Miracle-dz/UniFAR-FTUs huggingface dataset:
Heat transfer and wall temperature effects in shock wave turbulent boundary layer interactions

Direct numerical simulations are carried out to investigate the effect of the wall temperature on the behavior of oblique shock-wave/turbulent boundary layer interactions at freestream Mach number $2.28$ and shock angle of the wedge generator $\varphi = 8^{\circ}$. Five values of the wall-to-recovery-temperature ratio ($T_w/T_r$) are considered, corresponding to cold, adiabatic and hot wall thermal conditions. We show that the main effect of cooling is to decrease the characteristic scales of the interaction in terms of upstream influence and extent of the separation bubble. The opposite behavior is observed in the case of heating, that produces a marked dilatation of the interaction region. The distribution of the Stanton number shows that a strong amplification of the heat transfer occurs across the interaction, and the maximum values of thermal and dynamic loads are found in the case of cold wall. The analysis reveals that the fluctuating heat flux exhibits a strong intermittent behavior, characterized by scattered spots with extremely high values compared to the mean. Furthermore, the analogy between momentum and heat transfer, typical of compressible, wall-bounded, equilibrium turbulent flows does not apply for most part of the interaction domain. The pre-multiplied spectra of the wall heat flux do not show any evidence of the influence of the low-frequency shock motion, and the primary mechanism for the generation of peak heating is found to be linked with the turbulence amplification in the interaction region.
Radio, X-ray and γ-ray surface brightness profiles as powerful diagnostic tools for non-thermal SNR shells

Distributions of non-thermal surface brightness of supernova remnants (SNRs) contain important information about the properties of magnetic field and acceleration of charged particles. In the present paper, the synchrotron radio, X-ray, and inverse-Compton (IC) γ-ray maps of adiabatic SNRs in uniform interstellar medium and interstellar magnetic field are modelled and their morphology is analysed, with particular emphasis on comparison of azimuthal and radial variations of brightness in radio, X-rays and γ-rays. Approximate analytical formulae for the azimuthal and radial profiles of the synchrotron radio and X-ray as well as the IC γ-ray brightness are derived. They reveal the main factors which influence the pattern of the surface brightness distribution due to leptonic emission processes in shells of SNRs and can account for some non-linear effects of acceleration if necessary. These approximations provide observers and theorists with a set of simple diagnostic tools for quick analysis of the non-thermal maps of SNRs.
Variable Stars and Galactic Structure

Abstract Variable stars have a unique part to play in Galactic astronomy. Among the most important of these variables are the Cepheids (types I and II), the RR Lyraes and the Miras (O- and C-rich). The current status of the basic calibration of these stars in their roles as distance, structure and population indicators is outlined and some examples of recent applications of these stars to Galactic and extragalactic problems are reviewed. The expected impact of Gaia on this type of work is discussed and the need for complementary ground based observations, particularly large scale near-infrared photometry, is stressed.
THREE-DIMENSIONAL GAS DYNAMIC SIMULATION OF THE INTERACTION BETWEEN THE EXOPLANET WASP-12b AND ITS HOST STAR

Hubble Space Telescope transit observations in the near-UV performed in 2009 made WASP-12b one of the most “mysterious” exoplanets; the system presents an early ingress, which can be explained by the presence of optically thick matter located ahead of the planet at a distance of ∼4–5 planet radii. This work follows previous attempts to explain this asymmetry with an exospheric outflow or a bow shock, induced by a planetary magnetic field, and provides a numerical solution of the early ingress, though we did not perform any radiative transfer calculation. We performed pure 3D gas dynamic simulations of the plasma interaction between WASP-12b and its host star and describe the flow pattern in the system. In particular, we show that the overfilling of the planet's Roche lobe leads to a noticeable outflow from the upper atmosphere in the direction of the and points. Due to the conservation of the angular momentum, the flow to the point is deflected in the direction of the planet's orbital motion, while the flow toward is deflected in the opposite direction, resulting in a non-axisymmetric envelope, surrounding the planet. The supersonic motion of the planet inside the stellar wind leads to the formation of a bow shock with a complex shape. The existence of the bow shock slows down the outflow through the and points, allowing us to consider a long-living flow structure that is in the steady state.
Cosmological fluctuations of a random field and radiation fluid

