Daily Papers

Local feature frameworks are difficult to learn in an end-to-end fashion, due to the discreteness inherent to the selection and matching of sparse keypoints. We introduce DISK (DIScrete Keypoints), a novel method that overcomes these obstacles by leveraging principles from Reinforcement Learning (RL), optimizing end-to-end for a high number of correct feature matches. Our simple yet expressive probabilistic model lets us keep the training and inference regimes close, while maintaining good enough convergence properties to reliably train from scratch. Our features can be extracted very densely while remaining discriminative, challenging commonly held assumptions about what constitutes a good keypoint, as showcased in Fig. 1, and deliver state-of-the-art results on three public benchmarks.

Graph neural networks (GNNs) are machine learning models specialized for graph data and widely used in many applications. To train GNNs on large graphs that exceed CPU memory, several systems store data on disk and conduct out-of-core processing. However, these systems suffer from either read amplification when reading node features that are usually smaller than a disk page or degraded model accuracy by treating the graph as disconnected partitions. To close this gap, we build a system called DiskGNN, which achieves high I/O efficiency and thus fast training without hurting model accuracy. The key technique used by DiskGNN is offline sampling, which helps decouple graph sampling from model computation. In particular, by conducting graph sampling beforehand, DiskGNN acquires the node features that will be accessed by model computation, and such information is utilized to pack the target node features contiguously on disk to avoid read amplification. Besides, also adopts designs including four-level feature store to fully utilize the memory hierarchy to cache node features and reduce disk access, batched packing to accelerate the feature packing process, and pipelined training to overlap disk access with other operations. We compare DiskGNN with Ginex and MariusGNN, which are state-of-the-art systems for out-of-core GNN training. The results show that DiskGNN can speed up the baselines by over 8x while matching their best model accuracy.

Volatiles like H_2O are present as ice in solids in the outer cold regions of protoplanetary disks and as vapor in the warm inner regions within the water snow line. Icy pebbles drifting inwards from the outer disk sublimate after crossing the snow line, enriching the inner disk with solid mass and water vapor. Meanwhile, proto-planets forming within the disk open gaps in the disk gas, creating traps against the inward drift of pebbles and in turn reducing water enrichment in the inner disk. Recent disk observations from millimeter interferometry and infrared spectroscopy have supported this broad picture by finding a correlation between the outer radial distribution of pebbles and the properties of inner water vapor spectra. In this work, we aim at further informing previous and future observations by building on previous models to explore pebble drift in disks with multiple gaps. We systematically explore multiple gap locations and their depths (equivalent to specific masses of planets forming within), and different particle sizes to study their impact on inner disk water enrichment. We find that the presence of close-in deep gaps carved by a Jupiter-mass planet is likely crucial for blocking icy pebble delivery into the inner disk, while planets with lower masses only provide leaky traps. We also find that disks with multiple gaps show lower vapor enrichment in the inner disk. Altogether, these model results support the idea that inner disk water delivery and planet formation are regulated by the mass and location of the most massive planets.

Across the Galactic disk lies a diverse population of X-ray sources, with the fainter end remaining poorly understood due to past survey sensitivity limits. We aim to classify and characterize faint X-ray sources detected in the eROSITA All-Sky Survey (eRASS1) towards the inner Galactic disk (350^circ < l < 360^circ, -1^circ < b < 1^circ) using deeper XMM-Newton observations (typical exposure of sim 20,ks). We analyzed 189 eRASS1 sources, combining X-ray spectral fitting (0.2--10,keV) with Gaia astrometric and photometric data for robust classification. Our results show that the eRASS1 catalog towards the Galactic disk is overwhelmingly dominated by coronal sources (sim 74%), primarily active stars and binaries, with sim 8% being wind-powered massive stars and sim 18% being accreting compact objects. We propose an empirical hardness-ratio cut (HR > -0.2) to efficiently isolate these non-coronal sources. By stacking the classified population and comparing with the Galactic Ridge X-ray Emission (GRXE), we estimate that sim 6% of the GRXE flux in the 0.5--2.0,keV band is resolved into point sources above the eRASS1 flux limit (sim 5times 10^{-14},erg,cm^{-2},s^{-1}). This resolved soft-band emission is dominated by active stars, while hard-band flux originates primarily from X-ray binaries. We conclude that the eRASS1 catalog retains a non-negligible population of compact objects that can be effectively distinguished using X-ray color selection.

