Miracle-dz/UniFAR-FTUs huggingface dataset:
Stellar Variability in a Forming Massive Star Cluster

We present a near-infrared (NIR) variability analysis for a 6′ × 6′ region, which encompasses the massive protocluster G286.21+0.17. The total sample comprises more than 5000 objects, of which 562 show signs of a circumstellar disk based on their infrared colors. The data includes Hubble Space Telescope observations taken in two epochs separated by 3 yr in the F110W and F160W bands. 363 objects (7% of the sample) exhibit NIR variability at a significant level (Stetson index >1.7), and a higher variability fraction (14%) is found for the young stellar objects with disk excesses. We identified four high amplitude (>0.6 mag) variables seen in both NIR bands. Follow-up and archival observations of the most variable object in this survey (G286.2032+0.1740) reveal a rising light curve over 8 yr from 2011 to 2019, with a K band brightening of 3.5 mag. Overall the temporal behavior of G286.2032+0.1740 resembles that of typical FU Ori objects; however, its pre-burst luminosity indicates it has a very low mass (<0.12 M⊙), making it an extreme case of an outburst event that is still ongoing.
Magnetic nulls in interacting dipolar fields

The prominence of nulls in reconnection theory is due to the expected singular current density and the indeterminacy of field lines at a magnetic null. Electron inertia changes the implications of both features. Magnetic field lines are distinguishable only when their distance of closest approach exceeds a distance $\varDelta _d$. Electron inertia ensures $\varDelta _d\gtrsim c/\omega _{pe}$. The lines that lie within a magnetic flux tube of radius $\varDelta _d$ at the place where the field strength $B$ is strongest are fundamentally indistinguishable. If the tube, somewhere along its length, encloses a point where $B=0$ vanishes, then distinguishable lines come no closer to the null than $\approx (a^2c/\omega _{pe})^{1/3}$, where $a$ is a characteristic spatial scale of the magnetic field. The behaviour of the magnetic field lines in the presence of nulls is studied for a dipole embedded in a spatially constant magnetic field. In addition to the implications of distinguishability, a constraint on the current density at a null is obtained, and the time required for thin current sheets to arise is derived.
The EXOD search for faint transients in XMM-Newton observations: Method and discovery of four extragalactic Type I X-ray bursters

Context. The observations carried out with XMM-Newton have produced a very extensive X-ray source catalogue in which the standard pipeline determines the variability of sufficiently bright sources through χ2 and fractional variability tests. Faint sources, however, are not automatically checked for variability, and this means that faint, short timescale transients are overlooked. From dedicated X-ray searches, as well as optical and radio archive searches, we know that some such dim sources can still be identified with high confidence. Aims. Our goal is to find new faint, fast transients in XMM-Newton EPIC-pn observations. To that end we created the EPIC-pn XMM-Newton outburst detector (EXOD) algorithm, which we run on the EPIC-pn full-frame data available in the 3XMM-DR8 catalogue. Methods. In EXOD, we computed the variability of the whole field of view by first binning in time the counts detected in each pixel of the detector. We next computed the difference between the median and maximal number of counts in each time bin and pixel to detect variability. We applied EXOD to 5751 observations in the full frame mode and compared the variability of the detected sources to the standard χ2 and Kolmogorov–Smirnov (KS) variability tests. Results. The algorithm is able to detect periodic and aperiodic variability, with both short and long flares. Of the sources detected by EXOD, 60−95% are also shown to be variable by the standard χ2 and KS tests. EXOD computes the variability over the entire field of view faster than the light curve generation takes for all the individual sources. We detect a total of 2961 X-ray variable sources. After removing the spurious detections, we obtain a net number of 2536 variable sources. Of these we investigate the nature of 35 sources with no previously confirmed classification. Amongst the new sources, we find stellar flares and AGNs, in addition to four extragalactic type I X-ray bursters that double the known neutron-star population in M 31. Conclusions. This algorithm is a powerful tool for the prompt detection of interesting variable sources in XMM-Newton observations. EXOD also detects fast transients that other variability tests would classify as non-variable due to their short duration and low number of counts. This is of increasing importance for the multi-messenger detection of transient sources. Finally, EXOD allows us to identify the nature of compact objects through their variability and to detect rare compact objects. We demonstrate this through the discovery of four extragalactic neutron-star low-mass X-ray binaries, doubling the number of known neutron stars in M 31.
Floristic gradient of the northeast Paraense secondary forests

