A Modified Meta-Learner for Few-Shot Learning

Meta-Learning, or so-called Learning to learn, has become another important research branch in Machine Learning. Different from traditional deep learning, meta-learning can be used to solve one-to-many problems and has a better performance in few-shot learning which only few samples are available in each class. In these tasks, meta-learning is designed to quickly form a relatively reliable model through very limited samples. In this paper, we propose a modified LSTM-based meta-learning model, which can initialize and update the parameters of classifier (learner) considering both short-term knowledge of one task and long-term knowledge across multiple tasks. We reconstruct a Compound loss function to make up for the deficiency caused by the separate one in original model aiming for a quick start and better stability, without taking expensive operation. Our modification enables meta-learner to perform better under few-updates. Experiments conducted on the Mini-ImageNet demonstrate the improved accuracies

Machine Learning Enabled Prediction of Mechanical Properties of Tungsten Disulfide Monolayer

One of two-dimensional transition metal dichalcogenide materials, tungsten disulfide (WS2), has aroused much research interest, and its mechanical properties play an important role in a practical application. Here the mechanical properties of h-WS2 and t-WS2 monolayers in the armchair and zigzag directions are evaluated by utilizing the molecular dynamics (MD) simulations and machine learning (ML) technique. We mainly focus on the effects of chirality, system size, temperature, strain rate, and random vacancy defect on mechanical properties, including fracture strain, fracture strength, and Young’s modulus. We find that the mechanical properties of h-WS2 surpass those of t-WS2 due to the different coordination spheres of the transition metal atoms. It can also be observed that the fracture strain, fracture strength, and Young’s modulus decrease when temperature and vacancy defect ratio are enhanced. The random forest (RF) supervised ML algorithm is employed to model the correlations between different impact factors and target outputs. A total number of 3600 MD simulations are performed to generate the training and testing dataset for the ML model. The mechanical properties of WS2 (i.e., target outputs) can be predicted using the trained model with the knowledge of different input features, such as WS2 type, chirality, temperature, strain rate, and defect ratio. The mean square errors of ML predictions for the mechanical properties are orders of magnitude smaller than the actual values of each property, indicating good training results of the RF model

Robust Adaptive Neural Control of Morphing Aircraft with Prescribed Performance

This study proposes a low-computational composite adaptive neural control scheme for the longitudinal dynamics of a swept-back wing aircraft subject to parameter uncertainties. To efficiently release the constraint often existing in conventional neural designs, whose closed-loop stability analysis always necessitates that neural networks (NNs) be confined in the active regions, a smooth switching function is presented to conquer this issue. By integrating minimal learning parameter (MLP) technique, prescribed performance control, and a kind of smooth switching strategy into back-stepping design, a new composite switching adaptive neural prescribed performance control scheme is proposed and a new type of adaptive laws is constructed for the altitude subsystem. Compared with previous neural control scheme for flight vehicle, the remarkable feature is that the proposed controller not only achieves the prescribed performance including transient and steady property but also addresses the constraint on NN. Two comparative simulations are presented to verify the effectiveness of the proposed controller

High-throughput computations of cross-plane thermal conductivity in multilayer stanene

Computational materials science based on data-driven approach has gained increasing interest in recent years. The capability of trained machine learning (ML) models, such as an artificial neural network (ANN), to predict the material properties without repetitive calculations is an appealing idea to save computational time. Thermal conductivity in single or multilayer structure is a quintessential property that plays a pivotal role in electronic applications. In this work, we exemplified a data-driven approach based on ML and high-throughput computation (HTC) to investigate the cross-plane thermal transport in multilayer stanene. Stanene has attracted considerable attention due to its novel electronic properties such as topological insulating features with a wide bandgap, making it an appealing candidate to ferry current in electronic devices. Classical molecular dynamics simulations are performed to extract the lattice thermal conductivities (κL). The calculated cross-plane κL is orders of magnitude lower than its lateral counterparts. Impact factors such as layer number, system temperature, interlayer coupling strength, and compressive/tensile strains are explored. It is found that κL of multilayer stanene in the cross-plane direction can be diminished by 86.7% with weakened coupling strength, or 66.6% with tensile strains. A total of 2700 κL data are generated using HTC, which are fed into 9 different ANN models for training and testing. The best prediction performance is given by the 2-layer ANN with 30 neurons in each layer

