中医临床诊疗术语的翻译方法

El objetivo de este estudio es realizar un análisis de los métodos de traducción sobre los términos clínicos de la MTC. En el proceso de traducción, se utiliza el análisis cuantitativo y cualitativo para traducir e investigar los términos distintivos de la MTC según las partes de enfermedades, síntomas y métodos terapéuticos, también se resume las características lingüísticas de los términos de la MTC, las dificultades de traducción, los métodos y las estrategias de traducción en diferentes contextos. En el proceso de traducción de los términos de la MTC, se seguirá estrictamente las reglas de traducción: la veracidad, precisión y claridad para realizar la traducción y el estudio de los términos, es importante mantener el estilo lingüístico y las características del texto original, al mismo tiempo se debe satisfacer los hábitos de lectura del lector y ajustarse a las expresiones de lengua meta. Se han estudiado las siguientes características de la terminología de la MTC: 1) polisemia; 2) uso de abreviaturas; 3) metáfora; 4) no hay sujeto y uso frecuente de los verbos; 5) palabras poco comunes y chino antiguo. Debido a la incertidumbre de la terminología de la MTC, es necesario utilizar diferentes métodos de traducción para las distintas características de los términos de la MTC. Al final, este estudio confirma que el sistema lingüístico y la estructura gramatical de la MTC son complejos, por lo que los traductores deben conocer la esencia y las características de la terminología para poder traducir y expresar su significado con fluidez. No existe un método de traducción concreto (traducción literal o traducción libre) que se utilice siempre en la traducción de la terminología de la MTC, y los traductores pueden utilizar diversos métodos de traducción que tengan en cuenta las características de la terminología y el contexto para realizar “fidelidad, expresividad, elegancia” la finalidad del texto traducido该项研究的目的是对中医临床诊疗术语的翻译方法进行分析，用定量分 析和定性分析的方法对疾病，证候，治法这三个部分的中医特色术语进行翻译 和研究进而总结出中医术语的语言特点，翻译难点以及在不同语境之下的翻译 方法和策略。 在中医术语翻译过程中，将严格遵照翻译准则：真实性，准确性和简洁 性来进行术语翻译和研究，在保持原文的语言风格和特点的同时也要满足读者 的阅读习惯和符合目的语的表达方式。经研究中医术语有以下几个特点: 1)一 词多义；2) 缩略词；3) 隐喻；4) 无主语和惯用动词；5) 生僻字和古汉语。 由于中医术语具有不确定性，因此针对不同特点的中医术语则需要使用不同的 翻译方法。 最后，本项研究证实，中医的语言体系和语法结构较为复杂，因此译者 必须熟悉术语的本质和特点以确保能流利地翻译和表达出术语的含义。中医术 语的翻译并不存在一直使用某一种特定的翻译方法（直译或意译），译者可结 合术语自身的特点和文本的语境来使用多种译法，使译文实现到“信达雅”的 目标。Máster Universitario en Comunicación Intercultural, Interpretación y Traducción en los Servicios Públicos. Especialidad en chi-esp (M196

Incorporating Intra-Class Variance to Fine-Grained Visual Recognition

Fine-grained visual recognition aims to capture discriminative characteristics amongst visually similar categories. The state-of-the-art research work has significantly improved the fine-grained recognition performance by deep metric learning using triplet network. However, the impact of intra-category variance on the performance of recognition and robust feature representation has not been well studied. In this paper, we propose to leverage intra-class variance in metric learning of triplet network to improve the performance of fine-grained recognition. Through partitioning training images within each category into a few groups, we form the triplet samples across different categories as well as different groups, which is called Group Sensitive TRiplet Sampling (GS-TRS). Accordingly, the triplet loss function is strengthened by incorporating intra-class variance with GS-TRS, which may contribute to the optimization objective of triplet network. Extensive experiments over benchmark datasets CompCar and VehicleID show that the proposed GS-TRS has significantly outperformed state-of-the-art approaches in both classification and retrieval tasks.Comment: 6 pages, 5 figure

