A Hierarchical Spatio-Temporal Graph Convolutional Neural Network for Anomaly Detection in Videos

Deep learning models have been widely used for anomaly detection in surveillance videos. Typical models are equipped with the capability to reconstruct normal videos and evaluate the reconstruction errors on anomalous videos to indicate the extent of abnormalities. However, existing approaches suffer from two disadvantages. Firstly, they can only encode the movements of each identity independently, without considering the interactions among identities which may also indicate anomalies. Secondly, they leverage inflexible models whose structures are fixed under different scenes, this configuration disables the understanding of scenes. In this paper, we propose a Hierarchical Spatio-Temporal Graph Convolutional Neural Network (HSTGCNN) to address these problems, the HSTGCNN is composed of multiple branches that correspond to different levels of graph representations. High-level graph representations encode the trajectories of people and the interactions among multiple identities while low-level graph representations encode the local body postures of each person. Furthermore, we propose to weightedly combine multiple branches that are better at different scenes. An improvement over single-level graph representations is achieved in this way. An understanding of scenes is achieved and serves anomaly detection. High-level graph representations are assigned higher weights to encode moving speed and directions of people in low-resolution videos while low-level graph representations are assigned higher weights to encode human skeletons in high-resolution videos. Experimental results show that the proposed HSTGCNN significantly outperforms current state-of-the-art models on four benchmark datasets (UCSD Pedestrian, ShanghaiTech, CUHK Avenue and IITB-Corridor) by using much less learnable parameters.Comment: Accepted to IEEE Transactions on Circuits and Systems for Video Technology (T-CSVT

Influenza and COVID-19 co-infection and vaccine effectiveness against severe cases: a mathematical modeling study

BackgroundInfluenza A virus have a distinctive ability to exacerbate SARS-CoV-2 infection proven by in vitro studies. Furthermore, clinical evidence suggests that co-infection with COVID-19 and influenza not only increases mortality but also prolongs the hospitalization of patients. COVID-19 is in a small-scale recurrent epidemic, increasing the likelihood of co-epidemic with seasonal influenza. The impact of co-infection with influenza virus and SARS-CoV-2 on the population remains unstudied.MethodHere, we developed an age-specific compartmental model to simulate the co-circulation of COVID-19 and influenza and estimate the number of co-infected patients under different scenarios of prevalent virus type and vaccine coverage. To decrease the risk of the population developing severity, we investigated the minimum coverage required for the COVID-19 vaccine in conjunction with the influenza vaccine, particularly during co-epidemic seasons.ResultCompared to the single epidemic, the transmission of the SARS-CoV-2 exhibits a lower trend and a delayed peak when co-epidemic with influenza. Number of co-infection cases is higher when SARS-CoV-2 co-epidemic with Influenza A virus than that with Influenza B virus. The number of co-infected cases increases as SARS-CoV-2 becomes more transmissible. As the proportion of individuals vaccinated with the COVID-19 vaccine and influenza vaccines increases, the peak number of co-infected severe illnesses and the number of severe illness cases decreases and the peak time is delayed, especially for those >60 years old.ConclusionTo minimize the number of severe illnesses arising from co-infection of influenza and COVID-19, in conjunction vaccinations in the population are important, especially priority for the elderly

Radioacitivity of the aerosol collected in Nagasaki City due to the Fukushima Daiichi Nuclear Power Plant Accident

Radioactivity of 134Cs and 137Cs was detected in the aerosol collected in Nagasaki prefectural forest park "Nagasaki Kenmin No Mori" about 20 km north-west from central Nagasaki City from Mar. 23 to Jul. 27, 2011. The highest concentrations of the nuclides were detected in the sample collected from Apr. 6 to Apr. 13 and 110mAg was also detected in the sample. The wind of Apr. 6 in the park was found to come via Fukushima with back-trajectory analysis in the web-site of National Oceanic Atmospheric Administration (NOAA), United States Department of Commerce. The concentrations in the air of 134Cs, 137Cs and 110mAg evaluated were as small as 0.47, 0.52 and 0.0054 mBq/m3 respectively. However, the concentrations of them in the collected aerosol were as large as 11.3, 12.4 and 0.12 kBq/kg, and equivalent to the level of surface soil of 5 cm in Warabidaira Iitate Fukushima, highly contaminated area. It indicates that air filters in air-conditioning facilities should be handled carefully also at Nagasaki about 1,000 km apart from Fukushima Daiichi Nuclear Power Plant. In addition, the concentration of natural radioactivity Pb-210 was found as large as 19.9 kBq/kg. Therefore, it was ascertained that the risk of air filters was already existed before the accident and the radioactivity arisen from the accident increased the risk