Full-spectrum thermal analysis in twisted bilayer graphene

It was recently reported that a magic angle, i.e. 1.1º, exists in twisted bilayer graphene which could lead to intrinsic unconventional superconductivity. Variations of the twisting angle between different graphene layers could lead to altered electronic band structures, which gives the peculiar superconductivity phenomenon. Effects of twisting angles on different properties of bilayer graphene need to be comprehensively investigated in order to fully understand its mechanism. In this work, classical molecular dynamics simulations are performed to calculate the interfacial thermal resistance (R) at twisting angles from 0º to 359º. Due to the symmetric structures of the honeycomb lattice, only angles from 0º to 60º is needed but the full spectrum is explored to generate the complete picture of R with . It was reported the interfacial thermal resistance changes periodically with twisting angle, with the smallest R values at every 60º starting from 0º and the largest values at every 60º starting from 30º. Phonon density of states and radial distribution functions are calculated to explain the predicted results. Effects of temperature and single-direction, bi-direction tensile strains on the calculated interfacial thermal resistance are also studied. Results in this work contribute to the fundamental understanding of the thermal properties in twisted bilayer graphene and provide reasonable guidelines to its applications in thermal management devices

Development and validation of a prognostic nomogram for rectal cancer patients who underwent surgical resection

Objective: The purpose of this study was to develop and validate a nomogram model for the prediction of survival outcome in rectal cancer patients who underwent surgical resection.Methods: A total of 9,919 consecutive patients were retrospectively identified using the Surveillance, Epidemiology, and End Results (SEER) database. Significant prognostic factors were determined by the univariate and multivariate Cox analysis. The nomogram model for the prediction of cancer-specific survival (CSS) in rectal cancer patients were developed based on these prognostic variables, and its predictive power was assessed by the concordance index (C-index). Calibration curves were plotted to evaluate the associations between predicted probabilities and actual observations. The internal and external cohort were used to further validate the predictive performance of the prognostic nomogram.Results: All patients from the SEER database were randomly split into a training cohort (n = 6,944) and an internal validation cohort (n = 2,975). The baseline characteristics of two cohorts was comparable. Independent prognostic factors were identified as age, pT stage, lymph node metastasis, serum CEA level, tumor size, differentiation type, perineural invasion, circumferential resection margin involvement and inadequate lymph node yield. In the training cohort, the C-index of the nomogram was 0.719 (95% CI: 0.696–0.742), which was significantly higher than that of the TNM staging system (C-index: 0.606, 95% CI: 0.583–0.629). The nomogram had a C-index of 0.726 (95% CI: 0.691–0.761) for the internal validation cohort, indicating a good predictive power. In addition, an independent cohort composed of 202 rectal cancer patients from our institution were enrolled as the external validation. Compared with the TNM staging system (C-index: 0.573, 95% CI: 0.492–0.654), the prognostic nomogram still showed a better predictive performance, with the C-index of 0.704 (95% CI: 0.626–0.782). Calibration plots showed a good consistency between predicted probability and the actual observation in the training and two validation cohorts.Conclusion: The nomogram showed an excellent predictive ability for survival outcome of rectal cancer patients, and it might provide an accurate prognostic stratification and help clinicians determine individualized treatment strategies

Molecular dynamics and machine learning in catalysts

Given the importance of catalysts in the chemical industry, they have been extensively investigated by experimental and numerical methods. With the development of computational algorithms and computer hardware, large-scale simulations have enabled influential studies with more atomic details reflecting microscopic mechanisms. This review provides a comprehensive summary of recent developments in molecular dynamics, including ab initio molecular dynamics and reaction force-field molecular dynamics. Recent research on both approaches to catalyst calculations is reviewed, including growth, dehydrogenation, hydrogenation, oxidation reactions, bias, and recombination of carbon materials that can guide catalyst calculations. Machine learning has attracted increasing interest in recent years, and its combination with the field of catalysts has inspired promising development approaches. Its applications in machine learning potential, catalyst design, performance prediction, structure optimization, and classification have been summarized in detail. This review hopes to shed light and perspective on ML approaches in catalysts

