Zobrazeno 1 - 10
of 53 634
pro vyhledávání: '"Ercan, A"'
As irregularly structured data representations, graphs have received a large amount of attention in recent years and have been widely applied to various real-world scenarios such as social, traffic, and energy settings. Compared to non-graph algorith
Externí odkaz:
http://arxiv.org/abs/2412.00462
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
Autor:
Rothe, Stefan, Wisal, Kabish, Chen, Chun-Wei, Ercan, Mert, Jesacher, Alexander, Stone, A. Douglas, Cao, Hui
Multimode fibers provide a promising platform for realizing high-power laser amplifiers with suppressed nonlinearities and instabilities. The potential degradation of optical beam quality has been a major concern for highly multimode fiber amplifiers
Externí odkaz:
http://arxiv.org/abs/2410.23361
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
Low-light environments pose significant challenges for image enhancement methods. To address these challenges, in this work, we introduce the HUE dataset, a comprehensive collection of high-resolution event and frame sequences captured in diverse and
Externí odkaz:
http://arxiv.org/abs/2410.19164
This paper proposes Graph Signal Adaptive Message Passing (GSAMP), a novel message passing method that simultaneously conducts online prediction, missing data imputation, and noise removal on time-varying graph signals. Unlike conventional Graph Sign
Externí odkaz:
http://arxiv.org/abs/2410.17629
The assumption of using a static graph to represent multivariate time-varying signals oversimplifies the complexity of modeling their interactions over time. We propose a Dynamic Multi-hop model that captures dynamic interactions among time-varying n
Externí odkaz:
http://arxiv.org/abs/2410.17625
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
In today's world of globalized commerce, cross-market recommendation systems (CMRs) are crucial for providing personalized user experiences across diverse market segments. However, traditional recommendation algorithms have difficulties dealing with
Externí odkaz:
http://arxiv.org/abs/2409.07850
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