Miracle-dz/UniFAR-FTUs huggingface dataset:
Time-resolved spectroscopy and photometry of an M dwarf flare star YZ Canis Minoris with OISTER and TESS: Blue asymmetry in H$\alpha$ line during the non-white light flare

In this paper, we present the results from spectroscopic and photometric observations of the M-type flare star YZ CMi in the framework of the Optical and Infrared Synergetic Telescopes for Education and Research (OISTER) collaborations during the Transiting Exoplanet Survey Satellite (TESS) observation period. We detected 145 white-light flares from the TESS light curve and 4 H$\alpha$ flares from the OISTER observations performed between 2019-01-16 and 2019-01-18. Among them, 3 H$\alpha$ flares were associated with white-light flares. However, one of them did not show clear brightening in continuum; during this flare, the H$\alpha$ line exhibited blue-asymmetry which has lasted for $\sim 60$ min. The line of sight velocity of the blue-shifted component is $-80$ - $-100$ km s$^{-1}$. This suggests that there can be upward flows of chromospheric cool plasma even without detectable red/NIR continuum brightening. By assuming that the blue-asymmetry in H$\alpha$ line was caused by a prominence eruption on YZ CMi, we estimated the mass and kinetic energy of the upward-moving material to be $10^{16}$ - $10^{18}$ g and $10^{29.5}$ - $10^{31.5}$ erg, respectively. The estimated mass is comparable to expectations from the empirical relation between the flare X-ray energy and mass of upward-moving material for stellar flares and solar CMEs. In contrast, the estimated kinetic energy for the non-white-light flare on YZ CMi is roughly $2$ orders of magnitude smaller than that expected from the relation between flare X-ray energy and kinetic energy for solar CMEs. This could be understood by the difference in the velocity between CMEs and prominence eruptions.
Can Automatic Post-Editing Improve NMT?

Automatic post-editing (APE) aims to improve machine translations, thereby reducing human post-editing effort. APE has had notable success when used with statistical machine translation (SMT) systems but has not been as successful over neural machine translation (NMT) systems. This has raised questions on the relevance of APE task in the current scenario. However, the training of APE models has been heavily reliant on large-scale artificial corpora combined with only limited human post-edited data. We hypothesize that APE models have been underperforming in improving NMT translations due to the lack of adequate supervision. To ascertain our hypothesis, we compile a larger corpus of human post-edits of English to German NMT. We empirically show that a state-of-art neural APE model trained on this corpus can significantly improve a strong in-domain NMT system, challenging the current understanding in the field. We further investigate the effects of varying training data sizes, using artificial training data, and domain specificity for the APE task. We release this new corpus under CC BY-NC-SA 4.0 license at this https URL.
Blown-up toric surfaces with non-polyhedral effective cone

Abstract We construct projective toric surfaces whose blow-up at a general point has a non-polyhedral pseudo-effective cone. As a consequence, we prove that the pseudo-effective cone of the Grothendieck–Knudsen moduli space M ¯ 0 , n \overline{M}_{0,n} of stable rational curves is not polyhedral for n ≥ 10 n\geq 10 . These results hold both in characteristic 0 and in characteristic 𝑝, for all primes 𝑝. Many of these toric surfaces are related to an interesting class of arithmetic threefolds that we call arithmetic elliptic pairs of infinite order. Our analysis relies on tools of arithmetic geometry and Galois representations in the spirit of the Lang–Trotter conjecture, producing toric surfaces whose blow-up at a general point has a non-polyhedral pseudo-effective cone in characteristic 0 and in characteristic 𝑝, for an infinite set of primes 𝑝 of positive density.
Impact of dynamical dark energy on the neutron star equilibrium

