Miracle-dz/UniFAR-FTUs huggingface dataset:
Small dust grain dynamics on adaptive mesh refinement grids

Context. Small dust grains are essential ingredients of star, disk and planet formation. Aims. We present an Eulerian numerical approach to study small dust grain dynamics in the context of star and protoplanetary disk formation. It is designed for finite volume codes. We use it to investigate dust dynamics during the protostellar collapse. Methods. We present a method to solve the monofluid equations of gas and dust mixtures with several dust species in the diffusion approximation implemented in the adaptive-mesh-refinement code RAMSES. It uses a finite volume second-order Godunov method with a predictor-corrector MUSCL scheme to estimate the fluxes between the grid cells. Results. We benchmark our method against six distinct tests, DUSTYADVECT, DUSTYDIFFUSE, DUSTYSHOCK, DUSTYWAVE, SETTLING, and DUSTYCOLLAPSE. We show that the scheme is second-order accurate in space on uniform grids and intermediate between second- and first-order on non-uniform grids. We apply our method on various DUSTYCOLLAPSE simulations of 1 M⊙ cores composed of gas and dust. Conclusions. We developed an efficient approach to treat gas and dust dynamics in the diffusion regime on grid-based codes. The canonical tests were successfully passed. In the context of protostellar collapse, we show that dust is less coupled to the gas in the outer regions of the collapse where grains larger than ≃100 μm fall significantly faster than the gas.
Multivariate Signal Modeling With Applications to Inertial Sensor Calibration

The common approach to inertial sensor calibration has been to model the stochastic error signals of individual sensors independently, whether as components of a single inertial measurement unit (IMU) in different directions or arrayed in the same direction for redundancy. For this purpose, research in this domain has been focused on the proposal of various methods to improve the estimation of these models both from a computational and a statistical point of view. However, the separate calibration of the individual sensors is unable to take into account the dependence between each of them which can have an important impact on the precision of the navigation systems. In this paper, we develop a new approach to simultaneously model the individual signals and the dependence between them by studying the quantity called Wavelet Cross-Covariance and using it to extend the application of the Generalized Method of Wavelet Moments. This new method can be used in other settings for time series modeling, especially in cases where the dependence among signals may be hard to detect. Moreover, in the field of inertial sensor calibration, this approach can deliver important contributions among which the possibility to test dependence between sensors, integrate their dependence within the navigation filter and construct an optimal virtual sensor that can be used to simplify and improve navigation accuracy. The advantages of this method and its usefulness for inertial sensor calibration are highlighted through a simulation study and an applied example with a small array of XSens MTi-G IMUs.
Tensor models with series-parallel interactions

Tensor models are natural generalizations of matrix models. The interactions and observables in the case of unitary invariant models are generalizations of matrix traces. Some notable interactions in the literature include the melonic ones, the tetrahedral one as well as the planar ones in rank three, or necklaces in even ranks. Here we introduce series-parallel interactions which generalize the melonic and necklace interactions and are characterized by series-parallel bubbles. We characterize the local and global structure of those bubbles. We also completely characterize the Feynman graphs which contribute to the large N limit. For a subclass of series-parallel interactions called totally unbalanced series-parallel interactions, we prove that the large N limit is Gaussian and therefore the Feynman graphs are in bijection with trees. This result further extends the class of tensor models which fall into the Gaussian universality class. Another key aspect of tensor models with seriesparallel interactions is that they can be written as matrix models without increasing the number of degrees of freedom of the original tensor models. In the case of totally unbalanced interactions, this new matrix model formulation in fact decreases the number of degrees of freedom, meaning that some of the original ones are effectively integrated. We then show how the large N Gaussian behavior can be reproduced using a saddle point analysis.
Few-Shot Adaptive Gaze Estimation

Inter-personal anatomical differences limit the accuracy of person-independent gaze estimation networks. Yet there is a need to lower gaze errors further to enable applications requiring higher quality. Further gains can be achieved by personalizing gaze networks, ideally with few calibration samples. However, over-parameterized neural networks are not amenable to learning from few examples as they can quickly over-fit. We embrace these challenges and propose a novel framework for Few-shot Adaptive GaZE Estimation (Faze) for learning person-specific gaze networks with very few (≤ 9) calibration samples. Faze learns a rotation-aware latent representation of gaze via a disentangling encoder-decoder architecture along with a highly adaptable gaze estimator trained using meta-learning. It is capable of adapting to any new person to yield significant performance gains with as few as 3 samples, yielding state-of-the-art performance of 3.18-deg on GazeCapture, a 19% improvement over prior art. We open-source our code at https://github.com/NVlabs/few_shot_gaze
A Wide and Deep Exploration of Radio Galaxies with Subaru HSC (WERGS). II. Physical Properties Derived from the SED Fitting with Optical, Infrared, and Radio Data

