Miracle-dz/UniFAR-FTUs huggingface dataset:
Using the Markov chain Monte Carlo method to study the physical properties of GeV-TeV BL Lac objects

We fit the spectral energy distributions (SEDs) of 46 GeV-TeV BLLac objects in the frame of leptonic one-zone synchrotron self-Compton (SSC) model and investigate the physical properties of these objects. We use the Markov chain Monte Carlo (MCMC) method to obtain the basic parameters, such as magnetic field (B), the break energy of the relativistic electron distribution (gamma(b)'), and the electron energy spectral index. Based on the modeling results, we support the following scenarios for GeV-TeV BL Lac objects. (1) Some sources have large Doppler factors, implying other radiation mechanism should be considered. (2) Compared with flat spectrum quasars (FSRQs), GeV-TeV BL Lac objects have weaker magnetic fields and larger Doppler factors, which cause the ineffective cooling and shift the SEDs to higher bands. Their jet powers are around 4.0 x 10(45) erg s(-1), compared with radiation power, 5.0 x 10(42) erg s(-1), indicating that only a small fraction of jet power is transformed into the emission power. (3) For some BL Lacs with large Doppler factors, their jet components could have two substructures, e.g., the fast core and the slow sheath. For most GeV-TeV BL Lacs, Kelvin-Helmholtz instabilities are suppressed by their higher magnetic fields, leading to micro-variability or intro-day variability in the optical bands. (4) Combined with a sample of FSRQs, an anti-correlation between the peak luminosity, L-pk, and the peak frequency, nu(pk), is obtained, favoring the blazar sequence scenario. In addition, an anti-correlation between the jet power, P-jet, and the break Lorentz factor, gamma(b), also supports the blazar sequence.
Degree of arbitrariness directly from moving frames

For physical theories, the degree of arbitrariness of a system is of great importance, and is often closely linked to the concept of degree of freedom, and for most systems this number is far from obvious. In this paper we present an easy to apply algorithm for calculating the degree of arbitrariness for large classes of systems formulated in the language of moving frames, by mere manipulation of the indices of the differential invariants. We then give several examples illustrating our procedure, including a derivation of the degree of arbitrariness of solutions of the Einstein equation in arbitrary dimensions, which is vastly simpler than previous calculations, the degree of arbitrariness of gauge theories of arbitrary groups under Yang-Mills type equations, for which until now only specific cases have been calculated by rather messy means, and the degree of arbitrariness of relativistic rigid flow with or without additional constraints, which has not been derived before. Finally we give the proof of our algorithm.
Modeling transport through single-molecule junctions

Non-equilibrium Green's functions (NEGF) formalism combined with extended Hückel (EHT) and charging model are used to study electrical conduction through single-molecule junctions. The analyzed molecular complex is composed of the asymmetric 1,4-Bis((2′-para-mercaptophenyl)-ethinyl)-2-acetyl-amino-5-nitrobenzene molecule symmetrically coupled to two gold electrodes. Owing to this model, the accurate values of the current flowing through such junctions can be obtained by utilizing basic fundamentals and coherently deriving model parameters. Furthermore, the influence of the charging effect on the transport characteristics is emphasized. In particular, charging-induced reduction of conductance gap, charging-induced rectification effect and charging-generated negative value of the second derivative of the current with respect to voltage are observed and examined for the molecular complex.
A persistent neutrophil-associated immune signature characterizes post–COVID-19 pulmonary sequelae

