A Novel Image Segmentation Algorithm Based on Graph Cut Optimization Problem

Image segmentation, a fundamental task in computer vision, has been widely used in recent years in many fields. Dealing with the graph cut optimization problem obtains the image segmentation results. In this study, a novel algorithm with weighted graphs was constructed to solve the image segmentation problem through minimization of an energy function. A binary vector of the segmentation label was defined to describe both the foreground and the background of an image. To demonstrate the effectiveness of our proposed method, four various types of images were used to construct a series of experiments. Experimental results indicate that compared with other methods, the proposed algorithm can effectively promote the quality of image segmentation under three performance evaluation metrics, namely, misclassification error rate, rate of the number of background pixels, and the ratio of the number of wrongly classified foreground pixels

Synergy of Pd atoms and oxygen vacancies on In₂O₃ for methane conversion under visible light

Methane (CH4) oxidation to high value chemicals under mild conditions through photocatalysis is a sustainable and appealing pathway, nevertheless confronting the critical issues regarding both conversion and selectivity. Herein, under visible irradiation (420 nm), the synergy of palladium (Pd) atom cocatalyst and oxygen vacancies (OVs) on In2O3 nanorods enables superior photocatalytic CH4 activation by O2. The optimized catalyst reaches ca. 100 μmol h-1 of C1 oxygenates, with a selectivity of primary products (CH3OH and CH3OOH) up to 82.5%. Mechanism investigation elucidates that such superior photocatalysis is induced by the dedicated function of Pd single atoms and oxygen vacancies on boosting hole and electron transfer, respectively. O2 is proven to be the only oxygen source for CH3OH production, while H2O acts as the promoter for efficient CH4 activation through ·OH production and facilitates product desorption as indicated by DFT modeling. This work thus provides new understandings on simultaneous regulation of both activity and selectivity by the synergy of single atom cocatalysts and oxygen vacancies

Long-Period Fiber Grating Based on Side-Polished Optical Fiber and Its Sensing Application

A novel side-polished long-period fiber grating (LPFG) sensor was proposed and experimentally validated. Side-polished can provide a stronger evanescent field than traditional grating and bring superior sensitivity. The greater the side-polished depth, the higher the refractive index (RI) sensitivity. When d = 44,μm , the RI sensitivity reached 466.85 nm/RIU in the range of 1.3330-1.3580, which is fourfold higher than the LPFG prepared by the electric-arc discharge (EAD) method. A graphene oxide (GO) nano-film is coated on the LPFG to make it realize high sensitivity relative humidity (RH) sensing. Humidity sensitivity reached -0.193 nm/%RH in the range of 40%-80% RH. In addition, side-polished breaks the symmetry of the distribution of the cross-sectional light field, which determines the ability to achieve vector curvature measurement. It shows good sensing performance in the same/opposite bending direction as the side polished surface. When the input light polarization is 90°, the average sensitivity reaches 5.03 and -5.9 nmm-1 in the range of 0-19.67 m-1 , respectively. This strongly indicates that the fabricated sensors show high sensitivity, low-cost materials, and robust performance and break the limitations of the EDA method to prepare gratings, which have good application potential for biomedicine and the field of construction

A Survey on Evaluation of Large Language Models

Large language models (LLMs) are gaining increasing popularity in both academia and industry, owing to their unprecedented performance in various applications. As LLMs continue to play a vital role in both research and daily use, their evaluation becomes increasingly critical, not only at the task level, but also at the society level for better understanding of their potential risks. Over the past years, significant efforts have been made to examine LLMs from various perspectives. This paper presents a comprehensive review of these evaluation methods for LLMs, focusing on three key dimensions: what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide an overview from the perspective of evaluation tasks, encompassing general natural language processing tasks, reasoning, medical usage, ethics, educations, natural and social sciences, agent applications, and other areas. Secondly, we answer the `where' and `how' questions by diving into the evaluation methods and benchmarks, which serve as crucial components in assessing performance of LLMs. Then, we summarize the success and failure cases of LLMs in different tasks. Finally, we shed light on several future challenges that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to researchers in the realm of LLMs evaluation, thereby aiding the development of more proficient LLMs. Our key point is that evaluation should be treated as an essential discipline to better assist the development of LLMs. We consistently maintain the related open-source materials at: https://github.com/MLGroupJLU/LLM-eval-survey.Comment: 23 page