Oxidative stress is a crucial pathological process that contributes to secondary injury following intracerebral hemorrhage. P2X7 receptor (P2X7R), which activated by the abnormal accumulation of extracellular ATP, plays an important role in regulation oxidative central nervous system, although effects P2X7R-associated after hemorrhage remain unclear. Mouse models were established through stereotactic injection 0.075 U VII collagenase into right basal ganglia. The results revealed P2X7R...

Due to the popularity of Deep Neural Network (DNN) models, we have witnessed extreme-scale DNN models with continued increase scale in terms depth and width. However, extremely high memory requirements for them make it difficult run training processes on single many-core architectures such as a Graphic Processing Unit (GPU), which compels researchers use model parallelism over multiple GPUs work. always brings very heavy additional overhead. Therefore, running an GPU is urgently required....

On heterogeneous cluster systems, the convergence performances of neural network models are greatly troubled by different machines. In this paper, we propose a novel distributed Stochastic Gradient Descent (SGD) algorithm named Grouping-SGD for deep learning, which converges faster than Sync-SGD, Async-SGD, and Stale-SGD. Grouping-SGD, machines partitioned into multiple groups, ensuring that in same group have similar performances. Machines update synchronously, while groups asynchronously....

Although GPUs have emerged as the mainstream for acceleration of convolutional neural network (CNN) training processes, they usually limited physical memory, meaning that it is hard to train large-scale CNN models. Many methods memory optimization been proposed decrease consumption CNNs and mitigate increasing scale these networks; however, this comes at cost an obvious drop in time performance. We propose a new strategy named Layup realizes both better efficiency First, fast...

In the COVID-19 epidemic mildly symptomatic and asymptomatic infections generate a substantial portion of virus spread; these undetected individuals make it difficult to assess effectiveness preventive measures as most prevention strategies are based on detected data. Effectively identifying in local transmission will be great help control. this work, we propose an RNA network representation model graph attention networks (RVTR); is constructed using principle natural language processing...

In this work, we present a convolutional neural network (CNN) named CGFA-CNN for blind image quality assessment (BIQA). A unique two-stage strategy is utilized which firstly identifies the distortion type in an using Sub-Network I and then quantifies II. Different from most deep networks, extract hierarchical features as descriptors to enhance representation design feature aggregation layer end-to-end training manner applying Fisher encoding visual vocabularies modeled by Gaussian mixture...

With the development of artificial intelligence technology, intelligent weapon systems that can automatically identify, lock on and strike targets have gradually appeared replace humans in executing simple decision-making commands. Target detection is a key part weapons. At present, large-scale target has serious challenges such as long-tail data distributions, severe occlusion, category ambiguity. The main algorithms only detect each independent area without considering semantic...

Multimodal Large Language Models (MLLMs) have recently received substantial interest, which shows their emerging potential as general-purpose models for various vision-language tasks. MLLMs involve significant external knowledge within parameters; however, it is challenging to continually update these with the latest knowledge, involves huge computational costs and poor interpretability. Retrieval augmentation techniques proven be effective plugins both LLMs MLLMs. In this study, we propose...

Automatic localization of thoracic diseases has a wide range applications which can assist radiologists for more efficient and better diagnosis. However, it is still challenging task to locate the accurately since strong location annotation may not be available different vary in size greatly. In this paper, we propose novel multi-scale feature pyramids model weakly supervised disease on chest X-ray images. Our leverages maps learn representation lesions by fusing heatmaps generated from all...

This paper proposes a three-dimensional(3D) segmentation algorithm using hyper-complex edge detection operator and applies the new to three-dimensional hepatic vessel from computed tomography (CT) volumetric data. A 3D is constructed by combining octonion gradient operator. We replace every voxel of data one which consist its gray-level 6 neighborhoods' gray-level. Via this original defined as Similar Sobel operator, there are three principal directions (coordinate axes) in each element...

Abstract The unified stochastic particle method based on the Bhatnagar-Gross-Krook model (USP-BGK) has been proposed recently to overcome low accuracy and efficiency of traditional methods, such as direct simulation Monte Carlo (DSMC) method, for multi-scale gas flows. However, running with extra virtual particles space interpolation, previous USP-BGK cannot be directly transplanted into existing DSMC codes. In this work, implementation is simplified using new temporal evolution spatial...

Growing accuracy and robustness of Deep Neural Networks (DNN) models are accompanied by growing model capacity (going deeper or wider). However, high memory requirements those make it difficult to execute the training process in one GPU. To address it, we first identify usage characteristics for deep wide convolutional networks, demonstrate opportunities reuse on both intra-layer inter-layer levels. We then present Layrub, a runtime data placement strategy that orchestrates execution...

Stroke is the second leading cause of death worldwide. Therefore, research on prevention and treatment for stroke has great significance. In recent years, development artificial intelligence (AI) technology brought new hopes to healthcare. The resulting partnerships between clinicians computation science scientists, supported by growing strength clinical informatics, are beginning yield positive results. AI techniques have been used successfully in skin/cancer-related studies. Besides that,...

Growing accuracy and robustness of Deep Neural Networks (DNN) models are accompanied by growing model capacity (going deeper or wider). However, high memory requirements those make it difficult to execute the training process in one GPU. To address it, we first identify usage characteristics for deep wide convolutional networks, demonstrate opportunities reuse on both intra-layer inter-layer levels. We then present Layrub, a runtime data placement strategy that orchestrates execution...

Abstract In this work, we present a convolutional neural network (CNN) named CGFA-CNN for blind image quality assessment (BIQA). A unique 2-stage strategy is utilized which fifirstly identififies the distortion type in an using Sub-network I and then quantififies II. And difffferent from most deep networks, extract hierarchical features as descriptors to enhance representation design feature aggregation layer end-to-end training manner applying Fisher encoding visual vocabularies modeled by...

In order to improve the efficiency of transmission line maintenance scheduling, a multi-objective and multiscale mathematical model is proposed.The coloring problem graph theory analytic hierarchy process are combined, various constrains considered in model.In deal with discrete variables problems, new method based on harmony search algorithm combined ant colony developed an analogy music improvisation process.Musical performers seek find pleasing as determined by aesthetic standard, just...

Deep fully convolution neural network has opened a new field in semantic segmentation for remote sensing images. In this paper, an improved U-net model is proposed to extract buildings at the pixel level, so as obtain its contour and size information. model, highly modular ResNeXt50 used encoder of parallel dense residual module based on atrous multi-scale information segmentation. Moreover, transposed whose stride 2 upsampling grayscale mask. This modified adopts sum jaccard loss binary...