This study describes a floristic gradient of secondary forest chronosequence in northeast of Para State - Brazil, from 19 sites of different ages, evaluated in different years. We used density data and carried out regression analysis for richness, diversity, density, maximum relative density and equitability in relation to the age. We used the hierarchic grouping method of analysis and the Euclidean distance as dissimilarity measure, and applied a Principal Component Analysis (PCA) for confirming the groups. After defining the groups, we made an indicating species analysis (IndVal) on the same matrix used for the grouping analysis. We found a geographic gradient in the chronosequence analyzed and the species Tapirira guianensis, Vismia guianensis, Inga alba, Lacistema aggregatum, Croton maturensis, Abarema jupunba, Inga rubiginosa, Guateria poepigiana and Thyrsodium paraense to be indicators of the northeast Para (Brazil) secondary forests analyzed in this study.
End-to-End Lane Marker Detection via Row-wise Classification

In autonomous driving, detecting reliable and accurate lane marker positions is a crucial yet challenging task. The conventional approaches for the lane marker detection problem perform a pixel-level dense prediction task followed by sophisticated post-processing that is inevitable since lane markers are typically represented by a collection of line segments without thickness. In this paper, we propose a method performing direct lane marker vertex prediction in an end-to-end manner, i.e., without any post-processing step that is required in the pixel-level dense prediction task. Specifically, we translate the lane marker detection problem into a row-wise classification task, which takes advantage of the innate shape of lane markers but, surprisingly, has not been explored well. In order to compactly extract sufficient information about lane markers which spread from the left to the right in an image, we devise a novel layer, inspired by [8], which is utilized to successively compress horizontal components so enables an end-to-end lane marker detection system where the final lane marker positions are simply obtained via argmax operations in testing time. Experimental results demonstrate the effectiveness of the proposed method, which is on par or outperforms the state-of-the-art methods on two popular lane marker detection benchmarks, i.e., TuSimple and CULane.
Deep feature fusion for self-supervised monocular depth prediction

Recent advances in end-to-end unsupervised learning has significantly improved the performance of monocular depth prediction and alleviated the requirement of ground truth depth. Although a plethora of work has been done in enforcing various structural constraints by incorporating multiple losses utilising smoothness, left-right consistency, regularisation and matching surface normals, a few of them take into consideration multi-scale structures present in real world images. Most works utilise a VGG16 or ResNet50 model pre-trained on ImageNet weights for predicting depth. We propose a deep feature fusion method utilising features at multiple scales for learning self-supervised depth from scratch. Our fusion network selects features from both upper and lower levels at every level in the encoder network, thereby creating multiple feature pyramid sub-networks that are fed to the decoder after applying the CoordConv solution. We also propose a refinement module learning higher scale residual depth from a combination of higher level deep features and lower level residual depth using a pixel shuffling framework that super-resolves lower level residual depth. We select the KITTI dataset for evaluation and show that our proposed architecture can produce better or comparable results in depth prediction.
Neural Controlled Differential Equations for Irregular Time Series

Neural ordinary differential equations are an attractive option for modelling temporal dynamics. However, a fundamental issue is that the solution to an ordinary differential equation is determined by its initial condition, and there is no mechanism for adjusting the trajectory based on subsequent observations. Here, we demonstrate how this may be resolved through the well-understood mathematics of \emph{controlled differential equations}. The resulting \emph{neural controlled differential equation} model is directly applicable to the general setting of partially-observed irregularly-sampled multivariate time series, and (unlike previous work on this problem) it may utilise memory-efficient adjoint-based backpropagation even across observations. We demonstrate that our model achieves state-of-the-art performance against similar (ODE or RNN based) models in empirical studies on a range of datasets. Finally we provide theoretical results demonstrating universal approximation, and that our model subsumes alternative ODE models.
Triggered high-mass star formation in the H iiregion W 28 A2: A cloud–cloud collision scenario

