Applying the Convolutional Neural Network Deep Learning Technology to Behavioural Recognition in Intelligent Video

In order to improve the accuracy and real-time performance of abnormal behaviour identification in massive video monitoring data, the authors design intelligent video technology based on convolutional neural network deep learning and apply it to the smart city on the basis of summarizing video development technology. First, the technical framework of intelligent video monitoring algorithm is divided into bottom (object detection), middle (object identification) and high (behaviour analysis) layers. The object detection based on background modelling is applied to routine real-time detection and early warning. The object detection based on object modelling is applied to after-event data query and retrieval. The related optical flow algorithms are used to achieve the identification and detection of abnormal behaviours. In order to improve the accuracy, effectiveness and intelligence of identification, the deep learning technology based on convolutional neural network is applied to enhance the learning and identification ability of learning machine and realize the real-time upgrade of intelligence video’s "brain". This research has a good popularization value in the application field of intelligent video technology

Combination of Dendrobium Mixture and Metformin Curbs the Development and Progression of Diabetic Cardiomyopathy by Targeting the lncRNA NEAT1

OBJECTIVES: This study aimed to explore the efficacy of combination treatment with dendrobium mixture and metformin (Met) in diabetic cardiomyopathy (DCM) and its effects on NEAT1 and the Nrf2 signaling pathway. METHODS: H9c2 cells were maintained in medium supplemented with either low (5.5 mmol/L) or high (50 mmol/L) glucose. Male Sprague-Dawley rats were fed a high-glucose diet and administered a single, low dose of streptozotocin (35 mg/kg) via intraperitoneal injection to induce the development of DM. After induction of DM, the rats were treated with dendrobium mixture (10 g/kg) and Met (0.18 g/kg) daily for 4 weeks. Next, quantitative reverse transcription (qRT)-PCR and western blotting were performed to evaluate the expression levels of target genes and proteins. Flow cytometry was performed to assess apoptosis, and hematoxylin and eosin staining was performed to evaluate the morphological changes in rat cardiac tissue. RESULTS: In patients with diabetes mellitus (DM) and myocardial cells and heart tissues from rats with high glucose-induced DM, NEAT1 was downregulated, and the expression levels of Nrf2 were decreased (p<0.01, p<0.001). The combination of dendrobium mixture and Met upregulated the expression of NEAT1 which upregulated Nrf2 by targeting miR-23a-3p, resulting in reduced apoptosis and improved cardiac tissue morphology (p<0.01, p<0.001). CONCLUSION: Dendrobium mixture and Met upregulated the expression of NEAT1 in DCM, thereby inhibiting apoptosis of myocardial cells

Loss Rank Mining: A General Hard Example Mining Method for Real-time Detectors

Modern object detectors usually suffer from low accuracy issues, as foregrounds always drown in tons of backgrounds and become hard examples during training. Compared with those proposal-based ones, real-time detectors are in far more serious trouble since they renounce the use of region-proposing stage which is used to filter a majority of backgrounds for achieving real-time rates. Though foregrounds as hard examples are in urgent need of being mined from tons of backgrounds, a considerable number of state-of-the-art real-time detectors, like YOLO series, have yet to profit from existing hard example mining methods, as using these methods need detectors fit series of prerequisites. In this paper, we propose a general hard example mining method named Loss Rank Mining (LRM) to fill the gap. LRM is a general method for real-time detectors, as it utilizes the final feature map which exists in all real-time detectors to mine hard examples. By using LRM, some elements representing easy examples in final feature map are filtered and detectors are forced to concentrate on hard examples during training. Extensive experiments validate the effectiveness of our method. With our method, the improvements of YOLOv2 detector on auto-driving related dataset KITTI and more general dataset PASCAL VOC are over 5% and 2% mAP, respectively. In addition, LRM is the first hard example mining strategy which could fit YOLOv2 perfectly and make it better applied in series of real scenarios where both real-time rates and accurate detection are strongly demanded.Comment: 8 pages, 6 figure

TaintTrace: Efficient Flow Tracing with Dynamic Binary Rewriting

TaintTrace is a high performance flow tracing tool that protects systems against security exploits. It is based on dynamic execution binary rewriting empowering our tool with fine-grained monitoring of system activities such as the tracking of the usage and propagation of data origi-nated from the network. The challenge lies in minimizing the run-time overhead of the tool. TaintTrace uses a number of techniques such as direct memory mapping to optimize performance. In this paper, we demonstrate that TaintTrace is effective in protecting against various attacks while main-taining a modest slowdown of 5.5 times, offering significant improvements over similar tools.

Modified Glucose-Insulin-Potassium Regimen Provides Cardioprotection With Improved Tissue Perfusion in Patients Undergoing Cardiopulmonary Bypass Surgery

Background Laboratory studies demonstrate glucose-insulin-potassium (GIK) as a potent cardioprotective intervention, but clinical trials have yielded mixed results, likely because of varying formulas and timing of GIK treatment and different clinical settings. This study sought to evaluate the effects of modified GIK regimen given perioperatively with an insulin-glucose ratio of 1:3 in patients undergoing cardiopulmonary bypass surgery. Methods and Results In this prospective, randomized, double-blinded trial with 930 patients referred for cardiac surgery with cardiopulmonary bypass, GIK (200 g/L glucose, 66.7 U/L insulin, and 80 mmol/L KCl) or placebo treatment was administered intravenously at 1 mL/kg per hour 10 minutes before anesthesia and continuously for 12.5 hours. The primary outcome was the incidence of in-hospital major adverse cardiac events including all-cause death, low cardiac output syndrome, acute myocardial infarction, cardiac arrest with successful resuscitation, congestive heart failure, and arrhythmia. GIK therapy reduced the incidence of major adverse cardiac events and enhanced cardiac function recovery without increasing perioperative blood glucose compared with the control group. Mechanistically, this treatment resulted in increased glucose uptake and less lactate excretion calculated by the differences between arterial and coronary sinus, and increased phosphorylation of insulin receptor substrate-1 and protein kinase B in the hearts of GIK-treated patients. Systemic blood lactate was also reduced in GIK-treated patients during cardiopulmonary bypass surgery. Conclusions A modified GIK regimen administered perioperatively reduces the incidence of in-hospital major adverse cardiac events in patients undergoing cardiopulmonary bypass surgery. These benefits are likely a result of enhanced systemic tissue perfusion and improved myocardial metabolism via activation of insulin signaling by GIK. Clinical Trial Registration URL: clinicaltrials.gov. Identifier: NCT01516138