Distributed Graph Embedding with Information-Oriented Random Walks

Graph embedding maps graph nodes to low-dimensional vectors, and is widely adopted in machine learning tasks. The increasing availability of billion-edge graphs underscores the importance of learning efficient and effective embeddings on large graphs, such as link prediction on Twitter with over one billion edges. Most existing graph embedding methods fall short of reaching high data scalability. In this paper, we present a general-purpose, distributed, information-centric random walk-based graph embedding framework, DistGER, which can scale to embed billion-edge graphs. DistGER incrementally computes information-centric random walks. It further leverages a multi-proximity-aware, streaming, parallel graph partitioning strategy, simultaneously achieving high local partition quality and excellent workload balancing across machines. DistGER also improves the distributed Skip-Gram learning model to generate node embeddings by optimizing the access locality, CPU throughput, and synchronization efficiency. Experiments on real-world graphs demonstrate that compared to state-of-the-art distributed graph embedding frameworks, including KnightKing, DistDGL, and Pytorch-BigGraph, DistGER exhibits 2.33x-129x acceleration, 45% reduction in cross-machines communication, and > 10% effectiveness improvement in downstream tasks

DynVideo-E: Harnessing Dynamic NeRF for Large-Scale Motion- and View-Change Human-Centric Video Editing

Despite recent progress in diffusion-based video editing, existing methods are limited to short-length videos due to the contradiction between long-range consistency and frame-wise editing. Prior attempts to address this challenge by introducing video-2D representations encounter significant difficulties with large-scale motion- and view-change videos, especially in human-centric scenarios. To overcome this, we propose to introduce the dynamic Neural Radiance Fields (NeRF) as the innovative video representation, where the editing can be performed in the 3D spaces and propagated to the entire video via the deformation field. To provide consistent and controllable editing, we propose the image-based video-NeRF editing pipeline with a set of innovative designs, including multi-view multi-pose Score Distillation Sampling (SDS) from both the 2D personalized diffusion prior and 3D diffusion prior, reconstruction losses, text-guided local parts super-resolution, and style transfer. Extensive experiments demonstrate that our method, dubbed as DynVideo-E, significantly outperforms SOTA approaches on two challenging datasets by a large margin of 50% ~ 95% for human preference. Code will be released at https://showlab.github.io/DynVideo-E/.Comment: Project Page: https://showlab.github.io/DynVideo-E

The polygalacturonase gene BcMF2 from Brassica campestris is associated with intine development

Brassica campestris Male Fertility 2 (BcMF2) is a putative polygalacturonase (PG) gene previously isolated from the flower bud of Chinese cabbage (Brassica campestris L. ssp. chinensis Makino, syn. B. rapa ssp. chinensis). This gene was found to be expressed specifically in tapetum and pollen after the tetrad stage of anther development. Antisense RNA technology was used to study the function of BcMF2 in Chinese cabbage. Scanning and transmission electron microscopy revealed that there were deformities in the transgenic mature pollen grains such as abnormal location of germinal furrows. In addition, the homogeneous pectic exintine layer facing the exterior seemed to be overdeveloped and predominantly occupied the intine, thus reversing the normal proportional distribution of the internal endintine layer and the external exintine layer. Since it is a continuation of the intine layer, the pollen tube wall could not grow normally. This resulted in the formation of a balloon-like swelling structure in the pollen tube tip in nearly 80% of the transgenic pollen grains. Premature degradation of tapetum was also found in these transgenic plants, which displayed decreased expression of the BcMF2 gene. BcMF2 might therefore encode a new PG with an important role in pollen wall development, possibly via regulation of pectin's dynamic metabolism

An improved monarch butterfly spectrum allocation algorithm for multi-source data stream in complex electromagnetic environment

Abstract In the era of the Internet of Everything, various wireless devices and sensors use spectrum, which is a precious and non-renewable resource, to communication. Due to the characteristics of massive, heterogeneous, and multi-source, the generated multi-source data stream brings difficulties to spectrum cognition. As a result, unreasonable spectrum allocation strategy leads to low utilization of spectrum resources. Optimizing spectrum allocation strategy can effectively improve spectrum utilization. Aiming at the problem of trapped local optimum solution in the genetic algorithm (GA) and particle swarm optimization algorithm (PSO), an improved monarch butterfly algorithm is proposed. Firstly, this paper employs the simulated annealing algorithm to select the migration rate, which increases the diversity of monarch butterfly population. Secondly, chaos mapping algorithm is utilized to improve the optimization ability and convergence speed. Finally, in the view of the problem that the monarch butterfly algorithm is easy to fall into the local optimal solution, there is no better way to escape from the local optimal solution. The Wolf pack updating operator is selected to improve the diversity of the population to generate new monarch butterflies. This method updates the population by generating new monarch butterfly individuals, so as to increasing the diversity of the population. The experimental results show that the improved monarch butterfly algorithm outperforms the other two algorithms in terms of convergence speed and system revenue

A Class of Control Strategies for Energy Internet Considering System Robustness and Operation Cost Optimization

Aiming at restructuring the conventional energy delivery infrastructure, the concept of energy Internet (EI) has become popular in recent years. Outstanding benefits from an EI include openness, robustness and reliability. Most of the existing literatures focus on the conceptual design of EI and are lack of theoretical investigation on developing specific control strategies for the operation of EI. In this paper, a class of control strategies for EI considering system robustness and operation cost optimization is investigated. Focusing on the EI system robustness issue, system parameter uncertainty, external disturbance and tracking error are taken into consideration, and we formulate such robust control issue as a structure specified mixed H2/H∞ control problem. When formulating the operation cost optimization problem, three aspects are considered: realizing the bottom-up energy management principle, reducing the cost involved by power delivery from power grid (PG) to microgrid (MG), and avoiding the situation of over-control. We highlight that this is the very first time that the above targets are considered simultaneously in the field of EI. The integrated control issue is considered in frequency domain and is solved by a particle swarm optimization (PSO) algorithm. Simulation results show that our proposed method achieves the targets

Delay Analysis for End-to-End Synchronous Communication in Monitoring Systems

With the rapid development of smart grid technologies, communication systems are further integrated in the existing power grids. The real-time capability and reliability of the power applications are receiving increasing concerns. Thus, it is important to measure the end-to-end delay in communication systems. The network calculus theory has been widely applied in the communication delay measuring tasks. However, for better operation performance of power systems, most power applications require synchronous data communication, in which the network calculus theory cannot be directly applied. In this paper, we expand the network calculus theory such that it can be used to analyze the communication delay for power applications in smart grids. The problem of communication delay calculation for the synchronization system is converted into a maximum path problem in graph theory. Finally, our theoretical results are compared with the experimental ones obtained with the network simulation software EstiNet. The simulation results verify the feasibility and effectiveness of the proposed method

Energy sharing and frequency regulation in energy Internet via mixed H2/H∞ control with Markovian jump

In this paper, the problem of mixed optimization for energy sharing and frequency regulation in a typical energy network scenario where energy routers (ERs) interconnected AC microgrids (MGs) is investigated. Continuous-time Markov chains are introduced to describe the switching paths in the power dynamics of MGs. Such that the modelling of considered energy network system could be closer to the real-world engineering practice. Advanced parameter estimation techniques are inte- grated into the proposed method to achieve better modelling accuracy and controlling performance. Based on the parameters of MG power dynamics, the mixed H2/H∞ controllers are obtained via stochastic control theory. The feasibility and efficacy of the proposed approach are evaluated in numerical examples