Class-level Structural Relation Modelling and Smoothing for Visual Representation Learning

Representation learning for images has been advanced by recent progress in more complex neural models such as the Vision Transformers and new learning theories such as the structural causal models. However, these models mainly rely on the classification loss to implicitly regularize the class-level data distributions, and they may face difficulties when handling classes with diverse visual patterns. We argue that the incorporation of the structural information between data samples may improve this situation. To achieve this goal, this paper presents a framework termed \textbf{C}lass-level Structural Relation Modeling and Smoothing for Visual Representation Learning (CSRMS), which includes the Class-level Relation Modelling, Class-aware Graph Sampling, and Relational Graph-Guided Representation Learning modules to model a relational graph of the entire dataset and perform class-aware smoothing and regularization operations to alleviate the issue of intra-class visual diversity and inter-class similarity. Specifically, the Class-level Relation Modelling module uses a clustering algorithm to learn the data distributions in the feature space and identify three types of class-level sample relations for the training set; Class-aware Graph Sampling module extends typical training batch construction process with three strategies to sample dataset-level sub-graphs; and Relational Graph-Guided Representation Learning module employs a graph convolution network with knowledge-guided smoothing operations to ease the projection from different visual patterns to the same class. Experiments demonstrate the effectiveness of structured knowledge modelling for enhanced representation learning and show that CSRMS can be incorporated with any state-of-the-art visual representation learning models for performance gains. The source codes and demos have been released at https://github.com/czt117/CSRMS

Are We in The Zone? Exploring The Features and Method of Detecting Simultaneous Flow Experiences Based on EEG Signals

When executing interdependent personal tasks for the team's purpose, simultaneous individual flow(simultaneous flow) is the antecedent condition of achieving shared team flow. Detecting simultaneous flow helps better understanding the status of team members, which is thus important for optimizing multi-user interaction systems. However, there is currently a lack exploration on objective features and methods for detecting simultaneous flow. Based on brain mechanism of flow in teamwork and previous studies on electroencephalogram (EEG)-based individual flow detection, this study aims to explore the significant EEG features related to simultaneous flow, as well as effective detection methods based on EEG signals. First, a two-player simultaneous flow task is designed, based on which we construct the first multi-EEG signals dataset of simultaneous flow. Then, we explore the potential EEG signal features that may be related to individual and simultaneous flow and validate their effectiveness in simultaneous flow detection with various machine learning models. The results show that 1) the inter-brain synchrony features are relevant to simultaneous flow due to enhancing the models' performance in detecting different types of simultaneous flow; 2) the features from the frontal lobe area seem to be given priority attention when detecting simultaneous flows; 3) Random Forests performed best in binary classification while Neural Network and Deep Neural Network3 performed best in ternary classification

Comparative study of triage strategies for women with atypical squamous cells of undetermined significance in the post-vaccine era

ObjectiveThe research focused on a comparative analysis of triage strategies for women with Atypical Squamous Cells of Undetermined Significance (ASC-US) before and after receiving the HPV vaccine, aiming to optimize cervical cancer prevention strategies, especially in resource-limited healthcare settings.Materials and methodsBetween September 2018 and December 2023, 7,511 women aged 21 years or older who underwent liquid-based cytology for cervical cancer screening were recruited. Women diagnosed with ASC-US were included in the study. All participants underwent HPV testing and liquid-based cytology examination, and those with abnormal results were referred for colposcopy. Women with abnormal colposcopy findings underwent further histopathological examination. The gold standard for diagnosis was pathological, with cervical intraepithelial neoplasia grade 2 or higher (CIN2+) on histology as the endpoints. In the final analysis, 933 women with ASC-US were enrolled as the unvaccinated group, with 179 of them testing positive for HPV 16/18. Assuming that all women would receive the bivalent vaccine targeting HPV 16/18 in the post-vaccine era, and given that the vaccine protection rate is 100% against HPV 16/18, then 754 women excluding those of HPV 16/18 positive would comprise the vaccinated group.ResultsIn the unvaccinated group, the overall HPV positivity rate was 59.27% among ASC-US women, with a 100% HPV prevalence rate among those with CIN2+ lesions. The combination genotyping model of HPV16/18 showed the highest specificity (81.77%) and the lowest referral rate (32.37%). In the vaccinated group, the HPV positivity rate was 49.61% among ASC-US women, with a 100% HPV prevalence rate among those with CIN2+ lesions. The specificity of HPV33/58 was the highest (86.99%), and the colposcopy referral rate was lowest (27.54%), with statistical significance. Sensitivity, positive predictive value, and negative predictive value were not statistically significant.ConclusionHPV16/18 demonstrated a more efficacious triaging effect in the unvaccinated group. HPV33/58 will potentially replace HPV16/18 as the priority screening genotyping among vaccinated populations

Smoking patterns and sociodemographic factors associated with tobacco use among Chinese rural male residents: a descriptive analysis

