Global dynamics of a class of HIV-1 infection models with latently infected cells

In this paper, the global dynamics of a class of HIV-1 infection models with different infection rates and latently infected cells are investigated. We first modify the basic virus infection model and propose two models with bilinear infection rate and saturation infection rate, respectively, which take HIV-1 latency into consideration, and then study a model with a general nonlinear infection rate. By using proper Lyapunov functions and LaSalle's invariance principle, it is proved that in the first two models, if the basic reproduction ratio is less than unity, each of the infection-free equilibria is globally asymptotically stable; if the basic reproduction ratio is greater than unity, each of the chronic-infection equilibria is globally asymptotically stable. For the last model with general nonlinear infection rate, we obtain sufficient conditions for the global stability of both the infection-free and chronic-infection equilibria of the model

Histone modifications in embryo implantation and placentation: insights from mouse models

Embryo implantation and placentation play pivotal roles in pregnancy by facilitating crucial maternal-fetal interactions. These dynamic processes involve significant alterations in gene expression profiles within the endometrium and trophoblast lineages. Epigenetics regulatory mechanisms, such as DNA methylation, histone modification, chromatin remodeling, and microRNA expression, act as regulatory switches to modulate gene activity, and have been implicated in establishing a successful pregnancy. Exploring the alterations in these epigenetic modifications can provide valuable insights for the development of therapeutic strategies targeting complications related to pregnancy. However, our current understanding of these mechanisms during key gestational stages remains incomplete. This review focuses on recent advancements in the study of histone modifications during embryo implantation and placentation, while also highlighting future research directions in this field

Getting Sick After Seeing a Doctor? Diagnosing and Mitigating Knowledge Conflicts in Event Temporal Reasoning

Event temporal reasoning aims at identifying the temporal relations between two or more events. However, knowledge conflicts arise when there is a mismatch between the actual temporal relations of events in the context and the prior knowledge or biases learned by the model. We first systematically define distinct kinds of bias in event temporal reasoning, which include event relation prior bias, tense bias, narrative bias, and dependency bias, as indicators to study knowledge conflicts. To mitigate such event-related knowledge conflict, we introduce a Counterfactual Data Augmentation based method that can be applied to both Pre-trained Language Models (PLMs) and Large Language Models (LLMs) either as additional training data or demonstrations for In-Context Learning. Experiments suggest the importance of mitigating knowledge conflicts in event temporal reasoning tasks for reducing hallucination and highlight the potential of counterfactual data augmentation for improving model performance.Comment: 13 pages, 1 figur

HoMM: Higher-order Moment Matching for Unsupervised Domain Adaptation

Minimizing the discrepancy of feature distributions between different domains is one of the most promising directions in unsupervised domain adaptation. From the perspective of distribution matching, most existing discrepancy-based methods are designed to match the second-order or lower statistics, which however, have limited expression of statistical characteristic for non-Gaussian distributions. In this work, we explore the benefits of using higher-order statistics (mainly refer to third-order and fourth-order statistics) for domain matching. We propose a Higher-order Moment Matching (HoMM) method, and further extend the HoMM into reproducing kernel Hilbert spaces (RKHS). In particular, our proposed HoMM can perform arbitrary-order moment tensor matching, we show that the first-order HoMM is equivalent to Maximum Mean Discrepancy (MMD) and the second-order HoMM is equivalent to Correlation Alignment (CORAL). Moreover, the third-order and the fourth-order moment tensor matching are expected to perform comprehensive domain alignment as higher-order statistics can approximate more complex, non-Gaussian distributions. Besides, we also exploit the pseudo-labeled target samples to learn discriminative representations in the target domain, which further improves the transfer performance. Extensive experiments are conducted, showing that our proposed HoMM consistently outperforms the existing moment matching methods by a large margin. Codes are available at \url{https://github.com/chenchao666/HoMM-Master}Comment: Accept by AAAI-2020, codes are available at https://github.com/chenchao666/HoMM-Maste

Preliminary Design of a Small Unmanned Battery Powered Tailsitter

This paper presents a preliminary design methodology for small unmanned battery powered tailsitters. Subsystem models, including takeoff weight, power and energy consumption models, and battery discharge model, were investigated, respectively. Feasible design space was given by simulation with mission and weight constraints, while the influences of wing loading and battery ratio were analyzed. Case study was carried out according to the design process, and the results were validated by previous designs. The design methodology can be used to determine key parameters and make necessary preparations for detailed design and vehicle realization of small battery powered tailsitters