Miracle-dz/UniFAR-FTUs huggingface dataset:
Intermittent AGN episodes drive outflows with a large spread of observable loading factors

The properties of large-scale galactic outflows, such as their kinetic energy and momentum rates, correlate with the luminosity of the active galactic nucleus (AGN). This is well explained by the wind-driven outflow model, where a fraction of the AGN luminosity drives the outflow. However, significant departures from these correlations have been observed in a number of galaxies. This may happen because AGN luminosity varies on a much shorter timescale ($\sim 10^4-10^5$~yr) than outflow properties do ($\sim 10^6$~yr). We investigate the effect of AGN luminosity variations on outflow properties using 1D numerical simulations. This effect can explain the very weak outflow in PDS 456: if its nucleus is currently much brighter than the long-term average luminosity, the outflow has not had time to react to this luminosity change. Conversely, the outflow in Mrk 231 is consistent with being driven by an almost continuous AGN, while IRAS F11119+3257 represents an intermediate case between the two. Considering a population of AGN, we find that very low momentum loading factors $\dot{p}_{\rm out} < L_{\rm AGN}/c$ should be seen in a significant fraction of objects - up to $15\%$ depending on the properties of AGN variability and galaxy gas fraction. The predicted distribution of loading factors is consistent with the available observational data. We discuss how this model might help constrain the duty cycles of AGN during the period of outflow inflation, implications for multiphase and spatially distinct outflows, and suggest ways of improving AGN prescriptions in numerical simulations.
Identification of microRNAs Involved in mTOR Nutrient Signaling during Adipocyte Differentiation

Aims: In this study, we specifically aimed at identifying distinct miRNAs that are governing nutrientdriven adipogenesis via the mTOR pathway. Methodology: The murine pre-adipocyte cell line 3T3-L1 was differentiated in the presence and absence of mTOR inhibitor rapamycin. MiRNA profiles were generated using two different array platforms. Differential regulated miRNAs were subjected to in silico target prediction. Candidate miRNA-target pairs were studied via luciferase assays, transfection assays and functional assays. Results: This study confirmed 31 previously known and revealed 7 additional miRNAs, namely miROriginal Research Article Kaeuferle et al.; JABB, 2(2): 109-122, 2015; Article no.JABB. 2015.013 110 34a, -96, -106b, -148b, -214*, -678 and -690 as being involved in adipocyte differentiation. Furthermore, we identified a subset of 16 miRNAs showing both responsiveness to rapamycin and being predicted to target genes within the mTOR pathway. Within mTOR signaling, the novel miRNA-target pairs miR183-Tsc1 (tuberous sclerosis 1), miR378-Tsc1 and miR103-Pik3r1 (phosphatidylinositol 3-kinase regulatory subunit alpha), were confirmed via luciferase-based binding assays. Accordingly we observed down-regulation of Tsc1 on transcript level in cells transfected with miR-183 or -378 (18.3 % P = .004 and 40.4 % P < .0001). However, on protein level we could detect Tsc1 up-regulation (35.7 % and 39.8 %), which is consistent with an inhibitory influence on mTOR signaling (33.6 % and 32.9 %) and on adipocyte differentiation of these miRNAs. Conclusion: Our findings reveal a novel role for a set of miRNAs during adipocyte differentiation and mTOR signaling. Especially the attenuating effect of miR-183 and -378 on adipocyte differentiation and mTOR signaling suggests their potential as biomarkers for nutrient-driven dysregulation of adipogenesis.
Properties of Cataclysmic Variables in Globular Clusters

