Miracle-dz/UniFAR-FTUs huggingface dataset:
A Dynamic Deep-learning Model for Generating a Magnetogram Sequence from an SDO/AIA EUV Image Sequence

The solar magnetic field dominates solar activities in the solar atmosphere, such as solar flares and coronal mass ejections (CMEs). The Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO) has been in operation from 2010, providing a full-disk photospheric magnetogram. However, with a single view of observation, SDO/HMI cannot provide a global view of the Sun at the same time, so the farside of the Sun is blind to us. The Solar Terrestrial Relations Observatory (STEREO) provides two different views of the Sun with complementary viewing angles relative to SDO/HMI. However, it did not carry a magnetograph, but an extreme-ultraviolet (EUV) imager. Fortunately, deep learning has been proved to generate a solar farside magnetogram from STEREO farside EUV observation. Although a single generated magnetogram is morphologically very similar to ground truth, the sequence of the generated magnetogram has noticeable magnetic field fluctuation, which cannot be ignored when it is displayed as a time series, especially at an active region. This fluctuation is represented by sudden magnetic polarity reversal and drifting of magnetic field distribution. To mitigate this problem, a novel dynamic deep-learning model by integrating a convolutional gated recurrent units (convGRU) model into a pix2pix baseline is proposed in this paper. It can generate a sequence of a magnetogram with smooth transition among consecutive magnetograms by exploring spatio-temporal information of an input EUV image sequence. From both quantitative and qualitative comparisons, the proposed model can generate a magnetogram sequence more close to real observation.
A Massive Molecular Outflow in the Dense Dust Core AGAL G337.916-00.477

Massive molecular outflows erupting from high-mass young stellar objects (YSOs) provide important clues to understanding the mechanism of high-mass star formation. Based on new CO J = 3–2 and J = 1–0 observations using the Atacama Submillimeter Telescope Experiment (ASTE) and Mopra telescope facilities, we discovered a massive bipolar outflow associated with the dense dust core AGAL G337.916-00.477 (AGAL337.9-S), located 3.48 kpc from the Sun. The outflow lobes have extensions of less than 1 pc—and thus were not fully resolved in the angular resolutions of ASTE and Mopra—and masses of ∼50 M⊙. The maximum velocities of the outflow lobes are as high as 36–40 . Our analysis of the infrared and submillimeter data indicates that AGAL337.9-S is in an early evolutionary stage of high-mass star formation, having the total far-infrared luminosity of . We also found that another dust core, AGAL G337.922-00.456 (AGAL337.9-N), located 2′ north of AGAL337.9-S, is a high-mass YSO in an earlier evolutionary stage than AGAL337.9-S, as it is less bright in the mid-infrared than AGAL337.9-S.
CHANDRA AND XMM-NEWTON OBSERVATIONS OF THE MERGING CLUSTER OF GALAXIES PLCK G036.7+14.9

We present Chandra and XMM-Newton observations of PLCK G036.7+14.9 from the Chandra–Planck Legacy Program. The high resolution X-ray observations reveal two close (∼72″ = 193 kpc in projection) subclusters, G036N and G036S, which were not resolved by previous ROSAT, optical, or recent Planck observations. We perform detailed imaging and spectral analyses and use a simplified model to study the kinematics of this system. The basic picture is that PLCK G036.7+14.9 is undergoing a major merger (mass ratio close to unity) between the two massive subclusters, with the merger largely along the line of sight (∼80° between the merger axis and the plane of the sky from the simplified model) and probably at an early stage (less than ∼0.4–0.7 Gyr since the merger began). G036N hosts a small (∼27 kpc), moderate cool core (cooling time t cool ∼ 2.6 ? > –4.7 Gyr), while G036S has at most a very weak cool core ( t cool ∼ 5.7 ? > –10.3 Gyr) in the central ∼40 kpc region. The difference in core cooling times is unlikely to be caused by the ongoing merger disrupting a pre-existing cool core in G036S. G036N also hosts an unresolved radio source in the center, which may be heating the gas if the radio source is extended. The total mass of the whole cluster determined from XMM-Newton is ∼ ( 5.9 − 8.0 ) × 10 14 M ⊙ ? > , and is ∼ ( 6.7 − 9.9 ) × 10 14 M ⊙ ? > from Chandra. The Planck derived mass, ∼ ( 5.1 − 6.0 ) × 10 14 M ⊙ ? > , is higher than the X-ray measured mass of either subcluster, but is lower than the X-ray measured mass of the whole cluster, due to the fact that Planck does not resolve PLCK G036.7+14.9 into subclusters and interprets it as a single cluster. This mass discrepancy could induce significant bias to the mass function if such previously unresolved systems are common in the Planck cluster sample. High resolution X-ray observations are necessary to identify the fraction of such systems and correct such a bias for the purpose of precision cosmological studies.
Instabilities in Associative Memory Model with Synaptic Depression and Switching Phenomena among Attractors

