Miracle-dz/UniFAR-FTUs huggingface dataset:
Nondestructive depth‐resolved chemical state analysis of (La,Sr)MnO3 film under high temperature

Fluorescence yield (FY) X‐ray absorption spectroscopic experiments with a grazing‐ exit geometry for detection of emitted fluorescence were carried out. The observed XAFS spectra provide the information about depth‐resolved chemical state in the surface layer of film materials through the take‐off angle dependence of the escape depth of the fluorescent X‐ray emitted from the sample. For La1−xSrxMnO3 films which are the oxygen electrode materials of a solid‐oxide fuel cell (SOFC), the Mn K XANES spectra were measured using undulator radiation at BL37XU in SPring‐8 synchrotron radiation facility. The results of the conventional FY‐XAS indicated differences due to distortion of MnO6 caused by different temperature, oxygen partial pressure p(O2) and voltage loading conditions in the spectrum profiles. From the results of the angle‐resolved measurements at 973 K in air, a depth‐dependent variation of the Mn K XANES profile in surface region of the film was observed. Though such depth‐dependence was also measured in case of lower p(O2) or voltage loading conditions at the same temperature, the absorbance proportion of absorbing bands contributing the change was different among the split 1s −>4p transition bands. These results indicate a difference in surface region caused by a difference between the surface and the inside of the film in the chemical potential of oxygen. Copyright © 2010 John Wiley & Sons, Ltd.
Screened hybrid density functional study on Sr2Nb2O7 for visible light photocatalysis

The electronic structure of pure Sr2Nb2O7 and its electronic band position are being aligned with respect to the water oxidation/reduction potential level using hybrid functional (HSE06) theory. The experimental band gap (3.90 eV) of pure Sr2Nb2O7 can be reproduced (3.92 eV) using this level of theory. The cationic-anionic co-doping (Mo-N) in layered perovskite Sr2Nb2O7 structure reduces the band gap significantly, and its electronic band position is excellent for the visible-light photocatalysis. The respective cationic and anionic mono-doped systems create an occupied or unoccupied impurity states in the band gap, which can reduce the efficiency of the photocatalysis.
Quantum size effects in metal particles and thin films by an extended RPA

The RPA is extended in the framework of linear response theory to allow for the treatment of a finite free electron gas. The dielectric function taking quantum size effects into account, is calculated and shown to be finite in the limit of small wavevectors. The theory is applied to a simple free electron model for a small metal particle and analytical expressions are obtained for the electronic polarizability of a thin film and for the optical properties in the plasma resonance region.
Res-WGAN: Image Classification for Plant Small-scale Datasets

Background: Artificial identification of rare plants is an important yet challenging problem in plant taxonomy. Although deep learning-based method can accurately predict rare plant category from training samples, accuracy requirements of only few experts are satisfied due to the small sample size. Thus, effective data augmentation is vital to improve the expressive power and robustness of deep learning models, especially for plant small-sample classification tasks. Different from traditional methods, a generative adversarial network (GAN) can mimic the distribution of primary data and produce similar but nonidentical samples. Data augmentation for automated classification of rare plant samples with GAN has not been studied for a long time. Result: In this study, we present a novel GAN model, referred to as residual Wasserstein GAN (Res-WGAN), for extending rare plant dataset efficiently. With a premise of minimized size of training parameters, residual block serves as elementary building block to increase the model’s expression ability. A loss function based on Wasserstein distance with gradient penalty is used to ensure diversity of output. Moreover, we draw on the super-resolution GAN (SRGAN) and consider perceptual loss into the function. Perceptual loss guarantees similarity between generated samples and original samples in high-dimensional features. Benefiting from these improvements, the expanded dataset improves the classification accuracy on the residual neural network (ResNet), a classic convolutional neural network, and restrains the overfitting phenomenon effectively by using transfer learning. More similar experiments are presented in this paper to prove the practicality of the model. Conclusions: Our proposed method produces better diversity and sharpness of generated samples and more accurate classification results than other data augmentation methods. In addition, a user study confirms that it is an ideal alternative strategy for small-scale rare plant identification. Developing a robust and effective small-scale plant classification method to replace expert testimony is highly relevant for agricultural automation development.
DsUnet: A New Network Structure for Detection and Segmentation of Ultrasound Breast Lesions