The study of star clusters plays an important role in our understanding of the Universe since these systems are natural laboratories for testing theories of stellar dynamics and evolution. Particularly, globular clusters (GCs) are one of the most important objects for studying the formation and the physical nature of exotic systems which in turn provide basic information and tools that can help us to understand the formation and evolution processes of star clusters themselves, galaxies and, in general, the young Universe. Among the most interesting objects in GCs are the cataclysmic variables (CVs), which are interacting binaries harboring a white dwarf accreting from a low-mass companion. Since GC densities are sufficiently high that dynamical encounters involving binaries should be common, CV progenitors are expected to be affected by dynamics in some way in the early stages. In this article we review the formation channels and the influence of dynamics on the CV population in GCs. In particular, we review recent progress in numerical simulations. Furthermore, we discuss observational properties of CVs in GCs and the techniques used to identify and study them. We focus the discussion on the multi-wavelength observations carried out with HST and Chandra on the best-studied GCs NGC 6397, NGC 6752, 47 Tucanae and Omega Centauri; on the recent spectroscopic findings with MUSE, and on updates regarding the correlation between the number of faint X-ray sources and the cluster stellar encounter rate. Finally, we discuss some observational prospects that might potentially help future investigations.
A note on weighted poly-Bergman spaces

We introduce a la Vasilevski the weighted poly-Bergman spaces in the unit disc and provide concrete orthonormal basis and give close expression of their reproducing kernel. The main tool in the description if these spaces is the so-called disc polynomials that form an orthogonal basis of the whole weighted Hilbert space.
A Note on Debiased/Double Machine Learning Logistic Partially Linear Model

It is of particular interests in many application fields to draw doubly robust inference of a logistic partially linear model with the predictor specified as combination of a targeted low dimensional linear parametric function and a nuisance nonparametric function. In recent, Tan (2019) proposed a simple and flexible doubly robust estimator for this purpose. They introduced the two nuisance models, i.e. nonparametric component in the logistic model and conditional mean of the exposure covariates given the other covariates and fixed response, and specified them as fixed dimensional parametric models. Their framework could be potentially extended to machine learning or high dimensional nuisance modelling exploited recently, e.g. in Chernozhukovet al. (2018a,b) and Smucler et al. (2019); Tan (2020). Motivated by this, we derive the debiased/double machine learning logistic partially linear model in this note. For construction of the nuisance models, we separately consider the use of high dimensional sparse parametric models and general machine learning methods. By deriving certain moment equations to calibrate the first order bias of the nuisance models, we preserve a model double robustness property on high dimensional ultra-sparse nuisance models. We also discuss and compare the underlying assumption of our method with debiased LASSO (Van deGeer et al., 2014). To implement the machine learning proposal, we design a full model refitting procedure that allows the use of any blackbox conditional mean estimation method in our framework. Under the machine learning setting, our method is rate doubly robust in a similar sense as Chernozhukov et al. (2018a).
Predatory risk to insects foraging at flowers

Interactions between nectar-seeking insects (primarily bumble bees Bombus spp. and honey bees Apis mellifera L.) visiting common milkweed (Asciepias syriaca: Asclepiadaceae) inflorescences and their commonest predator at the inflorescences, the crab spider Misumena vatia (Clerk) (Thomisidae), were recorded. Densities of Misumena were low. On average a given insect would only be attacked once each 4-6 d by a spider, and predicted periods until capture of the different species ranged from 21 to 109 d, far longer than any individual visited milkweed. Insects showed no sign of avoiding spiders.
Application timing and concentration of abscisic acid affect the quality of ‘Redglobe’ grapes

Summary The plant hormone abscisic acid (ABA), the concentration of which increases in grape berry skins at the onset of maturation (veraison), appears to be involved in the regulation of anthocyanin accumulation. Preliminary tests suggested that exogenous applications of ABA could improve grape berry colour, but its high cost precluded the development of commercial applications. Recently, a lower-cost ABA production method was developed, which led to the evaluation of different concentrations of ABA, applied at or around veraison, on the quality of ‘Redglobe’ grapes. In two of three years of tests, several ABA treatments enhanced the anthocyanin content of grape skins. ABA, applied at approx. 300 mg l–1 at veraison, may be required to reliably increase pigmentation, and improve the colour of ‘Redglobe’ grapes. Although the total anthocyanin content was increased by ABA treatment, anthocyanin composition was not affected. Applications of ABA had few effects on fruit size or composition, although they did cause fruit softening, which is undesirable. A secondary objective of this study was to determine how the anthocyanin content of berry skins affected berry colour characteristics. Strong curvilinear relationships between anthocyanin content and lightness and hue showed that these colour characteristics were saturated by anthocyanin contents over 0.02 mg cm–2 of skin. These data suggest that colour measurements may be needed to evaluate the effect of cultural practices on colour in table grapes.
SEEDUNG INDEX AND YIElD AS INFLUENCED BY SEED SIZE IN PIGEONPEA

