Miracle-dz/UniFAR-FTUs huggingface dataset:
Origins of Type Ibn SNe 2006jc/2015G in interacting binaries and implications for pre-SN eruptions

Type Ibn supernovae (SNe Ibn) are intriguing stellar explosions whose spectra exhibit narrow helium lines with little hydrogen. They trace the presence of circumstellar material (CSM) formed via pre-SN eruptions of their stripped-envelope progenitors. Early work has generally assumed that SNe Ibn come from massive Wolf–Rayet (WR) stars via single-star evolution. In this paper, we report ultraviolet (UV) and optical observations of two nearby Type Ibn SNe 2006jc and 2015G conducted with the Hubble Space Telescope (HST) at late times. A point source is detected at the position of SN 2006jc, and we confirm the conclusion of Maund et al. that it is the progenitor’s binary companion. Its position on the Hertzsprung–Russell (HR) diagram corresponds to a star that has evolved off the main sequence (MS); further analysis implies a low initial mass for the companion star (M2 ≤ 12.3$^{+2.3}_{-1.5}$ M⊙) and a secondary-to-primary initial mass ratio very close to unity (q = M2/M1 ∼ 1); the SN progenitor’s hydrogen envelope had been stripped through binary interaction. We do not detect the binary companion of SN 2015G. For both SNe, the surrounding stellar populations have relatively old ages and argue against any massive WR stars as their progenitors. These results suggest that SNe Ibn may have lower mass origins in interacting binaries. As a result, they also provide evidence that the giant eruptions commonly seen in massive luminous blue variables (LBVs) can also occur in much lower mass, stripped-envelope stars just before core collapse.
GHASP: an H α kinematical survey of spiral galaxies – XIII. Distribution of luminous and dark matter in spiral and irregular nearby galaxies using H α and H i rotation curves and WISE photometry

We present the mass models of 31 spiral and irregular nearby galaxies obtained using hybrid rotation curves (RCs) combining high-resolution GHASP Fabry–Perot H α RCs and extended WHISP H i ones together with 3.4 $\mu$m WISE photometry. The aim is to compare the dark matter (DM) halo properties within the optical radius using only H α RCs with the effect of including and excluding the mass contribution of the neutral gas component, and when using H i or hybrid RCs. Pseudo-isothermal (ISO) core and Navarro–Frenk–White (NFW) cuspy DM halo profiles are used with various fiducial fitting procedures. Mass models using H α RCs including or excluding the H i gas component provide compatible disc M/L. The correlations between DM halo and baryon parameters do not strongly depend on the RC. Clearly, the differences between the fitting procedures are larger than between the different data sets. Hybrid and H i RCs lead to higher M/L values for both ISO and NFW best-fitting models but lower central densities for ISO haloes and higher concentration for NFW haloes than when using H α RCs only. The agreement with the mass model parameters deduced using hybrid RCs, considered as a reference, is better for H i than for H α RCs. ISO density profiles better fit the RCs than the NFW ones, especially when using H α or hybrid RCs. Halo masses at the optical radius determined using the various data sets are compatible even if they tend to be overestimated with H α RCs. Hybrid RCs are thus ideal to study the mass distribution within the optical radius.
Transfer Learning with Dynamic Distribution Adaptation

Transfer learning aims to learn robust classifiers for the target domain by leveraging knowledge from a source domain. Since the source and the target domains are usually from different distributions, existing methods mainly focus on adapting the cross-domain marginal or conditional distributions. However, in real applications, the marginal and conditional distributions usually have different contributions to the domain discrepancy. Existing methods fail to quantitatively evaluate the different importance of these two distributions, which will result in unsatisfactory transfer performance. In this article, we propose a novel concept called Dynamic Distribution Adaptation (DDA), which is capable of quantitatively evaluating the relative importance of each distribution. DDA can be easily incorporated into the framework of structural risk minimization to solve transfer learning problems. On the basis of DDA, we propose two novel learning algorithms: (1) Manifold Dynamic Distribution Adaptation (MDDA) for traditional transfer learning, and (2) Dynamic Distribution Adaptation Network (DDAN) for deep transfer learning. Extensive experiments demonstrate that MDDA and DDAN significantly improve the transfer learning performance and set up a strong baseline over the latest deep and adversarial methods on digits recognition, sentiment analysis, and image classification. More importantly, it is shown that marginal and conditional distributions have different contributions to the domain divergence, and our DDA is able to provide good quantitative evaluation of their relative importance, which leads to better performance. We believe this observation can be helpful for future research in transfer learning.
$C_p$ estimates for rough homogeneous singular integrals and sparse forms