We investigated how the stability of macroscopic states in the associative memory model is affected by synaptic depression. To this model, we applied the dynamical mean-field theory, which has recently been developed in stochastic neural network models with synaptic depression. By introducing a sublattice method, we derived macroscopic equations for firing state variables and depression variables. By using the macroscopic equations, we obtained the phase diagram when the strength of synaptic depression and the correlation level among stored patterns were changed. We found that there is an unstable region in which both the memory state and mixed state cannot be stable and that various switching phenomena can occur in this region.
DVGAN: Stabilize Wasserstein GAN training for time-domain Gravitational Wave physics

Simulating time-domain observations of gravitational wave (GW) detector environments will allow for a better understanding of GW sources, augment datasets for GW signal detection and help in characterizing the noise of the detectors, leading to better physics. This paper presents a novel approach to simulating fixed-length time-domain signals using a three-player Wasserstein Generative Adversarial Network (WGAN), called DVGAN, that includes an auxiliary discriminator that discriminates on the derivatives of input signals. An ablation study is used to compare the effects of including adversarial feedback from an auxiliary derivative discriminator with a vanilla two-player WGAN. We show that discriminating on derivatives can stabilize the learning of GAN components on 1D continuous signals during their training phase. This results in smoother generated signals that are less distinguishable from real samples and better capture the distributions of the training data. DVGAN is also used to simulate real transient noise events captured in the advanced LIGO GW detector.
Collaboration-Aware Graph Convolutional Network for Recommender Systems

Graph Neural Networks (GNNs) have been successfully adopted in recommender systems by virtue of the message-passing that implicitly captures collaborative effect. Nevertheless, most of the existing message-passing mechanisms for recommendation are directly inherited from GNNs without scrutinizing whether the captured collaborative effect would benefit the prediction of user preferences. In this paper, we first analyze how message-passing captures the collaborative effect and propose a recommendation-oriented topological metric, Common Interacted Ratio (CIR), which measures the level of interaction between a specific neighbor of a node with the rest of its neighbors. After demonstrating the benefits of leveraging collaborations from neighbors with higher CIR, we propose a recommendation-tailored GNN, Collaboration-Aware Graph Convolutional Network (CAGCN), that goes beyond 1-Weisfeiler-Lehman(1-WL) test in distinguishing non-bipartite-subgraph-isomorphic graphs. Experiments on six benchmark datasets show that the best CAGCN variant outperforms the most representative GNN-based recommendation model, LightGCN, by nearly 10% in Recall@20 and also achieves around 80% speedup. Our code/supplementary is at https://github.com/YuWVandy/CAGCN.
PRINCIPAL COMPONENT ANALYSIS OF COMPUTED EMISSION LINES FROM PROTOSTELLAR JETS