A generalization of the random fluid hydrodynamic fluctuation theory due to Landau and Lifshitz is applied to describe cosmological fluctuations in systems with radiation and scalar fields. The viscous pressures, parametrized in terms of the bulk and shear viscosity coefficients, and the respective random fluctuations in the radiation fluid are combined with the stochastic and dissipative scalar evolution equation. This results in a complete set of equations describing the perturbations in both scalar and radiation fluids. These derived equations are then studied, as an example, in the context of warm inflation. Similar treatments can be done for other cosmological early universe scenarios involving thermal or statistical fluctuations.
CHARACTERIZING ULTRAVIOLET AND INFRARED OBSERVATIONAL PROPERTIES FOR GALAXIES. II. FEATURES OF ATTENUATION LAW

Variations in the attenuation law have a significant impact on observed spectral energy distributions for galaxies. As one important observational property for galaxies at ultraviolet and infrared wavelength bands, the correlation between infrared-to-ultraviolet luminosity ratio and ultraviolet color index (or ultraviolet spectral slope), i.e., the IRX–UV relation (or IRX–β relation), offered a widely used formula for correcting dust attenuation in galaxies, but the usability appears to be in doubt now because of considerable dispersion in this relation found by many studies. In this paper, on the basis of spectral synthesis modeling and spatially resolved measurements of four nearby spiral galaxies, we provide an interpretation of the deviation in the IRX–UV relation with variations in the attenuation law. From both theoretical and observational viewpoints, two components in the attenuation curve, the linear background and the 2175 Å bump, are suggested to be the parameters in addition to the stellar population age (addressed in the first paper of this series) in the IRX–UV function; different features in the attenuation curve are diagnosed for the galaxies in our sample. Nevertheless, it is often difficult to ascertain the attenuation law for galaxies in actual observations. Possible reasons for preventing the successful detection of the parameters in the attenuation curve are also discussed in this paper, including the degeneracy of the linear background and the 2175 Å bump in observational channels, the requirement for young and dust-rich systems to study, and the difficulty in accurate estimates of dust attenuations at different wavelength bands.
Constraining the extension of a possible gamma-ray halo of 3C 279 from 2008–2014 solar occultations

The angular extension of the gamma-ray image of 3C 279 may be constrained by studying its solar occultations as suggested by Fairbairn et al. (2010). We perform this kind of analysis for seven occultations observed by Fermi-LAT in 2008-2014, using the Fermi-LAT Solar System tools. The results are interpreted in terms of models with extended gamma-ray halo of 3C 279; first constraints on the size and the flux of the halo are reported.
Predicting maternal social loneliness by passive sensing with wearable devices

