Miracle-dz/UniFAR-FTUs huggingface dataset:
Ab initio calculation of spin fluctuation spectra using time-dependent density functional perturbation theory, plane waves, and pseudopotentials

We present an implementation of time-dependent density functional perturbation theory for spin fluctuations, based on plane waves and pseudopotentials. We compute the dynamic spin susceptibility self-consistently by solving the time-dependent Sternheimer equation, within the adiabatic local density approximation to the exchange and correlation kernel. We demonstrate our implementation by calculating the spin susceptibility of representative elemental transition metals, namely bcc Fe, fcc Ni, and bcc Cr. The calculated magnon dispersion relations of Fe and Ni are in agreement with previous work. The calculated spin susceptibility of Cr exhibits a soft-paramagnon instability, indicating the tendency of the Cr spins to condense in an incommensurate spin density wave phase, in agreement with experiment.
A Model to Predict In-Hospital Mortality in HIV/AIDS Patients with Pneumocystis Pneumonia in China: The Clinical Practice in Real World

We aimed to develop and validate a predictive model to evaluate in-hospital mortality risk in HIV/AIDS patients with PCP in China. 1001 HIV/AIDS patients with PCP admitted in the Beijing Ditan hospital from August 2009 to January 2018 were included in this study. Multivariate Cox proportional hazard model was used to identify independent risk factors of death, and a predictive model was devised based on risk factors. The overall in-hospital mortality was 17.3%. The patients were randomly assigned into derivation cohort (801cases) and validation cohort (200 cases) in 8:2 ratio, respectively, in which in derivation cohort we found that 7 predictors, including LDH >350U/L, HR>130 times/min, room air PaO2 <70mmHg, later admission to ICU, Anemia (HGB≤90g/L), CD4<50cells/ul, and development of a pneumothorax, were associated with poor prognosis in HIV/AIDS patients with PCP and were included in the predictive model. The model had excellent discrimination with AUC of 0.904 and 0.921 in derivation and validation cohort, respectively. The predicted scores were divided into two groups to assess the in-hospital mortality risk: low-risk group (0-11 points with mortality with 2.15-12.77%) and high-risk group (12-21 points with mortality with 38.78%-81.63%). The cumulative mortality rate also indicated significant difference between two groups with Kaplan-Meier curve (p<0.001). A predictive model to evaluate mortality in HIV/AIDS patients with PCP was constructed based on routine laboratory and clinical parameters, which may be a simple tool for physicians to assess the prognosis in HIV/AIDS patients with PCP in China.
’ s repository of research publications and other research outputs An Investigation into Creep Cavity Development in 316 H Stainless Steel Journal Item

Creep-induced cavitation is an important failure mechanism in steel components operating at high temperature. Robust techniques are required to observe and quantify creep cavitation. In this paper, the use of two complementary analysis techniques: small-angle neutron scattering (SANS), and quantitative metallography, using scanning electron microscopy (SEM), is reported. The development of creep cavities that is accumulated under uniaxial load has been studied as a function of creep strain and life fraction, by carrying out interrupted tests on two sets of creep test specimens that are prepared from a Type-316H austenitic stainless steel reactor component. In order to examine the effects of pre-strain on creep damage formation, one set of specimens was subjected to a plastic pre-strain of 8%, and the other set had no pre-strain. Each set of specimens was subjected to different loading and temperature conditions, representative of those of current and future power plant operation. Cavities of up to 300 nm in size are quantified by using SANS, and their size distribution, as a function of determined creep strain. Cavitation increases significantly as creep strain increases throughout creep life. These results are confirmed by quantitative metallography analysis.
The second bovine β -galactoside- α 2,6-sialyltransferase (ST6Gal II): genomic organization and stimulation of its in vitro expression by IL-6 in bovine mammary epithelial cells

We have cloned a cDNA sequence encoding the second bovine β -galactoside- α 2,6-sialyltransferase whose sequence shares more than 75% of identity with hST6Gal II cDNA coding sequence. The bovine gene, located on BTA 11, spans over 50 kbp with ﬁve exons (E1–E5) containing the 1488 bp open reading frame and a 5 (cid:2) -untranslated exon (E0). The gene expression pattern reveals a speciﬁc tissue distribution (brain, lungs, spleen, salivary, and mammary glands) compared to ST6Gal I which is ubiquitously expressed. We identiﬁed for bovine ST6Gal II three kinds of transcripts which differ by their 5 (cid:2) -untranslated regions. Among them, two transcripts are brain speciﬁc whereas the third one is found in all of the tissues expressing the gene. Two pFlag- bST6Gal II vector constructions were separately transfected in COS-1 cells in order to express either membrane-bound or soluble active forms of ST6Gal II. Enzymatic assays with these two forms indicated that the enzyme used the LacdiNAc struc-ture (GalNAc β 1,4GlcNAc) as a better acceptor substrate than the Type II (Gal β 1-4GlcNAc) disaccharide. Moreover, the enzyme’s efﬁciency is improved when the acceptor substrate is provided as a free oligosaccharide rather than as a protein-bound oligosaccharide. In order to investigate the potential role of ST6Gal II during the acute phase of inﬂammation, we used primary cultures of bovine mammary epithelial cells which were stimulated with pro-inﬂammatory cytokines. It appears that the ST6Gal II gene was upregulated in cells stimulated by IL-6. This result suggested that α 2,6-sialylation mediated by this gene could contribute to organism’s response to infections.
The Effect of Fascia Relaxation and Mobilization of the Hyoid on the Range of Motion, Pain, and Deviation of the Hyoid in Neck Pain