Characterization and phylogenetic analysis of Acheilognathus chankaensis mitochondrial genome

A complete mitogenome of Acheilognathus chankaensis was assembled and analyzed in this study. The genome, with the length of 16,926 bp, contains 20 tRNAs, 13 coding genes, two rRNAs, and a D-loop region. The components of the genome are the same as available Acheilognathus mitogenomes in NCBI database, and GC (%) is also similar with other published mitogenomes in this genus. Phylogenetic analysis showed that A. chankaensis was genetically closed to A. macropterus. This complete mitogenome could contribute to the future phylogenetic and phylogeographic studies of genus Acheilognathus

The Relation Between the CO

Carbon dioxide can change the heat balance of the atmosphere. To study the relationship between CO2 and temperature change, we use the given CO2 concentrations to implement linear regression model, gray time series forecasting model, back-propagation model, and auto-regressive and moving average model to establish the growth function of CO2 concentration. Errors are evaluated to choose the most suitable model. then we use the most suitable model in step one to further predict future land-ocean temperature. Finally, we use grey relational analysis to analyze the relationship between CO2 concentrations and land-ocean temperatures since 1959

1,5-Bis(2-methoxybenzylidene)thiocarbonohydrazide methanol monosolvate

The title compound, C17H18N4O2S·CH3OH, was synthesized by the condensation reaction of o-methoxybenzaldehyde with thiocarbohydrazide in methanol. The two benzene rings are inclined each to other at 31.7 (1)°. Intermolecular N—H...O and bifurcated O—H...N(S) hydrogen bonds link two thiocarbonohydrazide and two solvent molecules into a centrosymmetric unit. These units, related by translation along the b axis, are further aggregated into columns through N—H...S hydrogen bonds

Inter-Comparison and Evaluation of Remote Sensing Precipitation Products over China from 2005 to 2013

Precipitation is a key aspect of the climate system. In this paper, the dependability of five satellite precipitation products (TRMM [Tropical Rainfall Measuring Mission] 3BV42, PERSIANN [Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks] CDR, GSMaP [Global Satellite Mapping of Precipitation] RENALYSIS, CMORPH [Climate Prediction Center’s morphing technique] BLD and CMORPH_RAW) were compared with in situ measurements over China for the period of 2005 to 2013. To completely evaluate these precipitation products, the annual, seasonal and monthly precipitation averages were calculated. Overall, the Huaihe River and Qinlin mountains are shown to have heavy precipitation to the southeast and lighter precipitation to the northwest. The comparison results indicate that Gauge correction (CMORPH_BLD) improves the quality of the original satellite products (CMORPH_RAW), resulting in the higher correlation coefficient (CC), the low relative bias (BIAS) and root mean square error (RMSE). Over China, the GSMaP_RENALYSIS outperforms other products and shows the highest CC (0.91) and lowest RMSE (0.85 mm/day) and all products except for PERSIANN_CDR exhibit underestimation. GSMaP_RENALYSIS gives the highest of probability of detection (81%), critical success index (63%) and lowest false alarm ratio (36%) while TRMM3BV42 gives the highest of frequency bias index (1.00). Over Tibetan Plateau, CMORPH_RAW demonstrates the poorest performance with the biggest BIAS (4.2 mm/month) and lowest CC (0.22) in December 2013. GSMaP_RENALYSIS displays quite consistent with in situ measurements in summer. However, GSMaP_RENALYSIS and CMORPH_RAW underestimate precipitation over South China. CMORPH_BLD and TRMM3BV42 show consistent with high CC (>0.8) but relatively large RMSE in summer