Background Maternal loneliness is associated with adverse physical and mental health outcomes for both the mother and her child. Detecting maternal loneliness non-invasively through wearable devices and passive sensing provides opportunities to prevent or reduce the impact of loneliness on the health and well-being of the mother and her child. Objective The aim of this study is to use objective health data collected passively by a wearable device to predict maternal (social) loneliness during pregnancy and the postpartum period based on and to identify the important objective physiological parameters in loneliness detection. Methods We conducted a longitudinal study using smartwatches to continuously collect physiological data from 31 women during pregnancy and the postpartum period. The participants completed the University of California, Los Angeles (UCLA) loneliness questionnaire in gestational week 36 and again at 12 weeks postpartum. Responses to this questionnaire and the background information of the participants were collected via our customized cross-platform mobile application. We leveraged participants smartwatch data from the 7 days before and the day of their completion of the UCLA questionnaire for loneliness prediction. We categorized the loneliness scores from the UCLA questionnaire as loneliness (scores >= 12) and non-loneliness (scores < 12). We developed decision tree and gradient boosting models to predict loneliness. We evaluated the models by using a leave-one-participant-out cross validation. Moreover, we discussed the importance of extracted health parameters in our models for loneliness prediction. Results The gradient boosting and decision tree models predicted maternal social loneliness with weighted F1 scores of 0.871 and 0.897, respectively. Our results also show that loneliness is highly associated with activity intensity, activity distribution during the day, resting heart rate (HR), and resting heart rate variability (HRV). Conclusion Our results show the potential benefit and feasibility of using passive sensing with a smartwatch to predict maternal loneliness. Our developed machine learning models achieved a high F1 score for loneliness prediction. We also show that intensity of activity, activity pattern, and resting HR and HRV are good predictors of loneliness. These results indicate the intervention opportunities made available by wearable devices and predictive models to improve maternal well-being by early detection of loneliness.
Galectin-3 is Associated with Heart Failure Incidence: A Meta-Analysis

Introduction: Heart failure (HF) is a leading cause of death worldwide. The global prevalence of heart failure is projected to increase rapidly in the coming decades, and significant attention has turned to improving biomarker-based risk prediction of incident HF. The aim of this paper was to qualitatively and quantitatively evaluate the evidence associating levels of galectin-3 with risk of incident HF. Methods: A review of PUBMED-indexed peer-reviewed literature was performed. Nine studies met inclusion criteria, and all nine had data eligible for conversion and pooling. A random-effects meta-analysis was performed using hazard ratios and 95% confidence intervals from a minimally adjusted model, a further adjusted model, and from subgroups within the further-adjusted model. Results: The minimally-adjusted model provided a HR of 1.97 (95% CI 1.74-2.23) when comparing the top quartile of log-gal-3 to the bottom quartile. The further-adjusted model provided a HR of 1.32 (95% CI 1.21-1.44) for the same comparison. The positive, significant association was conserved during sensitivity analysis. Conclusion: There is a significant positive association between levels of circulating galectin-3 and risk of incident heart failure. Given the complex mechanistic relationship between galectin-3 and cardiovascular pathophysiology, further investigation is recommended for possible implementation of galectin-3 into clinical risk prediction models.
TURBULENCE-INDUCED RELATIVE VELOCITY OF DUST PARTICLES. III. THE PROBABILITY DISTRIBUTION

Motivated by its important role in the collisional growth of dust particles in protoplanetary disks, we investigate the probability distribution function (PDF) of the relative velocity of inertial particles suspended in turbulent flows. Using the simulation from our previous work, we compute the relative velocity PDF as a function of the friction timescales, τp1 and τp2, of two particles of arbitrary sizes. The friction time of the particles included in the simulation ranges from 0. 1τη to 54TL, where τη and TL are the Kolmogorov time and the Lagrangian correlation time of the flow, respectively. The relative velocity PDF is generically non-Gaussian, exhibiting fat tails. For a fixed value of τp1, the PDF shape is the fattest for equal-size particles (τp2 = τp1), and becomes thinner at both τp2 < τp1 and τp2 > τp1. Defining f as the friction time ratio of the smaller particle to the larger one, we find that, at a given f in (1/2) ≲ f ≲ 1, the PDF fatness first increases with the friction time τp, h of the larger particle, peaks at τp, h ≃ τη, and then decreases as τp, h increases further. For 0 ⩽ f ≲ (1/4), the PDF becomes continuously thinner with increasing τp, h. The PDF is nearly Gaussian only if τp, h is sufficiently large (≫TL). These features are successfully explained by the Pan & Padoan model. Using our simulation data and some simplifying assumptions, we estimated the fractions of collisions resulting in sticking, bouncing, and fragmentation as a function of the dust size in protoplanetary disks, and argued that accounting for non-Gaussianity of the collision velocity may help further alleviate the bouncing barrier problem.
SemiFL: Semi-Supervised Federated Learning for Unlabeled Clients with Alternate Training

