Kavli Affiliate: Austin J. Minnich | First 5 Authors: Benjamin Hatanpää, Austin J. Minnich, , , | Summary: Ab-initio calculations of charge transport properties in materials without adjustable parameters have provided microscopic insights into electron-phonon interactions which govern charge transport properties. Other transport properties such as the diffusion coefficient provide additional microscopic information and are […]

Kavli Affiliate: Wei Gao | First 5 Authors: Xuan Zhang, Wei Gao, , , | Summary: While large pre-trained language models (LLMs) have shown their impressive capabilities in various NLP tasks, they are still under-explored in the misinformation domain. In this paper, we examine LLMs with in-context learning (ICL) for news claim verification, and find […]

Kavli Affiliate: James Bock | First 5 Authors: Grigory Heaton, Walter Cook, James Bock, Jill Burnham, Sam Condon | Summary: Intensity mapping observations measure galaxy clustering fluctuations from spectral-spatial maps, requiring stable noise properties on large angular scales. We have developed specialized readouts and analysis methods for achieving large-scale noise stability with Teledyne 2048$times$2048 H2RG […]

Kavli Affiliate: Wei Gao | First 5 Authors: Haoming Wang, Wei Gao, , , | Summary: The efficiency of Federated Learning (FL) is often affected by both data and device heterogeneities. Data heterogeneity is defined as the heterogeneity of data distributions on different clients. Device heterogeneity is defined as the clients’ variant latencies in uploading […]

Kavli Affiliate: Wei Gao | First 5 Authors: Haoming Wang, Wei Gao, , , | Summary: The efficiency of Federated Learning (FL) is often affected by both data and device heterogeneities. Data heterogeneity is defined as the heterogeneity of data distributions on different clients. Device heterogeneity is defined as the clients’ variant latencies in uploading […]

Kavli Affiliate: Wei Gao | First 5 Authors: Haoming Wang, Wei Gao, , , | Summary: Federated Learning (FL) can be affected by data and device heterogeneities, caused by clients’ different local data distributions and latencies in uploading model updates (i.e., staleness). Traditional schemes consider these heterogeneities as two separate and independent aspects, but this […]

Kavli Affiliate: Wei Gao | First 5 Authors: Haoming Wang, Wei Gao, , , | Summary: Federated Learning (FL) can be affected by data and device heterogeneities, caused by clients’ different local data distributions and latencies in uploading model updates (i.e., staleness). Traditional schemes consider these heterogeneities as two separate and independent aspects, but this […]

Kavli Affiliate: Wei Gao | First 5 Authors: Kai Huang, Hanyun Yin, Heng Huang, Wei Gao, | Summary: Fine-tuning is the most effective way of adapting pre-trained large language models (LLMs) to downstream applications. With the fast growth of LLM-enabled AI applications and democratization of open-souced LLMs, fine-tuning has become possible for non-expert individuals, but […]

Kavli Affiliate: Wei Gao | First 5 Authors: Kai Huang, Hanyun Yin, Heng Huang, Wei Gao, | Summary: Fine-tuning is the most effective way of adapting pre-trained large language models (LLMs) to downstream applications. With the fast growth of LLM-enabled AI applications and democratization of open-souced LLMs, fine-tuning has become possible for non-expert individuals, but […]

Kavli Affiliate: Alireza Marandi | First 5 Authors: Gordon H. Y. Li, Christian R. Leefmans, James Williams, Robert M. Gray, Midya Parto | Summary: Rapid advancements in deep learning over the past decade have fueled an insatiable demand for efficient and scalable hardware. Photonics offers a promising solution by leveraging the unique properties of light. […]