Miracle-dz/UniFAR-FTUs huggingface dataset:
Leveraging HST with MUSE: II. Na-abundance variations in intermediate age star clusters

Ancient ($>$10 Gyr) globular clusters (GCs) show chemical abundance variations in the form of patterns among certain elements, e.g. N correlates with Na and anti-correlates with O. Recently, N abundance spreads have also been observed in massive star clusters that are significantly younger than old GCs, down to an age of $\sim$2 Gyr. However, so far N has been the only element found to vary in such young objects. We report here the presence of Na abundance variations in the intermediate age massive star clusters NGC 416 ($\sim$6.5 Gyr old) and Lindsay 1 ($\sim$7.5 Gyr old) in the Small Magellanic Cloud, by combining HST and ESO-VLT MUSE observations. Using HST photometry we were able to construct ''chromosome maps'' and separate sub-populations with different N content, in the red giant branch of each cluster. MUSE spectra of individual stars belonging to each population were combined, resulting in high signal-to-noise spectra representative of each population, which were compared to search for mean differences in Na. We find a mean abundance variation of $\Delta$[Na/Fe]$=0.18\pm0.04$ dex for NGC 416 and $\Delta$[Na/Fe]$=0.24\pm0.05$ dex for Lindsay 1. In both clusters we find that the population that is enhanced in N is also enhanced in Na, which is the same pattern to the one observed in ancient GCs. Furthermore, we detect a bimodal distribution of core-helium burning Red Clump (RC) giants in the UV colour magnitude diagram of NGC 416. A comparison of the stacked MUSE spectra of the two RCs shows the same mean Na abundance difference between the two populations. The results reported in this work are a crucial hint that star clusters of a large age range share the same origin: they are the same types of objects, but only separated in age.
New Dynamical Mass Estimates of the beta Pictoris Planetary System Through Gaussian Process Stellar Activity Modelling

<p>Nearly 15 years of radial velocity (RV) monitoring and direct imaging (DI) enabled the detection oftwo giant planets orbiting the young, nearby star &#946; Pictoris. The &#948; Scuti pulsations of the star, overwhelming planetary signals, need however to be carefully suppress. In this talk, we propose a new and independent analysis of the system, making use of all available data, including photometric light curve from the ground and space, long term RV and DI monitoring. We demonstrate how all data can be consistently modelled in a Bayesian framework. We show how modern and physically motivated kernels for Gaussian Process can effectively model complex stellar activity. Using further carefull statistical treatment of the data to extend the monitoring, we detect both planets from RV data only for the first time. To characterize the system more accurately, we also perform a joint fit of&#160;<br />all available relative astrometry and RV data. We provide precise orbital parameters and discuss the whole system architecture. The inferred dynamical mass measurements for both planets are also compared to mass-luminosity evolutionary tracks. This work opens the path towards a precise characterization of young planetary systems combing photometry, spectroscopy, and astrometry. </p>
Embedding theorems for solvable groups

In this paper, we prove a series of results on group embeddings in groups with a small number of generators. We show that each finitely generated group $G$ lying in a variety ${\mathcal M}$ can be embedded in a $4$-generated group $H \in {\mathcal M}{\mathcal A}$ (${\mathcal A}$ means the variety of abelian groups). If $G$ is a finite group, then $H$ can also be found as a finite group. It follows, that any finitely generated (finite) solvable group $G$ of the derived length $l$ can be embedded in a $4$-generated (finite) solvable group $H$ of length $l+1$. Thus, we answer the question of V. H. Mikaelian and this http URL. Olshanskii. It is also shown that any countable group $G\in {\mathcal M}$, such that the abelianization $G_{ab}$ is a free abelian group, is embeddable in a $2$-generated group $H\in {\mathcal M}{\mathcal A}$.
It's Raining Cats or Dogs? Adversarial Rain Attack on DNN Perception