Background: Neck pain can be caused by any structure in the neck, such as intervertebral discs, ligaments, muscles, facet joints, dura mater, and nerve roots. The hyoid bone is a structure that is also related to head and neck posture, neck movement and pain, but there are no studies on hyoid deviation, neck pain, and range of motion (ROM). Objects: The purpose of this study was to investigate the effect of fascia relaxation and mobilization of the hyoid bone on the ROM, pain, and lateral deviation of the hyoid bone. Methods: Twenty-five patients with neck pain identified by the lateral motion test (10 males [35.13 ± 7.67 years, 172.69 ± 3.90 cm, 78.77 ± 6.96 kg] and 15 females [35.13 ± 10.05 years, 161.11 ± 4.09 cm, 52.59 ± 2.98 kg]) was chosen randomly. Baseline values for pain, neck ROM, and lateral deviation in the hyoid bone were recorded using a visual analogue scale (VAS), goniometer, and tape measure. Then, each patient was treated with hyoid fascia relaxation and mobilization, and all results were recorded after intervention. Comparison of the results before and after intervention was analyzed using paird t-test (p 0.05). Conclusion: Fascia relaxation and hyoid mobilization could improve the ROM of cervical extension, asymmetry of the cervical rotation and neck pain.
Information versus Investment

We quantify the real implications of trade-offs between firm information disclosure and long-term investment efficiency. We estimate a dynamic equilibrium model in which firm managers confront realistic incentives to misreport earnings and distort their real investment choices. The model implies a socially optimal level of disclosure regulation that exceeds the estimated value. Counterfactual analysis reveals that eliminating earnings misreporting completely through disclosure regulation incentivizes managers to distort real investment. Lower earnings informativeness raises the cost of capital, which results in a 5.7% drop in average firm value, but more modest effects on social welfare and aggregate growth.
AN EMPIRICAL ANALYSIS ON DETERMINANTS OF FOOD SECURITY AMONG FEMALE-HEADED HOUSEHOLDS IN SOUTH AFRICA

The importance of food security at household level has been identified as one of the important priorities globally. The second sustainable development goal (SDG2) involves dealing with hunger and food insecurity to the extent that no one goes to bed with an empty stomach. The literature shows that female-headed households, which in most cases are single-parent households - unless the de-facto head principle is, and a woman is a head with a man present- are the most vulnerable households in terms of both poverty and food security. The paper intends to investigate the extent of food insecurity among female-headed households in South Africa using the 2018 General Household Survey (GHS) that collected data from all the nine provinces in the country. The paper employs both descriptive statistics and inferential statistics with a regression model estimated to determine the household factors that are significant determinants of food security at household level among the female-headed household. The apriori expectation is that age, race, income and size of the household will be among the important determinants of food security. The study will also check if those that are receiving a grant are better off than those that do not receive one. The results of the paper will help policy makers to have a more focused approach in dealing with food insecurity at household level.
Emotional Quotient in Medical Students and its Influence on the Academic Performance A Cross Sectional Study

Original Research Article Background: During medical education students need various psychological constructs to sail through the course successfully. Emotional Intelligence is known for its influence on various aspects of life, especially in a trainee doctor, including claimed influence on academic performance. Very few studies have been done on medical students with these factors; in this background present study was planned. Method: Medical students who had cleared their final medical exam were included in the study. After consenting self-designed sociodemographic and academic performance scales were given along with EQ test by Chadha & Singh. Statistical analysis was done using IBM’s SPSS software. Results: Sample consisted of 167 participants with mean age of 23 years. On EQ assessment students had high Total EQ, Sensitivity and Competency scores but low maturity. EQ didn’t show any association with academic performance (r = -0.03, p>0.05). Males had lower academic performance than the female students (t = -2.89, p<0.01). Psychoactive substance users performed poorly in academics than non-users (t = 3.74, p<0.01). Multiple regression analysis predicted that substance use influences the academic performance significantly. Conclusion: EQ doesn’t mediate academic performance especially in group with above average intelligence. Gender and psychoactive substance use can influence academic scores.
Bargaining success in the reform of the Eurozone

