Translation from Chinese of Poems 1–9 from 'Wandering Spirit and Metaphysical Thoughts' by Gao Xingjian

The 2000 Nobel Laureate for Literature, Gao Xingjian, suffered cardiac arrest while directing rehearsals for his mega-scale opera Snow in August that was due to premiere in late 2002 at the National Opera House, Taipei. He recovered, and the opera premiered as scheduled with the help of a co-director before he returned to Paris to direct the Comédie Français premiere of his Quatre quatuors pour un week-end. He underwent surgery in February and March of 2003, but was soon again back at work. The year 2003 had been designated “Gao Xingjian Year” by the City of Marseille, and he would direct his new play Le Quêteur de la Mort at Théâtre du Gymnase, and then his Snow in August at Opéra de Marseille. It was during rehearsals for the former that he collapsed again, and was hospitalized: the play was co-directed by Romain Bonnin, 23–26 September 2003. Large exhibitions of Gao’s artworks had been held earlier that year, but the performance of Snow in August was postponed. During his recuperation for most of 2004, he sometimes wrote poems, some of which he later polished or rewrote for his collection Wandering Spirit and Metaphysical Thoughts (2012).These translations from the Chinese into English of the first 9 poems in Wandering Spirit and Metaphysical Thoughts (2012) are by acclaimed translator Mabel Lee

Media Exposure and Risk Perception as Predictors of Engagement in COVID-19 Preventive Behaviors: Extending the Theory of Planned Behavior Across Two Cultures

Purpose: This study examined the psychological and social factors that affect the performance of preventive behaviors toward COVID-19, by testing a model based on the theory of planned behavior (TPB). Our model featured media exposure and social networking site (SNS) involvement, and we tested it in two highly contrasted cultures regarding COVID-19 attitudes: U.S. and Japan. Method: An online survey collected 300 samples for each culture. Participation was voluntary, for monetary compensation through crowd-sourcing platforms. Findings: Overall, the results showed a good fit of our TPB model in each culture. Media exposure was a major predictor of risk perception in both cultures, while engagement in SNS predicted intention to perform preventive behavior for the Japanese only, and personal hygiene was found to be a significant predictor of protective behavior once again only for the Japanese. Implications and Value: While there were differences in the variables affecting preventive behaviors, overall, our proposed model proved to be robust across both cultures. Implications were made on differences between tight and loose cultures, as represented by Japan and the US, regarding COVID-19 preventive attitudes

Graph Contrastive Learning with Implicit Augmentations

Existing graph contrastive learning methods rely on augmentation techniques based on random perturbations (e.g., randomly adding or dropping edges and nodes). Nevertheless, altering certain edges or nodes can unexpectedly change the graph characteristics, and choosing the optimal perturbing ratio for each dataset requires onerous manual tuning. In this paper, we introduce Implicit Graph Contrastive Learning (iGCL), which utilizes augmentations in the latent space learned from a Variational Graph Auto-Encoder by reconstructing graph topological structure. Importantly, instead of explicitly sampling augmentations from latent distributions, we further propose an upper bound for the expected contrastive loss to improve the efficiency of our learning algorithm. Thus, graph semantics can be preserved within the augmentations in an intelligent way without arbitrary manual design or prior human knowledge. Experimental results on both graph-level and node-level tasks show that the proposed method achieves state-of-the-art performance compared to other benchmarks, where ablation studies in the end demonstrate the effectiveness of modules in iGCL

Visual Prompt Tuning for Test-time Domain Adaptation

Models should have the ability to adapt to unseen data during test-time to avoid performance drop caused by inevitable distribution shifts in real-world deployment scenarios. In this work, we tackle the practical yet challenging test-time adaptation (TTA) problem, where a model adapts to the target domain without accessing the source data. We propose a simple recipe called data-efficient prompt tuning (DePT) with two key ingredients. First, DePT plugs visual prompts into the vision Transformer and only tunes these source-initialized prompts during adaptation. We find such parameter-efficient finetuning can efficiently adapt the model representation to the target domain without overfitting to the noise in the learning objective. Second, DePT bootstraps the source representation to the target domain by memory bank-based online pseudo labeling. A hierarchical self-supervised regularization specially designed for prompts is jointly optimized to alleviate error accumulation during self-training. With much fewer tunable parameters, DePT demonstrates not only state-of-the-art performance on major adaptation benchmarks, but also superior data efficiency, i.e., adaptation with only 1\% or 10\% data without much performance degradation compared to 100\% data. In addition, DePT is also versatile to be extended to online or multi-source TTA settings

