YuZhao Yu

Schelling’s System of Transcendental Idealism (1800) cannot be interpreted as a work of transcendental philosophy in the Kantian sense of grounding philosophy in the conditions of possibility of knowledge, nor in the early Fichtean sense of deriving philosophy from the self’s activity. Instead, this paper shows that Schelling’s STI develops a new form of transcendental philosophy that provides the foundation for understanding the relationship between transcendental philosophy and the philosophy of nature. This new framework makes possible an independent philosophy of nature that stands in parallel to transcendental philosophy, rather than being derived from it. Through analysis of the structure of the STI and its key concepts—particularly the introduction of the Absolute and the role of artistic intuition—this paper argues how this new transcendental philosophy not only foreshadows the philosophy of identity but also calls for a reconsideration of what transcendental philosophy can mean.

Atmospheric pollution is deteriorating, which has affected the evolution of respiratory disease for the exposed human worldwide. Thus, exploring the influence of air pollution on the evolution of disease transmission dynamics is a significant issue. In this article, a stochastic susceptible-infective epidemic model in a polluted atmospheric environment is investigated. The existence and uniqueness of the global positive solution are established. In virtue of the aggregation methods and Lyapunov function, the sufficient conditions of disease extinction, persistence, and existence of the stationary distribution are established, respectively. TakingPM2.5concentration as the air pollutant index, numerical simulations are carried out to support these results. Our results indicated that the disease transmission dynamics are significantly associated with the environmental atmospheric pollution and fluctuation.

Knowledge graphs entity typing aims to predict the potential types to an entity, that is,. Recently, several embedding models are proposed for KG entity types prediction according to the existing typing information of the tuples in KGs. However, most of them unreasonably assume that all existing entity typing instances in KGs are completely correct, which ignore the nonnegligible entity type noises and may lead to potential errors for the downstream tasks. To address this problem, we propose ConfE, a novel confidence-aware embedding approach for modeling the tuples, which takes tuple confidence into consideration for learning better embeddings. Specifically, we learn the embeddings of entities and entity types in separate entity space and entity type space since they are different objects in KGs. We utilize an asymmetric matrix to specify the interaction of their embeddings and incorporate the tuple confidence as well. To make the tuple confidence more universal, we consider only the internal structural information in existing KGs. We evaluate our model on two tasks, including entity type noise detection and entity type prediction. The extensive experimental results in two public benchmark datasets demonstrate that our proposed model outperforms all baselines on all tasks, which verify the effectiveness of ConfE in learning better embeddings on noisy KGs. The source code and data of this work can be obtained from https://github.com/swufenlp/ConfE.

A deep reinforcement learning-based computational guidance method is presented, which is used to identify and resolve the problem of collision avoidance for a variable number of fixed-wing UAVs in limited airspace. The cooperative guidance process is first analyzed for multiple aircraft by formulating flight scenarios using multiagent Markov game theory and solving it by machine learning algorithm. Furthermore, a self-learning framework is established by using the actor-critic model, which is proposed to train collision avoidance decision-making neural networks. To achieve higher scalability, the neural network is customized to incorporate long short-term memory networks, and a coordination strategy is given. Additionally, a simulator suitable for multiagent high-density route scene is designed for validation, in which all UAVs run the proposed algorithm onboard. Simulated experiment results from several case studies show that the real-time guidance algorithm can reduce the collision probability of multiple UAVs in flight effectively even with a large number of aircraft.