We study the density distribution of the minimally-coupled scalar field dark energy inside a neutron star. The dark energy is considered in the hydrodynamical representation as a perfect fluid with three parameters (background density, equation of state, and effective sound speed). The neutron star matter is modeled with three unified equations of state, developed by the Brussels-Montreal group. With the calculated density distribution of the dark energy inside a neutron star (and its dependence on the dark energy parameters) we investigate how its presence impacts the macroscopic characteristics and the value of the mass limit for neutron stars. From this impact we derive the possible constraints on the effective speed of sound of dark energy with the help of maximal known masses of observed neutron stars. In this approach, we have found, that the squared effective speed of sound can not be smaller than ∼ 10−2 in units of squared speed of light.
Constraining the circumbinary disc tilt in the KH 15D system

KH 15D is a system which consists of a young, eccentric, eclipsing binary, and a circumbinary disk which obscures the binary as the disk precesses. We develop a self-consistent model that provides a reasonable fit to the photometric variability that was observed in the KH 15D system over the past more than 60 years. Our model suggests that the circumbinary disk has an inner edge $r_{\rm in}\lesssim 1 \ {\rm au}$, an outer edge $r_{\rm out} \sim {\rm a \ few \ au}$, and that the disk is misaligned relative to the stellar binary by $\sim$3-15 degrees, with the inner edge more inclined than the outer edge. The difference between the inclinations (warp) and longitude of ascending nodes (twist) at the inner and outer edges of the disk are of order $\sim$10 degrees and $\sim$15 degrees, respectively. We also provide constraints on other properties of the disk, such as the precession period and surface density profile. Our work demonstrates the power of photometric data in constraining the physical properties of planet-forming circumbinary disks.
Strategy and Benchmark for Converting Deep Q-Networks to Event-Driven Spiking Neural Networks

Spiking neural networks (SNNs) have great potential for energy-efficient implementation of Deep Neural Networks (DNNs) on dedicated neuromorphic hardware. Recent studies demonstrated competitive performance of SNNs compared with DNNs on image classification tasks, including CIFAR-10 and ImageNet data. The present work focuses on using SNNs in combination with deep reinforcement learning in ATARI games, which involves additional complexity as compared to image classification. We review the theory of converting DNNs to SNNs and extending the conversion to Deep Q-Networks (DQNs). We propose a robust representation of the firing rate to reduce the error during the conversion process. In addition, we introduce a new metric to evaluate the conversion process by comparing the decisions made by the DQN and SNN, respectively. We also analyze how the simulation time and parameter normalization influence the performance of converted SNNs. We achieve competitive scores on 17 top-performing Atari games. To the best of our knowledge, our work is the first to achieve state-of-the-art performance on multiple Atari games with SNNs. Our work serves as a benchmark for the conversion of DQNs to SNNs and paves the way for further research on solving reinforcement learning tasks with SNNs.
Measurement of proton-proton elastic scattering into the Coulomb region at $\text{P}_\text{beam}$ = 2.5, 2.8 and 3.2 GeV/c

The proton--proton elastic differential cross section at very small four momentum transfer squared has been measured at three different incident proton momenta in the range of 2.5 to 3.2 GeV/c by detecting the recoil proton at polar angles close to $90^\circ$. The measurement was performed at COSY with the KOALA detector covering the Coulomb-nuclear interference region. The total cross section $\sigma_\text{tot}$, which has been determined precisely, is consistent with previous measurements. The values of the slope parameter $B$ and the relative real amplitude ratio $\rho$ determined in this experiment alleviate the lack of data in the relevant energy region. This precise data on $\rho$ might be an important check for a new dispersion analysis.
Local and Global Gas Metallicity versus Stellar Age Relation in MaNGA Galaxies

