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After a merger of two massive black holes (MBHs), the remnant receives a gravitational wave (GW) recoil kick that can have a strong effect on its future evolution. The magnitude of the kick ($v_\mathrm{recoil}$) depends on the mass ratio and the alignment of the spins and orbital angular momenta, therefore on the previous evolution of the MBHs. We investigate the cosmic effect of GW recoil by running for the first time a high-resolution cosmological simulation including GW recoil that depends on the MBH spins (evolved through accretion and mergers), masses and dynamics computed self-consistently. We also run a twin simulation without GW recoil. The simulations are run down to $z=4.4$. We find that GW recoil reduces the growth of merger remnants, and can have a significant effect on the MBH-galaxy correlations and the merger rate. We find large recoil kicks across all galaxy masses in the simulation, up to a few $10^{11}\,\rm M_\odot$. The effect of recoil can be significant even if the MBHs are embedded in a rotationally supported gaseous structure. We investigate the dynamics of recoiling MBHs and find that MBHs remain in the centre of the host galaxy for low $v_\mathrm{recoil}/v_\mathrm{esc}$ and escape rapidly for high $v_\mathrm{recoil}/v_\mathrm{esc}$. Only if $v_\mathrm{recoil}$ is comparable to $v_\mathrm{esc}$ the MBHs escape the central region of the galaxy but might remain as wandering MBHs until the end of the simulation. Recoiling MBHs are a significant fraction of the wandering MBH population. Although the dynamics of recoiling MBHs may be complex, some retain their initial radial orbits but are difficult to discern from other wandering MBHs on radial orbits. Others scatter with the halo substructure or circularise in the asymmetric potential. Our work highlights the importance of including GW recoil in cosmological simulation models., Comment: submitted to A&A

The Radon cumulative distribution transform (R-CDT) exploits one-dimensional Wasserstein transport and the Radon transform to represent prominent features in images. It is closely related to the sliced Wasserstein distance and facilitates classification tasks, especially in the small data regime, like the recognition of watermarks in filigranology. Here, a typical issue is that the given data may be subject to affine transformations caused by the measuring process. The aim of this paper is to make the R-CDT and the related sliced Wasserstein distance invariant under affine transformations. For this, we propose a two-step normalization of the R-CDT and prove that our novel transform allows linear separation of affinely transformed image classes. The theoretical results are supported by numerical experiments showing a significant increase of the classification accuracy compared to the original R-CDT.

Negative differential resistance (NDR) behavior observed in several transition metal oxides is crucial for developing next-generation memory devices and neuromorphic computing systems. NbO2-based memristors exhibit two regions of NDR at room temperature, making them promising candidates for such applications. Despite this potential, the physical mechanisms behind the onset and the ability to engineer these NDR regions remain unclear, hindering further development of these devices for applications. This study employed electrical transport and ultra-low frequency noise spectroscopy measurements to investigate two distinct NDR phenomena in nanoscale thin films of NbO2. By analyzing the residual current fluctuations as a function of time, we find spatially inhomogeneous and non-linear conduction near NDR-1 and a two-state switching near NDR-2, leading to an insulator-to-metal (IMT) transition. The power spectral density of the residual fluctuations exhibits significantly elevated noise magnitudes around both NDR regions, providing insights into physical mechanisms and device size scaling for electronic applications. A simple theoretical model, based on the dimerization of correlated insulators, offers a comprehensive explanation of observed transport and noise behaviors near NDRs, affirming the presence of non-linear conduction followed by an IMT connecting macroscopic device response to transport signatures at atomic level.