Federated Learning allows the training of machine learning models by using the computation and private data resources of many distributed clients. Most existing results on Federated Learning (FL) assume the clients have ground-truth labels. However, in many practical scenarios, clients may be unable to label task-specific data due to a lack of expertise or resource. We propose SemiFL to address the problem of combining communication-efficient FL such as FedAvg with Semi-Supervised Learning (SSL). In SemiFL, clients have completely unlabeled data and can train multiple local epochs to reduce communication costs, while the server has a small amount of labeled data. We provide a theoretical understanding of the success of data augmentation-based SSL methods to illustrate the bottleneck of a vanilla combination of communication-efficient FL with SSL. To address this issue, we propose alternate training to `fine-tune global model with labeled data' and `generate pseudo-labels with the global model. ' We conduct extensive experiments and demonstrate that our approach significantly improves the performance of a labeled server with unlabeled clients training with multiple local epochs. Moreover, our method outperforms many existing SSFL baselines and performs competitively with the state-of-the-art FL and SSL results.
Fine-tuned Language Models are Continual Learners

Recent work on large language models relies on the intuition that most natural language processing tasks can be described via natural language instructions and that models trained on these instructions show strong zero-shot performance on several standard datasets. However, these models even though impressive still perform poorly on a wide range of tasks outside of their respective training and evaluation sets.To address this limitation, we argue that a model should be able to keep extending its knowledge and abilities, without forgetting previous skills. In spite of the limited success of Continual Learning, we show that Fine-tuned Language Models can be continual learners.We empirically investigate the reason for this success and conclude that Continual Learning emerges from self-supervision pre-training. Our resulting model Continual-T0 (CT0) is able to learn 8 new diverse language generation tasks, while still maintaining good performance on previous tasks, spanning in total of 70 datasets. Finally, we show that CT0 is able to combine instructions in ways it was never trained for, demonstrating some level of instruction compositionality.
The hard-core model on planar lattices: the disk-packing problem and high-density phases

We study dense packings of disks and related Gibbs distributions representing high-density phases in the hard-core model on unit triangular, honeycomb and square lattices. The model is characterized by a Euclidean exclusion distance D > 0 and a value of fugacity u > 0 . We use the Pirogov-Sinai theory to study the Gibbs distributions for a general D when u is large: u > u 0 ( D ) . For inﬁnite sequences of values D we describe a complete high-density phase diagram: it exhibits a multitude of co-existing pure phases, and their number grows as O ( D 2 ) . For the remaining values of D , except for those with sliding, the number of co-existing pure phases is still of the form E ( D ) ≥ O ( D 2 ) ; however, the exact identiﬁcation of the pure phases requires an additional analysis. Such an analysis is performed for a number of typical examples, which involves computer-assisted proofs. Consequently, for all values D > 0 where sliding does not occur, we establish the existence of a phase transition. The crucial steps in the study are (i) the identiﬁcation of periodic ground states and (ii) the veriﬁcation of the Peierls bound. This is done by using connections with algebraic number theory. In particular, a complete list of so-called sliding values of D has been speciﬁed. As a by-product, we solve the disk-packing problem on the lattices under consideration. The number and structure of maximally-dense packings depend on the disk-diameter D , unlike the case of R 2 . All assertions have been proved rigorously [26, 27], some of the proofs are computer-assisted.
Exploring Localization for Self-supervised Fine-grained Contrastive Learning

Self-supervised contrastive learning has demonstrated great potential in learning visual representations. Despite their success in various downstream tasks such as image classification and object detection, self-supervised pre-training for fine-grained scenarios is not fully explored. We point out that current contrastive methods are prone to memorizing background/foreground texture and therefore have a limitation in localizing the foreground object. Analysis suggests that learning to extract discriminative texture information and localization are equally crucial for fine-grained self-supervised pre-training. Based on our findings, we introduce cross-view saliency alignment (CVSA), a contrastive learning framework that first crops and swaps saliency regions of images as a novel view generation and then guides the model to localize on foreground objects via a cross-view alignment loss. Extensive experiments on both small- and large-scale fine-grained classification benchmarks show that CVSA significantly improves the learned representation.
Local Function Complexity for Active Learning via Mixture of Gaussian Processes