Breast cancer is one of the leading causes of death among the women worldwide. The clinical medical system urgently needs an accurate and automatic breast segmentation method in order to detect the breast ultrasound lesions. Recently, some studies show that deep learning methods based on fully convolutional network, have demonstrated a competitive performance in breast ultrasound segmentation. However, some features are missed in the Unet in case of down-sampling that results in a low segmentation accuracy. Furthermore, there is a semantic gap between the feature maps of decoder and encoder in Unet, so the simple fusion of high and low level features is not conducive to the semantic classification of pixels. In addition, the poor quality of breast ultrasound also affects the accuracy of image segmentation. To solve these problems, we propose a new end-toend network model called Dense skip Unet (DsUnet), which consists of the Unet backbone, short skip connection and deep supervision. The proposed method can effectively avoid the missing of feature information caused by down-sampling and implement the fusion of multilevel semantic information. We used a new loss function to optimize the DsUnet, which is composed of a binary cross-entropy and dice coefficient. We employed the True Positive Fraction (TPF), False Positives per image (FPs) and F -measure as performance metrics for evaluating various methods. In this paper, we adopted the UDIAT 212 dataset and the experimental results validate that our new approach achieved better performance than other existing methods in detecting and segmenting the ultrasound breast lesions. When we used the DsUnet model and new loss function (binary cross-entropy + dice coefficient), the best performance indexes can be achieved, i.e., 0.87 in TPF, 0.13 in FPs/image and 0.86 in F-measure.
The Interlanguage of Moroccan EFL learners: The Case of Complaints

The present study aims to compare complaint realizations of Moroccan learners of English (MLE) with American English speakers (AE) and Moroccan Arabic speakers (MA) through an interlanguage pragmatic analysis. The study is carried out with reference to the degree of directness. The study involves 135 subjects: 108 of them are Moroccan students from Faculty of Arts and Humanities of Ibn Toufail. Kenitra. 45 MLE participants were recruited from the English department as the second group of informants, while the 45 MA group were recruited from the department of History and Geography. The 45 American participants included some volunteers from American Peace Corps Morocco and students from Duke university, North Carolina. A written discourse completion test/ task was administered to the participants both native and EFL learners in order to elicit their complaint speech act productions through five hypothetical complaint situations. Responses of Moroccan EFL learners were reviewed to verify whether they approach native speakers complaint norms or Moroccan Arabic norms in terms of directness. In the analysis of the data, all responses were categorized according to Trosborg’s (1995) complaint speech act set. The results show that Learners of English in higher education do not possess the desirable norms of complaint strategies as compared to native speakers of English. Additionally, MLEs exhibit pragmatic transfer from Moroccan Arabic in their use of high complaint strategies. The study ends up with a series of suggestions and recommendations that aim to enhance linguistic and cultural understanding of the target language.
Nomogram for predicting axillary lymph node status after neoadjuvant chemotherapy in breast cancer

