Miracle-dz/UniFAR-FTUs huggingface dataset:
A near-infrared, optical and ultraviolet polarimetric and timing investigation of complex equatorial dusty structures

Context. From stars to active galactic nuclei, many astrophysical systems are surrounded by an equatorial distribution of dusty material that are, in a number of cases, spatially unresolved even with cutting edge facilities. Aims. In this paper, we investigate if and how one can determine the unresolved and heterogeneous morphology of dust distribution around a central bright source using time-resolved polarimetric observations. Methods. We use polarized radiative transfer simulations to study a sample of circumnuclear dusty morphologies. We explore a grid of uniform, fragmented, density-stratified, geometrically-variable models in the near-infrared, optical and ultraviolet bands, and present their distinctive time-dependent polarimetric signatures. Results. As expected, varying the structure of the obscuring equatorial disk has a deep impact on the inclination-dependent flux, polarization degree and angle, and time-lags we observe. We find that stratified media are distinguishable by time-resolved polarimetric observations, and that the expected polarization is much higher in the infrared band than in the ultraviolet. However, due to the physical scales imposed by dust sublimation, the average time-lags between the total and polarized fluxes are important (months to years), lengthening the observational campaigns necessary to break more sophisticated (and therefore also more degenerated) models. In the ultraviolet band, time-lags are slightly shorter than in the infrared or optical bands, and, coupled to lower diluting starlight fluxes, time-resolved polarimetry in the UV appears more promising for future campaigns.
A UBVR CCD Survey of the Magellanic Clouds

We present photometry and a preliminary interpretation of a UBVR survey of the Large and Small Magellanic Clouds, which covers 14.5 deg2 and 7.2 deg2, respectively. This study is aimed at obtaining well-calibrated data on the brighter, massive stars, complementing recent, deeper CCD surveys. Our catalog contains 179,655 LMC and 84,995 SMC stars brighter than V ∼ 18.0, and is photometrically complete to U ∼ B ∼ V ∼ 15.7 and R ∼ 15.2, although stars in crowded regions are selectively missed. We compare our photometry to that of others, and describe the need for gravity-dependent corrections to our CCD U-band photometry. We provide a preliminary cross-reference between our catalog stars and the stars with existing spectroscopy. We discuss the population of stars seen toward the two Clouds, identifying the features in the color-magnitude diagram, and using existing spectroscopy to help construct H-R diagrams. We derive improved values for the blue-to-red star ratios in the two Clouds, emphasizing the uncertainties involved in this before additional spectroscopy. We compare the relative number of red supergiants and Wolf-Rayet stars in the LMC and SMC with that of other galaxies in the Local Group, demonstrating a very strong, tight trend with metallicity, with the ratio changing by a factor of 160 from the SMC to M31. We also reinvestigate the initial mass function of the massive stars found outside of the OB associations. With the newer data, we find that the initial mass function (IMF) slope of this field population is very steep, with Γ ∼ -4 ± 0.5, in agreement with our earlier work. This is in sharp contrast to the IMF slope found for the massive stars with OB associations (Γ ∼ -1.3). Although much more spectroscopy is needed to make this result firm, incompleteness can no longer be invoked as an explanation.
Application of rifling comparison technology based on deep learning

In order to improve the efficiency of bullet marks inspection and meet the requirements of timeliness and accuracy of bullet marks inspection, it is urgent to make use of the powerful performance of artificial intelligence to develop a deep learing-based gun rifle-comparing system that can achieve accurate identification, rapid inspection and scientific qualitative. To achieve rapid combination and rapid tracing, to provide strong clues and technical support for rapid case detection. This paper introduces the development background of traditional bullet mark inspection, summarizes the existing automatic recognition technology of bullet mark inspection and its shortcomings, analyzes the application status of deep learning in bullet mark inspection, and finally prospects the application of deep learning in rifling comparison technology of bullet.
Microstructure-based calculations and experimental results for sound absorbing porous layers of randomly packed rigid spherical beads

