Pedestrian Spatio-Temporal Information Fusion For Video Anomaly Detection

Aiming at the problem that the current video anomaly detection cannot fully use the temporal information and ignore the diversity of normal behavior, an anomaly detection method is proposed to integrate the spatiotemporal information of pedestrians. Based on the convolutional autoencoder, the input frame is compressed and restored through the encoder and decoder. Anomaly detection is realized according to the difference between the output frame and the true value. In order to strengthen the characteristic information connection between continuous video frames, the residual temporal shift module and the residual channel attention module are introduced to improve the modeling ability of the network on temporal information and channel information, respectively. Due to the excessive generalization of convolutional neural networks, in the memory enhancement modules, the hopping connections of each codec layer are added to limit autoencoders' ability to represent abnormal frames too vigorously and improve the anomaly detection accuracy of the network. In addition, the objective function is modified by a feature discretization loss, which effectively distinguishes different normal behavior patterns. The experimental results on the CUHK Avenue and ShanghaiTech datasets show that the proposed method is superior to the current mainstream video anomaly detection methods while meeting the real-time requirements.Comment: International Conference on Intelligent Media, Big Data and Knowledge Minin

Learning to Adaptively Scale Recurrent Neural Networks

Recent advancements in recurrent neural network (RNN) research have demonstrated the superiority of utilizing multiscale structures in learning temporal representations of time series. Currently, most of multiscale RNNs use fixed scales, which do not comply with the nature of dynamical temporal patterns among sequences. In this paper, we propose Adaptively Scaled Recurrent Neural Networks (ASRNN), a simple but efficient way to handle this problem. Instead of using predefined scales, ASRNNs are able to learn and adjust scales based on different temporal contexts, making them more flexible in modeling multiscale patterns. Compared with other multiscale RNNs, ASRNNs are bestowed upon dynamical scaling capabilities with much simpler structures, and are easy to be integrated with various RNN cells. The experiments on multiple sequence modeling tasks indicate ASRNNs can efficiently adapt scales based on different sequence contexts and yield better performances than baselines without dynamical scaling abilities

Data Augmentation Vision Transformer for Fine-grained Image Classification

Recently, the vision transformer (ViT) has made breakthroughs in image recognition. Its self-attention mechanism (MSA) can extract discriminative labeling information of different pixel blocks to improve image classification accuracy. However, the classification marks in their deep layers tend to ignore local features between layers. In addition, the embedding layer will be fixed-size pixel blocks. Input network Inevitably introduces additional image noise. To this end, we study a data augmentation vision transformer (DAVT) based on data augmentation and proposes a data augmentation method for attention cropping, which uses attention weights as the guide to crop images and improve the ability of the network to learn critical features. Secondly, we also propose a hierarchical attention selection (HAS) method, which improves the ability of discriminative markers between levels of learning by filtering and fusing labels between levels. Experimental results show that the accuracy of this method on the two general datasets, CUB-200-2011, and Stanford Dogs, is better than the existing mainstream methods, and its accuracy is 1.4\% and 1.6\% higher than the original ViT, respectivelyComment: IEEE Signal Processing Letter

A Lightweight Reconstruction Network for Surface Defect Inspection

Currently, most deep learning methods cannot solve the problem of scarcity of industrial product defect samples and significant differences in characteristics. This paper proposes an unsupervised defect detection algorithm based on a reconstruction network, which is realized using only a large number of easily obtained defect-free sample data. The network includes two parts: image reconstruction and surface defect area detection. The reconstruction network is designed through a fully convolutional autoencoder with a lightweight structure. Only a small number of normal samples are used for training so that the reconstruction network can be A defect-free reconstructed image is generated. A function combining structural loss and $\mathit{L}1$ loss is proposed as the loss function of the reconstruction network to solve the problem of poor detection of irregular texture surface defects. Further, the residual of the reconstructed image and the image to be tested is used as the possible region of the defect, and conventional image operations can realize the location of the fault. The unsupervised defect detection algorithm of the proposed reconstruction network is used on multiple defect image sample sets. Compared with other similar algorithms, the results show that the unsupervised defect detection algorithm of the reconstructed network has strong robustness and accuracy.Comment: Journal of Mathematical Imaging and Vision(JMIV

