Research on the measurement and characteristics of museum visitors’ emotions under digital technology environment

What kind of emotional experience does the application of digital technology in museums create for museum visitors? Can it be measured accurately and in real-time? What are its characteristics? This paper utilizes EEG signals and the PAD emotional model as research methods to conduct real-time digital measurement of visitors’ emotional experiences at Tianyi Pavilion Museum in Ningbo City, focusing on their physiological and psychological reactions.The results show that: (1) In a quasi-experimental environment, linear SVM, polynomial kernel SVM, and Gaussian kernel SVM can all accurately classify the emotional tendencies of museum visitors with success rate of over 72%. (2) In a quasi-experimental environment, it is feasible and reliable to measure the immediate digital emotional experiences of visitors using EEG signals and the PAD emotion model. Based on this, we can summarize the characteristics of emotional tendencies among different demographic groups of museum visitors

Implicit Ray-Transformers for Multi-view Remote Sensing Image Segmentation

The mainstream CNN-based remote sensing (RS) image semantic segmentation approaches typically rely on massive labeled training data. Such a paradigm struggles with the problem of RS multi-view scene segmentation with limited labeled views due to the lack of considering 3D information within the scene. In this paper, we propose ''Implicit Ray-Transformer (IRT)'' based on Implicit Neural Representation (INR), for RS scene semantic segmentation with sparse labels (such as 4-6 labels per 100 images). We explore a new way of introducing multi-view 3D structure priors to the task for accurate and view-consistent semantic segmentation. The proposed method includes a two-stage learning process. In the first stage, we optimize a neural field to encode the color and 3D structure of the remote sensing scene based on multi-view images. In the second stage, we design a Ray Transformer to leverage the relations between the neural field 3D features and 2D texture features for learning better semantic representations. Different from previous methods that only consider 3D prior or 2D features, we incorporate additional 2D texture information and 3D prior by broadcasting CNN features to different point features along the sampled ray. To verify the effectiveness of the proposed method, we construct a challenging dataset containing six synthetic sub-datasets collected from the Carla platform and three real sub-datasets from Google Maps. Experiments show that the proposed method outperforms the CNN-based methods and the state-of-the-art INR-based segmentation methods in quantitative and qualitative metrics

DiffTalker: Co-driven audio-image diffusion for talking faces via intermediate landmarks

Generating realistic talking faces is a complex and widely discussed task with numerous applications. In this paper, we present DiffTalker, a novel model designed to generate lifelike talking faces through audio and landmark co-driving. DiffTalker addresses the challenges associated with directly applying diffusion models to audio control, which are traditionally trained on text-image pairs. DiffTalker consists of two agent networks: a transformer-based landmarks completion network for geometric accuracy and a diffusion-based face generation network for texture details. Landmarks play a pivotal role in establishing a seamless connection between the audio and image domains, facilitating the incorporation of knowledge from pre-trained diffusion models. This innovative approach efficiently produces articulate-speaking faces. Experimental results showcase DiffTalker's superior performance in producing clear and geometrically accurate talking faces, all without the need for additional alignment between audio and image features.Comment: submmit to ICASSP 202

Coupling Vision and Proprioception for Navigation of Legged Robots

We exploit the complementary strengths of vision and proprioception to develop a point-goal navigation system for legged robots, called VP-Nav. Legged systems are capable of traversing more complex terrain than wheeled robots, but to fully utilize this capability, we need a high-level path planner in the navigation system to be aware of the walking capabilities of the low-level locomotion policy in varying environments. We achieve this by using proprioceptive feedback to ensure the safety of the planned path by sensing unexpected obstacles like glass walls, terrain properties like slipperiness or softness of the ground and robot properties like extra payload that are likely missed by vision. The navigation system uses onboard cameras to generate an occupancy map and a corresponding cost map to reach the goal. A fast marching planner then generates a target path. A velocity command generator takes this as input to generate the desired velocity for the walking policy. A safety advisor module adds sensed unexpected obstacles to the occupancy map and environment-determined speed limits to the velocity command generator. We show superior performance compared to wheeled robot baselines, and ablation studies which have disjoint high-level planning and low-level control. We also show the real-world deployment of VP-Nav on a quadruped robot with onboard sensors and computation. Videos at https://navigation-locomotion.github.ioComment: CVPR 2022 final version. Website at https://navigation-locomotion.github.i

