More Attached, Less Stressed: Viewers’ Parasocial Attachment to Virtual Youtubers and Its Influence on the Stress of Viewers During the COVID-19 Pandemic

The COVID-19 pandemic is stressful for people, yet it has witnessed an exponential growth in the number of Virtual Youtubers and their viewers in China. Virtual Youtubers (VTubers) are people represented by virtual 2D or 3D avatars on different platforms. From the perspective of media psychology, an online survey of 669 participants was conducted to examine the intensity of Chinese viewers’ parasocial attachment to the VTubers and the influence of such attachment on the stress of these viewers during the pandemic. As a result, positive correlations were found between viewers’ parasocial attachment to VTubers and the stress relief of these viewers

Survey on some key technologies of virtual tourism system based on Web3D

Some key technologies on how to build large-scale virtual tourism systssems comprehensively on Web browsers and mobiles were analyzed and the current R&D status on Web3D virtual tourism was surveyed insightfully. Then, some methods were summarized, including 3D trees or plants modeling, 3D architectural modeling, 3D Virtual Human behavior modeling, virtual agents path planning, collision detection and progressive transmission strategy suitable for developing large scale Web3D tourism scenarios. Also, some bottleneck problems of Web3D virtual tourism system were investigated. At the same time, the lightweight 3D engine, the lightweight 3D modeling, the lightweight 3D streaming and P2P based progressive transmission of huge Web3D tourism contents would become much helpful to breakthrough those bottlenecks of Web3D tourism systems were pointed out. In addition, all kinds of Web3D engines in terms of lightweight, realism and efficiency that would be a good reference for developers to choose during various applications were compared comprehensively. Finally, the prospect of future investigation of Web3D tourism system is presented, which will be going on in terms of four characteristics lightweight, high-speed, realism, beauty.

Event-Centric Question Answering via Contrastive Learning and Invertible Event Transformation

Human reading comprehension often requires reasoning of event semantic relations in narratives, represented by Event-centric Question-Answering (QA). To address event-centric QA, we propose a novel QA model with contrastive learning and invertible event transformation, call TranCLR. Our proposed model utilizes an invertible transformation matrix to project semantic vectors of events into a common event embedding space, trained with contrastive learning, and thus naturally inject event semantic knowledge into mainstream QA pipelines. The transformation matrix is fine-tuned with the annotated event relation types between events that occurred in questions and those in answers, using event-aware question vectors. Experimental results on the Event Semantic Relation Reasoning (ESTER) dataset show significant improvements in both generative and extractive settings compared to the existing strong baselines, achieving over 8.4% gain in the token-level F1 score and 3.0% gain in Exact Match (EM) score under the multi-answer setting. Qualitative analysis reveals the high quality of the generated answers by TranCLR, demonstrating the feasibility of injecting event knowledge into QA model learning. Our code and models can be found at https://github.com/LuJunru/TranCLR.Comment: Findings of EMNLP 202

Extracting event temporal relations via hyperbolic geometry

Detecting events and their evolution through time is a crucial task in natural language understanding. Recent neural approaches to event temporal relation extraction typically map events to embeddings in the Euclidean space and train a classifier to detect temporal relations between event pairs. However, embeddings in the Euclidean space cannot capture richer asymmetric relations such as event temporal relations. We thus propose to embed events into hyperbolic spaces, which are intrinsically oriented at modeling hierarchical structures. We introduce two approaches to encode events and their temporal relations in hyperbolic spaces. One approach leverages hyperbolic embeddings to directly infer event relations through simple geometrical operations. In the second one, we devise an end-to-end architecture composed of hyperbolic neural units tailored for the temporal relation extraction task. Thorough experimental assessments on widely used datasets have shown the benefits of revisiting the tasks on a different geometrical space, resulting in state-of-the-art performance on several standard metrics. Finally, the ablation study and several qualitative analyses highlighted the rich event semantics implicitly encoded into hyperbolic spaces

Event-centric question answering via contrastive learning and invertible event transformation

