Adaptive Graph Convolutional Network with Attention Graph Clustering for Co-saliency Detection

Co-saliency detection aims to discover the common and salient foregrounds from a group of relevant images. For this task, we present a novel adaptive graph convolutional network with attention graph clustering (GCAGC). Three major contributions have been made, and are experimentally shown to have substantial practical merits. First, we propose a graph convolutional network design to extract information cues to characterize the intra- and interimage correspondence. Second, we develop an attention graph clustering algorithm to discriminate the common objects from all the salient foreground objects in an unsupervised fashion. Third, we present a unified framework with encoder-decoder structure to jointly train and optimize the graph convolutional network, attention graph cluster, and co-saliency detection decoder in an end-to-end manner. We evaluate our proposed GCAGC method on three cosaliency detection benchmark datasets (iCoseg, Cosal2015 and COCO-SEG). Our GCAGC method obtains significant improvements over the state-of-the-arts on most of them.Comment: CVPR202

Build Efficient Framework to Support Real-Time Computation-Intensive Applications on Mobile and Edge Devices

Modern society is witnessing an exponentially increasing number of smart devices in daily life. As the proliferation of mobile devices and edge devices bring convenience to everyone, their close proximity to the user, various embedded sensor readings, and increasingly powerful computability also provide many possible solutions to traditional challenges. In the present technical world, the cloud-based approach dominates the system requiring intensive computation. However, the offloading process introduces non-negligible delay. Also, the data sent to the server may raise people’s worries as privacy becomes a growing concern nowadays. This dissertation takes the advantage of widespread mobile and edge devices to build an efficient framework to support real-time computation-intensive applications. Specifically, the framework focuses on providing solutions to applications involving two challenging tasks: feature point comparison and machine learning inference. Both of them consume a noteworthy portion of the limited computing resource on current mobile and edge devices, hindering the development of the real-time applications that require performing either of these two jobs frequently. In the first part of this dissertation, a system to support feature point comparison on mobile and edge devices is presented. The widely deployed feature point comparison algorithms suffer from the view angle change problem. This dissertation devises a novel approach to overcome the view angle challenge with the assistance of the embedded sensors in smartphones. Our solution is practical and efficient without introducing noticeable overhead in our experiment. In the second part, this dissertation introduces a real-time object detection system running on resource-insufficient devices, e.g., smart camera, Jetson Nano, and Raspberry Pi. Our study shows those edge devices fail to run object detection at an acceptable rate (~30 FPS) independently. Instead of keep running object detection, edge devices can track objects to improve their responsiveness. However, tracking may lead to lower accuracy as time passes by. The time to trigger tracking and detection becomes an important decision to make. Our system introduces a cloud server to assist in making this decision. The experiments validate the great potential in our solution. The last part focuses on a real-time multi-user mobile AR application that incorporates both machine learning inference and feature point comparison. Our AR application is based on reference object recognition, which requires recognizing the exact object that appeared before. This dissertation designs an innovative approach to achieving this goal with the consideration of resource deficit and temporal requirements. Taking the knowledge that the number of feature points influences the response time significantly and the traditional feature points comparison algorithms fall short of accuracy when viewpoint changes, our solution embraces the mobile onboard sensor readings and machine learning model to decrease the number of feature points and increase the comparison accuracy. The performance strongly supports the superiority of our method over existing solutions

Opposite size dependences of the red/green upconversion intensity ratio in sub-20 nm Yb

Upconversion nanophosphors (UCNPs) of lanthanide-doped efficient ternary fluoride (β-NaGdF4:Yb3+,Er3+) with sizes below 20 nm are synthesized in a facile solvothermal route. Upconversion luminescence (UCL) of the UCNPs is found to increase with increase of the nanoparticle size prepared at a higher solvothermal-treatment temperature, due to reduction of the surface defects. Interestingly, opposite size dependences of the red/green UCL intensity ratio are observed for the same series of UCNPs in different surface conditions, i.e., with increase of the nanoparticle size, the ratio decreases for as-prepared dry powders, but increases for moisturized counterparts after being exposed in air for some days, and interesting the ratio decreases again after annealing of the moisturized samples. The phenomenon is explained due to the effect of surface defects and adsorbed water molecules, respectively, dominating in nonradiative relaxations, which usually facilitate red UCL. The disclosed phenomenon clarifies some contradictory observations on UCL in the literature

Opposite size dependences of the red/green upconversion intensity ratio in sub-20 nm Yb3+, Er3+-doped beta-NaGdF4 nanophosphors

National Natural Science Foundation of China [61275063, 61205051]; National Key Scientific Program [2012CB933503]; Fundamental Research Funds for the Central Universities [2012121009]Upconversion nanophosphors (UCNPs) of lanthanide-doped efficient ternary fluoride (beta-NaGdF4: Yb3+, Er3+) with sizes below 20 nm are synthesized in a facile solvothermal route. Upconversion luminescence (UCL) of the UCNPs is found to increase with increase of the nanoparticle size prepared at a higher solvothermal-treatment temperature, due to reduction of the surface defects. Interestingly, opposite size dependences of the red/green UCL intensity ratio are observed for the same series of UCNPs in different surface conditions, i.e., with increase of the nanoparticle size, the ratio decreases for as-prepared dry powders, but increases for moisturized counterparts after being exposed in air for some days, and interesting the ratio decreases again after annealing of the moisturized samples. The phenomenon is explained due to the effect of surface defects and adsorbed water molecules, respectively, dominating in nonradiative relaxations, which usually facilitate red UCL. The disclosed phenomenon clarifies some contradictory observations on UCL in the literature. Copyright (C) EPLA, 201

RoVEr: Robust and Verifiable Erasure Code for Hadoop Distributed File Systems

Erasure Coding based Storage (ECS) is replacing tradition replica-based systems because of its low storage overhead. In an ECS, however, every task needs to fetch remote pieces of data for its execution, and data verification is missing in the current framework. As security issues keep rising and there have been security incidents occurred in big data platforms, the compromised nodes in a computing cluster may manipulate its hosted data fed for other nodes yielding misleading results. Without replicas, it is quite challenging to efficiently verify the data integrity in ECS. In this paper, we develop ROVER, which is an efficient and verifiable ECS for big data platforms. In ROVER, every piece of data is monitored by its checksums stored on a set of witnesses. Bloom filter technique is used on each witness to efficiently keep the records of the checksums. The data verification is based on the majority voting. ROVER also supports a quick reconstruction of Bloom Filter when a node recovers from a failure. We present a complete system framework, security analysis, and a guideline for setting the parameters. The implementation and evaluation show that ROVER is robust and efficient against the attack from the compromised nodes