Miracle-dz/UniFAR-FTUs huggingface dataset:
The mass spectrum of interstellar clouds

The abundance of 21-cm absorption lines seen in surveys at high latitudes can be translated into a line of sight abundance of clouds vs. column density using an empirical relationship between temperature and optical depth. As VLA surveys of 21-cm absorption at low latitudes are now becoming available, it is possible to study the variation of this function with galactic radius. It is interesting to compare the abundance of these diffuse atomic clouds (with temperatures of 50 to 100 K and masses of 1 to 10 M⊙) to the abundance of molecular clouds. To do the latter we must make assumptions about cloud cross-sections in order to convert the line of sight abundance of diffuse clouds into a number per unit volume, and to convert from cloud column density to mass. The spectrum of diffuse clouds matches fairly well the spectrum of molecular clouds, although observationally there is a gap of several orders of magnitude in cloud mass. Optical absorption studies also agree well with the 21-cm results for clouds of column density a few times 1020 M⊙.
Diphenyl Ether Derivatives as Novel GPR120 Agonists for the Treatment of Type 2 Diabetes Mellitus

Diabetes mellitus (DM) is a serious disease affecting human health. Numerous attempts have been made to develop safe and effective new antidiabetic drugs. Recently, a series of G protein‐coupled receptors for free fatty acids (FFAs) have been described and characterized, and small molecule agonists and antagonists of these receptors show considerable promise for managing diabetes and related complications. FFA‐activated GPR120 could stimulate the release of glucagon‐like peptide‐1(GLP‐1), which can enhance the glucose‐dependent secretion of insulin from pancreatic β cells. GPR120 is a promising target for treating type 2 DM (T2DM). Herein we designed and synthesized a series of novel GPR120 agonists based on the structure of TUG‐891, which was the first potent and selective GPR120 agonist. Among the designed compounds, 18 f showed excellent GPR120 activation activity and high selectivity for GPR40 in vitro. Compound 18 f dose‐dependently improved glucose tolerance in normal mice, and no hypoglycemic side effects were observed at high dose. In addition, compound 18 f increased insulin release and displayed good antidiabetic effect in diet‐induced obese mice. Molecular simulations illustrated that compound 18 f could enter the active site of GPR120 and interact with Arg99. Based on these observations, compound 18 f may be a promising lead compound for the design of novel GPR120 agonists to treat T2DM.
Assessing future technological impacts of patents based on the classification algorithms in machine learning: The case of electric vehicle domain

Introduction Identifying the technologies that will drive technological changes over the coming years is important for the optimal allocation of firms’ R&D resources and the deployment of innovation strategies. The citation frequency of a patent is widely recognized as representative of the patent’s value. Thus, identifying potential highly cited patents is an important goal. A number of studies have attempted to distinguish highly cited patents from others based on statistical models, but a more effective and applicable method needs to be further developed. Methods This paper treats the prediction of later patent citations as a classification problem and proposes a novel framework based on machine learning methods. First, a indices system to identify highly cited patents is constructed using multiple factors that are believed to influence citation frequency. Second, various machine learning models are utilized to identify highly cited patents. The optimized model with the best generalization capability is selected to predict the future impacts of newly applied patents, which may be representative of emerging significant technologies. Finally, we select the electric vehicle (EV) domain as a case study to empirically test the validity of this framework. Results The optimized support vector machine (SVM) model performs well in identifying highly cited EV patents. Technological frontiers in the EV domain are identified, which are related to the topics of information systems, batteries, stability control, wireless charging, and vehicle operation. Discussion The good performance in prediction accuracy and generalization capability of the method proposed in this paper verifies its effectiveness and feasibility.
Available evidence and potential for vaccines for reduction in antibiotic prescriptions

ABSTRACT Bacterial antibiotic resistance is a public health issue. It means that drugs become ineffective, infections persist and have a huge impact on the health of patients and their spreading increases. To address a complex threat such as bacterial antibiotic resistance different and integrated approaches are needed including discovery of new antibiotics, improvement of diagnostics tools and improvement of antibiotic stewardship. Absolutely relevant are prevention of infections as well as decrease in the use of antibiotics. Vaccines are an important tool in the fight against bacterial antibiotic resistance and can help prevent it in several ways. Indeed, vaccines are highly effective in preventing diseases that might otherwise require the use of antibiotics to treat symptoms and associated complications. Preventing infections through vaccination helps reduce the need for and widespread and inappropriate use of antibiotics, including for secondary bacterial infections.
A semiparametric estimation procedure of dependence parameters in multivariate families of distributions

