Miracle-dz/UniFAR-FTUs huggingface dataset:
The age–chemical abundance structure of the Galactic disc – II. α-dichotomy and thick disc formation

We extend our previous work on the age–chemical abundance structure of the Galactic outer disc to the inner disc (4 < r < 8 kpc) based on the SDSS/APOGEE survey. Different from the outer disc, the inner disc stars exhibit a clear bimodal distribution in the [Mg/Fe]–[Fe/H] plane. While a number of scenarios have been proposed in the literature, it remains challenging to recover this bimodal distribution with theoretical models. To this end, we present a chemical evolution model embedding a complex multiphase inner disc formation scenario that matches the observed bimodal [Mg/Fe]–[Fe/H] distribution. In this scenario, the formation of the inner disc is dominated by two main starburst episodes $6\,$Gyr apart with secular, low-level star formation activity in between. In our model, the first starburst occurs at early cosmic times ($t\sim 1\,$ Gyr) and the second one $6\,$ Gyr later at a cosmic time of $t\sim 7\,$ Gyr. Both these starburst episodes are associated with gas accretion events in our model, and are quenched rapidly. The first starburst leads to the formation of the high-α sequence, and the second starburst leads to the formation of the metal-poor low-α sequence. The metal-rich low-α stars, instead, form during the secular evolution phase between the two bursts. Our model shows that the α-dichotomy originates from the rapid suppression of star formation after the first starburst. The two starburst episodes are likely to be responsible for the formation of the geometric thick disc (z >1 kpc), with the old inner thick disc and the young outer thick disc forming during the first and the second starbursts, respectively.
Semi-Supervised Learning with Meta-Gradient

In this work, we propose a simple yet effective meta-learning algorithm in thesemi-supervised settings. We notice that existing consistency-based approachesmostly do not consider the essential role of the label information for consistencyregularization. To alleviate this issue, we bridge the relationship between theconsistency loss and label information by unfolding and differentiating throughone optimization step. Specifically, we exploit the pseudo labels of the unlabeledexamples which are guided by the meta-gradients of the labeled data loss so thatthe model can generalize well on the labeled examples. In addition, we introduce asimple first-order approximation to avoid computing higher-order derivatives andguarantee scalability. Extensive evaluations on the SVHN, CIFAR, and ImageNetdatasets demonstrate that the proposed algorithm performs favorably against thestate-of-the-art methods.
Self-Supervised Policy Adaptation during Deployment

In most real world scenarios, a policy trained by reinforcement learning in one environment needs to be deployed in another, potentially quite different environment. However, generalization across different environments is known to be hard. A natural solution would be to keep training after deployment in the new environment, but this cannot be done if the new environment offers no reward signal. Our work explores the use of self-supervision to allow the policy to continue training after deployment without using any rewards. While previous methods explicitly anticipate changes in the new environment, we assume no prior knowledge of those changes yet still obtain significant improvements. Empirical evaluations are performed on diverse environments from DeepMind Control suite and ViZDoom. Our method improves generalization in 25 out of 30 environments across various tasks, and outperforms domain randomization on a majority of environments.
Determining the Hubble constant without the sound horizon scale: measurements from CMB lensing

Measurements of the Hubble constant, $H_0$, from the cosmic distance ladder are currently in tension with the value inferred from Planck observations of the CMB and other high redshift datasets if a flat $\Lambda$CDM cosmological model is assumed. One of the few promising theoretical resolutions of this tension is to invoke new physics that changes the sound horizon scale in the early universe; this can bring CMB and BAO constraints on $H_0$ into better agreement with local measurements. In this paper, we discuss how a measurement of the Hubble constant can be made from the CMB without using information from the sound horizon scale, $r_s$. In particular, we show how measurements of the CMB lensing power spectrum can be used to place interesting constraints on $H_0$ when combined with measurements of either supernovae or galaxy weak lensing, which constrain the matter density parameter. The constraints arise from the sensitivity of the CMB lensing power spectrum to the horizon scale at matter-radiation equality (in projection); this scale could have a different dependence on new physics than the sound horizon. From an analysis of current CMB lensing data from Planck and Pantheon supernovae with conservative external priors, we derive an $r_s$-independent constraint of $H_0 = 73.5\pm 5.3$ km/s/Mpc. Forecasts for future CMB surveys indicate that improving constraints beyond an error of $\sigma(H_0) = 3$ km/s/Mpc will be difficult with CMB lensing, although applying similar methods to the galaxy power spectrum may allow for further improvements.
Dilatancy in dense suspensions of model hard-sphere-like colloids under shear and extensional flow

