GeoSay: A Geometric Saliency for Extracting Buildings in Remote Sensing Images

Automatic extraction of buildings in remote sensing images is an important but challenging task and finds many applications in different fields such as urban planning, navigation and so on. This paper addresses the problem of buildings extraction in very high-spatial-resolution (VHSR) remote sensing (RS) images, whose spatial resolution is often up to half meters and provides rich information about buildings. Based on the observation that buildings in VHSR-RS images are always more distinguishable in geometry than in texture or spectral domain, this paper proposes a geometric building index (GBI) for accurate building extraction, by computing the geometric saliency from VHSR-RS images. More precisely, given an image, the geometric saliency is derived from a mid-level geometric representations based on meaningful junctions that can locally describe geometrical structures of images. The resulting GBI is finally measured by integrating the derived geometric saliency of buildings. Experiments on three public and commonly used datasets demonstrate that the proposed GBI achieves the state-of-the-art performance and shows impressive generalization capability. Additionally, GBI preserves both the exact position and accurate shape of single buildings compared to existing methods

Anomaly Detection in Aerial Videos with Transformers

Unmanned aerial vehicles (UAVs) are widely applied for purposes of inspection, search, and rescue operations by the virtue of low-cost, large-coverage, real-time, and high-resolution data acquisition capacities. Massive volumes of aerial videos are produced in these processes, in which normal events often account for an overwhelming proportion. It is extremely difficult to localize and extract abnormal events containing potentially valuable information from long video streams manually. Therefore, we are dedicated to developing anomaly detection methods to solve this issue. In this paper, we create a new dataset, named DroneAnomaly, for anomaly detection in aerial videos. This dataset provides 37 training video sequences and 22 testing video sequences from 7 different realistic scenes with various anomalous events. There are 87,488 color video frames (51,635 for training and 35,853 for testing) with the size of $640 \times 640$ at 30 frames per second. Based on this dataset, we evaluate existing methods and offer a benchmark for this task. Furthermore, we present a new baseline model, ANomaly Detection with Transformers (ANDT), which treats consecutive video frames as a sequence of tubelets, utilizes a Transformer encoder to learn feature representations from the sequence, and leverages a decoder to predict the next frame. Our network models normality in the training phase and identifies an event with unpredictable temporal dynamics as an anomaly in the test phase. Moreover, To comprehensively evaluate the performance of our proposed method, we use not only our Drone-Anomaly dataset but also another dataset. We will make our dataset and code publicly available. A demo video is available at https://youtu.be/ancczYryOBY. We make our dataset and code publicly available

3-(1,3-Dithio­lan-2-yl­idene)-1-(4-meth­oxy­phen­yl)pyridine-2,4(1H,3H)-dione

In the title compound, C15H13NO3S2, the dithiol­ane ring adopts a twisted conformation. The mol­ecule exhibits a V-shaped conformation, with a dihedral angle of 79.05 (7)° between the benzene ring and the pyridine ring. In the crystal, C—H⋯O inter­actions are observed

Supersymmetric Electroweak Corrections to W±H∓W^{\pm}H^{\mp} Associated Production at the CERN Large Hadron Collider

The $O(\alpha_{ew}m_{t(b)}^{2}/m_{W}^{2})$ and $O(\alpha_{ew} m_{t(b)}^4/m_W^4)$ supersymmetric electroweak corrections to the cross section for $W^{\pm}H^{\mp}$ associated production at the LHC are calculated in the minimal supersymmetric standard model. Those corrections arise from the quantum effects which are induced by the Yukawa couplings from the Higgs sector and the chargino-top(bottom)-sbottom(stop) couplings, neutralino-top(bottom)-stop(sbottom) couplings and charged Higgs-stop-sbottom couplings. The numerical results show that the Yukawa corrections arising from the Higgs sector can decrease the total cross sections significantly for low $\tan\beta(=1.5$ and $2)$ when $m_{H^+}(<300)$GeV, which exceed -12%. For high $\tan\beta$ the Yukawa corrections become negligibly small. The genuine supersymmetric electroweak corrections can increase or decrease the total cross sections depending on the supersymmetric parameters, which can exceed -25% for the favorable supersymmetric parameter values. We also show that the genuine supersymmetric electroweak corrections depend strongly on the choice of $\tan\beta$, $A_t$, $M_{\tilde Q}$ and $\mu$. For large values of $A_t$, or large values of $\mu$ and $\tan\beta$, one can get much larger corrections. The corrections can become very small, in contrast, for larger values of $M_{\tilde Q}$

Empirical study on clique-degree distribution of networks

The community structure and motif-modular-network hierarchy are of great importance for understanding the relationship between structures and functions. In this paper, we investigate the distribution of clique-degree, which is an extension of degree and can be used to measure the density of cliques in networks. The empirical studies indicate the extensive existence of power-law clique-degree distributions in various real networks, and the power-law exponent decreases with the increasing of clique size.Comment: 9 figures, 4 page