Zobrazeno 1 - 10
of 7 583
pro vyhledávání: '"topological feature"'
In recent years, the preliminary diagnosis of Attention Deficit Hyperactivity Disorder (ADHD) using electroencephalography (EEG) has garnered attention from researchers. EEG, known for its expediency and efficiency, plays a pivotal role in the diagno
Externí odkaz:
http://arxiv.org/abs/2404.06676
Autor:
Ying, Zu-Jian
Publikováno v:
Advanced Quantum Technologies 7, 2400053 (2024)
The Jaynes-Cummings Model (JCM) is a fundamental model and building block for light-matter interactions, quantum information and quantum computation. We analytically analyze the topological feature manifested by the JCM in the presence of non-Hermiti
Externí odkaz:
http://arxiv.org/abs/2402.06370
Autor:
Zeinalipour, Kamyar, Gori, Marco
The electrocardiogram (ECG) is a dependable instrument for assessing the function of the cardiovascular system. There has recently been much emphasis on precisely classifying ECGs. While ECG situations have numerous similarities, little attention has
Externí odkaz:
http://arxiv.org/abs/2311.04228
Autor:
Bubenik, Peter, Bush, Johnathan
We use tools from applied topology for feature selection on vector-valued time series data. We employ persistent homology and sliding window embeddings to quantify the coordinated dynamics of time series. We describe an algorithm for gradient descent
Externí odkaz:
http://arxiv.org/abs/2310.17494
Machine learning for point clouds has been attracting much attention, with many applications in various fields, such as shape recognition and material science. For enhancing the accuracy of such machine learning methods, it is often effective to inco
Externí odkaz:
http://arxiv.org/abs/2307.09259
Akademický článek
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Deep learning (DL) algorithms have been widely applied to short-term voltage stability (STVS) assessment in power systems. However, transferring the knowledge learned in one power grid to other power grids with topology changes is still a challenging
Externí odkaz:
http://arxiv.org/abs/2303.07138
Autor:
Briola, Antonio, Aste, Tomaso
In this paper, we introduce a novel unsupervised, graph-based filter feature selection technique which exploits the power of topologically constrained network representations. We model dependency structures among features using a family of chordal gr
Externí odkaz:
http://arxiv.org/abs/2302.09543
Akademický článek
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Faces-classes of grains, often referred to as topological features, largely dictate the evolution of polycrystalline microstructures during grain growth. Realising these topological features is generally an arduous task, often demanding sophisticated
Externí odkaz:
http://arxiv.org/abs/2212.14628