Low-mass X-ray binaries with a neutron star as the primary object show a complex array of phenomenology during outbursts. The observed variability in X-ray emission primarily arises from changes in the innermost regions of the accretion disk, neutron star surface, and corona. In this work, we present the results of a comprehensive X-ray spectral and timing analysis of the neutron star transient MAXI J1807+132 during its 2023 outburst using data from the NICER observatory. The outburst is marked by a very rapid rise in the count rate by about a factor of 20 in a day. The source undergoes full state transitions and displays hysteresis effect in the hardness and rms intensity diagrams. Spectral analysis with a three-component model is consistent with disk truncation during the hard states and reaching the last stable orbit during the intermediate and soft states. We discuss the different values of the last stable radius in the context of possible distance of the source and magnetic field strength. The characteristic frequencies throughout the hard and intermediate states are found to be strongly correlated with the inner radius of the disk. Together with the spectral and fast variability properties, we attempt to trace the evolution of the size of the corona along the outburst. Following the main outburst, the source undergoes a high amplitude reflare wherein it shows a complex behavior with relatively high variability (10 %), but low hardness.

How embedded, actively accreting low-mass protostars accrete their mass is still greatly debated. Observations are now piecing together the puzzle of embedded protostellar accretion, in particular with new facilities in the near-infrared. However, high-resolution theoretical models are still lacking, with a stark paucity of detailed simulations of these early phases. Here we present high-resolution non-ideal magneto-hydrodynamic simulations of a Solar mass protostar accreting at rates exceeding 10^{-6} M_{odot} yr^{-1}. We show the results of the accretion flow for four different protostellar magnetic fields, 10 G, 500 G, 1 kG, and 2 kG, combined with a disk magnetic field. For weaker (10 G and 500 G) protostar magnetic fields, accretion occurs via a turbulent boundary layer mode, with disk material impacting across the protostellar surface. In the 500 G model, the presence of a magnetically dominated outflow focuses the accretion towards the equator, slightly enhancing and ordering the accretion. For kG magnetic fields, the disk becomes truncated due to the protostellar dipole and exhibits magnetospheric accretion, with the 2 kG model having accretion bursts induced by the interchange instability. We present bolometric light curves for the models and find that they reproduce observations of Class I protostars from YSOVAR, with high bursts followed by an exponential decay possibly being a signature of instability-driven accretion. Finally, we present the filling fractions of accretion and find that 90\% of the mass is accreted in a surface area fraction of 10-20\%. These simulations will be extended in future work for a broader parameter space, with their high resolution and high temporal spacing able to explore a wide range of interesting protostellar physics.

Spectro-astrometry is used to investigate the low velocity component (LVC) of the optical forbidden emission from the T Tauri stars RU Lupi and AS 205 N. Both stars also have high velocity forbidden emission (HVC) which is tracing a jet. For AS 205 N, analysis reveals a complicated outflow system. For RU Lupi, the [O I] 6300 and [S II] 6716, 6731 LV narrow component (NC) is offset along the same position angle (PA) as the HVC but with a different velocity gradient than the jet, in that displacement from the stellar position along the rotation axis is decreasing with increasing velocity. From the LVC NC PA and velocity gradient, it is inferred that the NC is tracing a wide angled MHD disk wind. A photoevaporative wind is ruled out. This is supported by a comparison with a previous spectro-astrometric study of the CO fundamental line. The decrease in offset with increasing velocity is interpreted as tracing an increase in the height of the wind with increasing disk radius. This is one of the first measurements of the spatial extent of the forbidden emission line LVC NC (~ 40 au, 8 au for RU~Lupi in the [S II] 6731 and [O I] 6300 lines) and the first direct confirmation that the LVC narrow component can trace an MHD disk wind.

