Credit-based Pricing for Multi-user Class Transportation Facilities

This paper proposes an innovative arc-based credit (ABC) congestion pricing scheme to improve the system performance in a transportation network. By associating each arc with apositive or negative credit rate, the strategy can accomplish multiple planning goals, such as efficiency, fairness, and public acceptance simultaneously. We first demonstrate that on a one-origin or one-destination network, a pareto-improving, system-optimal and revenue-neutral credit scheme always exists and can be obtained by solving a set of linear equations. Recognizing that such a credit scheme may not exist in a multi-origin network, we then define the maximum-revenue problem with pareto-improving constrains (MRPI): find the maximum possible revenue collected by the credit scheme with optimal arc flows and non-increasing origin-destination (OD) travel costs. We discover that the dual of MRPI is equivalent to a typical Transportation Problem which, therefore, provides a simple way to calculate the revenue by just examining the dual problem. At the end of the paper, a numerical example with a small synthetic network is provided for the comparison of the credit scheme with other existing toll schemes in terms of OD travel disutilities

Conformal off-policy prediction

Off-policy evaluation is critical in a number of applications where new policies need to be evaluated offline before online deployment. Most existing methods focus on the expected return, define the target parameter through averaging and provide a point estimator only. In this paper, we develop a novel procedure to produce reliable interval estimators for a target policy’s return starting from any initial state. Our proposal accounts for the variability of the return around its expectation, focuses on the individual effect and offers valid uncertainty quantification. Our main idea lies in designing a pseudo policy that generates subsamples as if they were sampled from the target policy so that existing conformal prediction algorithms are applicable to prediction interval construction. Our methods are justified by theories, synthetic data and real data from short-video platforms

Pattern transfer learning for reinforcement learning in order dispatching

Order dispatch is one of the central problems to ridesharing platforms. Recently, value-based reinforcement learning algorithms have shown promising performance to solve this task. However, in real-world applications, the demand-supply system is typically nonstationary over time, posing challenges to reutilizing data generated in different time periods to learn the value function. In this work, motivated by the fact that the relative relationship between the values of some states is largely stable across various environments, we propose a pattern transfer learning framework for value-based reinforcement learning in the order dispatch problem. Our method efficiently captures the value patterns by incorporating a concordance penalty. The superior performance of the proposed method is supported by experiments

Graph Attention Transformer Network for Multi-Label Image Classification

Multi-label classification aims to recognize multiple objects or attributes from images. However, it is challenging to learn from proper label graphs to effectively characterize such inter-label correlations or dependencies. Current methods often use the co-occurrence probability of labels based on the training set as the adjacency matrix to model this correlation, which is greatly limited by the dataset and affects the model's generalization ability. In this paper, we propose a Graph Attention Transformer Network (GATN), a general framework for multi-label image classification that can effectively mine complex inter-label relationships. First, we use the cosine similarity based on the label word embedding as the initial correlation matrix, which can represent rich semantic information. Subsequently, we design the graph attention transformer layer to transfer this adjacency matrix to adapt to the current domain. Our extensive experiments have demonstrated that our proposed methods can achieve state-of-the-art performance on three datasets

Multi-frame image restoration method for novel rotating synthetic aperture imaging system

Abstract The novel rotating synthetic aperture (RSA) optical imaging system is an important development direction for future high-resolution optical remote sensing satellites in geostationary orbit. However, owing to the rotating rectangular pupil, the point spread function of the RSA system has an asymmetric spatial distribution, and the images obtained using the primary mirror from different rotation angles have nonuniform blur degradation. Moreover, platform vibration and pupil rotation have coupling effects on the RSA imaging, resulting in further radiometric and geometric quality degradation. To address these problems, the image degradation characteristics are first analyzed according to the imaging mechanism. Then, combined with the theory of mutual information, an image registration method is suggested by introducing the orientation gradient information. From this, a multi-frame image restoration model is proposed based on the directional gradient prior of the RSA system image. From the perspective of interpretation and application, when the aspect ratio is less than 3, the proposed inversion restoration method can achieve a satisfactory processing performance. This work can provide engineering application reference for the future space application of RSA imaging technology

