Solving a class of zero-sum stopping game with regime switching

This paper studies a class of zero-sum stopping game in a regime switching model. A verification theorem as a sufficient criterion for Nash equilibriums is established based on a set of variational inequalities (VIs). Under an appropriate regularity condition for solutions to the VIs, a suitable system of algebraic equations is derived via the so-called smooth-fit principle. Explicit Nash equilibrium stopping rules of threshold-type for the two players and the corresponding value function of the game in closed form are obtained. Numerical experiments are reported to demonstrate the dependence of the threshold levels on various model parameters. A reduction to the case with no regime switching is also presented as a comparison

Analysis and Design of Intelligent Logistics System Based on Internet of Things

Based on Internet of things, .NET software development technology and GIS technology, this paper analyzes and designs a system of intelligent distribution information with software engineering life cycle theory as the guide to solve the problem of high complexity and low efficiency of manual operation in logistics and distribution, improve the level of intelligent operation and then improve the operating efficiency. It analyzes the business requirements of the system, then designs its physical architecture, software architecture and system structure, and constructs the terminal node distribution dynamic model of transmission route, realizing the main function modules of the system and verifying the correctness and effectiveness of the system results through systematic and comprehensive tests. DOI: 10.17762/ijritcc2321-8169.15065

Dynamic Low-Rank Instance Adaptation for Universal Neural Image Compression

The latest advancements in neural image compression show great potential in surpassing the rate-distortion performance of conventional standard codecs. Nevertheless, there exists an indelible domain gap between the datasets utilized for training (i.e., natural images) and those utilized for inference (e.g., artistic images). Our proposal involves a low-rank adaptation approach aimed at addressing the rate-distortion drop observed in out-of-domain datasets. Specifically, we perform low-rank matrix decomposition to update certain adaptation parameters of the client's decoder. These updated parameters, along with image latents, are encoded into a bitstream and transmitted to the decoder in practical scenarios. Due to the low-rank constraint imposed on the adaptation parameters, the resulting bit rate overhead is small. Furthermore, the bit rate allocation of low-rank adaptation is \emph{non-trivial}, considering the diverse inputs require varying adaptation bitstreams. We thus introduce a dynamic gating network on top of the low-rank adaptation method, in order to decide which decoder layer should employ adaptation. The dynamic adaptation network is optimized end-to-end using rate-distortion loss. Our proposed method exhibits universality across diverse image datasets. Extensive results demonstrate that this paradigm significantly mitigates the domain gap, surpassing non-adaptive methods with an average BD-rate improvement of approximately $19\%$ across out-of-domain images. Furthermore, it outperforms the most advanced instance adaptive methods by roughly $5\%$ BD-rate. Ablation studies confirm our method's ability to universally enhance various image compression architectures.Comment: Accepted by ACM MM 2023, 13 pages, 12 figure

Cryopreservation in Ophthalmology

Amniotic membranes (AMs) and corneas are critical materials in ocular surface reconstruction. AM has specific structures (e.g., basement and two types of cells with stemness characteristics: amniotic epithelial cells and amniotic mesenchymal cells), which contribute to its attractive physical and biological properties that make it fundamental to clinical application. The corneal endothelial cell is a vital part of the cornea, which can influence postoperative vision directly. However, widespread use of fresh AM and cornea has been limited due to their short use span and safety concerns. To overcome these concerns, different preservation methods have been introduced. Cryopreservation is distinguished from many preservation methods for its attractive advantages of prolonged use span, optimally retained tissue structure, and minimized infection risk. This review will focus on recent advances of cryopreserved AM and cornea, including different cryopreservation methods and their indications in ophthalmology

Hydrate-based CO2 (carbon dioxide) capture from IGCC (integrated gasification combined cycle) synthesis gas using bubble method with a set of visual equipment

The hydrate-based carbon dioxide (CO2) capture from the integrated gasification combined cycle (IGCC) synthesis gas using the bubble method is investigated with a set of visual equipment in this work. The gas bubble is created with a bubble plate on the bottom of the equipment. By the visual equipment, the hydrate formation and the hydrate shape are visually captured. With the move of the gas bubble from the bottom to the top of the reactor, gas hydrate forms firstly from the gas-liquid boundary around the bubble, then the hydrate gradually grows up and piles up in the bottom side of the bubble to form a hydrate particle. The gas hydrate shape is affected by the gas flow rate. The hydrate is acicular crystal at the low gas flow rate while the hydrate is fine sand-like crystal at the high gas flow rate. The bubble size and the gas flow rate have an obvious impact on the hydrate-based CO2 separation process. The experimental results show the gas bubble of 50 mu m and the gas flow rate of 6.75 mL/min/L are ideal for CO2 capture from IGCC synthesis gas under the condition of 3.0 MPa and 274.15 K. (C) 2012 Elsevier Ltd. All rights reserved.</p

Estimation of monthly pan evaporation using support vector machine in Three Gorges Reservoir Area, China

Pan evaporation plays a critical role in estimating water budget and modeling crop water requirements. However, it has been measured at a very limited number of meteorological stations. Estimation of pan evaporation from measured meteorological variables offers an important alternative and drawn increasing attention in the recent years. This paper investigated the performance of support vector machine (SVM) in the estimation of monthly pan evaporation using commonly measured meteorological variables in Three Gorges Reservoir Area in China. Evaluation suggested that SVM models showed remarkable performances and significantly outperformed the empirical model. The SVM model with polynomial as kernel function outperformed that with radial basis function. In the case of unavailable measurements of pan evaporation and meteorological variables to construct the SVM model, pan evaporation can be well-estimated by SVM model developed using data at other sites. The results indicated that the SVM method would be a promising alternative over the traditional approaches for estimating pan evaporation from measured meteorological variables

Learning to Reweight for Graph Neural Network

Graph Neural Networks (GNNs) show promising results for graph tasks. However, existing GNNs' generalization ability will degrade when there exist distribution shifts between testing and training graph data. The cardinal impetus underlying the severe degeneration is that the GNNs are architected predicated upon the I.I.D assumptions. In such a setting, GNNs are inclined to leverage imperceptible statistical correlations subsisting in the training set to predict, albeit it is a spurious correlation. In this paper, we study the problem of the generalization ability of GNNs in Out-Of-Distribution (OOD) settings. To solve this problem, we propose the Learning to Reweight for Generalizable Graph Neural Network (L2R-GNN) to enhance the generalization ability for achieving satisfactory performance on unseen testing graphs that have different distributions with training graphs. We propose a novel nonlinear graph decorrelation method, which can substantially improve the out-of-distribution generalization ability and compares favorably to previous methods in restraining the over-reduced sample size. The variables of the graph representation are clustered based on the stability of the correlation, and the graph decorrelation method learns weights to remove correlations between the variables of different clusters rather than any two variables. Besides, we interpose an efficacious stochastic algorithm upon bi-level optimization for the L2R-GNN framework, which facilitates simultaneously learning the optimal weights and GNN parameters, and avoids the overfitting problem. Experimental results show that L2R-GNN greatly outperforms baselines on various graph prediction benchmarks under distribution shifts