As host to two accreting planets, PDS 70 provides a unique opportunity to probe the chemical complexity of atmosphere-forming material. We present ALMA Band 6 observations of the PDS~70 disk and report the first chemical inventory of the system. With a spatial resolution of 0.4''-0.5'' (sim50 au), 12 species are detected, including CO isotopologues and formaldehyde, small hydrocarbons, HCN and HCO+ isotopologues, and S-bearing molecules. SO and CH3OH are not detected. All lines show a large cavity at the center of the disk, indicative of the deep gap carved by the massive planets. The radial profiles of the line emission are compared to the (sub-)mm continuum and infrared scattered light intensity profiles. Different molecular transitions peak at different radii, revealing the complex interplay between density, temperature and chemistry in setting molecular abundances. Column densities and optical depth profiles are derived for all detected molecules, and upper limits obtained for the non detections. Excitation temperature is obtained for H2CO. Deuteration and nitrogen fractionation profiles from the hydro-cyanide lines show radially increasing fractionation levels. Comparison of the disk chemical inventory to grids of chemical models from the literature strongly suggests a disk molecular layer hosting a carbon to oxygen ratio C/O>1, thus providing for the first time compelling evidence of planets actively accreting high C/O ratio gas at present time.

Aromaticity is a common chemical functionalities in bioactive molecules. In interstellar and circumstellar environments benzene and other small aromatics are considered the precursor for more complex prebiotic molecules and they have shown to potentially have rich ice-phase photochemistry. The availability of small organic molecules in prebiotic networks depends on their photostability in astrophysical environments preceding planet formation, particularly during the protoplanetary disk stage, as the disk composition is linked to the chemical make-up of planets and planetesimals. We study the ultraviolet (UV) photodestruction (120-160 nm) of five aromatic molecules in undiluted ices and, for selected cases, in astrophysically relevant ice matrices (H2O, CO, CO2). For each ice, we measure the destruction cross sections as a function of photon exposure. In undiluted ices, aromatic molecules exhibit substantially lower photodestruction cross sections (sigma < 10-19 cm2) than aliphatic hydrocarbons, including cyclohexane, (sigma = 2.8-4x10-18 cm2). Furthermore, neither substituent nature nor size affects the aromatic stability in pure ices, suggesting that the strong intermolecular interactions among aromatic molecules provide protection against VUV exposure, even with small to mid-sized ring substituents. In mixed ices, the photodestruction and reactivity of aromatic molecules (sigma = 2.5-6.1x10-18 cm2) increases by more than an order of magnitude, but are still lower than in the gas-phase. We attribute this to a weaker cage effect and matrix-specific interactions. We use the experimental photodestruction cross sections to estimate the lifetime of aromatic molecules in protoplanetary disks, denileating the disks regions in which aromatic photochemistry is expected to be the most active.

Young exoplanets provide a critical link between understanding planet formation and atmospheric evolution. Direct imaging spectroscopy allows us to infer the properties of young, wide orbit, giant planets with high signal-to-noise. This allows us to compare this young population to exoplanets characterized with transmission spectroscopy, which has indirectly revealed the presence of clouds, photochemistry, and a diversity of atmospheric compositions. Direct detections have also been made for brown dwarfs, but direct studies of young giant planets in the mid-infrared were not possible prior to JWST. With two exoplanets around a solar type star, the YSES-1 system is an ideal laboratory for studying this early phase of exoplanet evolution. We report the first direct observations of silicate clouds in the atmosphere of the exoplanet YSES-1 c through its 9-11 micron absorption feature, and the first circumplanetary disk silicate emission around its sibling planet, YSES-1 b. The clouds of YSES-1 c are composed of either amorphous iron-enriched pyroxene or a combination of amorphous MgSiO3 and Mg2SiO4, with particle sizes of less than or equal to 0.1 micron at 1 millibar of pressure. We attribute the emission from the disk around YSES-1 b to be from submicron olivine dust grains, which may have formed through collisions of planet-forming bodies in the disk.