Dense suspensions of model hard-sphere (HS)-like colloids, with different particle sizes, are examined experimentally near and in the glass state, under shear and extensional rheology. Under steady shear flow, we detect both continuous and discontinuous shear thickening (DST) above a critical shear rate (or shear stress), depending on the particle size and volume fraction. Start-up shear experiments show stress overshoots in the vicinity of the onset of DST indicative of microscopic structural changes, while the sample macroscopically shows dilatancy effects. Measurement of shear and normal stresses together with direct video imaging of the sample probes the appearance of the positive first normal stress difference, N1, at the onset of shear thickening at high shear rates and glassy states. This is followed by dilatancy effects accompanied by large fluctuations of shear and normal stress and stick-slip phenomena. Similarly, under extensional flow probed by capillary breakup and filament stretching setups, we find liquidlike response for low strain rates, while above a critical strain rate, the samples exhibit a solidlike behavior where thickening is accompanied by a macroscopic dilatancy and granulation. Monitoring the filament thinning processes under different conditions (volume fractions and strain rates), we have created a state diagram where all responses of a HS suspension (liquidlike, shear thinning, shear thickening, and dilatant) are shown. We, finally, compare the shear thickening response of these HS-like suspensions and glasses in shear with that in the extensional flow.
Skeptic: Automatic, Justified and Privacy-Preserving Password Composition Policy Selection

The choice of password composition policy to enforce on a password-protected system represents a critical security decision, and has been shown to significantly affect the vulnerability of user-chosen passwords to guessing attacks. In practice, however, this choice is not usually rigorous or justifiable, with a tendency for system administrators to choose password composition policies based on intuition alone. In this work, we propose a novel methodology that draws on password probability distributions constructed from large sets of real-world password data which have been filtered according to various password composition policies. Password probabilities are then redistributed to simulate different user password reselection behaviours in order to automatically determine the password composition policy that will induce the distribution of user-chosen passwords with the greatest uniformity, a metric which we show to be a useful proxy to measure overall resistance to password guessing attacks. Further, we show that by fitting power-law equations to the password probability distributions we generate, we can justify our choice of password composition policy without any direct access to user password data. Finally, we present Skeptic---a software toolkit that implements this methodology, including a DSL to enable system administrators with no background in password security to compare and rank password composition policies without resorting to expensive and time-consuming user studies. Drawing on 205,176,321 passwords across 3 datasets, we lend validity to our approach by demonstrating that the results we obtain align closely with findings from a previous empirical study into password composition policy effectiveness.
The Scattering Compositional Learner: Discovering Objects, Attributes, Relationships in Analogical Reasoning

In this work, we focus on an analogical reasoning task that contains rich compositional structures, Raven's Progressive Matrices (RPM). To discover compositional structures of the data, we propose the Scattering Compositional Learner (SCL), an architecture that composes neural networks in a sequence. Our SCL achieves state-of-the-art performance on two RPM datasets, with a 48.7% relative improvement on Balanced-RAVEN and 26.4% on PGM over the previous state-of-the-art. We additionally show that our model discovers compositional representations of objects' attributes (e.g., shape color, size), and their relationships (e.g., progression, union). We also find that the compositional representation makes the SCL significantly more robust to test-time domain shifts and greatly improves zero-shot generalization to previously unseen analogies.
The Variability of Selected Features of Gladiolus imbricatus L. in Relation to Successive Stages of Meadow Communities Following the Mowing Cessation

