RPDP: An Efficient Data Placement based on Residual Performance for P2P Storage Systems

Storage systems using Peer-to-Peer (P2P) architecture are an alternative to the traditional client-server systems. They offer better scalability and fault tolerance while at the same time eliminate the single point of failure. The nature of P2P storage systems (which consist of heterogeneous nodes) introduce however data placement challenges that create implementation trade-offs (e.g., between performance and scalability). Existing Kademlia-based DHT data placement method stores data at closest node, where the distance is measured by bit-wise XOR operation between data and a given node. This approach is highly scalable because it does not require global knowledge for placing data nor for the data retrieval. It does not however consider the heterogeneous performance of the nodes, which can result in imbalanced resource usage affecting the overall latency of the system. Other works implement criteria-based selection that addresses heterogeneity of nodes, however often cause subsequent data retrieval to require global knowledge of where the data stored. This paper introduces Residual Performance-based Data Placement (RPDP), a novel data placement method based on dynamic temporal residual performance of data nodes. RPDP places data to most appropriate selected nodes based on their throughput and latency with the aim to achieve lower overall latency by balancing data distribution with respect to the individual performance of nodes. RPDP relies on Kademlia-based DHT with modified data structure to allow data subsequently retrieved without the need of global knowledge. The experimental results indicate that RPDP reduces the overall latency of the baseline Kademlia-based P2P storage system (by 4.87%) and it also reduces the variance of latency among the nodes, with minimal impact to the data retrieval complexity

A real-time dynamic obstacle tracking and mapping system for UAV navigation and collision avoidance with an RGB-D camera

The real-time dynamic environment perception has become vital for autonomous robots in crowded spaces. Although the popular voxel-based mapping methods can efficiently represent 3D obstacles with arbitrarily complex shapes, they can hardly distinguish between static and dynamic obstacles, leading to the limited performance of obstacle avoidance. While plenty of sophisticated learning-based dynamic obstacle detection algorithms exist in autonomous driving, the quadcopter's limited computation resources cannot achieve real-time performance using those approaches. To address these issues, we propose a real-time dynamic obstacle tracking and mapping system for quadcopter obstacle avoidance using an RGB-D camera. The proposed system first utilizes a depth image with an occupancy voxel map to generate potential dynamic obstacle regions as proposals. With the obstacle region proposals, the Kalman filter and our continuity filter are applied to track each dynamic obstacle. Finally, the environment-aware trajectory prediction method is proposed based on the Markov chain using the states of tracked dynamic obstacles. We implemented the proposed system with our custom quadcopter and navigation planner. The simulation and physical experiments show that our methods can successfully track and represent obstacles in dynamic environments in real-time and safely avoid obstacles

Deep Learning Techniques for Video Instance Segmentation: A Survey

Video instance segmentation, also known as multi-object tracking and segmentation, is an emerging computer vision research area introduced in 2019, aiming at detecting, segmenting, and tracking instances in videos simultaneously. By tackling the video instance segmentation tasks through effective analysis and utilization of visual information in videos, a range of computer vision-enabled applications (e.g., human action recognition, medical image processing, autonomous vehicle navigation, surveillance, etc) can be implemented. As deep-learning techniques take a dominant role in various computer vision areas, a plethora of deep-learning-based video instance segmentation schemes have been proposed. This survey offers a multifaceted view of deep-learning schemes for video instance segmentation, covering various architectural paradigms, along with comparisons of functional performance, model complexity, and computational overheads. In addition to the common architectural designs, auxiliary techniques for improving the performance of deep-learning models for video instance segmentation are compiled and discussed. Finally, we discuss a range of major challenges and directions for further investigations to help advance this promising research field

An Efficient and Reliable Asynchronous Federated Learning Scheme for Smart Public Transportation

Since the traffic conditions change over time, machine learning models that predict traffic flows must be updated continuously and efficiently in smart public transportation. Federated learning (FL) is a distributed machine learning scheme that allows buses to receive model updates without waiting for model training on the cloud. However, FL is vulnerable to poisoning or DDoS attacks since buses travel in public. Some work introduces blockchain to improve reliability, but the additional latency from the consensus process reduces the efficiency of FL. Asynchronous Federated Learning (AFL) is a scheme that reduces the latency of aggregation to improve efficiency, but the learning performance is unstable due to unreasonably weighted local models. To address the above challenges, this paper offers a blockchain-based asynchronous federated learning scheme with a dynamic scaling factor (DBAFL). Specifically, the novel committee-based consensus algorithm for blockchain improves reliability at the lowest possible cost of time. Meanwhile, the devised dynamic scaling factor allows AFL to assign reasonable weights to stale local models. Extensive experiments conducted on heterogeneous devices validate outperformed learning performance, efficiency, and reliability of DBAFL

ReDas: Supporting Fine-Grained Reshaping and Multiple Dataflows on Systolic Array

Current systolic arrays still suffer from low performance and PE utilization on many real workloads due to the mismatch between the fixed array topology and diverse DNN kernels. We present ReDas, a flexible and lightweight systolic array that can adapt to various DNN models by supporting dynamic fine-grained reshaping and multiple dataflows. The key idea is to construct reconfigurable roundabout data paths using only the short connections between neighbor PEs. The array with 128$\times$128 size supports 129 different logical shapes and 3 dataflows (IS/OS/WS). Experiments on DNN models of MLPerf demonstrate that ReDas can achieve 3.09x speedup on average compared to state-of-the-art work.Comment: 7 pages, 11 figures, conferenc