The search for new global scaling relations linking the physical properties of galaxies has a fundamental interest. Furthermore, their recovery from spatially resolved relations has been in the spotlight of integral field spectroscopy (IFS). In this study, we investigate the existence of global and local relations between stellar age (Age⋆) and gas-phase metallicity (Z g ). To this aim, we analyze IFS data for a sample of 736 star-forming disk galaxies from the MaNGA survey. We report a positive correlation between the global Z g and D(4000) (an indicator of stellar age), with a slope that decreases with increasing galaxy mass. Locally, a similar trend is found when analyzing the Z g and D(4000) of the star-forming regions, as well as the residuals resulting from removing the radial gradients of both parameters. The local laws have systematically smaller slopes than the global one. We ascribe this difference to random errors that cause the true slope of the Age⋆–Z g relation to be systematically underestimated when performing a least-squares fitting. The explored relation is intimately linked with the already known relation between gas metallicity and star formation rate at fixed mass, both presenting a common physical origin.
On the Reliability of Photometric and Spectroscopic Tracers of Halo Relaxation

We characterize the relaxation state of galaxy systems by providing an assessment of the reliability of the photometric and spectroscopic probe via the semianalytic galaxy evolution model. We quantify the correlations between the dynamical age of simuglated galaxy groups and popular proxies of halo relaxation in observation, which are mainly either spectroscopic or photometric. We find the photometric indicators demonstrate a stronger correlation with the dynamical relaxation of galaxy groups compared to the spectroscopic probes. We take advantage of the Anderson Darling statistic (A 2) and the velocity segregation ( ) as our spectroscopic indicators, and use the luminosity gap (Δm 12) and the luminosity decentering (D offset) as photometric ones. First, we find that a combination of Δm 12 and D offset evaluated by a bivariant relation ( ) shows a good correlation with the dynamical age compared to all other indicators. Second, by using the observational X-ray surface brightness map, we show that the bivariant relation brings about some acceptable correlations with X-ray proxies. These correlations are as well as the correlations between A 2 and X-ray proxies, offering a reliable yet fast and economical method of quantifying the relaxation of galaxy systems. This study demonstrates that using photometric data to determine the relaxation status of a group will lead to some promising results that are comparable with the more expensive spectroscopic counterpart.
Energy Expenditure during Molt in Dippers (Cinclus cinclus): No Evidence of Elevated Costs

The energetic cost of molt in birds is generally considered to be high and is frequently regarded as being a main reason for the temporal separation of molt from reproduction and other activities. We assessed the energetic cost of the postnuptial molt in dippers, Cinclus cinclus, using laboratory measurements of O₂ consumption and CO₂ production of molting and nonmolting individuals, timeactivity budgets in the field, and the doubly labeled water technique on free-living, molting birds. Energy expenditures of molting dippers within their thermal neutral zone in the laboratory (males, 92 kJ d−1; females, 74 kJ d−1) were not significantly diferent from those of nonmolting individuals (males, 85 kJ d−1; females, 76 kJ d−1). Free-living, molting dippers spent most of their active day resting (55%) and foraging along riverbanks (39%). Flying and diving were infrequent, accounting for less than 1% of their activity budget. Mean daily energoy expenditures offree-living dippers during molt, obtained from time-activity laboratory estimates and from doubly labeled water estimates, were in close agreement, with the latter averaging 177 kJ d−1 for males and 162 kJ d−1 for females. These rates of energy expenditure are lower than at any other time of the year, and een if normal levels of flying and diving are superimposed on daily energy expenditure during molt, energy expenditure is elevated only to levels typical of males during mate guarding and females attending young. We conclude that molt in dippers does not elevate basal metabolic rate or daily energy expenditure and that the timing of molt in this species is therefore not constrained by the direct energetic costs of feather replacement. We propose several explanations for the timing of molt that may apply to dippers and other species.
Direct Multi-hop Attention based Graph Neural Network