Acoustics of stiff porous media with open porosity can be very effectively modelled using the so-called Johnson-Champoux-Allard-Pride-Lafarge model for sound absorbing porous media with rigid frame. It is an advanced semi-phenomenological model with eight parameters, namely, the total porosity, the viscous permeability and its thermal analogue, the tortuosity, two characteristic lengths (one specific for viscous forces, the other for thermal effects), and finally, viscous and thermal tortuosities at the frequency limit of 0 Hz. Most of these parameters can be measured directly, however, to this end specific equipment is required different for various parameters. Moreover, some parameters are difficult to determine. This is one of several reasons for the so-called multiscale approach, where the parameters are computed from specific finite-element analyses based on some realistic geometric representations of the actual microstructure of porous material. Such approach is presented and validated for layers ma.
Isochrone ages for ∼3 million stars with the second Gaia data release

We present a catalogue of distances, masses, and ages for ∼3 million stars in the second Gaia data release with spectroscopic parameters available from the large spectroscopic surveys: APOGEE, Gaia-ESO, GALAH, LAMOST, RAVE, and SEGUE. We use a Bayesian framework to characterize the probability density functions of distance, mass, and age using photometric, spectroscopic, and astrometric information, supplemented with spectroscopic masses where available for giant stars. Furthermore, we provide posterior extinction estimates (AV) to every star using published extinction maps as a prior input. We provide an appendix with extinction coefficients for Gaia photometry derived from stellar models, which account for variation with intrinsic colour and total extinction. Our pipeline provides output estimates of the spectroscopic parameters, which can be used to inform improved spectroscopic analysis. We complement our catalogues with Galactocentric coordinates and actions with associated uncertainties. As a demonstration of the power of our catalogue, we produce velocity dispersion profiles of the disc separated by age and Galactocentric radius (between 3 and 15 kpc from the Galactic centre). This suggests that the velocity dispersion profiles flatten with radius in the outer Galaxy (>8 kpc) and that at all radii the velocity dispersion follows the smooth power law with age observed in the solar neighbourhood.
The spectroscopic orbits and physical parameters of GG Car

Abstract GG Car is a peculiar B type star with emission lines classified as a B[e] supergiant star. In this work we present a spectral analysis of this system based on spectra obtained at Casleo. We fit the spectral energy distribution adopting a model for the gas and dust circumstellar components and thus we obtain the physical parameters of the star and its environment.
Chinese medicine based on word embedding Deberta-BiLSTM-CRF model named entity recognition

In this paper, a Deberta-BiLSTM-CRF model based on the fusion of character and lexical features is proposed to solve the problems of semantic lack and entity nesting in Chinese medical texts in entity recognition tasks. The model uses the Deberta pre-training model and the Soft-lexicon method to dynamically fuse dictionary information and enhance the semantic representation of text. After the learning and decoding of BiLSTM and CRF models, the effective recognition of medical entities is realized. Follow-up work will consider making a large-scale Chinese medical dictionary to improve the recognition effect of the model. The experimental results show that the model proposed in this paper has a greater improvement in the F1 value of entity recognition than the RoBERTa-wwm-BiLSTM-CRF and Bert-BiLSTM-CRF models, and the speed has also improved. Therefore, the model has broad application prospects in Chinese medical entity recognition.
Low overhead quantum computation using lattice surgery

When calculating the overhead of a quantum algorithm made fault-tolerant using the surface code, many previous works have used defects and braids for logical qubit storage and state distillation. In this work, we show that lattice surgery reduces the storage overhead by over a factor of 4, and the distillation overhead by nearly a factor of 5, making it possible to run algorithms with $10^8$ T gates using only $3.7\times 10^5$ physical qubits capable of executing gates with error $p\sim 10^{-3}$. These numbers strongly suggest that defects and braids in the surface code should be deprecated in favor of lattice surgery.
Traditional Chinese Medicine (TCM) synonym recognition based on BERT