The effect of seed size on seedling vigour index and yield was studied in pigeonpea. Hundred seed weight and seedling vigour index revealed significant differences among seed grades of all the varieties. Large seeds expressed high seedling vigour index than the small size seeds in all the varieties, however, minimum differences among seed grades were noticed in UPAS 120 and Pant A104. Poor seedling vigour index was seen in small seed grades in all the varieties except Pant A-3 and Pant A-10.
Holocene crustaceans from the Tarioba shell mound, Rio das Ostras, Rio de Janeiro, Brazil

Crustacean remains from the Tarioba shell mound (Sambaqui da Tarioba) archaeological site were investigated in order to produce a reference inventory of this taxonomic group. Information on past crustacean diversity is important for establishing baselines on biodiversity, as well as recovering paleoenvironmental information and comprehension of evolutionary change patterns for the Brazilian coast. The archaeological site was excavated by delayering the soil by artificial 10 cm sections which revealed five archaeological stratigraphic layers. A sample of mollusk shells for each of these layers was used for dating based on the C 14 method. The results show an occupation period of 550 yr for the Tarioba shell mound, with dates ranging between 4,070 cal BP (beginning of occupation) and 3,520 cal BP (occupation ending) and record nine species of crustaceans from the shell mound. However, a reduction of biodiversity over time was found that was not statically significant. Therefore, it seems that patterns of composition, richness and distribution of crustaceans have been stable in the last 4,070 years BP.
Relative abundance and resistance traits of two Collembola species under metal stress

Population differentiation was studied in Onychiurus armatus (Tullb.) and Isotoma notabilis Schaffer from a reference site and two highly polluted (Cu, Zn) sites (long-term and short-term exposed, respectively) in the Gusum area, SE Sweden. Growth, survival and reproduction were followed in a F1 generation of O. armatus reared on a diet of the fungus Verticillium bullosum (W. Gams and Malla) grown on a series of Cu+Zn amended agar plates. Survival and reproduction were observed in a P generation of I. notabilis incubated in soils with enhanced concentrations of Cu and Zn. Metals affected growth rate but not mean maximum length of O. armatatus. The growth rates of both populations decreased significantly with increasing metal concentration
ROOT-SGD: Sharp Nonasymptotics and Asymptotic Efficiency in a Single Algorithm

The theory and practice of stochastic optimization has focused on stochastic gradient descent (SGD) in recent years, retaining the basic first-order stochastic nature of SGD while aiming to improve it via mechanisms such as averaging, momentum, and variance reduction. Improvement can be measured along various dimensions, however, and it has proved difficult to achieve improvements both in terms of nonasymptotic measures of convergence rate and asymptotic measures of distributional tightness. In this work, we consider first-order stochastic optimization from a general statistical point of view, motivating a specific form of recursive averaging of past stochastic gradients. The resulting algorithm, which we refer to as \emph{Recursive One-Over-T SGD} (ROOT-SGD), matches the state-of-the-art convergence rate among online variance-reduced stochastic approximation methods. Moreover, under slightly stronger distributional assumptions, the rescaled last-iterate of ROOT-SGD converges to a zero-mean Gaussian distribution that achieves near-optimal covariance.
Discovery of a Candidate Binary Supermassive Black Hole in a Periodic Quasar from Circumbinary Accretion Variability

