Asymptotic properties of a multicolored random reinforced urn model with an application to multi-armed bandits

The random self-reinforcement mechanism, characterized by the principle of ``the rich get richer'', has demonstrated significant utility across various domains. One prominent model embodying this mechanism is the random reinforcement urn model. This paper investigates a multicolored, multiple-drawing variant of the random reinforced urn model. We establish the limiting behavior of the normalized urn composition and demonstrate strong convergence upon scaling the counts of each color. Additionally, we derive strong convergence estimators for the reinforcement means, i.e., for the expectations of the replacement matrix's diagonal elements, and prove their joint asymptotic normality. It is noteworthy that the estimators of the largest reinforcement mean are asymptotically independent of the estimators of the other smaller reinforcement means. Additionally, if a reinforcement mean is not the largest, the estimators of these smaller reinforcement means will also demonstrate asymptotic independence among themselves. Furthermore, we explore the parallels between the reinforced mechanisms in random reinforced urn models and multi-armed bandits, addressing hypothesis testing for expected payoffs in the latter context

Dual Dynamic Inference: Enabling More Efficient, Adaptive and Controllable Deep Inference

State-of-the-art convolutional neural networks (CNNs) yield record-breaking predictive performance, yet at the cost of high-energy-consumption inference, that prohibits their widely deployments in resource-constrained Internet of Things (IoT) applications. We propose a dual dynamic inference (DDI) framework that highlights the following aspects: 1) we integrate both input-dependent and resource-dependent dynamic inference mechanisms under a unified framework in order to fit the varying IoT resource requirements in practice. DDI is able to both constantly suppress unnecessary costs for easy samples, and to halt inference for all samples to meet hard resource constraints enforced; 2) we propose a flexible multi-grained learning to skip (MGL2S) approach for input-dependent inference which allows simultaneous layer-wise and channel-wise skipping; 3) we extend DDI to complex CNN backbones such as DenseNet and show that DDI can be applied towards optimizing any specific resource goals including inference latency or energy cost. Extensive experiments demonstrate the superior inference accuracy-resource trade-off achieved by DDI, as well as the flexibility to control such trade-offs compared to existing peer methods. Specifically, DDI can achieve up to 4 times computational savings with the same or even higher accuracy as compared to existing competitive baselines

Quantitative study of microscopic formation water distribution in tight gas reservoirs based on the thermogravimetric method

The microscopic characterization of the distribution of formation water in tight gas reservoirs has always been one of the challenges in the industry. The traditional nuclear magnetic resonance method has certain limitations in characterizing the microscopic distribution of formation water. Thermogravimetric analysis can correlate with mass, and combined with nuclear magnetic resonance spectra, it can further optimize the characterization method for the microscopic distribution of formation water. Multiple tight sandstone gas reservoirs are vertically developed in the Shenfu Block of the Ordos Basin. Due to the strong heterogeneity of the reservoir, given the complexity of the characterization of the microscopic occurrence law of formation water, the typical argillaceous tight sandstone reservoirs Qian 5 and Tai 2 members are selected as the research objects. The distribution characteristics of the microscopic formation water of the tight gas reservoir in the Shenfu block were quantitatively characterized by the thermogravimetric method using dry distillation experiment, nuclear magnetic resonance experiment, and displacement experiment. The results show that 35°C is the boundary temperature between free water and microporous water. According to the characterization of various types of water occurrence in clay and tight sandstone by thermogravimetric experiment, free water below 35°C, microporous water (including capillary water and adsorbed water on the surface of mineral particles) in the range of 35°C–427°C, and clay-bound water (crystal water, structural water/carboxyl water) above 427°C. The type of water occurrence in tight sandstone is consistent with that of clay minerals, but the amount of water occurrence and water loss rate are different. From the perspective of water occurrence, microporous water is typically the most abundant form, while in terms of water loss rate, free water generally exhibits the highest rate. The full-scale quantitative study of micro-formation water distribution in tight gas reservoirs based on the thermogravimetric method has important guiding significance for solving the accurate characterization of water saturation logging in tight gas reservoirs, and enriches the understanding of the occurrence characteristics and laws of micro-formation water in tight gas reservoirs

Seasonal variability of sea surface pCO2 and air-sea CO2 flux in a high turbidity coastal ocean in the vicinity of the East China Sea

The sea surface partial pressure of CO2 (pCO2) and air-sea carbon flux in estuarine and bay areas, influenced by both natural and anthropogenic factors, remain poorly understood and inadequately assessed. This study, based on seasonal underway observations conducted in 2024, analyzed the seasonal variations in surface seawater pCO2 and air-sea CO2 flux in the high-turbidity coastal waters of Zhejiang, including Hangzhou Bay (HZB), Xiangshan Bay (XSB), Sanmen Bay (SMB), and the nearshore waters (NSW). The results indicate that the pCO2 in the study area ranged from 194 to 739 μatm throughout the year, exhibiting significant spatiotemporal heterogeneity. In HZB, the lowest pCO2 was observed in winter, averaging 453 μatm, whereas the values in spring and summer were around 600 μatm, with a subsequent decline to 481 μatm in autumn. In XSB, pCO2 reached its minimum in winter (194 μatm), attributed to vigorous biological activity, and peaked in spring, averaging 639 μatm. In SMB, pCO2 was relatively lower in autumn and winter (~470 μatm), and higher in spring and summer (~640 μatm). In the NSW, pCO2 was lower in winter and spring (~445 μatm), and increased to ~510 μatm in summer and autumn. The pCO2 was predominantly regulated by sea surface temperature and horizontal mixing, while other factors like biological activity also had significant impacts. The annual average CO2 flux was 6.0±3.7 mmol m-2 d-1 in HZB, 1.2±2.3 mmol m-2 d-1 in XSB, 7.0±3.2 mmol m-2 d-1 in SMB and 5.2±5.9 mmol m-2 d-1 in the NSW. Higher wind speeds in autumn and winter, coupled with elevated the pCO2 difference between the surface water and the atmosphere (ΔpCO2) in spring and summer, collectively drove the seasonal variations in CO2 flux. On an annual scale, both the estuarine and bay areas and the nearshore regions functioned as carbon sources

A Review of Asynchronous Byzantine Consensus Protocols

Blockchain technology can be used in the IoT to ensure the data privacy collected by sensors. In blockchain systems, consensus mechanisms are a key technology for maintaining data consistency and correctness. Among the various consensus protocols, asynchronous Byzantine consensus protocols offer strong robustness as they do not rely on any network timing assumptions during design. As a result, these protocols have become a research hotspot in the field of blockchain. Based on different structural design approaches, asynchronous Byzantine consensus protocols can be divided into two categories: protocols based on the DAG structure and protocols based on the ACS structure. The paper describes their principles and summarizes the related research works. The advantages and disadvantages of the protocols are also compared and analyzed. At the end of the paper, future research directions are identified