Rain is a common phenomenon in nature and an essential factor for many deep neural network (DNN) based perception systems. Rain can often post inevitable threats that must be carefully addressed especially in the context of safety and security-sensitive scenarios (e.g., autonomous driving). Therefore, a comprehensive investigation of the potential risks of the rain to a DNN is of great importance. Unfortunately, in practice, it is often rather difficult to collect or synthesize rainy images that can represent all raining situations that possibly occur in the real world. To this end, in this paper, we start from a new perspective and propose to combine two totally different studies, i.e., rainy image synthesis and adversarial attack. We present an adversarial rain attack, with which we could simulate various rainy situations with the guidance of deployed DNNs and reveal the potential threat factors that can be brought by rain, helping to develop more rain-robust DNNs. In particular, we propose a factor-aware rain generation that simulates rain steaks according to the camera exposure process and models the learnable rain factors for adversarial attack. With this generator, we further propose the adversarial rain attack against the image classification and object detection, where the rain factors are guided by the various DNNs. As a result, it enables to comprehensively study the impacts of the rain factors to DNNs. Our largescale evaluation on three datasets, i.e., NeurIPS'17 DEV, MS COCO and KITTI, demonstrates that our synthesized rainy images can not only present visually realistic appearances, but also exhibit strong adversarial capability, which builds the foundation for further rain-robust perception studies.
The H i Structure of the Local Volume Dwarf Galaxy Pisces A

Dedicated H i surveys have recently led to a growing category of low-mass galaxies found in the Local Volume. We present synthesis imaging of one such galaxy, Pisces A, a low-mass dwarf originally confirmed via optical imaging and spectroscopy of neutral hydrogen (H i) sources in the Galactic Arecibo L-band Feed Array H i survey. Using H i observations taken with the Karl G. Jansky Very Large Array, we characterize the kinematic structure of the gas and connect it to the galaxy’s environment and evolutionary history. While the galaxy shows overall ordered rotation, a number of kinematic features indicate a disturbed gas morphology. These features are suggestive of a tumultuous recent history and represent % of the total baryonic mass. We find a total baryon fraction if we include these features. We also quantify the cosmic environment of Pisces A, finding an apparent alignment of the disturbed gas with nearby, large-scale filamentary structure at the edge of the Local Void. We consider several scenarios for the origin of the disturbed gas, including gas stripping via ram pressure or galaxy–galaxy interactions, as well as accretion and ram pressure compression. Though we cannot rule out a past interaction with a companion, our observations best support the suggestion that the neutral gas morphology and recent star formation in Pisces A are a direct result of its interactions with the intergalactic medium.
Learning Soft Labels via Meta Learning

One-hot labels do not represent soft decision boundaries among concepts, and hence, models trained on them are prone to overfitting. Using soft labels as targets provide regularization, but different soft labels might be optimal at different stages of optimization. Also, training with fixed labels in the presence of noisy annotations leads to worse generalization. To address these limitations, we propose a framework, where we treat the labels as learnable parameters, and optimize them along with model parameters. The learned labels continuously adapt themselves to the model's state, thereby providing dynamic regularization. When applied to the task of supervised image-classification, our method leads to consistent gains across different datasets and architectures. For instance, dynamically learned labels improve ResNet18 by 2.1% on CIFAR100. When applied to dataset containing noisy labels, the learned labels correct the annotation mistakes, and improves over state-of-the-art by a significant margin. Finally, we show that learned labels capture semantic relationship between classes, and thereby improve teacher models for the downstream task of distillation.
The Evolution of Disk Winds from a Combined Study of Optical and Infrared Forbidden Lines