The illumination of the accretion disks is frequently studied assuming that the incident X-ray flux is a point-like source. The approach is referred as lamppost model.The most recent computations of the X-ray reprocessing by the disk take into account the departure from the simple lamppost models. However, in computations of the incident flux thermalization and subsequent re-emission in the optical-UV band the lamppost approximation is most frequently assumed. We test if the UV-optical reverberation mapping and time delay measurements are sensitive to this assumption. We assume that the incident radiation originates from a region extended along the symmetry axis. To model this, we adopt a simple setup by representing the emission as two lamps irradiating the disk simultaneously from two different heights. We then compare the resulting predictions with those obtained for a single lamppost located at an intermediate height. We show at the basis of the transfer function that the deviation of the wavelength-dependent delay curve shows at most a difference of 20% in comparison to a single lamppost, assuming the black hole mass of 10^8 M_{odot}, Eddington ratio 1, and the location of the lamps at 5 and 100 rg. The maximum deviation happens for the lamp luminosity ratio sim3. When simulating light curves for a two-lamp setup and a standard lamppost with the same black hole mass and a sampling rate of 0.1 days, we find no measurable differences in the ICCF profiles between the two setups. Larger black hole mass and considerably lower Eddington ratio would allow to see larger differences between a single lamppost and a two-lampost model. UV/optical reverberation mapping is not very sensitive to the vertical extension of the corona.

In this paper, we aim to examine the electric Penrose process (PP) around a charged black hole in 4D Einstein-Gauss-Bonnet (EGB) gravity and bring out the effect of the Gauss-Bonnet (GB) coupling parameter alpha and black hole charge on the efficiency of the energy extraction from the black hole. This research is motivated by the fact that electrostatic interactions significantly influence the behavior of charged particles in the vicinity of a charged static black hole. Under this interaction, decaying charged particles can have negative energies, causing energy to be released from black holes with no ergosphere. We show that the GB coupling parameter has a significant impact on the energy efficiency of the electric PP, but the efficiency can be strongly enhanced by the black hole charge due to the Coulomb force. Finally, we consider the accretion disk around the black hole and investigate in detail its radiation properties, such as the electromagnetic radiation flux, the temperature, and the differential luminosity. We show that the GB coupling parameter can have a significant impact on the radiation parameters, causing them to increase in the accretion disk in the vicinity of the black hole. Interestingly, it is found that the 4D EGB charged black hole is more efficient and favorable for the accretion disk radiation compared to a charged black hole in Einstein gravity.

A detailed measurement is made of the metallicity distributions, kinematics and dynamics of the thin and thick disks, across a large disk volume (5.0 leq R leq 15.0 kpc and |Z| leq3.0 kpc), by using the LAMOST-APOGEE red clump stars. The metallicity distributions results show that the radial metallicity gradient Delta[Fe/H]/DeltaR of the thin disk weakens with |Z| from -0.06 dex kpc^{-1} at around |Z| < 0.25 kpc to -0.02 dex kpc^{-1} at around |Z| > 2.75 kpc, while the thick disk displays a global weak positive Delta[Fe/H]/DeltaR, generally weaker than 0.01 dex kpc^{-1}. The vertical metallicity gradient Delta[Fe/H]/Delta|Z| weakened steadily from -0.36 dex kpc^{-1} at R sim 5.5 kpc to -0.05 dex kpc^{-1} at around R > 11.5 kpc for the thin disk, while the thick disk presents an almost constant value (nearly -0.06 sim -0.08 dex kpc^{-1}) for all the R bins. These results indicate the contribution of the radial migration to the disk evolution, and the obvious north-south asymmetry in [Fe/H] may be linked to the disk warp and/or the disk perturbation events. The oscillations of the corrected Delta[Fe/H]/Delta|Z| with R are likely because of the resonances with the Galactic Bar. Our detailed measurements of DeltaV_{phi}/Delta[Fe/H] indicate an "inside-out" and "upside-down" star formation scenario for the thick disk. The results of eccentricity distributions and [alpha/Fe]--velocity dispersion relations are likely to suggest that the thick disk stars require an obvious contribution from other heating mechanisms such as merger and accretion, or born in the chaotic mergers of gas-rich systems and/or turbulent interstellar medium.