We consider Coifman--Fefferman inequalities for rough homogeneous singular integrals $T_\Omega$ and $C_p$ weights. It was recently shown by Li-Perez-Rivera-Rios-Roncal that $$ \|T_\Omega \|_{L^p(w)} \le C_{p,T,w} \|Mf\|_{L^p(w)} $$ for every $0 \max\{1,p\}$ without using extrapolation theory. Although the bounds we prove are new even in a qualitative sense, we also give the quantitative bound with respect to the $C_q$ characteristic. Our techniques rely on recent advances in sparse domination theory and we actually prove most of our estimates for sparse forms. Our second goal is to continue the structural analysis of $C_p$ classes. We consider some weak self-improving properties of $C_p$ weights and weak and dyadic $C_p$ classes. We also revisit and generalize a counterexample by Kahanpaa and Mejlbro who showed that $C_p \setminus \bigcup_{q > p} C_q \neq \emptyset$. We combine their construction with techniques of Lerner to define an explicit weight class $\widetilde{C}_p$ such that $\bigcup_{q > p} C_q \subsetneq \widetilde{C}_p \subsetneq C_p$ and every $w \in \widetilde{C}_p$ satisfies Muckenhoupt's conjecture. In particular, we give a different, self-contained proof for the fact that the $C_{p+\varepsilon}$ condition is not necessary for the Coifman--Fefferman inequality and our ideas allow us to consider also dimensions higher than $1$.
A scalable noisy speech dataset and online subjective test framework

Background noise is a major source of quality impairments in Voice over Internet Protocol (VoIP) and Public Switched Telephone Network (PSTN) calls. Recent work shows the efficacy of deep learning for noise suppression, but the datasets have been relatively small compared to those used in other domains (e.g., ImageNet) and the associated evaluations have been more focused. In order to better facilitate deep learning research in Speech Enhancement, we present a noisy speech dataset (MS-SNSD) that can scale to arbitrary sizes depending on the number of speakers, noise types, and Speech to Noise Ratio (SNR) levels desired. We show that increasing dataset sizes increases noise suppression performance as expected. In addition, we provide an open-source evaluation methodology to evaluate the results subjectively at scale using crowdsourcing, with a reference algorithm to normalize the results. To demonstrate the dataset and evaluation framework we apply it to several noise suppressors and compare the subjective Mean Opinion Score (MOS) with objective quality measures such as SNR, PESQ, POLQA, and VISQOL and show why MOS is still required. Our subjective MOS evaluation is the first large scale evaluation of Speech Enhancement algorithms that we are aware of.
The ALMA Survey of 70 μm Dark High-mass Clumps in Early Stages (ASHES). I. Pilot Survey: Clump Fragmentation