Binary supermassive black holes (BSBHs) are expected to be a generic byproduct from hierarchical galaxy formation. The final coalescence of BSBHs is thought to be the loudest gravitational wave (GW) siren, yet no confirmed BSBH is known in the GW-dominated regime. While periodic quasars have been proposed as BSBH candidates, the physical origin of the periodicity has been largely uncertain. Here we report discovery of a periodicity (P=1607±7 days) at 99.95% significance (with a global p-value of ∼10−3 accounting for the look elsewhere effect) in the optical light curves of a redshift 1.53 quasar, SDSS J025214.67−002813.7. Combining archival Sloan Digital Sky Survey data with new, sensitive imaging from the Dark Energy Survey, the total ∼20-yr time baseline spans ∼4.6 cycles of the observed 4.4-yr (restframe 1.7-yr) periodicity. The light curves are best fit by a bursty model predicted by hydrodynamic simulations of circumbinary accretion disks. The periodicity is likely caused by accretion rate modulation by a milli-parsec BSBH emitting GWs, dynamically coupled to the circumbinary accretion disk. A bursty hydrodynamic variability model is statistically preferred over a smooth, sinusoidal model expected from relativistic Doppler boost, a kinematic effect proposed for PG1302−102. Furthermore, the frequency dependence of the variability amplitudes disfavors Doppler boost, lending independent support to the circumbinary accretion variability hypothesis. Given our detection rate of one BSBH candidate from circumbinary accretion variability out of 625 quasars, it suggests that future large, sensitive synoptic surveys such as the Vera C. Rubin Observatory Legacy Survey of Space and Time may be able to detect hundreds to thousands of candidate BSBHs from circumbinary accretion with direct implications for Laser Interferometer Space Antenna.
Dynamical Variational Autoencoders: A Comprehensive Review

The Variational Autoencoder (VAE) is a powerful deep generative model that is now extensively used to represent high-dimensional complex data via a low-dimensional latent space that is learned in an unsupervised manner. In the original VAE model, input data vectors are processed independently. In the recent years, a series of papers have presented different extensions of the VAE to sequential data, that not only model the latent space, but also model the temporal dependencies within a sequence of data vectors and/or corresponding latent vectors, relying on recurrent neural networks or state space models. In this paper we perform an extensive literature review of these models. Importantly, we introduce and discuss a general class of models called Dynamical Variational Autoencoders (DVAEs) that encompass a large subset of these temporal VAE extensions. Then we present in details seven different instances of DVAE that were recently proposed in the literature, with an effort to homogenize the notations and presentation lines, as well as to relate those models with existing classical temporal models (that are also presented for the sake of completeness). We reimplemented those seven DVAE models and we present the results of an experimental benchmark that we conducted on the speech analysis-resynthesis task (the PyTorch code will be made publicly available). An extensive discussion is presented at the end of the paper, aiming to comment on important issues concerning the DVAE class of models and to describe future research guidelines.
On the model-based stochastic value gradient for continuous reinforcement learning

Model-based reinforcement learning approaches add explicit domain knowledge to agents in hopes of improving the sample-efficiency in comparison to model-free agents. However, in practice model-based methods are unable to achieve the same asymptotic performance on challenging continuous control tasks due to the complexity of learning and controlling an explicit world model. In this paper we investigate the stochastic value gradient (SVG), which is a well-known family of methods for controlling continuous systems which includes model-based approaches that distill a model-based value expansion into a model-free policy. We consider a variant of the model-based SVG that scales to larger systems and uses 1) an entropy regularization to help with exploration, 2) a learned deterministic world model to improve the short-horizon value estimate, and 3) a learned model-free value estimate after the model's rollout. This SVG variation captures the model-free soft actor-critic method as an instance when the model rollout horizon is zero, and otherwise uses short-horizon model rollouts to improve the value estimate for the policy update. We surpass the asymptotic performance of other model-based methods on the proprioceptive MuJoCo locomotion tasks from the OpenAI gym, including a humanoid. We notably achieve these results with a simple deterministic world model without requiring an ensemble.
Description of eggs and larvae of laboratory reared red snapper (Lutjanus campechanus)

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A Taxonomy of Knowledge Gaps for Wikimedia Projects (First Draft)