Fluoxetine treatment for major depression decreases the plasma levels of cytokines

Elevated levels of pro-inflammatory biomarkers have been reported in major depressive disorder (MDD). The aim of this study is to investigate the plasma levels of interleukin-18 (IL-18), macrophageinflammatory protein-1α (MIP-1α), monocyte chemoattractant protein 1 (MCP-1), stromal cell derived factor-1 (SDF-1), and regulated upon activation, normal T cell  expressed and secreted (RANTES) in patients with MDD before and after  eight week treatment of fluoxetine hydrochloride in comparison with normal controls. All subjects were assessed before and after treatment with the Hamilton Depression Rating Scale (HDRS). Our results showed that the symptoms of forty healthy controls and thirty-four patients with MDD were correlated with their plasma levels of IL-18, MIP-1α, MCP-1, SDF-1α, and RANTES. The levels of all five cytokine of patients with MDD were significantly decreased after treatment.  However, the levels remained significantly higher than those of the healthy controls (p<0.001). In the seven depressed subjects whose HDRS score fell to below seven after antidepressant therapy comparing with those subjects whose HDRS score larger than seven, the mean levels of IL-18 (p=0.01) and SDF-1α(p<0.05) were significantly lower. Conversely, higher levels of cytokines correlated with a persistently increased severity of symptoms, as measured by the HDRS scores. In conclusion, these findings suggest that MDD is associated with activation of the immune system, and the antidepressant effect of fluoxetine may be mediated in part through its anti-inflammatory effects.Key words: Fluoxetine hydrochloride, major depression, cytokine, chemokine, inflammation

Decreased Glomerular Filtration Rate Is Associated with Mortality and Cardiovascular Events in Patients with Hypertension: A Prospective Study

BACKGROUND: Few studies reported the associations between decreased glomerular filtration rate (GFR) and mortality, coronary heart disease (CHD), and stroke in hypertensive patients. We aim to assess the associations between GFR and mortality, CHD, and stroke in hypertensive patients and to evaluate whether low GFR can improve the prediction of these outcomes in addition to conventional cardiovascular risk factors. METHODS AND FINDINGS: This is an observational prospective study and 3,711 eligible hypertensive patients aged ≥5 years from rural areas of China were used for the present analysis. The associations between eGFR and outcomes, followed by a median of 4.9 years, were evaluated using Cox proportional hazards models adjusting for other potential confounders. Low eGFR was independently associated with risk of all-cause mortality, cardiovascular mortality, and incident stroke [multivariable adjusted hazard ratios (95% confidence intervals) for eGFR <60 ml/min/1.73 m(2) relative to eGFR ≥90 ml/min/1.73 m(2) were 1.824 (1.047-3.365), 2.371 (1.109-5.068), and 2.493 (1.193-5.212), respectively]. We found no independent association between eGFR and the risk of CHD. For 4-year all-cause and cardiovascular mortality, integrated discrimination improvement (IDI) was positive when eGFR were added to traditional risk factors (1.51%, P = 0.016, and 1.99%, P = 0.017, respectively). For stroke and CHD events, net reclassification improvements (NRI) were 5.9% (P = 0.012) and 1.8% (P = 0.083) for eGFR, respectively. CONCLUSIONS: We have established an inversely independent association between eGFR and all-cause mortality, cardiovascular mortality, and stroke in hypertensive patients in rural areas of China. Further, addition of eGFR significantly improved the prediction of 4-year mortality and stroke over and above that of conventional risk factors. We recommend that eGFR be incorporated into prognostic assessment for patients with hypertension in rural areas of China. LIMITATIONS: We did not have sufficient information on atrial fibrillation to control for the potential covariate. These associations should be further confirmed in future

Solving a Class of Modular Polynomial Equations and its Relation to Modular Inversion Hidden Number Problem and Inversive Congruential Generator