Background Many breast cancer patients benefit from neoadjuvant chemotherapy (NAC). However, sentinel lymph node biopsy (SLNB) after NAC remains controversial, especially for patients with axillary lymph node metastasis (ALNM) at diagnosis. We developed a nomogram for predicting axillary lymph node (ALN) status after NAC to screen for patients for whom SLNB may be beneficial. Methods A total of 320 cT1–4N0–1M0 breast cancer patients receiving ALN dissection (ALND) after NAC were included. Univariate and multivariate logistic regression analyses determined significant factors for predicting ALN status. Efficacy of the resulting nomogram was assessed using receiver operating characteristic (ROC) and calibration curves, while decision curve analysis (DCA) was used to evaluated net clinical benefit. Our nomogram was validated using female patients grouped according to a diagnosis of node-positive (cN1) or node-negative (cN0) by ultrasound-guided needle biopsy of suspected lymph nodes before NAC. Results Logistic regression analyses indicated that estrogen receptor (ER), Ki67, degree of tumor regression, clinical tumor T stage after NAC, and ALN Breast Imagining-Reporting and Data System (BI-RADS) category after NAC, were associated with ALN status. The resulting nomogram had an area under the curve (AUC) of 0.802 [95% confidence interval (CI), 0.7485–0.8554], and the calibration plot showed strong uniformity between predicted and actual ALN status. DCA indicated a positive net benefit of nomogram predictions in our cohort. After internal validation, the cN1 and cN0 groups had an AUC of 0.7926 (95% CI, 0.7187–0.8665) and 0.8165 (95% CI, 0.7381–0.8949), respectively. The calibration plot indicated better performance in the cN0 group. Conclusions After NAC, some patients may benefit from SLNB. Our nomogram predicts ALN status after NAC and has great potential to assist in clinical decision-making.
Studies on Surface Facets and Chemical Composition of Vapor Grown One-Dimensional Magnetite Nanostructures

Investigations on shape and chemical composition of one-dimensional magnetite nanostructures grown by a catalyst-assisted vapor phase procedure are reported. Intrinsic crystal chemistry (preferred growth of most stable surfaces) could be modulated by seeding the magnetite growth through Au nanoclusters, which led to elongated nanostructures (VLS mode); however, the structures have similar facets as observed in uncatalyzed growth. Geometric and energetic contributions to the evolution of the predominately observed {111} surface facets are discussed on the basis of high-angle annular dark field (HAADF) images and electron energy loss spectroscopy (EELS). The Fe:O stoichiometry in magnetite nanowire was determined by EELS, which manifested the reproducibility of nanowire growth by molecule-based CVD and the slightly nonstoichiometric nature of magnetite (Fe3O4−0.15). In combination with HAADF-TEM techniques, Au nanoclusters were identified on the surface of single-crystalline nanowires, which ably result fro.
Loop effective model for Schwarzschild black hole: A modified 
μ¯
 dynamics

In this article, we introduce a new effective model for the Kantowski-Sachs spacetime in the context of loop quantum gravity, and we use it to evaluate departures from general relativity in the case of Schwarzschild black hole interior. The model is based on an effective Hamiltonian constructed via the regularized Thiemann identities in the $\bar \mu$-scheme. We show that, in contrast with the $\mu_o$-scheme studied in [1], the classical limit imposes certain alterations of Thiemann identities as well as restrictions on the choice of regulators. Once we define the Hamiltonian, we derive the equations of motion for the relevant variables and proceed with the solving using numerical methods, focusing on a specific choice of $\bar \mu$. We establish that for a Schwarzschild black hole interior, the effective dynamics leads to a resolution of the classical singularity and the emergence of an anti-trapped region bounded by a second Killing horizon. We then perform a comparison of the dynamical trajectories and their properties obtained in the new model and some models present in the literature. We finally conclude with few comments on other choices of the regulators and their consequences.
Synthesis and biodistribution of 99mTc-Vancomycin in a model of bacterial infection

Abstract Vancomycin Hydrochloride is an antibiotic produced by the growth of certain strains of Streptomyces orientalis. As Vancomycin Hydrochloride is poorly absorbed after oral administration; it is given intravenously for therapy of systemic infections. Vancomycin was labeled with technetium-99m pertechnetate using SnCl2·2H2O as reducing agent. The labeling efficiency depends on ligand/reductant ratio, pH, and volume of reaction mixture. Radiochemical purity and stability of 99mTc-Vancomycin was determined by thin layer chromatography. Biodistribution studies of 99mTc-Vancomycin were performed in a model of bacterial infection in Sprague–Dawley rats. A significantly higher accumulation of 99mTc-Vancomycin was seen at sites of S. aureus infected animals. Whereas uptake of 99mTc-Vancomycin in turpentine inflamed rats were quite low.
Enabling Collaborative Data Science Development with the Ballet Framework

