Miracle-dz/UniFAR-FTUs huggingface dataset:
Numerical hydrodynamic simulations based on semi-analytic galaxy merger trees: method and Milky Way-like galaxies

We present a new approach to study galaxy evolution in a cosmological context. We combine cosmological merger trees and semi-analytic models of galaxy formation to provide the initial conditions for multi-merger hydrodynamic simulations. In this way we exploit the advantages of merger simulations (high resolution and inclusion of the gas physics) and semi-analytic models (cosmological background and low computational cost), and integrate them to create a novel tool. This approach allows us to study the evolution of various galaxy properties, including the treatment of the hot gaseous halo from which gas cools and accretes onto the central disc, which has been neglected in many previous studies. This method shows several advantages over other methods. As only the particles in the regions of interest are included, the run time is much shorter than in traditional cosmological simulations, leading to greater computational efficiency. Using cosmological simulations, we show that multiple mergers are expected to be more common than sequences of isolated mergers, and therefore studies of galaxy mergers should take this into account. In this pilot study, we present our method and illustrate the results of simulating ten Milky Way-like galaxies since z=1. We find good agreement with observations for the total stellar masses, star formation rates, cold gas fractions and disc scale length parameters. We expect that this novel numerical approach will be very useful for pursuing a number of questions pertaining to the transformation of galaxy internal structure through cosmic time.
Collisionless reconnection in the large guide field regime: Gyrokinetic versus particle-in-cell simulations

Results of the first validation of large guide field, Bg/δB0≫1, gyrokinetic simulations of magnetic reconnection at a fusion and solar corona relevant βi=0.01 and solar wind relevant βi=1 are presented, where δB0 is the reconnecting field. Particle-in-cell (PIC) simulations scan a wide range of guide magnetic field strength to test for convergence to the gyrokinetic limit. The gyrokinetic simulations display a high degree of morphological symmetry, to which the PIC simulations converge when βiBg/δB0≳1 and Bg/δB0≫1. In the regime of convergence, the reconnection rate, relative energy conversion, and overall magnitudes are found to match well between the PIC and gyrokinetic simulations, implying that gyrokinetics is capable of making accurate predictions well outside its regime of formal applicability. These results imply that in the large guide field limit many quantities resulting from the nonlinear evolution of reconnection scale linearly with the guide field.
A Way Out of the Bubble Trouble? - Upon Reconstructing the Origin of the Local Bubble and Loop I via Radioisotopic Signatures on Earth

Deep-sea archives all over the world show an enhanced concentration of the radionuclide $^{60}$Fe, isolated in layers dating from about 2.2 Myr ago. Since this comparatively long-lived isotope is not naturally produced on Earth, such an enhancement can only be attributed to extraterrestrial sources, particularly one or several nearby supernovae in the recent past. It has been speculated that these supernovae might have been involved in the formation of the Local Superbubble, our Galactic habitat. Here, we summarize our efforts in giving a quantitative evidence for this scenario. Besides analytical calculations, we present results from high-resolution hydrodynamical simulations of the Local Superbubble and its presumptive neighbor Loop I in different environments, including a self-consistently evolved supernova-driven interstellar medium. For the superbubble modeling, the time sequence and locations of the generating core-collapse supernova explosions are taken into account, which are derived from the mass spectrum of the perished members of certain, carefully preselected stellar moving groups. The release and turbulent mixing of $^{60}$Fe is followed via passive scalars, where the yields of the decaying radioisotope were adjusted according to recent stellar evolution calculations. The models are able to reproduce both the timing and the intensity of the $^{60}$Fe excess observed with rather high precision. We close with a discussion of recent developments and give future perspectives.
FORMATION OF LATE-TYPE SPIRAL GALAXIES: GAS RETURN FROM STELLAR POPULATIONS REGULATES DISK DESTRUCTION AND BULGE GROWTH

Spiral galaxies have most of their stellar mass in a large rotating disk, and only a modest fraction in a central spheroidal bulge. This challenges present models of galaxy formation: galaxies form at the center of dark matter halos through a combination of hierarchical merging and gas accretion along cold streams. Cosmological simulations thus predict that galaxies rapidly grow their bulge through mergers and instabilities and end up with most of their mass in the bulge and an angular momentum much below the observed level, except in dwarf galaxies. We propose that the continuous return of gas by stellar populations over cosmic times could help to solve this issue. A population of stars formed at a given instant typically returns half of its initial mass in the form of gas over 10 billion years, and the process is not dominated by supernovae explosions but by the long-term mass-loss from low- and intermediate-mass stars. Using simulations of galaxy formation, we show that this gas recycling can strongly affect the structural evolution of massive galaxies, potentially solving the bulge fraction issue, as the bulge-to-disk ratio of a massive galaxy can be divided by a factor of 3. The continuous recycling of baryons through star formation and stellar mass loss helps the growth of disks and their survival to interactions and mergers. Instead of forming only early-type, spheroid-dominated galaxies (S0 and ellipticals), the standard cosmological model can successfully account for massive late-type, disk-dominated spiral galaxies (Sb-Sc).
On the central stellar mass density and the inside-out growth of early-type galaxies