Multiomics and bioinformatics identify differentially expressed effectors in the brain of Toxoplasma gondii infected masked palm civet

IntroductionThe masked palm civet (Paguma larvata) serves as a reservoir in transmitting pathogens, such as Toxoplasma gondii, to humans. However, the pathogenesis of T. gondii infection in masked palm civets has not been explored. We studied the molecular changes in the brain tissue of masked palm civets chronically infected with T. gondii ME49.MethodsThe differentially expressed proteins in the brain tissue were investigated using iTRAQ and bioinformatics.ResultsA total of 268 differential proteins were identified, of which 111 were upregulated and 157 were downregulated. KEGG analysis identified pathways including PI3K-Akt signaling pathway, proteoglycans in cancer, carbon metabolism, T-cell receptor signaling pathway. Combing transcriptomic and proteomics data, we identified 24 genes that were differentially expressed on both mRNA and protein levels. The top four upregulated proteins were REEP3, REEP4, TEP1, and EEPD1, which was confirmed by western blot and immunohistochemistry. KEGG analysis of these 24 genes identified signaling cascades that were associated with small cell lung cancer, breast cancer, Toll-like receptor signaling pathway, Wnt signaling pathways among others. To understand the mechanism of the observed alteration, we conducted immune infiltration analysis using TIMER databases which identified immune cells that are associated with the upregulation of these proteins. Protein network analysis identified 44 proteins that were in close relation to all four proteins. These proteins were significantly enriched in immunoregulation and cancer pathways including PI3K-Akt signaling pathway, Notch signaling pathway, chemokine signaling pathway, cell cycle, breast cancer, and prostate cancer. Bioinformatics utilizing two cancer databases (TCGA and GEPIA) revealed that the four genes were upregulated in many cancer types including glioblastoma (GBM). In addition, higher expression of REEP3 and EEPD1 was associated with better prognosis, while higher expression of REEP4 and TEP1 was associated with poor prognosis in GBM patients.DiscussionWe identified the differentially expressed genes in the brain of T. gondii infected masked palm civets. These genes were associated with various cellular signaling pathways including those that are immune- and cancer-related

The Mechanism and Diagnosis of Insulation Deterioration Caused by Moisture Ingress into Oil-Impregnated Paper Bushing

The healthy state of insulation in oil-impregnated bushings is traditionally evaluated by tanδ and capacitance at power frequency and mostly at 10 kV in the test standard. However, there has frequently been insulation accidents induced by moisture ingress (MI) for bushings that have passed the standard. The mechanism and new diagnostic features for MI into bushings were not distinct enough and an accurate test method is urgently needed research. To address this technical gap, a bushing model with a transparent sheath was designed and an ultrasonic humidifier device was adopted to simulate the environment of MI in bushings and recorded by digital camera. The parameters of dielectric dissipation factor, capacitance, partial discharge (PD), frequency domain response, and moisture content in oil were measured at room temperature with time. The results presented that both the increment dissipation factor at low frequency of 0.001 Hz and the increment dissipation factor of 1.2 Um could be used for detecting the earlier insulation defect of oil-impregnated paper (OIP) bushings. The phase resolved partial discharge (PRPD) can serve as the diagnostic basis of the severe state (S3) of insulation deterioration caused by MI into bushings around the phases of 0–117°, 151–303°, and 325–360°. The research findings would provide a useful reference for the condition diagnosis and maintenance of OIP bushings. Especially, the increment detection of Frequency Domain Spectroscopy (FDS) at the frequency of 1 mHz and 10 kHz was recommended firstly for the operative bushings in real sites