<p>Abstract</p> <p>Background</p> <p>Although evidence has shown high prevalence rates of tobacco use in the general urban populations in China, relatively little is known in its rural population. The purposes of this study were to examine smoking patterns and sociodemographic correlates of smoking in a sample of rural Chinese male residents.</p> <p>Methods</p> <p>The study employed a cross-sectional, multi-stage sampling design. Residents (N = 4,414; aged 15 years and older) were recruited from four geographic regions in China. Information on participants' tobacco use (of all forms), including their daily use, and sociodemographic characteristics were collected via survey questionnaires and the resultant data were analyzed using chi-square tests and logistic regression procedures.</p> <p>Results</p> <p>The overall smoking prevalence in the study sample was 66.8% (n = 2,950). Of these, the average use of tobacco products per day was 12.70 (SD = 7.99) and over 60% reported daily smoking of more than 10 cigarettes. Geographic regions of the study areas, age of the participants, marital status, ethnicity, education, occupation, and average personal annual income were found to be significantly associated with an increased likelihood of smoking among rural Chinese male residents.</p> <p>Conclusion</p> <p>There is a high smoking prevalence in the Chinese rural population and smoking behaviors are associated with important sociodemographic factors. Findings suggest the need for tobacco control and intervention policies aimed at reducing tobacco use in Chinese rural smoking populations.</p

Enhanced Photocatalytic Performance and Mechanism of Au@CaTiO₃ Composites with Au Nanoparticles Assembled on CaTiO₃ Nanocuboids

Using P25 as the titanium source and based on a hydrothermal route, we have synthesized CaTiO3 nanocuboids (NCs) with the width of 0.3–0.5 μm and length of 0.8–1.1 μm, and systematically investigated their growth process. Au nanoparticles (NPs) of 3–7 nm in size were assembled on the surface of CaTiO3 NCs via a photocatalytic reduction method to achieve excellent Au@CaTiO3 composite photocatalysts. Various techniques were used to characterize the as-prepared samples, including X-ray powder diffraction (XRD), scanning/transmission electron microscopy (SEM/TEM), diffuse reflectance spectroscopy (UV-vis DRS), Fourier transform infrared spectroscopy (FTIR), and X-ray photoelectron spectroscopy (XPS). Rhodamine B (RhB) in aqueous solution was chosen as the model pollutant to assess the photocatalytic performance of the samples separately under simulated-sunlight, ultraviolet (UV) and visible-light irradiation. Under irradiation of all kinds of light sources, the Au@CaTiO3 composites, particularly the 4.3%Au@CaTiO3 composite, exhibit greatly enhanced photocatalytic performance when compared with bare CaTiO3 NCs. The main roles of Au NPs in the enhanced photocatalytic mechanism of the Au@CaTiO3 composites manifest in the following aspects: (1) Au NPs act as excellent electron sinks to capture the photoexcited electrons in CaTiO3, thus leading to an efficient separation of photoexcited electron/hole pairs in CaTiO3; (2) the electromagnetic field caused by localized surface plasmon resonance (LSPR) of Au NPs could facilitate the generation and separation of electron/hole pairs in CaTiO3; and (3) the LSPR-induced electrons in Au NPs could take part in the photocatalytic reactions

Emissions of Airport Monitoring with Solar Occultation Flux-Fourier Transform Infrared Spectrometer

Both domestic and international aviation industries have experienced a boom, which results in a dramatic increase in emissions of the aviation industry in recent decades. Therefore, domestic and abroad scientists adopted different methods to measure emissions; however, there are no appropriate methods to measure the emissions of the whole airport. In order to provide data support for the relevant departments to take appropriate emission reduction measures, solar occultation flux-Fourier transform infrared spectrometer (SOF-FT-IR) is used to monitor the emissions of Beijing Capital International Airport. CO, CO2, C2H4, and CH2O are selected as the target gases and are quantitatively analyzed with the nonlinear least squares method to get the column concentration. Then, the flux can also be calculated by linking the wind velocity and direction with the column concentration. A comparison between the results measured by SOF-FT-IR and the results measured by the method published by the Intergovernmental Panel on Climate Change (IPCC) indicates that auxiliary power equipment and ground support equipment for the emission of the airport are also important emission sources besides the aircraft and the concentration distribution gives powerful and useful pieces of evidence to locate the emission sources. In order to decrease the contribution of the airport to the air pollution, the key point is to reduce the emissions of the APU and mobiles in the airport

Emissions of Airport Monitoring with Solar Occultation Flux-Fourier Transform Infrared Spectrometer

Both domestic and international aviation industries have experienced a boom, which results in a dramatic increase in emissions of the aviation industry in recent decades. Therefore, domestic and abroad scientists adopted different methods to measure emissions; however, there are no appropriate methods to measure the emissions of the whole airport. In order to provide data support for the relevant departments to take appropriate emission reduction measures, solar occultation flux-Fourier transform infrared spectrometer (SOF-FT-IR) is used to monitor the emissions of Beijing Capital International Airport. CO, CO2, C2H4, and CH2O are selected as the target gases and are quantitatively analyzed with the nonlinear least squares method to get the column concentration. Then, the flux can also be calculated by linking the wind velocity and direction with the column concentration. A comparison between the results measured by SOF-FT-IR and the results measured by the method published by the Intergovernmental Panel on Climate Change (IPCC) indicates that auxiliary power equipment and ground support equipment for the emission of the airport are also important emission sources besides the aircraft and the concentration distribution gives powerful and useful pieces of evidence to locate the emission sources. In order to decrease the contribution of the airport to the air pollution, the key point is to reduce the emissions of the APU and mobiles in the airport