Identification of anomalous fast bulk events in a p-type point-contact germanium detector

The ultralow detection threshold,ultralow intrinsic background,and excellent energy resolution of ptype point-contact germanium detectors are important for rare-event searches,in particular for the detection of direct dark matter interactions,coherent elastic neutrino-nucleus scattering,and neutrinoless double beta decay.Anomalous bulk events with an extremely fast rise time are observed in the CDEX-1B detector.We report a method of extracting fast bulk events from bulk events using a pulse shape simulation and reconstructed source experiment signature.Calibration data and the distribution of X-rays generated by intrinsic radioactivity verified that the fast bulk experienced a single hit near the passivation layer.The performance of this germanium detector indicates that it is capable of single-hit bulk spatial resolution and thus provides a background removal technique.

Anomalous Situations Recognition in Surveillance Images Using Deep Learning

Anomalous situations in surveillance videos or images that may result in security issues,such as disasters,accidents,crime,violence,or terrorism,can be identified through video anomaly detection.However,differentiat-ing anomalous situations from normal can be challenging due to variations in human activity in complex environments such as train stations,busy sporting fields,airports,shopping areas,military bases,care centers,etc.Deep learning models’learning capability is leveraged to identify abnormal situations with improved accuracy.This work proposes a deep learning architecture called Anomalous Situation Recognition Network(ASRNet)for deep feature extraction to improve the detection accuracy of various anomalous image situations.The proposed framework has five steps.In the first step,pretraining of the proposed architecture is performed on the CIFAR-100 dataset.In the second step,the proposed pre-trained model and Inception V3 architecture are used for feature extraction by utilizing the suspicious activity recognition dataset.In the third step,serial feature fusion is performed,and then the Dragonfly algorithm is utilized for feature optimization in the fourth step.Finally,using optimized features,various Support Vector Machine(SVM)and K-Nearest Neighbor(KNN)based classification models are utilized to detect anomalous situations.The proposed framework is validated on the suspicious activity dataset by varying the number of optimized features from 100 to 1000.The results show that the proposed method is effective in detecting anomalous situations and achieves the highest accuracy of 99.24%using cubic SVM.

Anomalous Midsummer Rainfall in Yangtze River-Huaihe River Valleys and Its Association with the East Asia Westerly Jet

In this study, the interannual and interdecadal relationship between midsummer Yangtze River-Huaihe River valley （YHRV） rainfall and the position of the East Asia westerly jet （EAWJ） were investigated. The midsummer YHRV rainfall was found to significantly increase after the 1980s. Moreover, the location of the EAWJ was found abnormally south of the climatic mean during 1980–2008 （ID2） compared to 1951–1979 （ID1）. During ID2, associated with the southward movement of the EAWJ, an anomalous upper-level conver-gence occurred over middle-high latitudes （35° –55° N） and divergence occurred over lower latitudes （～30°N） of East Asia. Correspondingly, anomalous descending and ascending motion was observed in middle-high and lower latitudes along 90°–130° E, respectively, favoring more precipitation over YHRV. On an interan-nual time scale, the EAWJ and YHRV rainfall exhibited similar relationships during the two periods. When the EAWJ was centered abnormally southward, rainfall over YHRV tended to increase. However, EAWJ-related circulations were significantly different during the two periods. During ID1, the circulation of the southward-moving EAWJ exhibited alternating positive–negative–positive distributions from low to middle– high latitudes along the East Asian coast; the most significant anomaly appeared west of the Okhotsk Sea. However, during ID2 the EAWJ was more closely correlated with the tropical and subtropical circulations. Significant differences between ID1 and ID2 were also recorded sea surface temperatures （SSTs）. During ID1, the EAWJ was influenced by the extratropical SST over the northern Pacific; however, the EAWJ was more significantly affected by the SST of the tropical western Pacific during ID2.

Environment-dependent continuous time random walk

A generalized continuous time random walk model which is dependent on environmental damping is proposed in which the two key parameters of the usual random walk theory： the jumping distance and the waiting time, are replaced by two new ones： the pulse velocity and the flight time. The anomalous diffusion of a free particle which is characterized by the asymptotical mean square displacement （x^2（t）） - t^a is realized numerically and analysed theoretically, where the value of the power index a is in a region of 0 〈 a 〈 2. Particularly, the damping leads to a sub-diffusion when the impact velocities are drawn from a Gaussian density function and the super-diffusive effect is related to statistical extremes, which are called rare-though-dominant events.