Miracle-dz/UniFAR-FTUs huggingface dataset:
THE STAR FORMATION HISTORY AND CHEMICAL EVOLUTION OF STAR-FORMING GALAXIES IN THE NEARBY UNIVERSE

We have determined the metallicity (O/H) and nitrogen abundance (N/O) of a sample of 122,751 star-forming galaxies (SFGs) from the Data Release 7 of the Sloan Digital Sky Survey. For all these galaxies we have also determined their morphology and obtained a comprehensive picture of their star formation history (SFH) using the spectral synthesis code STARLIGHT. The comparison of the chemical abundance with the SFH allows us to describe the chemical evolution of the SFGs in the nearby universe (z ⩽ 0.25) in a manner consistent with the formation of their stellar populations and morphologies. A high fraction (45%) of the SFGs in our sample show an excess abundance of nitrogen relative to their metallicity. We also find this excess to be accompanied by a deficiency of oxygen, which suggests that this could be the result of effective starburst winds. However, we find no difference in the mode of star formation of the nitrogen-rich and nitrogen-poor SFGs. Our analysis suggests that they all form their stars through a succession of bursts of star formation extended over a period of few Gyr. What produces the chemical differences between these galaxies seems therefore to be the intensity of the bursts: the galaxies with an excess of nitrogen are those that are presently experiencing more intense bursts or have experienced more intense bursts in their past. We also find evidence relating the chemical evolution process to the formation of the galaxies: the galaxies with an excess of nitrogen are more massive, and have more massive bulges and earlier morphologies than those showing no excess. Contrary to expectation, we find no evidence that the starburst wind efficiency decreases with the mass of the galaxies. As a possible explanation we propose that the loss of metals consistent with starburst winds took place during the formation of the galaxies, when their potential wells were still building up, and consequently were weaker than today, making starburst winds more efficient and independent of the final mass of the galaxies. In good agreement with this interpretation, we also find evidence consistent with downsizing, according to which the more massive SFGs formed before the less massive ones.
FINDING THE INSTABILITY STRIP FOR ACCRETING PULSATING WHITE DWARFS FROM HUBBLE SPACE TELESCOPE AND OPTICAL OBSERVATIONS

Time-resolved low resolution Hubble Space Telescope ultraviolet spectra together with ground-based optical photometry and spectra are used to constrain the temperatures and pulsation properties of six cataclysmic variables containing pulsating white dwarfs (WDs). Combining our temperature determinations for the five pulsating WDs that are several years past outburst with past results on six other systems shows that the instability strip for accreting pulsating WDs ranges from 10,500 to 15,000 K, a wider range than evident for ZZ Ceti pulsators. Analysis of the UV/optical pulsation properties reveals some puzzling aspects. While half the systems show high pulsation amplitudes in the UV compared to their optical counterparts, others show UV/optical amplitude ratios that are less than one or no pulsations at either wavelength region.
Bosons in high-temperature superconductors: an experimental survey

We review a number of experimental techniques that are beginning to reveal fine details of the bosonic spectrum α2F(Ω) that dominates the interaction between the quasiparticles in high-temperature superconductors. Angle-resolved photoemission spectroscopy (ARPES) shows kinks in electronic dispersion curves at characteristic energies that agree with similar structures in the optical conductivity and tunnelling spectra. Each technique has its advantages. ARPES is momentum resolved and offers independent measurements of the real and imaginary part of the contribution of the bosons to the self-energy of the quasiparticles. The optical conductivity can be used on a larger variety of materials and with the use of maximum entropy techniques reveals rich details of the spectra including their evolution with temperature and doping. Scanning tunnelling spectroscopy offers spatial resolution on the unit cell level. We find that together the various spectroscopies, including recent Raman results, are pointing to a unified picture of a broad spectrum of bosonic excitations at high temperatures which evolves, as the temperature is lowered, into a peak in the 30–60 meV region and a featureless high-frequency background in most of the materials studied. This behaviour is consistent with the spectrum of spin fluctuations as measured by magnetic neutron scattering. However, there is evidence for a phonon contribution to the bosonic spectrum as well.
Rapid CVD growth of millimetre-sized single crystal graphene using a cold-wall reactor

