Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of stratospheric aerosol intervention geoengineering on surface air temperature in China: A surface energy budget perspective

Abstract. Stratospheric aerosol intervention (SAI) geoengineering is a rapid, effective, and promising means to counteract anthropogenic global warming, but the climate response to SAI, with great regional disparities, remains uncertain. In this study, we use Geoengineering Model Intercomparison Project G4 experiment simulations from three models (HadGEM2-ES, MIROC-ESM, and MIROC-ESM-CHEM) that offset anthropogenic forcing under medium-low emissions (RCP4.5) by injecting a certain amount of SO2 into the stratosphere every year, to investigate the surface air temperature response to SAI geoengineering over China. It has been shown that the SAI leads to surface cooling over China over the last 40 years of injection simulation (2030–2069), which varies among models, regions and seasons. The spatial pattern of SAI-induced temperature changes over China is mainly due to net surface shortwave radiation changes. We find that changes in solar radiation modification strength, surface albedo, atmospheric water vapor and cloudiness affect surface shortwave radiation. In summer, the increased cloud cover in some regions reduces net surface shortwave radiation, causing strong surface cooling. In winter, both the strong cooling in all three models and the abnormal warming in MIROC-ESM are related to surface albedo changes. Our results suggest that cloud and land surface processes in models may dominate the spatial pattern of SAI-induced surface air temperature changes over China.
In-Depth Analysis of Bacillus anthracis 16S rRNA Genes and Transcripts Reveals Intra- and Intergenomic Diversity and Facilitates Anthrax Detection

For severe infectious diseases, precise pathogen detection is crucial for antibiotic therapy and patient survival. Identification of Bacillus anthracis, the causative agent of the zoonosis anthrax, can be challenging when querying specific nucleotide sequences such as in small subunit rRNA (16S rRNA) genes, which are commonly used for typing of bacteria. ABSTRACT Analysis of 16S rRNA (rRNA) genes provides a central means of taxonomic classification of bacterial species. Based on presumed sequence identity among species of the Bacillus cereus sensu lato group, the 16S rRNA genes of B. anthracis have been considered unsuitable for diagnosis of the anthrax pathogen. With the recent identification of a single nucleotide polymorphism in some 16S rRNA gene copies, specific identification of B. anthracis becomes feasible. Here, we designed and evaluated a set of in situ, in vitro, and in silico assays to assess the unknown 16S state of B. anthracis from different perspectives. Using a combination of digital PCR, fluorescence in situ hybridization, long-read genome sequencing, and bioinformatics, we were able to detect and quantify a unique 16S rRNA gene allele of B. anthracis (16S-BA-allele). This allele was found in all available B. anthracis genomes and may facilitate differentiation of the pathogen from any close relative. Bioinformatics analysis of 959 B. anthracis SRA data sets inferred that abundances and genomic arrangements of the 16S-BA-allele and the entire rRNA operon copy numbers differ considerably between strains. Expression ratios of 16S-BA-alleles were proportional to the respective genomic allele copy numbers. The findings and experimental tools presented here provide detailed insights into the intra- and intergenomic diversity of 16S rRNA genes and may pave the way for improved identification of B. anthracis and other pathogens with diverse rRNA operons. IMPORTANCE For severe infectious diseases, precise pathogen detection is crucial for antibiotic therapy and patient survival. Identification of Bacillus anthracis, the causative agent of the zoonosis anthrax, can be challenging when querying specific nucleotide sequences such as in small subunit rRNA (16S rRNA) genes, which are commonly used for typing of bacteria. This study analyzed on a broad genomic scale a cryptic and hitherto underappreciated allelic variant of the bacterium’s 16S rRNA genes and their transcripts using a set of in situ, in vitro, and in silico assays and found significant intra- and intergenomic heterogeneity in the distribution of the allele and overall rRNA operon copy numbers. This allelic variation was uniquely species specific, which enabled sensitive pathogen detection on both DNA and transcript levels. The methodology used here is likely also applicable to other pathogens that are otherwise difficult to discriminate from their less harmful relatives.
Electron Beam Induced Current Investigations of Electrical Inhomogeneities with High Spatial Resolution