The Zirconium (Z = 40) isotopic chain has attracted interest for more than four decades. The abrupt lowering of the energy of the first $2^+$ state and the increase in the transition strength B(E2; $2_1^\rightarrow 0_1^+$ going from $^{98}$Zr to $^{100}$Zr has been the first example of "quantum phase transition" in nuclear shapes, which has few equivalents in the nuclear chart. Although a multitude of experiments have been performed to measure nuclear properties related to nuclear shapes and collectivity in the region, none of the measured lifetimes were obtained using the Recoil Distance Doppler Shift method in the $\gamma\gamma$-coincidence mode where a gate on the direct feeding transition of the state of interest allows a strict control of systematical errors. This work reports the results of lifetime measurements for the first yrast excited states in $^{98-104}$Zr carried out to extract reduced transition probabilities. The new lifetime values in $\gamma\gamma$-coincidence and $\gamma$-single mode are compared with the results of former experiments. Recent predictions of the Interacting Boson Model with Configuration Mixing, the Symmetry Conserving Configuration Mixing model based on the Hartree-Fock-Bogoliubov approach and the Monte Carlo Shell Model are presented and compared with the experimental data.

The detection of gravitational waves (GWs) from massive black hole binary (MBHB) coalescence motivates the development of a sub-grid model. We present RAMCOAL, integrated into the RAMSES code, which simulates the orbital evolution of MBHBs, accounting for stellar and gaseous dynamical friction (DF), stellar scattering, circumbinary disk interactions, and GW emission at scales below the simulation resolution. Unlike post-processing approaches, RAMCOAL tracks the real-time evolution of MBHBs within hydrodynamical simulations of galaxies using local quantities to model dynamics and accretion. This enables more accurate predictions of both GW signals and the properties of merging black holes. We validate RAMCOAL across isolated and merging galaxy setups at resolutions of 10, 50, and 100 pc, with and without black hole accretion and feedback. In addition, we test the model in seven galaxy merger scenarios at 100 pc resolution. These tests demonstrate that RAMCOAL is largely resolution-independent and successfully captures the effects of DF from stars, dark matter, and gas, loss-cone scattering, viscous drag from circumbinary disks, and GW emission -- all within a realistic galactic environment, even at low resolutions. With RAMCOAL, we can better estimate MBHB coalescence rates and the GW background, while providing insights into the electromagnetic counterparts of GW sources. This approach bridges the gap between electromagnetic observations and GW detection, offering a more comprehensive understanding of MBHB evolution in cosmological simulations., Comment: Submitted to Astronomy & Astrophysics, 20 pages, 11 figures

Astrophysical black holes (BHs) have two fundamental properties: mass and spin. While the mass-evolution of BHs has been extensively studied, much less work has been done on predicting the distribution of BH spins. In this paper we present the spin evolution for a sample of intermediate-mass and massive BHs from the newHorizon simulation, which evolved BH spin across cosmic time in a full cosmological context through gas accretion, BH-BH mergers and BH feedback including jet spindown. As BHs grow, their spin evolution alternates between being dominated by gas accretion and BH mergers. Massive BHs are generally highly spinning. Accounting for the spin energy extracted through the Blandford-Znajek mechanism increases the scatter in BH spins, especially in the mass range $10^{5-7} \rm \ M_\odot$, where BHs had previously been predicted to be almost universally maximally spinning. We find no evidence for spin-down through efficient chaotic accretion. As a result of their high spin values, massive BHs have an average radiative efficiency of $<\varepsilon_{\rm r}^{\rm thin}> \approx 0.19$. As BHs spend much of their time at low redshift with a radiatively inefficient thick disc, BHs in our sample remain hard to observe. Different observational methods probe different sub-populations of BHs, significantly influencing the observed distribution of spins. Generally, X-ray-based methods and higher luminosity cuts increase the average observed BH spin. When taking BH spin evolution into account, BHs inject on average between 3 times (in quasar mode) and 8 times (in radio mode) as much feedback energy into their host galaxy as previously assumed., Comment: 21 pages, accepted for publication in MNRAS