We analyze high-resolution (Δv ≤ 10 km s−1) optical and infrared spectra covering the [O i] λ6300 and [Ne ii] 12.81 μm lines from a sample of 31 disks in different evolutionary stages. Following work at optical wavelengths, we use Gaussian profiles to fit the [Ne ii] lines and classify them into high-velocity component (HVC) or low-velocity component (LVC) if the line centroid is more or less blueshifted than 30 km s−1 with respect to the stellar radial velocity, respectively. Unlike for the [O i], where an HVC is often accompanied by an LVC, all 17 sources with an [Ne ii] detection have either an HVC or an LVC. [Ne ii] HVCs are preferentially detected toward high accretors ( M⊙ yr−1), while LVCs are found in sources with low , low [O i] luminosity, and large infrared spectral index (n13–31). Interestingly, the [Ne ii] and [O i] LVC luminosities display an opposite behavior with n13–31: as the inner dust disk depletes (higher n13–31), the [Ne ii] luminosity increases while the [O i] weakens. The [Ne ii] and [O i] HVC profiles are generally similar, with centroids and FWHMs showing the expected behavior from shocked gas in microjets. In contrast, the [Ne ii] LVC profiles are typically more blueshifted and narrower than the [O i] profiles. The FWHM and centroid versus disk inclination suggest that the [Ne ii] LVC predominantly traces unbound gas from a slow, wide-angle wind that has not lost completely the Keplerian signature from its launching region. We sketch an evolutionary scenario that could explain the combined [O i] and [Ne ii] results and includes screening of hard (∼1 keV) X-rays in inner, mostly molecular, MHD winds.
Hierarchical black hole mergers in multiple systems: constrain the formation of GW190412-, GW190814-, and GW190521-like events

The merging black-hole (BH) binaries GW190412, GW190814 and GW190521 from the third LIGO/VIRGO observing run exhibit some extraordinary properties, including highly asymmetric masses, significant spin, and component mass in the "mass gap". These features can be explained if one or both components of the binary are the remnants of previous mergers. In this paper, we explore hierarchical mergers in multiple stellar systems, taking into account the natal kick and mass loss due to the supernova explosion (SN) on each component, as well as the merger kick received by the merger remnant. The binaries that have survived the SNe and kicks generally have too wide orbital separations to merge by themselves, but can merge with the aid of an external companion that gives rise to Lidov-Kozai oscillations. The BH binaries that consist of second-generation BHs can also be assembled in dense star clusters through binary interactions. We characterize the parameter space of these BH binaries by merger fractions in an analytical approach. Combining the distributions of the survived binaries, we further constrain the parameters of the external companion, using the analytically formulated tertiary perturbation strength. We find that to produce the three LIGO/VIRGO O3 events, the external companions must be at least a few hundreds $M_\odot$, and fall in the intermediate-mass BH and supermassive BH range. We suggest that GW190412, GW190814 and GW190521 could all be produced via hierarchical mergers in multiples, likely in a nuclear star cluster, with the final merger induced by a massive BH. Our rate estimates for such events are consistent with the LIGO/VIRGO findings.
Learning a Contact-Adaptive Controller for Robust, Efficient Legged Locomotion

We present a hierarchical framework that combines model-based control and reinforcement learning (RL) to synthesize robust controllers for a quadruped (the Unitree Laikago). The system consists of a high-level controller that learns to choose from a set of primitives in response to changes in the environment and a low-level controller that utilizes an established control method to robustly execute the primitives. Our framework learns a controller that can adapt to challenging environmental changes on the fly, including novel scenarios not seen during training. The learned controller is up to 85~percent more energy efficient and is more robust compared to baseline methods. We also deploy the controller on a physical robot without any randomization or adaptation scheme.
Reconciling escape fractions and observed line emission in Lyman-continuum-leaking galaxies

