Visualization Challenges of Virtual Reality 3D Images in New Media Environments

This paper proposes a three-dimensional image visualization process to face-drawing three-dimensional image reconstruction algorithm to obtain the data field with three-dimensional space, using color adjustment based on global color correction and local Poisson fusion to optimize the splicing seams between the texture color blocks and updating the visualization technology of three-dimensional images. Divide the digital display design and create a virtual reality visualization display using 3D modeling in combination with the new media environment. Propose design steps to visualize virtual reality three-dimensional images in the new media environment by combining the key algorithms of three-dimensional image visualization from the previous section. Combined with the application of new media displaying 3D images, the concept of artifact shape in reconstructed images is proposed to analyze the quality of 3D image reconstruction by taking the Herman model and Sheep-Logan model as the research object. Test experiments are conducted to examine the visual impact of texture mapping algorithms, and different sampling intervals are set to measure the drawing time of 3D reconstruction. For the data size and number of pictures of other organizations, the processing time of the 3D image reconstruction algorithm based on surface drawing is no more than 2s. The denser the sampling points are, the higher the degree of fitting, the more complete the preservation of isosurface information is, the finer the effect of 3D reconstruction, and the higher the quality of the image

A free-standing, phase-change liquid metal mold for 3D flexible microfluidics

This paper describes a method to fabricate the 3D microfluidic channel using the free-standing, phase-change gallium mold. Three approaches to prepare the free-standing gallium molds are described. The solid metal framework is strong enough to stand against the gravity. After casting, the embedded gallium molds are melted from solid to liquid and then extracted from the encasing elastomer to form the 3D microfluidic channel due to the phase change property. Since this method is compatible with many encasing materials (e.g., elastomers, gels, resins, ceramics), the encasing materials will bring novel functionalities to the microfluidic chip. Two proof-of-concept experiments have been demonstrated. Firstly, a soft, sticky, on-skin microfluidic cooler is developed based on this method to deliver the focused, minimal invasive cooling power at arbitrary skins of human body with temperature control. Secondly, an ultra-stretchable viscoelastic microchannel with the ultra-soft base is fabricated to continuously tune the viscoelastic particle focusing with a large dynamic range. This proposed technique suggests the new possibilities for the development of lab-on-a-chip applications

Exploring Structured Semantic Prior for Multi Label Recognition with Incomplete Labels

Multi-label recognition (MLR) with incomplete labels is very challenging. Recent works strive to explore the image-to-label correspondence in the vision-language model, \ie, CLIP, to compensate for insufficient annotations. In spite of promising performance, they generally overlook the valuable prior about the label-to-label correspondence. In this paper, we advocate remedying the deficiency of label supervision for the MLR with incomplete labels by deriving a structured semantic prior about the label-to-label correspondence via a semantic prior prompter. We then present a novel Semantic Correspondence Prompt Network (SCPNet), which can thoroughly explore the structured semantic prior. A Prior-Enhanced Self-Supervised Learning method is further introduced to enhance the use of the prior. Comprehensive experiments and analyses on several widely used benchmark datasets show that our method significantly outperforms existing methods on all datasets, well demonstrating the effectiveness and the superiority of our method. Our code will be available at https://github.com/jameslahm/SCPNet.Comment: Accepted by IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 202

Who is Here: Location Aware Face Recognition

Abstract Face recognition has many challenges. For instance, the illumination, various facial expression and different viewpoints add difficulties to identify the same person from a bunch of images. Searching over a huge set of images will only amplify such difficulties. We introduce the location aware face recognition framework for mobile-taken photos to alleviate the hardness. With the help of location sensor on the mobile devices, we collect images with location information. We propose an algorithm to reduce the search space of face recognition and therefore achieve better accuracy. Photos are clustered by locations on the server. Each location is then associated with a face classifier. Every client can send a "Who is Here" type query to the server by uploading an image with the location. The algorithm on the server will search over the given location and identify the person on the image. Experiments are conducted on mobile devices. The results are quite promising that higher accuracy is achieved and the query can be answered in near real-time

Research on the influence factors of accident severity of new energy vehicles based on ensemble learning