Research Progress of High Entropy Ceramic Materials

High-entropy materials (HEMs) have better mechanical, thermal, and electrical properties than traditional materials due to their special "high entropy effect". They can also adjust the performance of high entropy ceramics by adjusting the proportion of raw materials, and have broad application prospects in many fields. This article provides a review of the high entropy effect, preparation methods, and main applications of high entropy ceramic materials, especially exploring relevant research on high entropy perovskite ceramics. It is expected to provide reference for the promotion of scientific research and the development of further large-scale applications of high-entropy ceramic materials

Study of Ammonia Concentration Characteristics and Optimization in Broiler Chamber during Winter Based on Computational Fluid Dynamics

Poultry breeding is one of the most significant components of agriculture and an essential link of material exchange between humans and nature. Moreover, poultry breeding technology has a considerable impact on the life quality of human beings, and could even influence the survival of human beings. As one of the most popular poultry, broiler has a good economic benefit due to its excellent taste and fast growing cycle. This paper aims to improve the efficiency of raising broilers by understanding the impact of ammonia concentration distribution within a smart broiler breeding chamber, and the rationality of the system’s design. More specifically, we used computational fluid dynamics (CFD) technology to simulate the process of ammonia production and identify the characteristics of ammonia concentration. Based on the simulation results, the structure of the broiler chamber was reformed, and the ammonia uniformity was significantly improved after the structural modification of the broiler chamber and the ammonia concentration in the chamber had remained extremely low. In general, this study provides a reference for structural optimization of the design of broiler chambers and the environmental regulation of ammonia

On the Out-Of-Distribution Generalization of Multimodal Large Language Models

We investigate the generalization boundaries of current Multimodal Large Language Models (MLLMs) via comprehensive evaluation under out-of-distribution scenarios and domain-specific tasks. We evaluate their zero-shot generalization across synthetic images, real-world distributional shifts, and specialized datasets like medical and molecular imagery. Empirical results indicate that MLLMs struggle with generalization beyond common training domains, limiting their direct application without adaptation. To understand the cause of unreliable performance, we analyze three hypotheses: semantic misinterpretation, visual feature extraction insufficiency, and mapping deficiency. Results identify mapping deficiency as the primary hurdle. To address this problem, we show that in-context learning (ICL) can significantly enhance MLLMs' generalization, opening new avenues for overcoming generalization barriers. We further explore the robustness of ICL under distribution shifts and show its vulnerability to domain shifts, label shifts, and spurious correlation shifts between in-context examples and test data

The adaptability of three Arctic microalgae to different low temperatures

In order to study the adaptability of Arctic microalgae to different environmental temperatures, the growth curves and antioxidase system of three microalgae (Skeletonema marinoi, Chlorella sp. and Chlamydomonas sp.) that were separated from the Ny-Ålesund, the high Arctic, at different low temperatures (0°C, 4°C and 8°C) were determined. The result showed that the adaptability of the microalgae to temperatures depended on the species. The growth rate, SOD and CAT activities of Skeletonema marinoi were the highest at 4°C, but MDA content was the lowest. The growth rate and enzyme activity of Chlorella sp. were the highest at 8°C, while the lowest MDA content presented at 0°C. The growth of Chlamydomonas sp. at the different temperatures was not so significant, the lowest MDA content presented at 8°C. The change of antioxidase system also depended on species and temperatures. Three indexes of antioxidase system of Skeletone mamarinoi between 0°C and 4°C showed extremely significant difference (p <0.01).SOD activity of Skeletonema marinoi and Chlorella sp. between 0°C and 8°C showed significant difference (p<0.05), and the other two indexes of them differed insignificantly. Antioxidase systems of Chlamydomonas sp. at the three temperatures differed insignificantly. In conclusion, the three microalgae had good adaptability to the three temperatures; their MDA content presented a low level, and had unique physiological mechanism to adapt to the environment with different low temperatures