Context. Finding and elucidating the properties of Lyman-continuum(LyC)-emitting galaxies is an important step in improving our understanding of cosmic reionization. Aims. Although the z ∼ 0.3 − 0.4 LyC emitters found recently show strong optical emission lines, no consistent quantitative photoionization model taking into account the escape of ionizing photons and inhomogenous interstellar medium (ISM) geometry of these galaxies has yet been constructed. Furthermore, it is unclear to what extent these emission lines can be used to distinguish LyC emitters. Methods. To address these questions we construct one- and two-zone photoionization models accounting for the observed LyC escape, which we compare to the observed emission line measurements. The main diagnostics used include lines of [O III], [O II], and [O I] plus sulfur lines ([S II], [S III]) and a nitrogen line ([N II]), which probe regions of different ionization in the ISM. Results. We find that single (one-zone) density-bounded photoionization models cannot reproduce the emission lines of the LyC leakers, as pointed out by earlier studies, because they systematically underpredict the lines of species of low ionization potential, such as [O I] and [S II]. Introducing a two-zone model, with differing ionization parameter and a variable covering fraction and where one of the zones is density-bounded, we show that the observed emission line ratios of the LyC emitters are well reproduced. Furthermore, our model yields LyC escape fractions, which are in fair agreement with the observations and independent measurements. The [O I] λ6300 excess, which is observed in some LyC leakers, can be naturally explained in this model, for example by emission from low-ionization and low-filling-factor gas. LyC emitters with a high escape fraction (fesc ≳ 38%) are deficient both in [O I] λ6300 and in [S II] λλ6716,6731. We also confirm that a [S II] λλ6716,6731 deficiency can be used to select LyC emitter candidates, as suggested earlier. Finally, we find indications for a possible dichotomy in terms of escape mechanisms for LyC photons between galaxies with relatively low (fesc ≲ 10%) and higher escape fractions. Conclusions. We conclude that two-zone photoionization models are sufficient and required to explain the observed emission line properties of z ∼ 0.3 − 0.4 LyC emitters. This is in agreement with UV absorption line studies, which also show the co-existence of regions with high hydrogen column density (i.e., no escape of ionizing photons) and density-bounded or very low column density regions responsible for the observed escape of LyC radiation. These simple but consistent models provide a first step towards the use of optical emission lines and their ratios as quantitative diagnostics of LyC escape from galaxies.
Deep Reinforcement Learning for Closed-Loop Blood Glucose Control

People with type 1 diabetes (T1D) lack the ability to produce the insulin their bodies need. As a result, they must continually make decisions about how much insulin to self-administer to adequately control their blood glucose levels. Longitudinal data streams captured from wearables, like continuous glucose monitors, can help these individuals manage their health, but currently the majority of the decision burden remains on the user. To relieve this burden, researchers are working on closed-loop solutions that combine a continuous glucose monitor and an insulin pump with a control algorithm in an `artificial pancreas. ' Such systems aim to estimate and deliver the appropriate amount of insulin. Here, we develop reinforcement learning (RL) techniques for automated blood glucose control. Through a series of experiments, we compare the performance of different deep RL approaches to non-RL approaches. We highlight the flexibility of RL approaches, demonstrating how they can adapt to new individuals with little additional data. On over 2.1 million hours of data from 30 simulated patients, our RL approach outperforms baseline control algorithms: leading to a decrease in median glycemic risk of nearly 50% from 8.34 to 4.24 and a decrease in total time hypoglycemic of 99.8%, from 4,610 days to 6. Moreover, these approaches are able to adapt to predictable meal times (decreasing average risk by an additional 24% as meals increase in predictability). This work demonstrates the potential of deep RL to help people with T1D manage their blood glucose levels without requiring expert knowledge. All of our code is publicly available, allowing for replication and extension.
TED: Triple Supervision Decouples End-to-end Speech-to-text Translation

An end-to-end speech-to-text translation (ST) takes audio in a source language and outputs the text in a target language. Inspired by neuroscience, humans have perception systems and cognitive systems to process different information, we propose TED, \textbf{T}ransducer-\textbf{E}ncoder-\textbf{D}ecoder, a unified framework with triple supervision to decouple the end-to-end speech-to-text translation task. In addition to the target sentence translation loss, \method includes two auxiliary supervising signals to guide the acoustic transducer that extracts acoustic features from the input, and the semantic encoder to extract semantic features relevant to the source transcription text. Our method achieves state-of-the-art performance on both English-French and English-German speech translation benchmarks.
Epidemic mitigation by statistical inference from contact tracing data

