Anti-Kekulé number of the {(3, 4), 4}-fullerene*

A {(3,4),4}-fullerene graphG is a 4-regular plane graph with exactly eight triangular faces and other quadrangular faces. An edge subset S of G is called an anti-Kekulé set, if G − S is a connected subgraph without perfect matchings. The anti-Kekulé number of G is the smallest cardinality of anti-Kekulé sets and is denoted by akG. In this paper, we show that 4≤akG≤5; at the same time, we determine that the {(3, 4), 4}-fullerene graph with anti-Kekulé number 4 consists of two kinds of graphs: one of which is the graph H1 consisting of the tubular graph Qnn≥0, where Qn is composed of nn≥0 concentric layers of quadrangles, capped on each end by a cap formed by four triangles which share a common vertex (see Figure 2 for the graph Qn); and the other is the graph H2, which contains four diamonds D1, D2, D3, and D4, where each diamond Di1≤i≤4 consists of two adjacent triangles with a common edge ei1≤i≤4 such that four edges e1, e2, e3, and e4 form a matching (see Figure 7D for the four diamonds D1 − D4). As a consequence, we prove that if G∈H1, then akG=4; moreover, if G∈H2, we give the condition to judge that the anti-Kekulé number of graph G is 4 or 5

Video Desnowing and Deraining via Saliency and Dual Adaptive Spatiotemporal Filtering

Outdoor vision sensing systems often struggle with poor weather conditions, such as snow and rain, which poses a great challenge to existing video desnowing and deraining methods. In this paper, we propose a novel video desnowing and deraining model that utilizes the salience information of moving objects to address this problem. First, we remove the snow and rain from the video by low-rank tensor decomposition, which makes full use of the spatial location information and the correlation between the three channels of the color video. Second, because existing algorithms often regard sparse snowflakes and rain streaks as moving objects, this paper injects salience information into moving object detection, which reduces the false alarms and missed alarms of moving objects. At the same time, feature point matching is used to mine the redundant information of moving objects in continuous frames, and a dual adaptive minimum filtering algorithm in the spatiotemporal domain is proposed by us to remove snow and rain in front of moving objects. Both qualitative and quantitative experimental results show that the proposed algorithm is more competitive than other state-of-the-art snow and rain removal methods

Connection between the decadal variability in the Southern Ocean circulation and the Southern Annular Mode

Author Posting. © American Geophysical Union, 2007. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Geophysical Research Letters 34 (2007): L16604, doi:10.1029/2007GL030526.Previous studies demonstrated the remarkable upward trend of the Southern Annular Mode (SAM) and Southern Ocean wind stress in association with anthropogenic forcing. An oceanic reanalysis data set is used to investigate the response of the circulation in the Southern Ocean to the decadal variability of SAM. Our results indicate the strengthening and the poleward shift of the northward Ekman velocity as well as the Ekman pumping rate, which led to a corresponding strengthening trend in the Deacon Cell. This strengthening, in turn, intensified the meridional density gradient and the tilting of the isopycnal surfaces. On the interannual time scale, the Antarctic Circumpolar Currents (ACC) transport exhibits a positive correlation with SAM index as seen separately in observations. However, there is no significant trend in the total transport of ACC. Possible reasons are discussed.This work was supported by the Chinese Academy of Sciences (Grant KZSW2-YW-214), the Natural Science Foundation of China (Grant 40640420557) and National Basic Research Program of China (Grant 2006CB403604) for X-Y. Yang and D. Wang, and by W. Alan Clark Chair from Woods Hole Oceanographic Institution for R.X. Huang

3-[(1H-Benzimidazol-2-yl)sulfanyl­methyl]benzonitrile

In the title compound, C15H11N3S, the dihedral angle between the benzimidazole ring system and the benzene ring is 51.8 (2)°. The crystal structure exhibits inter­molecular N—H⋯N hydrogen bonds which lead to the formation of C(4) chains along the [001] direction

PNet—A Deep Learning Based Photometry and Astrometry Bayesian Framework

Time-domain astronomy has emerged as a vibrant research field in recent years, focusing on celestial objects that exhibit variable magnitudes or positions. Given the urgency of conducting follow-up observations for such objects, the development of an algorithm capable of detecting them and determining their magnitudes and positions has become imperative. Leveraging the advancements in deep neural networks, we present PNet, an end-to-end framework designed not only to detect celestial objects and extract their magnitudes and positions, but also to estimate the photometric uncertainty. PNet comprises two essential steps. First, it detects stars and retrieves their positions, magnitudes, and calibrated magnitudes. Subsequently, in the second phase, PNet estimates the uncertainty associated with the photometry results, serving as a valuable reference for the light-curve classification algorithm. Our algorithm has been tested using both simulated and real observation data, demonstrating the ability of PNet to deliver consistent and reliable outcomes. Integration of PNet into data-processing pipelines for time-domain astronomy holds significant potential for enhancing response speed and improving the detection capabilities for celestial objects with variable positions and magnitudes

TripleNet: A Low Computing Power Platform of Low-Parameter Network

With the excellent performance of deep learning technology in the field of computer vision, convolutional neural network (CNN) architecture has become the main backbone of computer vision task technology. With the widespread use of mobile devices, neural network models based on platforms with low computing power are gradually being paid attention. This paper proposes a lightweight convolutional neural network model, TripleNet, an improved convolutional neural network based on HarDNet and ThreshNet, inheriting the advantages of small memory usage and low power consumption of the mentioned two models. TripleNet uses three different convolutional layers combined into a new model architecture, which has less number of parameters than that of HarDNet and ThreshNet. CIFAR-10 and SVHN datasets were used for image classification by employing HarDNet, ThreshNet, and our proposed TripleNet for verification. Experimental results show that, compared with HarDNet, TripleNet's parameters are reduced by 66% and its accuracy rate is increased by 18%; compared with ThreshNet, TripleNet's parameters are reduced by 37% and its accuracy rate is increased by 5%.Comment: 4 pages, 2 figure