Preliminary study on a predacious natural enemy, Broad vein-longitudinal striped ladybug Brumoides lineatus (Weise)

Originating text in Chinese.Citation: Weng, Wenshen, Huang, Yuqing. (1988). Preliminary study on a predacious natural enemy, Broad vein-longitudinal striped ladybug Brumoides lineatus (Weise). Entomological Knowledge, 25, 105-108

High-Dimensional Stochastic Gradient Quantization for Communication-Efficient Edge Learning

Edge machine learning involves the deployment of learning algorithms at the wireless network edge so as to leverage massive mobile data for enabling intelligent applications. The mainstream edge learning approach, federated learning, has been developed based on distributed gradient descent. Based on the approach, stochastic gradients are computed at edge devices and then transmitted to an edge server for updating a global AI model. Since each stochastic gradient is typically high-dimensional (with millions to billions of coefficients), communication overhead becomes a bottleneck for edge learning. To address this issue, we propose in this work a novel framework of hierarchical stochastic gradient quantization and study its effect on the learning performance. First, the framework features a practical hierarchical architecture for decomposing the stochastic gradient into its norm and normalized block gradients, and efficiently quantizes them using a uniform quantizer and a low-dimensional codebook on a Grassmann manifold, respectively. Subsequently, the quantized normalized block gradients are scaled and cascaded to yield the quantized normalized stochastic gradient using a so-called hinge vector designed under the criterion of minimum distortion. The hinge vector is also efficiently compressed using another low-dimensional Grassmannian quantizer. The other feature of the framework is a bit-allocation scheme for reducing the quantization error. The scheme determines the resolutions of the low-dimensional quantizers in the proposed framework. The framework is proved to guarantee model convergency by analyzing the convergence rate as a function of the quantization bits. Furthermore, by simulation, our design is shown to substantially reduce the communication overhead compared with the state-of-the-art signSGD scheme, while both achieve similar learning accuracies

A STEM PROFILE MODEL CALIBRATED BY NONLINEAR MIXED-EFFECTS MODELING

A stem profile model was developed for black spruce (Picea mariana (Mill.) B.S.P.) trees in Alberta, Canada using a nonlinear mixed model approach. The model included two random parameters to capture between-subject variation and a general covariance structure to model within-subject residual autocorrelation. After evaluating various covariance structures, the 4-banded toeplitz and the spatial power structures were chosen for further evaluation. The 4-banded toeplitz structure provided a better fit. The model was further evaluated using an independent data set to examine its validation accuracy. Model validation results showed that the model was able to accurately predict stem diameters at the population and subject-specific levels. Both covariance structures produced reliable model predictions, but the spatial power structure was superior to the 4-banded toeplitz structure. One to four stem diameters were used to predict random parameters and to subsequently generate subject-specific predictions. At least three stem diameters were needed to achieve better subject-specific predictions than population-average predictions

The current opportunities and challenges of Web 3.0

With recent advancements in AI and 5G technologies,as well as the nascent concepts of blockchain and metaverse,a new revolution of the Internet,known as Web 3.0,is emerging. Given its significant potential impact on the internet landscape and various professional sectors,Web 3.0 has captured considerable attention from both academic and industry circles. This article presents an exploratory analysis of the opportunities and challenges associated with Web 3.0. Firstly, the study evaluates the technical differences between Web 1.0, Web 2.0, and Web 3.0, while also delving into the unique technical architecture of Web 3.0. Secondly, by reviewing current literature, the article highlights the current state of development surrounding Web 3.0 from both economic and technological perspective. Thirdly, the study identifies numerous research and regulatory obstacles that presently confront Web 3.0 initiatives. Finally, the article concludes by providing a forward-looking perspective on the potential future growth and progress of Web 3.0 technology