of small and highly mosaic crystalline domains that are not handled well by existing methods. Samples of interest include bio-minerals and a range of small-graines microstructures common in engineering metals. By choosing a particular kind of basis functions, we can effectively utilize non-negativity in orientation-space for samples with sparse texture. This enables us to achieve stable solutions at high angular resolutions where the problem would otherwise be under determined. We demonstrate the new approach using data from a shot peened martensite sample where we are able to map the twinning micro structure in the interior of a bulk sample without resolving the individual lattice domains. We also demonstrate the approach on a piece of gastropods shell with a mosaic micro structure.

Jet flavour tagging is crucial in experimental high-energy physics. A tagging algorithm, DeepJetTransformer, is presented, which exploits a transformer-based neural network that is substantially faster to train. The DeepJetTransformer network uses information from particle flow-style objects and secondary vertex reconstruction as is standard for $b$- and $c$-jet identification supplemented by additional information, such as reconstructed V$^0$s and $K^{\pm}/\pi^{\pm}$ discrimination, typically not included in tagging algorithms at the LHC. The model is trained as a multiclassifier to identify all quark flavours separately and performs excellently in identifying $b$- and $c$-jets. An $s$-tagging efficiency of $40\%$ can be achieved with a $10\%$ $ud$-jet background efficiency. The impact of including V$^0$s and $K^{\pm}/\pi^{\pm}$ discrimination is presented. The network is applied on exclusive $Z \to q\bar{q}$ samples to examine the physics potential and is shown to isolate $Z \to s\bar{s}$ events. Assuming all other backgrounds can be efficiently rejected, a $5\sigma$ discovery significance for $Z \to s\bar{s}$ can be achieved with an integrated luminosity of $60~\text{nb}^{-1}$, corresponding to less than a second of the FCC-ee run plan at the $Z$ resonance., Comment: Version submitted to the European Physical Journal C

Fecal Microbiota Transplant (FMT) is an FDA approved treatment for recurrent Clostridium difficile infections, and is being explored for other clinical applications, from alleviating digestive and neurological disorders, to priming the microbiome for cancer treatment, and restoring microbiomes impacted by cancer treatment. Quantifying the extent of engraftment following an FMT is important in determining if a recipient didn't respond because the engrafted microbiome didn't produce the desired outcomes (a successful FMT, but negative treatment outcome), or the microbiome didn't engraft (an unsuccessful FMT and negative treatment outcome). The lack of a consistent methodology for quantifying FMT engraftment extent hinders the assessment of FMT success and its relation to clinical outcomes, and presents challenges for comparing FMT results and protocols across studies. Here we review 46 studies of FMT in humans and model organisms and group their approaches for assessing the extent to which an FMT engrafts into three criteria: 1) Chimeric Asymmetric Community Coalescence investigates microbiome shifts following FMT engraftment. 2) Donated Microbiome Indicator Features tracks donated microbiome features as a signal of engraftment with methods such as differential abundance testing based on the current sample collection, or tracking changes in feature abundances that have been previously identified. 3) Temporal Stability examines how resistant post-FMT recipient's microbiomes are to reverting back to their baseline microbiome. Investigated together, these criteria provide a clear assessment of microbiome engraftment. We discuss the pros and cons of each of these criteria, providing illustrative examples of their application. We also introduce key terminology and recommendations on how FMT studies can be analyzed for rigorous engraftment extent assessment., Comment: 18 pages, 6 figures, 2 supplemental tables

We examine the capacity of the complementarity spectrum to distinguish non-isomorphic digraphs. We focus on the seven families with exactly three complementarity eigenvalues. Our findings reveal that in some, but not all families, any two non-isomorphic members have different complementarity spectrum. Complementarity eigenvalues outperform traditional eigenvalues in the task of identifying graphs. Indeed, the question of whether graphs are uniquely determined by their complementarity spectrum remains unresolved, highlighting the significance of this tool in graph theory. Moreover, since the complementarity spectrum of a digraph was characterized as the set of spectral radii of the induced strongly connected subdigraphs, the results of this study provides useful structural information for important families of digraphs.

Cosmological models featuring an elastic interaction in the dark sector at linear order has been shown to provide a promising scenario for alleviating the $\sigma_8$ tension. A natural question for these scenarios is whether there could be a degeneracy between the interaction and massive neutrinos that also contribute to erasing structures at late times. In this work we investigate the presence of such a degeneracy and show that the two effects do not exhibit strong correlations.