While the open-source software development model has led to successful large-scale collaborations in building software systems, data science projects are frequently developed by individuals or small teams. We describe challenges to scaling data science collaborations and present a conceptual framework and ML programming model to address them. We instantiate these ideas in Ballet, the first lightweight framework for collaborative, open-source data science through a focus on feature engineering, and an accompanying cloud-based development environment. Using our framework, collaborators incrementally propose feature definitions to a repository which are each subjected to software and ML performance validation and can be automatically merged into an executable feature engineering pipeline. We leverage Ballet to conduct a case study analysis of an income prediction problem with 27 collaborators, and discuss implications for future designers of collaborative projects.
SenSeNet: Neural Keyphrase Generation with Document Structure

Keyphrase Generation (KG) is the task of generating central topics from a given document or literary work, which captures the crucial information necessary to understand the content. Documents such as scientific literature contain rich meta-sentence information, which represents the logical-semantic structure of the documents. However, previous approaches ignore the constraints of document logical structure, and hence they mistakenly generate keyphrases from unimportant sentences. To address this problem, we propose a new method called Sentence Selective Network (SenSeNet) to incorporate the meta-sentence inductive bias into KG. In SenSeNet, we use a straight-through estimator for end-to-end training and incorporate weak supervision in the training of the sentence selection module. Experimental results show that SenSeNet can consistently improve the performance of major KG models based on seq2seq framework, which demonstrate the effectiveness of capturing structural information and distinguishing the significance of sentences in KG task.
Interactive Radiotherapy Target Delineation with 3D-Fused Context Propagation

Gross tumor volume (GTV) delineation on tomography medical imaging is crucial for radiotherapy planning and cancer diagnosis. Convolutional neural networks (CNNs) has been predominated on automatic 3D medical segmentation tasks, including contouring the radiotherapy target given 3D CT volume. While CNNs may provide feasible outcome, in clinical scenario, double-check and prediction refinement by experts is still necessary because of CNNs' inconsistent performance on unexpected patient cases. To provide experts an efficient way to modify the CNN predictions without retrain the model, we propose 3D-fused context propagation, which propagates any edited slice to the whole 3D volume. By considering the high-level feature maps, the radiation oncologists would only required to edit few slices to guide the correction and refine the whole prediction volume. Specifically, we leverage the backpropagation for activation technique to convey the user editing information backwardly to the latent space and generate new prediction based on the updated and original feature. During the interaction, our proposed approach reuses the extant extracted features and does not alter the existing 3D CNN model architectures, avoiding the perturbation on other predictions. The proposed method is evaluated on two published radiotherapy target contouring datasets of nasopharyngeal and esophageal cancer. The experimental results demonstrate that our proposed method is able to further effectively improve the existing segmentation prediction from different model architectures given oncologists' interactive inputs.
Toward a universal model for spatially structured populations

A key question in evolution is how likely a mutant is to take over. This depends on natural selection and on stochastic fluctuations. Population spatial structure can impact mutant fixation probabilities. We introduce a model for structured populations on graphs that generalizes previous ones by making migrations independent of birth and death. We demonstrate that by tuning migration asymmetry, the star graph transitions from amplifying to suppressing natural selection. Our results are universal in the sense that they do not hinge on a modeling choice of microscopic dynamics or update rules. Instead, they depend on migration asymmetry, which can be experimentally tuned and measured.
TF-CR: Weighting Embeddings for Text Classification

Text classification, as the task consisting in assigning categories to textual instances, is a very common task in information science. Methods learning distributed representations of words, such as word embeddings, have become popular in recent years as the features to use for text classification tasks. Despite the increasing use of word embeddings for text classification, these are generally used in an unsupervised manner, i.e. information derived from class labels in the training data are not exploited. While word embeddings inherently capture the distributional characteristics of words, and contexts observed around them in a large dataset, they aren't optimised to consider the distributions of words across categories in the classification dataset at hand. To optimise text representations based on word embeddings by incorporating class distributions in the training data, we propose the use of weighting schemes that assign a weight to embeddings of each word based on its saliency in each class. To achieve this, we introduce a novel weighting scheme, Term Frequency-Category Ratio (TF-CR), which can weight high-frequency, category-exclusive words higher when computing word embeddings. Our experiments on 16 classification datasets show the effectiveness of TF-CR, leading to improved performance scores over existing weighting schemes, with a performance gap that increases as the size of the training data grows.
Near Real-Time Social Distancing in London