We develop a linear perturbative formalism to compute the response of an inhomogeneous stellar disk embedded in a non-responsive dark matter halo to perturbations like bars, spiral arms and satellite galaxy encounters. Without self-gravity to reinforce it, the response of a Fourier mode phase mixes away due to an intrinsic spread in the vertical (Omega_z), radial (Omega_r) and azimuthal (Omega_phi) frequencies, giving rise to local phase-space spirals. Collisional diffusion due to scattering of stars by structures like giant molecular clouds causes super-exponential damping of the phase-spiral amplitude. The z-v_z phase-spiral is 1-armed (2-armed) for vertically anti-symmetric (symmetric) bending (breathing) modes. Only transient perturbations with timescales (tau_{P}) comparable to the vertical oscillation period (tau_z sim 1/Omega_z) trigger z-v_z phase-spirals. Each (n,l,m) mode of the response to impulsive (tau_{P}<tau=1/(nOmega_z+lOmega_r+mOmega_phi)) perturbations is power law (sim tau_{P}/tau) suppressed, but that to adiabatic (tau_{P}>tau) perturbations is exponentially weak (sim left[-left(tau_{mathrm{P}/tauright)^alpharight]}) except resonant (tauto infty) modes. Slower (tau_{P}>tau_z) perturbations, e.g., distant encounters with satellite galaxies, induce stronger bending modes. If the Gaia phase-spiral was triggered by a satellite, Sagittarius is the leading contender as it dominates the Solar neighborhood response of the Milky Way disk to satellite encounters. However, survival against collisional damping necessitates that the impact occurred within sim 0.6-0.7 Gyr ago. We discuss how the detailed galactic potential dictates the phase-spiral shape: phase mixing occurs slower and phase-spirals are less wound in the outer disk and in presence of an ambient halo.

Along with binary neutron star mergers, the in-spiral and merger of a black hole and a neutron star is a predicted site of r-process nucleosynthesis and associated kilonovae. For the right mass ratio, very large amounts of neutron rich material may become unbound from the post-merger accretion disk. We simulate a suite of four post-merger disks with full-transport general relativistic neutrino radiation magnetohydrodynamics. We find that the outflows from these disks are very close to the threshold conditions for robust r-process nucleosynthesis. For these conditions, the detailed properties of the outflow determine whether a full r-process can or cannot occur, implying that a wide range of observable phenomena are possible. We show that on average the disk outflow lanthanide fraction is suppressed relative to the solar isotopic pattern. In combination with the dynamical ejecta, these outflows imply a kilonova with both blue and red components.

Observed dwarf galaxies tend to have linearly rising rotation curves, which indicate flat density cores in their centers. Furthermore, disk galaxies show a wide range of rotation curves shapes. High resolution simulations of cold collisionless dark matter do not reproduce flat central profiles, or the observed diversity of rotation curve shapes; even hydrodynamic simulations incorporating baryonic feedback cannot do that robustly. However, numerical simulations are not the only way to make predictions about density profiles of equilibrium dark matter halos. A theoretical model based on statistical mechanics shows that maximum entropy solutions for cold collisionless self-gravitating dark matter halos can have a range of inner density profiles, including flat density cores. These theoretical profiles, called DARKexp, have only one shape parameter, and are able to fit the observed rotation curves of galaxies with last measured velocities in the range ~20-200 km/s. Here we present fits to 96 SPARC catalog galaxies, and the Milky Way. DARKexp also provides good fits to the projected stellar density distributions of ultrafaint dwarfs that show cores, suggesting that the dark matter halo hosts could have flat density cores. Thus, DARKexp appears to be able to address the core-cusp problem and the diversity of rotation curves with cold collisionless dark matter alone, without baryonic feedback.

Extreme-mass-ratio inspirals (EMRIs) and intermediate-mass-ratio inspirals (IMRIs) are important gravitational wave (GW) sources for the Laser Interferometer Space Antenna (LISA). It has been recently suggested that EMRIs and IMRIs can both form in the accretion disk of an active galactic nucleus (AGN). Considering the likely encounter between a sBH and an IMBH during the migration in the AGN disk, Paper I showed that a gap-opening IMBH can drive a surrounding sBH to migrate synchronously. In this work, we extend the study in Paper I with a more sophisticated model. We first use 3D hydrodynamical simulations to study the co-evolution of the disk and the migration of a sBH in the vicinity of an IMBH. We find that the gaseous torque, together with the tidal torque exerted by the IMBH, can drive synchronized migration until sim 10 Schwarzschild radii from the central supermassive black hole (SMBH). We further use a relativistic three-body code to study the final fate of the sBH in the GW-dominated regime. We find that the sBH can be either captured or kicked out by the IMBH, which will result in either two subsequent IMRIs or an EMRI followed by an IMRI. These events will bring rich information about the formation and evolution of sBHs and IMBHs in AGNs.