ABSTRACT: Molinietum caeruleae meadows belong to the most endangered plant communities in Central Europe, nowadays suffering due to the lack of management. The land abandonment promotes strong secondary expansion of macroforbs, sedges or willows, contributing to decrease of the species diversity. In light of these findings very interesting seems to be the evaluation of the variability of features of populations of rare meadow species. In Poland one of strictly protected plant is Gladiolus imbricatus L. It is a tuberous, clonal plant with sword-like leaves and tall stalks bearing purple flowers collected in one-sided inflorescence. The studies of spatio-temporal variability of selected features of subpopoulations of Gladiolus imbricatus were carried out in the years 2009– 2011 in Kraków-Kostrze (Southern Poland) in patches of Molinietum caeruleae with different species composition. In Patch I chiefly occurred small meadow species, Patch II was prevailed by shrub willow, Patch III was dominated by tall-growing macroforbs, Patch IV was characterized by substantial share of large-tussock grasses, while Patch V was overgrown by tall rhizomatous grass Phragmites communis. The plant canopy height and ground moisture raised from Patch I, via Patches II, III and IV, to Patch V. Moreover, in all sites the above mentioned parameters increased moderately in consecutive seasons. The decline of abundance of subpopulations observed in the consecutive years and in the successive patches might be caused by a mortality of individuals, a physical damage of tubers, as well as a prolonged dormancy of genets. The structure of developmental stages were similar in consecutive years but differed significantly among Patches. The increasing trend toward senility noted in successive sites could be an effect of lack of microsites suitable for seedling recruitment or absence of vegetative regeneration. The dimensions of leaf blades, flowering stalks and inflorescences remained similar in the subsequent seasons, but they augmented significantly in the successive patches. The length and the width of capsules did not change significantly between seasons and sites. Concluding, it might be assumed, that the perspectives for survivability of subpopulations diminish gradually from Patch I, via Patches II, III and IV to Patch V. Simultaneously, it should be pointed out that prospects for persistence in each place steadily decrease in effect of continuous deterioration of site conditions.
The shade and fertiliser requirements of cacao (Theobroma cacao) in Ghana

Experiments to examine the shade and nutrient requirements of cacao1 are described and preliminary results are presented. A significant response to phosphate was obtained under shade when pests, diseases and weeds were controlled. Removing shade results in highly significant increases in yield with a positive interaction between increased light and applied nutrients. Fertilised, unshaded cacao gave yields double that previously considered to be the maximum for Amelonado cacao. The application of these results to farmers' shaded cacao and to a more intensive system of growing the crop without shade are discussed. Attention is drawn to the dangers of removing shade and to the importance of associated factors such as pest distribution, incidence of weeds, adverse dry season effects and soil degradation.
Seasonal patterns of soil carbon dioxide efflux from a wet-dry tropical savanna of northern Australia

Soil CO2 efflux rates were measured in a eucalypt open forest in a tropical savanna of northern Australia, with a portable closed chamber and CO2 gas analyser. Both abiotic (soil temperature and water content) and biotic (litterfall and fine-root growth) factors that may influence soil CO2 efflux were examined. Daytime rates of soil CO2 efflux rate were consistently higher than nocturnal values. Maximal rates occurred during late afternoons when soil temperatures were also maximal and minimum values were recorded during the early morning (0400-0800 hours). Average soil CO2 efflux was 5.37 mol m -2 s -1 (range 3.5-6.7 mol m -2 s -1 ) during the wet season and declined to 2.20 mol m -2 s -1 (range 1.2-3.6 mol m -2 s -1 ) during the dry season. The amount of carbon released from soil was 14.3 t ha -1 year -1 , with approximately 70% released during the wet season and 30% during the dry season. The rate of efflux was correlated with soil moisture content and soil temperature only during the wet season, when root growth and respiration were high. During the dry season there was no correlation with soil temperature. These results are discussed in relation to the carbon balance of tropical savannas.
Winning with Simple Learning Models: Detecting Earthquakes in Groningen, the Netherlands

