The rapid proliferation of Internet of Things (IoT) devices in smart cities has led to an exponential growth in task requests, posing significant challenges to real-time and efficient task scheduling within large-scale distributed edge computing networks. This paper proposes a novel dynamics-based adaptive scheduling (DAS) method to address this NP-hard problem. The core innovation of DAS lies in its transformation of the complex task scheduling challenge into a two-dimensional geometric model. By leveraging the principles of forward dynamics simulation, DAS achieves efficient and non-iterative task assignment. This approach avoids the reliance on iterative optimization or complex model training, successfully reducing the computational complexity toO(n log n), which makes it highly suitable for large-scale, dynamic smart city edge networks. Simulation results demonstrate that the DAS method significantly outperforms existing benchmark strategies across key performance indicators. Specifically, DAS reduces service response time by 9% to 15%, improves system load balanceooo by 93% to 98%, and simultaneously decreases overall energy consumption by 2% to 12%. These quantitative findings strongly validate that DAS provides an efficient, scalable, and real-time responsive scheduling solution for large-scale edge computing environments, substantially enhancing the performance and reliability of smart city services.

In large-scale edge computing networks, efficient task scheduling is essential for applications with stringent requirements on reliability and low latency, such as autonomous driving and industrial Internet of Things (IoT). However, traditional scheduling approaches often fall short in dynamic environments involving node failures, user mobility, and network congestion. The dynamic adaptive scheduling (DAS) method based on a dynamic framework is proposed, which combines geometric mapping with a dynamic model to simulate edge node load conditions and the task allocation process, enabling efficient and adaptive task distribution. Experimental results indicate that DAS demonstrates superior adaptability and performance under complex and dynamic scenarios, significantly outperforming traditional methods such as random and greedy scheduling. These results validate the effectiveness of DAS in enhancing system reliability and scheduling efficiency in large-scale edge computing environments.

3D Gaussian Splatting (3DGS) has emerged as a key rendering pipeline for digital asset creation due to its balance between efficiency and visual quality. To address the issues of unstable pose estimation and scene representation distortion caused by geometric texture inconsistency in large outdoor scenes with weak or repetitive textures, we approach the problem from two aspects: pose estimation and scene representation. For pose estimation, we leverage LiDAR-IMU Odometry to provide prior poses for cameras in large-scale environments. These prior pose constraints are incorporated into COLMAP’s triangulation process, with pose optimization performed via bundle adjustment. Ensuring consistency between pixel data association and prior poses helps maintain both robustness and accuracy. For scene representation, we introduce normal vector constraints and effective rank regularization to enforce consistency in the direction and shape of Gaussian primitives. These constraints are jointly optimized with the existing photometric loss to enhance the map quality. We evaluate our approach using both public and self-collected datasets. In terms of pose optimization, our method requires only one-third of the time while maintaining accuracy and robustness across both datasets. In terms of scene representation, the results show that our method significantly outperforms conventional 3DGS pipelines. Notably, on self-collected datasets characterized by weak or repetitive textures, our approach demonstrates enhanced visualization capabilities and achieves superior overall performance. Codes and data will be publicly available at https://github.com/justinyeah/normaljshape.git.

Efficient, accurate, and flexible relative localization is crucial in air-ground collaborative tasks. However, current approaches for robot relative localization are primarily realized in the form of distributed multi-robot SLAM systems with the same sensor configuration, which are tightly coupled with the state estimation of all robots, limiting both flexibility and accuracy. To this end, we fully leverage the high capacity of Unmanned Ground Vehicle (UGV) to integrate multiple sensors, enabling a semi-distributed cross-modal air-ground relative localization framework. In this work, both the UGV and the Unmanned Aerial Vehicle (UAV) independently perform SLAM while extracting deep learning-based keypoints and global descriptors, which decouples the relative localization from the state estimation of all agents. The UGV employs a local Bundle Adjustment (BA) with LiDAR, camera, and an IMU to rapidly obtain accurate relative pose estimates. The BA process adopts sparse keypoint optimization and is divided into two stages: First, optimizing camera poses interpolated from LiDAR-Inertial Odometry (LIO), followed by estimating the relative camera poses between the UGV and UAV. Additionally, we implement an incremental loop closure detection algorithm using deep learning-based descriptors to maintain and retrieve keyframes efficiently. Experimental results demonstrate that our method achieves outstanding performance in both accuracy and efficiency. Unlike traditional multi-robot SLAM approaches that transmit images or point clouds, our method only transmits keypoint pixels and their descriptors, effectively constraining the communication bandwidth under 0.3 Mbps. Codes and data will be publicly available on https://github.com/Ascbpiac/cross-model-relative-localization.git.