Electron beam induced current (EBIC) microscopy is a very promising SEM technique for the study of diffusion length an~ . deple~ion _region width inhomogeneities with high spatial resolution. HowevE;r, t~is resolution is limited by the dimensions of the electron-hole pair generation region. In this paper the possibilities to improve spatial resolution are discussed. Electron beam tomography, i.e. the reconstruction of physical property distributions from sets of two-dimensional images, seems to be the most promising for this purpose. It is shown that in the case of dislocations it is possible to obtain information about dislocation impurity atmosphere parameters from EBIC measurements. The spatial resolution limitations in SEM techniques are discussed and it is shown that for many structures the spatial resolution is restricted by electron beam damage.
Bilirubin Oxidation End Products (BOXes) Induce Neuronal Oxidative Stress Involving the Nrf2 Pathway

Delayed ischemic neurological deficit (DIND) is a severe complication after subarachnoid hemorrhage (SAH). Previous studies have suggested that bilirubin oxidation end products (BOXes) are probably associated with the DIND after SAH, but there is a lack of direct evidence yet even on cellular levels. In the present study, we aim to explore the potential role of BOXes and the involved mechanisms in neuronal function. We synthesized high-purity (>97%) BOX A and BOX B isomers. The pharmacokinetics showed they are permeable to the blood-brain barrier. Exposure of a moderate concentration (10 or 30 μM) of BOX A or BOX B to isolated primary cortical neurons increased the production of reactive oxygen species. In the human neuroblastoma SH-SY5Y cells, BOX A and BOX B decreased the mitochondrial membrane potential and enhanced nuclear accumulation of the protein Nrf2 implicated in oxidative injury repair. In addition, both chemicals increased the mRNA and protein expression levels of multiple antioxidant response genes including Hmox1, Gsta3, Blvrb, Gclm, and Srxn1, indicating that the antioxidant response element (ARE) transcriptional cascade driven by Nrf2 is activated. In conclusion, we demonstrated that primary cortical neurons and neuroblastoma cells undergo an adaptive response against BOX A- and BOX B-mediated oxidative stress by activation of multiple antioxidant responses, in part through the Nrf2 pathway, which provides in-depth insights into the pathophysiological mechanism of DIND after SAH or other neurological dysfunctions related to cerebral hemorrhage.
Preparation of hydrocortisone

The invention relates to a preparation method of a steroid compound, in particular to the preparation of hydrocortisone, which takes 17-hydroxyl-4, 9-diene-pregna-3, 20-diketone as the initiator and is improved by 16, 17-grignard, 9, 11 th and 21st to obtain the hydrocortisone and a hydrocortisone-21st esterified ester thereof. As the production process adopts the existing intermediate of the company as the initiator, the line is concise, the material is easy to obtain, expensive auxiliary materials are saved, a line can be used for obtaining a plurality of products, and the yield and the cost are obviously superior to the historical synthetic method of the hydrocortisone and the derivative thereof; in addition, the adoption of the existing intermediate realizes the doubling production of the triamcinolone products, the hydrocortisone products and the anecortave acetate products, thus greatly reducing the production cost and industrial conditions, wherein, R is equal to -OCOR1, R1 is equal to the alkyl with less than 11carbon atoms.
Resin-modified glass ionomer cement v composite for orthodontic bonding; a randomised controlled trial

eprints@whiterose.ac.uk https://eprints.whiterose.ac.uk/ Reuse This article is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs (CC BY-NC-ND) licence. This licence only allows you to download this work and share it with others as long as you credit the authors, but you can’t change the article in any way or use it commercially. More information and the full terms of the licence here: https://creativecommons.org/licenses/
Faster Attention for Question Answering

