Zobrazeno 1 - 10
of 462
pro vyhledávání: '"Austin Z"'
Autor:
Michael S. Westphall, Kenneth W. Lee, Austin Z. Salome, Jean M. Lodge, Timothy Grant, Joshua J. Coon
Publikováno v:
Nature Communications, Vol 13, Iss 1, Pp 1-6 (2022)
Mass spectrometry (MS) is a powerful tool for the structural characterization of protein complexes. Here the authors offer a path for direct integration of MS and electron microscopy with a MS approach that enables grid deposition and structural pres
Externí odkaz:
https://doaj.org/article/6e45a8f643a24a1f9f34b640053ec2a6
Publikováno v:
Hepatology Communications, Vol 6, Iss 4, Pp 652-664 (2022)
Liver cancer is one of the leading causes of cancer deaths worldwide. Among all primary liver cancers, hepatocellular carcinoma (HCC) is the most common type, representing 75%‐85% of all primary liver cancer cases. Median survival following diagnos
Externí odkaz:
https://doaj.org/article/1d590dd53a0e4f3099ede34519fddfa6
Novice programmers are increasingly relying on Large Language Models (LLMs) to generate code for learning programming concepts. However, this interaction can lead to superficial engagement, giving learners an illusion of learning and hindering skill
Externí odkaz:
http://arxiv.org/abs/2410.08922
Autor:
Jindian Li, Juno Van Valkenburgh, Jianyang Fang, Deliang Zhang, Yingxi Chen, Quan Chen, Guorong Jia, Austin Z. Chen, Xianzhong Zhang, Kai Chen
Publikováno v:
Pharmacological Research, Vol 183, Iss , Pp 106395- (2022)
Riboflavin receptor 3 (RFVT3) is a key protein in energetic metabolism reprogramming and is overexpressed in multiple cancers involved in malignant proliferation, angiogenesis, chemotherapy resistance, and immunosuppression. To enable non-invasive re
Externí odkaz:
https://doaj.org/article/74acc90cd0e0456694aa239a84fb3605
In this paper, we present Wandercode, a novel interaction design for recommender systems that recommend code locations to aid programmers in software development tasks. In particular, our design aims to improve upon prior designs by reducing informat
Externí odkaz:
http://arxiv.org/abs/2408.14589
Autor:
Singha, Ananya, Chopra, Bhavya, Khatry, Anirudh, Gulwani, Sumit, Henley, Austin Z., Le, Vu, Parnin, Chris, Singh, Mukul, Verbruggen, Gust
Automated insight generation is a common tactic for helping knowledge workers, such as data scientists, to quickly understand the potential value of new and unfamiliar data. Unfortunately, automated insights produced by large-language models can gene
Externí odkaz:
http://arxiv.org/abs/2405.01556
Autor:
Henley, Austin Z., Piorkowski, David
Without well-labeled ground truth data, machine learning-based systems would not be as ubiquitous as they are today, but these systems rely on substantial amounts of correctly labeled data. Unfortunately, crowdsourced labeling is time consuming and e
Externí odkaz:
http://arxiv.org/abs/2403.07762
Autor:
Kazemitabaar, Majeed, Ye, Runlong, Wang, Xiaoning, Henley, Austin Z., Denny, Paul, Craig, Michelle, Grossman, Tovi
Timely, personalized feedback is essential for students learning programming. LLM-powered tools like ChatGPT offer instant support, but reveal direct answers with code, which may hinder deep conceptual engagement. We developed CodeAid, an LLM-powered
Externí odkaz:
http://arxiv.org/abs/2401.11314
Autor:
Parnin, Chris, Soares, Gustavo, Pandita, Rahul, Gulwani, Sumit, Rich, Jessica, Henley, Austin Z.
A race is underway to embed advanced AI capabilities into products. These product copilots enable users to ask questions in natural language and receive relevant responses that are specific to the user's context. In fact, virtually every large techno
Externí odkaz:
http://arxiv.org/abs/2312.14231
Autor:
Chopra, Bhavya, Singha, Ananya, Fariha, Anna, Gulwani, Sumit, Parnin, Chris, Tiwari, Ashish, Henley, Austin Z.
Large Language Models (LLMs) are being increasingly employed in data science for tasks like data preprocessing and analytics. However, data scientists encounter substantial obstacles when conversing with LLM-powered chatbots and acting on their sugge
Externí odkaz:
http://arxiv.org/abs/2310.16164