Miracle-dz/UniFAR-FTUs huggingface dataset:
The change of myopic prevalence in children and adolescents before and after COVID-19 pandemic in Suqian, China

Purpose The aim of this study was to investigate the change of myopic prevalence in students with different demographic characteristics before and after the COVID-19 pandemic in Suqian, China. Methods A retrospective, cross-sectional study was conducted. Student data from 52 schools in 2019 and 2020 were collected from the electronic medical records database through cluster sampling. Ophthalmic examinations were conducted on students from September to December in 2019 and 2020. Measurements of uncorrected visual acuity (UCVA) and noncycloplegic autorefraction were included to obtain the spherical equivalent refraction (SER) and prevalence of myopia. The difference in the rate of myopia and SER of students ages 6 to 18 with various demographic characteristics was compared between the two years. Results Records from 118,479 students in 2019 and the 121,881 students in 2020 were obtained. In 2019 and 2020, the prevalence of overall myopia increased from 43.1% to 48.9% (5.8 percentage point), and a substantial shift in myopic rate occurred in grades 4 to 6 (6.9 percentage point). The change in the prevalence of myopia in girls (5.9 percentage point) was approximately equal to that in boys (5.8 percentage point) and it was more common in rural students (5.9 percentage point) than in urban students (5.1 percentage point). The prevalence of low myopia increased more in children, and the prevalence of moderate myopia increased more in adolescents. The mean spherical equivalent refraction (SER) (-1.34±2.03 D) was lower in 2020 than in 2019 (-1.16±1.92 D), while SER decreased mainly at ages 7 to 15. The SER presented myopic status at the age of 9 (-0.55±1.26 D in 2019, -0.71±1.42 D in 2020), and attained moderate myopia at the age of 15 (-3.06±2.41 D in 2019, -3.22±2.40 D in 2020). Conclusions After the COVID-19 pandemic, myopia increased in this population with variable rates of increase in different demographic groups. The change of myopia in children was comparatively greater than that in adolescents. Therefore, we should take measures to prevent and control the development of myopia after the COVID-19 pandemic, especially for younger students.
Active Pharmacovigilance for Primaquine Radical Cure of Plasmodium vivax Malaria in Odisha, India

ABSTRACT. Plasmodium vivax malaria elimination requires radical cure with chloroquine/primaquine. However, primaquine causes hemolysis in glucose-6-phosphate dehydrogenase-deficient (G6PDd) individuals. Between February 2016 and July 2017 in Odisha State, India, a prospective, observational, active pharmacovigilance study assessed the hematologic safety of directly observed 25 mg/kg chloroquine over 3 days plus primaquine 0.25 mg/kg/day for 14 days in 100 P. vivax patients (≥ 1 year old) with hemoglobin (Hb) ≥ 7 g/dL. Pretreatment G6PDd screening was not done, but patients were advised on hemolysis signs and symptoms using a visual aid. For evaluable patients, the mean absolute change in Hb between day 0 and day 7 was −0.62 g/dL (95% confidence interval [CI]: −0.93, −0.31) for males (N = 53) versus −0.24 g/dL (95%CI: −0.59, 0.10) for females (N = 45; P = 0.034). Hemoglobin declines ≥ 3 g/dL occurred in 5/99 (5.1%) patients (three males, two females); none had concurrent clinical symptoms of hemolysis. Based on G6PD qualitative testing after study completion, three had a G6PD-normal phenotype, one female was confirmed by genotyping as G6PDd heterozygous, and one male had an unknown phenotype. A G6PDd prevalence survey was conducted between August 2017 and March 2018 in the same region using qualitative G6PD testing, confirmed by genotyping. G6PDd prevalence was 12.0% (14/117) in tribal versus 3.1% (16/509) in nontribal populations, with G6PD Orissa identified in 29/30 (96.7%) of G6PDd samples. Following chloroquine/primaquine, notable Hb declines were observed in this population that were not recognized by patients based on clinical signs and symptoms.
Adaptive Patch Exiting for Scalable Single Image Super-Resolution

