Block-Randomized Gradient Descent Methods with Importance Sampling for CP Tensor Decomposition

This work considers the problem of computing the CANDECOMP/PARAFAC (CP) decomposition of large tensors. One popular way is to translate the problem into a sequence of overdetermined least squares subproblems with Khatri-Rao product (KRP) structure. In this work, for tensor with different levels of importance in each fiber, combining stochastic optimization with randomized sampling, we present a mini-batch stochastic gradient descent algorithm with importance sampling for those special least squares subproblems. Four different sampling strategies are provided. They can avoid forming the full KRP or corresponding probabilities and sample the desired fibers from the original tensor directly. Moreover, a more practical algorithm with adaptive step size is also given. For the proposed algorithms, we present their convergence properties and numerical performance. The results on synthetic data show that our algorithms outperform the existing algorithms in terms of accuracy or the number of iterations

Block-Randomized Stochastic Methods for Tensor Ring Decomposition

Tensor ring (TR) decomposition is a simple but effective tensor network for analyzing and interpreting latent patterns of tensors. In this work, we propose a doubly randomized optimization framework for computing TR decomposition. It can be regarded as a sensible mix of randomized block coordinate descent and stochastic gradient descent, and hence functions in a double-random manner and can achieve lightweight updates and a small memory footprint. Further, to improve the convergence, especially for ill-conditioned problems, we propose a scaled version of the framework that can be viewed as an adaptive preconditioned or diagonally-scaled variant. Four different probability distributions for selecting the mini-batch and the adaptive strategy for determining the step size are also provided. Finally, we present the theoretical properties and numerical performance for our proposals

Resources and future availability of agricultural biomass for energy use in Beijing

The increasing importance of lignocellulosic biomass based energy production has led to an urgent need to conduct a reliable resource supply assessment. This study analyses and estimates the availability of agricultural residue biomass in Beijing, where biomass energy resources are relatively rich and is mainly distributed in the suburbs. The major types of crops considered across Beijing include food crops (e.g., maize, winter wheat, soybean, tubers and rice), cotton crops and oil-bearing crops (e.g., peanuts). The estimates of crop yields are based on historical data between 1996 and 2017 collected from the Beijing Municipal Bureau of Statistics. The theoretical and collectable amount of agricultural residues was calculated on the basis of the agricultural production for each crop, multiplied by specific parameters collected from the literature. The assessment of current and near future agricultural residues from crop harvesting and processing resources in Beijing was performed by employing three advanced modeling methods: the Time Series Analysis Autoregressive moving average (ARMA) model, Least Squares Linear Regression and Gray System Gray Model (GM) (1,1). The results show that the time series model prediction is suitable for short-term prediction evaluation; the least squares fitting result is more accurate but the factors affecting agricultural waste production need to be considered; the gray system prediction is suitable for trend prediction but the prediction accuracy is low

Spectral 3D Computer Vision -- A Review

Spectral 3D computer vision examines both the geometric and spectral properties of objects. It provides a deeper understanding of an object's physical properties by providing information from narrow bands in various regions of the electromagnetic spectrum. Mapping the spectral information onto the 3D model reveals changes in the spectra-structure space or enhances 3D representations with properties such as reflectance, chromatic aberration, and varying defocus blur. This emerging paradigm advances traditional computer vision and opens new avenues of research in 3D structure, depth estimation, motion analysis, and more. It has found applications in areas such as smart agriculture, environment monitoring, building inspection, geological exploration, and digital cultural heritage records. This survey offers a comprehensive overview of spectral 3D computer vision, including a unified taxonomy of methods, key application areas, and future challenges and prospects

Research on Factors Affecting the Use of E-commerce Consumer Credit Services: A Study of Ant Check Later

This study uses “Ant Check Later”, the e-commerce consumer credit service of Alibaba, as the artifact and explores factors affecting its use. This study first summarized initiatives that Alibaba has launched to stimulate the use of “Ant Check Later”. Three factors, bonus, quota lifting, and scenario enrichment, were then distinguished from the initiatives using principal component analysis. These factors were anticipated to affect consumers’ intention to use the service. The research model was tested using 373 respondents collected from an online survey. Results indicate that bonus, quota lifting, and scenario enrichment are three predictors of consumers’ intention to continue using the service, and bonus and scenario enrichment positively affect non-users’ intention to use the service. This study found that scenario enrichment is the most important factor among the three factors in boosting consumers’ behavioral intention toward using the service. Keywords E-commerce consumer credit services, bonus, quota lifting, scenario enrichment, acceptance

Side Channel Attack-Aware Resource Allocation for URLLC and eMBB Slices in 5G RAN

Network slicing is a key enabling technology to realize the provisioning of customized services in 5G paradigm. Due to logical isolation instead of physical isolation, network slicing is facing a series of security issues. Side Channel Attack (SCA) is a typical attack for slices that share resources in the same hardware. Considering the risk of SCA among slices, this paper investigates how to effectively allocate heterogeneous resources for the slices under their different security requirements. Then, a SCA-aware Resource Allocation (SCA-RA) algorithm is proposed for Ultra-reliable and Low-latency Communications (URLLC) and Enhanced Mobile Broadband (eMBB) slices in 5G RAN. The objective is to maximize the number of slices accommodated in 5G RAN. With dynamic slice requests, simulation is conducted to evaluate the performance of the proposed algorithm in two different network scenarios. Simulation results indicate that compared with benchmark, SCA-RA algorithm can effectively reduce blocking probability of slice requests. In addition, the usage of IT and transport resources is also optimized