Multimodal remote sensing images provide complementary information, enhancing the effectiveness of object detection tasks in open-world scenarios. To address the imbalance of information richness between modalities in multimodal object detection, we propose a simple yet effective multi-source image object detection method (DKDNet). Our contributions are twofold: (a) we introduce diffusion deformation convolution (DDConv), which combines deformation convolution with adaptive long-range receptive fields to further enhance the ability to perceive object pose variations and capture distant information. (b) We propose the bidirectional feature distillation and information complementary fusion network (BDFusion), where different modalities exchange information through a knowledge distillation strategy, explicitly enhancing the information interaction between modalities. Finally, we adaptively build spatial domain complementarity between different modalities via self-correction, revealing implicit correlations. Experimental results on the publicly available vehicle detection in aerial imagery (VEDAI) dataset and the optical and SAR ship detection dataset (OSSDD), collected in the Suez Canal region, demonstrate that our proposed method achieves superior performance with acceptable inference time, making it suitable for various realworld scenarios.

Issues such as insufficient feature representation and background confusion make detection tasks for small object in remote sensing arduous. Particularly when the algorithm will be deployed on board for real-time processing, which requires extensive optimization of accuracy and speed under limited computing resources. To tackle these problems, an efficient detector called FFCA-YOLO(Feature enhancement, Fusion and Context Aware YOLO) is proposed in this paper. FFCA-YOLO includes three innovative lightweight and plug-and-play modules: feature enhancement module(FEM), feature fusion module(FFM) and spatial context aware module(SCAM). These three modules improve the network capabilities of local area awareness, multi-scale feature fusion and global association cross channels and space, respectively, while trying to avoid increasing complexity as possible. Thus the weak feature representations of small objects are enhanced and the confusable backgrounds are suppressed. Two public remote sensing datasets(VEDAI and AI-TOD) for small object detection and one self-built dataset(USOD) are used to validate the effectiveness of FFCA-YOLO. The accuracy of FFCA-YOLO reaches 0.748, 0.617 and 0.909(in terms of mAP50) that exceeds several benchmark models and state-of-the-art methods. Meanwhile, the robustness of FFCA-YOLO is also validated under different simulated degradation conditions. Moreover, to further reduce computational resource consumption while ensuring efficiency, a lite version of FFCA-YOLO(L-FFCA-YOLO) is optimized by reconstructing the backbone and neck of FFCA-YOLO based on partial convolution. L-FFCA-YOLO has faster speed, smaller parameter scale, lower computing power requirement but little accuracy loss compared with FFCA-YOLO. The source code will be available at https://github.com/yemu1138178251/FFCA-YOLO.

We describe our experience with Fathom, a system for identifying the network performance bottlenecks of any service running in the Google fleet. Fathom passively samples RPCs, the principal unit of work for services. It segments the overall latency into host and network components with kernel and RPC stack instrumentation. It records these detailed latency metrics, along with detailed transport connection state, for every sampled RPC. This lets us determine if the completion is constrained by the client, network or server. To scale while enabling analysis, we also aggregate samples into distributions that retain multi-dimensional breakdowns. This provides us with a macroscopic view of individual services. Fathom runs globally in our datacenters for all production traffic, where it monitors billions of TCP connections 24x7. For five years Fathom has been our primary tool for troubleshooting service network issues and assessing network infrastructure changes. We present case studies to show how it has helped us improve our production services.

We present a new, host-based design for link load balancing and report the first experiences of link imbalance in datacenters. Our design, PLB (Protective Load Balancing), builds on transport protocols and ECMP/WCMP to reduce network hotspots. PLB randomly changes the paths of connections that experience congestion, preferring to repath after idle periods to minimize packet reordering. It repaths a connection by changing the IPv6 Flow Label on its packets, which switches include as part of ECMP/WCMP. Across hosts, this action drives down hotspots in the network, and lowers the latency of RPCs.

Machine slot-time spent on data transmission has direct impact on average job completion time (JCT). In this paper, we propose Macroflow, a networking abstraction that can capture the primitive scheduling granularity of machine slot-time. We demonstrate that minimizing machine slot-time is equivalent to minimizing the average macroflow completion time (MCT). We prove that minimizing MCT to be strongly NP-hard and focus on developing effective heuristics. We propose the Smallest-Macroflow-First (SMF) and Smallest-Average-Macroflow-First (SAMF) heuristics that greedily schedule macroflows based on their network footprint. To work with existing commodity switches, priority discretization is performed to classify macroflows into a small number of priority queues.

Abstract Website fingerprinting based on TCP/IP headers is of significant relevance to several Internet entities. Prior work has focused only on a limited set of features, and does not help understand the extents of fingerprint-ability. We address this by conducting an exhaustive feature analysis within eight different communication scenarios. Our analysis helps reveal several previously-unknown features in several scenarios, that can be used to fingerprint websites with much higher accuracy than previously demonstrated. This work helps the community better understand the extents of learnability (and vulnerability) from TCP/IP headers.

For a datacenter running a data-parallel analytic framework, minimizing job completion time (JCT) is crucial for application performance. The key observation is that JCT could be improved, if network scheduling can exploit the opportunity of decreasing the amount of occupied machine slot-time spend on communication. We propose Macroflow, a networking abstraction that captures the primitive resource granularity of data-parallel frameworks. We study the inter-macroflow scheduling problem for decreasing application JCT. We propose the Smallest-Macroflow-First (SMF) and Smallest-Average-Macroflow-First (SAMF) heuristics that greedily schedule macroflows based on their network footprint. Trace-driven simulations demonstrate that our algorithms can reduce the average and tail JCT of network-intensive jobs by up to 20% and 25%, respectively; at the same time, the throughput of computation-intensive jobs is increased by up to 2.2×.

To resist the adverse effect of shadow interference, illumination changes, indigent texture and scenario jitter in object detection and improve performance, a background modelling method based on local fusion feature and variational Bayesian learning is proposed. First, U‐LBSP (uniform‐local binary similarity patterns) texture feature, lab colour and location feature are used to construct local fusion feature. U‐LBSP is modified from local binary patterns in order to reduce computational complexity and better resist the influence of shadow and illumination changes. Joint colour and location feature are introduced to deal with the problem of indigent texture and scenario jitter. Then, LFGMM (Gaussian mixture model based on local fusion feature) is updated and learned by variational Bayes. In order to adapt to dynamic changing scenarios, the variational expectation maximisation algorithm is applied for distribution parameters optimisation. In this way, the optimal number of Gaussian components as well as their parameters can be automatically estimated with less time expended. Experimental results show that the authors’ method achieves outstanding detection performance especially under conditions of shadow disturbances, illumination changes, indigent texture and scenario jitter. Strong robustness and high accuracy have been achieved.