A very important issue concerning protostellar jets is the mechanism behind their formation. Obtaining information on the region at the base of a jet can shed light on the subject, and some years ago this was done through a search for a rotational signature in the jet line spectrum. The existence of such signatures, however, remains controversial. In order to contribute to the clarification of this issue, in this paper we show that principal component analysis (PCA) can potentially help to distinguish between rotation and precession effects in protostellar jet images. This method reduces the dimensions of the data, facilitating the efficient extraction of information from large data sets such as those arising from integral field spectroscopy. PCA transforms the system of correlated coordinates into a system of uncorrelated coordinates, the eigenvectors, ordered by principal components of decreasing variance. The projection of the data on these coordinates produces images called tomograms, while eigenvectors can be displayed as eigenspectra. The combined analysis of both can allow the identification of patterns correlated to a particular physical property that would otherwise remain hidden, and can help to separate the effects of physically uncorrelated phenomena in the data. These are, for example, rotation and precession in the kinematics of a stellar jet. In order to show the potential of PCA analysis, we apply it to synthetic spectro-imaging datacubes generated as an output of numerical simulations of protostellar jets. In this way we generate a benchmark with which a PCA diagnostics of real observations can be confronted. Using the computed emission line profiles for [O i]λ6300 and [S ii]λ6716, we recover and analyze the effects of rotation and precession in tomograms generated by PCA. We show that different combinations of the eigenvectors can be used to enhance and to identify the rotation features present in the data. Our results indicate that PCA can be useful for disentangling rotation from precession in jets with an inclination of the jet with respect to the plane of the sky as high as 45 ° ? > . We have been able to recover the initially imposed rotation jet profile for models at a moderate inclination angle ( φ ≤ 15 ° ? > ) and without precession.
Learning Dissipative Dynamics in Chaotic Systems

Chaotic systems are notoriously challenging to predict because of their sensitivity to perturbations and errors due to time stepping. Despite this unpredictable behavior, for many dissipative systems the statistics of the long term trajectories are governed by an invariant measure supported on a set, known as the global attractor; for many problems this set is finite dimensional, even if the state space is infinite dimensional. For Markovian systems, the statistical properties of long-term trajectories are uniquely determined by the solution operator that maps the evolution of the system over arbitrary positive time increments. In this work, we propose a machine learning framework to learn the underlying solution operator for dissipative chaotic systems, showing that the resulting learned operator accurately captures short-time trajectories and long-time statistical behavior. Using this framework, we are able to predict various statistics of the invariant measure for the turbulent Kolmogorov Flow dynamics with Reynolds numbers up to 5000.
The energy and time saving coordinated control methods of CO2, VOCs, and PM2.5 in office buildings

Indoor air pollution is complex and serious. In fact, an on-site investigation of an office building revealed that the concentration of three typical pollutants (CO2, VOCs, PM2.5) exceeded the Chinese standard. To identify a better control method to achieve good indoor air quality, an orthogonal experiment was carried out in an environmental chamber to compare the control time and energy consumption of four control methods (purifier+ and window+, purifier+ and window-, purified fresh air 240 m3/h and purified fresh air 400 m3/h) to meet the standard established for pollutants. The purifier+ and window+ method was found to be more effective in most conditions, with a control time reduced by 8.06% and energy consumption reduced by 11.91% compared with the traditional control method of purified fresh air 240 m3/h. This research highlights the optimal control strategy for the air quality in office buildings under different pollution conditions.
An agglomeration-based discontinuous Galerkin method for compressible flows

This thesis investigates the flexibility associated to Discontinuous Galerkin (DG) discretization on very general meshes obtained by means of agglomeration techniques. The work begins with a brief overview of the main tools that have been extended or specifically developed to deal with arbitrarily shaped elements in the DG context. Then two different implementations of the BRMPS scheme introduced by Bassi, Rebay, Mariotti, Pedinotti and Savini in [16] for the DG discretization of the Laplace operator on arbitrarily shaped elements have been presented. The validation of the scheme on a Poisson problem shows that the discrete polynomial space preserves optimal convergence properties. The discretization of the second order differential operator has been directly extended to the Navier-Stokes equations and the Reynolds Averaged Navier-Stokes (RANS) equations coupled with the k-w turbulence model of Wilcox [54]. In this regard, an implicit time integration strategy has been considered and assessed on classical validation test cases for the compressible f uid dynamics. Then a simple alternative approach to high-order mesh generation is presented. Indeed, once a standard fine grid able to provide an accurate domain discretization has been produced by means of standard low-order grid generation tools, a computational mesh suitable for the desired accuracy and computationally affordable can be obtained via agglomeration while keeping the boundary resolution of the fine grid. The effectiveness of this approach in representing the geometry of the domain is numerically assessed both on a Poisson model problem and on challenging inviscid and viscous test cases. Finally, the freedom in simply defining the topology of agglomerated meshes leads to a nonstandard approach to h-adaptivity that exploits adaptive agglomeration coarsening of a properly fine underlying grid. The effectiveness of this approach has been assessed on test cases involving both error-based and fl ow feature-based simple estimators.
Reduction in grey matter atrophy in patients with relapsing multiple sclerosis following treatment with cladribine tablets