The research of TCM informatization is usually based on the text of the patient's symptoms. TCM physicians use synonyms to describe a patient's symptoms, which can cause data redundancy in the patient's symptom text. Therefore, in the process of TCM informatization, it is first necessary to process the TCM text and standardize the synonyms in the symptom text. In this paper, a synonym set is constructed based on the symptom text of patients, and the ED-BERT model is proposed for the normalization of symptom texts in traditional Chinese medicine, which lays a foundation for the follow-up research of traditional Chinese medicine informatization. The accuracy of the proposed model for TCM synonym recognition reaches 0.8613, which has advanced advantages over traditional methods.
Spectral scaling technique to determine extreme Carrington‐level geomagnetically induced currents effects

Space weather events produce variations in the electric current in the Earth's magnetosphere and ionosphere. From these high‐altitude atmospheric regions, resulting geomagnetically induced currents (GICs) can lead to fluctuations in ground currents that affect the electric power grid and potentially overload transformers during extreme storms. The most extreme geomagnetic storm on record, known as the 1859 Carrington event, was so intense that ground‐based magnetometers were saturated at high magnetic latitudes. The most reliable, unsaturated observation is the hour resolution data from the Colaba Magnetic Observatory in India. However, higher‐frequency components—fluctuations at second through minute time cadence—to the magnetic field can play a significant role in GIC‐related effects. We present a new method for scaling higher‐frequency observations to create a realistic Carrington‐like event magnetic field model, using modern magnetometer observations. Using the magnetic field model and ground conductivity models, we produce an electric field model. This method can be applied to create similar magnetic and electric field models for studies of GIC effects on power grids.
Phases of the generalized two-leg spin ladder : A view from the SU(4) symmetry

The zero-temperature phases of a generalized two-leg spin ladder with four-spin exchanges are discussed by means of a low-energy field theory approach starting from an SU(4) quantum critical point. The latter fixed point is shown to be a rich multicritical point that unifies different competing dimerized orders and a scalar chirality phase that breaks spontaneously the time-reversal symmetry. The quantum phase transition between these phases is dominated by spin-singlet fluctuations and belongs to the Luttinger universality class due to the existence of a $\mathrm{U}(1)$ self-duality symmetry.
A Multi-Objective Robust Preference Genetic Algorithm Based on Decision Variable Perturbation

Multi-objective optimization is a challenging research topic because it involves the simultaneous optimization of several complex and conflicting objectives. However multi-objectivity is only one aspect of real-world applications and there is a growing interest in the optimization of solutions that are insensitive to parametric variations as well. A new robust preference multi-objective optimization algorithm is proposed in this paper, and the robust measurement of solution is designed based on Latin Hypercube Sampling, which is embedded in the optimization process to guide the optimization direction and help the better robust solution have more chance to survive. In order to obtain different preference of the robust solutions, a new fitness scheme is also presented. Through the adjustment of fitness function parameter preference, robust solutions can be obtained. Results suggest that the proposed algorithm has a bias towards the region where the preference robust solutions lie.
Evolution of sequence traits of prion-like proteins linked to amyotrophic lateral sclerosis (ALS)

Prions are proteinaceous particles that can propagate an alternative conformation to further copies of the same protein. They have been described in mammals, fungi, bacteria and archaea. Furthermore, across diverse organisms from bacteria to eukaryotes, prion-like proteins that have similar sequence characters are evident. Such prion-like proteins have been linked to pathomechanisms of amyotrophic lateral sclerosis (ALS) in humans, in particular TDP43, FUS, TAF15, EWSR1 and hnRNPA2. Because of the desire to study human disease-linked proteins in model organisms, and to gain insights into the functionally important parts of these proteins and how they have changed across hundreds of millions of years of evolution, we analyzed how the sequence traits of these five proteins have evolved across eukaryotes, including plants and metazoa. We discover that the RNA-binding domain architecture of these proteins is deeply conserved since their emergence. Prion-like regions are also deeply and widely conserved since the origination of the protein families for FUS, TAF15 and EWSR1, and since the last common ancestor of metazoa for TDP43 and hnRNPA2. Prion-like composition is uncommon or weak in any plant orthologs observed, however in TDP43 many plant proteins have equivalent regions rich in other amino acids (namely glycine and tyrosine and/or serine) that may be linked to stress granule recruitment. Deeply conserved low-complexity domains are identified that likely have functional significance.
Meta-Learning for Beam Prediction in a Dual-Band Communication System