The James Webb Space telescope has discovered an abundant population of broad line emitters, typical signposts for Active Galactic Nuclei (AGN). Many of these sources have red colors and a compact appearance that has led to naming them `Little Red Dots'. In this paper we develop a detailed framework to estimate the photometry of AGN embedded in galaxies extracted from the Obelisk cosmological simulation to understand the properties of color-selected Little Red Dots (cLRDs) in the context of the full AGN and massive black hole population. We find that using realistic spectral energy distributions (SEDs) and attenuation for AGN we can explain the shape of the cLRD SED as long as galaxies host a sufficiently luminous AGN that is not too much or too little attenuated. When attenuation is too low or too high, AGN do not enter the cLRD selection, because the AGN dominates over the host galaxy too much in blue filters, or it does not contribute to photometry anywhere, respectively. cLRDs are also characterized by high Eddington ratios, possibility super-Eddington, and/or high ratios between black hole and stellar mass., Comment: Submitted to A&A

The method of filtered back projection (FBP) is a widely used reconstruction technique in X-ray computerized tomography (CT), which is particularly important in clinical diagnostics. To reduce scanning times and radiation doses in medical CT settings, enhancing the reconstruction quality of the FBP method is essential. To this end, this paper focuses on analytically optimizing the applied filter function. We derive a formula for the filter that minimizes the expected squared $\mathrm{L}^2$-norm of the difference between the FBP reconstruction, given infinite noisy measurement data, and the true target function. Additionally, we adapt our derived filter to the case of finitely many measurements. The resulting filter functions have a closed-form representation, do not require a training dataset, and, thus, provide an easy-to-implement, out-of-the-box solution. Our theoretical findings are supported by numerical experiments based on both simulated and real CT data., Comment: 35 pages, 9 figures

The electrical properties and performance characteristics of niobium dioxide (NbO$_\mathrm{2}$)-based memristive devices are examined at cryogenic temperatures. Sub-stoichiometric Nb$_\mathrm{2}$O$_\mathrm{5}$ was deposited via magnetron sputtering and patterned in microscale (2$\times$2 - 15$\times$15 $\mu$m$^2$) cross-bar Au/Ru/NbO$_\mathrm{x}$/Pt devices and electroformed at 3-5 V to make NbO$_\mathrm{2}$ filaments. At cryogenic temperatures, the threshold voltage ($V_\mathrm{th}$) increased by more than a factor of 3. The hold voltage ($V_\mathrm{h}$) was significantly lower than the threshold voltage for fast voltage sweeps (200 ms per measurement). If the sample is allowed to cool between voltage measurements, the hold voltage increases, but never reaches the threshold voltage, indicating the presence of non-volatile Nb$_\mathrm{2}$O$_\mathrm{5}$ in the filament. The devices have an activation energy of $E_a \approx 1.4$ eV, lower than other NbO$_\mathrm{2}$ devices reported. Our works shows that even nominally ``bad" memristive devices can be improved by reducing the leakage current and increases the sample resistance at cryogenic temperatures., Comment: 16 pages, 5 figures

Recent advancements in propagation-based phase-contrast imaging, such as hierarchical imaging, have enabled the visualization of internal structures in large biological specimens and material samples. However, wavefront marker-based techniques, which provide quantitative electron density information, face challenges when imaging larger objects due to stringent beam stability requirements and potential structural changes in objects during longer measurements. Extending the fields-of-view of these methods is crucial for obtaining comparable quantitative results across beamlines and adapting to the smaller beam profiles of fourth-generation synchrotron sources. We introduce a novel technique combining an adapted eigenflat optimization with deformable image registration to address the challenges and enable quantitative high-resolution scans of centimeter-sized objects with micrometre resolution. We demonstrate the potential of the method by obtaining an electron density map of a rat brain sample 15 mm in diameter using speckle-based imaging, despite the limited horizontal field-of-view of 6 mm of the beamline (PETRA III, P05, operated by Hereon at DESY). This showcases the ability of the technique to significantly widen the range of application of wavefront marker-based techniques in both biological and materials science research., Comment: *The authors contributed equally to this work

Reference classes in healthcare establish healthy norms, such as pediatric growth charts of height and weight, and are used to chart deviations from these norms which represent potential clinical risk. How the demographics of the reference class influence clinical interpretation of deviations is unknown. Using normative modeling, a method for building reference classes, we evaluate the fairness (racial bias) in reference models of structural brain images that are widely used in psychiatry and neurology. We test whether including race in the model creates fairer models. We predict self-reported race using the deviation scores from three different reference class normative models, to better understand bias in an integrated, multivariate sense. Across all of these tasks, we uncover racial disparities that are not easily addressed with existing data or commonly used modeling techniques. Our work suggests that deviations from the norm could be due to demographic mismatch with the reference class, and assigning clinical meaning to these deviations should be done with caution. Our approach also suggests that acquiring more representative samples is an urgent research priority.