Inhomogeneities in real-world data, e.g., due to changes in the observation noise level or variations in the structural complexity of the source function, pose a unique set of challenges for statistical inference. Accounting for them can greatly improve predictive power when physical resources or computation time is limited. In this paper, we draw on recent theoretical results on the estimation of local function complexity (LFC), derived from the domain of local polynomial smoothing (LPS), to establish a notion of local structural complexity, which is used to develop a model-agnostic active learning (AL) framework. Due to its reliance on pointwise estimates, the LPS model class is not robust and scalable concerning large input space dimensions that typically come along with real-world problems. Here, we derive and estimate the Gaussian process regression (GPR)-based analog of the LPS-based LFC and use it as a substitute in the above framework to make it robust and scalable. We assess the effectiveness of our LFC estimate in an AL application on a prototypical low-dimensional synthetic dataset, before taking on the challenging real-world task of reconstructing a quantum chemical force field for a small organic molecule and demonstrating state-of-the-art performance with a significantly reduced training demand.
Break the Wall Between Homophily and Heterophily for Graph Representation Learning

Homophily and heterophily are intrinsic properties of graphs that describe whether two linked nodes share similar properties. Although many Graph Neural Network (GNN) models have been proposed, it remains unclear how to design a model so that it can generalize well to the whole spectrum of homophily. This work addresses the challenge by identifying three graph features, including the ego node feature, the aggregated node feature, and the graph structure feature, that are essential for graph representation learning. It further proposes a new GNN model called OGNN (Omnipotent Graph Neural Network) that extracts all three graph features and adaptively fuses them to achieve generalizability across the whole spectrum of homophily. Extensive experiments on both synthetic and real datasets demonstrate the superiority (average rank 1.56) of our OGNN compared with state-of-the-art methods.
Joint Human Orientation-Activity Recognition Using WIFI Signals for Human-Machine Interaction

WiFi sensing is an important part of the new WiFi 802.11bf standard, which can detect motion and measure distances. In recent years, some machine learning methods have been proposed for human activity recognition from WiFi signals. However, to the best of our knowledge, none of these methods have explored orientation prediction of the user using WiFi signals. Orientation prediction is particularly critical for human-machine interaction in an environment with multiple smart devices. In this paper, we propose a data collection setup and machine learning models for joint human orientation and activity recognition using WiFi signals from a single access point (AP) or multiple APs. The results show feasibility of joint orientation-activity recognition in an indoor environment with a high accuracy.
THE GLOBAL EVOLUTION OF GIANT MOLECULAR CLOUDS. II. THE ROLE OF ACCRETION

We present virial models for the global evolution of giant molecular clouds (GMCs). Focusing on the presence of an accretion flow and accounting for the amount of mass, momentum, and energy supplied by accretion and star formation feedback, we are able to follow the growth, evolution, and dispersal of individual GMCs. Our model clouds reproduce the scaling relations observed in both galactic and extragalactic clouds. We find that accretion and star formation contribute roughly equal amounts of turbulent kinetic energy over the lifetime of the cloud. Clouds attain virial equilibrium and grow in such a way as to maintain roughly constant surface densities, with typical surface densities of order 50–200 M☉ pc−2, in good agreement with observations of GMCs in the Milky Way and nearby external galaxies. We find that as clouds grow, their velocity dispersion and radius must also increase, implying that the linewidth–size relation constitutes an age sequence. Lastly, we compare our models to observations of GMCs and associated young star clusters in the Large Magellanic Cloud and find good agreement between our model clouds and the observed relationship between H ii regions, young star clusters, and GMCs.