Deep-learning assisted reduced order model for high-dimensional flow prediction from sparse data

The reconstruction and prediction of full-state flows from sparse data are of great scientific and engineering significance yet remain challenging, especially in applications where data are sparse and/or subjected to noise. To this end, this study proposes a deep-learning assisted non-intrusive reduced order model (named DCDMD) for high-dimensional flow prediction from sparse data. Based on the compressed sensing (CS)-Dynamic Mode Decomposition (DMD), the DCDMD model is distinguished by two novelties. Firstly, a sparse matrix is defined to overcome the strict random distribution condition of sensor locations in CS, thus allowing flexible sensor deployments and requiring very few sensors. Secondly, a deep-learning-based proxy is invoked to acquire coherent flow modes from the sparse data of high-dimensional flows, thereby addressing the issue of defining sparsity and the stringent incoherence condition in the conventional CSDMD. The two advantageous features, combined with the fact that the model retains flow physics in the online stage, lead to significant enhancements in accuracy and efficiency, as well as superior insensitivity to data noises (i.e., robustness), in both reconstruction and prediction of full-state flows. These are demonstrated by three benchmark examples, i.e., cylinder wake, weekly-mean sea surface temperature and isotropic turbulence in a periodic square area.Comment: 36 Pages, 23 Figures, 5 Table

What do we know about multidimensional poverty in China: its dynamics, causes, and implications for sustainability

Poverty is a primary obstacle to achieving sustainable development. Therefore, exploring the spatiotemporal dynamics and causes of poverty is of great significance to the sustainable poverty reduction of the “post poverty alleviation era” in China. This paper used the multisource big data of 2022 counties in China from 2000 to 2015 to establish a comprehensive evaluation framework to explore the multidimensional poverty situation in China. The results showed the following findings: There is an obvious spatiotemporal heterogeneity of multidimensional poverty, showing a typical stair-like gradient from high in the west to low in the east, with the poverty level in state-designated poverty counties higher and intensifying over time. The spatial differentiation of multidimensional poverty is contributed to by multiple factors, in which the geographical condition has a stronger impact on state-designated poverty counties, while natural endowment and human resources have a stronger effect on non-state-designated poverty counties. These things considered, the regional poverty causes were relatively stable before 2015, but the poverty spatial agglomeration of some regions in the Northwest, Northeast, and Yangtze River Economic Belt has undergone significant changes after 2015. These findings can help policymakers better target plans to eliminate various types of poverty in different regions

Variational Metric Scaling for Metric-Based Meta-Learning

Metric-based meta-learning has attracted a lot of attention due to its effectiveness and efficiency in few-shot learning. Recent studies show that metric scaling plays a crucial role in the performance of metric-based meta-learning algorithms. However, there still lacks a principled method for learning the metric scaling parameter automatically. In this paper, we recast metric-based meta-learning from a Bayesian perspective and develop a variational metric scaling framework for learning a proper metric scaling parameter. Firstly, we propose a stochastic variational method to learn a single global scaling parameter. To better fit the embedding space to a given data distribution, we extend our method to learn a dimensional scaling vector to transform the embedding space. Furthermore, to learn task-specific embeddings, we generate task-dependent dimensional scaling vectors with amortized variational inference. Our method is end-to-end without any pre-training and can be used as a simple plug-and-play module for existing metric-based meta-algorithms. Experiments on mini-ImageNet show that our methods can be used to consistently improve the performance of existing metric-based meta-algorithms including prototypical networks and TADAM. The source code can be downloaded from https://github.com/jiaxinchen666/variational-scaling.Comment: AAAI202

Spokewise iridotomy combined with Descemet stripping automated endothelial keratoplasty in iridocorneal endothelial syndrome

PurposeIridocorneal endothelial (ICE) syndrome is a progressive anterior segment disorder that can be tricky to treat. Keratoplasty is commonly used to treat corneal edema in ICE syndrome. However, glaucoma is an important risk factor affecting graft survival. To address this question, we designed a retrospective cohort study to evaluate the effect of Spokewise Iridotomy (SI) on Descemet Stripping Automated Endothelial Keratoplasty (DSAEK) Grafts in Iridocorneal Endothelial (ICE) Syndrome.MethodsThis was a retrospective cohort study. A total of 29 patients were included; 31 eyes with ICE syndrome underwent DSAEK at Peking University Third Hospital between June 2015 and June 2022, including 11 eyes with combined SI during DSAEK. The aim was to explore the effect of SI on vision, glaucoma control, complications, peripheral anterior synechiae recurrence, endothelial cell count, and graft survival.ResultsThe median follow-up time was 30.83 months (mo.) in the SI+Endothelial Keratoplasty (EK) group and 6.17 mo in the EK group. The 2-year cumulative survival rate of grafts in the SI+EK group was 100%, compared with the 6-month and 1-year cumulative survival rates of 80.2 and 63.2%, respectively, in the EK group (p = 0.043). The SI+EK group had a lower incidence of immediate postoperative complications (p = 0.005), fewer postoperative anti-glaucoma medications (AGMs) (p = 0.029), smaller peripheral anterior synechiae recurrence (p = 0.001), and significant visual acuity improvement (p < 0.05). More AGMs were used in failed grafts (p = 0.002).ConclusionSI can help control intraocular pressure, improve visual acuity, and increase graft survival after DSAEK in ICE syndrome patients