SUMMARY This paper investigates the properties of a semiparametric method for estimating the dependence parameters in a family of multivariate distributions. The proposed estimator, obtained as a solution of a pseudo-likelihood equation, is shown to be consistent, asymptotically normal and fully efficient at independence. A natural estimator of its asymptotic variance is proved to be consistent. Comparisons are made with alternative semiparametric estimators in the special case of Clayton's model for association in bivariate data.
Correlation of high frequency ultrasound backscatter with cartilage matrix constituents

We have shown in a previous work that high frequency quantitative backscatter ultrasound (US) was sensitive to progressive changes related to various stages of osteoarthritis (OA) and to normal development of rat patellar cartilage. The goal of the study was to identify the cartilage matrix constituents that are responsible for the changes in the echographic signal. To this end, echographic signals from the cartilage were explored after proteoglycan (PG) and collagen degradation by hyaluronidase and papain enzymes. The patellar cartilages of immature rats were degraded and examined in vitro using a three-dimensional (3-D) 55 MHz backscatter ultrasound scanner. B-scan images were correlated with histologic images. For all the specimens US images of control cartilages showed a hyperechoic internal structure involving mainly the first two-thirds of the tissue and consisting of the transitional and deep layers of the cartilage. The backscattered signal from degraded cartilage did not change when hyaluronidase has selectively extracted an important part of the PGs during 6 hours. Whereas the echographic signal from the cartilage surface was significantly affected after 10-minute-papain degradation which induced a first stage of collagen network alteration. These preliminary results demonstrate that high frequency ultrasound does not provide information about variations in PG content but seems to be very sensitive to changes in collagen fibrillar network.
The easiest way to estimate the Oaxaca–Blinder decomposition

This article studies the relation between the Oaxaca–Blinder decomposition and the estimated coefficient of a dummy variable for a linear regression. We calculate the unexplained part of the decomposition from the estimated coefficient of the dummy variable. More precisely, if the exogenous variables are uncorrelated to the dummy variable, the estimated coefficient and its variance give directly the value and the variance of the unexplained part of the decomposition. If the independent variables are correlated a simple relation is obtained. Moreover, we show that the significance of the unexplained part can be deduced from the significance of the estimated coefficient of the dummy variable.
PARP1 and OGG1 in Medicated Patients With Depression and the Response to ECT

Abstract Background Oxidative stress and oxidation-induced DNA damage may contribute to the pathophysiology of depression. Two key mediators of base excision repair (BER) in response to oxidative damage of DNA are OGG1 and PARP1. Few studies have examined changes in OGG1 or PARP1 mRNA in patients with depression or following antidepressant treatment. We examined PARP1 and OGG1 mRNA levels in patients with depression at baseline/pre-electroconvulsive therapy (baseline/pre-ECT) vs in healthy controls and in patients following a course of ECT. Methods PARP1 and OGG1 were examined in whole blood samples from medicated patients with depression and controls using quantitative real-time polymerase chain reaction. Exploratory subgroup correlational analyses were performed to determine associations between PARP1 and OGG1 and mood (Hamilton Depression Rating Scale 24-item version) scores as well as with vitamin B3, SIRT1, PGC1α, and tumor necrosis factor alpha levels, as previously reported on in this cohort. Results PARP1 levels were reduced in samples from patients with depression vs controls (P = .03), though no difference was noted in OGG1. ECT had no effect on PARP1 or OGG1. Higher baseline PARP1 weakly correlated with greater mood improvement post ECT (P = .008). Moreover, PARP1 positively correlated with SIRT1 at baseline and post ECT, and positive correlations were noted between change in PARP1 and change in OGG1 with change in tumor necrosis factor alpha post ECT. Conclusions To our knowledge, this is the first study to examine the effect of ECT on BER enzymes. A better understanding of BER enzymes and DNA repair in depression could unearth new mechanisms relevant to the pathophysiology of this condition and novel antidepressant treatments.
Generation of highly monochromatic positrons using cold moderators

Slow positron beams generally employ a radioactive β+ emitter with a crystal moderator in which the positrons thermalize. A small fraction of the thermalized positrons are desorbed at low (1–3 eV) energy, characteristic of the positron work function in the crystal. The energy width of the emitted positrons from W(110) and Ni(111) moderators can be narrowed significantly by cooling to liquid N2 temperatures. A practical configuration for a cooled moderator positron gun is presented which produces a narrow energy positron beam with good efficiency.
Healthy eating and physical activity: Analysing Soweto’s young adults’ perspectives with an intersectionality lens

