Exploring Users’ Intention to use QQ\u27s Various Functions based on Social Cognitive Theory

Based upon social cognitive theory, this study explores the effect of personal and environment factors on users’ intention to use QQ’s various functions. Online survey is used to collect data in China. The results show that relationship benefit, switching cost, compatibility and subjective norms can significantly affect users’ intention to use QQ’s various functions. Whereas image benefit, perceived advantage and popularity have no effect. Finally, we propose the theoretical contribution and practical implication of this study

KINEMATIC ANALYSIS OF THE SUPPORTING LEG BETWEEN DIFFERENT WEIGHT DIVISIONS IN THE ROUNDHOUSE KICK OF TAEKWONDO

The purpose of this study was to compare kinematic differences in the supporting leg between two weight divisions in the Taekwondo Roundhouse Kick. Collegiate Taekwondo athletes participated in the study and differences in maximum joint angles and ranges of motion on the supporting leg during executing the Roundhouse Kick were examined. The results showed significantly larger (

NeAI: A Pre-convoluted Representation for Plug-and-Play Neural Ambient Illumination

Recent advances in implicit neural representation have demonstrated the ability to recover detailed geometry and material from multi-view images. However, the use of simplified lighting models such as environment maps to represent non-distant illumination, or using a network to fit indirect light modeling without a solid basis, can lead to an undesirable decomposition between lighting and material. To address this, we propose a fully differentiable framework named neural ambient illumination (NeAI) that uses Neural Radiance Fields (NeRF) as a lighting model to handle complex lighting in a physically based way. Together with integral lobe encoding for roughness-adaptive specular lobe and leveraging the pre-convoluted background for accurate decomposition, the proposed method represents a significant step towards integrating physically based rendering into the NeRF representation. The experiments demonstrate the superior performance of novel-view rendering compared to previous works, and the capability to re-render objects under arbitrary NeRF-style environments opens up exciting possibilities for bridging the gap between virtual and real-world scenes. The project and supplementary materials are available at https://yiyuzhuang.github.io/NeAI/.Comment: Project page: <a class="link-external link-https" href="https://yiyuzhuang.github.io/NeAI/" rel="external noopener nofollow">https://yiyuzhuang.github.io/NeAI/</a

Executable Knowledge Base for Virtual Chat System

A virtual chat system enables the end user to interact with knowledge base by chatting with a virtual assistant. Besides knowledge article, a virtual assistant can also perform automation flows such as restart a virtual machine, reset the password for a PC. In many virtual chat systems, AIML (Artificial Intelligence Markup Language) is used to train the virtual agent to interact with human beings. It is also possible to integrate knowledge system and automation flow system with AIML interpreter to quickly empower virtual assistances with various domain knowledge. The disclosure provides a method to convert or link an automation flow to virtual agent understandable and executable format and enable them to perform and interact seamlessly with the users, the knowledge base system and the automation system

Factors influencing the quality of clinical trials on traditional Chinese medicine— Qualitative interviews with trial auditors, clinicians and academic researchers

Background: As clinical trials evaluating the efficacy of traditional Chinese medicine (TCM) therapies have increased, several empirical studies have shown that the quality of TCM trials are generally low in terms of risk of bias. This qualitative study aimed to investigate the factors influencing the quality of TCM clinical trials to provide strategic advice on trial quality improvement. Methods: One focus group with clinical trial auditors (n=4) and six indepth semi-structured interviews with clinical research organization managers (n=2), lecturers and researchers in TCM academic institutions (n=2), a chief physician in a TCM oncology department and a PhD candidate specialized in non-pharmaceutical TCM interventions were conducted. The interviews were audio-recorded, transcribed verbatim and thematically analyzed. Results: Factors that influenced the quality of TCM clinical trials merged on the following 6 themes: trial design; trialists/ participants; trial conducting; TCM specified problems; trial monitoring, and finally societal influences. The lack of expertise and time inputs of the trialists were repeatedly mentioned. Methodological difficulties experienced when conducting TCM trials included calculating sample size, analyzing the efficacy of TCM decoctions with multiple ingredients, blinding in trials investigating non-pharmaceutical TCM interventions were highlighted. Interviewees agreed that third-party monitoring can help improving trial quality and improved participant welfare and may accelerate recruiting processes and increase compliance; however more comprehensive regulations and funding requirements would be needed. Conclusions: This study identified real-life issues influencing the quality of TCM clinical trials from design to reporting. In addition to mandatory training for TCM trial designers and coordinators, more effective institutional oversight is required. Future studies should explore specific measures to address the methodological problems in TCM trials and explore how the quality of TCM trials can affect further evidence synthesis and clinical practice