In this work we present a simple pathway to obtain large single-crystal graphene on copper (Cu) foils with high growth rates using a commercially available cold-wall chemical vapour deposition (CVD) reactor. We show that graphene nucleation density is drastically reduced and crystal growth is accelerated when: (i) using ex situ oxidized foils; (ii) performing annealing in an inert atmosphere prior to growth; (iii) enclosing the foils to lower the precursor impingement flux during growth. Growth rates as high as 14.7 and 17.5 μm min−1 are obtained on flat and folded foils, respectively. Thus, single-crystal grains with lateral size of about 1 mm can be obtained in just 1 h. The samples are characterized by optical microscopy, scanning electron microscopy, x-ray photoelectron spectroscopy, Raman spectroscopy as well as selected area electron diffraction and low-energy electron diffraction, which confirm the high quality and homogeneity of the films. The development of a process for the quick production of large grain graphene in a commonly used commercial CVD reactor is a significant step towards an increased accessibility to millimetre-sized graphene crystals.
The Gaia mission

Gaia is a cornerstone mission in the science programme of the European Space Agency (ESA). The spacecraft construction was approved in 2006, following a study in which the original interferometric concept was changed to a direct-imaging approach. Both the spacecraft and the payload were built by European industry. The involvement of the scientific community focusses on data processing for which the international Gaia Data Processing and Analysis Consortium (DPAC) was selected in 2007. Gaia was launched on 19 December 2013 and arrived at its operating point, the second Lagrange point of the Sun-Earth-Moon system, a few weeks later. The commissioning of the spacecraft and payload was completed on 19 July 2014. The nominal five-year mission started with four weeks of special, ecliptic-pole scanning and subsequently transferred into full-sky scanning mode. We recall the scientific goals of Gaia and give a description of the as-built spacecraft that is currently (mid-2016) being operated to achieve these goals. We pay special attention to the payload module, the performance of which is closely related to the scientific performance of the mission. We provide a summary of the commissioning activities and findings, followed by a description of the routine operational mode. We summarise scientific performance estimates on the basis of in-orbit operations. Several intermediate Gaia data releases are planned and the data can be retrieved from the Gaia Archive, which is available through the Gaia home page at http://www.cosmos. esa.int/gaia.
Cluster Analysis of Educational Data: an Example of Quantitative Study on the answers to an Open-Ended Questionnaire

In the last years many studies examined the consistency of students' answers in a variety of contexts. Some of these papers tried to develop more detailed models of the consistency of students' reasoning, or to subdivide a sample of students into intellectually similar subgroups. The problem of taking a set of data and separating it into subgroups where the elements of each subgroup are more similar to each other than they are to elements not in the subgroup has been extensively studied through the methods of Cluster Analysis. This method can separate students into groups that can be recognized and characterized by common traits in their answers, without any prior knowledge of what form those groups would take (unbiased classification). In this paper we start from a detailed analysis of the data coding needed in Cluster Analysis, in order to discuss the meaning and the limits of the interpretation of quantitative results. Then two methods commonly used in Cluster Analysis are described and the variables and parameters involved are outlined and criticized. Section III deals with the application of these methods to the analysis of data from an open-ended questionnaire administered to a sample of university students, and the quantitative results are discussed. Finally, the quantitative results are related to student answers and compared with previous results reported in the literature, by pointing out the new insights resulting from the application of such new methods.
Generalized parametrization dependence in quantum gravity

We critically examine the gauge, and field-parametrization dependence of renormalization group flows in the vicinity of non-Gau\ss{}ian fixed points in quantum gravity. While physical observables are independent of such calculational specifications, the construction of quantum gravity field theories typically relies on off-shell quantities such as $\beta$ functions and generating functionals and thus face potential stability issues with regard to such generalized parametrizations. We analyze a two-parameter class of covariant gauge conditions, the role of momentum-dependent field rescalings and a class of field parametrizations. Using the product of Newton and cosmological constant as an indicator, the principle of minimum sensitivity identifies stationary points in this parametrization space which show a remarkable insensitivity to the parametrization. In the most insensitive cases, the quantized gravity system exhibits a non-Gau\ss{}ian UV stable fixed point, lending further support to asymptotically free quantum gravity. One of the stationary points facilitates an analytical determination of the quantum gravity phase diagram and features ultraviolet and infrared complete RG trajectories with a classical regime.
GREENER: Graph Neural Networks for News Media Profiling