Observed variability of intertropical convergence zone over 1998-2018

The intertropical convergence zone (ITCZ) accounts for more than 30% of the global precipitation and its variability has a great effect on the people living in the tropical area. It is the manifestation of the Hadley circulation, tropical dynamic and thermodynamic coupling and the air-sea interaction. Therefore, it is essential to understand the changes and variability of the ITCZ, including its position and intensity. Using observed precipitation from GPCP and TRMM 3B43, as well as the ERA5 reanalysis data, we examine the ITCZ variations over the global and seven regions from 1998-2018. These data sets show consistent ITCZ climatology and inter-annual variability. Except over Atlantic and eastern Pacific where ITCZ stays in the northern hemisphere, the ITCZ crosses the equator after equinoxes over other sections. There is no overall significant shift of annual mean ITCZ position over the globe and seven regions, except over the America section where the GPCP data show a significant increase trend of 0.8°decade-1 and over the western Pacific section where ERA5 data show a significant decrease trend of -1.2°decade-1. The ITCZ positions over the globe and Africa are related to both ENSO and NAO, while ITCZ position over eastern Pacific is significantly affected by ENSO and the Atlantic ITCZ is mainly related to NAO. Except for the western Pacific, all other sections are significantly related to local meridional surface temperature gradient, particularly over America, Atlantic and eastern Pacific. The meridional gradient variation of the vertically integrated moist static energy has generally good agreement with the shift of the intertropical convergence zone, particularly for the seasonal climatology over the Africa region. The relationship between ITCZ position and intensity shows complicated patterns, with positive correlation over the globe, but different correlations over different sections. The two observed data sets are more or less consistent, but ERA5 shows discrepancies over some sections. It is also found that the local meridional temperature gradient has more influences on the ITCZ positions than the global on

Transcriptome Analysis Reveals the Profile of Long Non-coding RNAs During Chicken Muscle Development.

peer reviewedThe developmental complexity of muscle arises from elaborate gene regulation. Long non-coding RNAs (lncRNAs) play critical roles in muscle development through the regulation of transcription and post-transcriptional gene expression. In chickens, previous studies have focused on the lncRNA profile during the embryonic periods, but there are no studies that explore the profile from the embryonic to post-hatching period. Here, we reconstructed 14,793 lncRNA transcripts and identified 2,858 differentially expressed lncRNA transcripts and 4,282 mRNAs from 12-day embryos (E12), 17-day embryos (E17), 1-day post-hatch chicks (D1), 14-day post-hatch chicks (D14), 56-day post-hatch chicks (D56), and 98-day post-hatch chicks (D98), based on our published RNA-seq datasets. We performed co-expression analysis for the differentially expressed lncRNAs and mRNAs, using STEM, and identified two profiles with opposite expression trends: profile 4 with a downregulated pattern and profile 21 with an upregulated pattern. The cis- and trans-regulatory interactions between the lncRNAs and mRNAs were predicted within each profile. Functional analysis of the lncRNA targets showed that lncRNAs in profile 4 contributed to the cell proliferation process, while lncRNAs in profile 21 were mainly involved in metabolism. Our work highlights the lncRNA profiles involved in the development of chicken breast muscle and provides a foundation for further experiments on the role of lncRNAs in the regulation of muscle development

Duration-dependent impact of cardiometabolic diseases and multimorbidity on all-cause and cause-specific mortality: a prospective cohort study of 0.5 million participants

Background The association of incident cardiometabolic multimorbidity (CMM) with mortality risk is rarely studied, and neither are the durations of cardiometabolic diseases (CMDs). Whether the association patterns of CMD durations with mortality change as individuals progress from one CMD to CMM is unclear. Methods Data from China Kadoorie Biobank of 512,720 participants aged 30–79 was used. CMM was defined as the simultaneous presence of two or more CMDs of interest, including diabetes, ischemic heart disease, and stroke. Cox regression was used to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs) for the duration-dependent associations of CMDs and CMM with all-cause and cause-specific mortality. All information on exposures of interest was updated during follow-up. Results During a median follow-up of 12.1 years, 99,770 participants experienced at least one incident CMD, and 56,549 deaths were documented. Among 463,178 participants free of three CMDs at baseline, compared with no CMD during follow-up, the adjusted HRs (95% CIs) between CMM and all-cause mortality, mortality from circulatory system diseases, respiratory system diseases, cancer, and other causes were 2.93 (2.80–3.07), 5.05 (4.74–5.37), 2.72 (2.35–3.14), 1.30 (1.16–1.45), and 2.30 (2.02–2.61), respectively. All CMDs exhibited a high mortality risk in the first year of diagnosis. Subsequently, with prolonged disease duration, mortality risk increased for diabetes, decreased for IHD, and sustained at a high level for stroke. With the presence of CMM, the above association estimates inflated, but the pattern of which remained. Conclusion Among Chinese adults, mortality risk increased with the number of the CMDs and changed with prolonged disease duration, the patterns of which varied among the three CMDs