Introducing self-attention mechanism in graph neural networks (GNNs) achieved state-of-the-art performance for graph representation learning. However, at every layer, attention is only computed between two connected nodes and depends solely on the representation of both nodes. This attention computation cannot account for the multi-hop neighbors which supply graph structure context information and have influence on the node representation learning as well. In this paper, we propose Direct Multi-hop Attention based Graph neural Network (DAGN) for graph representation learning, a principled way to incorporate multi-hop neighboring context into attention computation, enabling long-range interactions at every layer. To compute attention between nodes that are multiple hops away, DAGN diffuses the attention scores from neighboring nodes to non-neighboring nodes, thus increasing the receptive field for every message passing layer. Unlike previous methods, DAGN uses a diffusion prior on attention values, to efficiently account for all paths between the pair of nodes when computing multi-hop attention weights. This helps DAGN capture large-scale structural information in a single layer, and learn more informative attention distribution. Experimental results on standard semi-supervised node classification as well as the knowledge graph completion show that DAGN achieves state-of-the-art results: DAGN achieves up to 5.7% relative error reduction over the previous state-of-the-art on Cora, Citeseer, and Pubmed. DAGN also obtains the best performance on a large-scale Open Graph Benchmark dataset. On knowledge graph completion DAGN advances state-of-the-art on WN18RR and FB15k-237 across four different performance metrics.
Dorado and its member galaxies

ABRIDGED: Dorado is a nearby, rich and clumpy galaxy group that extends for several degrees in the Southern Hemisphere. For the first time, we map the Ha distribution as a possible indicator of star formation (SF) activity of Dorado members a large fraction of which show interaction and merging signatures. With the 2.5m du Pont and the 1m Swope telescopes we obtained narrow-band, calibrated images of 14 galaxies, forming the backbone of the group, mapping Ha+[N II] down to few 10$^{-17}$ erg~cm$^{-2}$~s$^{-1}$~arcsec$^{-2}$. We estimated the galaxy star formation rate (SFR) from the Ha fluxes, corrected for Galaxy foreground extinction and [N II] contamination. Ha+[N II] emission has been detected in all galaxies. HII regions clearly emerge in late-type galaxies (LTGs) , while in early-type galaxies (ETGs) the Ha+[N II] emission is dominated by [N II], especially in the central regions. However, HII complexes are also revealed in four ETGs. Considering the Dorado group as a whole, we notice that the Ha+[N II] equivalent width, a measure of the specific SF, increases with the morphological type, from early to late-type members, although it remains lower that what observed in similar surveys of spiral galaxies. The SFR of the spiral members is in the range of what observed in similar galaxies surveys James+ (2004), although, in three spirals the SFR is well below the median for their morphological classes. The SFR of some early-type members tends, at odds, to be higher than the average derived from Ha+[N II] surveys of this morphological family. We detected in Ha+[N II] all the ETGs observed and half of them show HII regions. These findings suggest that ETGs in this group are not dead galaxies: their SF has not shut down yet. Mechanisms such as gas stripping and gas accretion, through galaxy-galaxy interaction, seem relevant in modifying the SF in this evolutionary phase of Dorado.
Pruning Filter in Filter

Pruning has become a very powerful and effective technique to compress and accelerate modern neural networks. Existing pruning methods can be grouped into two categories: filter pruning (FP) and weight pruning (WP). FP wins at hardware compatibility but loses at the compression ratio compared with WP. To converge the strength of both methods, we propose to prune the filter in the filter. Specifically, we treat a filter $F \in \mathbb{R}^{C\times K\times K}$ as $K \times K$ stripes, \emph{i.e.}, $1\times 1$ filters $\in \mathbb{R}^{C}$, then by pruning the stripes instead of the whole filter, we can achieve finer granularity than traditional FP while being hardware friendly. We term our method as SWP (\emph{Stripe-Wise Pruning}). SWP is implemented by introducing a novel learnable matrix called Filter Skeleton, whose values reflect the shape of each filter. As some recent work has shown that the pruned architecture is more crucial than the inherited important weights, we argue that the architecture of a single filter, \emph{i.e.}, the shape, also matters. Through extensive experiments, we demonstrate that SWP is more effective compared to the previous FP-based methods and achieves the state-of-art pruning ratio on CIFAR-10 and ImageNet datasets without obvious accuracy drop. Code is available at this https URL
Multiple Word Embeddings for Increased Diversity of Representation