Recent theoretical and observational studies on the assembly of early-type galaxies (ETGs) point towards an inside-out growth of their stellar mass characterized by extended low-mass-density haloes grown around compact and dense cores. Models can form ETGs at high-z as compact spheroids that then grow in size through dry minor mergers. Dry mergers would affect mainly the outskirts of the galaxy, enlarging the size (i.e. the effective radius), keeping the inner parts and the total stellar mass nearly unchanged. Hence, the central stellar mass density will not change with time, in contrast to the stellar mass density within the effective radius, which should decrease with time as the effective radius increases. Some previous observations are interpreted as supporting inside-out growth, as the central stellar mass density of high-z ETGs is found to be similar to that of local ETGs. In this paper we derive the central stellar mass density within a fixed radius and the effective stellar mass density within the effective radius for a complete sample of 34 ETGs morphologically selected at 0.9 2. Furthermore, we show that the small scatter of the central mass density of ETGs compared with the large scatter of the effective mass density is simply a peculiar feature of the Sersic profile and hence is independent of redshift and of any assembly history experienced by galaxies. Thus, it has no connection with the possible inside-out growth of ETGs. Finally, we show a tight correlation between the central stellar mass density and the total stellar mass of ETGs in the sense that the central mass density increases with mass as . This implies that the fraction of the central stellar mass of ETGs decreases with the mass of the galaxy. These correlations are valid for the whole population of ETGs considered, independently of their redshift, suggesting that they originate in the early phases of their formation.
Regularized R\'enyi divergence minimization through Bregman proximal gradient algorithms

We study the variational inference problem of minimizing a regularized R\'enyi divergence over an exponential family, and propose a relaxed moment-matching algorithm, which includes a proximal-like step. Using the information-geometric link between Bregman divergences and the Kullback-Leibler divergence, this algorithm is shown to be equivalent to a Bregman proximal gradient algorithm. This novel perspective allows us to exploit the geometry of our approximate model while using stochastic black-box updates. We use this point of view to prove strong convergence guarantees including monotonic decrease of the objective, convergence to a stationary point or to the minimizer, and geometric convergence rates. These new theoretical insights lead to a versatile, robust, and competitive method, as illustrated by numerical experiments.
Fast and Scalable Channels in Kotlin Coroutines

Asynchronous programming has gained significant popularity over the last decade: support for this programming pattern is available in many popular languages via libraries and native language implementations, typically in the form of coroutines or the async/await construct. Instead of programming via shared memory, this concept assumes implicit synchronization through message passing. The key data structure enabling such communication is the rendezvous channel. Roughly, a rendezvous channel is a blocking queue of size zero, so both send(e) and receive() operations wait for each other, performing a rendezvous when they meet. To optimize the message passing pattern, channels are usually equipped with a fixed-size buffer, so sends do not suspend and put elements into the buffer until its capacity is exceeded. This primitive is known as a buffered channel. This paper presents a fast and scalable algorithm for both rendezvous and buffered channels. Similarly to modern queues, our solution is based on an infinite array with two positional counters for send(e) and receive() operations, leveraging the unconditional Fetch-And-Add instruction to update them. Yet, the algorithm requires non-trivial modifications of this classic pattern, in order to support the full channel semantics, such as buffering and cancellation of waiting requests. We compare the performance of our solution to that of the Kotlin implementation, as well as against other academic proposals, showing up to 9.8× speedup. To showcase its expressiveness and performance, we also integrated the proposed algorithm into the standard Kotlin Coroutines library, replacing the previous channel implementations.
Magnesium isotopes: a tool to understand self-enrichment in globular clusters

A critical issue in the asymptotic giant branch (AGB) self-enrichment scenario for the formation of multiple populations in Globular Clusters (GCs) is the inability to reproduce the magnesium isotopic ratios, despite the model in principle can account for the depletion of magnesium. In this work we analyze how the uncertainties on the various p-capture cross sections affect the results related to the magnesium content of the ejecta of AGB stars. The observed distribution of the magnesium isotopes and of the overall Mg-Al trend in M13 and NGC 6752 are successfully reproduced when the proton-capture rate by 25Mg at the temperatures 100 MK, in particular the 25Mg(p, gamma)26Alm channel, is enhanced by a factor 3 with respect to the most recent experimental determinations. This assumption also allows to reproduce the full extent of the Mg spread and the Mg-Si anticorrelation observed in NGC 2419. The uncertainties in the rate of the 25Mg(p,gamma)26Alm reaction at the temperatures of interest here leave space for our assumption and we suggest that new experimental measurements are needed to settle this problem. We also discuss the competitive model based on the super massive star nucleosynthesis.
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.
Numerical simulations of high Lundquist number relativistic magnetic reconnection

