A simple accurate way to model noise-seeded ultrafast nonlinear processes

Kavli Affiliate: Frank Wise | First 5 Authors: Yi-Hao Chen, Frank Wise, , , | Summary: Noise can play an important role in nonlinear pulse propagation. It is not only the origin of fluctuations in supercontinuum but can also determine the generated signal amplitude and phase, as seen in phenomena such as noise-seeded four-wave mixing […]


Continue.. A simple accurate way to model noise-seeded ultrafast nonlinear processes

A simple accurate way to model noise-seeded ultrafast nonlinear processes

Kavli Affiliate: Frank Wise | First 5 Authors: Yi-Hao Chen, Frank Wise, , , | Summary: Noise can play an important role in nonlinear pulse propagation. It is not only the origin of fluctuations in supercontinuum but can also determine the generated signal amplitude and phase, as seen in phenomena such as noise-seeded four-wave mixing […]


Continue.. A simple accurate way to model noise-seeded ultrafast nonlinear processes

A simple accurate way to model noise-seeded ultrafast nonlinear processes

Kavli Affiliate: Frank Wise | First 5 Authors: Yi-Hao Chen, Frank Wise, , , | Summary: Noise can play an important role in nonlinear pulse propagation. It is not only the origin of fluctuations in supercontinuum but can also determine the generated signal amplitude and phase, as seen in phenomena such as noise-seeded four-wave mixing […]


Continue.. A simple accurate way to model noise-seeded ultrafast nonlinear processes

A simple accurate way to model noise-seeded ultrafast nonlinear processes

Kavli Affiliate: Frank Wise | First 5 Authors: Yi-Hao Chen, Yi-Hao Chen, , , | Summary: Noise can play an important role in nonlinear pulse propagation. It is not only the origin of fluctuations in supercontinuum but can also determine the generated signal amplitude and phase, as seen in phenomena such as noise-seeded four-wave mixing […]


Continue.. A simple accurate way to model noise-seeded ultrafast nonlinear processes

EACO-RAG: Edge-Assisted and Collaborative RAG with Adaptive Knowledge Update

Kavli Affiliate: Feng Wang | First 5 Authors: Jiaxing Li, Chi Xu, Lianchen Jia, Feng Wang, Cong Zhang | Summary: Large Language Models are revolutionizing Web, mobile, and Web of Things systems, driving intelligent and scalable solutions. However, as Retrieval-Augmented Generation (RAG) systems expand, they encounter significant challenges related to scalability, including increased delay and […]


Continue.. EACO-RAG: Edge-Assisted and Collaborative RAG with Adaptive Knowledge Update

EACO-RAG: Towards Distributed Tiered LLM Deployment using Edge-Assisted and Collaborative RAG with Adaptive Knowledge Update

Kavli Affiliate: Feng Wang | First 5 Authors: Jiaxing Li, Chi Xu, Lianchen Jia, Feng Wang, Cong Zhang | Summary: Large language models (LLMs) have demonstrated impressive capabilities in language tasks, but they require high computing power and rely on static knowledge. To overcome these limitations, Retrieval-Augmented Generation (RAG) incorporates up-to-date external information into LLMs […]


Continue.. EACO-RAG: Towards Distributed Tiered LLM Deployment using Edge-Assisted and Collaborative RAG with Adaptive Knowledge Update

Counting Ability of Large Language Models and Impact of Tokenization

Kavli Affiliate: Xiang Zhang | First 5 Authors: Xiang Zhang, Juntai Cao, Chenyu You, , | Summary: Transformers, the backbone of modern large language models (LLMs), face inherent architectural limitations that impede their reasoning capabilities. Unlike recurrent networks, Transformers lack recurrent connections, confining them to constant-depth computation. This restriction places them in the complexity class […]


Continue.. Counting Ability of Large Language Models and Impact of Tokenization

Counting Ability of Large Language Models and Impact of Tokenization

Kavli Affiliate: Xiang Zhang | First 5 Authors: Xiang Zhang, Juntai Cao, Chenyu You, , | Summary: Transformers, the backbone of modern large language models (LLMs), face inherent architectural limitations that impede their reasoning capabilities. Unlike recurrent networks, Transformers lack recurrent connections, confining them to constant-depth computation. This restriction places them in the complexity class […]


Continue.. Counting Ability of Large Language Models and Impact of Tokenization

Modeling the Superlattice Phase Diagram of Transition Metal Intercalation in Bilayer 2H-TaS$_2$

Kavli Affiliate: David T. Limmer | First 5 Authors: Isaac M. Craig, B. Junsuh Kim, David T. Limmer, D. Kwabena Bediako, SinĂ©ad M. Griffin | Summary: Van der Waals hosts intercalated with transition metal (TM) ions exhibit a range of magnetic properties strongly influenced by the structural order of the intercalants. However, predictive computational models […]


Continue.. Modeling the Superlattice Phase Diagram of Transition Metal Intercalation in Bilayer 2H-TaS$_2$

Semi-supervised Chinese Poem-to-Painting Generation via Cycle-consistent Adversarial Networks

Kavli Affiliate: Feng Wang | First 5 Authors: Zhengyang Lu, Tianhao Guo, Feng Wang, , | Summary: Classical Chinese poetry and painting represent the epitome of artistic expression, but the abstract and symbolic nature of their relationship poses a significant challenge for computational translation. Most existing methods rely on large-scale paired datasets, which are scarce […]


Continue.. Semi-supervised Chinese Poem-to-Painting Generation via Cycle-consistent Adversarial Networks