36 research outputs found
Methane as an effective hydrogen source for single-layer graphene synthesis on Cu foil by plasma enhanced chemical vapor deposition
A single-layer graphene is synthesized on Cu foil in the absence of H2 flow
by plasma enhanced chemical vapor deposition (PECVD). In lieu of an explicit H2
flow, hydrogen species are produced during methane decomposition process into
their active species (CHx<4), assisted by the plasma. Notably, the early stage
of growth depends strongly on the plasma power. The resulting grain size (the
nucleation density) has a maximum (minimum) at 50 W and saturates when the
plasma power is higher than 120 W because hydrogen partial pressures are
effectively tuned by a simple control of the plasma power. Raman spectroscopy
and transport measurements show that decomposed methane alone can provide
sufficient amount of hydrogen species for high-quality graphene synthesis by
PECVD.Comment: 22 pages, 6 figure
Effects of the height of shoe heels on muscle activation of cervical and lumbar spine in healthy women
Who Speaks Like a Style of Vitamin:Towards Syntax-Aware Dialogue Summarization Using Multi-Task Learning
Abstractive dialogue summarization is a challenging task for several reasons.
First, most of the important pieces of information in a conversation are
scattered across utterances through multi-party interactions with different
textual styles. Second, dialogues are often informal structures, wherein
different individuals express personal perspectives, unlike text summarization,
tasks that usually target formal documents such as news articles. To address
these issues, we focused on the association between utterances from individual
speakers and unique syntactic structures. Speakers have unique textual styles
that can contain linguistic information, such as voiceprint. Therefore, we
constructed a syntax-aware model by leveraging linguistic information (i.e.,
POS tagging), which alleviates the above issues by inherently distinguishing
sentences uttered from individual speakers. We employed multi-task learning of
both syntax-aware information and dialogue summarization. To the best of our
knowledge, our approach is the first method to apply multi-task learning to the
dialogue summarization task. Experiments on a SAMSum corpus (a large-scale
dialogue summarization corpus) demonstrated that our method improved upon the
vanilla model. We further analyze the costs and benefits of our approach
relative to baseline models.Comment: This work has been accepted to the IEEE for possible publication.
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Exploring the Data Efficiency of Cross-Lingual Post-Training in Pretrained Language Models
Language model pretraining is an effective method for improving the performance of downstream natural language processing tasks. Even though language modeling is unsupervised and thus collecting data for it is relatively less expensive, it is still a challenging process for languages with limited resources. This results in great technological disparity between high- and low-resource languages for numerous downstream natural language processing tasks. In this paper, we aim to make this technology more accessible by enabling data efficient training of pretrained language models. It is achieved by formulating language modeling of low-resource languages as a domain adaptation task using transformer-based language models pretrained on corpora of high-resource languages. Our novel cross-lingual post-training approach selectively reuses parameters of the language model trained on a high-resource language and post-trains them while learning language-specific parameters in the low-resource language. We also propose implicit translation layers that can learn linguistic differences between languages at a sequence level. To evaluate our method, we post-train a RoBERTa model pretrained in English and conduct a case study for the Korean language. Quantitative results from intrinsic and extrinsic evaluations show that our method outperforms several massively multilingual and monolingual pretrained language models in most settings and improves the data efficiency by a factor of up to 32 compared to monolingual training
Exploring the Data Efficiency of Cross-Lingual Post-Training in Pretrained Language Models
Language model pretraining is an effective method for improving the performance of downstream natural language processing tasks. Even though language modeling is unsupervised and thus collecting data for it is relatively less expensive, it is still a challenging process for languages with limited resources. This results in great technological disparity between high- and low-resource languages for numerous downstream natural language processing tasks. In this paper, we aim to make this technology more accessible by enabling data efficient training of pretrained language models. It is achieved by formulating language modeling of low-resource languages as a domain adaptation task using transformer-based language models pretrained on corpora of high-resource languages. Our novel cross-lingual post-training approach selectively reuses parameters of the language model trained on a high-resource language and post-trains them while learning language-specific parameters in the low-resource language. We also propose implicit translation layers that can learn linguistic differences between languages at a sequence level. To evaluate our method, we post-train a RoBERTa model pretrained in English and conduct a case study for the Korean language. Quantitative results from intrinsic and extrinsic evaluations show that our method outperforms several massively multilingual and monolingual pretrained language models in most settings and improves the data efficiency by a factor of up to 32 compared to monolingual training
A Novel T Cell-Engaging Bispecific Antibody for Treating Mesothelin-Positive Solid Tumors
As mesothelin is overexpressed in various types of cancer, it is an attractive target for therapeutic antibodies. T-cell bispecific antibodies bind to target cells and engage T cells via binding to CD3, resulting in target cell killing by T-cell activation. However, the affinity of the CD3-binding arm may influence CD3-mediated plasma clearance or antibody trapping in T-cell-containing tissues. This may then affect the biodistribution of bispecific antibodies. In this study, we used scFab and knob-into-hole technologies to construct novel IgG-based 1 + 1 MG1122-A and 2 + 1 MG1122-B bispecific antibodies against mesothelin and CD3ε. MG1122-B was designed to be bivalent to mesothelin and monovalent to CD3ε, using a 2 + 1 head-to-tail format. Activities of the two antibodies were evaluated in mesothelin-positive tumor cells in vitro and xenograft models in vivo. Although both antibodies exhibited target cell killing efficacy and produced regression of xenograft tumors with CD8+ T-cell infiltration, the antitumor efficacy of MG1122-B was significantly higher. MG1122-B may improve tumor targeting because of its bivalency for tumor antigen. It may also reduce systemic toxicity by limiting the activation of circulating T cells. Thus, MG1122-B may be useful for treating mesothelin-positive solid tumors