Biomedical Image Splicing Detection using Uncertainty-Guided Refinement

Recently, a surge in biomedical academic publications suspected of image manipulation has led to numerous retractions, turning biomedical image forensics into a research hotspot. While manipulation detectors are concerning, the specific detection of splicing traces in biomedical images remains underexplored. The disruptive factors within biomedical images, such as artifacts, abnormal patterns, and noises, show misleading features like the splicing traces, greatly increasing the challenge for this task. Moreover, the scarcity of high-quality spliced biomedical images also limits potential advancements in this field. In this work, we propose an Uncertainty-guided Refinement Network (URN) to mitigate the effects of these disruptive factors. Our URN can explicitly suppress the propagation of unreliable information flow caused by disruptive factors among regions, thereby obtaining robust features. Moreover, URN enables a concentration on the refinement of uncertainly predicted regions during the decoding phase. Besides, we construct a dataset for Biomedical image Splicing (BioSp) detection, which consists of 1,290 spliced images. Compared with existing datasets, BioSp comprises the largest number of spliced images and the most diverse sources. Comprehensive experiments on three benchmark datasets demonstrate the superiority of the proposed method. Meanwhile, we verify the generalizability of URN when against cross-dataset domain shifts and its robustness to resist post-processing approaches. Our BioSp dataset will be released upon acceptance

Rapid and Efficient Extraction and HPLC Analysis of Sesquiterpene Lactones from Aucklandia lappa Root.

The root of Aucklandia lappa Decne, family Asteraceae, is widely used in Asian traditional medicine due to its sesquiterpene lactones. The aim of this study was the development and optimization of the extraction and analysis of these sesquiterpene lactones. The current Chinese Pharmacopoeia reports a monograph for "Aucklandiae Radix", but the extraction method is very long and tedious including maceration overnight and ultrasonication. Different extraction protocols were evaluated with the aim of optimizing the maceration period, solvent, and shaking and sonication times. The optimized method consists of only one hour of shaking plus 30 minutes of sonication using 100% MeOH as solvent. 1H NMR spectroscopy was used as a complementary analytical tool to monitor the residual presence of sesquitepene lactones in the herbal material. A suitable LC-DAD method was set up to quantify the sesquiterpene lactones. Recovery was ca. 97%, but a very high instability of constituents was found after powdering the herbal drug. A loss of about 20% of total sesquiterpenes was found after 15–20 days; as a consequence, it is strongly endorsed to use fresh powdered herbal material to avoid errors in the quantification

Not All Models Are Equal: Predicting Model Transferability in a Self-challenging Fisher Space

This paper addresses an important problem of ranking the pre-trained deep neural networks and screening the most transferable ones for downstream tasks. It is challenging because the ground-truth model ranking for each task can only be generated by fine-tuning the pre-trained models on the target dataset, which is brute-force and computationally expensive. Recent advanced methods proposed several lightweight transferability metrics to predict the fine-tuning results. However, these approaches only capture static representations but neglect the fine-tuning dynamics. To this end, this paper proposes a new transferability metric, called \textbf{S}elf-challenging \textbf{F}isher \textbf{D}iscriminant \textbf{A}nalysis (\textbf{SFDA}), which has many appealing benefits that existing works do not have. First, SFDA can embed the static features into a Fisher space and refine them for better separability between classes. Second, SFDA uses a self-challenging mechanism to encourage different pre-trained models to differentiate on hard examples. Third, SFDA can easily select multiple pre-trained models for the model ensemble. Extensive experiments on $33$ pre-trained models of $11$ downstream tasks show that SFDA is efficient, effective, and robust when measuring the transferability of pre-trained models. For instance, compared with the state-of-the-art method NLEEP, SFDA demonstrates an average of $59.1$\% gain while bringing $22.5$x speedup in wall-clock time. The code will be available at \url{https://github.com/TencentARC/SFDA}.Comment: ECCV 2022 camera ready. 24 pages, 11 tables, 5 figure