Miracle-dz/UniFAR-FTUs huggingface dataset:
X-ray embedded stars as driving sources of outflow-driven turbulence in OMC1-S

Outflows arising from very young stars affect their surroundings and influence the star formation in the parental core. Multiple molecular outflows and Herbig-Haro (HH) objects have been observed in Orion, many of them originating from the embedded massive star-forming region known as OMC1-S. The detection of the outflow driving sources is commonly difficult, because they are still hidden behind large extinction, preventing their direct observation at optical and even near and mid-IR wavelengths. With the aim of improving the identification of the driving sources of the multiple outflows detected in OMC1-S, we used the catalog provided by deep X-ray observations, which have unveiled the very embedded population of pre-main sequence stars. We compared the position of stars observed by the Chandra Orion Ultra Deep project (COUP) in OMC1-S with the morphology of the molecular outflows and the directions of measured proper motions of HH optical objects. We find that 6 out of 7 molecular outflows reported in OMC1-S (detection rate of 86 %) have an extincted X-ray COUP star located at the expected position of the driving source. In several cases, X-rays detected the possible driving sources for the first time. This clustered embedded population revealed by Chandra is very young, with an estimated average age of few 10^{5} yr. It is also likely responsible for the multiple HH objects, which are the optical correspondence of flows arising from the cloud. We show that the molecular outflows driven by the members of the OMC1-S cluster can account for the observed turbulence at core-scales and regulate the star formation efficiency. We discuss the effects of outflow feedback in the formation of massive stars, concluding that the injected turbulence in OMC1-S is compatible with a competitive accretion scenario.
The covariant formulation of f(T) gravity

We show that the well-known problem of frame dependence and violation of local Lorentz invariance in the usual formulation of f(T) gravity is a consequence of neglecting the role of spin connection. We re-formulate f(T) gravity starting from, instead of the ‘pure tetrad’ teleparallel gravity, the covariant teleparallel gravity, using both the tetrad and the spin connection as dynamical variables, resulting in a fully covariant, consistent, and frame-independent version of f(T) gravity, which does not suffer from the notorious problems of the usual, pure tetrad, f(T) theory. We present the method to extract solutions for the most physically important cases, such as the Minkowski, the Friedmann–Robertson–Walker (FRW) and the spherically symmetric ones. We show that in covariant f(T) gravity we are allowed to use an arbitrary tetrad in an arbitrary coordinate system along with the corresponding spin connection, resulting always in the same physically relevant field equations.
THE COSMIC EVOLUTION OF FAINT SATELLITE GALAXIES AS A TEST OF GALAXY FORMATION AND THE NATURE OF DARK MATTER

The standard cosmological model based on cold dark matter (CDM) predicts a large number of subhalos for each galaxy-size halo. Matching the subhalos to the observed properties of luminous satellites of galaxies in the local universe poses a significant challenge to our understanding of the astrophysics of galaxy formation. We show that the cosmic evolution and host mass dependence of the luminosity function of satellites provide a powerful new diagnostic to disentangle astrophysical effects from variations in the underlying dark matter mass function. We illustrate this by comparing recent observations of satellites between redshifts 0.1 < z < 0.8 based on Hubble Space Telescope images, with predictions from three different state-of-the-art semi-analytic models applied to CDM power spectra, with one model also applied to a warm dark matter (WDM) spectrum. We find that even though CDM models provide a reasonable fit to the local luminosity function of satellites around galaxies comparable to the Milky Way, they do not reproduce the data as well for different redshifts and host galaxy stellar masses, indicating that further improvements in the description of star formation are likely needed. The WDM model matches the observed mass dependence and redshift evolution of satellite galaxies more closely, indicating that a modification of the underlying power spectrum may offer an alternative solution to this tension. We conclude by presenting predictions for the color distribution of satellite galaxies to demonstrate how future observations will be able to further distinguish between these models and to help constrain baryonic and non-baryonic physics.
INTERMETALLICS AND THERMOPHYSICAL PROPERTIES OF LIQUID Ag-La AND Ag-Pr ALLOYS

The stable intermetallic compounds for Ag-La and Ag-Pr liquid alloys at 1346K have been predicted. The intermetallic compound Ag5La exists in Ag-La liquid alloy, while Ag2Pr is the intermetallic compound that is predominant in Ag-Pr liquid alloy at 1346K. Some thermophysical properties of liquid Ag-La and Ag-Pr alloys have also been studied at 1346K throughout the concentration range using quantites obtained from the quasi chemical studies of liquid binary alloys. The properties studied include diffusivities, viscosities, surface tension and surface concentration. This study shows that the thermophysical properties of these alloys exhibit similar behavioural trends. Diffusivity values are high within the region of concentrations where compound formation is more prominent for both alloys. A fitting parameter of m = −0.1 is predicted for the viscosity values of both alloys.
Classification of multispectral data by joint supervised-unsupervised learning