This article provides a systematic assessment of bargaining success in the reform of the Eurozone 2010 to 2015. Theoretically, we develop an argument about preferences and institutions as determinants of bargaining success and contrast this argument with an alternative account privileging states’ power resources. Empirically, we conduct a statistical analysis of new data covering all key reform proposals. Our findings are three-fold. First, contrary to a conventional narrative of German dominance, the negotiations produced no clear winners and losers. Second, while power resources were of limited importance, holding preferences that were centrist or close to the European Commission favored bargaining success—particularly when adoption only required the support of a qualified majority. Third, these descriptive and explanatory results reflect dynamics of compromise and reciprocity.
Shape Recovery on Low-Quality or Missing Images

Shape segmentation or recovery is an essential task in image processing, especially in remote sensing imagery. Due to the complicated imaging conditions, satellite or natural images are usually noisy and blurred, which makes it challenging to recognize the actual shapes of ground objects. In this project, we aim at recovering shapes of natural and remote sensing objects (e.g. aircraft) based on an MCMC sampling algorithm and operations learned from the class. We found that the MCMC sampling algorithm often generates artifacts which impact the quality of shape recovery. We propose a post-processing algorithm that recovers the shape without artifacts. According to the test on objects with different noise levels and real-world radar objects, we demonstrate the effectiveness of our method.
UvA-DARE ( Digital Academic Repository ) Persuasion through facts and feelings : integrating affect and cognition into behavioral decision models and health

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Cosmological horizons, Noether charges and entropy

It has recently been shown that, in the vicinity of their event horizons, black holes exhibit an infinite-dimensional symmetry. This symmetry captures relevant physical information about the black hole, and in particular about its thermodynamics. Here, we show that the same holds in a cosmological setup. More precisely, we show that around the de Sitter cosmological horizon, an infinite set of diffeomorphisms preserving a sensible set of boundary conditions emerges. These boundary conditions are similar to those considered by Price and Thorne in the context of the membrane paradigm, and permit to accommodate interesting gravity solutions. As for other boundary conditions considered previously, they are preserved by an infinite-dimensional asymptotic isometry algebra that includes supertranslations and two copies of the Virasoro algebra. This symmetry has associated a set of Noether charges that turn out to be conserved, finite, and integrable. We derive these charges explicitly using the covariant formalism and analyze their physical meaning by evaluating them to the case of de Sitter space. In this case, the zero-mode of the charge is found to account for the Gibbons–Hawking entropy of the cosmological horizon. We then consider a much more general set of solutions, including asymptotically de-Sitter black holes and asymptotically Taub–NUT–de Sitter black holes.
Reinforcement Learning for Clinical Decision Support in Critical Care: Comprehensive Review

Background Decision support systems based on reinforcement learning (RL) have been implemented to facilitate the delivery of personalized care. This paper aimed to provide a comprehensive review of RL applications in the critical care setting. Objective This review aimed to survey the literature on RL applications for clinical decision support in critical care and to provide insight into the challenges of applying various RL models. Methods We performed an extensive search of the following databases: PubMed, Google Scholar, Institute of Electrical and Electronics Engineers (IEEE), ScienceDirect, Web of Science, Medical Literature Analysis and Retrieval System Online (MEDLINE), and Excerpta Medica Database (EMBASE). Studies published over the past 10 years (2010-2019) that have applied RL for critical care were included. Results We included 21 papers and found that RL has been used to optimize the choice of medications, drug dosing, and timing of interventions and to target personalized laboratory values. We further compared and contrasted the design of the RL models and the evaluation metrics for each application. Conclusions RL has great potential for enhancing decision making in critical care. Challenges regarding RL system design, evaluation metrics, and model choice exist. More importantly, further work is required to validate RL in authentic clinical environments.
On the support recovery of marginal regression.