The ALMA Survey of 70 μm dark High-mass clumps in Early Stages (ASHES) is designed to systematically characterize the earliest stages and constrain theories of high-mass star formation. Twelve massive (>500 ), cold (≤15 K), 3.6–70 μm dark prestellar clump candidates, embedded in infrared dark clouds, were carefully selected in the pilot survey to be observed with the Atacama Large Millimeter/submillimeter Array (ALMA). We have mosaicked each clump (∼1 arcmin2) in continuum and line emission with the 12 m, 7 m, and Total Power (TP) arrays at 224 GHz (1.34 mm), resulting in ∼1. ″2 resolution (∼4800 au, at the average source distance). As the first paper in the series, we concentrate on the continuum emission to reveal clump fragmentation. We detect 294 cores, from which 84 (29%) are categorized as protostellar based on outflow activity or “warm core” line emission. The remaining 210 (71%) are considered prestellar core candidates. The number of detected cores is independent of the mass sensitivity range of the observations and, on average, more massive clumps tend to form more cores. We find a large population of low-mass (<1 ) cores and no high-mass (>30 ) prestellar cores (maximum mass 11 ). From the prestellar core mass function, we derive a power-law index of 1.17 ± 0.10, which is slightly shallower than Salpeter. We used the minimum spanning tree (MST) technique to characterize the separation between cores and their spatial distribution, and to derive mass segregation ratios. While there is a range of core masses and separations detected in the sample, the mean separation and mass per clump are well explained by thermal Jeans fragmentation and are inconsistent with turbulent Jeans fragmentation. Core spatial distribution is well described by hierarchical subclustering rather than centrally peaked clustering. There is no conclusive evidence of mass segregation. We test several theoretical conditions and conclude that overall, competitive accretion and global hierarchical collapse scenarios are favored over the turbulent core accretion scenario.
Supporting knowledge workers in practice : how do they understand and use work units?

If basic assumptions about how knowledge workers conceptualize and use work units are wrong, then any solutions resting on those assumptions are unlikely to be successful since, instead of decreasing costs, they will lead to increasing them. This paper reports on how knowledge workers understand, use and switch between units of work. We furthermore identify where current computing support causes problems and discuss implications for designing intelligent user interfaces.
Disentangling the effects of doping, strain and disorder in monolayer WS2 by optical spectroscopy

Monolayers of transition metal dichalcogenides (TMdC) are promising candidates for realization of a new generation of optoelectronic devices. The optical properties of these two-dimensional materials, however, vary from flake to flake, or even across individual flakes, and change over time, all of which makes control of the optoelectronic properties challenging. There are many different perturbations that can alter the optical properties, including charge doping, defects, strain, oxidation, adsorbed molecules and water intercalation. Identifying which perturbations are present is usually not straightforward and requires multiple measurements using multiple experimental modalities, which presents barriers when attempting to optimise preparation of these materials. Here, we apply high-resolution photoluminescence and differential reflectance hyperspectral imaging in situ to CVD-grown WS2 monolayers. By combining these two optical measurements and using a statistical correlation analysis we are able to disentangle three contributions modulating optoelectronic properties of these materials: electron doping, strain and disorder arising from adsorbates, substrate fluctuations and/or defects. In separating these contributions, we also observe that the B-exciton energy is less sensitive to variations in doping density than A-excitons. Our approach is not limited to monolayers of WS2 and is also applicable to other 2D materials with optical transitions, and the general approach could be extended to other characterization modalities.
CLASH-VLT: Enhancement of (O/H) in z  =  0.35 RX J2248−4431 cluster galaxies