Significant research has focused on low-power stochastic devices built from memristive materials. These devices foster neuromorphic approaches to computational efficiency enhancement in merged biomimetic and CMOS architectures due to their ability to phase transition from a dielectric to a metal at an increased temperature. Niobium dioxide has a volatile memristive phase change that occurs $\sim$800$^\circ$C~that makes it an ideal candidate for future neuromorphic electronics. A straightforward optical system has been developed on a horizontal tube furnace for \emph{in situ} spectral measurements as an as-grown \NbtOf\ film is annealed and ultimately crystallizes as \NbOt. The system measures the changing spectral transmissivity of \NbtOf\ as it undergoes both reduction and crystallization processes. We were also able to measure the transition from metallic-to-non-metallic \NbOt\ during the cooldown phase, which is shown to occur about 100$^\circ$C~ lower on a sapphire substrate than fused silica. After annealing, the material properties of the \NbtOf\ and \NbOt\ were assessed via X-ray photoelectron spectroscopy, X-ray diffraction, and 4-point resistivity, confirming that we have made crystalline \NbOt.

The current standard model of cosmology successfully describes a variety of measurements, but the nature of its main ingredients, dark matter and dark energy, remains unknown. Euclid is a medium-class mission in the Cosmic Vision 2015-2025 programme of the European Space Agency (ESA) that will provide high-resolution optical imaging, as well as near-infrared imaging and spectroscopy, over about 14,000 deg^2 of extragalactic sky. In addition to accurate weak lensing and clustering measurements that probe structure formation over half of the age of the Universe, its primary probes for cosmology, these exquisite data will enable a wide range of science. This paper provides a high-level overview of the mission, summarising the survey characteristics, the various data-processing steps, and data products. We also highlight the main science objectives and expected performance., Comment: Accepted for publication in the A&A special issue`Euclid on Sky'

Since the advent of Deepfakes in digital media, the development of robust and reliable detection mechanism is urgently called for. In this study, we explore a novel approach to Deepfake detection by utilizing electroencephalography (EEG) measured from the neural processing of a human participant who viewed and categorized Deepfake stimuli from the FaceForensics++ datset. These measurements serve as input features to a binary support vector classifier, trained to discriminate between real and manipulated facial images. We examine whether EEG data can inform Deepfake detection and also if it can provide a generalized representation capable of identifying Deepfakes beyond the training domain. Our preliminary results indicate that human neural processing signals can be successfully integrated into Deepfake detection frameworks and hint at the potential for a generalized neural representation of artifacts in computer generated faces. Moreover, our study provides next steps towards the understanding of how digital realism is embedded in the human cognitive system, possibly enabling the development of more realistic digital avatars in the future.

The low efficiency of conventional liquefaction technologies based on the Joule-Thomson expansion makes liquid hydrogen currently not attractive enough for large-scale energy-related technologies that are important for the transition to a carbon-neutral society. Magnetocaloric hydrogen liquefaction has great potential to achieve higher efficiency and is therefore a crucial enabler for affordable liquid hydrogen. Cost-effective magnetocaloric materials with large magnetic entropy and adiabatic temperature changes in the temperature range of 77 $\sim$ 20 K under commercially practicable magnetic fields are the foundation for the success of magnetocaloric hydrogen liquefaction. Heavy rare-earth-based magnetocaloric intermetallic compounds generally show excellent magnetocaloric performances, but the heavy rare-earth elements (Gd, Tb, Dy, Ho, Er, and Tm) are highly critical in resources. Yttrium and light rare-earth elements (La, Ce, Pr, and Nd) are relatively abundant, but their alloys generally show less excellent magnetocaloric properties. A dilemma appears: higher performance or lower criticality? In this review, we study how cryogenic temperature influences magnetocaloric performance by first reviewing heavy rare-earth-based intermetallic compounds. Next, we look at light rare-earth-based, "mixed" rare-earth-based, and Gd-based intermetallic compounds with the nature of the phase transition order taken into consideration, and summarize ways to resolve the dilemma.