Ferrofluids kept in place by permanent magnet quadrupoles can act as liquid walls to surround a second non-magnetic inside, resulting in a liquid fluidic channel with diameter size ranging from mm down to less than 10 micrometer. Micro particle tracking velocimetry (micro PTV) experiments and modeling show that near ideal plug flow is possible in such liquid-in-liquid channels due to the reduced friction at the walls. The measured fluids velocity profiles agree with the predictions of a hydrodynamic model of cylindrical symmetry with a minimal set of hypotheses. By introducing symmetry breaking elements in the system, we show how unique velocity and flow properties can be obtained. Our liquid-in-liquid confinement opens new possibilities for < 10 micrometer-sized microfluidics with low pressures and low shear, with flow characteristics not attainable in comparable solid-wall devices., Comment: 12 pages 7 figures

We show that the unsteadiness of turbulence has a drastic effect on turbulence parameters and in particle cluster formation. To this end we use direct numerical simulations of particle laden flows with a steady forcing that generates an unsteady large-scale flow. Particle clustering correlates with the instantaneous Taylor-based flow Reynolds number, and anti-correlates with its instantaneous turbulent energy dissipation constant. A dimensional argument for these correlations is presented. In natural flows, unsteadiness can result in extreme particle clustering, which is stronger than the clustering expected from averaged inertial turbulence effects.

The description of the $T\bar{T}$ deformation in terms of two-dimensional gravity is analyzed from the Hamiltonian point of view, in a manner analogous to the ADM description of general relativity. We find that the Hamiltonian constraints of the theory imply relations between target-space momentum at finite volume which are equivalent to the $T\bar{T}$ finite volume flow equations. This fully-quantum $T\bar{T}$ result emerges already at the classical level within the gravitational theory. We exemplify the analysis for the case when the undeformed sector is a collection of $D-2$ free massless scalars, where it is shown that -- somewhat non-trivially -- the target-space two-dimensional Poincar\'e symmetry is extended to $D$ dimensions. The connection between canonical quantization of this constrained Hamiltonian system and previous path integral quantizations is also discussed. We extend our analysis to the ``gravitational'' description of $J\bar{T}$-type deformations, where it is found that the flow equations obtained involve deformations that twist the spatial boundary conditions., Comment: 41 pages, no figures, comments welcome

We provide the quantization of a charged black hole. We consider a redefinition of the scalar constraint in order to render the algebra of constraints as a Lie algebra. We apply loop quantum gravity techniques adhered to a novel improved dynamics scheme. We show that the model is solvable in closed form. We compute effective geometries, and show that the resulting effective space-times replace the inner horizon with a transition surface that connects trapped and antitrapped regions within the charged black hole interior. Quantum effects therefore stabilize the classical inner Cauchy horizons, as long as the charge is small compared to the mass. We further discuss the properties of these effective geometries by defining an effective stress-energy tensor out of the Einstein tensor, concluding that the null energy condition is violated., Comment: 18 pages, 2 figures

Introduction. Noisy measurements frequently cause noisy and inaccurate images in impedance imaging. No post-processing technique exists to calculate the propagation of measurement noise and use this to suppress noise in the image. Objectives. The objectives of this work were (1) to develop a post-processing method for noise-based correction (NBC) in impedance tomography, (2) to test whether NBC improves image quality in electrical impedance tomography (EIT), (3) to determine whether it is preferable to use correlated or uncorrelated noise for NBC, (4) to test whether NBC works with in vivo data and (5) to test whether NBC is stable across model and perturbation geometries. Methods. EIT was performed in silico in a 2D homogeneous circular domain and an anatomically realistic, heterogeneous 3D human head domain for four perturbations and 25 noise levels in each case. This was validated by performing EIT for four perturbations in a circular, saline tank in 2D as well as a human head-shaped saline tank with a realistic skull-like layer in 3D. Images were assessed on the weighted spatial variance (WSV) with respect to the target image. The effect of NBC was also tested for example in vivo EIT data of lung ventilation in a human thorax and cortical activity in a rat brain. Results. On visual inspection, NBC maintained or increased image quality for all perturbations and noise levels in 2D and 3D experimentally and in silico. Analysis of the WSV showed that NBC significantly improved the WSV in nearly all cases. When the WSV was inferior with NBC, this was either visually imperceptible or a transformation between noisy images. For in vivo data, NBC improved image quality in all cases and preserved the expected shape of the reconstructed perturbation. In practice, uncorrelated NBC performed better than correlated NBC and is recommended as a general-use post-processing technique in EIT., Comment: 23 pages, 13 figures