Miracle-dz/UniFAR-FTUs huggingface dataset:
The Star Formation Rate of the Milky Way as Seen by Herschel

We present a new derivation of the Milky Way’s current star formation rate (SFR) based on the data of the Herschel InfraRed Galactic Plane Survey (Hi-GAL). We estimate the distribution of the SFR across the Galactic plane from the star-forming clumps identified in the Hi-GAL survey and calculate the total SFR from the sum of their contributions. The estimate of the global SFR amounts to 2.0 ± 0.7 M ⊙ yr−1, of which 1.7 ± 0.6 M ⊙ yr−1 coming from clumps with reliable heliocentric distance assignment. This value is in general agreement with estimates found in the literature of last decades. The profile of SFR density averaged in Galactocentric rings is found to be qualitatively similar to others previously computed, with a peak corresponding to the Central Molecular Zone and another one around Galactocentric radius R gal ∼ 5 kpc, followed by an exponential decrease as log(ΣSFR/[M⊙yr−1kpc−2])=aRgal/[kpc]+b , with a = −0.28 ± 0.01. In this regard, the fraction of SFR produced within and outside the solar circle is 84% and 16%, respectively; the fraction corresponding to the far outer Galaxy (R gal > 13.5 kpc) is only 1%. We also find that, for R gal > 3 kpc, our data follow a power law as a function of density, similarly to the Kennicutt–Schmidt relation. Finally, we compare the distribution of the SFR density across the face-on Galactic plane and those of median parameters, such as temperature, luminosity/mass ratio, and bolometric temperature, describing the evolutionary stage of Hi-GAL clumps. We found no clear correlation between the SFR and the clump evolutionary stage.
DisentQA: Disentangling Parametric and Contextual Knowledge with Counterfactual Question Answering

Question answering models commonly have access to two sources of “knowledge” during inference time: (1) parametric knowledge - the factual knowledge encoded in the model weights, and (2) contextual knowledge - external knowledge (e.g., a Wikipedia passage) given to the model to generate a grounded answer. Having these two sources of knowledge entangled together is a core issue for generative QA models as it is unclear whether the answer stems from the given non-parametric knowledge or not. This unclarity has implications on issues of trust, interpretability and factuality. In this work, we propose a new paradigm in which QA models are trained to disentangle the two sources of knowledge. Using counterfactual data augmentation, we introduce a model that predicts two answers for a given question: one based on given contextual knowledge and one based on parametric knowledge. Our experiments on the Natural Questions dataset show that this approach improves the performance of QA models by making them more robust to knowledge conflicts between the two knowledge sources, while generating useful disentangled answers.
Optimizing Server-side Aggregation For Robust Federated Learning via Subspace Training

Non-IID data distribution across clients and poisoning attacks are two main challenges in real-world federated learning systems. While both of them have attracted great research interest with speciﬁc strategies developed, no known solution manages to address them in a uniﬁed framework. To jointly overcome both challenges, we propose SmartFL, a generic approach that optimizes the server-side aggregation process with a small clean server-collected proxy dataset (e.g., around one hundred samples, 0.2% of the dataset) via a subspace training technique. Speciﬁcally, the aggregation weight of each participating client at each round is optimized using the server-collected proxy data, which is essentially the optimization of the global model in the convex hull spanned by client models. Since at each round, the number of tunable parameters optimized on the server side equals the number of participating clients (thus independent of the model size), we are able to train a global model with massive parameters using only a small amount of proxy data. We provide theoretical analyses of the convergence and generalization capacity for SmartFL. Empirically, SmartFL achieves state-of-the-art performance on both federated learning with non-IID data distribution and federated learning with malicious clients. The source code will be released.
Grammatical Error Correction: A Survey of the State of the Art