We report on a study of the high-mass star formation in the H ii region W 28 A2 by investigating the molecular clouds that extend over ∼5–10 pc from the exciting stars using the 12CO and 13CO (J = 1–0) and 12CO (J = 2–1) data taken by NANTEN2 and Mopra observations. These molecular clouds consist of three velocity components with CO intensity peaks at VLSR ∼ −4 km s−1, 9 km s−1, and 16 km s−1. The highest CO intensity is detected at VLSR ∼ 9 km s−1, where the high-mass stars with spectral types O6.5–B0.5 are embedded. We found bridging features connecting these clouds toward the directions of the exciting sources. Comparisons of the gas distributions with the radio continuum emission and 8 μm infrared emission show spatial coincidence/anti-coincidence, suggesting physical associations between the gas and the exciting sources. The 12CO J = 2–1 to 1–0 intensity ratio shows a high value (≳0.8) toward the exciting sources for the −4 km s−1 and +9 km s−1 clouds, possibly due to heating by the high-mass stars, whereas the intensity ratio at the CO intensity peak (VLSR ∼ 9 km s−1) decreases to ∼0.6, suggesting self absorption by the dense gas in the near side of the +9 km s−1 cloud. We found partly complementary gas distributions between the −4 km s−1 and +9 km s−1 clouds, and the −4 km s−1 and +16 km s−1 clouds. The exciting sources are located toward the overlapping region in the −4 km s−1 and +9 km s−1 clouds. Similar gas properties are found in the Galactic massive star clusters RCW 38 and NGC 6334, where an early stage of cloud collision to trigger the star formation is suggested. Based on these results, we discuss the possibility of the formation of high-mass stars in the W 28 A2 region being triggered by cloud–cloud collision.
Genetic variation in susceptibility to Dothistroma needle blight among provenances and families of Pinusmuricata

The susceptibility of Pinusmuricata D. Don to needle blight caused by Dothistromaseptospora (Dorog.) Morelet var. septospora was investigated in three provenance tests and a progeny test in New South Wales, Australia, and compared with that in Pinusradiata D. Don, the major commercially planted species. Provenances of P. muricata from Humboldt, Mendocino, and Sonoma counties in northern California were moderately susceptible for the first 3 or 4 years but then developed a high degree of field resistance. There was a general trend to increasing susceptibility in provenances progressively southwards along the California coast, with one notable exception, Santa Rosa Island provenance, which appeared to be resistant. All provenances from Monterey southwards, with that one exception, were at least as susceptible as P. radiata. The Mendocino County provenance of P. muricata is suggested as an alternative to P. radiata for planting on sites where there is a high risk of severe needle blight, as it possesses a co.
Federated Neural Architecture Search

To preserve user privacy while enabling mobile intelligence, techniques have been proposed to train deep neural networks on decentralized data. However, training over decentralized data makes the design of neural architecture quite difficult as it already was. Such difficulty is further amplified when designing and deploying different neural architectures for heterogeneous mobile platforms. In this work, we propose an automatic neural architecture search into the decentralized training, as a new DNN training paradigm called Federated Neural Architecture Search, namely federated NAS. To deal with the primary challenge of limited on-client computational and communication resources, we present FedNAS, a highly optimized framework for efficient federated NAS. FedNAS fully exploits the key opportunity of insufficient model candidate re-training during the architecture search process, and incorporates three key optimizations: parallel candidates training on partial clients, early dropping candidates with inferior performance, and dynamic round numbers. Tested on large-scale datasets and typical CNN architectures, FedNAS achieves comparable model accuracy as state-of-the-art NAS algorithm that trains models with centralized data, and also reduces the client cost by up to two orders of magnitude compared to a straightforward design of federated NAS.
Fast variable density node generation on parametric surfaces with application to mesh-free methods

