Event-based Simultaneous Localization and Mapping: A Comprehensive Survey

In recent decades, visual simultaneous localization and mapping (vSLAM) has gained significant interest in both academia and industry. It estimates camera motion and reconstructs the environment concurrently using visual sensors on a moving robot. However, conventional cameras are limited by hardware, including motion blur and low dynamic range, which can negatively impact performance in challenging scenarios like high-speed motion and high dynamic range illumination. Recent studies have demonstrated that event cameras, a new type of bio-inspired visual sensor, offer advantages such as high temporal resolution, dynamic range, low power consumption, and low latency. This paper presents a timely and comprehensive review of event-based vSLAM algorithms that exploit the benefits of asynchronous and irregular event streams for localization and mapping tasks. The review covers the working principle of event cameras and various event representations for preprocessing event data. It also categorizes event-based vSLAM methods into four main categories: feature-based, direct, motion-compensation, and deep learning methods, with detailed discussions and practical guidance for each approach. Furthermore, the paper evaluates the state-of-the-art methods on various benchmarks, highlighting current challenges and future opportunities in this emerging research area. A public repository will be maintained to keep track of the rapid developments in this field at {\url{https://github.com/kun150kun/ESLAM-survey}}

Enhancing thermoelectric figure-of-merit by low-dimensional electrical transport in phonon-glass crystals

Low-dimensional electronic and glassy phononic transport are two important ingredients of highly-efficient thermoelectric material, from which two branches of the thermoelectric research emerge. One focuses on controlling electronic transport in the low dimension, while the other on multiscale phonon engineering in the bulk. Recent work has benefited much from combining these two approaches, e.g., phonon engineering in low-dimensional materials. Here, we propose to employ the low-dimensional electronic structure in bulk phonon-glass crystal as an alternative way to increase the thermoelectric efficiency. Through first-principles electronic structure calculation and classical molecular dynamics simulation, we show that the $\pi$-$\pi$ stacking Bis-Dithienothiophene molecular crystal is a natural candidate for such an approach. This is determined by the nature of its chemical bonding. Without any optimization of the material parameter, we obtain a maximum room-temperature figure of merit, $ZT$, of $1.48$ at optimal doping, thus validating our idea.Comment: Nano Lett.201

A new class of fractional impulsive differential hemivariational inequalities with an application

We consider a new fractional impulsive differential hemivariational inequality, which captures the required characteristics of both the hemivariational inequality and the fractional impulsive differential equation within the same framework. By utilizing a surjectivity theorem and a fixed point theorem we establish an existence and uniqueness theorem for such a problem. Moreover, we investigate the perturbation problem of the fractional impulsive differential hemivariational inequality to prove a convergence result, which describes the stability of the solution in relation to perturbation data. Finally, our main results are applied to obtain some new results for a frictional contact problem with the surface traction driven by the fractional impulsive differential equation

Toward Real-world Single Image Deraining: A New Benchmark and Beyond

Single image deraining (SID) in real scenarios attracts increasing attention in recent years. Due to the difficulty in obtaining real-world rainy/clean image pairs, previous real datasets suffer from low-resolution images, homogeneous rain streaks, limited background variation, and even misalignment of image pairs, resulting in incomprehensive evaluation of SID methods. To address these issues, we establish a new high-quality dataset named RealRain-1k, consisting of $1,120$ high-resolution paired clean and rainy images with low- and high-density rain streaks, respectively. Images in RealRain-1k are automatically generated from a large number of real-world rainy video clips through a simple yet effective rain density-controllable filtering method, and have good properties of high image resolution, background diversity, rain streaks variety, and strict spatial alignment. RealRain-1k also provides abundant rain streak layers as a byproduct, enabling us to build a large-scale synthetic dataset named SynRain-13k by pasting the rain streak layers on abundant natural images. Based on them and existing datasets, we benchmark more than 10 representative SID methods on three tracks: (1) fully supervised learning on RealRain-1k, (2) domain generalization to real datasets, and (3) syn-to-real transfer learning. The experimental results (1) show the difference of representative methods in image restoration performance and model complexity, (2) validate the significance of the proposed datasets for model generalization, and (3) provide useful insights on the superiority of learning from diverse domains and shed lights on the future research on real-world SID. The datasets will be released at https://github.com/hiker-lw/RealRain-1

Unsupervised Text Topic-Related Gene Extraction for Large Unbalanced Datasets

There is a common notion that traditional unsupervised feature extraction algorithms follow the assumption that the distribution of the different clusters in a dataset is balanced. However, feature selection is guided by the calculation of similarities among features when topic keywords are extracted from a large number of unmarked, unbalanced text datasets. As a result, the selected features cannot truly reflect the information of the original data set, which thus affects the subsequent performance of classifiers. To solve this problem, a new method of extracting unsupervised text topic-related genes is proposed in this paper. Firstly, a sample cluster group is obtained by factor analysis and a density peak algorithm, based on which the dataset is marked. Then, considering the influence of the unbalanced distribution of sample clusters on feature selection, the CHI statistical matrix feature selection method, which combines average local density and information entropy together, is used to strengthen the features of low-density small-sample clusters. Finally, a related gene extraction method based on the exploration of high-order relevance in multidimensional statistical data is described, which uses independent component analysis to enhance the generalisability of the selected features. In this way, unsupervised text topic-related genes can be extracted from large unbalanced datasets. The results of experiments suggest that the proposed method of extracting unsupervised text topic-related genes is better than existing methods in extracting text subject terms from low-density small-sample clusters, and has higher prematurity and feature dimension-reduction ability

Interprovincial reliance for improving air quality in China:A case study on black carbon aerosol

Black carbon (BC) is of global concern because of its adverse effects on climate and human health. It can travel long distances via atmospheric movement and can be geographically relocated through trade. Here, we explored the integrated patterns of BC transport within 30 provinces in China from the perspective of meteorology and interprovincial trade using the Weather Research and Forecasting with Chemistry (WRF/Chem) model and multiregional input-output analysis. In general, cross-border BC transport, which accounts for more than 30% of the surface concentration, occurs mainly between neighboring provinces. Specifically, Hebei contributes 1.2 μg·m(-3) BC concentration in Tianjin. By contrast, trade typically drives virtual BC flows from developed provinces to heavily industrial provinces, with the largest net flow from Beijing to Hebei (4.2 Gg). Shanghai is most vulnerable to domestic consumption with an average interprovincial consumption influence efficiency of 1.5 × 10(-4) (μg·m(-3))/(billion Yuan·yr(-1)). High efficiencies (∼8 × 10(-5) (μg·m(-3))/(billion Yuan·yr(-1))) are also found from regions including Beijing, Jiangsu, and Shanghai to regions including Hebei, Shandong, and Henan. The above source-receptor relationship indicates two control zones: Huabei and Huadong. Both mitigating end-of-pipe emissions and rationalizing the demand for pollution-intense products are important within the two control zones to reduce BC and other pollutants