We study the problem of profiling news media on the Web with respect to their factuality of reporting and bias. This is an important but under-studied problem related to disinformation and “fake news” detection, but it addresses the issue at a coarser granularity compared to looking at an individual article or an individual claim. This is useful as it allows to profile entire media outlets in advance. Unlike previous work, which has focused primarily on text (e.g., on the text of the articles published by the target website, or on the textual description in their social media profiles or in Wikipedia), here our main focus is on modeling the similarity between media outlets based on the overlap of their audience. This is motivated by homophily considerations, i.e., the tendency of people to have connections to people with similar interests, which we extend to media, hypothesizing that similar types of media would be read by similar kinds of users. In particular, we propose GREENER (GRaph nEural nEtwork for News mEdia pRofiling), a model that builds a graph of inter-media connections based on their audience overlap, and then uses graph neural networks to represent each medium. We find that such representations are quite useful for predicting the factuality and the bias of news media outlets, yielding improvements over state-of-the-art results reported on two datasets. When augmented with conventionally used representations obtained from news articles, Twitter, YouTube, Facebook, and Wikipedia, prediction accuracy is found to improve by 2.5-27 macro-F1 points for the two tasks.
Privacy-Utility Balanced Voice De-Identification Using Adversarial Examples

Faced with the threat of identity leakage during voice data publishing, users are engaged in a privacy-utility dilemma when enjoying convenient voice services. Existing studies employ direct modification or text-based re-synthesis to de-identify users' voices, but resulting in inconsistent audibility in the presence of human participants. In this paper, we propose a voice de-identification system, which uses adversarial examples to balance the privacy and utility of voice services. Instead of typical additive examples inducing perceivable distortions, we design a novel convolutional adversarial example that modulates perturbations into real-world room impulse responses. Benefit from this, our system could preserve user identity from exposure by Automatic Speaker Identification (ASI) while remaining the voice perceptual quality for non-intrusive de-identification. Moreover, our system learns a compact speaker distribution through a conditional variational auto-encoder to sample diverse target embeddings on demand. Combining diverse target generation and input-specific perturbation construction, our system enables any-to-any identify transformation for adaptive de-identification. Experimental results show that our system could achieve 98% and 79% successful de-identification on mainstream ASIs and commercial systems with an objective Mel cepstral distortion of 4.31dB and a subjective mean opinion score of 4.48.
Secondary instabilities in the flow past a cylinder: insights from a local stability analysis

We perform a three-dimensional, short-wavelength stability analysis on the numerically simulated two-dimensional flow past a circular cylinder for Reynolds numbers in the range 50 ≤ Re ≤ 300; here, Re = U∞D/ν with U∞, D and ν being the free-stream velocity, the diameter of the cylinder and the kinematic viscosity of the fluid, respectively. For a given Re, inviscid local stability equations from the geometric optics approach are solved on three distinct closed fluid particle trajectories (denoted as orbits 1, 2 & 3) for purely transverse perturbations. The inviscid instability on orbits 1 & 2, which are symmetric counterparts of one another, is shown to undergo bifurcations at Re ≈ 50 and Re ≈ 250. Upon incorporating finite-wavenumber, finite-Reynolds number effects to compute corrected local instability growth rates, the inviscid instability on orbits 1 & 2 is shown to be suppressed for Re . 262. Orbits 1 & 2 are thus shown to exhibit a synchronous instability for Re & 262, which is remarkably close to the critical Reynolds number for the mode-B secondary instability. Further evidence for the connection between the local instability on orbits 1 & 2, and the mode-B secondary instability, is provided via a comparison of the growth rate variation with span-wise wavenumber between the local and global stability approaches. In summary, our results strongly suggest that the three-dimensional short-wavelength instability on orbits 1 & 2 is a possible mechanism for the emergence of the mode B secondary instability. Usage: Secondary publications and information retrieval purposes. PACS numbers: May be entered using the \pacs{#1} command. Structure: You may use the description environment to structure your abstract; use the optional argument of the \item command to give the category of each item. PACS numbers: Valid PACS appear here ∗ manims@ae.iitm. ac.in
DiaASQ : A Benchmark of Conversational Aspect-based Sentiment Quadruple Analysis

