Non-local Attention Optimized Deep Image Compression

This paper proposes a novel Non-Local Attention Optimized Deep Image Compression (NLAIC) framework, which is built on top of the popular variational auto-encoder (VAE) structure. Our NLAIC framework embeds non-local operations in the encoders and decoders for both image and latent feature probability information (known as hyperprior) to capture both local and global correlations, and apply attention mechanism to generate masks that are used to weigh the features for the image and hyperprior, which implicitly adapt bit allocation for different features based on their importance. Furthermore, both hyperpriors and spatial-channel neighbors of the latent features are used to improve entropy coding. The proposed model outperforms the existing methods on Kodak dataset, including learned (e.g., Balle2019, Balle2018) and conventional (e.g., BPG, JPEG2000, JPEG) image compression methods, for both PSNR and MS-SSIM distortion metrics

A well-balanced lattice Boltzmann model for binary fluids based on the incompressible phase-field theory

Spurious velocities arising from the imperfect offset of the undesired term at the discrete level are frequently observed in numerical simulations of equilibrium multiphase flow systems using the lattice Boltzmann equation (LBE) method. To capture the physical equilibrium state of two-phase fluid systems and eliminate spurious velocities, a well-balanced LBE model based on the incompressible phase-field theory is developed. In this model, the equilibrium distribution function for the Cahn-Hilliard (CH) equation is designed by treating the convection term as a source to avoid the introduction of undesired terms, enabling achievement of possible discrete force balance. Furthermore, this approach allows for the attainment of a divergence-free velocity field, effectively mitigating the impact of artificial compression effects and enhancing numerical stability. Numerical tests, including a flat interface problem, a stationary droplet, and the coalescence of two droplets, demonstrate the well-balanced properties and improvements in the stability of the present model

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

UFO-Net: A Linear Attention-Based Network for Point Cloud Classification

Three-dimensional point cloud classification tasks have been a hot topic in recent years. Most existing point cloud processing frameworks lack context-aware features due to the deficiency of sufficient local feature extraction information. Therefore, we designed an augmented sampling and grouping module to efficiently obtain fine-grained features from the original point cloud. In particular, this method strengthens the domain near each centroid and makes reasonable use of the local mean and global standard deviation to extract point cloud’s local and global features. In addition to this, inspired by the transformer structure UFO-ViT in 2D vision tasks, we first tried to use a linearly normalized attention mechanism in point cloud processing tasks, investigating a novel transformer-based point cloud classification architecture UFO-Net. An effective local feature learning module was adopted as a bridging technique to connect different feature extraction modules. Importantly, UFO-Net employs multiple stacked blocks to better capture feature representation of the point cloud. Extensive ablation experiments on public datasets show that this method outperforms other state-of-the-art methods. For instance, our network performed with 93.7% overall accuracy on the ModelNet40 dataset, which is 0.5% higher than PCT. Our network also achieved 83.8% overall accuracy on the ScanObjectNN dataset, which is 3.8% better than PCT

Intelligent Quality Control of Surface Defects in Fabrics: A Comprehensive Research Progress

Fabric defect detection is an important part of quality control in textile enterprises. The use of computer vision inspection technology in the textile industry is key to achieving intelligent manufacturing. This study sought to determine the progress made and future research directions in intelligent fabric surface defect detection by comprehensively reviewing published literature in terms of algorithms, datasets, and detection systems. Initially, the detection methods are classified as traditional and learning-based methods. The traditional methods are subdivided into model, spectral, statistical, and structural approaches. Learning-based methods are categorized into classical machine learning methods and deep learning methods. The principles, model performance, detection rate, real-time performance, and applicability of deep learning methods are highlighted and compared. In addition, the strengths and weaknesses of all the approaches are elaborated. The use of fabric defect datasets and deep learning frameworks is analyzed. Public datasets and commonly used frameworks are collated and organized. The application of existing fabric inspection systems on the market is outlined. Fabric defect types are systematically named and analyzed. Finally, future research directions are discussed to provide guidance for researchers in related fields

Effects of textured insoles and elastic braces on dynamic stability in patients with functional ankle instability

Abstract Background Functional ankle instability (FAI) is a common condition that affects individuals who have experienced previous ankle sprains. Textured insoles and elastic ankle braces have been previously used as interventions to improve stability in FAI patients. However, the optimal combination of these interventions has not been fully explored. The objective of this study was to investigate the effects of different types of textured insoles and elastic ankle braces on the dynamic stability of individuals diagnosed with FAI. Methods The study involved 18 FAI patients who performed single-leg landing tasks with and without wearing an eight-band elastic ankle brace while wearing textured insoles with protrusion heights of 0 mm, 1 mm, and 2 mm. The dynamic posture stability index (DPSI) and its components in the anterior-posterior (APSI), mediolateral (MLSI) and vertical (VSI) directions were calculated from the ground reaction force collected from the Kistler force plate during the first three seconds of the landing tasks. Results A significant interaction was found between textured insole type and ankle brace for DPSI (P = 0.026), APSI (P = 0.001), and VSI (P = 0.021). However, no significant interaction was observed for MLSI (P = 0.555). With elastic ankle braces, textured insoles with 1-mm protrusions significantly enhanced anterior-posterior, mediolateral, vertical, and overall stability compared to textured insoles with no and 2 mm protrusions (P < 0.05). Without elastic ankle braces, textured insoles with 1-mm protrusions significantly improved the anterior-posterior (P = 0.012) and overall stability (P = 0.014) of FAI patients compared to smooth insoles. Conclusions The combination of textured insoles with 1-mm protrusion heights and an elastic ankle brace could enhance the dynamic stability of individuals with FAI, potentially mitigating the risk of ankle sprains