SCEI: A Smart-Contract Driven Edge Intelligence Framework for IoT Systems

Federated learning (FL) utilizes edge computing devices to collaboratively train a shared model while each device can fully control its local data access. Generally, FL techniques focus on learning model on independent and identically distributed (iid) dataset and cannot achieve satisfiable performance on non-iid datasets (e.g. learning a multi-class classifier but each client only has a single class dataset). Some personalized approaches have been proposed to mitigate non-iid issues. However, such approaches cannot handle underlying data distribution shift, namely data distribution skew, which is quite common in real scenarios (e.g. recommendation systems learn user behaviors which change over time). In this work, we provide a solution to the challenge by leveraging smart-contract with federated learning to build optimized, personalized deep learning models. Specifically, our approach utilizes smart contract to reach consensus among distributed trainers on the optimal weights of personalized models. We conduct experiments across multiple models (CNN and MLP) and multiple datasets (MNIST and CIFAR-10). The experimental results demonstrate that our personalized learning models can achieve better accuracy and faster convergence compared to classic federated and personalized learning. Compared with the model given by baseline FedAvg algorithm, the average accuracy of our personalized learning models is improved by 2% to 20%, and the convergence rate is about 2$\times$ faster. Moreover, we also illustrate that our approach is secure against recent attack on distributed learning.Comment: 12 pages, 9 figure

Coral Bleaching in the Persian/Arabian Gulf Is Modulated by Summer Winds

Corals in the Persian/Arabian Gulf are the most thermally tolerant in the world, but live very near the thresholds of their thermal tolerance. Warming sea temperatures associated with climate change have resulted in numerous coral bleaching events regionally since the mid-1990s, but it has been unclear why unusually warm sea temperatures occur some years but not others. Using a combination of 5 years of observed sea-bottom temperatures at three reef sites and a meteorologically linked hydrodynamic model that extends through the past decade, we show that summer sea-bottom temperatures are tightly linked to regional wind regimes, and that strong ‘shamal’ wind events control the occurrence and severity of bleaching. Sea bottom temperatures were primarily controlled by latent heat flux from wind-driven surface evaporation which exceeded 300 W m-2 during shamal winds, double that of typical breeze conditions. Daily temperature change was highly correlated with wind speed, with breeze winds (<4 m s-1) resulting in increased warming, while faster winds caused cooling, with the magnitude of temperature decline increasing with wind speed. Using observed and simulated data from 2012 to 2017, we show that years with reported bleaching events (2012, 2017) were characterized by low winds speeds that resulted in temperatures persisting above coral bleaching threshold temperatures for >5 weeks, while the cooler intervening years (2013–2016) had summers with more frequent and/or strong shamal events which repeatedly cooled temperatures below bleaching thresholds for days to weeks, providing corals temporary respite from thermal stress. Using observed data from 2012 onward and simulated data from 2008 to 2011, we show that the severity of bleaching events over the past decade was linked to both the number of cumulative days above bleaching thresholds (rather than total days, which obfuscates the cooling effects of occasional brief shamal events), as well as to maxima. We show that winds of 4 m s-1 represents a critical threshold for whether or not corals cross bleaching threshold temperatures, and provide simulations to forecast sea-bottom temperature change and recovery times under a range of wind conditions. The role that wind-driven cooling may play on coral reefs globally is discussed

Quantitative assessment of retinal microvascular remodeling in eyes that underwent idiopathic epiretinal membrane surgery

Purpose: To explore the surgical outcomes of the macular microvasculature and visual function in eyes with idiopathic epiretinal membrane (iERM) using spectral-domain optical coherence tomography angiography (SD-OCTA).Methods: This observational, cross-sectional study included 41 participants who underwent iERM surgery with a 3-month (3M) follow-up. Forty-one healthy eyes formed the control group. The assessments included best-corrected visual acuity (BCVA) and mean sensitivity (MS) by microperimetry and SD-OCTA assessment of vessel tortuosity (VT), vessel density (VD), foveal avascular zone, and retinal thickness (RT).Results: The findings showed statistically significant differences in VT, foveal avascular zone parameters, RT, BCVA, and MS between the iERM and control groups (p < 0.05). After iERM surgery, the macular VT, SCP VD, and RT decreased significantly (p < 0.01) while the DCP VD increased (p = 0.029). The BCVA improved significantly (p < 0.001) and was associated with the MS (rs = −0.377, p = 0.015). MS was associated with the SCP VD and RT at 3M (SCP VD rs = 0.511, p = 0.001; RT rs = 0.456, p = 0.003). In the superior quadrant, the MS improved significantly (p < 0.001) and the improvement of MS was associated with the reduction of VT (β = −0.330, p = 0.034).Conclusion: Microcirculatory remodeling and perfusion recovery were observed within 3 months after iERM surgery. VT was a novel index for evaluating the morphology of the retinal microvasculature in eyes with iERM and was associated with MS in the superior quadrant