Since the future of computing is heterogeneous, scalability is a crucial problem for single image super-resolution. Recent works try to train one network, which can be deployed on platforms with different capacities. However, they rely on the pixel-wise sparse convolution, which is not hardware-friendly and achieves limited practical speedup. As image can be divided into patches, which have various restoration difficulties, we present a scalable method based on Adaptive Patch Exiting (APE) to achieve more practical speedup. Specifically, we propose to train a regressor to predict the incremental capacity of each layer for the patch. Once the incremental capacity is below the threshold, the patch can exit at the specific layer. Our method can easily adjust the trade-off between performance and efficiency by changing the threshold of incremental capacity. Furthermore, we propose a novel strategy to enable the network training of our method. We conduct extensive experiments across various backbones, datasets and scaling factors to demonstrate the advantages of our method. Code is available at https://github.com/littlepure2333/APE
Nematic phases and elastoresistivity from a multiorbital non-Fermi liquid

Significance Strong interactions can cause electronic fluids to lose their well-defined electron-like excitations and behave as non-Fermi liquids with anomalous transport properties. Understanding spontaneous symmetry breaking and its experimental consequences in such non-Fermi liquids is an important endeavor in condensed matter physics. Here, we study crystalline rotational symmetry breaking, also called nematic ordering, in a non-Fermi liquid using a paradigmatic theoretical model. We explore the resulting thermodynamic, spectral, and transport properties, including the impact of strain which acts as a probe as well as a tuning parameter. Our results are of broad interest for a broad variety of quantum materials including cuprate and iron pnictide superconductors and twisted bilayer graphene, which feature an interplay of non-Fermi liquid behavior and electronic nematic order.
Exploiting Neighbor Effect: Conv-Agnostic GNNs Framework for Graphs with Heterophily

Due to the homophily assumption in graph convolution networks (GCNs), a common consensus in the graph node classification task is that graph neural networks (GNNs) perform well on homophilic graphs but may fail on heterophilic graphs with many interclass edges. However, the previous interclass edges' perspective and related homo-ratio metrics cannot well explain the GNNs' performance under some heterophilic datasets, which implies that not all the interclass edges are harmful to GNNs. In this work, we propose a new metric based on the von Neumann entropy to reexamine the heterophily problem of GNNs and investigate the feature aggregation of interclass edges from an entire neighbor identifiable perspective. Moreover, we propose a simple yet effective Conv-Agnostic GNN framework (CAGNNs) to enhance the performance of most GNNs on the heterophily datasets by learning the neighbor effect for each node. Specifically, we first decouple the feature of each node into the discriminative feature for downstream tasks and the aggregation feature for graph convolution (GC). Then, we propose a shared mixer module to adaptively evaluate the neighbor effect of each node to incorporate the neighbor information. The proposed framework can be regarded as a plug-in component and is compatible with most GNNs. The experimental results over nine well-known benchmark datasets indicate that our framework can significantly improve performance, especially for the heterophily graphs. The average performance gain is 9.81%, 25.81%, and 20.61% compared with graph isomorphism network (GIN), graph attention network (GAT), and GCN, respectively. Extensive ablation studies and robustness analysis further verify the effectiveness, robustness, and interpretability of our framework. Code is available at https://github.com/JC-202/CAGNN.
Design Considerations for 2D Dirac-Source FETs: Device Parameters, Non-Idealities and Benchmarking

