Zobrazeno 1 - 10
of 9 893
pro vyhledávání: '"Zina, A."'
Topological phases of matter provide a flexible platform to engineer unconventional quantum excitations in quantum materials. Beyond single particle topological matter, in systems with strong quantum many-body correlations, many-body effects can be t
Externí odkaz:
http://arxiv.org/abs/2409.17202
Autor:
Jorry, Victoria, Duma, Zina-Sabrina, Sihvonen, Tuomas, Reinikainen, Satu-Pia, Roininen, Lassi
In the domain of rotating machinery, bearings are vulnerable to different mechanical faults, including ball, inner, and outer race faults. Various techniques can be used in condition-based monitoring, from classical signal analysis to deep learning m
Externí odkaz:
http://arxiv.org/abs/2407.17236
Autor:
Qian, Linglong, Wang, Tao, Wang, Jun, Ellis, Hugh Logan, Mitra, Robin, Dobson, Richard, Ibrahim, Zina
We introduce a novel classification framework for time-series imputation using deep learning, with a particular focus on clinical data. By identifying conceptual gaps in the literature and existing reviews, we devise a taxonomy grounded on the induct
Externí odkaz:
http://arxiv.org/abs/2407.08442
Autor:
Du, Wenjie, Wang, Jun, Qian, Linglong, Yang, Yiyuan, Ibrahim, Zina, Liu, Fanxing, Wang, Zepu, Liu, Haoxin, Zhao, Zhiyuan, Zhou, Yingjie, Wang, Wenjia, Ding, Kaize, Liang, Yuxuan, Prakash, B. Aditya, Wen, Qingsong
Effective imputation is a crucial preprocessing step for time series analysis. Despite the development of numerous deep learning algorithms for time series imputation, the community lacks standardized and comprehensive benchmark platforms to effectiv
Externí odkaz:
http://arxiv.org/abs/2406.12747
The advent of Intelligent Reflecting Surfaces (IRS) and Unmanned Aerial Vehicles (UAVs) is setting a new benchmark in the field of wireless communications. IRS, with their groundbreaking ability to manipulate electromagnetic waves, have opened avenue
Externí odkaz:
http://arxiv.org/abs/2407.01576
In this study, we explore the impact of different masking strategies on time series imputation models. We evaluate the effects of pre-masking versus in-mini-batch masking, normalization timing, and the choice between augmenting and overlaying artific
Externí odkaz:
http://arxiv.org/abs/2405.17508
Autor:
Duma, Zina-Sabrina, Zemcik, Tomas, Bilik, Simon, Sihvonen, Tuomas, Honec, Peter, Reinikainen, Satu-Pia, Horak, Karel
Hyperspectral (HS) imagery in agriculture is becoming increasingly common. These images have the advantage of higher spectral resolution. Advanced spectral processing techniques are required to unlock the information potential in these HS images. The
Externí odkaz:
http://arxiv.org/abs/2403.14359
Missingness is ubiquitous in multivariate time series and poses an obstacle to reliable downstream analysis. Although recurrent network imputation achieved the SOTA, existing models do not scale to deep architectures that can potentially alleviate is
Externí odkaz:
http://arxiv.org/abs/2401.02258
Autor:
Qian, Linglong, Raj, Joseph Arul, Ellis, Hugh Logan, Zhang, Ao, Zhang, Yuezhou, Wang, Tao, Dobson, Richard JB, Ibrahim, Zina
We present an end-to-end architecture for managing complex missingness in multivariate time series derived from hospital electronic health records (EHRs). Our Conditional Self-Attention Imputation (CSAI) is a recurrent neural network architecture equ
Externí odkaz:
http://arxiv.org/abs/2312.16713
Autor:
Wu, Jinge, Kim, Yunsoo, Keller, Eva C., Chow, Jamie, Levine, Adam P., Pontikos, Nikolas, Ibrahim, Zina, Taylor, Paul, Williams, Michelle C., Wu, Honghan
This paper proposes one of the first clinical applications of multimodal large language models (LLMs) as an assistant for radiologists to check errors in their reports. We created an evaluation dataset from real-world radiology datasets (including X-
Externí odkaz:
http://arxiv.org/abs/2312.13103