In January 2019, prompted by the Wikimedia Movement's 2030 strategic direction, the Research team at the Wikimedia Foundation identified the need to develop a knowledge gaps index -- a composite index to support the decision makers across the Wikimedia movement by providing: a framework to encourage structured and targeted brainstorming discussions; data on the state of the knowledge gaps across the Wikimedia projects that can inform decision making and assist with measuring the long term impact of large scale initiatives in the Movement. Since July 2019 and as the first step toward building the knowledge gap index, the Research team has developed the first complete draft of a taxonomy of knowledge gaps for the Wikimedia projects. We studied more than 200 references by scholars, researchers, practitioners, community members and affiliates -- exposing evidence of knowledge gaps in readership, contributorship, and content of Wikimedia projects. We elaborated the findings and compiled the taxonomy of knowledge gaps in this paper, where we describe, group and classify knowledge gaps into a structured framework. The taxonomy you will learn more about in the rest of this work will serve as a basis to operationalize and quantify knowledge equity, one of the two 2030 strategic directions, through the knowledge gaps index.
W-Net: Dense Semantic Segmentation of Subcutaneous Tissue in Ultrasound Images by Expanding U-Net to Incorporate Ultrasound RF Waveform Data

We present W-Net, a novel Convolution Neural Network (CNN) framework that employs raw ultrasound waveforms from each A-scan, typically referred to as ultrasound Radio Frequency (RF) data, in addition to the gray ultrasound image to semantically segment and label tissues. Unlike prior work, we seek to label every pixel in the image, without the use of a background class. To the best of our knowledge, this is also the first deep-learning or CNN approach for segmentation that analyses ultrasound raw RF data along with the gray image. International patent(s) pending [PCT/US20/37519]. We chose subcutaneous tissue (SubQ) segmentation as our initial clinical goal since it has diverse intermixed tissues, is challenging to segment, and is an underrepresented research area. SubQ potential applications include plastic surgery, adipose stem-cell harvesting, lymphatic monitoring, and possibly detection/treatment of certain types of tumors. A custom dataset consisting of hand-labeled images by an expert clinician and trainees are used for the experimentation, currently labeled into the following categories: skin, fat, fat fascia/stroma, muscle and muscle fascia. We compared our results with U-Net and Attention U-Net. Our novel \emph{W-Net}'s RF-Waveform input and architecture increased mIoU accuracy (averaged across all tissue classes) by 4.5\% and 4.9\% compared to regular U-Net and Attention U-Net, respectively. We present analysis as to why the Muscle fascia and Fat fascia/stroma are the most difficult tissues to label. Muscle fascia in particular, the most difficult anatomic class to recognize for both humans and AI algorithms, saw mIoU improvements of 13\% and 16\% from our W-Net vs U-Net and Attention U-Net respectively.
Conformal Bjorken flow in the general frame and its attractor: Similarities and discrepancies with the Müller-Israel-Stewart formalism

We investigate the implications of the general frame approach for conformal Bjorken flow beyond the earlier studies. We show that the power series solution at late times is not unique and is accompanied by an exact solution of the form $1/\tau$, which becomes unphysical if taken on shell. In contrast to the Muller-Israel-Stewart formalism, a matching between $\NFSYM$ results and the hydro expansion is only possible up to the first order, which gives rise to $\eta/s=1/4\pi$. Matching the results to the next order gives rise to causality/stability-violating values. Furthermore, we show that the pressure anisotropy in the general frame cannot capture the hydrodynamization, and we introduce an alternative measure to find the attractor. Using slow-roll expansion, we find an analytical approximation form for the attractor. We also show that the early-time behavior of attractors is related to stability and causality conditions. The attractor solutions outside the stable and causal regime give rise to reheating and negative longitudinal pressures in early times, in contrast to the stable and causal ones. We also comment on the violation of the second law of thermodynamics by the off-shell parameters. We show that for the stable and causal choice of parameters, the off-shell canonical entropy of the attractors, which is not a physical quantity, has a negative divergence in early times before tending to its on-shell limit. On the other hand, the unstable and acausal attractors have non-negative entropy divergence. We speculate that the violation of the second law by stable and causal off-shell parameters is required for stability of the first-order hydrodynamics. We investigate the analytical structure of the Borel-transformed series and find the proper relation between the poles and nonhydro modes.