Dirac-source field-effect transistors (DS-FETs) have been proposed as promising candidates for lowpower switching devices by leveraging the Dirac cone of graphene as a low-pass energy filter. In particular, using two-dimensional (2D) materials as the channel in a DS-FET is of interest for ultimate scaling purposes. In this paper, we investigate the design considerations for 2D DS-FETs using ballistic simulations based on Landauer formalism. We study the impact of several key device parameters on the device performance, such as graphene doping, Schottky barrier heights, and effective mass of the 2D channel. In addition, we study the impact of non-idealities on the performance of DS-FETs, such as graphene disorder and rethermalization, as well as ways to mitigate them. Finally, we benchmark the performance of DS-FETs for different channel materials, providing a guide for the proper choice of material for 2D DS-FETs.
Fast Toeplitz eigenvalue computations, joining interpolation-extrapolation matrix-less algorithms and simple-loop conjectures: the preconditioned setting

Under appropriate technical assumptions, the simple-loop theory allows to deduce various types of asymptotic expansions for the eigenvalues of Toeplitz matrices $T_{n}(f)$ generated by a function $f$, unfortunately, such a theory is not available in the preconditioning setting, that is for matrices of the form $T_{n}^{-1}(g)T_{n}(l)$ with $l,g$ real-valued, $g$ nonnnegative and not identically zero almost everywhere. Independently and under the milder hypothesis that $f=\frac{l}{g}$ is even and monotonic over $[0,\pi]$, matrix-less algorithms have been developed for the fast eigenvalue computation of large preconditioned matrices of the type above, within a linear complexity in the matrix order: behind the high efficiency of such algorithms there are the expansions as in the case $g\equiv 1$, combined with the extrapolation idea, and hence we conjecture that the simple-loop theory has to be extended in such a new setting, as the numerics strongly suggest.Here we focus our attention on a change of variable, followed by the asymptotic expansion of the new variable, and we consider new matrix-less algorithms ad hoc for the current case. Numerical experiments show a much higher precision till machine precision and the same linear computation cost, when compared with the matrix-less procedures already proposed in the literature.
Factual Consistency of Multilingual Pretrained Language Models

Pretrained language models can be queried for factual knowledge, with potential applications in knowledge base acquisition and tasks that require inference. However, for that, we need to know how reliable this knowledge is, and recent work has shown that monolingual English language models lack consistency when predicting factual knowledge, that is, they fill-in-the-blank differently for paraphrases describing the same fact. In this paper, we extend the analysis of consistency to a multilingual setting. We introduce a resource, mParaRel, and investigate (i) whether multilingual language models such as mBERT and XLM-R are more consistent than their monolingual counterparts;and (ii) if such models are equally consistent across languages.We find that mBERT is as inconsistent as English BERT in English paraphrases, but that both mBERT and XLM-R exhibit a high degree of inconsistency in English and even more so for all the other 45 languages.
SSD-KD: A Self-supervised Diverse Knowledge Distillation Method for Lightweight Skin Lesion Classification Using Dermoscopic Images

Skin cancer is one of the most common types of malignancy, affecting a large population and causing a heavy economic burden worldwide. Over the last few years, computer-aided diagnosis has been rapidly developed and make great progress in healthcare and medical practices due to the advances in artificial intelligence, particularly with the adoption of convolutional neural networks. However, most studies in skin cancer detection keep pursuing high prediction accuracies without considering the limitation of computing resources on portable devices. In this case, the knowledge distillation (KD) method has been proven as an efficient tool to help improve the adaptability of lightweight models under limited resources, meanwhile keeping a high-level representation capability. To bridge the gap, this study specifically proposes a novel method, termed SSD-KD, that unifies diverse knowledge into a generic KD framework for skin disease classification. Our method models an intra-instance relational feature representation and integrates it with existing KD research. A dual relational knowledge distillation architecture is self-supervised trained while the weighted softened outputs are also exploited to enable the student model to capture richer knowledge from the teacher model. To demonstrate the effectiveness of our method, we conduct experiments on ISIC 2019, a large-scale open-accessed benchmark of skin diseases dermoscopic images. Experiments show that our distilled MobileNetV2 can achieve an accuracy as high as 85% for the classification tasks of 8 different skin diseases with minimal parameters and computing requirements. Ablation studies confirm the effectiveness of our intra- and inter-instance relational knowledge integration strategy. Compared with state-of-the-art knowledge distillation techniques, the proposed method demonstrates improved performance. To the best of our knowledge, this is the first deep knowledge distillation application for multi-disease classification on the large-scale dermoscopy database. Our codes and models are available at https://github.com/enkiwang/Portable-Skin-Lesion-Diagnosis.
Rough Solutions of the Relativistic Euler Equations

