In fringe projection profilometry systems, accurately reconstructing 3D objects with varying surface reflectivity requires high dynamic range (HDR) imaging. However, the limited dynamic range of single-exposure cameras poses challenges for capturing HDR fringe patterns efficiently. This paper introduces a deep learning-based HDR structured light 3D reconstruction pipeline, comprising an HDR Fringe Generation Module and a Phase Calculation Module. The HDR Fringe Generation Module employs an end-to-end network with attention guidance and feature distillation to reconstruct HDR fringe images from short- and long-exposure low dynamic range (LDR) inputs. The Phase Calculation Module processes the phase information from HDR fringes to enable 3D reconstruction. On a metallic HDR dataset, the method achieved a phase error of 0.105, comparable to the 4-exposure 6-step Phase Shifting Profilometry (PSP) method (0.069), with only 8.3% of the projection time. Experimental results demonstrate the robustness of our approach under diverse object geometries, exposure levels, and challenging global illumination environments. In quantitative measurements, our method achieved accuracies of sub-50 $\mu $ m on ceramic spheres, flat plates and metal step object. Ablation experiments confirmed that feature distillation and attention module effectively enhance the HDR Fringe Generation Module, producing high-quality HDR fringe patterns critical for reconstructing objects with HDR surface reflectivity. Furthermore, we constructed an HDR imaging metal dataset comprising 1,700 samples of machined metal parts with diverse shapes, sizes, and materials, making it a benchmark in the field of HDR structured light measurement. Our method offers a general HDR imaging-based structured light 3D reconstruction approach, integrating the two modules into an efficient, end-to-end solution for objects with HDR reflective surfaces.

All-around, real-time navigation and sensing across the water environments by miniature soft robotics are promising, for their merits of small size, high agility and good compliance to the unstructured surroundings. In this paper, we propose and demonstrate a mantas-like soft aquatic robot which propels itself by flapping-fins using rolled dielectric elastomer actuators (DEAs) with bending motions. This robot exhibits fast-moving capabilities of swimming at 57mm/s or 1.25 body length per second (BL/s), skating on water surface at 64 mm/s (1.36 BL/s) and vertical ascending at 38mm/s (0.82 BL/s) at 1300 V, 17 Hz of the power supply. These results show the feasibility of adopting rolled DEAs for mesoscale aquatic robots with high motion performance in various water-related scenarios.

Automation testing is widely used in the functional testing of web applications. However, during the evolution of web applications, such web test scripts tend to break. It is essential to repair such broken test scripts to make regression testing run successfully. As manual repairing is time-consuming and expensive, researchers focus on automatic repairing techniques. Empirical study shows that the web element locator is the leading cause of web test breakages. Most existing repair techniques utilize Document Object Model attributes or visual appearances of elements to find their location but neglect their semantic information.

Aspect-based Sentiment Classification (ASC) task is a challenge in Natural Language Processing (NLP) and is especially important for fields that require detailed analysis like finance. It aims to identify the sentiment polarity of specific aspects in sentences. In addition to tweets and posts directly related to finance, news from such as restaurants and e-commerce may also indirectly affect its stock prices. In previous approaches, attention-based neural network models were mostly adopted to implicitly connect aspects with opinion words for better aspect representations. However, due to the complexity of language and the presence of multiple aspects in a single sentence, these existing models often confuse connections. To tackle this problem, we propose a model named GAS-CL which encodes syntactical structure into aspect representations and refines it with a contrastive loss. Experiments on several datasets confirm that our approach can have better aspect representations and achieve a significant improvement.