The rapid development of aspect-based sentiment analysis (ABSA) within recent decades shows great potential for real-world society. The current ABSA works, however, are mostly limited to the scenario of a single text piece, leaving the study in dialogue contexts unexplored. To bridge the gap between fine-grained sentiment analysis and conversational opinion mining, in this work, we introduce a novel task of conversational aspect-based sentiment quadruple analysis, namely DiaASQ, aiming to detect the quadruple of target-aspect-opinion-sentiment in a dialogue. We manually construct a large-scale high-quality DiaASQ dataset in both Chinese and English languages. We deliberately develop a neural model to benchmark the task, which advances in effectively performing end-to-end quadruple prediction, and manages to incorporate rich dialogue-specific and discourse feature representations for better cross-utterance quadruple extraction. We hope the new benchmark will spur more advancements in the sentiment analysis community.
Stay Home Safe with Starving Federated Data

—Over the past few years, the ﬁeld of adversarial attack received numerous attention from various researchers with the help of successful attack success rate against well-known deep neural networks that were acknowledged to achieve high classiﬁcation ability in various tasks. However, majority of the experiments were completed under a single model, which we believe it may not be an ideal case in a real-life situation. In this paper, we introduce a novel federated adversarial training method for smart home face recognition, named FLATS, where we observed some interesting ﬁndings that may not be easily noticed in a traditional adversarial attack to federated learning experiments. By applying different variations to the hyperparameters, we have spotted that our method can make the global model to be robust given a starving federated environment. Our code can be found on https://github.com/jcroh0508/FLATS .
Absolute parameters of young stars: GG Lup and μ1 Sco

New high-resolution spectroscopy and $BVR$ photometry, together with literature data, on the Gould's Belt close binary systems GG Lup and $\mu^1$ Sco are presented and analysed. In the case of GG Lup, light and radial velocity curve fittings confirm a near-Main-Sequence picture of a pair of close stars. Absolute parameters are found, to within a few percent, thus: $M_1$ = 4.16$\pm$0.12, $M_2$ = 2.64$\pm$0.12, $R_{1}$ = 2.42$\pm$0.05, $R_2$ = 1.79$\pm$0.04, ($\odot$); $T_{1}$ $\sim$13000, $T_2$ $\sim$10600 (K); photometric distance $\sim$ 160 (pc). The high eccentricity and relatively short period (105 y) of apsidal revolution may be related to an apparent `slow B-type pulsator' (SPB) oscillation. Disturbances of the outer envelope of at least one of the components then compromise comparisons to standard evolutionary models, at least regarding the age of the system. A rate of apsidal advance is derived, which allows a check on the mean internal structure constant $\bar{k_2} = 0.0058 \pm 0.0004$. This is in agreement with values recently derived for young stars of solar composition and mass $\sim$3${\odot}$. For $\mu^1$ Sco, we agree with previous authors that the secondary component is considerably oversized for its mass, implying binary (interactive) stellar evolution, probably of the `Case A' type. The primary appears relatively little affected by this evolution, however. Its parameters show consistency with a star of its derived mass at age about 13 My, consistent with the star's membership of the Sco-Cen OB2 Association. The absolute parameters are as follows: $M_1$ = 8.3$\pm$1.0, $M_2$ = 4.6$\pm$1.0, $R_{1}$ = 3.9$\pm$0.3, $R_2$ = 4.6$\pm$0.4, ($\odot$); $T_{1}$ $\sim$24000, $T_2$ $\sim$17000 (K); photometric distance $\sim$ 135 (pc).
DO NOT FORGET THE FOREST FOR THE TREES: THE STELLAR-MASS HALO-MASS RELATION IN DIFFERENT ENVIRONMENTS