In this project (a default final project on the IID track), I built a question-answering system for SQUAD 2.0 by exploring both the BiDAF model [1] through modifications of the default baseline as well as a from scratch implementation of QANet [2], a self-attention [3] based question-answering architecture. The BiDAF modifications which added character embeddings achieved a small, but significant improvement over the baseline model on the test set with F1: 64.083, EM: 60.423. However, the QANet models only nearly matched the baseline BiDAF scoring with character embeddings. Curiously, not only did my QANet under-perform the baseline in model performance, it also turned out to be significantly slower to train and at inference time on GPUs. Though profiling, I found that the QANet model is indeed faster on CPUs, however significantly under-performs the baseline BiDAF model on GPUs because the BiDAF model’s slowest component, the RNN, is implemented as a highly optimized CuDNN routine on GPUs that the custom QANet encoder block did not benefit from. Finally, this profiling also shows that faster attention mechanisms, as explored in the literature [4] [5], are unlikely to improve performance on this particular SQUAD 2.0 workload as additional instruction overhead would likely wash out any performance gains absent better operation compilation for GPUs or a custom GPU kernel.
Micro-financing in the Era of Good Governance: Economic Benefits. An Empirical Study

In an era of good governance, micro-financing can benefit from rules of the game enabling individuals in developing economies to borrow funds while ensuring the institutions lending such funds will be repaid. This study looked at the types of mechanisms making this possible. Even at this exploratory stage, the current study showed the benefit of good governance for both lenders and borrowers. African people, the primary focus of this study, were encouraged to help themselves out of poverty by putting in place well governed institutions, in order to attract desperately needed funding. Data from a micro-segment of African natives found individuals’ familiarity with Non-Bank Financing Institutions (NBFIs), and individuals’ acceptance of good governance directly impacted their likelihood to patronise these NBFIs. This study’s contribution is to suggest Micro-financing, implemented the right way, that is, guided by good governance, could be one way to help minority populations get out of poverty.
Appendix A Application of mixreg to PPO and Rainbow

Background Proximal Policy Optimization (PPO) [12] introduces a novel objective function, resulting in a policy gradient method which is simpler, more general and performs better than previous work Trust Region Policy Optimization (TRPO) [10]. In each update, the algorithm collects a batch of transition samples using a rollout policy πθold(at|st) and maximizes a clipped surrogate objective: LCLIP(θ) = Êt [min(rt(θ)At, clip(rt(θ), 1− , 1 + )At)] , (9) where rt(θ) denotes the probability ratio rt(θ) = πθ(at|st) πθold (at|st) , At is the advantage at timestep t estimated by generalized advantage estimation (GAE) [11] and is a hyperparameter controlling the width of the clipping interval. GAE makes use of a learned state-value function V (s) for computing the advantage estimation. To learn the value function, the following loss is adopted:
SofGAN: A Portrait Image Generator with Dynamic Styling

Recently, Generative Adversarial Networks (GANs) have been widely used for portrait image generation. However, in the latent space learned by GANs, different attributes, such as pose, shape, and texture style, are generally entangled, making the explicit control of specific attributes difficult. To address this issue, we propose a SofGAN image generator to decouple the latent space of portraits into two subspaces: a geometry space and a texture space. The latent codes sampled from the two subspaces are fed to two network branches separately, one to generate the 3D geometry of portraits with canonical pose, and the other to generate textures. The aligned 3D geometries also come with semantic part segmentation, encoded as a semantic occupancy field (SOF). The SOF allows the rendering of consistent 2D semantic segmentation maps at arbitrary views, which are then fused with the generated texturemaps and stylized to a portrait photo using our semantic instance-wise module. Through extensive experiments, we show that our system can generate high-quality portrait images with independently controllable geometry and texture attributes. The method also generalizes well in various applications, such as appearance-consistent facial animation and dynamic styling.
Enterprise Analytics using Graph Database and Graph-based Deep Learning