PreDiff: Precipitation Nowcasting with Latent Diffusion Models

Earth system forecasting has traditionally relied on complex physical models that are computationally expensive and require significant domain expertise. In the past decade, the unprecedented increase in spatiotemporal Earth observation data has enabled data-driven forecasting models using deep learning techniques. These models have shown promise for diverse Earth system forecasting tasks but either struggle with handling uncertainty or neglect domain-specific prior knowledge, resulting in averaging possible futures to blurred forecasts or generating physically implausible predictions. To address these limitations, we propose a two-stage pipeline for probabilistic spatiotemporal forecasting: 1) We develop PreDiff, a conditional latent diffusion model capable of probabilistic forecasts. 2) We incorporate an explicit knowledge control mechanism to align forecasts with domain-specific physical constraints. This is achieved by estimating the deviation from imposed constraints at each denoising step and adjusting the transition distribution accordingly. We conduct empirical studies on two datasets: N-body MNIST, a synthetic dataset with chaotic behavior, and SEVIR, a real-world precipitation nowcasting dataset. Specifically, we impose the law of conservation of energy in N-body MNIST and anticipated precipitation intensity in SEVIR. Experiments demonstrate the effectiveness of PreDiff in handling uncertainty, incorporating domain-specific prior knowledge, and generating forecasts that exhibit high operational utility.Comment: Technical repor

The host transcriptome change involved in the inhibitory effect of exogenous interferon-γ on Getah virus replication

IntroductionGetah virus (GETV) has become a growing potential threat to the global livestock industry and public health. However, little is known about the viral pathogenesis and immune escape mechanisms, leading to ineffective control measures.MethodsIn this study, the antiviral activity of exogenous interferons (IFNs) was assessed by using western blotting (WB), real-time quantitative PCR (RT-qPCR) and indirect immunofluorescence assay (IFA). The comparative transcriptomics among mock- and GETV-infected (MOI = 0.1) ST cells with or without IFN-γ was performed by RNA-seq, and then the transcriptome profiling of GETV-infected ST cells and key pathways and putative factors involved in inhibitory effect of IFN-γ on GETV replication were analyzed by bioinformatics methods and RT-qPCR.ResultsThe results showed that treatment with IFN-γ could suppress GETV replication, and the inhibitory effect lasted for at least 48 h, while the exogenous IFN-α/ω and IFN-λ3 treatments failed to inhibit the viral infection and early replication in vitro. Furthermore, the blueprint of virus-host interaction was plotted by RNA-seq and RT-qPCR, showing systemic activation of inflammatory, apoptotic, and antiviral pathways in response to GETV infection, indicating viral hijacking and inhibition of innate host immunity such as IFN-I/III responses. Last and most importantly, activation of the JAK-STAT signaling pathway and complement and coagulation cascades may be a primary driver for IFN-γ-mediated inhibition of GETV replication.DiscussionThese findings revealed that GETV possessed the capability of viral immune escape and indicated that IFN-γ aided in the prevention and control of GETV, implying the potential molecular mechanism of suppression of GETV by IFN-γ, all of which warrant emphasis or further clarification

Petroleum Hydrocarbon-Degrading Bacteria for the Remediation of Oil Pollution Under Aerobic Conditions: A Perspective Analysis

With the sharp increase in population and modernization of society, environmental pollution resulting from petroleum hydrocarbons has increased, resulting in an urgent need for remediation. Petroleum hydrocarbon-degrading bacteria are ubiquitous in nature and can utilize these compounds as sources of carbon and energy. Bacteria displaying such capabilities are often exploited for the bioremediation of petroleum oil-contaminated environments. Recently, microbial remediation technology has developed rapidly and achieved major gains. However, this technology is not omnipotent. It is affected by many environmental factors that hinder its practical application, limiting the large-scale application of the technology. This paper provides an overview of the recent literature referring to the usage of bacteria as biodegraders, discusses barriers regarding the implementation of this microbial technology, and provides suggestions for further developments