The connection between dark matter halos and galactic baryons is often not well constrained nor well resolved in cosmological hydrodynamical simulations. Thus, halo occupation distribution models that assign galaxies to halos based on halo mass are frequently used to interpret clustering observations, even though it is well known that the assembly history of dark matter halos is related to their clustering. In this paper we use high-resolution hydrodynamical cosmological simulations to compare the halo and stellar mass growth of galaxies in a large-scale overdensity to those in a large-scale underdensity (on scales of about 20 Mpc). The simulation reproduces assembly bias, in which halos have earlier formation times in overdense environments than in underdense regions. We find that the ratio of stellar mass to halo mass is larger in overdense regions in central galaxies residing in halos with masses between 1011 and 1012.9 M⊙. When we force the local density (within 2 Mpc) at z = 0 to be the same for galaxies in the large-scale over- and underdensities, we find the same results. We posit that this difference can be explained by a combination of earlier formation times, more interactions at early times with neighbors, and more filaments feeding galaxies in overdense regions. This result puts the standard practice of assigning stellar mass to halos based only on their mass, rather than considering their larger environment, into question.
Low X-ray luminosity galaxy clusters – III. Weak lensing mass determination at 0.18 < z < 0.70

This is the third of a series of papers of low X-ray luminosity galaxy clusters. In this work we present the weak lensing analysis of eight clusters, based on observations obtained with the Gemini Multi-Object Spectrograph in the $g'$, $r'$ and $i'$ passbands. For this purpose, we have developed a pipeline for the lensing analysis of ground-based images and we have performed tests applied to simulated data. We have determined the masses of seven galaxy clusters, six of them measured for the first time. For the four clusters with availably spectroscopic data, we find a general agreement between the velocity dispersions obtained via weak lensing assuming a Singular Isothermal Sphere profile, and those obtained from the redshift distribution of member galaxies. The correlation between our weak lensing mass determinations and the X-ray luminosities are suitably fitted by other observations of the $M-L_{X}$ relation and models.
Impact of half-wave plate systematics on the measurement of cosmic birefringence from CMB polarization

Polarization of the cosmic microwave background (CMB) can probe new parity-violating physics such as cosmic birefringence (CB), which requires exquisite control over instrumental systematics. The non-idealities of the half-wave plate (HWP) represent a source of systematics when used as a polarization modulator. We study their impact on the CMB angular power spectra, which is partially degenerate with CB and miscalibration of the polarization angle. We use full-sky beam convolution simulations including HWP to generate mock noiseless time-ordered data, process them through a bin averaging map-maker, and calculate the power spectra including TB and EB correlations. We also derive analytical formulae which accurately model the observed spectra. For our choice of HWP parameters, the HWP-induced angle amounts to a few degrees, which could be misinterpreted as CB. Accurate knowledge of the HWP is required to mitigate this. Our simulation and analytical formulae will be useful for deriving requirements for the accuracy of HWP calibration.
Estimating Soft Labels for Out-of-Domain Intent Detection

Out-of-Domain (OOD) intent detection is important for practical dialog systems. To alleviate the issue of lacking OOD training samples, some works propose synthesizing pseudo OOD samples and directly assigning one-hot OOD labels to these pseudo samples. However, these one-hot labels introduce noises to the training process because some “hard” pseudo OOD samples may coincide with In-Domain (IND) intents. In this paper, we propose an adaptive soft pseudo labeling (ASoul) method that can estimate soft labels for pseudo OOD samples when training OOD detectors. Semantic connections between pseudo OOD samples and IND intents are captured using an embedding graph. A co-training framework is further introduced to produce resulting soft labels following the smoothness assumption, i.e., close samples are likely to have similar labels. Extensive experiments on three benchmark datasets show that ASoul consistently improves the OOD detection performance and outperforms various competitive baselines.