We present the results of two-dimensional and three-dimensional magnetohydrodynamical numerical simulations of relativistic magnetic reconnection, with particular emphasis on the dynamics of the plasma in a Petschek-type configuration with high Lundquist numbers, S � 10 5 10 8 . The numerical scheme adopted, allowing for unprecedented accuracy for this type of calculations, is based on high order finite volume and discontinuous Galerkin methods as recently proposed by Dumbser & Zanotti (2009). The possibility of producing high Lorentz factors is discussed, showing that Lorentz factors close to � 4 can be produced for a plasma parameter σm = 20. Moreover, we find that the Sweet-Parker layers are unstable, generating secondary magnetic islands, but only for S > Sc � 10 8 , much larger than what is reported in the Newtonian regime. Finally, the effects of a mildly anisotropic Ohm law are considered in a configuration with a guide magnetic field. Such effects produce only slightly faster reconnection rates and Lorentz factors of about 1% larger with respect to the perfectly isotropic Ohm law.
Deep IMU Bias Inference for Robust Visual-Inertial Odometry With Factor Graphs

Visual Inertial Odometry (VIO) is one of the most established state estimation methods for mobile platforms. However, when visual tracking fails, VIO algorithms quickly diverge due to rapid error accumulation during inertial data integration. This error is typically modeled as a combination of additive Gaussian noise and a slowly changing bias which evolves as a random walk. In this work, we propose to train a neural network to learn the true bias evolution. We implement and compare two common sequential deep learning architectures: LSTMs and Transformers. Our approach follows from recent learning-based inertial estimators, but, instead of learning a motion model, we target IMU bias explicitly, which allows us to generalize to locomotion patterns unseen in training. We show that our proposed method improves state estimation in visually challenging situations across a wide range of motions by quadrupedal robots, walking humans, and drones. Our experiments show an average 15% reduction in drift rate, with much larger reductions when there is total vision failure. Importantly, we also demonstrate that models trained with one locomotion pattern (human walking) can be applied to another (quadruped robot trotting) without retraining.
DISCORDANCE OF THE UNIFIED SCHEME WITH OBSERVED PROPERTIES OF QUASARS AND HIGH-EXCITATION GALAXIES IN THE 3CRR SAMPLE

We examine the consistency of the unified scheme of Fanaroff–Riley type II radio galaxies and quasars with their observed number and size distributions in the 3CRR sample. We separate the low-excitation galaxies from the high-excitation ones, as the former might not harbor a quasar within and thus may not be partaking in the unified scheme models. In the updated 3CRR sample, at low redshifts (z < 0.5), the relative number and luminosity distributions of high-excitation galaxies and quasars roughly match the expectations from the orientation-based unified scheme model. However, a foreshortening in the observed sizes of quasars, which is a must in the orientation-based model, is not seen with respect to radio galaxies even when the low-excitation galaxies are excluded. This dashes the hope that the unified scheme might still work if one includes only the high-excitation galaxies.
A study of Be stars in the Magellanic Clouds

We present the results of a photometric survey for Be stars in eleven young clusters in the Large Magellanic Cloud and fourteen young clusters in the Small Magellanic Cloud. B stars with hydrogen in emission are identified on the basis of their R-Hα colour. We find that Be star fraction in clusters decreases with cluster age, and also decreases with the metallicity.
Finite Sample Identification of Wide Shallow Neural Networks with Biases

Artificial neural networks are functions depending on a finite number of parameters typically encoded as weights and biases. The identification of the parameters of the network from finite samples of input-output pairs is often referred to as the \emph{teacher-student model}, and this model has represented a popular framework for understanding training and generalization. Even if the problem is NP-complete in the worst case, a rapidly growing literature -- after adding suitable distributional assumptions -- has established finite sample identification of two-layer networks with a number of neurons $m=\mathcal O(D)$, $D$ being the input dimension. For the range $D<m<D^2$ the problem becomes harder, and truly little is known for networks parametrized by biases as well. This paper fills the gap by providing constructive methods and theoretical guarantees of finite sample identification for such wider shallow networks with biases. Our approach is based on a two-step pipeline: first, we recover the direction of the weights, by exploiting second order information; next, we identify the signs by suitable algebraic evaluations, and we recover the biases by empirical risk minimization via gradient descent. Numerical results demonstrate the effectiveness of our approach.
The Star Formation&Chemical Evolution History of the Sculptor Dwarf Spheroidal Galaxy