Agile beam management is key for providing seamless millimeter wave (mm-wave) connectivity given the site-specific spatio-temporal variations of the mm-wave channel. Leveraging non radio frequency (RF) sensor inputs for environment awareness, e.g. via machine learning (ML) techniques, can greatly enhance RF-based beam steering. To overcome the lack of diverse publicly available multi-modal mm-wave datasets for the design and evaluation of such novel beam steering approaches, we demonstrate our software-defined radio multi-band mm-wave measurement platform which integrates multi-modal sensors towards environment-aware beam management., Comment: 2 pages, 1 figure

We present JWST MIRI/NIRSpec observations of the extremely red quasar SDSS J165202.64+172852.3 at z~3, one of the most luminous quasars known to date, driving powerful outflows and hosting a clumpy starburst, amidst several interacting companions. We estimate the black hole (BH) mass of the system based on the broad H$\alpha$ and H$\beta$ lines, as well as the Pa$\beta$ emission in the IR and MgII in the UV. We recover a very broad range of mass estimates, with constraints ranging between log $M_{\rm BH}$=9 and 10.1, which is exacerbated if imposing a uniform BLR geometry at all wavelengths. Several factors may contribute to the large spread: measurement uncertainties (insufficient sensitivity to detect the broadest component of the faint Pa$\beta$ line, spectral blending, ambiguities in the broad/narrow component distinction), lack of virial equilibrium (in a system characterised by powerful outflows and rapid accretion), and uncertainties on the luminosity-inferred size of the broad line region, a.o. given central dust obscuration. We constrain the stellar mass via SED fitting, suggesting the host to be extremely massive at $10^{12.8\pm 0.5} M_\odot$ - ~2 dex above the characteristic mass of the Schechter fit to the z=3 stellar mass function. Notably, J1652's central BH might be interpreted as being either undermassive, overmassive, or in line with the BH mass-stellar mass relation, depending on the choice of assumptions. The recovered Eddington ratio varies accordingly, but exceeds 10% in any case. We put our results into context by providing an extensive overview and discussion of recent literature results and their associated assumptions. Our findings provide an important demonstration of the uncertainties inherent in virial BH mass estimates, which are of particular relevance in the JWST era given the growing number of studies on rapidly accreting quasars at high redshift., Comment: 15 pages, 7 figures, submitted to A&A, comments welcome

NeRF-based 3D-aware Generative Adversarial Networks (GANs) like EG3D or GIRAFFE have shown very high rendering quality under large representational variety. However, rendering with Neural Radiance Fields poses challenges for 3D applications: First, the significant computational demands of NeRF rendering preclude its use on low-power devices, such as mobiles and VR/AR headsets. Second, implicit representations based on neural networks are difficult to incorporate into explicit 3D scenes, such as VR environments or video games. 3D Gaussian Splatting (3DGS) overcomes these limitations by providing an explicit 3D representation that can be rendered efficiently at high frame rates. In this work, we present a novel approach that combines the high rendering quality of NeRF-based 3D-aware GANs with the flexibility and computational advantages of 3DGS. By training a decoder that maps implicit NeRF representations to explicit 3D Gaussian Splatting attributes, we can integrate the representational diversity and quality of 3D GANs into the ecosystem of 3D Gaussian Splatting for the first time. Additionally, our approach allows for a high resolution GAN inversion and real-time GAN editing with 3D Gaussian Splatting scenes. Project page: florian-barthel.github.io/gaussian_decoder, Comment: CVPRW