We present physical properties of radio galaxies (RGs) with f1.4 GHz > 1 mJy discovered by Subaru Hyper Suprime-Cam (HSC) and Very Large Array Faint Images of the Radio Sky at Twenty-Centimeters (FIRST) survey. For 1056 FIRST RGs at 0 < z ≤ 1.7 with HSC counterparts in about 100 deg2, we compiled multi-wavelength data of optical, near-infrared (IR), mid-IR, far-IR, and radio (150 MHz). We derived their color excess (E(B − V)*), stellar mass, star formation rate (SFR), IR luminosity, the ratio of IR and radio luminosity (qIR), and radio spectral index (αradio) that are derived from the spectral energy distribution (SED) fitting with CIGALE. We also estimated Eddington ratio based on stellar mass and integration of the best-fit SEDs of active galactic nucleus (AGN) component. We found that E(B − V)*, SFR, and IR luminosity clearly depend on redshift while stellar mass, qIR, and αradio do not significantly depend on redshift. Since optically faint (iAB ≥ 21.3) RGs that are newly discovered by our RG survey tend to be high redshift, they tend to not only have a large dust extinction and low stellar mass but also have high SFR and AGN luminosity, high IR luminosity, and high Eddington ratio compared with optically bright ones. The physical properties of a fraction of RGs in our sample seem to differ from a classical view of RGs with massive stellar mass, low SFR, and low Eddington ratio, demonstrating that our RG survey with HSC and FIRST provides us curious RGs among entire RG population.
Edge-Labeling Graph Neural Network for Few-Shot Learning

In this paper, we propose a novel edge-labeling graph neural network (EGNN), which adapts a deep neural network on the edge-labeling graph, for few-shot learning. The previous graph neural network (GNN) approaches in few-shot learning have been based on the node-labeling framework, which implicitly models the intra-cluster similarity and the inter-cluster dissimilarity. In contrast, the proposed EGNN learns to predict the edge-labels rather than the node-labels on the graph that enables the evolution of an explicit clustering by iteratively updating the edge-labels with direct exploitation of both intra-cluster similarity and the inter-cluster dissimilarity. It is also well suited for performing on various numbers of classes without retraining, and can be easily extended to perform a transductive inference. The parameters of the EGNN are learned by episodic training with an edge-labeling loss to obtain a well-generalizable model for unseen low-data problem. On both of the supervised and semi-supervised few-shot image classification tasks with two benchmark datasets, the proposed EGNN significantly improves the performances over the existing GNNs.
A cautionary tale of attenuation in star-forming regions

The attenuation of light from star-forming galaxies is correlated with a multitude of physical parameters including star formation rate, metallicity and total dust content. This variation in attenuation is even more evident on kiloparsec scales, which is the relevant size for many current spectroscopic integral field unit surveys. To understand the cause of this variation, we present and analyse Swift/UVOT near-UV (NUV) images and SDSS/MaNGA emission-line maps of 29 nearby (z < 0.084) star-forming galaxies. We resolve kiloparsec-sized star-forming regions within the galaxies and compare their optical nebular attenuation (i.e. the Balmer emission line optical depth, $\tau ^{l}_{B}\equiv \tau _{\textrm {H}\beta }-\tau _{\textrm {H}\alpha }$) and NUV stellar continuum attenuation (via the NUV power-law index, β) to the attenuation law described by Battisti et al. We show the data agree with that model, albeit with significant scatter. We explore the dependence of the scatter of the β–$\tau ^{l}_{B}$ measurements from the star-forming regions on different physical parameters, including distance from the nucleus, star formation rate and total dust content. Finally, we compare the measured $\tau ^{l}_{B}$ and β values for the individual star-forming regions with those of the integrated galaxy light. We find a strong variation in β between the kiloparsec scale and the larger galaxy scale that is not seen in $\tau ^{l}_{B}$. We conclude that the sightline dependence of UV attenuation and the reddening of β due to the light from older stellar populations could contribute to the scatter in the β–$\tau ^{l}_{B}$ relation.
Minimal Recognizable Configurations Elicit Category-selective Responses in Higher Order Visual Cortex

Visual object recognition is performed effortlessly by humans notwithstanding the fact that it requires a series of complex computations, which are, as yet, not well understood. Here, we tested a novel account of the representations used for visual recognition and their neural correlates using fMRI. The rationale is based on previous research showing that a set of representations, termed “minimal recognizable configurations” (MIRCs), which are computationally derived and have unique psychophysical characteristics, serve as the building blocks of object recognition. We contrasted the BOLD responses elicited by MIRC images, derived from different categories (faces, objects, and places), sub-MIRCs, which are visually similar to MIRCs, but, instead, result in poor recognition and scrambled, unrecognizable images. Stimuli were presented in blocks, and participants indicated yes/no recognition for each image. We confirmed that MIRCs elicited higher recognition performance compared to sub-MIRCs for all three categories. Whereas fMRI activation in early visual cortex for both MIRCs and sub-MIRCs of each category did not differ from that elicited by scrambled images, high-level visual regions exhibited overall greater activation for MIRCs compared to sub-MIRCs or scrambled images. Moreover, MIRCs and sub-MIRCs from each category elicited enhanced activation in corresponding category-selective regions including fusiform face area and occipital face area (faces), lateral occipital cortex (objects), and parahippocampal place area and transverse occipital sulcus (places). These findings reveal the psychological and neural relevance of MIRCs and enable us to make progress in developing a more complete account of object recognition.
Formation and decay of eddy currents generated by crossed surface waves