Interstitial lung disease and associated fibrosis occur in a proportion of individuals who have recovered from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection through unknown mechanisms. We studied individuals with severe coronavirus disease 2019 (COVID-19) after recovery from acute illness. Individuals with evidence of interstitial lung changes at 3 to 6 months after recovery had an up-regulated neutrophil-associated immune signature including increased chemokines, proteases, and markers of neutrophil extracellular traps that were detectable in the blood. Similar pathways were enriched in the upper airway with a concomitant increase in antiviral type I interferon signaling. Interaction analysis of the peripheral phosphoproteome identified enriched kinases critical for neutrophil inflammatory pathways. Evaluation of these individuals at 12 months after recovery indicated that a subset of the individuals had not yet achieved full normalization of radiological and functional changes. These data provide insight into mechanisms driving development of pulmonary sequelae during and after COVID-19 and provide a rational basis for development of targeted approaches to prevent long-term complications. Description Individuals with persistent interstitial lung changes following COVID-19 exhibit a systemic proinflammatory neutrophil-associated immune signature. Casting a wide NET on long COVID A substantial number of individuals who recover from COVID-19 still present with long-term sequelae. Here, George et al. followed individuals after recovery from severe COVID-19 to identify features that distinguished those that had evidence of long-term pulmonary sequelae from those who made a full recovery. They found that a neutrophil-associated inflammatory phenotype was apparent in those who had persistent pulmonary symptoms, and evidence of neutrophil extracellular traps, or NETs, was found in the blood of these individuals. These data highlight a potential role for neutrophils in pulmonary long COVID.
Particles as Wilson lines of the gravitational field

Since the work of Mac-Dowell-Mansouri it is well known that gravity can be written as a gauge theory for the de Sitter group. In this paper we consider the coupling of this theory to the simplest gauge invariant observables that is, Wilson lines. The dynamics of these Wilson lines is shown to reproduce exactly the dynamics of relativistic particles coupled to gravity, the gauge charges carried by Wilson lines being the mass and spin of the particles. Insertion of Wilson lines breaks in a controlled manner the diffeomorphism symmetry of the theory and the gauge degree of freedom are transmuted to particles degree of freedom.
Evidence for M-theory based on fractal nearly tri-bimaximal neutrino mixing

Developing a theory that can describe everything in the universe is of great interest, and is closely relevant to M-theory, neutrino oscillation and charge-parity (CP) violation. Although M-theory is claimed as a grand unified theory, it has not been tested by any direct experiment. Here we show that existing neutrino oscillation experimental data supports one kind of high dimensional unified theory, such as M-theory. We propose a generalization of the tri-bimaximal neutrino mixing ansatz, and we find that the latest neutrino oscillation experimental data constraints dimension in a range between 10.46 and 12.93 containing 11, which is an important prediction of M-theory. This ansatz naturally incorporates the fractal feature of the universe and leptonic CP violation into the resultant scenario of \textit{fractal} nearly tri-bimaximal flavor mixing. We also analyze the consequences of this new ansatze on the latest experimental data of neutrino oscillations, and this theory matches the experimental data. Furthermore, an approach to construct lepton mass matrices in fractal universe under permutation symmetry is discussed. The proposed theory opens an unexpected window on the physics beyond the Standard Model.
Interpretations of gamma-ray burst spectroscopy. II. Bright BATSE bursts

We analyze the spectral lags of a sample of bright gamma-ray burst pulses observed by CGRO BATSE and compare these with the results of high-resolution spectroscopical investigations. We find that pulses with hard spectra have the largest lags, and that there is a similar, but weaker correlation between hardness-intensity corre- lation index, �, and lag. We also find that the lags differ considerably between pulses within a burst. Furthermore, the peak energy mainly decreases with increasing lag. Assuming a lag-luminosity relation as suggested by Norris et al., there will thus be a positive luminosity-peak-energy correlation. We also find that the hardness ratio, of the total flux in two channels, only weakly correlates with the spectral evolution parameters. These results are consistent with those found in the analytical and numerical analysis in Paper I. Finally, we find that for these bursts, dominated by a single pulse, there is a correlation between the observed energy-flux, F, and the inverse of the lag, �t: F / �t 1 . We interpret this flux-lag relation found as a consequence of the lag-luminosity relation and that these bursts have to be relatively narrowly distributed in z. However, they still have to, mainly, lie beyond z � 0.01, since they do not coincide with the local super-cluster of galaxies. We discuss the observed correlations within the collapsar model, in which the collimation of the outflow varies. Both the thermal photospheric emission as well as non-thermal, optically-thin synchrotron emission should be important.
Asphericity for certain groups of cohomological dimension 2

