Kavli Affiliate: Xiang Zhang | First 5 Authors: Xiang Zhang, Makars Siskins, Yaroslav Blanter, , | Summary: We developed a theoretical scheme of incorporating the magnetoelastic contribution into the thermal elastic dynamics for the thin membranes of 2D antiferromagnetic material with restricted geometry. We extended the elastic Gr"uneisen relation into an effective version which includes […]
Kavli Affiliate: Xiang Zhang | First 5 Authors: Xiang Zhang, Makars Siskins, Yaroslav Blanter, , | Summary: We developed a theoretical scheme of incorporating the magnetoelastic contribution into the thermal elastic dynamics for the thin membranes of 2D antiferromagnetic material with restricted geometry. We extended the elastic Gr"uneisen relation into an effective version which includes […]
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. […]
Kavli Affiliate: Michael P. Brenner | First 5 Authors: Jacob Page, Joe Holey, Michael P. Brenner, Rich R. Kerswell, | Summary: Convolutional autoencoders are used to deconstruct the changing dynamics of two-dimensional Kolmogorov flow as $Re$ is increased from weakly chaotic flow at $Re=40$ to a chaotic state dominated by a domain-filling vortex pair at […]