Human reading comprehension often requires reasoning of event semantic relations in narratives, represented by Event-centric Question-Answering (QA). To address event-centric QA, we propose a novel QA model with contrastive learning and invertible event transformation, call TranCLR. Our proposed model utilizes an invertible transformation matrix to project semantic vectors of events into a common event embedding space, trained with contrastive learning, and thus naturally inject event semantic knowledge into mainstream QA pipelines. The transformation matrix is fine-tuned with the annotated event relation types between events that occurred in questions and those in answers, using event-aware question vectors. Experimental results on the Event Semantic Relation Reasoning (ESTER) dataset show significant improvements in both generative and extractive settings compared to the existing strong baselines, achieving over 8.4% gain in the token-level F1 score and 3.0% gain in Exact Match (EM) score under the multi-answer setting. Qualitative analysis reveals the high quality of the generated answers by TranCLR, demonstrating the feasibility of injecting event knowledge into QA model learning. Our code and models can be found at https://github.com/LuJunru/TranCLR

Solving a Class of Singular Fifth-Order Boundary Value Problems Using Reproducing Kernel Hilbert Space Method

We use the reproducing kernel Hilbert space method to solve the fifth-order boundary value problems. The exact solution to the fifth-order boundary value problems is obtained in reproducing kernel space. The approximate solution is given by using an iterative method and the finite section method. The present method reveals to be more effective and convenient compared with the other methods

Exploring the perspectives of pharmaceutical experts and healthcare practitioners on senolytic drugs for vascular aging-related disorder: a qualitative study

Objective: The field of targeting cellular senescence with drug candidates to address age-related comorbidities has witnessed a notable surge of interest and research and development. This study aimed to gather valuable insights from pharmaceutical experts and healthcare practitioners regarding the potential and challenges of translating senolytic drugs for treatment of vascular aging-related disorders.Methods: This study employed a qualitative approach by conducting in-depth interviews with healthcare practitioners and pharmaceutical experts. Participants were selected through purposeful sampling. Thematic analysis was used to identify themes from the interview transcripts.Results: A total of six individuals were interviewed, with three being pharmaceutical experts and the remaining three healthcare practitioners. The significant global burden of cardiovascular diseases presents a potentially large market size that offer an opportunity for the development and marketability of novel senolytic drugs. The pharmaceutical sector demonstrates a positive inclination towards the commercialization of new senolytic drugs targeting vascular aging-related disorders. However potential important concerns have been raised, and these include increasing specificity toward senescent cells to prevent off-site targeting, thus ensuring the safety and efficacy of these drugs. In addition, novel senolytic therapy for vascular aging-related disorders may encounter competition from existing drugs that treat or manage risk factors of cardiovascular diseases. Healthcare practitioners are also in favor of recommending the novel senolytic drugs for vascular aging-related disorders but cautioned that its high cost may hinder its acceptance among patients. Besides sharing the same outcome-related concerns as with the pharmaceutical experts, healthcare practitioners anticipated a lack of awareness among the general public regarding the concept of targeting cellular senescence to delay vascular aging-related disorders, and this knowledge gap extends to healthcare practitioner themselves as well.Conclusion: Senolytic therapy for vascular aging-related disorders holds great promise, provided that crucial concerns surrounding its outcomes and commercial hurdles are effectively addressed

Synchronous post-acceleration of laser-driven protons in helical coil targets by controlling the current dispersion

Post-acceleration of protons in helical coil targets driven by intense, ultrashort laser pulses can enhance ion energy by utilizing the transient current from the targets’ self-discharge. The acceleration length of protons can exceed a few millimeters, and the acceleration gradient is of the order of GeV/m. How to ensure the synchronization between the accelerating electric field and the protons is a crucial problem for efficient post-acceleration. In this paper, we study how the electric field mismatch induced by current dispersion affects the synchronous acceleration of protons. We propose a scheme using a two-stage helical coil to control the current dispersion. With optimized parameters, the energy gain of protons is increased by four times. Proton energy is expected to reach 45 MeV using a hundreds-of-terawatts laser, or more than 100 MeV using a petawatt laser, by controlling the current dispersion