Large antenna arrays and beamforming are necessary for the mmWave communication system, resulting in heavy time and energy consumption in the beam training stage. Therefore, dual-band operations are expected to be deployed in future communication systems, where low-frequency channels are used to meet basic communication needs, and millimeter wave (mmWave) channels are exploited when the high-rate transmission is required. Existing works utilize deep learning methods to extract low-frequency channel state information (CSI) to reduce the mmWave beam training overheads. However, an important limitation of deep learning approaches is that the model is usually trained in a given environment. When employed in an unseen environment, it usually requires a large amount of data to retrain. In this paper, a model-agnostic optimization algorithm based on meta-learning is proposed to provide a general mmWave beam prediction model. This model can be deployed to edge base stations and effectively adapted to the environment without the need for a heavy collection of data. Simulation results demonstrate that the proposed approach could reduce the model adaptation overheads. The meta-learning-based beam prediction model is robust and achieves high prediction accuracy and spectral efficiency in different signal-to-noise ratio (SNR) regimes.
Bulk and surface excitons in solid neon

Transmission and reflection spectra of solid neon in the valence-exciton range ($16\ensuremath{\le}h\ensuremath{\nu}\ensuremath{\le}22$ eV) have been carefully reinvestigated using highly monochromatized ($\ensuremath{\Delta}E=4$ meV) synchrotron radiation. A surface-exciton state at $h\ensuremath{\nu}=17.15$ eV located 210 meV below the $n=1$ ($j=\frac{3}{2}$) bulk exciton is observed and unambiguously identified. Energy positions of the bulk exciton states and line shapes as well as new and more precise values for derived quantities have been determined and are discussed in view of recent theoretical predictions. For the first time we are able to resolve the spin-orbit splitting of the $j=\frac{3}{2}$ and $j=\frac{1}{2}$ states and study the influence of exchange interaction. The value of 90 meV for the spin-orbit splitting is close to the gas-phase result. Within a quantum-defect approach we evaluate a new band gap of 21.58 eV for solid Ne.
What is the real accretion rate on to a black hole for low-angular-momentum accretion?

Mass accretion rate is a key parameter in accretion disk theory. It determines black hole accretion mode. In large scale cosmological simulations studying galaxy formation and evolution, Bondi radius can at most be marginally resolved. In those simulations, Bondi accretion formula is always used to estimate black hole accretion rate. Bondi solution is too simple, which cannot represent the real accretion process. We perform simulations of hot accretion flow at parsec scale irradiated by a low luminosity active galactic nucleus (AGN). We perform 77 simulations with varying density and temperature at outer boundary (10 parsec). Our purpose is to find a formula to calculate real black hole accretion rate based on the gas density and temperature at parsec scale. We define Eddington accretion rate to be $\dot M_{\rm Edd}=10L_{\rm Edd}/c^2$, with $L_{\rm Edd}$ and $c$ been Eddington luminosity and speed of light respectively. We set black hole mass to be $10^8M_{\odot}$, $M_{\odot}$ is solar mass. We find that black hole accretion rate can be expressed as $\frac{\dot M_{\rm BH} }{\dot M_{\rm Edd}}=10^{-3.11} (\rho_0/10^{-22}{\rm g cm}^{-3})^{1.36} (T_0/10^7 {\rm K})^{-1.9}$, with $\rho_0$ and $T_0$ being density and temperature at parsec scale, respectively. We find the formula can accurately predict the luminosity of observed low-luminosity AGNs (with black hole mass $\sim 10^8M_{\odot}$). This formula can be used in the sub-grid models in large scale cosmological simulations with a black hole mass of $\sim 10^8M_{\odot}$.
Effect of Lactobacillus rhamnosus hsryfm 1301 Fermented Milk on Lipid Metabolism Disorders in High-Fat-Diet Rats