Computation in recurrent networks of neurons has been hypothesized to occur at the level of low-dimensional latent dynamics, both in artificial systems and in the brain. This hypothesis seems at odds with evidence from large-scale neuronal recordings in mice showing that neuronal population activity is high-dimensional. To demonstrate that low-dimensional latent dynamics and high-dimensional activity can be two sides of the same coin, we present an analytically solvable recurrent neural network (RNN) model whose dynamics can be exactly reduced to a low-dimensional dynamical system, but generates an activity manifold that has a high linear embedding dimension. This raises the question: Do low-dimensional latents explain the high-dimensional activity observed in mouse visual cortex? Spectral theory tells us that the covariance eigenspectrum alone does not allow us to recover the dimensionality of the latents, which can be low or high, when neurons are nonlinear. To address this indeterminacy, we develop Neural Cross-Encoder (NCE), an interpretable, nonlinear latent variable modeling method for neuronal recordings, and find that high-dimensional neuronal responses to drifting gratings and spontaneous activity in visual cortex can be reduced to low-dimensional latents, while the responses to natural images cannot. We conclude that the high-dimensional activity measured in certain conditions, such as in the absence of a stimulus, is explained by low-dimensional latents that are nonlinearly processed by individual neurons.

Accurate localization is an essential technology for the flexible navigation of robots in large-scale environments. Both SLAM-based and map-based localization will increase the computing load due to the increase in map size, which will affect downstream tasks such as robot navigation and services. To this end, we propose a localization system based on Block Maps (BMs) to reduce the computational load caused by maintaining large-scale maps. Firstly, we introduce a method for generating block maps and the corresponding switching strategies, ensuring that the robot can estimate the state in large-scale environments by loading local map information. Secondly, global localization according to Branch-and-Bound Search (BBS) in the 3D map is introduced to provide the initial pose. Finally, a graph-based optimization method is adopted with a dynamic sliding window that determines what factors are being marginalized whether a robot is exposed to a BM or switching to another one, which maintains the accuracy and efficiency of pose tracking. Comparison experiments are performed on publicly available large-scale datasets. Results show that the proposed method can track the robot pose even though the map scale reaches more than 6 kilometers, while efficient and accurate localization is still guaranteed on NCLT [6] and M2DGR [35]. Codes and data will be publicly available on https://github.com/YixFeng/blocklocalization.

Despite advances in network security, attacks targeting mission critical systems and applications remain a significant problem for network and datacenter providers. Existing telemetry platforms detect volumetric attacks at terabit scales using approximation techniques and coarse grain analysis. However, the prevalence of low and slow attacks that require very little bandwidth, makes flow-state tracking critical to overall attack mitigation. Traffic queries deployed on network switches are often limited by hardware constraints, preventing them from carrying out flow tracking features required to detect stealthy attacks. Such attacks can go undetected in the midst of high traffic volumes.

Recent developments in In-band Network Telemetry (INT) provide granular monitoring of performance and load on network elements by collecting information in the data plane. INT enables traffic sources to embed telemetry instructions in data packets, avoiding separate probing or infrequent management-based monitoring. INT sink nodes track and collect metrics by retrieving INT metadata instructions appended by different sources of INT information. However, tracking the INT state in packets arriving at the sink is both compute intensive (requiring complex operations on each packet), and challenging for the standard P4 match-action packet processing pipeline to maintain line-rate. We propose a network telemetry platform in which the INT sink is implemented using distinct (C-based) algorithms on a SmartNIC in the monitoring host, complementing the P4 packet processing pipeline. This design accelerates packet processing and handles complex INT-related operations more efficiently than P4 match-action processing alone. While the P4 pipeline parses INT headers, a general-purpose Micro-C algorithms performs complex INT tasks (e.g. aggregation, event-detection, notification, etc.). We demonstrate that partitioning of INT processing significantly reduces processing overhead vs. a P4-on1y implementation, providing accurate, timely and almost loss-free event notification.