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$^3$-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$^3$-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$^3$-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