An important problem in pattern recognition is the effect of small design sample size on classification performance. When the ratio of the number of training samples to the number of feature measurements is small, the estimates of the discriminant functions are not accurate and therefore the classification results might not be satisfactory. This problem is becoming more and more important in remote sensing, as the number of available spectral bands is becoming greater and greater. In order to utilize fully the information contained in the high dimensional data, training samples are needed from all of the classes of interest. A large number of classes of interest, and a large number of features to be used, necessitate a large number of training samples. Such training samples are usually very expensive and time consuming to acquire. In this thesis, we study the use of unlabeled samples, that are usually available in large numbers and with no extra effort, in reducing the small sample size problems. It is shown that by adding the unlabeled samples to the classifier design process, better estimates for the discriminant functions can be obtained. Therefore, the peaking phenomenon that is observed in the performance versus dimensionality curves, can be mitigated. Bounds on the expected amount of improvement in the classification performance are derived for the case of two multivariate Gaussian classes with a known common covariance matrix. These bounds, explicitly show the relationship between dimensionality and samples size for the case when parameters are estimated by simultaneously using training and unlabeled samples. A semi-parametric method for estimating the parameters of the class density functions, that uses both training and unlabeled samples, is proposed, and its parallel implementation is discussed. The problem of density model selection for classification is studied. An algorithm based on backtrack search strategy is presented for generating candidate models for the density functions. The candidate models are evaluated by several criteria that are based on both training and unlabeled samples.
High-fidelity Radio Astronomical Polarimetry Using a Millisecond Pulsar as a Polarized Reference Source

A new method of polarimetric calibration is presented in which the instrumental response is derived from regular observations of PSR J0437-4715 based on the assumption that the mean polarized emission from this millisecond pulsar remains constant over time. The technique is applicable to any experiment in which high-fidelity polarimetry is required over long timescales; it is demonstrated by calibrating 7.2 years of high-precision timing observations of PSR J1022+1001 made at the Parkes Observatory. Application of the new technique followed by arrival time estimation using matrix template matching yields post-fit residuals with an uncertainty-weighted standard deviation of 880 ns, two times smaller than that of arrival time residuals obtained via conventional methods of calibration and arrival time estimation. The precision achieved by this experiment yields the first significant measurements of the secular variation of the projected semimajor axis, the precession of periastron, and the Shapiro delay; it also places PSR J1022+1001 among the 10 best pulsars regularly observed as part of the Parkes Pulsar Timing Array (PPTA) project. It is shown that the timing accuracy of a large fraction of the pulsars in the PPTA is currently limited by the systematic timing error due to instrumental polarization artifacts. More importantly, long-termmore » variations of systematic error are correlated between different pulsars, which adversely affects the primary objectives of any pulsar timing array experiment. These limitations may be overcome by adopting the techniques presented in this work, which relax the demand for instrumental polarization purity and thereby have the potential to reduce the development cost of next-generation telescopes such as the Square Kilometre Array. « less
ELECTRON DENSITY OF ACTIVE REGION OUTFLOWS MEASURED BY THE EUV IMAGING SPECTROMETER ON BOARD HINODE

In order to better understand the nature of active region outflows, the electron density was measured by using a density-sensitive line pair, Fe xiv 264.78 Å/274.20 Å. Because coronal line profiles of the outflow region are composed of a major component with a Doppler shift of ≤ 10 km s - 1 ? > and a minor component (enhanced blue wing, EBW) blueshifted by up to 100 km s - 1 ? > , we extracted EBW from the line profiles through double-Gaussian fitting. We tried applying the simultaneous fitting to those two Fe xiv lines with several physical restrictions. Electron density for both components ( n Major ? > and n EBW ? > , respectively) was calculated by referring to the theoretical intensity ratio as a function of electron density as per the CHIANTI database. We studied six locations in the outflow regions around NOAA AR10978. The average electron density was n Major = 10 9.16 ± 0.16 cm − 3 ? > and n EBW = 10 8.74 ± 0.29 cm − 3 ? > . The magnitude relationship between n Major ? > and n EBW ? > was the opposite in the eastern and western outflow regions. The column depth was also calculated for each component, which leads to the result that the outflows possess only a small fraction (∼0.1) in the eastern region, whereas they dominate over the major component in the line profiles by a factor of five in the western region. When taking into account the extended coronal structures, the western region can be thought to represent the mass leakage. In contrast, we suggest a possibility that the eastern region actually contributes to mass supply to coronal loops.
Is Sextans dwarf galaxy in a scalar field dark matter halo?