During the COVID-19 pandemic, policy makers at the Greater London Authority, the regional governance body of London, UK, are reliant upon prompt and accurate data sources. Large well-defined heterogeneous compositions of activity throughout the city are sometimes difficult to acquire, yet are a necessity in order to learn 'busyness' and consequently make safe policy decisions. One component of our project within this space is to utilise existing infrastructure to estimate social distancing adherence by the general public. Our method enables near immediate sampling and contextualisation of activity and physical distancing on the streets of London via live traffic camera feeds. We introduce a framework for inspecting and improving upon existing methods, whilst also describing its active deployment on over 900 real-time feeds.
TEMPERATURE AND VISCOSITY DEPENDENCE OF FLUORESCENCE QUENCHING BY OXYGEN IN MODEL SYSTEMS

Abstract— The bimolecular rate constant, kq, for the quenching of the fluorescence of pyrenebutyric acid and naphthaleneacetic acid by molecular oxygen has been studied as a function of temperature and viscosity, η. Fluorescence lifetime measurements were used to monitor the degree of quenching. Glycerol‐water and sucrose‐water mixtures were used to increase the bulk viscosity. Plots of log kqvs log η show deviations from Stokes‐Einstein behavior, but the data are consistent with the patterns that have been observed for other diffusion limited reactions. This work provides background information, regarding the viscosity dependence of oxygen quenching reactions, which is essential for the correct interpretation of oxygen quenching/viscosity dependence studies with more complex systems (i.e. quenching of tryptophan residues in proteins).
An Old Stellar Population or Diffuse Nebular Continuum Emission Discovered in Green Pea Galaxies

We use new Hubble Space Telescope (HST) images of nine Green Pea galaxies (GPGs) to study their resolved structure and color. The choice of filters, F555W and F850LP, together with the redshift of the galaxies (z ∼ 0.25), minimizes the contribution of the nebular [O iii] and Hα emission lines to the broadband images. While these galaxies are typically very blue in color, our analysis reveals that it is only the dominant stellar clusters that are blue. Each GPG does clearly show the presence of at least one bright and compact star-forming region, but these are invariably superimposed on a more extended and lower surface brightness emission. Moreover, the colors of the star-forming regions are on average bluer than those of the diffuse emission, reaching up to 0.6 magnitudes bluer. Assuming that the diffuse and compact components have constant and single-burst star formation histories, respectively, the observed colors imply that the diffuse components (possibly the host galaxy of the star formation episode) have, on average, old stellar ages (>1 Gyr), while the star clusters are younger than 500 Myr. While a redder stellar component is perhaps the most plausible explanation for these results, the limitations of our current data set lead us to examine possible alternative mechanisms, particularly recombination emission processes, which are unusually prominent in systems with such strong line emission. With the available data, however, it is not possible to distinguish between these two interpretations. A substantial presence of old stars would indicate that the mechanisms allowing large escape fractions in these local galaxies may be different from those at play during the reionization epoch.
Completely regular codes in Johnson and Grassmann graphs with small covering radii

Let ${\cal L}$ be a Desarguesian 2-spread in the Grassmann graph $J_q(n,2)$. We prove that the collection of the $4$-subspaces, which do not contain subspaces from ${\cal L}$ is a completely regular code in $J_q(n,4)$. Similarly, we construct a completely regular code in the Johnson graph $J(n,6)$ from the Steiner quadruple system of the extended Hamming code. We obtain several new completely regular codes with covering radius $1$ in the Grassmann graph $J_2(6,3)$ using binary linear programming.