Zobrazeno 1 - 10
of 11 259
pro vyhledávání: '"An, Shizhong"'
A new method is presented to generate atomic structures that reproduce the essential characteristics of arbitrary material systems, phases, or ensembles. Previous methods allow one to reproduce the essential characteristics (e.g. chemical disorder) o
Externí odkaz:
http://arxiv.org/abs/2409.13871
Combining multiple predictors obtained from distributed data sources to an accurate meta-learner is promising to achieve enhanced performance in lots of prediction problems. As the accuracy of each predictor is usually unknown, integrating the predic
Externí odkaz:
http://arxiv.org/abs/2408.07796
Autor:
Dale, Colin J., Xie, Kevin G. S., Grehan, Kiera Pond, Zhang, Shizhong, Maki, Jeff, Thywissen, Joseph H.
We investigate the scattering properties and bound states of a quasi-two-dimensional (q2D) spin-polarized Fermi gas near a $p$-wave Feshbach resonance. Strong confinement promotes the out-of-plane spatial wave functions to a discrete, gapped orbital
Externí odkaz:
http://arxiv.org/abs/2408.00737
Radio-Frequency Spectroscopy and the Dimensional Crossover in Interacting Spin-Polarized Fermi Gases
Low-dimensional ultracold gases are created in the laboratory by confining three-dimensional (3D) gases inside highly anisotropic trapping potentials. Such trap geometries not only provide access to simulating one-dimensional (1D) and two-dimensional
Externí odkaz:
http://arxiv.org/abs/2407.21106
Autor:
Letourneau, Pierre-David, Singh, Manish Kumar, Cheng, Hsin-Pai, Han, Shizhong, Shi, Yunxiao, Jones, Dalton, Langston, Matthew Harper, Cai, Hong, Porikli, Fatih
We present Polynomial Attention Drop-in Replacement (PADRe), a novel and unifying framework designed to replace the conventional self-attention mechanism in transformer models. Notably, several recent alternative attention mechanisms, including Hyena
Externí odkaz:
http://arxiv.org/abs/2407.11306
Autor:
Li, Junfan, Liao, Shizhong
Online kernel selection is a fundamental problem of online kernel methods.In this paper,we study online kernel selection with memory constraint in which the memory of kernel selection and online prediction procedures is limited to a fixed budget. An
Externí odkaz:
http://arxiv.org/abs/2407.00916
Autor:
Zhang, Yi-Cai, Zhang, Shizhong
In this paper, we develop the appropriate set of hydrodynamic equations in a U(N) invariant superfluid that couple the dynamics of superflow and magnetization. In the special case when both the superfluid and normal velocities are zero, the hydrodyna
Externí odkaz:
http://arxiv.org/abs/2405.02613
Autor:
Yasarla, Rajeev, Singh, Manish Kumar, Cai, Hong, Shi, Yunxiao, Jeong, Jisoo, Zhu, Yinhao, Han, Shizhong, Garrepalli, Risheek, Porikli, Fatih
In this paper, we propose a novel video depth estimation approach, FutureDepth, which enables the model to implicitly leverage multi-frame and motion cues to improve depth estimation by making it learn to predict the future at training. More specific
Externí odkaz:
http://arxiv.org/abs/2403.12953
Autor:
Yang, Zhou, Ren, Zhaochun, Yufeng, Wang, Peng, Shizhong, Sun, Haizhou, Zhu, Xiaofei, Liao, Xiangwen
Empathetic response generation is increasingly significant in AI, necessitating nuanced emotional and cognitive understanding coupled with articulate response expression. Current large language models (LLMs) excel in response expression; however, the
Externí odkaz:
http://arxiv.org/abs/2402.11801
Self-supervised methods have gained prominence in time series anomaly detection due to the scarcity of available annotations. Nevertheless, they typically demand extensive training data to acquire a generalizable representation map, which conflicts w
Externí odkaz:
http://arxiv.org/abs/2401.15123