A finite connected 2-complex K whose fundamental group is of cohomological dimension 2 is aspherical iff the subgroup \Sigma_K of H_2(K) consisting of spherical 2-cycles is zero. A finite connected subcomplex of an aspherical 2-complex is aspherical iff its fundamental group is of cohomological dimension 2. If G is a countable group such that extension of scalars from Z[G] to \ell_2(G) kills \bar K_0(Z[G]), and if P is a finitely generated projective Z[G]-module with P/IP=0, where I is the augmentation ideal of Z[G], then P=0. In particular, if G is a countable group of cohomological dimension 2 and P is a finitely generated projective Z[G]-module such that P/IP=0, then P=0.
GAMMA-RADIATION SPECTRA OF 1200 MeV ELECTRONS IN THICK BERYLLIUM, SILICON AND TUNGSTEN SINGLE CRYSTALS

Gamma radiation spectra of 1200 MeV electrons from the single crystals of the beryllium 1.2 mm thick, silicon 1.5 mm and 15 mm thick and tungsten 1.18 mm thick along of the crystallographic axes were measured. Also spectral-angular distributions of gamma radiation from the silicon single crystals 1.5 mm thick along of the crystallographic axes < 100 >, < 110 > and < 111 > were measured. On the basis of these measurements the γ-radiation spectra for the different solid angles up to 6.97 × 10−6 sr were obtained.
The RR Lyrae Period-Luminosity Relation. I. Theoretical Calibration

We present a theoretical calibration of the RR Lyrae period-luminosity (PL) relation in the UBVRIJHK Johnson-Cousins-Glass system. Our theoretical work is based on calculations of synthetic horizontal branches (HBs) for several different metallicities, fully taking into account evolutionary effects besides the effect of chemical composition. Extensive tabulations of our results are provided, including convenient analytical formulae for the calculation of the coefficients of the period-luminosity relation in the different passbands as a function of HB type. We also provide "average" PL relations in IJHK, for applications in cases where the HB type is not known a priori; as well as a new calibration of the MV-[M/H] relation. These can be summarized as follows: and
Semantic Image Segmentation for Efficiently Adding Recognition Objects

With the development of artificial intelligence technology, various methods have been developed for recognizing objects in images using machine learning. Image segmentation is the most effective among these methods for recognizing objects within an image. Conventionally, image datasets of various classes are trained simultaneously. In situations where several classes require segmentation, all datasets have to be trained thoroughly. Such repeated training results in low training efficiency because most of the classes have already been trained. In addition, the number of classes that appear in the datasets affects training. Some classes appear in datasets in remarkably smaller numbers than others, and hence, the training errors will not be properly reflected when all the classes are trained simultaneously. Therefore, a new method that separates some classes from the dataset is proposed to improve efficiency during training. In addition, the accuracies of the conventional and proposed methods are compared.
Feature Extraction of Non-proliferative Diabetic Retinopathy Using Faster R-CNN and Automatic Severity Classification System Using Random Forest Method

Non-proliferative diabetic retinopathy is a representative complication of diabetic patients and is known to be a major cause of impaired vision and blindness. There has been ongoing research on automatic detection of diabetic retinopathy, however, there is also a growing need for research on an automatic severity classification system. This study proposes an automatic detection system for pathological symptoms of diabetic retinopathy such as microaneurysms, retinal hemorrhage, and hard exudate by applying the Faster R-CNN technique. An automatic severity classification system was devised by training and testing a Random Forest classifier based on the data obtained through preprocessing of detected features. An experiment of classifying 228 test fundus images with the proposed classification system showed 97.8% accuracy.
Double distributions and generalized parton distributions from the parton number conserved light front wave function overlap representation