Pyramid Fusion Transformer for Semantic Segmentation

The recently proposed MaskFormer gives a refreshed perspective on the task of semantic segmentation: it shifts from the popular pixel-level classification paradigm to a mask-level classification method. In essence, it generates paired probabilities and masks corresponding to category segments and combines them during inference for the segmentation maps. In our study, we find that per-mask classification decoder on top of a single-scale feature is not effective enough to extract reliable probability or mask. To mine for rich semantic information across the feature pyramid, we propose a transformer-based Pyramid Fusion Transformer (PFT) for per-mask approach semantic segmentation with multi-scale features. The proposed transformer decoder performs cross-attention between the learnable queries and each spatial feature from the feature pyramid in parallel and uses cross-scale inter-query attention to exchange complimentary information. We achieve competitive performance on three widely used semantic segmentation datasets. In particular, on ADE20K validation set, our result with Swin-B backbone surpasses that of MaskFormer's with a much larger Swin-L backbone in both single-scale and multi-scale inference, achieving 54.1 mIoU and 55.7 mIoU respectively. Using a Swin-L backbone, we achieve single-scale 56.1 mIoU and multi-scale 57.4 mIoU, obtaining state-of-the-art performance on the dataset. Extensive experiments on three widely used semantic segmentation datasets verify the effectiveness of our proposed method

Distribution and origination of zinc contamination in newly reclaimed heterogeneous dredger fills: Field investigation and numerical simulation.

Heavy metal elements, including Zn, Cd, As, Ni, Cu, Pb and Cr, were detected in soils (no deeper than 75 m) from newly reclaimed zones of Shanghai, China. The Zn concentration exceeded soil quality limits. The Zn contamination was tested in both dredger fills and sedimentary layers (①3-3, ②3, ④ and ⑤1-1). However, it was not detected in layer ⑤1-2-⑨. PCA and HCA analysis show that exogenous Zn probably was the contaminant source of dredger fills before the fills were dredged from the neighboring waters. Stochastic heterogeneity of the dredger fills affects the Zn-depollution remarkably. Numerical simulations show both acid precipitation and widespread drainage channels in the zones contributed to Zn-decrease in the dredger fills no deeper than 1.2 m. Acid rainstorms work better than acid constant precipitation in Zn-remediation for layers below 0.4 m. To remove Zn contamination in deep dredger fills, un-consolidation of the fills should be utilized

Dynamic Characteristics of Meteorological Drought and Its Impact on Vegetation in an Arid and Semi-Arid Region

Under the background of global climate warming, meteorological drought disasters have become increasingly frequent. Different vegetation types exhibit varying responses to drought, thus, exploring the heterogeneity of the impact of meteorological drought on vegetation is particularly important. In this study, we focused on Inner Mongolia (IM) as the research area and employed Standardized Precipitation Evapotranspiration Index (SPEI) and Vegetation Health Index (VHI) as meteorological drought and vegetation indices, respectively. The Breaks for Additive Seasons and Trend algorithm (BFAST) was utilized to reveal the dynamic characteristics of both meteorological drought and vegetation changes. Additionally, the Pixel-Based Trend Identification Method (PTIM) was employed to identify the trends of meteorological drought and vegetation during spring, summer, autumn, and the growing season. Subsequently, we analyzed the correlation between meteorological drought and vegetation growth. Finally, the response of vegetation growth to various climate factors was explored using the standardized multivariate linear regression method. The results indicated that: (1) During the study period, both SPEI and VHI exhibited a type of interrupted decrease. The meteorological drought was aggravated and the vegetation growth was decreased. (2) Deserts and grasslands exhibited higher sensitivity to meteorological drought compared to forests. The strongest correlation between SPEI-3 and VHI was observed in desert and grassland regions. In forest areas, the strongest correlation was found between SPEI-6 and VHI. (3) The r between severity of meteorological drought and status of vegetation growth was 0.898 (p < 0.01). Vegetation exhibits a more pronounced response to short-term meteorological drought events. (4) Evapotranspiration is the primary climatic driving factor in the IM. The findings of this study provide valuable insights for the rational utilization of water resources, the formulation of effective irrigation and replenishment policies, and the mitigation of the adverse impacts of meteorological drought disasters on vegetation growth in the IM