Deep learning is fast emerging as a potential disruptive tool to tackle longstanding research problems across the sciences. Notwithstanding its success across disciplines, the recent trend of the overuse of deep learning is concerning to many machine learning practitioners. Recently, seismologists have also demonstrated the efficacy of deep learning algorithms in detecting low magnitude earthquakes. Here, we revisit the problem of seismic event detection but using a logistic regression model with feature extraction. We select well-discriminating features from a huge database of time-series operations collected from interdisciplinary time-series analysis methods. Using a simple learning model with only five trainable parameters, we detect several low-magnitude induced earthquakes from the Groningen gas field that are not present in the catalog. We note that the added advantage of simpler models is that the selected features add to our understanding of the noise and event classes present in the dataset. Since simpler models are easy to maintain, debug, understand, and train, through this study we underscore that it might be a dangerous pursuit to use deep learning without carefully weighing simpler alternatives.
Structure motif–centric learning framework for inorganic crystalline systems

A structure motif–centric, graph-based deep learning framework for inorganic crystalline materials is proposed. Incorporation of physical principles in a machine learning (ML) architecture is a fundamental step toward the continued development of artificial intelligence for inorganic materials. As inspired by the Pauling’s rule, we propose that structure motifs in inorganic crystals can serve as a central input to a machine learning framework. We demonstrated that the presence of structure motifs and their connections in a large set of crystalline compounds can be converted into unique vector representations using an unsupervised learning algorithm. To demonstrate the use of structure motif information, a motif-centric learning framework is created by combining motif information with the atom-based graph neural networks to form an atom-motif dual graph network (AMDNet), which is more accurate in predicting the electronic structures of metal oxides such as bandgaps. The work illustrates the route toward fundamental design of graph neural network learning architecture for complex materials by incorporating beyond-atom physical principles.
Learning from DPPs via Sampling: Beyond HKPV and symmetry

Determinantal point processes (DPPs) have become a significant tool for recommendation systems, feature selection, or summary extraction, harnessing the intrinsic ability of these probabilistic models to facilitate sample diversity. The ability to sample from DPPs is paramount to the empirical investigation of these models. Most exact samplers are variants of a spectral meta-algorithm due to Hough, Krishnapur, Peres and Vir\'ag (henceforth HKPV), which is in general time and resource intensive. For DPPs with symmetric kernels, scalable HKPV samplers have been proposed that either first downsample the ground set of items, or force the kernel to be low-rank, using e.g. Nystr\"om-type decompositions. In the present work, we contribute a radically different approach than HKPV. Exploiting the fact that many statistical and learning objectives can be effectively accomplished by only sampling certain key observables of a DPP (so-called linear statistics), we invoke an expression for the Laplace transform of such an observable as a single determinant, which holds in complete generality. Combining traditional low-rank approximation techniques with Laplace inversion algorithms from numerical analysis, we show how to directly approximate the distribution function of a linear statistic of a DPP. This distribution function can then be used in hypothesis testing or to actually sample the linear statistic, as per requirement. Our approach is scalable and applies to very general DPPs, beyond traditional symmetric kernels.
Second-order delay ordinary differential equations, their symmetries and application to a traffic problem

This article is the third in a series, the aim of which is to use Lie group theory to obtain exact analytic solutions of delay ordinary differential systems (DODSs). Such a system consists of two equations involving one independent variable x and one dependent variable y. As opposed to ordinary differential equations (ODEs) the variable x figures in more than one point (we consider the case of two points, x and x −). The dependent variable y and its derivatives figure in both x and x −. Two previous articles were devoted to first-order DODSs, here we concentrate on a large class of second-order ones. We show that within this class the symmetry algebra can be of dimension n with 0 ⩽ n ⩽ 6 for nonlinear DODSs and must be infinite-dimensional for linear or linearizable ones. The symmetry algebras can be used to obtain exact particular group invariant solutions. As a specific application we present some exact solutions of a DODS model of traffic flow.
Investigation Into Open-Ended Fitness Landscape Through Evolutionary Logical Circuits