Measures of atrophy in the whole brain can be used to reliably assess treatment effect in clinical trials of patients with multiple sclerosis (MS). Trials assessing the effect of treatment on grey matter (GM) and white matter (WM) atrophy are very informative, but hindered by technical limitations. This study aimed to measure GM and WM volume changes, using a robust longitudinal method, in patients with relapsing MS randomized to cladribine tablets 3.5 mg/kg or placebo in the CLARITY study.
Fish and Fish-Based Products for Nutrition and Health in the First 1000 Days: A Systematic Review of the Evidence from Low and Middle-Income Countries

ABSTRACT Fish provide essential nutrients for the critical window of growth and development in the first 1000 d of life and are thus an attractive option for inclusion in nutrition-sensitive and nutrition-specific programming. We conducted a systematic review of the evidence for fish and fish-based products for nutrition and health outcomes during the first 1000 d of life in low- and middle-income countries, searching the peer-reviewed and gray literature from 1999 to 2020. Databases included PubMed, Web of Science, Embase, ProQuest, and the Clinical Trials repository. Our search returned 1135 articles, 39 of which met the inclusion criteria. All studies were dual evaluated for risk of bias. Of the included studies, 18 measured maternal health and nutrition outcomes and 24 measured infant/child outcomes (3 measured both). Our search uncovered 10 impact evaluations, all of which measured consumption of fish or fish-based complementary food products in children aged 6–24 mo. We did not find strong evidence for fish consumption in children improving child growth from the impact evaluations; however, the studies were highly heterogeneous in their design and likely underpowered to detect an effect. Results from observational studies were mixed but provided evidence that adding fish to maternal and child diets is associated with improved nutrition outcomes, such as reducing the risk of anemia and improving vitamin D status. Given the nutrient richness of fish and the fact that production is often more environmentally friendly as compared with other animal source foods, more robust evidence is needed on the role of fish consumption in nutrition interventions to inform policy and programming recommendations in low- and middle-income countries.
A short survey of Stein's method

Stein's method is a powerful technique for proving central limit theorems in probability theory when more straightforward approaches cannot be implemented easily. This article begins with a survey of the historical development of Stein's method and some recent advances. This is followed by a description of a "general purpose" variant of Stein's method that may be called the generalized perturbative approach, and an application of this method to minimal spanning trees. The article concludes with the descriptions of some well known open problems that may possibly be solved by the perturbative approach or some other variant of Stein's method.
Breaking the disc-halo degeneracy in NGC 1291 using hydrodynamic simulations

Abstract We present a pilot study on the nearby massive galaxy NGC 1291, in which we aim to constrain the dark matter in the inner regions, by obtaining a dynamical determination of the disc mass-to-light ratio (M/L). To this aim, we model the bar-induced dust lanes in the galaxy, using hydrodynamic gas response simulations. The models have three free parameters, the M/L of the disc, the bar pattern speed and the disc height function. We explore the parameter space to find the best fit models, i.e. those in which the morphology of the shocks in the gas simulations matches the observed dust lanes. The best-fit models suggest that the M/L of NGC 1291 agrees with that predicted by stellar population synthesis models in the near-infrared (≈ 0.6 M ⊙/L ⊙), which leads to a borderline maximum disc for this galaxy. The bar rotates fast, with corotation radius ⩽ 1.4 times the bar length. Additionally, we find that the height function has a significant effect on the results, and can bias them towards lower or higher M/L.
Monitoring genetic diversity with new indicators applied to an alpine freshwater top predator