We show that Mellin moments of generalized parton distributions, given as even polynomials in the skewness parameter, are obtained from the Taylor expansion of light front wave functions. Furthermore, we derive non-standard versions of the inverse Radon transform to obtain the double distribution from the parton number conserved light front wave function overlap. These transformations are utilized to extend a generalized parton distribution from the outer region to the central one. We exemplify the formalism for a light front wave function that arises from an AdS/QCD duality conjecture.
Aspect-Based Sentiment Analysis with Position Embedding Interactive Attention Network

Aspect-based sentiment analysis is to discover the sentiment polarity towards an aspect from user-generated natural language. So far, most of the methods only use the implicit position information of the aspect in the context, instead of directly utilizing the position relationship between the aspect and the sentiment terms. In fact, neighboring words of the aspect terms should be given more attention than other words in the context. This paper studies the influence of different position embedding methods on the sentimental polarities of given aspects, and proposes a position embedding interactive attention network based on a long short-term memory network. Firstly, it uses the position information of the context simultaneously in the input layer and the attention layer. Secondly, it mines the importance of different context words for the aspect with the interactive attention mechanism. Finally, it generates a valid representation of the aspect and the context for sentiment classification. The model which has been posed was evaluated on the datasets of the Semantic Evaluation 2014. Compared with other baseline models, the accuracy of our model increases by about 2% on the restaurant dataset and 1% on the laptop dataset.
Assessing the Intrinsic Uncertainty and Structural Stability of Planetary Models: 1. Parameterized Thermal‐Tectonic History Models

Thermal history models, historically used to understand Earth's geologic history, are being coupled to climate models to map conditions that allow planets to maintain life. However, the lack of structural uncertainty assessment has blurred guidelines for how thermal history models can be used toward this end. Structural uncertainty is intrinsic to the modeling process. Model structure refers to the cause and effect relations that define a model and are assumed to adequately represent a particular real world system. Intrinsic/structural uncertainty is different from input and parameter uncertainties (which are often evaluated for thermal history models). A full uncertainty assessment requires that input/parametric and intrinsic/structural uncertainty be evaluated (one is not a substitute for the other). We quantify the intrinsic uncertainty for several parameterized thermal history models (a subclass of planetary models). We use single perturbation analysis to determine the reactance time of different models. This provides a metric for how long it takes low‐amplitude, unmodeled effects to decay or grow. Reactance time is shown to scale inversely with the strength of the dominant model feedback (negative or positive). A perturbed physics analysis is then used to determine uncertainty shadows for model outputs. This provides probability distributions for model predictions. It also tests the structural stability of a model (do model predictions remain qualitatively similar, and within assumed model limits, in the face of intrinsic uncertainty? ). Once intrinsic uncertainty is accounted for, model outputs/predictions and comparisons to observational data should be treated in a probabilistic way.
Architecture, Protocols, and Applications of the Internet of Medical Things (IoMT)

The Internet of Things (IoT) refers to the interconnected framework of web-connected objects that can collect and transfer information over a remote network without requiring any human intervention. The rapid progression in the development of IoT-based devices and their expansion towards making the medical care facility financially more savvy, proactive, and customized, has given rise to the development of the "Internet of Medical Things (IoMT)" that are assumed to function proactively in all domains of the healthcare industry. Within this framework, the IoMT-based healthcare system delivers various advantages, such as quick and unfailing treatment, enhanced communication, cost minimization, etc., through the exploitation of several new technologies. For instance, machine learning has significantly helped with the exploitation of various healthcare systems; fog computing not only minimises the cost of communication but also provides low latency; blockchain delivers its users a much better way of protecting sensitive and confidential information and data they possess. In this survey, a comprehensive elaboration of the IoMT-based healthcare systems based on modern technologies was conducted. This article describes various techniques and solutions of IoMT healthcare systems in the context of emerging technologies, and the related future trends and applications for a better understanding of how IoMT can enhance the healthcare industry now and in future.
Design and Analysis of Single Layer Proximity Fed Sierpinski Fractal 2x1 Array Antenna Embedded on Hexagon Shaped Patch with Defected Ground Structure