The superfluid phase transition dynamics and associated spontaneous vortex formation with the crossing of the critical temperature in a disk geometry is studied in the framework of the AdS/CFT correspondence by solving the Einstein-Abelian-Higgs model in an AdS_4 black hole. For a slow quench, the vortex density admits a universal scaling law with the cooling rate as predicted by the Kibble-Zurek mechanism (KZM), while for fast quenches, the density shows a universal scaling behavior as a function of the final temperature, that lies beyond the KZM prediction. The vortex number distribution in both the power-law and saturation regimes can be approximated by a normal distribution. However, the study of the universal scaling of the cumulants reveals non-normal features and indicates that vortex statistics in the newborn superfluid is best described by the Poisson binomial distribution, previously predicted in the KZM regime [Phys. Rev. Lett. 124, 240602 (2020)]. This is confirmed by studying the cumulant scalings as a function of the quench time and the quench depth. Our work supports the existence of a universal defect number distribution that accommodates the KZM scaling, its breakdown at fast quenches, and the additional universal scaling laws as a function of the final value of the control parameter.

We present the TESS discovery of only the second system of transiting exocomets with a sufficient number of events to measure the size distribution in the RZ Psc system, enabling comparisons with the beta Pictoris and Solar System size distributions. Twenty-four transits with absorption depths (AD) of 1--20\% were observed across three TESS sectors of the 20-50 Myr K0V star, detected as part of our TESS survey of extreme debris disks identified by their IR excess. We discover that the ADs (and hence exocomet radii) follow a broken power-law cumulative frequency distribution not previously seen in extrasolar contexts but similar to that observed in Solar System Kuiper Belt Object sizes, with power-law slopes above and below the break of gamma_AD>break=2.32pm0.12 and gamma_AD<break=0.11pm0.04, respectively. We derive size distributions of 1--7~km from two independent lines of evidence. We use the RZ Psc exocomet rate to predict exocomet yields for the Early eVolution Explorer (EVE) NASA astrophysics Small Explorer (SMEX) mission concept to obtain simultaneous photometry of 10^4 young stars in NUV, optical, and NIR bands. Assuming occurrence rates scaled from RZ Psc, EVE would detect 590 exocomets from approx70 young systems in the optical band, with approx120 simultaneous 5sigma detections in all three bands. These data would enable grain sizes of 200--700~nm and graphite--olivine compositions of dozens of events to be distinguished at 2.5--3sigma, as well as a 4sigma determination of the accuracy of the Herschel-derived M-debris disk fraction.

Modern deep learning applications require increasingly more compute to train state-of-the-art models. To address this demand, large corporations and institutions use dedicated High-Performance Computing clusters, whose construction and maintenance are both environmentally costly and well beyond the budget of most organizations. As a result, some research directions become the exclusive domain of a few large industrial and even fewer academic actors. To alleviate this disparity, smaller groups may pool their computational resources and run collaborative experiments that benefit all participants. This paradigm, known as grid- or volunteer computing, has seen successful applications in numerous scientific areas. However, using this approach for machine learning is difficult due to high latency, asymmetric bandwidth, and several challenges unique to volunteer computing. In this work, we carefully analyze these constraints and propose a novel algorithmic framework designed specifically for collaborative training. We demonstrate the effectiveness of our approach for SwAV and ALBERT pretraining in realistic conditions and achieve performance comparable to traditional setups at a fraction of the cost. Finally, we provide a detailed report of successful collaborative language model pretraining with 40 participants.