The Bose-Einstein condensate/scalar field dark matter model, considers that the dark matter is composed by spinless-ultra-light particles which can be described by a scalar field. This model is an alternative model to the Λ-cold dark matter paradigm, and therefore should be studied at galactic and cosmological scales. Dwarf spheroidal galaxies have been very useful when studying any dark matter theory, because the dark matter dominates their dynamics. In this paper we study the Sextans dwarf spheroidal galaxy, embedded in a scalar field dark matter halo. We explore how the dissolution time-scale of the stellar substructures in Sextans, constrain the mass, and the self-interacting parameter of the scalar field dark matter boson. We find that for masses in the range (0.12< mϕ<8) ×10-22 eV, scalar field dark halos without self-interaction would have cores large enough to explain the longevity of the stellar substructures in Sextans, and small enough mass to be compatible with dynamical limits. If the self-interacting parameter is distinct to zero, then the mass of the boson could be as high as mϕ≈2×10-21 eV, but it would correspond to an unrealistic low mass for the Sextans dark matter halo . Therefore, the Sextans dwarf galaxy could be embedded in a scalar field/BEC dark matter halo with a preferred self-interacting parameter equal to zero.
RKKY coupling in graphene

We study the carrier-mediated exchange interaction, the so-called RKKY coupling, between two magnetic moments in graphene using exact diagonalization on the honeycomb lattice. By using the tight-binding nearest neighbor band structure of graphene we avoid the use of a momentum cut-off which plagues results in the Dirac continuum model formulation. We extract both the short and long impurity-impurity distance behavior and show several corrections to earlier long distance results. In the bulk the RKKY coupling is proportional to $1/|{\bf R}|^3$ and displays $(1+\cos(2{\bf k}_D\cdot {\bf R})$-type oscillations. A-A sublattice coupling is always ferromagnetic whereas A-B subattice coupling is always antiferromagnetic and three times as large. We also study the effect of edges in zigzag graphene nanoribbons (ZGNRs) and find that for impurities on the edge, the RKKY coupling decays exponentially because of the localized zero energy edge states. For impurities inside a ZGNR the bulk characteristics are quickly regained.
Signature of primordial non-Gaussianity on the matter power spectrum

Employing the perturbative treatment of gravitational clustering, we discuss possible effects of primordial non-Gaussianity on the matter power spectrum. As gravitational clustering develops, the coupling between different Fourier modes of density perturbations becomes important and the primordial non-Gaussianity which intrinsically possesses a nontrivial mode correlation can affect the late-time evolution of the power spectrum. We quantitatively estimate the non-Gaussian effect on the power spectrum from the perturbation theory. The potential impact on the cosmological parameter estimation using the power spectrum are investigated based on the Fisher-matrix formalism. In addition, on the basis of the local biasing prescription, non-Gaussian effects on the galaxy power spectrum are considered, showing that the scale-dependent biasing arises from a local-type primordial non-Gaussianity. On the other hand, an equilateral-type non-Gaussianity does not induce such scale dependence because of weaker mode correlations between small and large Fourier modes.
How Does Value Distribution in Distributional Reinforcement Learning Help Optimization?

We consider the problem of learning a set of probability distributions from the Bellman dynamics in distributional reinforcement learning~(RL) that learns the whole return distribution compared with only its expectation in classical RL. Despite its success to obtain superior performance, we still have a poor understanding of how the value distribution in distributional RL works. In this study, we analyze the optimization benefits of distributional RL by leverage of additional value distribution information over classical RL in the Neural Fitted Z-Iteration~(Neural FZI) framework. To begin with, we demonstrate that the distribution loss of distributional RL has desirable smoothness characteristics and hence enjoys stable gradients, which is in line with its tendency to promote optimization stability. Furthermore, the acceleration effect of distributional RL is revealed by decomposing the return distribution. It turns out that distributional RL can perform favorably if the value distribution approximation is appropriate, measured by the variance of gradient estimates in each environment for any specific distributional RL algorithm. Rigorous experiments validate the stable optimization behaviors of distributional RL, contributing to its acceleration effects compared to classical RL. The findings of our research illuminate how the value distribution in distributional RL algorithms helps the optimization.
The Upsilon spectrum and the determination of the lattice spacing from lattice QCD including charm quarks in the sea

eld congurations from the MILC collaboration. Using the 2 S 1S splitting to determine the lattice spacing, we are able to obtain the 1P 1S splitting to 1.4% and the 3S 1S splitting to 2.4%. Our improved result for M() M( b) is 70(9) MeV and we predict M( 0 ) M( 0) = 35(3) MeV. We also calculate ;K and s correlators using the Highly Improved Staggered Quark action and perform a chiral and continuum extrapolation to give values for M s (0.6893(12) GeV) and f s (0.1819(5) GeV) that allow us to tune the strange quark mass as well as providing an independent and consistent determination of the lattice spacing. Combining the NRQCD and HISQ analyses gives mb=ms = 54.7(2.5) and a value for the heavy quark potential parameter of r1 = 0.3209(26) fm.
Neighborhood Gradient Clustering: An Efficient Decentralized Learning Method for Non-IID Data Distributions

