语料库在中国医疗体系作用的分析: 疫情领域的口笔译(中文-西班牙语)

El objetivo de este trabajo es analizar el papel de los corpus en el ámbito sanitario público en China. El brote de la pandemia de COVID-19 a principios de 2020 causó una gran preocupación en varios países, y también para China, por lo que la comunicación sobre este acontecimiento de salud pública ha cobrado especial importancia. El corpus como una herramienta de análisis de datos, un repositorio electrónico de textos, proporciona una importante referencia para los intérpretes y traductores de este repentino acontecimiento de salud pública. Este trabajo toma la pandemia de COVID-19 como el punto de partida para investigar específicamente el papel del corpus en el ámbito sanitario público en China. Para ilustrar mejor el impacto del corpus en el trabajo de interpretación y traducción en este campo, se analizan y estudian las funciones de dos herramientas de corpus, Sketch Engine y AntConc, por medio de la búsqueda de los términos de palabras y los términos de varias palabras relacionados con la pandemia de COVID-19. Además, Se utiliza el estado actual del uso de corpus en el ámbito educativo y judicial de China como dos partes complementarias del trabajo, y se perfecciona con ejemplos relevantes. Por último, se analizan en detalle los resultados obtenidos en relación con el punto de partida de este trabajo, se destaca el papel de los corpus en el sector sanitario público en China y su repercusión en la traducción y la interpretación. Esperamos que el presente trabajo aporte un valor de referencia para investigaciones futuras sobre el uso de corpus en el ámbito médico.本文旨在分析语料库在中国公共医疗领域的作用。2020 年初，新冠疫情的 爆发引起了各国的广泛关注，中国也不例外，因此，对这次公共卫生事件的交 流变得尤为重要。语料库作为一种数据分析工具，一种电子文本储存库，为这 次突发的公共卫生事件的口笔译工作人员提供了重要的参考资料。 本文以新冠疫情为出发点具体研究语料库在中国公共医疗领域的作用。以 搜索与新冠疫情相关的单词术语和多词术语的方式，对 Sketch Engine 和 AntConc 两个语料库工具的功能进行分析和研究，进一步说明语料库在此领域 对口笔译工作的影响。此外，语料库在中国教育和司法领域的使用现状为本文 的两个补充部分，并通过相关例子来进一步完善相关内容。 最后，结合本篇文章的出发点对得出的结论进行详细的分析，进一步强调 语料库在中国公共医疗领域的作用以及对口笔译工作的影响。我们希望本论文 可以为将来有关语料库在医疗领域中使用的研究提供参考Máster Universitario en Comunicación Intercultural, Interpretación y Traducción en los Servicios Públicos. Especialidad en CHI-ESP (M196

Instantaneous physico-chemical analysis of suspension-based nanomaterials

High-throughput manufacturing of nanomaterial-based products demands robust online characterization and quality control tools capable of continuously probing the in-suspension state. But existing analytical techniques are challenging to deploy in production settings because they are primarily geared toward small-batch ex-situ operation in research laboratory environments. Here we introduce an approach that overcomes these limitations by exploiting surface complexation interactions that emerge when a micron-scale chemical discontinuity is established between suspended nanoparticles and a molecular tracer. The resulting fluorescence signature is easily detectable and embeds surprisingly rich information about composition, quantity, size, and morphology of nanoparticles in suspension independent of their agglomeration state. We show how this method can be straightforwardly applied to enable continuous sizing of commercial ZnO nanoparticles, and to instantaneously quantify the anatase and rutile composition of multicomponent TiO(2) nanoparticle mixtures pertinent to photocatalysis and solar energy conversion

