Nearly diffraction-limited picosecond pulse amplification from LMA fluoride fiber at 2.8 µm.

We demonstrated the generation of a nearly diffraction-limited picosecond pulse from a large-mode-area (LMA) fluoride fiber amplifier. Seeded with a mode-locked fiber oscillator at 2.8 µm, the LMA Er:ZBLAN fiber amplifier delivered the pulse of 16 µJ with a duration of 70 ps at 5 kHz. The nearly diffraction-limited beam was obtained from the 50 µm LMA fiber using the fundamental mode excitation technique, with a measured M2 value of 1.25 for x axis and 1.27 for y axis, respectively. This high-beam-quality high-energy picosecond fiber-based system of 2.8 µm exhibits a great potential in the high-precision biomaterial processing.

LPN: Label-Enhanced Prototypical Network for Legal Judgment Prediction.

As one of the most critical tasks in legal artificial intelligence, legal judgment prediction (LJP) has garnered growing attention, especially in the civil law system. However, current methods often overlook the challenge of imbalanced label distributions, treating each label with equal importance, which can lead the model to be biased toward labels with high frequency. In this paper, we propose a label-enhanced prototypical network (LPN) suitable for LJP, that adopts a strategy of uniform encoding and separate decoding. Specifically, LPN adopts a multi-scale convolutional neural network to uniformly encode case factual description to capture long-distance features of the document. At the decoding end, a prototypical network incorporating label semantic features is used to guide the learning of prototype representations of high-frequency and low-frequency labels, respectively. At the same time, we also propose a prototype-prototype loss to optimize the prototypical representation. We conduct extensive experiments on two real datasets and show that our proposed method effectively improves the performance of LJP, with an average F1 of 1.23% and 1.13% higher than the state-of-the-art model on two subtasks, respectively.

A Depth Evidence Score Fusion Algorithm for Chinese Medical Intelligence Question Answering System.

Question answering (QA) system is becoming the focus of the research in medical health in terms of providing fleetly accurate answers to users. Numerous traditional QA systems are faced to simple factual questions and do not obtain accurate answers for complex questions. In order to realize the intelligent QA system for disease diagnosis and treatment in medical informationization, in this paper, we propose a depth evidence score fusion algorithm for Chinese Medical Intelligent Question Answering System, which can measure the text information in many algorithmic ways and ensure that the QA system outputs accurately the optimal candidate answer. At the semantic level, a new text semantic evidence score based on Word2vec is proposed, which can calculate the semantic similarity between texts. Experimental results on the medical text corpus show that the depth evidence score fusion algorithm has better performance in the evidence-scoring module of the intelligent QA system.