Optimizing Deep Neural Networks for Single Cell Segmentation

Analysis of live-cell imaging experiments at the resolution of single cells provides exciting insights into the inner workings of biological systems. Advances in biological imaging and computer vision allow for segmentation of natural images with a high degree of accuracy. However, automation of the segmentation pipeline at the single cell resolution remains a challenging task. Complex deep learning models require large, well-annotated datasets that are rarely available in biology. In this research, we explore various methods that optimize state of the art deep learning frameworks, despite limited resources. We trained a large permutation of models to quantify their capacity and to measure the effects of temporal information, spatial awareness and transfer learning on model performance. We find that, although training set size is most impactful in improving model accuracy, we can leverage techniques like spatial awareness and transfer learning to compromise for the lack of data. These insights show that, with an abundance of data, light-weight models can be as performant as their heavy-weight counterparts in cellular analysis

Exploring Fashion Geometric Pattern Design Process Using a Semiotic Method

In recent years, the rapid growth of the economy and increasing degree of globalization have led to a continuous increase in demand for cross-cultural design. To meet this demand, the academic community and the fashion industry have continuously analyzed and developed methods for providing cross-cultural fashion pattern design. This study explores a symbol-based fashion pattern design method. Several commonly used geometric patterns in fashion pattern design were selected as research objects, and a method of symbolizing patterns in fashion pattern design was proposed to suit cross-cultural design. When redesigning fashion geometric patterns, typical patterns are symbolized. The results showed that the processed samples exhibited good adaptability for design applications. Therefore, this study indicates that the symbolic method can be applied to fashion pattern design, and this design method can be further systematized to achieve cross-cultural fashion design

Fatigue Analysis for Void Repair of Cement Concrete Pavement with Under Slab by Polymer Grouting

After the appearing of voids beneath cement concrete slabs, the pavement loses a continuous and uniform lower support structure, and the stress state of the road panel is extremely unfavorable. The polymer grouting repair is timesaving, efficient and pollution-free. In order to verify the performance improvement and fatigue damage evolution of cement concrete pavement before and after grouting repair, a material damage constitutive model was established. The UMAT subprogram was introduced into the finite element software ABAQUS to analyze the structure under the action of moving cyclic loading, stress response and fatigue damage evolution process before and after regional grouting repair. The results show that the Mises stress and vertical displacement of the grouting repairing slab are very close to the normal state, which indicates that the grouting repair has a prominent influence on the bottom void of the slab. With the rise of loading time, the fatigue damage of the pavement structure is increasing, but the trend is gradually reduced, and the number of load times and the degree of fatigue damage are nonlinear. From the long-term cyclic loading and comprehensive analysis of the construction period, the polymer grouting repair is better than cementitious grout

Radar Enlighten the Dark: Enhancing Low-Visibility Perception for Automated Vehicles with Camera-Radar Fusion

Sensor fusion is a crucial augmentation technique for improving the accuracy and reliability of perception systems for automated vehicles under diverse driving conditions. However, adverse weather and low-light conditions remain challenging, where sensor performance degrades significantly, exposing vehicle safety to potential risks. Advanced sensors such as LiDARs can help mitigate the issue but with extremely high marginal costs. In this paper, we propose a novel transformer-based 3D object detection model "REDFormer" to tackle low visibility conditions, exploiting the power of a more practical and cost-effective solution by leveraging bird's-eye-view camera-radar fusion. Using the nuScenes dataset with multi-radar point clouds, weather information, and time-of-day data, our model outperforms state-of-the-art (SOTA) models on classification and detection accuracy. Finally, we provide extensive ablation studies of each model component on their contributions to address the above-mentioned challenges. Particularly, it is shown in the experiments that our model achieves a significant performance improvement over the baseline model in low-visibility scenarios, specifically exhibiting a 31.31% increase in rainy scenes and a 46.99% enhancement in nighttime scenes.The source code of this study is publicly available