Multi-Cell, Multi-Channel Scheduling with Probabilistic Per-Packet Real-Time Guarantee

For mission-critical sensing and control applications such as those to be enabled by 5G Ultra-Reliable, Low-Latency Communications (URLLC), it is critical to ensure the communication quality of individual packets. Prior studies have considered Probabilistic Per-packet Real-time Communications (PPRC) guarantees for single-cell, single-channel networks with implicit deadline constraints, but they have not considered real-world complexities such as inter-cell interference and multiple communication channels. Towards ensuring PPRC in multi-cell, multi-channel wireless networks, we propose a real-time scheduling algorithm based on \emph{local-deadline-partition (LDP)}. The LDP algorithm is suitable for distributed implementation, and it ensures probabilistic per-packet real-time guarantee for multi-cell, multi-channel networks with general deadline constraints. We also address the associated challenge of the schedulability test of PPRC traffic. In particular, we propose the concept of \emph{feasible set} and identify a closed-form sufficient condition for the schedulability of PPRC traffic. We propose a distributed algorithm for the schedulability test, and the algorithm includes a procedure for finding the minimum sum work density of feasible sets which is of interest by itself. We also identify a necessary condition for the schedulability of PPRC traffic, and use numerical studies to understand a lower bound on the approximation ratio of the LDP algorithm. We experimentally study the properties of the LDP algorithm and observe that the PPRC traffic supportable by the LDP algorithm is significantly higher than that of a state-of-the-art algorithm

Zero-knowledge Proof Meets Machine Learning in Verifiability: A Survey

With the rapid advancement of artificial intelligence technology, the usage of machine learning models is gradually becoming part of our daily lives. High-quality models rely not only on efficient optimization algorithms but also on the training and learning processes built upon vast amounts of data and computational power. However, in practice, due to various challenges such as limited computational resources and data privacy concerns, users in need of models often cannot train machine learning models locally. This has led them to explore alternative approaches such as outsourced learning and federated learning. While these methods address the feasibility of model training effectively, they introduce concerns about the trustworthiness of the training process since computations are not performed locally. Similarly, there are trustworthiness issues associated with outsourced model inference. These two problems can be summarized as the trustworthiness problem of model computations: How can one verify that the results computed by other participants are derived according to the specified algorithm, model, and input data? To address this challenge, verifiable machine learning (VML) has emerged. This paper presents a comprehensive survey of zero-knowledge proof-based verifiable machine learning (ZKP-VML) technology. We first analyze the potential verifiability issues that may exist in different machine learning scenarios. Subsequently, we provide a formal definition of ZKP-VML. We then conduct a detailed analysis and classification of existing works based on their technical approaches. Finally, we discuss the key challenges and future directions in the field of ZKP-based VML

Species-based Mapping of Carbon Stocks in Salt Marsh: Tianjin Coastal Zone as a Case Study

Because of geographical position and high carbon storage potential, coastal salt marshes are recognized as an essential component of blue carbon and play an indispensable role in regulating climate and reaching carbon neutrality targets. Nonetheless, accurately mapping salt marsh carbon stock on a regional scale remains challenging. The framework of mapping salt marsh carbon stock was developed by using machine learning (temporal–phenological–spatial) models, vegetation index aboveground biomass inversion models, and above/belowground biomass allometric models. Here, we employed Sentinel-2 time series images based on Google Earth Engine in combination with field survey data to produce a 10-m map of salt marsh carbon stocks in the Tianjin coastal zone (TCZ). The total and average carbon stocks of TCZ salt marsh vegetation in 2020 were approximately 6.24 × 103 Mg C and 45.02 Mg C/ha, respectively. In terms of vegetative species, the carbon stock was ranked by Spartina alterniflora (2.89 × 103 Mg C) > Phragmites australis (1.74 × 103 Mg C) > Suaeda salsa (1.61 × 103 Mg C). The carbon density of 3 representative salt marsh species sampled in Tianjin were calculated: S. alterniflora (18.63 Mg/ha) > P. australis (6.49 Mg/ha) > S. salsa (1.40 Mg/ha). The random forest algorithm shows the best performance in classifying, with an overall accuracy of 87.21%. This work created the replicable and generic technical framework for mapping carbon stocks in salt marshes, which supports blue carbon accounting and provides case support for “nature-based solutions.

Mechanism of Magnesium Oxide Hydration Based on the Multi-Rate Model

The hydration of different active MgO under an unforced and ultrasonic condition was conducted in this paper to investigate the chemical kinetics model of the apparent reaction and discuss the mechanism combined with the product morphology. The dynamics fitting result shows that both the first-order and multi-rate model describe the hydration process under ultrasound well, while only the multi-rate model was right for the hydration process under an unforced condition. It indicated that the rate order of hydration was different in the hydration process under an unforced condition. The XRD and SEM show that the MgO hydration was a process of dissolution and crystallization. Part of the magnesium ions produced by dissolution of MgO did not diffuse into the solution in time, and adhered to the magnesium oxide surface and grew in situ instead. As a result, the difference in the hydration rate of the remaining MgO particles becomes wider and not in the same order (order of magnitude). The ultrasonic cavitation could prevent the in-situ growth of Mg(OH)2 crystal nuclei on the surface of MgO. It not only greatly improved the hydration rate of MgO and produced monodisperse Mg(OH)2 particles, but also made the first-order kinetics model fit the hydration process of MgO well

Thermal Error Prediction and Compensation of Digital Twin Laser Cutting Based on T-XGBoost

Laser cutting belongs to non-contact processing, which is different from traditional turning and milling. In order to improve the machining accuracy of laser cutting, a thermal error prediction and dynamic compensation strategy for laser cutting is proposed. Based on the time-varying characteristics of the digital twin technology, a hybrid model combining the thermal elastic–plastic finite element (TEP-FEM) and T-XGBoost algorithms is established. The temperature field and thermal deformation under 12 common working conditions are simulated and analyzed with TEP-FEM. Real-time machining data obtained from TEP-FEM simulation is used in intelligent algorithms. Based on the XGBoost algorithm and the simulation data set as the training data set, a time-series-based segmentation algorithm (T-XGBoost) is proposed. This algorithm can reduce the maximum deformation at the slit by more than 45%. At the same time, by reducing the average volume strain under most working conditions, the lifting rate can reach 63% at the highest, and the machining result is obviously better than XGBoost. The strategy resolves the uncontrollable thermal deformation during cutting and provides theoretical solutions to the implementation of the intelligent operation strategies such as predictive machining and quality monitoring