Anomaly (natural sciences)

About: Anomaly (natural sciences) is a research topic. Over the lifetime, 4256 publications have been published within this topic receiving 88808 citations.

Real-World Anomaly Detection in Surveillance Videos

Abstract: Surveillance videos are able to capture a variety of realistic anomalies. In this paper, we propose to learn anomalies by exploiting both normal and anomalous videos. To avoid annotating the anomalous segments or clips in training videos, which is very time consuming, we propose to learn anomaly through the deep multiple instance ranking framework by leveraging weakly labeled training videos, i.e. the training labels (anomalous or normal) are at video-level instead of clip-level. In our approach, we consider normal and anomalous videos as bags and video segments as instances in multiple instance learning (MIL), and automatically learn a deep anomaly ranking model that predicts high anomaly scores for anomalous video segments. Furthermore, we introduce sparsity and temporal smoothness constraints in the ranking loss function to better localize anomaly during training. We also introduce a new large-scale first of its kind dataset of 128 hours of videos. It consists of 1900 long and untrimmed real-world surveillance videos, with 13 realistic anomalies such as fighting, road accident, burglary, robbery, etc. as well as normal activities. This dataset can be used for two tasks. First, general anomaly detection considering all anomalies in one group and all normal activities in another group. Second, for recognizing each of 13 anomalous activities. Our experimental results show that our MIL method for anomaly detection achieves significant improvement on anomaly detection performance as compared to the state-of-the-art approaches. We provide the results of several recent deep learning baselines on anomalous activity recognition. The low recognition performance of these baselines reveals that our dataset is very challenging and opens more opportunities for future work. The dataset is available at: http://crcv.ucf.edu/projects/real-world/

Persistent Positive North Atlantic Oscillation Mode Dominated the Medieval Climate Anomaly

Abstract: The Medieval Climate Anomaly (MCA) was the most recent pre-industrial era warm interval of European climate, yet its driving mechanisms remain uncertain. We present here a 947-year-long multidecadal North Atlantic Oscillation (NAO) reconstruction and find a persistent positive NAO during the MCA. Supplementary reconstructions based on climate model results and proxy data indicate a clear shift to weaker NAO conditions into the Little Ice Age (LIA). Globally distributed proxy data suggest that this NAO shift is one aspect of a global MCA-LIA climate transition that probably was coupled to prevailing La Nina-like conditions amplified by an intensified Atlantic meridional overturning circulation during the MCA.

A signal analysis of network traffic anomalies

Abstract: Identifying anomalies rapidly and accurately is critical to the efficient operation of large computer networks. Accurately characterizing important classes of anomalies greatly facilitates their identification; however, the subtleties and complexities of anomalous traffic can easily confound this process. In this paper we report results of signal analysis of four classes of network traffic anomalies: outages, flash crowds, attacks and measurement failures. Data for this study consists of IP flow and SNMP measurements collected over a six month period at the border router of a large university. Our results show that wavelet filters are quite effective at exposing the details of both ambient and anomalous traffic. Specifically, we show that a pseudo-spline filter tuned at specific aggregation levels will expose distinct characteristics of each class of anomaly. We show that an effective way of exposing anomalies is via the detection of a sharp increase in the local variance of the filtered data. We evaluate traffic anomaly signals at different points within a network based on topological distance from the anomaly source or destination. We show that anomalies can be exposed effectively even when aggregated with a large amount of additional traffic. We also compare the difference between the same traffic anomaly signals as seen in SNMP and IP flow data, and show that the more coarse-grained SNMP data can also be used to expose anomalies effectively.

Propagation of the Arctic Oscillation from the stratosphere to the troposphere

Abstract: Geopotential anomalies ranging from the Earth's surface to the middle stratosphere in the northern hemisphere are dominated by a mode of variability known as the Arctic Oscillation (AO). The AO is represented herein by the leading mode (the first empirical orthogonal function) of low-frequency variability of wintertime geopotential between 1000 and 10 hPa. In the middle stratosphere the signature of the AO is a nearly zonally symmetric pattern representing a strong or weak polar vortex. At 1000 hPa the AO is similar to the North Atlantic Oscillation, but with more zonal symmetry, especially at high latitudes. In zonal-mean zonal wind the AO is seen as a north-south dipole centered on 40°–45°N; in zonal-mean temperature it is seen as a deep warm or cold polar anomaly from the upper troposphere to ∼10 hPa. The association of the AO pattern in the troposphere with modulation of the strength of the stratospheric polar vortex provides perhaps the best measure of coupling between the stratosphere and the troposphere. By examining separately time series of AO signatures at tropospheric and stratospheric levels, it is shown that AO anomalies typically appear first in the stratosphere and propagate downward. The midwinter correlation between the 90-day low-pass-filtered 10-hPa anomaly and the 1000-hPa anomaly exceeds 0.65 when the surface anomaly time series is lagged by about three weeks. The tropospheric signature of the AO anomaly is characterized by substantial changes to the storm tracks and strength of the midtropospheric flow, especially over the North Atlantic and Europe. The implications of large stratospheric anomalies as precursors to changes in tropospheric weather patterns are discussed.