A Study of Pulsation properties of 57 Non-Blazhko effect ab-type RR Lyrae stars with homogeneous metallicities from the LAMOST-Kepler/K2 survey

Homogeneous metallicities and continuous high-precision light curves play key roles in studying the pulsation properties of RR Lyrae stars. By cross-matching with LAMOST DR6, we have determined 7 and 50 Non-Blazhko RRab stars in the Kepler and K2 fields, respectively, who have homogeneous metallicities determined from low-resolution spectra of the LAMOST-Kepler/K2 project. The Fourier Decomposition method is applied to the light curves of these stars provided by the Kepler space based telescope to determine the fundamental pulsation periods and the pulsation parameters. The calculated amplitude ratios of R21, R31 and the phase differences of {\phi}21, {\phi}31 are consistent with the parameters of the RRab stars in both the Globular Clusters and the Large Magellanic Cloud. We find a linear relationship between the phase differences {\phi}21 and {\phi}31, which is in good agreement with the results in previous literature. As far as the amplitude, we find that the amplitude of primary frequency A1 and the total amplitude Atot follow either a cubic or linear relationship. For the rise time RT, we do not find its relevance with the period of the fundamental pulsation mode P1, or Atot and {\phi}21. However, it might follow a linear relationship with R31. Based on the homogeneous metallicities, we have derived a new calibration formula for the relationship of period-{\phi}31-[Fe/H], which agrees well with the previous studies

Dysnatremia is associated with increased risk of all-cause mortality within 365 days post-discharge in patients with atrial fibrillation without heart failure: A prospective cohort study

Aim: The aim of this study is to evaluate the association between serum sodium concentrations at hospital admission and all-cause mortality within 365 days post-discharge in patients with atrial fibrillation (AF) without heart failure (HF). Methods: The prospective cohort study enrolled 1,446 patients with AF without HF between November 2018 and October 2020. A follow-up was performed 30, 90, 180, and 365 days after enrollment through outpatient visits or telephone interviews. All-cause mortality was estimated in three groups according to serum sodium concentrations: hyponatremia ( \u3c 135 mmol/L), normonatremia (135 – 145 mmol/L), and hypernatremia ( \u3e 145 mmol/L). We estimated the risk of all-cause mortalities using univariable and multivariable Cox proportional hazards models with normonatremia as the reference. Results: The all-cause mortalities of hyponatremia, normonatremia, and hypernatremia were 20.6, 9.4, and 33.3 % within 365 days post-discharge, respectively. In the univariable analysis, hyponatremia (HR: 2.19, CI 1.5 – 3.2) and hypernatremia (HR: 4.03, CI 2.32 – 7.02) increased the risk of all-cause mortality. The HRs for hyponatremia and hypernatremia were 1.55 (CI 1.05 – 2.28) and 2.55 (CI 1.45 – 4.46) after adjustment for age, diabetes mellitus, loop diuretics, antisterone, antiplatelet drugs, and anticoagulants in the patients with AF without HF. The association between serum sodium concentrations and the HRs of all-cause mortality was U-shaped. Conclusion: Dysnatremia at hospital admission was an independent factor for all-cause mortality in patients with AF without HF within 365 days post-discharge

Mirror protected Dirac fermions on a Weyl semimetal NbP surface

The first Weyl semimetal was recently discovered in the NbP class of compounds. Although the topology of these novel materials has been identified, the surface properties are not yet fully understood. By means of scanning tunneling spectroscopy, we find that NbPs (001) surface hosts a pair of Dirac cones protected by mirror symmetry. Through our high resolution spectroscopic measurements, we resolve the quantum interference patterns arising from these novel Dirac fermions, and reveal their electronic structure, including the linear dispersions. Our data, in agreement with our theoretical calculations, uncover further interesting features of the Weyl semimetal NbPs already exotic surface. Moreover, we discuss the similarities and distinctions between the Dirac fermions here and those in topological crystalline insulators in terms of symmetry protection and topology

Vegetation greenness and photosynthetic phenology in response to climatic determinants

Vegetation phenology is a key indicator of vegetation-climate interactions and carbon sink changes in ecosystems. Therefore, it is very important to understand the temporal and spatial variability of vegetation phenology and the driving climatic determinants [e.g., temperature (Ts) and soil moisture (SM)]. Vegetation greenness and photosynthetic phenology were derived using the double logistic (DL) method to enhance vegetation index (EVI) and solar-induced chlorophyll fluorescence (SIF) spring and autumn phenology, respectively. The growing season length (GSL) of greenness phenology (about 100 days) derived EVI was longer than GSL of photosynthetic phenology (about 80 days) derived SIF. Although their overall spatiotemporal pattern trends were consistent, photosynthetic phenology varied 1.4 to 3.1 times more than greenness phenology over time. In addition, SIF-based photosynthetic phenology and EVI-based greenness phenology showed consistent factors of drivers but differed to some extent in spatial patterns and the most relevant preseason dates. Spring photosynthetic phenology was mainly influenced by pre-season mean cumulative Ts (about 90 days). However, greenness phenology was controlled by both pre-seasons mean cumulative Ts [(about 55 days) and mean cumulative SM (about 40 days)]. Autumn photosynthetic phenology was controlled by both periods’ mean cumulative Ts [(about 20 days) and SM (about 20 days)], but autumn greenness phenology was mainly influenced by pre-season mean cumulative Ts (85 days). The comparison analysis of SIF and EVI phenology helps to understand the difference between photosynthetic phenology and greenness phenology at a regional scale