Aims. Gas-phase metallicities offer insight into the chemical evolution of galaxies as they reflect the recycling of gas through star formation and galactic inflows and outflows. Environmental effects such as star-formation quenching mechanisms play an important role in shaping the evolution of galaxies. Clusters of galaxies at z < 0.5 are expected to be the sites where environmental effects can be clearly observed with present-day telescopes. Methods. We explored the Frontier Fields cluster RX J2248−443 at z = 0.348 with VIMOS/VLT spectroscopy from CLASH-VLT, which covers a central region corresponding to almost 2 virial radii. The fluxes of [OII] λ3727, Hβ, [OIII] λ5007, Hα and [NII] λ6584 emission lines were measured allowing the derivation of (O/H) gas metallicities, star formation rates based on extinction-corrected Hα fluxes, and contamination from active galactic nuclei. We compared our sample of cluster galaxies to a population of field galaxies at similar redshifts. Results. We use the location of galaxies in projected phase-space to distinguish between cluster and field galaxies. Both populations follow the star-forming sequence in the diagnostic diagrams, which allow the ionising sources in a galaxy to be disentangled, with only a low number of galaxies classified as Seyfert II. Both field and cluster galaxies follow the “main sequence” of star-forming galaxies, with no substantial difference observed between the two populations. In the mass–metallicity (MZ) plane, both high-mass field and cluster galaxies show comparable (O/H)s to the local SDSS MZ relation, with an offset of low-mass galaxies (log(M/M⊙) < 9.2) towards higher metallicities. While both the metallicities of “accreted” (R < R500) and “infalling” (R > R500) cluster members are comparable at all masses, the cluster galaxies from the “mass complete” bin (which is the intermediate mass bin in this study: 9.2 < log(M/M⊙) < 10.2), show more enhanced metallicities than their field counterparts by a factor of 0.065 dex with a ∼1. 8σ significance. The intermediate-mass field galaxies are in accordance with the expected (O/H)s from the fundamental metallicity relation, while the cluster members deviate strongly from the model predictions, namely by a factor of ∼0.12 dex. The results of this work are in accordance with studies of other clusters at z < 0.5 and favour the scenario in which the hot halo gas of low- and intermediate-mass cluster galaxies is removed due to ram pressure stripping, leading to an increase in their gas-phase metallicity.
Dzyaloshinskii–Moriya Coupling in 3d Insulators

We present an overview of the microscopic theory of the Dzyaloshinskii–Moriya (DM) coupling in strongly correlated 3d compounds. Most attention in the paper centers around the derivation of the Dzyaloshinskii vector, its value, orientation, and sense (sign) under different types of the (super)exchange interaction and crystal field. We consider both the Moriya mechanism of the antisymmetric interaction and novel contributions, in particular, that of spin–orbital coupling on the intermediate ligand ions. We have predicted a novel magnetic phenomenon, weak ferrimagnetism in mixed weak ferromagnets with competing signs of Dzyaloshinskii vectors. We revisit a problem of the DM coupling for a single bond in cuprates specifying the local spin–orbital contributions to the Dzyaloshinskii vector focusing on the oxygen term. We predict a novel puzzling effect of the on-site staggered spin polarization to be a result of the on-site spin–orbital coupling and the cation-ligand spin density transfer. The intermediate ligand nuclear magnetic resonance (NMR) measurements are shown to be an effective tool to inspect the effects of the DM coupling in an external magnetic field. We predict the effect of a strong oxygen-weak antiferromagnetism in edge-shared CuO 2 chains due to uncompensated oxygen Dzyaloshinskii vectors. We revisit the effects of symmetric spin anisotropy directly induced by the DM coupling. A critical analysis will be given of different approaches to exchange-relativistic coupling based on the cluster and the DFT (density functional theory) based calculations. Theoretical results are applied to different classes of 3d compounds from conventional weak ferromagnets ( α -Fe 2 O 3 , FeBO 3 , FeF 3 , RFeO 3 , RCrO 3 , .) to unconventional systems such as weak ferrimagnets (e.g., RFe 1 - x Cr x O 3 ), helimagnets (e.g., CsCuCl 3 ), and parent cuprates (La 2 CuO 4 , .).
Limited-Budget Output Consensus for Descriptor Multiagent Systems With Energy Constraints

This article deals with limited-budget output consensus for descriptor multiagent systems with two types of switching communication topologies, that is, switching connected ones and jointly connected ones. First, a singular dynamic output feedback control protocol with switching communication topologies is proposed on the basis of the observable decomposition, where an energy constraint is involved and protocol states of neighboring agents are utilized to derive a new two-step design approach of gain matrices. Then, limited-budget output consensus problems are transformed into asymptotic stability ones and a valid candidate of the output consensus function is determined. Furthermore, sufficient conditions for limited-budget output consensus design and analysis for two types of switching communication topologies are proposed, respectively, and an explicit expression of the output consensus function is given, which is identical for two types of switching communication topologies. Finally, two numerical simulations are shown to demonstrate theoretical conclusions.
Evidence for Late-stage Eruptive Mass Loss in the Progenitor to SN2018gep, a Broad-lined Ic Supernova: Pre-explosion Emission and a Rapidly Rising Luminous Transient