Most state-of-the-art models in natural language processing (NLP) are neural models built on top of large, pre-trained, contextual language models that generate representations of words in context and are fine-tuned for the task at hand. The improvements afforded by these "contextual embeddings" come with a high computational cost. In this work, we explore a simple technique that substantially and consistently improves performance over a strong baseline with negligible increase in run time. We concatenate multiple pre-trained embeddings to strengthen our representation of words. We show that this concatenation technique works across many tasks, datasets, and model types. We analyze aspects of pre-trained embedding similarity and vocabulary coverage and find that the representational diversity between different pre-trained embeddings is the driving force of why this technique works. We provide open source implementations of our models in both TensorFlow and PyTorch.
Uncovering Feature Interdependencies with Non-Greedy Random Forests

A "non-greedy" variation of the random forest algorithm is presented to better uncover feature interdependencies inherent in complex data. Conventionally, random forests are built from "greedy" decision trees which each consider only one split at a time during their this http URL contrast, the decision trees included in this random forest algorithm each consider three split nodes simultaneously in tiers of depth two. It is demonstrated on synthetic data and bitcoin price time series that the non-greedy version significantly outperforms the greedy one if certain non-linear relationships between feature-pairs are present. In particular, both greedy and a non-greedy random forests are trained to predict the signs of daily bitcoin returns and backtest a long-short trading strategy. The better performance of the non-greedy algorithm is explained by the presence of "XOR-like" relationships between long-term and short-term technical indicators. When no such relationships exist, performance is similar.
Natcat: Weakly Supervised Text Classification with Naturally Annotated Datasets

We seek to improve text classification by leveraging naturally annotated data. In particular, we construct a general purpose text categorization dataset (NatCat) from three online resources: Wikipedia, Reddit, and Stack Exchange. These datasets consist of document-category pairs derived from manual curation that occurs naturally by their communities. We build general purpose text classifiers by training on NatCat and evaluate them on a suite of 11 text classification tasks (CatEval). We benchmark different modeling choices and dataset combinations, and show how each task benefits from different NatCat training resources.
Helical magnetic order and Fermi surface nesting in noncentrosymmetric ScFeGe

An investigation of the structural, magnetic, thermodynamic, and charge transport properties of non-centrosymmetric hexagonal ScFeGe reveals it to be an anisotropic metal with a transition to a weak itinerant incommensurate helimagnetic state below $T_N = 36$ K. Neutron diffraction measurements discovered a temperature and field independent helical wavevector \textbf{\textit{k}} = (0 0 0.193) with magnetic moments of 0.53 $\mu_{B}$ per formula unit confined to the {\it ab}-plane. Density functional theory calculations are consistent with these measurements and find several bands that cross the Fermi level along the {\it c}-axis with a nearly degenerate set of flat bands just above the Fermi energy. The anisotropy found in the electrical transport is reflected in the calculated Fermi surface, which consists of several warped flat sheets along the $c$-axis with two regions of significant nesting, one of which has a wavevector that closely matches that found in the neutron diffraction. The electronic structure calculations, along with a strong anomaly in the {\it c}-axis conductivity at $T_N$, signal a Fermi surface driven magnetic transition, similar to that found in spin density wave materials. Magnetic fields applied in the {\it ab}-plane result in a metamagnetic transition with a threshold field of $\approx$ 6.7 T along with a sharp, strongly temperature dependent, discontinuity and a change in sign of the magnetoresistance for in-plane currents. Thus, ScFeGe is an ideal system to investigate the effect of in-plane magnetic fields on an easy-plane magnetic system, where the relative strength of the magnetic interactions and anisotropies determine the topology and magnetic structure.