Domain discretization is considered a dominant part of solution procedures for solving partial differential equations. It is widely accepted that mesh generation is among the most cumbersome parts of the FEM analysis and often requires human assistance, especially in complex 3D geometries. When using alternative mesh-free approaches, the problem of mesh generation is simplified to the problem of positioning nodes, a much simpler task, though still not trivial. In this paper we present an algorithm for generation of nodes on arbitrary $d$-dimensional surfaces. This algorithm complements a recently published algorithm for generation of nodes in domain interiors, and represents another step towards a fully automated dimension-independent solution procedure for solving partial differential equations. The proposed algorithm generates nodes with variable density on surfaces parameterized over arbitrary parametric domains in a dimension-independent way in $O(N\log N)$ time. It is also compared with existing algorithms for generation of surface nodes for mesh-free methods in terms of quality and execution time.
Approximate Denial Constraints

The problem of mining integrity constraints from data has been extensively studied over the past two decades for commonly used types of constraints, including the classic Functional Dependencies (FDs) and the more general Denial Constraints (DCs). In this paper, we investigate the problem of mining from data approximate DCs, that is, DCs that are "almost" satisfied. Approximation allows us to discover more accurate constraints in inconsistent databases and detect rules that are generally correct but may have a few exceptions. It also allows to avoid overfitting and obtain constraints that are more general, more natural, and less contrived. We introduce the algorithm ADCMiner for mining approximate DCs. An important feature of this algorithm is that it does not assume any specific approximation function for DCs, but rather allows for arbitrary approximation functions that satisfy some natural axioms that we define in the paper. We also show how our algorithm can be combined with sampling to return highly accurate results considerably faster.
A Comparison of X-Ray Photon Indices among the Narrow- and Broad-line Seyfert 1 Galaxies

We present a detailed comparative systematic study using a sample of 221 narrow-line Seyfert 1 (NLSy1) galaxies in comparison to a redshift-matched sample of 154 broad-line Seyfert 1 (BLSy1) galaxies based on their observations using ROSAT and/or XMM-Newton in soft X-ray band (0.1–2.0 keV). A homogeneous analysis is carried out to estimate their soft X-ray photon indices ( ) and its correlations with other parameters of nuclear activities such as Eddington ratios (REdd), bolometric luminosities (Lbol), black hole masses (MBH), and the widths of the broad component of Hβ lines (FWHM(Hβ)). In our analysis, we found clear evidence of the difference in the and REdd distributions among NLSy1 and BLSy1 galaxies, with steeper and higher REdd for the former. Such a difference also exists in the spectral index distribution in hard X-ray ( ), based on the analysis of 53 NLSy1 and 46 BLSy1 galaxies in the 2–10 keV energy band. The difference in REdd distributions does exist even after applying the average correction for the difference in the inclination angle of NLSy1 and BLSy1 galaxies. We also estimated REdd, based on SED fitting of 34 NLSy1 and 30 BLSy1 galaxies over the 0.3–10 keV energy band, and found that results are still consistent with REdd estimates based on the optical bolometric luminosity. Our analysis suggests that the higher REdd in NLSy1 is responsible for its steeper X-ray spectral slope compared to the BLSy1, consistent with the disk-corona model as proposed for the luminous AGNs.
Parasitic Crustacea from Bimini, Bahamas

This paper deals with the parasitic copepods, isopods, and barnacles collected from fishes and crustaceans during a month's work (October 13 to November 15, 1948) at the Lerner Marine Laboratory of the American Museum of Natural History at Bimini, Bahama Islands. In all, 368 fishes of 73 species were examined. Of these, 171 individuals (46 percent) of 23 species (31 percent) yielded 290 parasitic copepods, and 140 individuals of 50 species carried none. Of the crustaceans that might be expected to harbor copepods, 10 species were examined; 504 individuals of 4 species harbored 67 copepods, and 129 individuals of 6 species carried
Floquet-surface bound states in the continuum in a resonantly driven one-dimensional tilted defect-free lattice