We present detailed observations of ZTF18abukavn (SN2018gep), discovered in high-cadence data from the Zwicky Transient Facility as a rapidly rising (1.4 ± 0.1 mag hr−1) and luminous ( mag) transient. It is spectroscopically classified as a broad-lined stripped-envelope supernova (Ic-BL SN). The high peak luminosity ( ), the short rise time ( in g band), and the blue colors at peak ( ) all resemble the high-redshift Ic-BL iPTF16asu, as well as several other unclassified fast transients. The early discovery of SN2018gep (within an hour of shock breakout) enabled an intensive spectroscopic campaign, including the highest-temperature ( ) spectra of a stripped-envelope SN. A retrospective search revealed luminous ( mag) emission in the days to weeks before explosion, the first definitive detection of precursor emission for a Ic-BL. We find a limit on the isotropic gamma-ray energy release , a limit on X-ray emission , and a limit on radio emission . Taken together, we find that the early ( ) data are best explained by shock breakout in a massive shell of dense circumstellar material (0.02 ) at large radii ( ) that was ejected in eruptive pre-explosion mass-loss episodes. The late-time ( ) light curve requires an additional energy source, which could be the radioactive decay of Ni-56.
Visual Tracking by Means of Deep Reinforcement Learning and an Expert Demonstrator

In the last decade many different algorithms have been proposed to track a generic object in videos. Their execution on recent large-scale video datasets can produce a great amount of various tracking behaviours. New trends in Reinforcement Learning showed that demonstrations of an expert agent can be efficiently used to speed-up the process of policy learning. Taking inspiration from such works and from the recent applications of Reinforcement Learning to visual tracking, we propose two novel trackers, A3CT, which exploits demonstrations of a state-of-the-art tracker to learn an effective tracking policy, and A3CTD, that takes advantage of the same expert tracker to correct its behaviour during tracking. Through an extensive experimental validation on the GOT-10k, OTB-100, LaSOT, UAV123 and VOT benchmarks, we show that the proposed trackers achieve state-of-the-art performance while running in real-time.
Periodic asymptotic dynamics of the measure solutions to an equal mitosis equation

We prove, in the framework of measure solutions, that the equal mito-sis equation present persistent asymptotic oscillations. To do so we adopt a duality approach, which is also well suited for proving the well-posedness when the division rate is unbounded. The main difficulty for characterizing the asymptotic behavior is to define the projection onto the subspace of periodic (rescaled) solutions. We achieve this by using the generalized relative entropy structure of the dual problem.
Robustly representing inferential uncertainty in deep neural networks through sampling

As deep neural networks (DNNs) are applied to increasingly challenging problems, they will need to be able to represent their own uncertainty. Modelling uncertainty is one of the key features of Bayesian methods. Using Bernoulli dropout with sampling at prediction time has recently been proposed as an efficient and well performing variational inference method for DNNs. However, sampling from other multiplicative noise based variational distributions has not been investigated in depth. We evaluated Bayesian DNNs trained with Bernoulli or Gaussian multiplicative masking of either the units (dropout) or the weights (dropconnect). We tested the calibration of the probabilistic predictions of Bayesian fully connected and convolutional DNNs on two visual inference tasks (MNIST and CIFAR-10). Sampling at prediction time increased the quality of the DNNs' uncertainty estimates. Sampling weights, whether Gaussian or Bernoulli, led to more accurate representation of uncertainty compared to sampling of units. However, sampling units using either Gaussian or Bernoulli dropout led to increased convolutional neural network (CNN) classification accuracy. Based on these findings we used both Bernoulli dropout and Gaussian dropconnect concurrently, which we show approximates the use of a spike-and-slab variational distribution without increasing the number of learned parameters. We found that spike-and-slab sampling efficiently combined the advantages of the other methods: it classifies with high accuracy and robustly represents inferential uncertainty for all tested architectures.