Continuous Nanoparticle Sizing and Characterization via Microfluidics

High-throughput manufacturing of nanomaterial-based products demands robust online characterization and quality control tools capable of continuously probing the in suspension state. But existing analytical techniques are challenging to deploy in production settings because they are primarily geared toward small-batch ex-situ operation in research laboratory environments. Here we introduce an approach that overcomes these limitations by exploiting surface complexation interactions that emerge when a micron-scale chemical discontinuity is established between suspended nanoparticles and a molecular tracer. The resulting fluorescence signature is easily detectable and embeds surprisingly rich information about composition, quantity, size, and morphology of nanoparticles in suspension independent of their agglomeration state. We show how this method can be straightforwardly applied to enable continuous sizing of commercial ZnO nanoparticles, and to instantaneously quantify the anatase and rutile composition of multi-component TiO2 nanoparticle mixtures pertinent to photo catalysis and solar energy conversion. A transport model of the interfacial complexation process is formulated to qualitatively confirm the experimental discovery and to provide understanding of the transport and binding processes. Practical utility is demonstrated by combining our detection method with a cyclone sampler to enable continuous monitoring of airborne nanoparticles. Our method uniquely combines ultra-high flow rate sampling (up to thousands of liters per minute) with sensitive detection based on localized fluorescent complexation, permitting rapid quantitative measurement of airborne nanoparticle concentration. By coupling these components, we show initial results demonstrating detection of airborne ultrafine Al2O3 nanoparticles at environmental concentrations below 200 μg m^−3 in air sampled at 200 L min^−1. This capability suggests potential for online monitoring, making it possible to establish dynamic exposure profiles not readily obtainable using current-generation personal sampling instruments. The underlying fluorescent complexation interactions are inherently size and composition dependent, offering potential to straightforwardly obtain continuous detailed characterization. The increasing commercial prevalence of nanoparticle-based materials also introduces a new demand for robust online characterization tools amenable toward online monitoring in manufacturing settings. We address this need by showing how electrical conductivity measurements can be exploited to instantaneously obtain size and species information in oxide nanoparticle suspensions. This approach is readily implemented in an easy to build platform that can be employed either online to provide real-time feedback during continuous synthesis and processing, or offline for evaluation of test samples obtained from larger batches. Our implementation enables accurate results to be obtained using inexpensive digital multimeters, suggesting broad potential for on-site deployment in industrial settings

Parrot Mind: Towards Explaining the Complex Task Reasoning of Pretrained Large Language Models with Template-Content Structure

The pre-trained large language models (LLMs) have shown their extraordinary capacity to solve reasoning tasks, even on tasks that require a complex process involving multiple sub-steps. However, given the vast possible generation space of all the tasks, how the pretrained model learns the reasoning ability remains an open question. We firstly propose that an intrinsic structural constraint on the generated sequence of language-based reasoning -- we called it template-content structure (T-C structure) -- is the key to explain why LLMs can solve a large number of complex reasoning problems with limited training data by showing this structure can reduce the possible space from exponential level to linear level. Furthermore, by generalizing this structure to the hierarchical case, we demonstrate that models can achieve task composition, further reducing the space needed to learn from linear to logarithmic, thereby effectively learning on complex reasoning involving multiple steps. We provide both examples and formal theory of our T-C structure. We also experimentally validate the existence of the T-C structure in some current LLMs and its effectiveness for reasoning

Large Language Models are In-Context Semantic Reasoners rather than Symbolic Reasoners

The emergent few-shot reasoning capabilities of Large Language Models (LLMs) have excited the natural language and machine learning community over recent years. Despite of numerous successful applications, the underlying mechanism of such in-context capabilities still remains unclear. In this work, we hypothesize that the learned \textit{semantics} of language tokens do the most heavy lifting during the reasoning process. Different from human's symbolic reasoning process, the semantic representations of LLMs could create strong connections among tokens, thus composing a superficial logical chain. To test our hypothesis, we decouple semantics from the language reasoning process and evaluate three kinds of reasoning abilities, i.e., deduction, induction and abduction. Our findings reveal that semantics play a vital role in LLMs' in-context reasoning -- LLMs perform significantly better when semantics are consistent with commonsense but struggle to solve symbolic or counter-commonsense reasoning tasks by leveraging in-context new knowledge. The surprising observations question whether modern LLMs have mastered the inductive, deductive and abductive reasoning abilities as in human intelligence, and motivate research on unveiling the magic existing within the black-box LLMs. On the whole, our analysis provides a novel perspective on the role of semantics in developing and evaluating language models' reasoning abilities. Code is available at {\url{https://github.com/XiaojuanTang/ICSR}}