A novel innovative technique for producing a single layer proximity fed triple band antenna based on second iteration level Sierpinski fractal geometry implanted on a hexagonal shaped array antenna is being presented for next-generation cellular networks. The steps of fractal array antenna growth are examined. The array antenna is supplied using a single layer proximity fed method. The suggested single layer proximity fed hexagonal shaped array antenna with defective ground structure is developed and simulated using CST Microwave Studio, and characteristics such as bandwidth, return loss, directivity, and radiation pattern are investigated. The second iteration level single layer proximity fed array antenna's simulated -10 dB impedance bandwidth for the lower band is 237 MHz (1.6480 – 1.8850 GHz) with peak resonance at 1.8010 GHz, the middle band bandwidth is 66 MHz (2.4280 – 2.4940 GHz) with peak resonance at 2.4640 GHz, and the upper band bandwidth is 69 MHz (2.9080 – 2.9778 GHz) with peak resonance at 2.9 This antenna may be used for a wide range of wireless communication applications.
Post-main-sequence Evolution of Icy Minor Planets. II. Water Retention and White Dwarf Pollution around Massive Progenitor Stars

Most studies suggest that the pollution of white dwarf (WD) atmospheres arises from the accretion of minor planets, but the exact properties of polluting material, and in particular the evidence for water in some cases, are not yet understood. Here we study the water retention of small icy bodies in exo-solar planetary systems, as their respective host stars evolve through and off the main sequence and eventually become WDs. We explore, for the first time, a wide range of star masses and metallicities. We find that the mass of the WD progenitor star is of crucial importance for the retention of water, while its metallicity is relatively unimportant. We predict that minor planets around lower-mass WD progenitors would generally retain more water and would do so at closer distances from the WD than compared with high-mass progenitors. The dependence of water retention on progenitor mass and other parameters has direct implications for the origin of observed WD pollution, and we discuss how our results and predictions might be tested in the future as more observations of WDs with long cooling ages become available.
Log-Linear Models for Categorical Data with Misclassification and Double Sampling

Abstract Errors in the collection of categorical data lead to misclassification of observed counts. Several authors have proposed a double sampling scheme. This article develops a method for analysis of double sampling data. First, a log-linear model is selected for the misclassification matrix that relates the fallible to the correct data; then another log-linear model is built on the distribution of the correct classifications. Thus, the error structure can be utilized in inference of the relationships among the correct classifications. The statistical principles used are maximum likelihood estimation and goodness-of-fit tests. An example from epidemiology illustrates the methodology.
Frequency Domain Data Encoding in Apache IoTDB

Frequency domain analysis is widely conducted on time series. While online transforming from time domain to frequency domain is costly, e.g., by Fast Fourier Transform (FFT), it is highly demanded to store the frequency domain data for reuse. However, frequency domain data encoding for efficient storage is surprisingly untouched. We notice that (1) the precision of data value is unnecessarily high after transforming to frequency domain and (2) the data values are with skewed distribution leading to a very large bit width for encoding. To avoid such space waste in both precision and skewness, we devise a descending bit-packing encoding for frequency domain data. Specifically, we quantize the data values in proper precision referring to the signal-noise-ratio (SNR) in frequency domain analysis. Moreover, we sort the data values in descending order so that the bit width could be dynamically reduced in encoding. The method has been deployed in Apache IoTDB, an open-source time-series database, not only for directly encoding frequency domain data, but also as a lossy compression of the time domain data. The extensive experiments on the system demonstrate the superiority of our encoding for both frequency domain and time domain data.