We prove that the time of classical existence of smooth solutions to the relativistic Euler equations can be bounded from below in terms of norms that measure the “(sound) wave-part” of the data in Sobolev space and “transport-part” in higher regularity Sobolev space and Hölder spaces. The solutions are allowed to have nontrivial vorticity and entropy. We use the geometric framework from [8], where the relativistic Euler flow is decomposed into a “wave-part”, that is, geometric wave equations for the velocity components, density and enthalpy, and a “transport-part”, that is, transport-div-curl systems for the vorticity and entropy gradient. Our main result is that the Sobolev norm H of the variables in the “wave-part” and the Hölder norm C of the variables in the “transport-part” can be controlled in terms of initial data for short times. We note that the Sobolev norm assumption H is the optimal result for the variables in the “wave-part. ” Compared to low regularity results for quasilinear wave equations and the 3D non-relativistic compressible Euler equations, the main new challenge of the paper is that when controlling the acoustic geometry and bounding the wave equation energies, we must deal with the difficulty that the vorticity and entropy gradient are four-dimensional space-time vectors satisfying a space-time div-curl-transport system, where the space-time div-curl part is not elliptic. Due to lack of ellipticity, one can not immediately rely on the approach taken in [8] to control these terms. To overcome this difficulty, we show that the space-time div-curl systems imply elliptic div-curl-transport systems on constant-time hypersurfaces plus error terms that involve favorable differentiations and contractions with respect to the four-velocity. By using these structures, we are able to adequately control the vorticity and entropy gradient with the help of energy estimates for transport equations, elliptic estimates, Schauder estimates, and Littlewood-Paley theory.
Correlating atom probe tomography with x-ray and electron spectroscopies to understand microstructure–activity relationships in electrocatalysts

The search for a new energy paradigm with net-zero carbon emissions requires new technologies for energy generation and storage that are at the crossroad between engineering, chemistry, physics, surface, and materials sciences. To keep pushing the inherent boundaries of device performance and lifetime, we need to step away from a cook-and-look approach and aim to establish the scientific ground to guide the design of new materials. This requires strong efforts in establishing bridges between microscopy and spectroscopy techniques, across multiple scales. Here, we discuss how the complementarities of x-ray- and electron-based spectroscopies and atom probe tomography can be exploited in the study of surfaces and subsurfaces to understand structure–property relationships in electrocatalysts. Graphical abstract
Diagnosis of Schizophrenia: A Comprehensive Evaluation

Machine learning models have been successfully employed in the diagnosis of Schizophrenia disease. The impact of classification models and the feature selection techniques on the diagnosis of Schizophrenia have not been evaluated. Here, we sought to access the performance of classification models along with different feature selection approaches on the structural magnetic resonance imaging data. The data consist of 72 subjects with Schizophrenia and 74 healthy control subjects. We evaluated different classification algorithms based on support vector machine (SVM), random forest, kernel ridge regression and randomized neural networks. Moreover, we evaluated T-Test, Receiver Operator Characteristics (ROC), Wilcoxon, entropy, Bhattacharyya, Minimum Redundancy Maximum Relevance (MRMR) and Neighbourhood Component Analysis (NCA) as the feature selection techniques. Based on the evaluation, SVM based models with Gaussian kernel proved better compared to other classification models and Wilcoxon feature selection emerged as the best feature selection approach. Moreover, in terms of data modality the performance on integration of the grey matter and white matter proved better compared to the performance on the grey and white matter individually. Our evaluation showed that classification algorithms along with the feature selection approaches impact the diagnosis of Schizophrenia disease. This indicates that proper selection of the features and the classification models can improve the diagnosis of Schizophrenia.
Performance of Deep Learning models with transfer learning for multiple-step-ahead forecasts in monthly time series

