Deep Learning-enabled Spatial Phase Unwrapping for 3D Measurement

In terms of 3D imaging speed and system cost, the single-camera system projecting single-frequency patterns is the ideal option among all proposed Fringe Projection Profilometry (FPP) systems. This system necessitates a robust spatial phase unwrapping (SPU) algorithm. However, robust SPU remains a challenge in complex scenes. Quality-guided SPU algorithms need more efficient ways to identify the unreliable points in phase maps before unwrapping. End-to-end deep learning SPU methods face generality and interpretability problems. This paper proposes a hybrid method combining deep learning and traditional path-following for robust SPU in FPP. This hybrid SPU scheme demonstrates better robustness than traditional quality-guided SPU methods, better interpretability than end-to-end deep learning scheme, and generality on unseen data. Experiments on the real dataset of multiple illumination conditions and multiple FPP systems differing in image resolution, the number of fringes, fringe direction, and optics wavelength verify the effectiveness of the proposed method.Comment: 26 page

Pesticide control, physical control, or biological control? How to manage forest pests and diseases more effectively

The frequent occurrence of forest diseases and insect pests has a significant impact on the forest ecosystem. The government needs to take measures to protect the forest ecosystem. The common management modes for forest pests and diseases include pesticide control, physical control, and biological control. In the process of governance, governments need to consider not only cost-effectiveness but also the impact on the ecosystem. In this article, the differential game model under these three modes is constructed, and the equilibrium results are compared and analyzed. Finally, the research conclusion is drawn that under the biological control mode, the income generated by the unit control quantity is inversely proportional to the balanced control quantity. However, under pesticide control and physical control modes, the revenue generated by the unit control quantity is proportional to the balanced control quantity. At the same time, under the biological control mode, the unit governance cost is proportional to the balanced control quantity. Under the pesticide control and physical control modes, the unit control cost is inversely proportional to the balanced control quantity. Social forces tend to adopt pesticide control. The government prefers physical control

How to reduce the impact of contaminated seafood on public health with the discharge of Fukushima nuclear wastewater

As wastewater from the Fukushima nuclear disaster continues to drain into the sea, the supply system for healthy seafood is being seriously challenged. To protect public health, it is necessary to restructure the seafood supply mode. The seafood supply mode is divided into the original mode, land farming mode, and strengthen monitoring mode. To derive the applicable scope of the various modes of the seafood supply chain and to provide recommendations for the safety and sustainability of seafood supply chains for governments and enterprises, three differential game models are constructed in this study. Then, the equilibrium results obtained by the models are compared and analyzed. Based on the findings, the health impact of seafood pollution is relatively small, and the government tends to choose the original supply mode. As the health impact of seafood grows, governments tend to prefer land-based farming. The social benefit to the government is directly proportional to the monitoring cost of seafood. To protect public health, enterprises tend to choose the mode of strengthen monitoring if the proportion of unqualified seafood is low. In addition, if sea products show a high degree of adaptation to the land environment, they tend to choose land farming

SOOD: Towards Semi-Supervised Oriented Object Detection

Semi-Supervised Object Detection (SSOD), aiming to explore unlabeled data for boosting object detectors, has become an active task in recent years. However, existing SSOD approaches mainly focus on horizontal objects, leaving multi-oriented objects that are common in aerial images unexplored. This paper proposes a novel Semi-supervised Oriented Object Detection model, termed SOOD, built upon the mainstream pseudo-labeling framework. Towards oriented objects in aerial scenes, we design two loss functions to provide better supervision. Focusing on the orientations of objects, the first loss regularizes the consistency between each pseudo-label-prediction pair (includes a prediction and its corresponding pseudo label) with adaptive weights based on their orientation gap. Focusing on the layout of an image, the second loss regularizes the similarity and explicitly builds the many-to-many relation between the sets of pseudo-labels and predictions. Such a global consistency constraint can further boost semi-supervised learning. Our experiments show that when trained with the two proposed losses, SOOD surpasses the state-of-the-art SSOD methods under various settings on the DOTA-v1.5 benchmark. The code will be available at https://github.com/HamPerdredes/SOOD.Comment: Accepted to CVPR 2023. Code will be available at https://github.com/HamPerdredes/SOO

An In-Context Schema Understanding Method for Knowledge Base Question Answering

The Knowledge Base Question Answering (KBQA) task aims to answer natural language questions based on a given knowledge base. As a kind of common method for this task, semantic parsing-based ones first convert natural language questions to logical forms (e.g., SPARQL queries) and then execute them on knowledge bases to get answers. Recently, Large Language Models (LLMs) have shown strong abilities in language understanding and may be adopted as semantic parsers in such kinds of methods. However, in doing so, a great challenge for LLMs is to understand the schema of knowledge bases. Therefore, in this paper, we propose an In-Context Schema Understanding (ICSU) method for facilitating LLMs to be used as a semantic parser in KBQA. Specifically, ICSU adopts the In-context Learning mechanism to instruct LLMs to generate SPARQL queries with examples. In order to retrieve appropriate examples from annotated question-query pairs, which contain comprehensive schema information related to questions, ICSU explores four different retrieval strategies. Experimental results on the largest KBQA benchmark, KQA Pro, show that ICSU with all these strategies outperforms that with a random retrieval strategy significantly (from 12\% to 78.76\% in accuracy)

ProtoEM: A Prototype-Enhanced Matching Framework for Event Relation Extraction

Event Relation Extraction (ERE) aims to extract multiple kinds of relations among events in texts. However, existing methods singly categorize event relations as different classes, which are inadequately capturing the intrinsic semantics of these relations. To comprehensively understand their intrinsic semantics, in this paper, we obtain prototype representations for each type of event relation and propose a Prototype-Enhanced Matching (ProtoEM) framework for the joint extraction of multiple kinds of event relations. Specifically, ProtoEM extracts event relations in a two-step manner, i.e., prototype representing and prototype matching. In the first step, to capture the connotations of different event relations, ProtoEM utilizes examples to represent the prototypes corresponding to these relations. Subsequently, to capture the interdependence among event relations, it constructs a dependency graph for the prototypes corresponding to these relations and utilized a Graph Neural Network (GNN)-based module for modeling. In the second step, it obtains the representations of new event pairs and calculates their similarity with those prototypes obtained in the first step to evaluate which types of event relations they belong to. Experimental results on the MAVEN-ERE dataset demonstrate that the proposed ProtoEM framework can effectively represent the prototypes of event relations and further obtain a significant improvement over baseline models.Comment: Work in progres