A Graph-Based Semi-Supervised k Nearest-Neighbor Method for Nonlinear Manifold Distributed Data Classification

$k$ Nearest Neighbors ($k$NN) is one of the most widely used supervised learning algorithms to classify Gaussian distributed data, but it does not achieve good results when it is applied to nonlinear manifold distributed data, especially when a very limited amount of labeled samples are available. In this paper, we propose a new graph-based $k$NN algorithm which can effectively handle both Gaussian distributed data and nonlinear manifold distributed data. To achieve this goal, we first propose a constrained Tired Random Walk (TRW) by constructing an $R$-level nearest-neighbor strengthened tree over the graph, and then compute a TRW matrix for similarity measurement purposes. After this, the nearest neighbors are identified according to the TRW matrix and the class label of a query point is determined by the sum of all the TRW weights of its nearest neighbors. To deal with online situations, we also propose a new algorithm to handle sequential samples based a local neighborhood reconstruction. Comparison experiments are conducted on both synthetic data sets and real-world data sets to demonstrate the validity of the proposed new $k$NN algorithm and its improvements to other version of $k$NN algorithms. Given the widespread appearance of manifold structures in real-world problems and the popularity of the traditional $k$NN algorithm, the proposed manifold version $k$NN shows promising potential for classifying manifold-distributed data.Comment: 32 pages, 12 figures, 7 table

Research on the Compliant Control of Electro-Hydraulic Servo Drive Force/Position Switching for a Lower Limb Exoskeleton Robot

In order to improve the flexibility of the foot landing of a lower limb exoskeleton robot based on an electro-hydraulic servo drive and to reduce its impact with the ground, an active compliance control method for force/position switching based on fuzzy control is proposed. According to the mathematical model of each component of the electro-hydraulic servo system of the core drive unit of the lower limb exoskeleton robot, the transfer functions of the position control system and the force control system are obtained respectively, and then its specific working characteristics are studied. Before the feet hit the ground, the position servo control system under the action of a fuzzy controller is used to achieve the movement of legs in free and unconstrained space, and the moment the foot touches the ground, the system is switched to a force servo control system to precisely control the output force, thereby reducing the rigid impact between the feet. In the meantime, the validity of the designed switching method and controller is verified by the joint simulation of MATLAB and AMESIM. The simulation results show that the electro-hydraulic servo force/position switching method based on a fuzzy algorithm is able not only to guarantee the movement accuracy of the foot end of the lower limb exoskeleton robot, but can also effectively reduce the impact force between the foot end and the ground

Rega-Net:Retina Gabor Attention for Deep Convolutional Neural Networks

Extensive research works demonstrate that the attention mechanism in convolutional neural networks (CNNs) effectively improves accuracy. But little works design attention mechanisms using large receptive fields. In this work, we propose a novel attention method named Rega-net to increase CNN accuracy by enlarging the receptive field. Inspired by the mechanism of the human retina, we design convolutional kernels to resemble the non-uniformly distributed structure of the human retina. Then, we sample variable-resolution values in the Gabor function distribution and fill these values in retina-like kernels. This distribution allows important features to be more visible in the center position of the receptive field. We further design an attention module including these retina-like kernels. Experiments demonstrate that our Rega-Net achieves 79.963\% top-1 accuracy on ImageNet-1K classification and 43.1\% mAP on COCO2017 object detection. The mAP of the Rega-Net increased by up to 3.5\% compared to baseline networks

Satellite-detected ammonia changes in the United States: Natural or anthropogenic impacts

Ammonia (NH3) is the most abundant alkaline component and can react with atmospheric acidic species to form aerosols that can lead to numerous environmental and health issues. Increasing atmospheric NH3 over agricultural regions in the US has been documented. However, spatiotemporal changes of NH3 concentrations over the entire US are still not thoroughly understood, and the factors that drive these changes remain unknown. Herein, we applied the Atmospheric Infrared Sounder (AIRS) monthly NH3 dataset to explore spatiotemporal changes in atmospheric NH3 and the empirical relationships with synthetic N fertilizer application, livestock manure production, and climate factors across the entire US at both regional and pixel levels from 2002 to 2016. We found that, in addition to the US Midwest, the Mid-South and Western regions also experienced striking increases in NH3 concentrations. NH3 released from livestock manure during warmer winters contributed to increased annual NH3 concentrations in the Western US. The influence of temperature on temporal evolution of NH3 concentrations was associated with synthetic N fertilizer use in the Northern Great Plains. With a strong positive impact of temperature on NH3 concentrations in the US Midwest, this region could possibly become an atmospheric NH3 hotspot in the context of future warming. Our study provides an essential scientific basis for US policy makers in developing mitigation strategies for agricultural NH3 emissions under future climate change scenarios

CLIP Brings Better Features to Visual Aesthetics Learners

The success of pre-training approaches on a variety of downstream tasks has revitalized the field of computer vision. Image aesthetics assessment (IAA) is one of the ideal application scenarios for such methods due to subjective and expensive labeling procedure. In this work, an unified and flexible two-phase \textbf{C}LIP-based \textbf{S}emi-supervised \textbf{K}nowledge \textbf{D}istillation paradigm is proposed, namely \textbf{\textit{CSKD}}. Specifically, we first integrate and leverage a multi-source unlabeled dataset to align rich features between a given visual encoder and an off-the-shelf CLIP image encoder via feature alignment loss. Notably, the given visual encoder is not limited by size or structure and, once well-trained, it can seamlessly serve as a better visual aesthetic learner for both student and teacher. In the second phase, the unlabeled data is also utilized in semi-supervised IAA learning to further boost student model performance when applied in latency-sensitive production scenarios. By analyzing the attention distance and entropy before and after feature alignment, we notice an alleviation of feature collapse issue, which in turn showcase the necessity of feature alignment instead of training directly based on CLIP image encoder. Extensive experiments indicate the superiority of CSKD, which achieves state-of-the-art performance on multiple widely used IAA benchmarks

Risk Assessment and Mapping of Hand, Foot, and Mouth Disease at the County Level in Mainland China Using Spatiotemporal Zero-Inflated Bayesian Hierarchical Models

Hand, foot, and mouth disease (HFMD) is a worldwide infectious disease, prominent in China. China’s HFMD data are sparse with a large number of observed zeros across locations and over time. However, no previous studies have considered such a zero-inflated problem on HFMD’s spatiotemporal risk analysis and mapping, not to mention for the entire Mainland China at county level. Monthly county-level HFMD cases data combined with related climate and socioeconomic variables were collected. We developed four models, including spatiotemporal Poisson, negative binomial, zero-inflated Poisson (ZIP), and zero-inflated negative binomial (ZINB) models under the Bayesian hierarchical modeling framework to explore disease spatiotemporal patterns. The results showed that the spatiotemporal ZINB model performed best. Both climate and socioeconomic variables were identified as significant risk factors for increasing HFMD incidence. The relative risk (RR) of HFMD at the local scale showed nonlinear temporal trends and was considerably spatially clustered in Mainland China. The first complete county-level spatiotemporal relative risk maps of HFMD were generated by this study. The new findings provide great potential for national county-level HFMD prevention and control, and the improved spatiotemporal zero-inflated model offers new insights for epidemic data with the zero-inflated problem in environmental epidemiology and public health