The mass-transport induced by crossed surface waves consists of the Stokes and Euler contributions which are very different in nature. The first contribution is a generalization of Stokes drift for a plane wave in ideal fluid and the second contribution arises due to the fluid viscosity and it is excited by a force applied in the viscous sublayer near the fluid surface. We study the formation and decay of the induced mass-transport theoretically and experimentally and demonstrate that both contributions have different time scales for typical experimental conditions. The evolution of the Euler contribution is described by a diffusion equation, where the fluid kinematic viscosity plays the role of the diffusion coefficient, while the Stokes contribution evolves faster, feeling the additional damping near the system boundaries. The difference becomes more pronounced if the fluid surface is contaminated. We model the effect of contamination by a thin insoluble liquid film presented on the fluid surface with the compression modulus being the only non-zero rheological parameter of the film. Then the Euler contribution into the mass-transport becomes parametrically larger and the evolution of the Stokes contribution becomes parametrically faster. The parameter is the same in both cases and it is equal to the quality factor of surfaces waves, which is modified by the presence of a surface film. We infer the value of the compression modulus of the film by fitting the results of transient measurements of eddy currents and demonstrate that the obtained value leads to the correct ratio of amplitudes of horizontal and vertical velocities of the wave motion and is in reasonable agreement with the measured dissipation rate of surface waves.
Lynceus: Tuning and Provisioning Data Analytic Jobs on a Budget

Many enterprises need to run data analytic jobs on the cloud. Significant cost savings can be achieved if one can find the right combination of virtual machine type, cluster size, and parameter settings for the job (e.g., hyper-parameters of a machine learning algorithm). Unfortunately, this task is very challenging given that the search space is composed of hundreds or even thousands of different configurations. Lynceus is a new tool to provision and tune data analytic applications on the cloud. It does so automatically and in a cost-efficient manner. Lynceus implements a new budget-aware approach that builds the performance model of the target job by profiling the job on the best set of cloud/parameter configurations possible given constraints of both quality of service and monetary nature. Lynceus departs from state-of-the-art approaches that simply aim to reduce the number of configurations to try, disregarding the corresponding profiling costs, and that hence achieve a worse trade-off between the accuracy of the model and the cost to build it. We evaluate Lynceus on several heterogeneous data analytic jobs, running on different frameworks and with search spaces of different sizes. We compare Lynceus with the state-of-the-art approach, implemented by recent systems such as CherryPick, and show that it can consistently identify better (i.e., less expensive) configurations. This leads to cost reductions that range from 1.7x to 1.9x on average, and from 2x to 4x at the 90-th percentile.
Correlations between stacked structures and weak itinerant magnetic properties of La2−xYxNi7 compounds

Hexagonal La2Ni7 and rhombohedral Y2Ni7 are weak itinerant antiferromagnet (wAFM) and ferromagnet (wFM), respectively. To follow the evolution between these two compounds, the crystal structure and magnetic properties of A2B7 intermetallic compounds (A = La, Y, B = Ni) have been investigated combining x-ray powder diffraction and magnetic measurements. The La2−xYxNi7 intermetallic compounds with 0 ⩽ x ⩽ 1 crystallize in the hexagonal Ce2Ni7-type structure with Y preferentially located in the [A2B4] units. The compounds with larger Y content (1.2 ⩽ x < 2) crystallize in both hexagonal and rhombohedral (Gd2Co7-type) structures with a substitution of Y for La in both [A2B4] and [AB5] units. Y2Ni7 crystallizes in the rhombohedral structure only. The average cell volume decreases linearly versus Y content, whereas the c/a ratio presents a minimum at x = 1 due to geometric constrains. The magnetic properties are strongly dependent on the structure type and the Y content. La2Ni7 displays a complex metamagnetic behavior with split AFM peaks. Compounds with x = 0.25 and 0.5 display a wAFM ground state and two metamagnetic transitions, the first one toward an intermediate wAFM state and the second one toward a FM state. TN and the second critical field μ0Hc2 increase with the Y content, indicating a stabilization of the AFM state. LaYNi7, which is as the boundary between the two structure types, presents a very wFM state at low field and an AFM state as the applied field increases. All the compounds with x > 1, and which contains a rhombohedral phase are wFM with TC = 53(2) K. In addition to the experimental studies, first principles calculations using spin polarization have been performed to interpret the evolution of structural phase stability for 0 ⩽ x ⩽ 2.
Dissipative dynamics of a driven qubit: Interplay between nonadiabatic dynamics and noise effects from the weak to strong coupling regime