Abstract Grammatical Error Correction (GEC) is the task of automatically detecting and correcting errors in text. The task not only includes the correction of grammatical errors, such as missing prepositions and mismatched subject–verb agreement, but also orthographic and semantic errors, such as misspellings and word choice errors, respectively. The field has seen significant progress in the last decade, motivated in part by a series of five shared tasks, which drove the development of rule-based methods, statistical classifiers, statistical machine translation, and finally neural machine translation systems, which represent the current dominant state of the art. In this survey paper, we condense the field into a single article and first outline some of the linguistic challenges of the task, introduce the most popular datasets that are available to researchers (for both English and other languages), and summarize the various methods and techniques that have been developed with a particular focus on artificial error generation. We next describe the many different approaches to evaluation as well as concerns surrounding metric reliability, especially in relation to subjective human judgments, before concluding with an overview of recent progress and suggestions for future work and remaining challenges. We hope that this survey will serve as a comprehensive resource for researchers who are new to the field or who want to be kept apprised of recent developments.
Dwarf spheroidal galaxies and Bose-Einstein condensate dark matter

We constrain the parameters of a self-interacting massive dark matter scalar particle in a condensate using the kinematics of the eight brightest dwarf spheroidal satellites of the Milky Way. For the case of a repulsive self-interaction the condensate develops a mass density profile with a characteristic scale radius that is closely related to the fundamental parameters of the theory. We find that the velocity dispersion of dwarf spheroidal galaxies suggests a scale radius of the order of 1 kpc, in tension with previous results found using the rotational curve of low-surface-brightness and dwarf galaxies. The new value is however favored marginally by the constraints coming from the number of relativistic species at Big-Bang nucleosynthesis. We discuss the implications of our findings for the particle dark matter model and argue that while a single classical coherent state can correctly describe the dark matter in dwarf spheroidal galaxies, it cannot play, in general, a relevant role for the description of dark matter in bigger objects.
The perturbed FLRW metric on all scales: Newtonian limit and top-hat collapse

The applicability of a linearized perturbed FLRW metric to the late, lumpy universe has been subject to debate. We consider in an elementary way the Newtonian limit of the Einstein equations with this ansatz for the case of structure formation in late-time cosmology, on small and on large scales, and argue that linearizing the Einstein tensor produces only a small error down to arbitrarily small, decoupled scales (e.g. solar system scales). On subhorizon patches, the metric scale factor becomes a coordinate choice equivalent to choosing the spatial curvature, and not a sign that the FLRW metric cannot perturbatively accommodate very different local physical expansion rates of matter; we distinguish these concepts, and show that they merge on large scales for the Newtonian limit to be globally valid. Furthermore, on subhorizon scales, a perturbed FLRW metric ansatz does not already imply assumptions on isotropy, and effects beyond a FLRW background, including those potentially caused by nonlinearities of GR, may be encoded into nontrivial boundary conditions. The corresponding cosmologies have already been developed in a Newtonian setting by Heckmann and Schücking and none of these boundary conditions can explain the accelerated expansion of the universe. Our analysis of the field equations is confirmed on the level of solutions by an example of pedagogical value, comparing a collapsing top-hat overdensity (embedded into a cosmological background) treated in such perturbative manner to the corresponding exact solution of GR, where we find good agreement even in the regimes of strong density contrast
Helioseismic and neutrino data-driven reconstruction of solar properties

In this work we use Bayesian inference to quantitatively reconstruct the solar properties most relevant to the solar composition problem using as inputs the information provided by helioseismic and solar neutrino data. In particular, we use a Gaussian process to model the functional shape of the opacity uncertainty to gain flexibility and become as free as possible from prejudice in this regard. With these tools we first readdress the statistical significance of the solar composition problem. Furthermore, starting from a composition unbiased set of standard solar models we are able to statistically select those with solar chemical composition and other solar inputs which better describe the helioseismic and neutrino observations. In particular, we are able to reconstruct the solar opacity profile in a data driven fashion, independently of any reference opacity tables, obtaining a 4% uncertainty at the base of the convective envelope and 0.8% at the solar core. When systematic uncertainties are included, results are 7.5% and 2% respectively. In addition we find that the values of most of the other inputs of the standard solar models required to better describe the helioseismic and neutrino data are in good agreement with those adopted as the standard priors, with the exception of the astrophysical factor $S_{11}$ and the microscopic diffusion rates, for which data suggests a 1% and 30% reduction respectively. As an output of the study we derive the corresponding data driven predictions for the solar neutrino fluxes.
AGN quenching in simulated dwarf galaxies

