37 research outputs found
Novel microwave mediated synthesis for photophysical studies.
Finally, a series of novel cyanobipyridine-derived zinc(II) bis(thiolate) complexes were prepared excellently by a microwave-assisted cross-coupling/complexation sequence and display luminescence that can be modulated using intrinsic functionality and ancillary ligands
Losses Can Be Blessings: Routing Self-Supervised Speech Representations Towards Efficient Multilingual and Multitask Speech Processing
Self-supervised learning (SSL) for rich speech representations has achieved
empirical success in low-resource Automatic Speech Recognition (ASR) and other
speech processing tasks, which can mitigate the necessity of a large amount of
transcribed speech and thus has driven a growing demand for on-device ASR and
other speech processing. However, advanced speech SSL models have become
increasingly large, which contradicts the limited on-device resources. This gap
could be more severe in multilingual/multitask scenarios requiring
simultaneously recognizing multiple languages or executing multiple speech
processing tasks. Additionally, strongly overparameterized speech SSL models
tend to suffer from overfitting when being finetuned on low-resource speech
corpus. This work aims to enhance the practical usage of speech SSL models
towards a win-win in both enhanced efficiency and alleviated overfitting via
our proposed S-Router framework, which for the first time discovers that
simply discarding no more than 10\% of model weights via only finetuning model
connections of speech SSL models can achieve better accuracy over standard
weight finetuning on downstream speech processing tasks. More importantly,
S-Router can serve as an all-in-one technique to enable (1) a new
finetuning scheme, (2) an efficient multilingual/multitask solution, (3) a
state-of-the-art ASR pruning technique, and (4) a new tool to quantitatively
analyze the learned speech representation. We believe S-Router has provided
a new perspective for practical deployment of speech SSL models. Our codes are
available at: https://github.com/GATECH-EIC/S3-Router.Comment: Accepted at NeurIPS 202
GPT4AIGChip: Towards Next-Generation AI Accelerator Design Automation via Large Language Models
The remarkable capabilities and intricate nature of Artificial Intelligence
(AI) have dramatically escalated the imperative for specialized AI
accelerators. Nonetheless, designing these accelerators for various AI
workloads remains both labor- and time-intensive. While existing design
exploration and automation tools can partially alleviate the need for extensive
human involvement, they still demand substantial hardware expertise, posing a
barrier to non-experts and stifling AI accelerator development. Motivated by
the astonishing potential of large language models (LLMs) for generating
high-quality content in response to human language instructions, we embark on
this work to examine the possibility of harnessing LLMs to automate AI
accelerator design. Through this endeavor, we develop GPT4AIGChip, a framework
intended to democratize AI accelerator design by leveraging human natural
languages instead of domain-specific languages. Specifically, we first perform
an in-depth investigation into LLMs' limitations and capabilities for AI
accelerator design, thus aiding our understanding of our current position and
garnering insights into LLM-powered automated AI accelerator design.
Furthermore, drawing inspiration from the above insights, we develop a
framework called GPT4AIGChip, which features an automated demo-augmented
prompt-generation pipeline utilizing in-context learning to guide LLMs towards
creating high-quality AI accelerator design. To our knowledge, this work is the
first to demonstrate an effective pipeline for LLM-powered automated AI
accelerator generation. Accordingly, we anticipate that our insights and
framework can serve as a catalyst for innovations in next-generation
LLM-powered design automation tools.Comment: Accepted by ICCAD 202
Clinical factors of post-chemoradiotherapy as valuable indicators for pathological complete response in locally advanced rectal cancer
OBJECTIVES: Pathological complete response has shown a better prognosis for patients with locally advanced rectal cancer after preoperative chemoradiotherapy. However, correlations between post-chemoradiotherapy clinical factors and pathologic complete response are not well confirmed. The aim of the current study was to identify post-chemoradiotherapy clinical factors that could serve as indicators of pathologic complete response in locally advanced rectal cancer. METHODS: This study retrospectively analyzed 544 consecutive patients with locally advanced rectal cancer treated at Sun Yat-sen University Cancer Center from December 2003 to June 2014. All patients received preoperative chemoradiotherapy followed by surgery. Univariate and multivariate regression analyses were performed to identify post-chemoradiotherapy clinical factors that are significant indicators of pathologic complete response. RESULTS: In this study, 126 of 544 patients (23.2%) achieved pathological complete response. In multivariate analyses, increased pathological complete response rate was significantly associated with the following factors: post-chemoradiotherapy clinical T stage 0-2 (odds ratio=2.098, 95% confidence interval=1.023-4.304, p=0.043), post-chemoradiotherapy clinical N stage 0 (odds ratio=2.011, 95% confidence interval=1.264-3.201, p=0.003), interval from completion of preoperative chemoradiotherapy to surgery of >;7 weeks (odds ratio=1.795, 95% confidence interval=1.151-2.801, p=0.010) and post-chemoradiotherapy carcinoembryonic antigen ≤2 ng/ml (odds ratio=1.579, 95% confidence interval=1.026-2.432, p=0.038). CONCLUSIONS: Post-chemoradiotherapy clinical T stage 0-2, post-chemoradiotherapy clinical N stage 0, interval from completion of chemoradiotherapy to surgery of >;7 weeks and post-chemoradiotherapy carcinoembryonic antigen ≤2 ng/ml were independent clinical indicators for pathological complete response. These findings demonstrate that post-chemoradiotherapy clinical factors could be valuable for post-operative assessment of pathological complete response