蜡样芽胞杆菌(Bacillus cereus)是一种常见的食源性致病菌。从食物中毒病人的食物残渣中分离到1株产呕吐毒素的蜡样芽胞杆菌BC307,其对青霉素类抗生素具有较强的抗性。抗SigP因子RsiP是蜡样芽胞杆菌族(Bacillus cereus sensu lato)细菌...蜡样芽胞杆菌(Bacillus cereus)是一种常见的食源性致病菌。从食物中毒病人的食物残渣中分离到1株产呕吐毒素的蜡样芽胞杆菌BC307,其对青霉素类抗生素具有较强的抗性。抗SigP因子RsiP是蜡样芽胞杆菌族(Bacillus cereus sensu lato)细菌青霉素耐药的重要调控因子。该实验利用原核系统表达RsiP蛋白并利用亲和层析法进行纯化,免疫小鼠后获得高效价多克隆抗体。结果表明,RsiP蛋白在炭疽芽胞杆菌、蜡样芽胞杆菌、苏云金芽胞杆菌中高度同源,在Western Blotting实验中RsiP抗血清与这3种菌的全菌蛋白均能发生特异性免疫反应。研究结果为探究RsiP对蜡样芽胞杆菌群细菌的青霉素耐药性调控打下了良好的基础。展开更多
This paper proposes a universal framework,termed as Multi-Task Hybrid Convolutional Neural Network(MHCNN),for joint face detection,facial landmark detection,facial quality,and facial attribute analysis.MHCNN consists ...This paper proposes a universal framework,termed as Multi-Task Hybrid Convolutional Neural Network(MHCNN),for joint face detection,facial landmark detection,facial quality,and facial attribute analysis.MHCNN consists of a high-accuracy single stage detector(SSD)and an efficient tiny convolutional neural network(T-CNN)for joint face detection refinement,alignment and attribute analysis.Though the SSD face detectors achieve promising results,we find that applying a tiny CNN on detections further boosts the detected face scores and bounding boxes.By multi-task training,our T-CNN aims to provide five facial landmarks,facial quality scores,and facial attributes like wearing sunglasses and wearing masks.Since there is no public facial quality data and facial attribute data as we need,we contribute two datasets,namely FaceQ and FaceA,which are collected from the Internet.Experiments show that our MHCNN achieves face detection performance comparable to the state of the art in face detection data set and benchmark(FDDB),and gets reasonable results on AFLW,FaceQ and FaceA.展开更多
Person re-identification(Re-ID)has achieved great progress in recent years.However,person Re-ID methods are still suffering from body part missing and occlusion problems,which makes the learned representations less re...Person re-identification(Re-ID)has achieved great progress in recent years.However,person Re-ID methods are still suffering from body part missing and occlusion problems,which makes the learned representations less reliable.In this paper,we pro⁃pose a robust coarse granularity part-level network(CGPN)for person Re-ID,which ex⁃tracts robust regional features and integrates supervised global features for pedestrian im⁃ages.CGPN gains two-fold benefit toward higher accuracy for person Re-ID.On one hand,CGPN learns to extract effective regional features for pedestrian images.On the other hand,compared with extracting global features directly by backbone network,CGPN learns to extract more accurate global features with a supervision strategy.The single mod⁃el trained on three Re-ID datasets achieves state-of-the-art performances.Especially on CUHK03,the most challenging Re-ID dataset,we obtain a top result of Rank-1/mean av⁃erage precision(mAP)=87.1%/83.6%without re-ranking.展开更多
Video conferencing systems face the dilemma between smooth streaming and decent visual quality because traditional video compression algorithms fail to produce bitstreams low enough for bandwidth-constrained networks....Video conferencing systems face the dilemma between smooth streaming and decent visual quality because traditional video compression algorithms fail to produce bitstreams low enough for bandwidth-constrained networks.An ultra-lightweight face-animation-based method that enables better video conferencing experience is proposed in this paper.The proposed method compresses high-quality upperbody videos with ultra-low bitrates and runs efficiently on mobile devices without high-end graphics processing units(GPU).Moreover,a visual quality evaluation algorithm is used to avoid image degradation caused by extreme face poses and/or expressions,and a full resolution image composition algorithm to reduce unnaturalness,which guarantees the user experience.Experiments show that the proposed method is efficient and can generate high-quality videos at ultra-low bitrates.展开更多