Significance Contact tracing mobile applications are clear candidates for enabling us to slow down an epidemic and keep society running while holding the health risks down. Currently used mobile applications aim to notify individuals who were recently in significant contact with an individual who tested COVID-19 positive. In our work, we aim to quantify the epidemiological gain one would obtain if, additionally, individuals who were recently in contact could exchange messages of information. With such a message-passing addition, the risk of contracting COVID-19 could be estimated with much better accuracy than simple contact tracing. We conclude that probabilistic risk estimation is capable of enhancing performance of digital contact tracing and should be considered in the mobile tracing applications. Contact tracing is an essential tool to mitigate the impact of a pandemic, such as the COVID-19 pandemic. In order to achieve efficient and scalable contact tracing in real time, digital devices can play an important role. While a lot of attention has been paid to analyzing the privacy and ethical risks of the associated mobile applications, so far much less research has been devoted to optimizing their performance and assessing their impact on the mitigation of the epidemic. We develop Bayesian inference methods to estimate the risk that an individual is infected. This inference is based on the list of his recent contacts and their own risk levels, as well as personal information such as results of tests or presence of syndromes. We propose to use probabilistic risk estimation to optimize testing and quarantining strategies for the control of an epidemic. Our results show that in some range of epidemic spreading (typically when the manual tracing of all contacts of infected people becomes practically impossible but before the fraction of infected people reaches the scale where a lockdown becomes unavoidable), this inference of individuals at risk could be an efficient way to mitigate the epidemic. Our approaches translate into fully distributed algorithms that only require communication between individuals who have recently been in contact. Such communication may be encrypted and anonymized, and thus, it is compatible with privacy-preserving standards. We conclude that probabilistic risk estimation is capable of enhancing the performance of digital contact tracing and should be considered in the mobile applications.
A simple method for isolation of lipopolysaccharides from Pseudomonas aeruginosa and some other bacterial strains

A simple procedure for the isolation of bacterial lipopolysaccharides was developed. The method consists of the following three steps; recovery of lipopolysaccharides in an insoluble form together with denatured proteins in a hot MgCl2-Triton X-100 solution (step 1), solubilization with EDTA-Triton X-100 (step 2) and precipitation with MgCl2 (step 3). A good yield of lipopolysaccharides was obtained with most of the Pseudomonas strains tested. Although the yield was lower with strains belonging to Salmonella and Escherichia, irrespective of whether they are smooth-type or rough-type strains, the amounts obtained from 100mg of cells as dry weight were sufficient for detailed chemical characterization. The gel-electrophoretic profiles of the lipopolysaccharides prepared by the present method were essentially the same as those of the lipopolysaccharides prepared by the conventional phenol-water method. The method allowed us to prepare lipopolysaccharides from a large number of bacterial strains in one day.
Further Experiments on Accessory Growth Factor Requirements of the Brucella Group

Received for publication, January 10, 1942. This investigation was aided in part by a grant from The John and Mary R. Markle Foundation and in part by a grant from the Dr. Wallace C. and Clara A. Abbott Memorial Fund of the University of Chicago. 1. Koser, S. A. , Breslove, B. B. , and Dorfman, A. : J. Infect. Dis. 69: 114, 1941. tothenic acid. Fifty to 100 jug of thioglycollic acid were added also in the case of the Br. abortus cultures since this seemed to facilitate to some extent the growth of 2 of these strains. The effect on growth of three strains of Br. abortus produced by biotin conentrate, pure biotin and biotin methyl ester is shown in table 1. These 3 cul-
AutoQA: From Databases to Q&A Semantic Parsers with Only Synthetic Training Data

We propose AutoQA, a methodology and toolkit to generate semantic parsers that answer questions on databases, with no manual effort. Given a database schema and its data, AutoQA automatically generates a large set of high-quality questions for training that covers different database operations. It uses automatic paraphrasing combined with template-based parsing to find alternative expressions of an attribute in different parts of speech. It also uses a novel filtered auto-paraphraser to generate correct paraphrases of entire sentences. We apply AutoQA to the Schema2QA dataset and obtain an average logical form accuracy of 62.9% when tested on natural questions, which is only 6.4% lower than a model trained with expert natural language annotations and paraphrase data collected from crowdworkers. To demonstrate the generality of AutoQA, we also apply it to the Overnight dataset. AutoQA achieves 69.8% answer accuracy, 16.4% higher than the state-of-the-art zero-shot models and only 5.2% lower than the same model trained with human data.