Intrinsic localized modes in a two-dimensional checkerboard ferromagnetic lattice

An analytical work on intrinsic localized modes in a two-dimensional Heisenberg ferromagnet on the checkerboard lattice is presented. Taking advantage of an asymptotic method, the governing lattice dynamical equations are reduced to one (2+1) -dimensional nonlinear Schr\"odinger. In our work, we obtain two types of nonlinear localized mode solutions, namely, Brillouin zone center modes and Brillouin zone corner modes. The occurrence conditions for these intrinsic localized modes are given in detail. Especially, we find that the competition between the Dzialozinskii-Moriy interaction and the next-nearest neighbor interaction of the checkerboard ferromagnet has an effect on the local structure of the Brillouin zone corner acoustic mode

The Cognitive Processes of Image Schema in Sino-American Economic News on The Belt and Road

This contrastive study decodes the language applications employed in news reports through image schematization to reproduce the ‘cognitive map’ of news writers in their ways of perceiving the world and exerting influence on news readers’ ways of perception. By analysing American economic news (AEN) and Chinese economic news (CEN) on the issue of ‘The Belt and Road’ (B&R) from the perspective of cognitive linguistics, the authors uncover and sketch out the hidden epistemic cognitive patterns and processes of both the Chinese and American writers. The study demonstrates that the schematic images and their constructions are organized in the mind of an individual as a network, with both metaphorical and formulaic schemas at different schematic levels, presenting a different process of cognitive entrenchment through which, in news discourses, image schema is utilized as a projection lens, projecting the covert cognitive processes onto overt language use and function

Influences of Several Insecticides on the Survival of Lysiphlebus japonicus

When pesticides are used to control soybean aphids, a fraction of larvae, pupae (mummies) and adults of Lysiphlebus japonicus survive. To understand the influence of pesticides on the development of those surviving parasitoids, we carried out toxicity experiments of pesticides commonly used in the field and surveyed the survival of parasitoids.Originating text in Chinese.Citation: Gao, Junffeng, Zhu, Junyi, Yu, Kai, Ren, Wenhui. (1993). Influences of Several Insecticides on the Survival of Lysiphlebus japonicus. Natural Enemies of Insects, 15(4), 160-161

Adaptive Control for Robotic Manipulators base on RBF Neural Network

An adaptive neural network controller is brought forward by the paper to solve trajectory tracking problems of robotic manipulators with uncertainties.  The  first  scheme consists of  a PD feedback  and  a  dynamic  compensator  which is  composed by  neural  network controller and  variable  structure controller.  Neutral network controller is designed to adaptive learn and compensate the unknown uncertainties, variable   structure controller is designed to eliminate approach errors of neutral network. The adaptive weight learning algorithm of neural network is designed to ensure online real-time adjustment, offline learning phase is not need; Global asymptotic stability (GAS) of system base on Lyapunov theory is analysised to ensure the convergence of the algorithm. The simulation results show that the kind of the control scheme is effective and has good robustness

PDL: Regularizing Multiple Instance Learning with Progressive Dropout Layers

Multiple instance learning (MIL) was a weakly supervised learning approach that sought to assign binary class labels to collections of instances known as bags. However, due to their weak supervision nature, the MIL methods were susceptible to overfitting and required assistance in developing comprehensive representations of target instances. While regularization typically effectively combated overfitting, its integration with the MIL model has been frequently overlooked in prior studies. Meanwhile, current regularization methods for MIL have shown limitations in their capacity to uncover a diverse array of representations. In this study, we delve into the realm of regularization within the MIL model, presenting a novel approach in the form of a Progressive Dropout Layer (PDL). We aim to not only address overfitting but also empower the MIL model in uncovering intricate and impactful feature representations. The proposed method was orthogonal to existing MIL methods and could be easily integrated into them to boost performance. Our extensive evaluation across a range of MIL benchmark datasets demonstrated that the incorporation of the PDL into multiple MIL methods not only elevated their classification performance but also augmented their potential for weakly-supervised feature localizations.Comment: The code is available in https://github.com/ChongQingNoSubway/PD

TetCNN: Convolutional Neural Networks on Tetrahedral Meshes

Convolutional neural networks (CNN) have been broadly studied on images, videos, graphs, and triangular meshes. However, it has seldom been studied on tetrahedral meshes. Given the merits of using volumetric meshes in applications like brain image analysis, we introduce a novel interpretable graph CNN framework for the tetrahedral mesh structure. Inspired by ChebyNet, our model exploits the volumetric Laplace-Beltrami Operator (LBO) to define filters over commonly used graph Laplacian which lacks the Riemannian metric information of 3D manifolds. For pooling adaptation, we introduce new objective functions for localized minimum cuts in the Graclus algorithm based on the LBO. We employ a piece-wise constant approximation scheme that uses the clustering assignment matrix to estimate the LBO on sampled meshes after each pooling. Finally, adapting the Gradient-weighted Class Activation Mapping algorithm for tetrahedral meshes, we use the obtained heatmaps to visualize discovered regions-of-interest as biomarkers. We demonstrate the effectiveness of our model on cortical tetrahedral meshes from patients with Alzheimer's disease, as there is scientific evidence showing the correlation of cortical thickness to neurodegenerative disease progression. Our results show the superiority of our LBO-based convolution layer and adapted pooling over the conventionally used unitary cortical thickness, graph Laplacian, and point cloud representation.Comment: Accepted as a conference paper to Information Processing in Medical Imaging (IPMI 2023) conferenc

Metal-to-semiconductor transition in squashed armchair carbon nanotubes

We investigate electronic transport properties of the squashed armchair carbon nanotubes, using tight-binding molecular dynamics and Green's function method. We demonstrate a metal-to-semiconductor transistion while squashing the nanotubes and a general mechanism for such transistion. It is the distinction of the two sublattices in the nanotube that opens an energy gap near the Fermi energy. We show that the transition has to be achieved by a combined effect of breaking of mirror symmetry and bond formation between the flattened faces in the squashed nanotubes.Comment: 4 papges, 4 figures, to appear in Phys. Rev. Let

NNMobile-Net: Rethinking CNN Design for Deep Learning-Based Retinopathy Research

Retinal diseases (RD) are the leading cause of severe vision loss or blindness. Deep learning-based automated tools play an indispensable role in assisting clinicians in diagnosing and monitoring RD in modern medicine. Recently, an increasing number of works in this field have taken advantage of Vision Transformer to achieve state-of-the-art performance with more parameters and higher model complexity compared to Convolutional Neural Networks (CNNs). Such sophisticated and task-specific model designs, however, are prone to be overfitting and hinder their generalizability. In this work, we argue that a channel-aware and well-calibrated CNN model may overcome these problems. To this end, we empirically studied CNN's macro and micro designs and its training strategies. Based on the investigation, we proposed a no-new-MobleNet (nn-MobileNet) developed for retinal diseases. In our experiments, our generic, simple and efficient model superseded most current state-of-the-art methods on four public datasets for multiple tasks, including diabetic retinopathy grading, fundus multi-disease detection, and diabetic macular edema classification. Our work may provide novel insights into deep learning architecture design and advance retinopathy research.Comment: Code will publish soon: https://github.com/Retinal-Research/NNMOBILE-NE