To further explore and improve the mechanism of probiotics to alleviate the disorder of lipid metabolism, transcriptomic and metabolomic with bioinformatic analysis were combined. In the present study, we successfully established a rat model of lipid metabolism disorder using a high-fat diet. Intervention with Lactobacillus rhamnosus hsryfm 1301 fermented milk resulted in a significant reduction in body weight, serum free fatty acid and blood lipid levels (p < 0.05), which predicted that the lipid metabolism disorder was alleviated in rats. Metabolomics and transcriptomics identified a total of 33 significantly different metabolites and 183 significantly different genes screened in the intervention group compared to the model group. Comparative analysis of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotations identified a total of 61 pathways in which differential metabolites and genes were jointly involved, with linoleic acid metabolism, glycine, serine and threonine metabolism and glutamatergic synapse in both transcriptome and metabolome being found to be significantly altered (p < 0.05). Lactobacillus rhamnosus hsryfm 1301 fermented milk was able to directly regulate lipid metabolism disorders by regulating the metabolic pathways of linoleic acid metabolism, glycerophospholipid metabolism, fatty acid biosynthesis, alpha-linolenic acid metabolism, fatty acid degradation, glycerolipid metabolism and arachidonic acid metabolism. In addition, we found that Lactobacillus rhamnosus hsryfm 1301 fermented milk indirectly regulates lipid metabolism through regulating amino acid metabolism, the nervous system, the endocrine system and other pathways. Lactobacillus rhamnosus hsryfm 1301 fermented milk could alleviate the disorders of lipid metabolism caused by high-fat diet through multi-target synergy.
Novel algorithm to compensate nonlinear response of photo detector to improve quality of image reconstruction for compressed sensing

Abstract. In imaging systems based on compressed sensing, error in the measured data is incurred due to the nonlinear response of the photo detector, which affects the quality of the reconstructed images. We propose an algorithm to compensate the nonlinear response from the detector. The compensation is based on the detector response curve on the measured data. The theoretical analysis and simulation results show that this algorithm can greatly reduce the reconstruction errors caused by the detector’s nonlinear response. Furthermore the peak signal-to-noise ratio of the reconstructed image and the system reconstruction rate have been significantly improved, while the fine feature of the images is better preserved and reconstructed as compared to that without using the algorithm.
Status of hadronic light-by-light scattering and the muon $(g-2)$

In this talk we review the recent progress on the numerical determination of the Hadronic Light-by-Light contribution to the anomalous magnetic moment of the muon and we discuss the role of experimental data on the accuracy of its determination. Special emphasis on the main contribution, the pseudoscalar piece, is made. Gathering recent progress in the light-by-light scattering contribution we consider $a_{\mu}^{\mathrm{HLBL}} = (12.1\pm3.0)\times10^{-10}$ as a good summary of the state-of-the-art calculations which still claims for a $4\sigma$ deviation between theory and experiment for the $(g-2)_{\mu}$.
Counting entropy in causal set quantum gravity

The finiteness of black hole entropy suggest that spacetime is fundamentally discrete, and hints at an underlying relationship between geometry and "information". The foundation of this relationship is yet to be uncovered, but should manifest itself in a theory of quantum gravity. We review recent attempts to define a microscopic measure for black hole entropy and for the maximum entropy of spherically symmetric spacelike regions, within the causal set approach to quantum gravity.