Background and objectives Non-communicable diseases (NCDs) are taking a toll on Africa’s youth at younger ages than in other regions. These are attributed to risk factors that usually advance in adolescence, such as unhealthy diets and reduced physical activity. Young adults in South Africa, particularly women, tend to be sedentary, consume energy-dense diets low in micronutrients, and are more likely to develop NCDs much earlier in life than those in high-income countries. With an intersectionality perspective, this study explored young adults’ barriers and solutions to addressing these risk factors in Soweto. Setting Soweto, Johannesburg, South Africa, is one of the most well-known historically disadvantaged townships known for its established communities, and socioeconomic and cultural diversity. Design: A qualitative investigation utilising focus group discussions (FGDs) with a topic guide. FGDs were transcribed verbatim and thematically analysed using a combination of deductive and inductive approaches. Participants 15 Men and 15 women 18–24 years of age living in Soweto (n = 30). Results: South African young adults have a basic understanding of the significance of nutrition, exercise, and their ties to health. However, numerous barriers (like taste, affordability and crime) to such behaviours were reported, arising from the participants’ personal, domestic, social, and local community levels. Young women experienced sexism and had safety concerns while exercising in the streets, while young men tended to describe themselves as lazy to engage in exercise as they find it boring. Conclusions Young adults face a multitude of intersecting barriers, making it difficult to adopt or sustain health-promoting behaviours. It is important that potential solutions focus on the intersections of barriers to healthy eating and physical activity in order to provide more realistic support for such behaviours.
Spline harmonic analysis and wavelets

We present here a new approach to wavelet transforms based on techniques of SHA. It is a version of HA which operates in spline spaces. SHA enjoys various applications but here we outline a recent application of SHA techniques, namely, accomplishing on the base of periodic splines the wavelet transforms of periodic signals as well as the remarkable informative digital representation of the signals. SHA approach to wavelets yields a tool just as for the constructing a diversity of spline wavelet bases, so for a fast implementation of a decomposition of a function into a fitting wavelet representation and its reconstruction. This approach allows to construct WP bases for refined frequency resolution of signals.
A Hyperspectral and RGB Dataset for Building Facade Segmentation

Hyperspectral Imaging (HSI) provides detailed spectral information and has been utilised in many real-world applications. This work introduces an HSI dataset of building facades in a light industry environment with the aim of classifying different building materials in a scene. The dataset is called the Light Industrial Building HSI (LIB-HSI) dataset. This dataset consists of nine categories and 44 classes. In this study, we investigated deep learning based semantic segmentation algorithms on RGB and hyperspectral images to classify various building materials, such as timber, brick and concrete.
HSTOF ENA observations and energetic ion distributions in the heliosheath

Context. Launched in 1996, HSTOF on board SOHO was the first instrument to detect the energetic neutral atoms (ENA) from the heliosheath. After mid 2003, the field of view of HSTOF was restricted to the flank sectors of the heliosheath, in which region the energetic ion distributions are still unknown. Interpretation of these data requires understanding of the energetic ion transport in the inner heliosheath. Aims. We update the HSTOF ENA hydrogen and helium spectra by adding the results from the recent (2006−2010) measurements. We calculate the energetic ion distributions in a numerical model of the heliosheath and use them as a basis for interpreting the HSTOF ENA observations, in particular those of the flank sectors. Methods. The hydrogen and helium ENA spectra were derived from the HSTOF observations of two 90 ◦ -wide ecliptic longitude sectors around the crosswind directions. The energetic ion distributions in the heliosheath were calculated in a simple model of the heliosphere, assuming that the main acceleration occurs at the termination shock. The effects of different processes (charge-exchange loss, adiabatic acceleration, parallel diffusion and escape across the boundary) were determined with different assumptions about the boundary conditions and the transport parameters. The resulting ion distributions were used to calculate the ENA fluxes, which can be compared with the observations by HSTOF and other instruments. Results. The energetic ion density in the flank sectors of the heliosphere is lower than in the forward (upwind) sector. As a result, the contribution of the flanks of the heliosheath to the production of ENA is not directly proportional to the thickness of the heliosheath, but instead is comparable to the contribution of the forward sector, which agrees with the HSTOF data. The HSTOF ENA flux intensity is nevertheless significantly lower than the model calculations. Near the heliopause there appears a region of low energetic ion density caused by the charge-exchange and the escape losses.
Predicted Trajectory Guidance Control Framework of Teleoperated Ground Vehicles Compensating for Delays