We examine the quenching characteristics of 328 isolated dwarf galaxies (cid:0) 10 8 < M star /M (cid:12) < 10 10 (cid:1) within the Romulus25 cosmological hydrodynamic simulation. Using mock observation methods, we identify isolated dwarf galaxies with quenched star formation and make direct comparisons to the quenched fraction in the NASA Sloan Atlas (NSA). Similar to other cosmological simulations, we ﬁnd a population of quenched, isolated dwarf galaxies below M star < 10 9 M (cid:12) not detected within the NSA. We ﬁnd that the presence of massive black holes (MBHs) in Romulus25 is largely responsible for the quenched, isolated dwarfs, while isolated dwarfs without an MBH are consistent with quiescent fractions observed in the ﬁeld. Quenching occurs between z = 0 . 5 − 1, during which the available supply of star-forming gas is heated or evacuated by MBH feedback. Mergers or interactions seem to play little to no role in triggering the MBH feedback. At low stellar masses, M star (cid:46) 10 9 . 3 M (cid:12) , quenching proceeds across several Gyr as the MBH slowly heats up gas in the central regions. At higher stellar masses, M star (cid:38) 10 9 . 3 M (cid:12) , quenching occurs rapidly within 1 Gyr, with the MBH evacuating gas from the central few kpc of the galaxy and driving it to the outskirts of the halo. Our results indicate the possibility of substantial star formation suppression via MBH feedback within dwarf galaxies in the ﬁeld. On the other hand, the apparent over-quenching of dwarf galaxies due to MBH suggests higher resolution and/or better modeling is required for MBHs in dwarfs, and quenched fractions oﬀer the opportunity to constrain current models.
Controlling Moments with Kernel Stein Discrepancies

Kernel Stein discrepancies (KSDs) measure the quality of a distributional approximation and can be computed even when the target density has an intractable normalizing constant. Notable applications include the diagnosis of approximate MCMC samplers and goodness-of-fit tests for unnormalized statistical models. The present work analyzes the convergence control properties of KSDs. We first show that standard KSDs used for weak convergence control fail to control moment convergence. To address this limitation, we next provide sufficient conditions under which alternative diffusion KSDs control both moment and weak convergence. As an immediate consequence we develop, for each $q>0$, the first KSDs known to exactly characterize $q$-Wasserstein convergence.
Causal Modeling of Soil Processes for Improved Generalization

Measuring and monitoring soil organic carbon is critical for agricultural productivity and for addressing critical environmental problems. Soil organic carbon not only enriches nutrition in soil, but also has a gamut of co-benefits such as improving water storage and limiting physical erosion. Despite a litany of work in soil organic carbon estimation, current approaches do not generalize well across soil conditions and management practices. We empirically show that explicit modeling of cause-and-effect relationships among the soil processes improves the out-of-distribution generalizability of prediction models. We provide a comparative analysis of soil organic carbon estimation models where the skeleton is estimated using causal discovery methods. Our framework provide an average improvement of 81% in test mean squared error and 52% in test mean absolute error.
Strong RR Lyrae excess in the Hercules-Aquila Cloud