Obtaining reliable distance estimates to gas clouds within the Milky Way is challenging in the absence of certain tracers. The kinematic distance approach has been used as an alternative, derived from the assumption of circular trajectories around the Galactic centre. Consequently, significant errors are expected in regions where gas flow deviates from purely circular motions. We aim to quantify the systematic errors that arise from the kinematic distance method in the presence of a Galactic potential that is non-axisymmetric. We investigate how these errors differ in certain regions of the Galaxy and how they relate to the underlying dynamics. We perform 2D hydrodynamical simulation of the gas disk with the moving-mesh code Arepo, adding the capability of using an external potential provided by the Agama library for galactic dynamics. We introduce a new analytic potential of the Milky Way, taking elements from existing models and adjusting parameters to match recent observational constraints. In line with results of previous studies, we report significant errors in the kinematic distance estimate for gas close to the Sun, along sight lines towards the Galactic centre and anti-centre, and associated with the Galactic bar. Kinematic distance errors are low within the spiral arms as gas resides close to local potential minima and the resulting LOS velocity is similar to what is expected for an axisymmetric potential. Interarm regions exhibit large deviations at any given Galactic radius. This is caused by the gas being sped up or slowed down as it travels into or out of spiral arms. In addition, we identify 'zones of avoidance' in the lv-diagram, where the kinematic distance method is particularly unreliable and should only be used with caution, and we find a power law relation between the kinematic distance error and the deviation of the projected LOS velocity from circular motion., Comment: 23 pages, 20 figures, 6 tables, accepted for publication in A&A

Denoising Diffusion Probabilistic models have become increasingly popular due to their ability to offer probabilistic modeling and generate diverse outputs. This versatility inspired their adaptation for image segmentation, where multiple predictions of the model can produce segmentation results that not only achieve high quality but also capture the uncertainty inherent in the model. Here, powerful architectures were proposed for improving diffusion segmentation performance. However, there is a notable lack of analysis and discussions on the differences between diffusion segmentation and image generation, and thorough evaluations are missing that distinguish the improvements these architectures provide for segmentation in general from their benefit for diffusion segmentation specifically. In this work, we critically analyse and discuss how diffusion segmentation for medical images differs from diffusion image generation, with a particular focus on the training behavior. Furthermore, we conduct an assessment how proposed diffusion segmentation architectures perform when trained directly for segmentation. Lastly, we explore how different medical segmentation tasks influence the diffusion segmentation behavior and the diffusion process could be adapted accordingly. With these analyses, we aim to provide in-depth insights into the behavior of diffusion segmentation that allow for a better design and evaluation of diffusion segmentation methods in the future.

Deep learning-based image generation has seen significant advancements with diffusion models, notably improving the quality of generated images. Despite these developments, generating images with unseen characteristics beneficial for downstream tasks has received limited attention. To bridge this gap, we propose Style-Extracting Diffusion Models, featuring two conditioning mechanisms. Specifically, we utilize 1) a style conditioning mechanism which allows to inject style information of previously unseen images during image generation and 2) a content conditioning which can be targeted to a downstream task, e.g., layout for segmentation. We introduce a trainable style encoder to extract style information from images, and an aggregation block that merges style information from multiple style inputs. This architecture enables the generation of images with unseen styles in a zero-shot manner, by leveraging styles from unseen images, resulting in more diverse generations. In this work, we use the image layout as target condition and first show the capability of our method on a natural image dataset as a proof-of-concept. We further demonstrate its versatility in histopathology, where we combine prior knowledge about tissue composition and unannotated data to create diverse synthetic images with known layouts. This allows us to generate additional synthetic data to train a segmentation network in a semi-supervised fashion. We verify the added value of the generated images by showing improved segmentation results and lower performance variability between patients when synthetic images are included during segmentation training. Our code will be made publicly available at [LINK].