Learning Gait Representation from Massive Unlabelled Walking Videos: A Benchmark

Gait depicts individuals' unique and distinguishing walking patterns and has become one of the most promising biometric features for human identification. As a fine-grained recognition task, gait recognition is easily affected by many factors and usually requires a large amount of completely annotated data that is costly and insatiable. This paper proposes a large-scale self-supervised benchmark for gait recognition with contrastive learning, aiming to learn the general gait representation from massive unlabelled walking videos for practical applications via offering informative walking priors and diverse real-world variations. Specifically, we collect a large-scale unlabelled gait dataset GaitLU-1M consisting of 1.02M walking sequences and propose a conceptually simple yet empirically powerful baseline model GaitSSB. Experimentally, we evaluate the pre-trained model on four widely-used gait benchmarks, CASIA-B, OU-MVLP, GREW and Gait3D with or without transfer learning. The unsupervised results are comparable to or even better than the early model-based and GEI-based methods. After transfer learning, our method outperforms existing methods by a large margin in most cases. Theoretically, we discuss the critical issues for gait-specific contrastive framework and present some insights for further study. As far as we know, GaitLU-1M is the first large-scale unlabelled gait dataset, and GaitSSB is the first method that achieves remarkable unsupervised results on the aforementioned benchmarks. The source code of GaitSSB will be integrated into OpenGait which is available at https://github.com/ShiqiYu/OpenGait

Capacity Constrained Influence Maximization in Social Networks

Influence maximization (IM) aims to identify a small number of influential individuals to maximize the information spread and finds applications in various fields. It was first introduced in the context of viral marketing, where a company pays a few influencers to promote the product. However, apart from the cost factor, the capacity of individuals to consume content poses challenges for implementing IM in real-world scenarios. For example, players on online gaming platforms can only interact with a limited number of friends. In addition, we observe that in these scenarios, (i) the initial adopters of promotion are likely to be the friends of influencers rather than the influencers themselves, and (ii) existing IM solutions produce sub-par results with high computational demands. Motivated by these observations, we propose a new IM variant called capacity constrained influence maximization (CIM), which aims to select a limited number of influential friends for each initial adopter such that the promotion can reach more users. To solve CIM effectively, we design two greedy algorithms, MG-Greedy and RR-Greedy, ensuring the $1/2$-approximation ratio. To improve the efficiency, we devise the scalable implementation named RR-OPIM+ with $(1/2-\epsilon)$-approximation and near-linear running time. We extensively evaluate the performance of 9 approaches on 6 real-world networks, and our solutions outperform all competitors in terms of result quality and running time. Additionally, we deploy RR-OPIM+ to online game scenarios, which improves the baseline considerably.Comment: The technical report of the paper entitled 'Capacity Constrained Influence Maximization in Social Networks' in SIGKDD'2

Conjunctival Lymphangiogenesis Was Associated with the Degree of Aggression in Substantial Recurrent Pterygia

Objective. To examine conjunctival lymphatic vessels and to analyze the relationship between lymphangiogenesis and aggressive recurrent pterygia. Methods. Tissues from 60 excised recurrent (including 19 of Grade 1, 28 of Grade 2, and 13 of Grade 3) pterygia were used in the study. Tissues from 9 nasal epibulbar conjunctivae segments were used as controls. Pterygium slices from each patient were immunostained with LYVE-1 monoclonal antibodies to identify lymphatic microvessels in order to calculate the lymphovascular area (LVA), the lymphatic microvessel density (LMD), and the lymphovascular luminal diameter (LVL). The relationship between lymphangiogenesis (LVA, LMD, and LVL) and pterygium aggression (width, extension, and area) was clarified. Results. Few LYVE-1 positive lymphatic vessels were found in the normal epibulbar conjunctiva segments. Lymphatic vessels were slightly increased in Grades 1 and 2 and were dramatically increased in Grade 3 recurrent pterygia. The LMD was correlated with the pterygium area in Grade 1 and 2 pterygia. In Grade 3, both LVA and LMD were significantly correlated with the pterygium area. Conclusions. Lymphangiogenesis was associated with the degree of aggression in recurrent pterygia, particularly in substantial Grade 3 recurrent pterygia