In a business-to-business (B2B) customer relationship management (CRM) use case, each client is a potential business organization/company with a solid business strategy and focused and rational decisions. This paper introduces a graph-based analytics approach to improve CRM within a B2B environment. In our approach, in the first instance, we have designed a graph database using the Neo4j platform. Secondly, the graph database has been investigated by using data mining and exploratory analysis coupled with cypher graph query language. Specifically, we have applied the graph convolution network (GCN) to enable CRM analytics to forecast sales. This is the first step towards a GCN-based binary classification based on graph databases in the domain of B2B CRM. We evaluate the performance of the proposed GCN model on graph databases and compare it with Random Forest (RF), Convolutional Neural Network (CNN), and Artificial Neural Network (ANN). The proposed GCN approach is further augmented with the shortest path and eigenvector centrality attribute to significantly improve the accuracy of sales prediction. Experimental results reveal that the proposed graph-based deep learning approach outperforms the Random Forests (RsF) and two deep learning models, i.e., CNN and ANN under different combinations of graph features.
AI-based Aortic Vessel Tree Segmentation for Cardiovascular Diseases Treatment: Status Quo

The aortic vessel tree is composed of the aorta and its branching arteries, and plays a key role in supplying the whole body with blood. Aortic diseases, like aneurysms or dissections, can lead to an aortic rupture, whose treatment with open surgery is highly risky. Therefore, patients commonly undergo drug treatment under constant monitoring, which requires regular inspections of the vessels through imaging. The standard imaging modality for diagnosis and monitoring is computed tomography (CT), which can provide a detailed picture of the aorta and its branching vessels if completed with a contrast agent, called CT angiography (CTA). Optimally, the whole aortic vessel tree geometry from consecutive CTAs is overlaid and compared. This allows not only detection of changes in the aorta, but also of its branches, caused by the primary pathology or newly developed. When performed manually, this reconstruction requires slice by slice contouring, which could easily take a whole day for a single aortic vessel tree, and is therefore not feasible in clinical practice. Automatic or semi-automatic vessel tree segmentation algorithms, however, can complete this task in a fraction of the manual execution time and run in parallel to the clinical routine of the clinicians. In this paper, we systematically review computing techniques for the automatic and semi-automatic segmentation of the aortic vessel tree. The review concludes with an in-depth discussion on how close these state-of-the-art approaches are to an application in clinical practice and how active this research field is, taking into account the number of publications, datasets and challenges.
Disentangled Lifespan Face Synthesis

A lifespan face synthesis (LFS) model aims to generate a set of photo-realistic face images of a person’s whole life, given only one snapshot as reference. The generated face image given a target age code is expected to be age-sensitive reflected by bio-plausible transformations of shape and texture, while being identity preserving. This is extremely challenging because the shape and texture characteristics of a face undergo separate and highly nonlinear transformations w.r. t. age. Most recent LFS models are based on generative adversarial networks (GANs) whereby age code conditional transformations are applied to a latent face representation. They benefit greatly from the recent advancements of GANs. However, without explicitly disentangling their latent representations into the texture, shape and identity factors, they are fundamentally limited in modeling the nonlinear age-related transformation on texture and shape whilst preserving identity. In this work, a novel LFS model is proposed to disentangle the key face characteristics including shape, texture and identity so that the unique shape and texture age transformations can be modeled effectively. This is achieved by extracting shape, texture and identity features separately from an encoder. Critically, two transformation modules, one conditional convolution based and the other channel attention based, are designed for modeling the nonlinear shape and texture feature transformations respectively. This is to accommodate their rather distinct aging processes and ensure that our synthesized images are both age-sensitive and identity preserving. Extensive experiments show that our LFS model is clearly superior to the state-of-the-art alternatives. Codes and demo are available on our project website: https://senhe.github. io/projects/iccv_2021_lifespan_face.
OO Dra: An Algol-type Binary Formed through an Extremely Helium-poor Mass Accretion Revealed by Asteroseismology