This paper reviews task scheduling frameworks,methods,and evaluation metrics of central processing unit-graphics processing unit(CPU-GPU)heterogeneous clusters.Task scheduling of CPU-GPU heterogeneous clusters can be ...This paper reviews task scheduling frameworks,methods,and evaluation metrics of central processing unit-graphics processing unit(CPU-GPU)heterogeneous clusters.Task scheduling of CPU-GPU heterogeneous clusters can be carried out on the system level,nodelevel,and device level.Most task-scheduling technologies are heuristic based on the experts’experience,while some technologies are based on statistic methods using machine learning,deep learning,or reinforcement learning.Many metrics have been adopted to evaluate and compare different task scheduling technologies that try to optimize different goals of task scheduling.Although statistic task scheduling has reached fewer research achievements than heuristic task scheduling,the statistic task scheduling still has significant research potential.展开更多
基金supported by ZTE Corporation and State Key Laboratory of Mobile Network and Mobile Multimedia Technology
文摘This paper proposes a universal framework,termed as Multi-Task Hybrid Convolutional Neural Network(MHCNN),for joint face detection,facial landmark detection,facial quality,and facial attribute analysis.MHCNN consists of a high-accuracy single stage detector(SSD)and an efficient tiny convolutional neural network(T-CNN)for joint face detection refinement,alignment and attribute analysis.Though the SSD face detectors achieve promising results,we find that applying a tiny CNN on detections further boosts the detected face scores and bounding boxes.By multi-task training,our T-CNN aims to provide five facial landmarks,facial quality scores,and facial attributes like wearing sunglasses and wearing masks.Since there is no public facial quality data and facial attribute data as we need,we contribute two datasets,namely FaceQ and FaceA,which are collected from the Internet.Experiments show that our MHCNN achieves face detection performance comparable to the state of the art in face detection data set and benchmark(FDDB),and gets reasonable results on AFLW,FaceQ and FaceA.
文摘Person re-identification(Re-ID)has achieved great progress in recent years.However,person Re-ID methods are still suffering from body part missing and occlusion problems,which makes the learned representations less reliable.In this paper,we pro⁃pose a robust coarse granularity part-level network(CGPN)for person Re-ID,which ex⁃tracts robust regional features and integrates supervised global features for pedestrian im⁃ages.CGPN gains two-fold benefit toward higher accuracy for person Re-ID.On one hand,CGPN learns to extract effective regional features for pedestrian images.On the other hand,compared with extracting global features directly by backbone network,CGPN learns to extract more accurate global features with a supervision strategy.The single mod⁃el trained on three Re-ID datasets achieves state-of-the-art performances.Especially on CUHK03,the most challenging Re-ID dataset,we obtain a top result of Rank-1/mean av⁃erage precision(mAP)=87.1%/83.6%without re-ranking.
文摘Video conferencing systems face the dilemma between smooth streaming and decent visual quality because traditional video compression algorithms fail to produce bitstreams low enough for bandwidth-constrained networks.An ultra-lightweight face-animation-based method that enables better video conferencing experience is proposed in this paper.The proposed method compresses high-quality upperbody videos with ultra-low bitrates and runs efficiently on mobile devices without high-end graphics processing units(GPU).Moreover,a visual quality evaluation algorithm is used to avoid image degradation caused by extreme face poses and/or expressions,and a full resolution image composition algorithm to reduce unnaturalness,which guarantees the user experience.Experiments show that the proposed method is efficient and can generate high-quality videos at ultra-low bitrates.
基金supported by ZTE‑University‑Institute Fund Project under Grant No.IA20230629009.
文摘This paper reviews task scheduling frameworks,methods,and evaluation metrics of central processing unit-graphics processing unit(CPU-GPU)heterogeneous clusters.Task scheduling of CPU-GPU heterogeneous clusters can be carried out on the system level,nodelevel,and device level.Most task-scheduling technologies are heuristic based on the experts’experience,while some technologies are based on statistic methods using machine learning,deep learning,or reinforcement learning.Many metrics have been adopted to evaluate and compare different task scheduling technologies that try to optimize different goals of task scheduling.Although statistic task scheduling has reached fewer research achievements than heuristic task scheduling,the statistic task scheduling still has significant research potential.