We study the exact solution of the Schrodinger equation for the dissipative dynamics of a qubit, achieved by means of Short Iterative Lanczos method (SIL), which allows us to describe the qubit and the bath dynamics from weak to strong coupling regimes. We focus on two different models of a qubit in contact with the external environment: the first is the Spin Boson Model (SBM), which gives a description of the qubit in terms of static tunnelling energy and a bias field. The second model describes an externally driven qubit, where both the bias field and the tunnelling rate are controlled by a time-dependent magnetic field obeying to a finite time protocol. We show that in the SBM case, our solution correctly describes the crossover from coherent to incoherent behavior of the magnetization, occurring at the Toulouse point. Furthermore, we show that the bath response dramatically changes during the system dynamics, going from non-resonant at small times to resonant behavior at long times. When the external driving field is present, for fixed values of the drive duration our results show that the bath can provide beneficial effects to the success of the protocol. We find evidence for a complex interplay between non-adiabaticity of the protocol due to the external drive and dissipation effects, which strongly depends on the detailed form of the qubit-bath interaction.
High-sensitivity C-reactive protein levels predict survival and are related to haemodynamics in alcoholic cirrhosis

Objectives Inflammation may be implicated in the haemodynamic deterioration and in the development of complications in patients with cirrhosis. High-sensitivity C-reactive protein (hsCRP) is a marker of low-grade inflammation, and predicts outcomes in patients at risk of ischaemic heart disease. Proinflammatory cytokines reflect immune activation and have been found to be elevated in cirrhosis. We investigated a possible association between markers of inflammation and splanchnic and systemic haemodynamics, complications and survival in patients with cirrhosis. Methods In 45 stable patients with cirrhosis on the basis of alcohol consumption, we measured hsCRP, as well as monocyte chemoattractant protein-1, tumour necrosis factor-&agr;, interleukin-6, interleukin-8 and vascular endothelial growth factor in patients and in 12 healthy controls. Systemic and splanchnic haemodynamics were investigated in patients. Results hsCRP levels were significantly higher in patients compared with controls (P<0.05) and the highest in patients belonging to Child–Pugh class C. hsCRP levels correlated with markers of liver dysfunction and with the hepatic venous pressure gradient (r=0.48, P<0.001). hsCRP values above the median level of 5.3 mg/l were associated with a highly increased mortality (P=0.001). Model for End-Stage Liver Disease score (P=0.01) and hsCRP (P<0.05) provided independent prognostic information. Cytokines had no discernible value in predicting survival. Conclusion hsCRP is elevated in patients with cirrhosis and is associated with portal hypertension and decreased survival. hsCRP is a promising prognostic marker in cirrhosis, which may improve the selection of candidates for liver transplantation.
Do AGN triggering mechanisms vary with radio power? – I. Optical morphologies of radio-intermediate HERGs

Active galactic nuclei (AGNs) with intermediate radio powers are capable of driving multiphase outflows in galaxy bulges, and are also more common than their high-radio-power counterparts. In-depth characterization of the typical host galaxies and likely triggering mechanisms for this population is therefore required in order to better understand the role of radio AGN feedback in galaxy evolution. Here, we use deep optical imaging data to study the detailed host morphologies of a complete sample of 30 local radio AGNs with high-excitation optical emission (HERG) spectra and intermediate radio powers [ z < 0.1; 22.5 < log($L_{\rm 1.4GHz}$) < 24.0 W Hz-1]. The fraction of hosts with morphological signatures of mergers and interactions is greatly reduced compared to the 2Jy radio-powerful galaxies [log($L_{\rm 1.4GHz}$) > 25.0 W Hz-1] with strong optical emission lines: 53 ± 9 per cent compared with 94 ± 4 per cent. In addition, the most radio-powerful half of the sample has a higher frequency of morphological disturbance than the least radio-powerful half (67 ± 12 per cent and 40 ± 13 per cent, respectively), including the eight most highly disturbed galaxies. This suggests that the importance of triggering nuclear activity in high-excitation radio galaxies (HERGs) through mergers and interactions reduces with radio power. Both visual inspection and detailed light profile modelling reveal a mixed population of early-type and late-type morphologies, contrary to the massive elliptical galaxy hosts of radio-powerful AGNs. The prevalence of late-type hosts could suggest that triggering via secular, disc-based processes has increased importance for HERGs with lower radio powers (e.g. disc instabilities and large-scale bars).