In this paper we revisit the modular inversion hidden number problem (MIHNP) and the inversive congruential generator (ICG) and consider how to attack them more efficiently. We consider systems of modular polynomial equations of the form a_{ij}+b_{ij}x_i+c_{ij}x_j+x_ix_j=0 (mod p) and show the relation between solving such equations and attacking MIHNP and ICG. We present three heuristic strategies using Coppersmith\u27s lattice-based root-finding technique for solving the above modular equations. In the first strategy, we use the polynomial number of samples and get the same asymptotic bound on attacking ICG proposed in PKC 2012, which is the best result so far. However, exponential number of samples is required in the work of PKC 2012. In the second strategy, a part of polynomials chosen for the involved lattice are linear combinations of some polynomials and this enables us to achieve a larger upper bound for the desired root. Corresponding to the analysis of MIHNP we give an explicit lattice construction of the second attack method proposed by Boneh, Halevi and Howgrave-Graham in Asiacrypt 2001. We provide better bound than that in the work of PKC 2012 for attacking ICG. Moreover, we propose the third strategy in order to give a further improvement in the involved lattice construction in the sense of requiring fewer samples

Water management affects arsenic and cadmium accumulation in different rice cultivars

Paddy rice (Oryza sativa L.) is a staple food and one of the major sources of dietary arsenic (As) and cadmium (Cd) in Asia. A field experiment was conducted to investigate the effects of four water management regimes (aerobic, intermittent irrigation, conventional irrigation and flooding) on As and Cd accumulation in seven major rice cultivars grown in Zhejiang province, east China. With increasing irrigation from aerobic to flooded conditions, the soil HCl-extractable As concentrations increased significantly and the HCl-extractable Cd concentrations decreased significantly. These trends were consistent with the As and Cd concentrations in the straw, husk and brown rice. Water management both before and after the full tillering stage affected As and Cd accumulation in the grains. The intermittent and conventional treatments produced higher grain yields than the aerobic and flooded treatments. Cd concentrations in brown rice varied 13.1-40.8 times and As varied 1.75-8.80 times among the four water management regimes. Cd and As accumulation in brown rice varied among the rice cultivars, with Guodao 6 (GD6) was a low Cd but high-As-accumulating cultivar while Indonesia (IR) and Yongyou 9 (YY9) were low As but high-Cd-accumulating cultivars. Brown rice Cd and As concentrations in the 7 cultivars were significantly negatively correlated. The results indicate that As and Cd accumulated in rice grains with opposite trends that were influenced by both water management and rice cultivar. Production of 'safe' rice with respect to As and Cd might be possible by balancing water management and rice cultivar according to the severity of soil pollution.Paddy rice (Oryza sativa L.) is a staple food and one of the major sources of dietary arsenic (As) and cadmium (Cd) in Asia. A field experiment was conducted to investigate the effects of four water management regimes (aerobic, intermittent irrigation, conventional irrigation and flooding) on As and Cd accumulation in seven major rice cultivars grown in Zhejiang province, east China. With increasing irrigation from aerobic to flooded conditions, the soil HCl-extractable As concentrations increased significantly and the HCl-extractable Cd concentrations decreased significantly. These trends were consistent with the As and Cd concentrations in the straw, husk and brown rice. Water management both before and after the full tillering stage affected As and Cd accumulation in the grains. The intermittent and conventional treatments produced higher grain yields than the aerobic and flooded treatments. Cd concentrations in brown rice varied 13.1-40.8 times and As varied 1.75-8.80 times among the four water management regimes. Cd and As accumulation in brown rice varied among the rice cultivars, with Guodao 6 (GD6) was a low Cd but high-As-accumulating cultivar while Indonesia (IR) and Yongyou 9 (YY9) were low As but high-Cd-accumulating cultivars. Brown rice Cd and As concentrations in the 7 cultivars were significantly negatively correlated. The results indicate that As and Cd accumulated in rice grains with opposite trends that were influenced by both water management and rice cultivar. Production of 'safe' rice with respect to As and Cd might be possible by balancing water management and rice cultivar according to the severity of soil pollution