Pricing decision research for TPL considering different logistics service level influencing the market demand

Purpose: With the rapid development of economy and the support of government policy, the development of the logistics industry has become a new economic growth engine. As we all know, the reasonable price of logistics service is the most critical factor for logistics enterprises to win market share and make profit. At the same time, the service level is one of the most important factors which will influence the size of the market share. Therefore, this paper constructs a pricing model considering a situation that the logistics service level affects the market demand. This model helps the enterprises to make scientific decisions. Methodology: To achieve this objective, this paper constructs the TPL service and the pricing decision models based on the game theory. Findings: The conclusion shows that under the situation of independent decision-making, the enterprise which has strong ability of logistics service does not necessarily have a competitive advantage, while pricing equilibrium under the situation of joint decision-making, not only make both sides get more income, but also be conducive to improve the level of service. Research limitations: In this research, there are some assumptions that might affect the accuracy the model such as there are only two TPL enterprises to participate in, and considerations are taken under the condition of complete information environment. These assumptions can be relaxed in the future work. Originality: In this research, logistics service level is taken account into the areas of logistics service pricing, which makes the models more practical and more perfect. And this paper constructs game models based on game theory to make up the limitations of traditional pricing theories in logistics service pricing.Peer Reviewe

Variability in the impacts of partisan conflict: a new perspective from bank credit

The purpose of this article is to analyse the impact of partisan conflict on bank credit, and take the global financial crisis as the time node to analyse the variability of this impact before and after the financial crisis. This article examines the impacts of partisan conflict on the bank credit by employing the US data covering the past 40 years and captures the variability in the effects of partisan conflict based on the rolling sample and time-varying parameter VAR analysis. The full sample results reveal that one standard deviation partisan conflict shock will shrink the bank credit growth rate to nonfinancial sectors, and the negative effects of partisan conflict on bank credit are more substantial after the global financial crisis. The rolling sample and time-varying parameter VAR analysis further confirm that the impacts of partisan conflict shock have varied substantially over time, where bank credit still negatively reacts to the impacts of partisan conflict in recent periods. Additionally, we estimate two extended models and support the intermediate role of economic policy uncertainty in transmitting the partisan conflict and the substitution effect of cross-border bank lending on domestic bank credit. Finally, our major results are unchanged by performing a series of robustness checks. The conclusion of this article is that partisan conflict has a significant impact on bank credit and shows obvious variability, which is more significant after the global financial crisis

Does Subprime Crisis Affect Chinese Stock Market Returns?

Abstract This paper aims at testing the influence of Subprime Crisis on Chinese stock market returns. By means of newly proposed time series spatial analysis methodology, we investigate the dominance behavior of daily returns on both Shanghai Stock Exchange Composite Index and Shenzhen Stock Exchange Component Index between before and after the crisis. Little spatial dominance could be found, even considering the appreciation of the RMB, no matter in short-term or long run investment. For rationale investors, there are no significant risk and preference changes about domestic stock market in the post Subprime Crisis era. JEL classification numbers: G01, G10, C5

Species196: A One-Million Semi-supervised Dataset for Fine-grained Species Recognition

The development of foundation vision models has pushed the general visual recognition to a high level, but cannot well address the fine-grained recognition in specialized domain such as invasive species classification. Identifying and managing invasive species has strong social and ecological value. Currently, most invasive species datasets are limited in scale and cover a narrow range of species, which restricts the development of deep-learning based invasion biometrics systems. To fill the gap of this area, we introduced Species196, a large-scale semi-supervised dataset of 196-category invasive species. It collects over 19K images with expert-level accurate annotations Species196-L, and 1.2M unlabeled images of invasive species Species196-U. The dataset provides four experimental settings for benchmarking the existing models and algorithms, namely, supervised learning, semi-supervised learning, self-supervised pretraining and zero-shot inference ability of large multi-modal models. To facilitate future research on these four learning paradigms, we conduct an empirical study of the representative methods on the introduced dataset. The dataset is publicly available at https://species-dataset.github.io/.Comment: Accepted by NeurIPS 2023 Track Datasets and Benchmark

MolFM: A Multimodal Molecular Foundation Model

Molecular knowledge resides within three different modalities of information sources: molecular structures, biomedical documents, and knowledge bases. Effective incorporation of molecular knowledge from these modalities holds paramount significance in facilitating biomedical research. However, existing multimodal molecular foundation models exhibit limitations in capturing intricate connections between molecular structures and texts, and more importantly, none of them attempt to leverage a wealth of molecular expertise derived from knowledge graphs. In this study, we introduce MolFM, a multimodal molecular foundation model designed to facilitate joint representation learning from molecular structures, biomedical texts, and knowledge graphs. We propose cross-modal attention between atoms of molecular structures, neighbors of molecule entities and semantically related texts to facilitate cross-modal comprehension. We provide theoretical analysis that our cross-modal pre-training captures local and global molecular knowledge by minimizing the distance in the feature space between different modalities of the same molecule, as well as molecules sharing similar structures or functions. MolFM achieves state-of-the-art performance on various downstream tasks. On cross-modal retrieval, MolFM outperforms existing models with 12.13% and 5.04% absolute gains under the zero-shot and fine-tuning settings, respectively. Furthermore, qualitative analysis showcases MolFM's implicit ability to provide grounding from molecular substructures and knowledge graphs. Code and models are available on https://github.com/BioFM/OpenBioMed.Comment: 31 pages, 15 figures, and 15 table