Genetic diversity is the basis for population adaptation and long‐term survival, yet rarely considered in biodiversity monitoring. One key issue is the need for useful and straightforward indicators of genetic diversity. We monitored genetic diversity over 40 years (1970–2010) in metapopulations of brown trout (Salmo trutta) inhabiting 27 small mountain lakes representing 10 lake systems in central Sweden using >1200 fish per time point. We tested six newly proposed indicators; three were designed for broad, international use in the UN Convention on Biological Diversity (CBD) and are currently applied in several countries. The other three were recently elaborated for national use by a Swedish science‐management effort and applied for the first time here. The Swedish indicators use molecular genetic data to monitor genetic diversity within and between populations (indicators ΔH and ΔFST, respectively) and assess the effective population size (Ne‐indicator). We identified 29 genetically distinct populations, all retained over time. Twelve of the 27 lakes harboured more than one population indicating that brown trout biodiversity hidden as cryptic, sympatric populations are more common than recognized. The Ne indicator showed values below the threshold (Ne ≤ 500) in 20 populations with five showing Ne < 100. Statistically significant genetic diversity reductions occurred in several populations. Metapopulation structure appears to buffer against diversity loss; applying the indicators to metapopulations suggest mostly acceptable rates of change in all but one system. The CBD indicators agreed with the Swedish ones but provided less detail. All these indicators are appropriate for managers to initiate monitoring of genetic biodiversity.
Reprograming of proteasomal degradation by branched chain amino acid metabolism

Branched‐chain amino acid (BCAA) metabolism is a central hub for energy production and regulation of numerous physiological processes. Controversially, both increased and decreased levels of BCAAs are associated with longevity. Using genetics and multi‐omics analyses in Caenorhabditis elegans, we identified adaptive regulation of the ubiquitin‐proteasome system (UPS) in response to defective BCAA catabolic reactions after the initial transamination step. Worms with impaired BCAA metabolism show a slower turnover of a GFP‐based proteasome substrate, which is suppressed by loss‐of‐function of the first BCAA catabolic enzyme, the branched‐chain aminotransferase BCAT‐1. The exogenous supply of BCAA‐derived carboxylic acids, which are known to accumulate in the body fluid of patients with BCAA metabolic disorders, is sufficient to regulate the UPS. The link between BCAA intermediates and UPS function presented here sheds light on the unexplained role of BCAAs in the aging process and opens future possibilities for therapeutic interventions.
Real-Time 3D Microwave Medical Imaging With Enhanced Variational Born Iterative Method

In this paper, we present a new variational Born iterative method (VBIM) for real-time microwave imaging (MWI) applications. The S-parameter volume integral equation and waveport vector Green’s function are implemented to utilize the measured signal of the MWI system. Meanwhile, the real and imaginary separation (RIS) approach is used at each iterative step to simultaneously reconstruct the dielectric permittivity and conductivity of unknown objects. Compared with the Born iterative method and distorted Born iterative method, VBIM requires less computational time to reach the convergence threshold. The graphics processing unit based acceleration technique is implemented for real-time imaging. To demonstrate the efficiency and accuracy of this VBIM-RIS method, synthetic analysis of a complex multi-layer spherical phantom is first conducted. Then, the algorithm is tested with measured data using our new MWI system prototype. Finally, a synthetic brain-tumor phantom model under a thermal therapy procedure is monitored to exemplify the real-time imaging with about 5 seconds per reconstruction frame.
Transgressions and Holographic Conformal Anomalies for Chern-Simons Gravities

I present two calculations of the holographic Weyl anomalies induced by Chern-Simons gravity theories alternative to the ones presented in the literature. The calculations presented here rest on the extension from Chern-Simons to Transgression forms as lagrangians, motivated by gauge invariance, which automatically yields the boundary terms suitable to regularize the theory. The procedure followed here sheds light in the structure of Chern-Simons gravities and their regularization.