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pro vyhledávání: '"Dayu AT"'
This work introduces the LLM Online Spatial-temporal Reconstruction (LLM-OSR) framework, which integrates Graph Signal Processing (GSP) and Large Language Models (LLMs) for online spatial-temporal signal reconstruction. The LLM-OSR utilizes a GSP-bas
Externí odkaz:
http://arxiv.org/abs/2411.15764
Organs-at-risk segmentation is critical for ensuring the safety and precision of radiotherapy and surgical procedures. However, existing methods for organs-at-risk image segmentation often suffer from uncertainties and biases in target selection, as
Externí odkaz:
http://arxiv.org/abs/2411.15526
Autor:
Yang, Dayu, Monaikul, Natawut, Ding, Amanda, Tan, Bozhao, Mosaliganti, Kishore, Iyengar, Giri
In the era of data-driven decision-making, accurate table-level representations and efficient table recommendation systems are becoming increasingly crucial for improving table management, discovery, and analysis. However, existing approaches to tabu
Externí odkaz:
http://arxiv.org/abs/2411.03356
The spatiotemporally continuous data of normalized difference snow index (NDSI) are key to understanding the mechanisms of snow occurrence and development as well as the patterns of snow distribution changes. However, the presence of clouds, particul
Externí odkaz:
http://arxiv.org/abs/2411.01450
In this paper, we propose a novel framework that leverages large language models (LLMs) for predicting missing values in time-varying graph signals by exploiting spatial and temporal smoothness. We leverage the power of LLM to achieve a message-passi
Externí odkaz:
http://arxiv.org/abs/2410.18718
Forecasting models are pivotal in a data-driven world with vast volumes of time series data that appear as a compound of vast Linear and Nonlinear patterns. Recent deep time series forecasting models struggle to utilize seasonal and trend decompositi
Externí odkaz:
http://arxiv.org/abs/2410.17159
Autism Spectrum Disorder (ASD) is a prevalent neurological disorder. However, the multi-faceted symptoms and large individual differences among ASD patients are hindering the diagnosis process, which largely relies on subject descriptions and lacks q
Externí odkaz:
http://arxiv.org/abs/2410.16874
Designing network parameters that can effectively represent complex networks is of significant importance for the analysis of time-varying complex networks. This paper introduces a novel thermodynamic framework for analyzing complex networks, focusin
Externí odkaz:
http://arxiv.org/abs/2409.01039
Autor:
Ganji, Mohammadreza, Fathi, Anas El, Fabris, Chiara, Lv, Dayu, Kovatchev, Boris, Breton, Marc
Background and Objective: Diabetes presents a significant challenge to healthcare due to the negative impact of poor blood sugar control on health and associated complications. Computer simulation platforms, notably exemplified by the UVA/Padova Type
Externí odkaz:
http://arxiv.org/abs/2409.00232
Autor:
Liu, Weiwei, Zhang, Zheng, Yan, Dayu, Li, Jianshu, Zhang, Zhitao, Ji, Jianting, Jin, Feng, Shi, Youguo, Zhang, Qingming
Publikováno v:
Chinese Physics Letters 41, 097503 (2024)
After the discovery of the ARECh2 (A=alkali or monovalent ions, RE=rare-earth, Ch= chalcogen) triangular lattice quantum spin liquid (QSL) family, a series of its oxide, sulfide, and selenide counterparts has been consistently reported and extensivel
Externí odkaz:
http://arxiv.org/abs/2408.09670