Deep Learning and transfer learning models are being used to generate time series forecasts; however, there is scarce evidence about their performance prediction that it is more evident for monthly time series. The purpose of this paper is to compare Deep Learning models with transfer learning and without transfer learning and other traditional methods used for monthly forecasts to answer three questions about the suitability of Deep Learning and Transfer Learning to generate predictions of time series. Time series of M4 and M3 competitions were used for the experiments. The results suggest that deep learning models based on TCN, LSTM, and CNN with transfer learning tend to surpass the performance prediction of other traditional methods. On the other hand, TCN and LSTM, trained directly on the target time series, got similar or better performance than traditional methods for some forecast horizons.
Autonomous Bikebot Control for Crossing Obstacles With Assistive Leg Impulsive Actuation

As a single-track mobile platform, bikebot (i.e., bicycle-based robot) has attractive capability to navigate through narrow, off-road terrain with high speed. However, running crossing step-like obstacles creates challenges for intrinsically unstable, underactuated bikebots. This article presents a novel autonomous bikebot control with two assistive legs to navigate crossing obstacles. The proposed design integrates the external/internal convertible form-based motion control with leg-assisted impulse actuation. The leg–terrain interaction generates impulsive torques to help maintain navigation and balance when running across obstacles. The control performance is analyzed and guaranteed. The experimental results confirm that under the control design, the bikebot can smoothly run crossing multiple step-like obstacles with heights more than one-third of the wheel radius. The comparison results demonstrate the superior performance than those under only the velocity and steering control without leg assistive actuation.
Federated Class-Incremental Learning

Federated learning (FL) has attracted growing attentions via data-private collaborative training on decentralized clients. However, most existing methods unrealistically assume object classes of the overall framework are fixed over time. It makes the global model suffer from significant catastrophic forgetting on old classes in real-world scenarios, where local clients often collect new classes continuously and have very limited storage memory to store old classes. Moreover, new clients with unseen new classes may participate in the FL training, further aggravating the catastrophic forgetting of global model. To address these challenges, we develop a novel Global-Local Forgetting Compensation (GLFC) model, to learn a global class-incremental model for alleviating the catastrophic forgetting from both local and global perspectives. Specifically, to address local forgetting caused by class imbalance at the local clients, we design a class-aware gradient compensation loss and a class-semantic relation distillation loss to balance the forgetting of old classes and distill consistent inter-class relations across tasks. To tackle the global forgetting brought by the non-i.i. d class imbalance across clients, we propose a proxy server that selects the best old global model to assist the local relation distillation. Moreover, a prototype gradient-based communication mechanism is developed to protect the privacy. Our model outperforms state-of-the-art methods by 4.4%~15.1% in terms of average accuracy on representative benchmark datasets. The code is available at https://github.com/conditionWang/FCIL.
DQ-BART: Efficient Sequence-to-Sequence Model via Joint Distillation and Quantization

Large-scale pre-trained sequence-to-sequence models like BART and T5 achieve state-of-the-art performance on many generative NLP tasks. However, such models pose a great challenge in resource-constrained scenarios owing to their large memory requirements and high latency. To alleviate this issue, we propose to jointly distill and quantize the model, where knowledge is transferred from the full-precision teacher model to the quantized and distilled low-precision student model. Empirical analyses show that, despite the challenging nature of generative tasks, we were able to achieve a 16.5x model footprint compression ratio with little performance drop relative to the full-precision counterparts on multiple summarization and QA datasets. We further pushed the limit of compression ratio to 27.7x and presented the performance-efficiency trade-off for generative tasks using pre-trained models. To the best of our knowledge, this is the first work aiming to effectively distill and quantize sequence-to-sequence pre-trained models for language generation tasks.