Node classification is a classical graph machine learning task on which Graph Neural Networks (GNNs) have recently achieved strong results. However, it is often believed that standard GNNs only work well for homophilous graphs, i.e., graphs where edges tend to connect nodes of the same class. Graphs without this property are called heterophilous, and it is typically assumed that specialized methods are required to achieve strong performance on such graphs. In this work, we challenge this assumption. First, we show that the standard datasets used for evaluating heterophily-specific models have serious drawbacks, making results obtained by using them unreliable. The most significant of these drawbacks is the presence of a large number of duplicate nodes in the datasets Squirrel and Chameleon, which leads to train-test data leakage. We show that removing duplicate nodes strongly affects GNN performance on these datasets. Then, we propose a set of heterophilous graphs of varying properties that we believe can serve as a better benchmark for evaluating the performance of GNNs under heterophily. We show that standard GNNs achieve strong results on these heterophilous graphs, almost always outperforming specialized models. Our datasets and the code for reproducing our experiments are available at https://github.com/yandex-research/heterophilous-graphs

Many deep learning applications benefit from using large models with billions of parameters. Training these models is notoriously expensive due to the need for specialized HPC clusters. In this work, we consider alternative setups for training large models: using cheap "preemptible" instances or pooling existing resources from multiple regions. We analyze the performance of existing model-parallel algorithms in these conditions and find configurations where training larger models becomes less communication-intensive. Based on these findings, we propose SWARM parallelism, a model-parallel training algorithm designed for poorly connected, heterogeneous and unreliable devices. SWARM creates temporary randomized pipelines between nodes that are rebalanced in case of failure. We empirically validate our findings and compare SWARM parallelism with existing large-scale training approaches. Finally, we combine our insights with compression strategies to train a large Transformer language model with 1B shared parameters (approximately 13B before sharing) on preemptible T4 GPUs with less than 200Mb/s network.

Variational inequalities in general and saddle point problems in particular are increasingly relevant in machine learning applications, including adversarial learning, GANs, transport and robust optimization. With increasing data and problem sizes necessary to train high performing models across various applications, we need to rely on parallel and distributed computing. However, in distributed training, communication among the compute nodes is a key bottleneck during training, and this problem is exacerbated for high dimensional and over-parameterized models. Due to these considerations, it is important to equip existing methods with strategies that would allow to reduce the volume of transmitted information during training while obtaining a model of comparable quality. In this paper, we present the first theoretically grounded distributed methods for solving variational inequalities and saddle point problems using compressed communication: MASHA1 and MASHA2. Our theory and methods allow for the use of both unbiased (such as Randk; MASHA1) and contractive (such as Topk; MASHA2) compressors. New algorithms support bidirectional compressions, and also can be modified for stochastic setting with batches and for federated learning with partial participation of clients. We empirically validated our conclusions using two experimental setups: a standard bilinear min-max problem, and large-scale distributed adversarial training of transformers.

Many areas of deep learning benefit from using increasingly larger neural networks trained on public data, as is the case for pre-trained models for NLP and computer vision. Training such models requires a lot of computational resources (e.g., HPC clusters) that are not available to small research groups and independent researchers. One way to address it is for several smaller groups to pool their computational resources together and train a model that benefits all participants. Unfortunately, in this case, any participant can jeopardize the entire training run by sending incorrect updates, deliberately or by mistake. Training in presence of such peers requires specialized distributed training algorithms with Byzantine tolerance. These algorithms often sacrifice efficiency by introducing redundant communication or passing all updates through a trusted server, making it infeasible to apply them to large-scale deep learning, where models can have billions of parameters. In this work, we propose a novel protocol for secure (Byzantine-tolerant) decentralized training that emphasizes communication efficiency.

The infrastructure necessary for training state-of-the-art models is becoming overly expensive, which makes training such models affordable only to large corporations and institutions. Recent work proposes several methods for training such models collaboratively, i.e., by pooling together hardware from many independent parties and training a shared model over the Internet. In this demonstration, we collaboratively trained a text-to-image transformer similar to OpenAI DALL-E. We invited the viewers to join the ongoing training run, showing them instructions on how to contribute using the available hardware. We explained how to address the engineering challenges associated with such a training run (slow communication, limited memory, uneven performance between devices, and security concerns) and discussed how the viewers can set up collaborative training runs themselves. Finally, we show that the resulting model generates images of reasonable quality on a number of prompts.