We map the large-scale sub-structure in the Galactic stellar halo using accurate 3D positions of ~14,000 RR Lyrae reported by the Catalina Sky Survey. In the heliocentric distance range of 10-25 kpc, in the region of the sky approximately bounded by 30{\deg} < l < 55{\deg} and -45{\deg} < b < -25{\deg}, there appears to be a strong excess of RRab stars. This overdensity, peaking at 18 kpc, is most likely associated with the so-called Hercules-Aquila Cloud, previously detected using Main Sequence tracers at similar distances in the Sloan Digital Sky Survey data. Our analysis of the period-amplitude distribution of RR Lyrae in this region indicates that the HAC is dominated by the Oosterhoff I type population. By comparing the measured RR Lyrae number density to models of a smooth stellar halo, we estimate the significance of the observed excess and provide an updated estimate of the total luminosity of the Cloud's progenitor.
Photometric survey, modelling, and scaling of long-period and low-amplitude asteroids

The available set of spin and shape modelled asteroids is strongly biased against slowly rotating targets and those with low lightcurve amplitudes. As a consequence of these selection effects, the current picture of asteroid spin axis distribution, rotation rates, or radiometric properties, might be affected too. To counteract these selection effects, we are running a photometric campaign of a large sample of main belt asteroids omitted in most previous studies. We determined synodic rotation periods and verified previous determinations. When a dataset for a given target was sufficiently large and varied, we performed spin and shape modelling with two different methods. We used the convex inversion method and the non-convex SAGE algorithm, applied on the same datasets of dense lightcurves. Unlike convex inversion, the SAGE method allows for the existence of valleys and indentations on the shapes based only on lightcurves. We obtained detailed spin and shape models for the first five targets of our sample: (159) Aemilia, (227) Philosophia, (329) Svea, (478) Tergeste, and (487) Venetia. When compared to stellar occultation chords, our models obtained an absolute size scale and major topographic features of the shape models were also confirmed. When applied to thermophysical modelling, they provided a very good fit to the infrared data and allowed their size, albedo, and thermal inertia to be determined. Convex and non-convex shape models provide comparable fits to lightcurves. However, some non-convex models fit notably better to stellar occultation chords and to infrared data in sophisticated thermophysical modelling (TPM). In some cases TPM showed strong preference for one of the spin and shape solutions. Also, we confirmed that slowly rotating asteroids tend to have higher-than-average values of thermal inertia.
Regional Disparities and Economic Growth in Ukraine

This research is devoted to assessing regional economic disparities in Ukraine, where regional economic inequality is a crucial issue the country faces in its medium and long-term development, recently, even in the short term. We analyze the determinants of regional economic growth, mainly industrial and agricultural productions, population, human capital, fertility, migration, and regional government expenditures. Using panel data estimations from 2004 to 2020 for 27 regions of Ukraine, our results show that the gaps between regions in Ukraine have widened last two decades. Natural resource distribution, agricultural and industrial productions, government spending, and migration can explain the disparities. We show that regional government spending is highly concentrated in Kyiv, and the potential of the other regions, especially the Western ones, needs to be used sufficiently. Moreover, despite its historical and economic opportunity, the East region performed little development during the last two decades. The inefficient and inconsistent regional policies played a crucial role in these disparities.
Okapi: Generalising Better by Making Statistical Matches Match

We propose Okapi, a simple, efficient, and general method for robust semi-supervised learning based on online statistical matching. Our method uses a nearest-neighbours-based matching procedure to generate cross-domain views for a consistency loss, while eliminating statistical outliers. In order to perform the online matching in a runtime- and memory-efficient way, we draw upon the self-supervised literature and combine a memory bank with a slow-moving momentum encoder. The consistency loss is applied within the feature space, rather than on the predictive distribution, making the method agnostic to both the modality and the task in question. We experiment on the WILDS 2.0 datasets Sagawa et al., which significantly expands the range of modalities, applications, and shifts available for studying and benchmarking real-world unsupervised adaptation. Contrary to Sagawa et al., we show that it is in fact possible to leverage additional unlabelled data to improve upon empirical risk minimisation (ERM) results with the right method. Our method outperforms the baseline methods in terms of out-of-distribution (OOD) generalisation on the iWildCam (a multi-class classification task) and PovertyMap (a regression task) image datasets as well as the CivilComments (a binary classification task) text dataset. Furthermore, from a qualitative perspective, we show the matches obtained from the learned encoder are strongly semantically related. Code for our paper is publicly available at https://github.com/wearepal/okapi/.
Moffatt eddies in electrohydrodynamics flows: numerical simulations and analyses