We study the Floquet-surface bound states embedded in the continuum (BICs) and bound states out the continuum (BOCs)in a resonantly driven 1D tilted defect-free lattice. In contrast to fragile single-particle BICs assisted by specially tailored potentials, we find that Floquet-surface BICs, stable against structural perturbations, can exist in a wide range of parameter space. By using a multiple-time-scale asymptotic analysis in the high-frequency limit, the appearance of Floquet-surface bound states can be analytically explained by effective Tamm-type defects at boundaries induced by the resonance between the periodic driving and tilt. The phase boundary of existing Floquet-surface states is also analytically given. Based on the repulsion effect of surface states, we propose to detect transition points and measure the number of Floquet-surface bound states by quantum walk. Our work opens a new door to experimental realization of BICs in quantum system.
Differences in choice of diet between sheep breeds grazing mountain pastures in Norway

In the present study, the foraging behaviour of ewes from three common Norwegian sheep breeds; the long-tailed Dala sheep, and the two short-tailed breeds Spæl sheep and Norwegian Fur sheep was studied. Based on difference in body size and digestive anatomy, we expected the lighter short-tailed ewes to browse more on woody plants than the Dala ewes. The experiments were conducted during two consecutive summers within the flocks’ usual grazing areas, and we sampled the proportion of grazing time that ewes spent feeding on woody plant species. We tested the hypothesis by applying generalized linear mixed models to the feeding behaviour data. As predicted, the short-tailed ewes spent substantially more of their grazing time browsing on trees, bushes and heather compared to the Dala ewes. We discuss the results in the context of maintaining the, now threatened, pastoral landscapes of mountainous regions of Norway; short-tailed breeds should be more effective than the Dala breed in reducing tree and bush encroachment, but more detailed studies are needed to quantify the breed differences in this respect.
A Fragmentation Phenomenon for a Nonenergetic Optimal Control Problem: Optimization of the Total Population Size in Logistic Diffusive Models

Following the recent works [9, 17, 30, 31, 37], we investigate the problem of optimising the total population size for logistic diffusive models with respect to resources distributions. Using the spatially heterogeneous Fisher-KPP equation, we obtain a surprising fragmentation phenomenon: depending on the scale of diffusivity (i.e the dispersal rate), it is better to either concentrate or fragment resources. Our main result is that, the smaller the dispersal rate of the species in the domain, the more optimal resources distributions tend to oscillate. This is in sharp contrast with other criteria in population dynamics, such as the classical problem of optimising the survival ability of a species, where concentrating resources is always favourable, regardless of the diffusivity. Our study is completed by numerous numerical simulations that confirm our results.
On the scaling of the instability of a flat sediment bed with respect to ripple-like patterns

Abstract We investigate the formation of subaqueous transverse bedforms in turbulent open channel flow by means of direct numerical simulations with fully resolved particles. The main goal of the present analysis is to address the question whether the initial pattern wavelength scales with the particle diameter or with the mean fluid height. A previous study (Kidanemariam & Uhlmann, J. Fluid Mech., vol. 818, 2017, pp. 716–743) has observed a lower bound for the most unstable pattern wavelength in the range 75–100 times the particle diameter, which was equivalent to 3–4 times the mean fluid height. In the current paper, we vary the streamwise box length in terms of the particle diameter and of the mean fluid height independently in order to distinguish between the two possible scaling relations. For the chosen parameter range, the obtained results clearly exhibit a scaling of the initial pattern wavelength with the particle diameter, with a lower bound around a streamwise extent of approximately $80$ particle diameters. In longer domains, on the other hand, patterns are observed at initial wavelengths in the range 150–180 times the particle diameter, which is in good agreement with experimental measurements. Variations of the mean fluid height, on the other hand, seem to have no significant influence on the most unstable initial pattern wavelength. We argue that the observed scaling with the particle diameter is due to the wake effect induced by the seeds which are formed by initially dislodged lumps of particles, in accordance with the ideas of Coleman & Nikora (Water Resour. Res., vol. 45, 2009, W04402). Finally, for the cases with the largest relative submergence, we observe spanwise and streamwise sediment waves of similar amplitude to evolve and superimpose, leading to three-dimensional sediment patterns.