Cumulative cultural evolution is what made humanity to thrive in various ecological and demographic environments. Solutions to the tasks that humans needed to solve could be mapped onto a task space which could take the form of either closed or open-ended fitness landscape, with the former being modeled more extensively than the latter in studies of cultural evolution. In this article, we modified a simulation by Arthur and Polak (2006) that modeled open-ended fitness landscape by using a computer simulation that builds logical circuits with circuits that were built in earlier trials. We used this simulation to clarify the nature of open-ended fitness landscape and to investigate whether the speed of accumulation of culture is increased by an increase in group size. The results indicated that group size increased the speed of accumulation but is limited than expected. Also, when two types of accumulation, invention and improvement, were distinguished the nature of the two differed. In improvement, the trajectory followed aconvex function with productivity of one agent decreasing as group size increased. In invention, the trajectory showed a continuous pattern of rapid increase followed by a plateau.
X-ray properties of He ii λ 1640 emitting galaxies in VANDELS

We explore X-ray emission from a sample of 18 He ii λ1640 emitting star-forming galaxies at z ∼ 2.3–3.6 from the VANDELS survey in the Chandra Deep Field South, to set constraints on the role of X-ray sources in powering the He ii emission. We find that 4 He ii emitters have tentative detections with S/N ∼ 2 and have X-ray luminosities, LX = 1.5−4.9 × 1041 erg s−1. The stacked luminosity of all 18 He ii emitters is 2.6 × 1041 erg s−1, and that of a subset of 13 narrow He ii emitters (FHWM(He ii) < 1000 km s−1) is 3.1 × 1041 erg s−1. We also measure stacked LX for non-He ii emitters through bootstrapping of matched samples, and find LX = 2.5 × 1041 erg s−1, which is not significantly different from LX measured for He ii emitters. The LX per star formation rate for He ii emitters (log (LX/SFR) ∼ 40.0) and non-emitters (log (LX/SFR) ∼ 39.9) are also comparable and in line with the redshift evolution and metallicity dependence predicted by models. Due to the non-significant difference between the X-ray emission from galaxies with and without He ii, we conclude that X-ray binaries or weak or obscured active galactic nuclei are unlikely to be the dominant producers of He ii ionizing photons in VANDELS star-forming galaxies at z ∼ 3. Given the comparable physical properties of both He ii emitters and non-emitters reported previously, alternative He ii ionizing mechanisms such as localized low-metallicity stellar populations, Pop-III stars, etc. may need to be explored.
Heating at the Remote Footpoints as a Brake on Jet Flows along Loops in the Solar Atmosphere

We report on observations of a solar jet propagating along coronal loops taken by the Solar Dynamics Observatory, the Interface Region Imaging Spectrograph, and the 1 m New Vacuum Solar Telescope. The ejecta of the jet consist of multithermal components and propagate with a speed greater than 100 km s−1. Brightenings are found in the remote footpoints of the coronal loops having compact and round shape in the Hα images. The emission peak of the remote brightening in the Atmospheric Imaging Assembly (AIA) 94 Å passband lags 60 s behind that in the jet base. The brightenings in the remote footpoints are believed to be consequences of heating by nonthermal electrons, MHD waves, and/or a conduction front generated by the magnetic reconnection processes of the jet. The heating in the remote footpoints leads to extension of the brightening along the loops toward the jet base, which is believed to be the chromospheric evaporation. This apparently acts as a brake on the ejecta, leading to a deceleration in the range from 1.5 to 3 km s−2 with an error of ∼1.0 km s−2 when the chromospheric evaporation and the ejecta meet at locations near the loop apexes. The dynamics of this jet allows for a unique opportunity to diagnose the chromospheric evaporation from the remote footpoints, from which we deduce a velocity in the range of 330–880 km s−1.