52 research outputs found
High-speed Internal Finishing of Capillary Tubes by Magnetic Abrasive Finishing
AbstractIn magnetic abrasive finishing, the development of a multiple pole-tip system using a partially heat-treated magnetic tool allows the finishing of multiple regions simultaneously in capillary tubes and thus improves the finishing efficiency. To further reduce the processing time required, a new high-speed machine is fabricated. This paper describes the development of the high-speed multiple pole-tip finishing equipment, which is capable of rotating the spindle up to 30000 min-1, and the effects of tube rotational speed on abrasive motion during the finishing experiments. Also, the finishing mechanisms of the high-speed machine are clarified
Let Me Know What to Ask: Interrogative-Word-Aware Question Generation
Question Generation (QG) is a Natural Language Processing (NLP) task that
aids advances in Question Answering (QA) and conversational assistants.
Existing models focus on generating a question based on a text and possibly the
answer to the generated question. They need to determine the type of
interrogative word to be generated while having to pay attention to the grammar
and vocabulary of the question. In this work, we propose
Interrogative-Word-Aware Question Generation (IWAQG), a pipelined system
composed of two modules: an interrogative word classifier and a QG model. The
first module predicts the interrogative word that is provided to the second
module to create the question. Owing to an increased recall of deciding the
interrogative words to be used for the generated questions, the proposed model
achieves new state-of-the-art results on the task of QG in SQuAD, improving
from 46.58 to 47.69 in BLEU-1, 17.55 to 18.53 in BLEU-4, 21.24 to 22.33 in
METEOR, and from 44.53 to 46.94 in ROUGE-L.Comment: Accepted at 2nd Workshop on Machine Reading for Question Answering
(MRQA), EMNLP 201
Schema-Driven Information Extraction from Heterogeneous Tables
In this paper, we explore the question of whether large language models can
support cost-efficient information extraction from tables. We introduce
schema-driven information extraction, a new task that transforms tabular data
into structured records following a human-authored schema. To assess various
LLM's capabilities on this task, we develop a benchmark composed of tables from
four diverse domains: machine learning papers, chemistry literature, material
science journals, and webpages. Alongside the benchmark, we present an
extraction method based on instruction-tuned LLMs. Our approach shows
competitive performance without task-specific labels, achieving F1 scores
ranging from 74.2 to 96.1, while maintaining great cost efficiency. Moreover,
we validate the possibility of distilling compact table-extraction models to
reduce API reliance, as well as extraction from image tables using multi-modal
models. By developing a benchmark and demonstrating the feasibility of this
task using proprietary models, we aim to support future work on open-source
schema-driven IE models
Emergence of the brain-border immune niches and their contribution to the development of neurodegenerative diseases
Historically, the central nervous system (CNS) was regarded as ‘immune-privileged’, possessing its own distinct immune cell population. This immune privilege was thought to be established by a tight blood-brain barrier (BBB) and blood-cerebrospinal-fluid barrier (BCSFB), which prevented the crossing of peripheral immune cells and their secreted factors into the CNS parenchyma. However, recent studies have revealed the presence of peripheral immune cells in proximity to various brain-border niches such as the choroid plexus, cranial bone marrow (CBM), meninges, and perivascular spaces. Furthermore, emerging evidence suggests that peripheral immune cells may be able to infiltrate the brain through these sites and play significant roles in driving neuronal cell death and pathology progression in neurodegenerative disease. Thus, in this review, we explore how the brain-border immune niches may contribute to the pathogenesis of neurodegenerative disorders such as Alzheimer’s disease (AD), Parkinson’s disease (PD), and multiple sclerosis (MS). We then discuss several emerging options for harnessing the neuroimmune potential of these niches to improve the prognosis and treatment of these debilitative disorders using novel insights from recent studies
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