With the deepening of the concept of green, low-carbon, and sustainable development, the continuous growth of the ownership of new energy vehicles has led to increasing public concerns about the traffic safety issues of these vehicles. In order to conduct research on the traffic safety of new energy vehicles, three sampling methods, namely, Synthetic Minority Over-sampling Technique (SMOTE), Edited Nearest Neighbours (ENN), and SMOTE-ENN hybrid sampling, were employed, along with cost-sensitive learning, to address the problem of imbalanced data in the UK road traffic accident dataset. Three algorithms, eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost), were selected for modeling work. Lastly, the evaluation criteria used for model selection were primarily based on G-mean, with AUC and accuracy as secondary measures. The TreeSHAP method was applied to explain the interaction mechanism between accident severity and its influencing factors in the constructed models. The results showed that LightGBM had a more stable overall performance and higher computational efficiency. XGBoost demonstrated a balanced combination of computational efficiency and model performance. CatBoost, however, was more time-consuming and showed less stability with different datasets. Studies have found that people using fewer protective means of transportation (bicycles, motorcycles) and vulnerable groups such as pedestrians are susceptible to serious injury and death

Tunable quantum dots in monolithic Fabry-Perot microcavities for high-performance single-photon sources

Cavity-enhanced single quantum dots (QDs) are the main approach towards ultra-high-performance solid-state quantum light sources for scalable photonic quantum technologies. Nevertheless, harnessing the Purcell effect requires precise spectral and spatial alignment of the QDs' emission with the cavity mode, which is challenging for most cavities. Here we have successfully integrated miniaturized Fabry-Perot microcavities with a piezoelectric actuator, and demonstrated a bright single photon source derived from a deterministically coupled QD within this microcavity. Leveraging the cavity-membrane structures, we have achieved large spectral-tunability via strain tuning. On resonance, we have obtained a high Purcell factor of approximately 9. The source delivers single photons with simultaneous high extraction efficiency of 0.58, high purity of 0.956(2) and high indistinguishability of 0.922(4). Together with a small footprint, our scheme facilitates the scalable integration of indistinguishable quantum light sources on-chip, and therefore removes a major barrier to the solid-state quantum information platforms based on QDs.Comment: 12 pages, 4 figure

Development and application of an amplified luminescent proximity homogeneous assay-linked immunosorbent assay for the accurate quantification of kidney injury molecule-1

Background: Kidney injury molecule-1 (Kim-1), a specific marker of kidney injury, is usually not expressed in normal kidneys or at very low levels but is highly expressed in injured renal tubular epithelial cells until the damaged cells recover completely. Therefore, we aimed to develop an efficient and highly sensitive assay to accurately quantify Kim-1 levels in human serum and urine.Methods: In this study, a novel immunoassay was developed and named amplified luminescent proximity homogeneous assay-linked immunosorbent assay (AlphaLISA). Anti-Kim-1 antibodies can be directly coupled to carboxyl-modified donor and acceptor beads for the rapid detection of Kim-1 by double-antibody sandwich method. Serum and urine samples for Kim-1 measurements were obtained from 129 patients with nephropathy and 17 healthy individuals.Results: The linear range of Kim-1 detected by AlphaLISA was 3.83–5000 pg/mL, the coefficients of variation of intra-assay and inter-assay batches were 3.36%–4.71% and 5.61%–11.84%, respectively, and the recovery rate was 92.31%–99.58%. No cross reactions with neutrophil gelatinase-associated lipocalin, liver-type fatty acid binding protein, and matrix metalloproteinase-3 were observed. A good correlation (R2 = 0.9086) was found between the findings of Kim-1-TRFIA and Kim-AlphaLISA for the same set of samples. In clinical trials, both serum and urine Kim-1 levels were significantly higher in patients with nephropathy than in healthy individuals, especially in patients with acute kidney injury. Furthermore, serum Kim-1 was superior to urinary Kim-1 in distinguishing between patients with nephropathy and healthy individuals.Conclusion: The developed Kim-1-AlphaLISA is highly efficient, precise, and sensitive, and it is suitable for the rapid detection of patients with acute kidney injury

YOLOv8-ACU: improved YOLOv8-pose for facial acupoint detection

IntroductionAcupoint localization is integral to Traditional Chinese Medicine (TCM) acupuncture diagnosis and treatment. Employing intelligent detection models for recognizing facial acupoints can substantially enhance localization accuracy.MethodsThis study introduces an advancement in the YOLOv8-pose keypoint detection algorithm, tailored for facial acupoints, and named YOLOv8-ACU. This model enhances acupoint feature extraction by integrating ECA attention, replaces the original neck module with a lighter Slim-neck module, and improves the loss function for GIoU.ResultsThe YOLOv8-ACU model achieves impressive accuracy, with an [email protected] of 97.5% and an [email protected]–0.95 of 76.9% on our self-constructed datasets. It also marks a reduction in model parameters by 0.44M, model size by 0.82 MB, and GFLOPs by 9.3%.DiscussionWith its enhanced recognition accuracy and efficiency, along with good generalization ability, YOLOv8-ACU provides significant reference value for facial acupoint localization and detection. This is particularly beneficial for Chinese medicine practitioners engaged in facial acupoint research and intelligent detection