We have combined deep photometry in the B,V and I bands from CTIO/MOSAIC of the Sculptor dwarf spheroidal galaxy, going down to the oldest Main Sequence Turn-Offs, with spectroscopic metallicity distributions of Red Giant Branch stars. This allows us to obtain the most detailed and complete Star Formation History to date, as well as an accurate timescale for chemical enrichment. The Star Formation History shows that Sculptor is dominated by old ($>$10 Gyr), metal-poor stars, but that younger, more metal-rich populations are also present. Using Star Formation Histories determined at different radii from the centre we show that Sculptor formed stars with an increasing central concentration with time. The old, metal-poor populations are present at all radii, while more metal-rich, younger stars are more centrally concentrated. We find that within an elliptical radius of 1 degree, or 1.5 kpc from the centre, a total mass in stars of 7.8$\times10^{6}$ M$_{\odot}$ was formed, between 14 and 7 Gyr ago, with a peak at 13$-$14 Gyr ago. We use the detailed Star Formation History to determine age estimates for individual Red Giant Branch stars with high resolution spectroscopic abundances. Thus, for the first time, we can directly determine detailed timescales for the evolution of individual chemical elements. We find that the trends in alpha-elements match what is expected from an extended, relatively uninterrupted period of star formation continuing for 6$-$7 Gyr. The knee in the alpha-element distribution occurs at an age of 10.9$\pm$1Gyr, suggesting that SNe Ia enrichment began $\approx2\pm$1Gyr after the start of star formation in Sculptor.
Disentangling Aesthetic and Technical Effects for Video Quality Assessment of User Generated Content

User-generated-content (UGC) videos have dominated the Internet during recent years. While it is well-recognized that the perceptual quality of these videos can be affected by diverse factors, few existing methods explicitly explore the effects of different factors in video quality assessment (VQA) for UGC videos, i.e. the UGC-VQA problem . In this work, we make the ﬁrst attempt to disentangle the effects of aesthetic quality issues and technical quality issues risen by the complicated video generation processes in the UGC-VQA problem. To overcome the absence of respective supervisions during disentanglement, we pro-pose the Limited View Biased Supervisions (LVBS) scheme where two separate evaluators are trained with decomposed views speciﬁcally designed for each issue. Composed of an Aesthetic Quality Evaluator (AQE) and a Technical Quality Evaluator (TQE) under the LVBS scheme, the proposed Disentangled Objective Video Quality Evaluator ( DOVER ) reach excellent performance (0.91 SRCC for KoNViD-1k, 0.89 SRCC for LSVQ, 0.88 SRCC for YouTube-UGC) in the UGC-VQA problem. More importantly, our blind subjective studies prove that the separate evaluators in DOVER can effectively match human perception on respective dis-entangled quality issues. Codes and demos are released in https://github.com/teowu/dover .
MFDNet: Towards Real-time Image Denoising On Mobile Devices

Deep convolutional neural networks have achieved great progress in image denoising tasks. However, their complicated architectures and heavy computational cost hinder their deployments on a mobile device. Some recent efforts in designing lightweight denoising networks focus on reducing either FLOPs (ﬂoating-point operations) or the number of parameters. However, these metrics are not directly correlated with the on-device latency. By performing extensive analysis and experiments, we identify the network architectures that can fully utilize powerful neural processing units (NPUs) and thus enjoy both low latency and excellent denoising performance. To this end, we propose a mobile-friendly denoising network, namely MFDNet . The experiments show that MFD-Net achieves state-of-the-art performance on real-world de-noising benchmarks SIDD and DND under real-time latency on mobile devices. The code and pre-trained models will be released.
THE STELLAR POPULATION STRUCTURE OF THE GALACTIC DISK

The spatial structure of stellar populations with different chemical abundances in the Milky Way (MW) contains a wealth of information on Galactic evolution over cosmic time. We use data on 14,699 red-clump stars from the APOGEE survey, covering , to determine the structure of mono-abundance populations (MAPs)—stars in narrow bins in and —accounting for the complex effects of the APOGEE selection function and the spatially variable dust obscuration. We determine that all MAPs with enhanced are centrally concentrated and are well-described as exponentials with a scale length of over the whole radial range of the disk. We discover that the surface-density profiles of low- MAPs are complex: they do not monotonically decrease outwards, but rather display a peak radius ranging from to at low . The extensive radial coverage of the data allows us to measure radial trends in the thickness of each MAP. While high- MAPs have constant scale heights, low- MAPs flare. We confirm, now with high-precision abundances, previous results that each MAP contains only a single vertical scale height and that low- , low- and high- , high- MAPs have intermediate ( ) scale heights that smoothly bridge the traditional thin- and thick-disk divide. That the high- , thick disk components do not flare is strong evidence against their thickness being caused by radial migration. The correspondence between the radial structure and chemical-enrichment age of stellar populations is clear confirmation of the inside-out growth of galactic disks. The details of these relations will constrain the variety of physical conditions under which stars form throughout the MW disk.