Abstract We study numerically a sequence of eddies in two-dimensional electrohydrodynamics (EHD) flows of a dielectric liquid, driven by an electric potential difference between a hyperbolic blade electrode and a flat plate electrode (or the blade–plate configuration). The electrically driven flow impinges on the plate to generate vortices, which resemble Moffatt eddies (Moffatt, J. Fluid Mech., vol. 18, 1964, pp. 1–18). Such a phenomenon in EHD was first reported in the experimental work of Perri et al. (J. Fluid Mech., vol. 900, 2020, A12). We conduct direct numerical simulations of the EHD flow with three Moffatt-type eddies in a large computational domain at moderate electric Rayleigh numbers ($T$, quantifying the strength of the electric field). The ratios of size and intensity of the adjacent eddies are examined, and they can be compared favourably to the theoretical prediction of Moffatt; interestingly, the quantitative comparison is remarkably accurate for the two eddies in the far field. Our investigation also shows that a larger $T$ strengthens the vortex intensity, and a stronger charge diffusion effect enlarges the vortex size. A sufficiently large $T$ can further result in an oscillating flow, consistent with the experimental observation. In addition, a global stability analysis of the steady blade–plate EHD flow is conducted. The global mode is characterised in detail at different values of $T$. When $T$ is large, the confinement effect of the geometry in the centre region may lead to an increased oscillation frequency. This work contributes to the quantitative characterisation of the Moffatt-type eddies in EHD flows.
AnimeRun: 2D Animation Visual Correspondence from Open Source 3D Movies

Existing correspondence datasets for two-dimensional (2D) cartoon suffer from simple frame composition and monotonic movements, making them insufficient to simulate real animations. In this work, we present a new 2D animation visual correspondence dataset, AnimeRun, by converting open source three-dimensional (3D) movies to full scenes in 2D style, including simultaneous moving background and interactions of multiple subjects. Our analyses show that the proposed dataset not only resembles real anime more in image composition, but also possesses richer and more complex motion patterns compared to existing datasets. With this dataset, we establish a comprehensive benchmark by evaluating several existing optical flow and segment matching methods, and analyze shortcomings of these methods on animation data. Data, code and other supplementary materials are available at https://lisiyao21.github. io/projects/AnimeRun.
X-ray spectra of hot accretion flows

We study radiative properties of hot accretion flows in a general relativistic model with an exact treatment of global Comptonization, developed in our recent works. We note a strong dependence of electron temperature on the strength of magnetic field and we clarify that the underlying mechanism involves the change of the flow structure, with more strongly magnetized flows approaching the slab geometry more closely. We find that the model with thermal synchrotron radiation being the main source of seed photons agrees with the spectral index versus Eddington ratio relation observed in black hole transients below 1 per cent of the Eddington luminosity, L-Edd, and models with a weak direct heating of electrons (small delta) are more consistent with observations. Models with large delta predict slightly too soft spectra, furthermore, they strongly overpredict electron temperatures at similar to 0.01 L-Edd. The low-luminosity spectra, at less than or similar to 0.001 L-Edd, deviate from a power-law shape in the soft X-ray range, and we note that the first-scattering bump often resembles a thermal like component, with the temperature of a few hundred eV, superimposed on a power-law spectrum. The model with thermal Comptonization of thermal synchrotron radiation does not agree with well-studied AGNs observed below similar to 0.01 L-Edd, for which there is a substantial evidence for the lack of an inner cold disc. This indicates that the model of hot flows powering AGNs should be revised, possibly by taking into account an additional (but internal to the flow) source of seed photons.