Leading methods for support recovery in high-dimensional regression, such as Lasso, have been well-studied and their limitations in the context of correlated design have been characterized with precise incoherence conditions. In this work, we present a similar treatment of selection consistency for marginal regression (MR), a computationally efficient family of methods with connections to decision trees. Selection based on marginal regression is also referred to as covariate screening or independence screening and is a popular approach in applied work, especially in ultra high-dimensional settings. We identify the underlying factors---which we denote as \emph{MR incoherence}---affecting MR's support recovery performance. Our near complete characterization provides a much more nuanced and optimistic view of MR in comparison to previous works. To ground our results, we provide a broad taxonomy of results for leading feature selection methods, relating the behavior of Lasso, OMP, SIS, and MR. We also lay the foundation for interesting generalizations of our analysis, e.g., to non-linear feature selection methods and to more general regression frameworks such as a general additive models.
On the Importance of Video Action Recognition for Visual Lipreading

We focus on the word-level visual lipreading, which requires to decode the word from the speaker's video. Recently, many state-of-the-art visual lipreading methods explore the end-to-end trainable deep models, involving the use of 2D convolutional networks (e.g., ResNet) as the front-end visual feature extractor and the sequential model (e.g., Bi-LSTM or Bi-GRU) as the back-end. Although a deep 2D convolution neural network can provide informative image-based features, it ignores the temporal motion existing between the adjacent frames. In this work, we investigate the spatial-temporal capacity power of I3D (Inflated 3D ConvNet) for visual lipreading. We demonstrate that, after pre-trained on the large-scale video action recognition dataset (e.g., Kinetics), our models show a considerable improvement of performance on the task of lipreading. A comparison between a set of video model architectures and input data representation is also reported. Our extensive experiments on LRW shows that a two-stream I3D model with RGB video and optical flow as the inputs achieves the state-of-the-art performance.
On the formality of the little disks operad in positive characteristic

Using a variant of the Boardman–Vogt tensor product, we construct an action of the Grothendieck–Teichmüller group on the completion of the little n ‐disks operad En . This action is used to establish a partial formality theorem for En with mod p coefficients and to give a new proof of the formality theorem in characteristic zero.
Characterization of mixed n-Jordan homomorphisms and pseudo n-Jordan homomorphisms

In this article, a new notion of $n$-Jordan homomorphism namely the mixed $n$-Jordan homomorphism is introduced. It is proved that how a mixed $(n+1)$-Jordan homomorphism can be a mixed $n$-Jordan homomorphism and vice versa. By means of some examples, it is shown that the mixed $n$-Jordan homomorphisma are different from the $n$-Jordan homomorphisms and the pseudo $n$-Jordan homomorphisms. As a consequence, it shown that every mixed Jordan homomorphism from Banach algebra $\mathcal{A}$ into commutative semisimple Banach algebra $\mathcal{B}$ is automatically continuous. Under some mild conditions, every unital pseudo $3$-Jordan homomorphism can be a homomorphism.
The Origins of Protostellar Core Angular Momenta

We present the results of a suite of numerical simulations designed to explore the origin of the angular momenta of protostellar cores. Using the hydrodynamic grid code Athena with a sink implementation, we follow the formation of protostellar cores and protostars (sinks) from the subvirial collapse of molecular clouds on larger scales to investigate the range and relative distribution of core properties. We find that the core angular momenta are relatively unaffected by large-scale rotation of the parent cloud; instead, we infer that angular momenta are mainly imparted by torques between neighboring mass concentrations and exhibit a log-normal distribution. Our current simulation results are limited to size scales ∼0.05 pc (∼104 au), but serve as first steps toward the ultimate goal of providing initial conditions for higher-resolution studies of core collapse to form protoplanetary disks.
Deep Radiomics for Brain Tumor Detection and Classification from Multi-Sequence MRI

Glioma constitutes 80% of malignant primary brain tumors and is usually classified as HGG and LGG. The LGG tumors are less aggressive, with slower growth rate as compared to HGG, and are responsive to therapy. Tumor biopsy being challenging for brain tumor patients, noninvasive imaging techniques like Magnetic Resonance Imaging (MRI) have been extensively employed in diagnosing brain tumors. Therefore automated systems for the detection and prediction of the grade of tumors based on MRI data becomes necessary for assisting doctors in the framework of augmented intelligence. In this paper, we thoroughly investigate the power of Deep ConvNets for classification of brain tumors using multi-sequence MR images. We propose novel ConvNet models, which are trained from scratch, on MRI patches, slices, and multi-planar volumetric slices. The suitability of transfer learning for the task is next studied by applying two existing ConvNets models (VGGNet and ResNet) trained on ImageNet dataset, through fine-tuning of the last few layers. LOPO testing, and testing on the holdout dataset are used to evaluate the performance of the ConvNets. Results demonstrate that the proposed ConvNets achieve better accuracy in all cases where the model is trained on the multi-planar volumetric dataset. Unlike conventional models, it obtains a testing accuracy of 95% for the low/high grade glioma classification problem. A score of 97% is generated for classification of LGG with/without 1p/19q codeletion, without any additional effort towards extraction and selection of features. We study the properties of self-learned kernels/ filters in different layers, through visualization of the intermediate layer outputs. We also compare the results with that of state-of-the-art methods, demonstrating a maximum improvement of 7% on the grading performance of ConvNets and 9% on the prediction of 1p/19q codeletion status.