A predicted trajectory guidance control (PTGC) framework is proposed to deal with the degradation of maneuverability and drivability of a teleoperated ground vehicle caused by large communication delays. Humans dominate the control of the teleoperated ground vehicle, and human sensitivity to delays is the main reason for control performance degradation. To address this problem, the proposed framework predicts the teleoperator's intended trajectory using the control commands and the 3D point cloud of the environment at the vehicle side, which is then used to guide the vehicle directly. The novelty of the approach is to attenuate the sensitivity of the human-in-loop system to delays by removing the teleoperator from the direct control loop. Delays can be compensated as long as the predicted horizon exceeds the delays. Human-in-loop simulation experiments are designed to evaluate the teleoperation performance of the proposed method at various delay levels. By means of repeated measurement analysis of variance, it is concluded that the proposed method can significantly improve maneuverability and drivability metrics at large delays(>200ms), such as the task completion time, deviation to centerline and steering efforts. In addition, the results also show that since teleoperators can adapt well to small delays ($ \leq 200\ \mathbf{ms}$), the presented method is ineffective in this case.
First-order perturbation theory of trust-region subproblems

Trust-region subproblem (TRS) is an important problem arising in many applications such as numerical optimization, Tikhonov regularization of ill-posed problems, and constrained eigenvalue problems. In recent decades, extensive works focus on how to solve the trust-region subproblem efficiently. To the best of our knowledge, there are few results on perturbation analysis of the trust-region subproblem. In order to fill in this gap, we focus on first-order perturbation theory of the trust-region subproblem. The main contributions of this paper are three-fold. First, suppose that the TRS is in \emph{easy case}, we give a sufficient condition under which the perturbed TRS is still in easy case. Second, with the help of the structure of the TRS and the classical eigenproblem perturbation theory, we perform first-order perturbation analysis on the Lagrange multiplier and the solution of the TRS, and define their condition numbers. Third, we point out that the solution and the Lagrange multiplier could be well-conditioned even if TRS is in {\it nearly hard case}. The established results are computable, and are helpful to evaluate ill-conditioning of the TRS problem beforehand. Numerical experiments show the sharpness of the established bounds and the effectiveness of the proposed strategies.
Rethinking Backdoor Data Poisoning Attacks in the Context of Semi-Supervised Learning

Semi-supervised learning methods can train high-accuracy machine learning models with a fraction of the labeled training samples required for traditional supervised learning. Such methods do not typically involve close review of the unlabeled training samples, making them tempting targets for data poisoning attacks. In this paper we investigate the vulnerabilities of semi-supervised learning methods to backdoor data poisoning attacks on the unlabeled samples. We show that simple poisoning attacks that influence the distribution of the poisoned samples' predicted labels are highly effective - achieving an average attack success rate as high as 96.9%. We introduce a generalized attack framework targeting semi-supervised learning methods to better understand and exploit their limitations and to motivate future defense strategies.
Applications of Machine Learning for the Ratemaking in Agricultural Insurances

This paper evaluates Machine Learning (ML) in establishing ratemaking for new insurance schemes. To make the evaluation feasible, we established expected indemnities as premiums. Then, we use ML to forecast indemnities using a minimum set of variables. The analysis simulates the introduction of an income insurance scheme, the so-called Income Stabilization Tool (IST), in Italy as a case study using farm-level data from the FADN from 2008-2018. We predicted the expected IST indemnities using three ML tools, LASSO, Elastic Net, and Boosting, that perform variable selection, comparing with the Generalized Linear Model (baseline) usually adopted in insurance investigations. Furthermore, Tweedie distribution is implemented to consider the peculiarity shape of the indemnities function, characterized by zero-inflated, no-negative value, and asymmetric fat-tail. The robustness of the results was evaluated by comparing the econometric and economic performance of the models. Specifically, ML has obtained the best goodness-of-fit than baseline, using a small and stable selection of regressors and significantly reducing the gathering cost of information. However, Boosting enabled it to obtain the best economic performance, balancing the most and most minor risky subjects optimally and achieving good economic sustainability. These findings suggest how machine learning can be successfully applied in agricultural insurance.This study represents one of the first to use ML and Tweedie distribution in agricultural insurance, demonstrating its potential to overcome multiple issues.
SDM: Spatial Diffusion Model for Large Hole Image Inpainting

Generative adversarial networks (GANs) have made great success in image inpainting yet still have difﬁculties tackling large missing regions. In contrast, iterative algorithms, such as autoregressive and denoising diffusion models, have to be deployed with massive computing resources for decent effect. To overcome the respective limitations, we present a novel spatial diffusion model (SDM) that uses a few iterations to gradually deliver informative pixels to the entire image, largely enhancing the inference efﬁciency. Also, thanks to the proposed decoupled probabilistic modeling and spatial diffusion scheme, our method achieves high-quality large-hole completion. On multiple benchmarks, we achieve new state-of-the-art performance. Code is released at https://github.com/fenglinglwb/SDM .