We introduce a model for dust evolution in the RAMSES code for simulations of galaxies with a resolved multiphase interstellar medium. Dust is modelled as a fluid transported with the gas component, and is decomposed into two sizes, 5 nm and 0.1 $\mu\rm m$, and two chemical compositions for carbonaceous and silicate grains. Using a suite of isolated disc simulations with different masses and metallicities, the simulations can explore the role of these processes in shaping the key properties of dust in galaxies. The simulated Milky Way analogue reproduces the dust-to-metal mass ratio (DTM), depletion factors, size distribution and extinction curves of the Milky Way. Galaxies with lower metallicities reproduce the observed decrease in the DTM with metallicity at around a few 0.1 $\rm Z_\odot$. This break in the DTM corresponds to a galactic gas metallicity threshold that marks the transition from an ejecta-dominated to an accretion-dominated grain growth, and that is different for silicate and carbonaceous grains, with $\simeq$ 0.1 $\rm Z_\odot$ and $\simeq$ 0.5 $\rm Z_\odot$ respectively. This leads to more Magellanic Cloud-like extinction curves, i.e. with steeper slopes in the ultraviolet and a weaker bump feature at 217.5 nm, in galaxies with lower masses and lower metallicities. Steeper slopes in these galaxies are caused by the combination of the higher efficiency of gas accretion by silicate relative to carbonaceous grains and by the low rates of coagulation that preserves the amount of small silicate grains. Weak bumps are due to the overall inefficient accretion growth of carbonaceous dust at low metallicity, whose growth is mostly supported by the release of large grains in SN ejecta. We also show that the formation of CO molecules is a key component to limit the ability of carbonaceous dust to grow, in particular in low-metallicity gas-rich galaxies., Comment: A&A accepted

The genetic blueprint for the essential functions of life is encoded in DNA, which is translated into proteins -- the engines driving most of our metabolic processes. Recent advancements in genome sequencing have unveiled a vast diversity of protein families, but compared to the massive search space of all possible amino acid sequences, the set of known functional families is minimal. One could say nature has a limited protein "vocabulary." The major question for computational biologists, therefore, is whether this vocabulary can be expanded to include useful proteins that went extinct long ago, or maybe never evolved in the first place. We outline a computational approach to solving this problem. By merging evolutionary algorithms, machine learning (ML), and bioinformatics, we can facilitate the development of completely novel proteins which have never existed before. We envision this work forming a new sub-field of computational evolution we dub evolutionary algorithms simulating molecular evolution (EASME)., Comment: 7 pages, 2 figures

(Reduced) Using the recent cosmological high-resolution zoom-in simulations, NewHorizon and Galactica, in which the evolution of black hole spin is followed on the fly, we have tracked the cosmic history of a hundred of black holes (BHs) with a mass greater than 2x10^4 Ms. For each of them, we have studied the variations of the three dimensional angle (Psi) subtended between the BH spins and the angular momentum vectors of their host galaxies. The analysis of the individual evolution of the most massive BHs suggests that they are generally passing by three different regimes. First, for a short period after their birth, low mass BHs (<3x10^4 Ms) are rapidly spun up by gas accretion and their spin tends to be aligned with their host galaxy spin. Then follows a second phase in which the accretion of gas onto low mass BHs (<10^5 Ms) is quite chaotic and inefficient, reflecting the complex and disturbed morphologies of forming proto-galaxies at high redshifts. The variations of Psi are rather erratic during this phase and are mainly driven by the rapid changes of the direction of the galaxy angular momentum. Then, in a third and long phase, BHs are generally well settled in the center of galaxies around which the gas accretion becomes much more coherent (>10^5 Ms). In this case, the BH spins tend to be well aligned with the angular momentum of their host galaxy and this configuration is generally stable even though BH merger episodes can temporally induce misalignment. We have also derived the distributions of cos(Psi) at different redshifts and found that BHs and galaxy spins are generally aligned. Finally, based on a Monte Carlo method, we also predict statistics for the 2-d projected spin-orbit angles lambda. In particular, the distribution of lambda traces well the alignment tendency in the 3-d analysis. Such predictions provide an interesting background for future observational analyses., Comment: Accepted for publication in A&A (12/03/2024)