Decentralized learning over distributed datasets can have significantly different data distributions across the agents. The current state-of-the-art decentralized algorithms mostly assume the data distributions to be Independent and Identically Distributed. This paper focuses on improving decentralized learning over non-IID data. We propose \textit{Neighborhood Gradient Clustering (NGC)}, a novel decentralized learning algorithm that modifies the local gradients of each agent using self- and cross-gradient information. Cross-gradients for a pair of neighboring agents are the derivatives of the model parameters of an agent with respect to the dataset of the other agent. In particular, the proposed method replaces the local gradients of the model with the weighted mean of the self-gradients, model-variant cross-gradients (derivatives of the neighbors' parameters with respect to the local dataset), and data-variant cross-gradients (derivatives of the local model with respect to its neighbors' datasets). The data-variant cross-gradients are aggregated through an additional communication round without breaking the privacy constraints. Further, we present \textit{CompNGC}, a compressed version of \textit{NGC} that reduces the communication overhead by $32 \times$. We theoretically analyze the convergence rate of the proposed algorithm and demonstrate its efficiency over non-IID data sampled from {various vision and language} datasets trained. Our experiments demonstrate that \textit{NGC} and \textit{CompNGC} outperform (by $0-6\%$) the existing SoTA decentralized learning algorithm over non-IID data with significantly less compute and memory requirements. Further, our experiments show that the model-variant cross-gradient information available locally at each agent can improve the performance over non-IID data by $1-35\%$ without additional communication cost.
Stop Wasting My Time! Saving Days of ImageNet and BERT Training with Latest Weight Averaging

Training vision or language models on large datasets can take days, if not weeks. We show that averaging the weights of the k latest checkpoints, each collected at the end of an epoch, can speed up the training progression in terms of loss and accuracy by dozens of epochs, corresponding to time savings up to ~68 and ~30 GPU hours when training a ResNet50 on ImageNet and RoBERTa-Base model on WikiText-103, respectively. We also provide the code and model checkpoint trajectory to reproduce the results and facilitate research on reusing historical weights for faster convergence.
The Role of Filamentary Structures in the Formation of Two Dense Cores, L1544 and L694-2

We present mapping results of two prestellar cores, L1544 and L694-2, embedded in filamentary clouds in C18O (3–2), 13CO (3–2), 12CO (3–2), HCO+ (4–3), and H13CO+ (4–3) lines with the James Clerk Maxwell Telescope to examine the role of the filamentary structures in the formation of dense cores in the clouds, with new distance estimates for L1544 ( 175−3+4 pc) and L694-2 ( 203−7+6 pc). From these observations, we found that the nonthermal velocity dispersion of two prestellar cores and their surrounding clouds is smaller than or comparable to the sound speed. This may indicate that the turbulence has already been dissipated for both filaments and cores during their formation time. We also found a λ/4 shift between the periodic oscillations in the velocity and the column density distributions, implying the possible presence of gravitational core-forming flow motion along the axis of the filament. The mass accretion rates due to these flow motions are estimated to be 2–3 M ⊙ Myr−1, being comparable to that for Serpens cloud but much smaller than those for the Hub filaments, cluster, or high-mass forming filaments by 1 or 2 orders of magnitude. From this study, we suggest that the filaments in our targets might be formed from the shock compression of colliding clouds, and then the cores are formed by gravitational fragmentation of the filaments to evolve to the prestellar stage. We conclude that the filamentary structures in the clouds play an important role in the entire process of formation of dense cores and their evolution.
Modeling and Learning on High-Dimensional Matrix-Variate Sequences

We propose a new matrix factor model, named RaDFaM, which is strictly derived based on the general rank decomposition and assumes a structure of a high-dimensional vector factor model for each basis vector. RaDFaM contributes a novel class of low-rank latent structure that makes tradeoff between signal intensity and dimension reduction from the perspective of tensor subspace. Based on the intrinsic separable covariance structure of RaDFaM, for a collection of matrix-valued observations, we derive a new class of PCA variants for estimating loading matrices, and sequentially the latent factor matrices. The peak signal-to-noise ratio of RaDFaM is proved to be superior in the category of PCA-type estimations. We also establish the asymptotic theory including the consistency, convergence rates, and asymptotic distributions for components in the signal part. Numerically, we demonstrate the performance of RaDFaM in applications such as matrix reconstruction, supervised learning, and clustering, on uncorrelated and correlated data, respectively.