Based on 2 minutes of Transiting Exoplanet Survey Satellite data, we analyzed intrinsic oscillations of the primary component and identified seven confident independent δ Scuti frequencies (f 1, f 2, f 3, f 4, f 7, f 11, and f 12). Both single-star evolutionary models and mass-accreting models are computed to reproduce the δ Scuti frequencies and the fitting results match well with each other. The stellar parameters of the primary star yielded by asteroseismology are M = 1.92−0.02+0.10 M ⊙, Z = 0.011−0.001+0.006 , R = 2.068−0.007+0.050 R ⊙, logg = 4.090−0.002+0.010 , T eff = 8346−320+244 K, and L = 18.65−2.82+3.31 L ⊙, which match well with the dynamic ones using the binary model. Furthermore, our asteroseismic results show that OO Dra is another Algol system that has just undergone the rapid mass-transfer stage. The fitting results of single-star evolutionary models indicate that the pulsator is a helium-poor star with an age of 8.22−1.33+0.12 Myr, and the further mass-accreting models show that the primary star looks like an almost unevolved star formed by an extremely helium-poor mass accretion in the Case A evolutionary scenario.
Stellar labels for hot stars from low-resolution spectra. I. The HotPayne method and results for 330,000 stars from LAMOST DR6

We set out to determine stellar labels from low-resolution survey spectra of hot stars, specifically OBA stars with T e ff ≳ 7500 K. This fills a gap in the scientific analysis of large spectroscopic stellar surveys such as LAMOST, which offers spectra for millions of stars at R ∼ 1800 and covers 3800 Å ≤ λ ≤ 9000 Å. We first explore the theoretical information content of such spectra to determine stellar labels via the Cramér-Rao bound. We show that in the limit of perfect model spectra and observed spectra with signal-to-noise ratio ∼ 50 − 100, precise estimates are possible for a wide range of stellar labels: not only the effective temperature, T e ff , surface gravity, log g , and projected rotation velocity, v sin i , but also the micro-turbulence velocity, v mic , helium abundance, N He / N tot , and the elemental abundances [C/H], [N/H], [O/H], [Si/H], [S/H], and [Fe/H]. Our analysis illustrates that the temperature regime of T e ff ∼ 9500 K is challenging as the dominant Balmer and Paschen line strengths vary little with T e ff . We implement the simultaneous fitting of these 11 stellar labels to LAMOST hot-star spectra using the P AYNE approach, drawing on Kurucz’s ATLAS12/SYNTHE local thermodynamic equilibrium spectra as the underlying models. We then obtain stellar parameter estimates for a sample of about 330000 hot stars with LAMOST spectra, an increase by about two orders of magnitude in sample size. Among them, about 260000 have good Gaia parallaxes ( ω/σ ω > 5), and their luminosities imply that ≳ 95% of them are luminous stars, mostly on the main sequence; the rest are evolved lower luminosity stars, such as hot subdwarfs and white dwarfs. We show that the fidelity of the results, particularly for the abundance estimates, is limited by the systematics of the underlying models as they do not account for nonlocal thermodynamic equilibrium effects. Finally, we show the detailed distribution of v sin i of stars with 8000 − 15000 K, illustrating that it extends to a sharp cutoff at the critical rotation velocity, v crit , across a wide range of temperatures.
Optimizing Probabilities in Probabilistic Logic Programs

Abstract Probabilistic logic programming is an effective formalism for encoding problems characterized by uncertainty. Some of these problems may require the optimization of probability values subject to constraints among probability distributions of random variables. Here, we introduce a new class of probabilistic logic programs, namely probabilistic optimizable logic programs, and we provide an effective algorithm to find the best assignment to probabilities of random variables, such that a set of constraints is satisfied and an objective function is optimized.
Pattern Recognition in Vital Signs Using Spectrograms

Spectrograms visualize the frequency components of a given signal which may be an audio signal or even a time-series signal. Audio signals have higher sampling rate and high variability of frequency with time. Spectrograms can capture such variations well. But, vital signs which are time-series signals have less sampling frequency and low-frequency variability due to which, spectrograms fail to express variations and patterns. In this paper, we propose a novel solution to introduce frequency variability using frequency modulation on vital signs. Then we apply spectrograms on frequency modulated signals to capture the patterns. The proposed approach has been evaluated on 4 different medical datasets across both prediction and classification tasks. Significant results are found showing the efficacy of the approach for vital sign signals. The results from the proposed approach are promising with an accuracy of 91.55% and 91.67% in prediction and classification tasks respectively.