Graph prolongation convolutional networks: explicitly multiscale machine learning on graphs with applications to modeling of cytoskeleton [0.03%]
图延长卷积网络：图上显式多尺度机器学习及其在骨架建模中的应用
Cory B Scott,Eric Mjolsness
Cory B Scott
We define a novel type of ensemble graph convolutional network (GCN) model. Using optimized linear projection operators to map between spatial scales of graph, this ensemble model learns to aggregate information from each scale for its fina...
Beyond Euclid: an illustrated guide to modern machine learning with geometric, topological, and algebraic structures [0.03%]
超越欧几里得：用几何、拓扑和代数结构插图介绍现代机器学习
Mathilde Papillon,Sophia Sanborn,Johan Mathe et al.
Mathilde Papillon et al.
The enduring legacy of Euclidean geometry underpins classical machine learning, which, for decades, has been primarily developed for data lying in Euclidean space. Yet, modern machine learning increasingly encounters richly structured data ...
Pedro Pessoa,Paul Campitelli,Douglas P Shepherd et al.
Pedro Pessoa et al.
State space models, such as Mamba, have recently garnered attention in time series forecasting (TSF) due to their ability to capture sequence patterns. However, in electricity consumption benchmarks, Mamba forecasts exhibit a mean error of ...
Prior guided deep difference meta-learner for fast adaptation to stylized segmentation [0.03%]
基于先导引导的深度差异元学习的快速风格化分割适应方法
Dan Nguyen,Anjali Balagopal,Ti Bai et al.
Dan Nguyen et al.
Radiotherapy treatment planning requires segmenting anatomical structures in various styles, influenced by guidelines, protocols, preferences, or dose planning needs. Deep learning-based auto-segmentation models, trained on anatomical defin...
Quality assurance for online adaptive radiotherapy: a secondary dose verification model with geometry-encoded U-Net [0.03%]
基于几何编码的U型网络二次剂量验证模型实现线上自适应放疗的质量控制
Shunyu Yan,Austen Maniscalco,Biling Wang et al.
Shunyu Yan et al.
In online adaptive radiotherapy (ART), quick computation-based secondary dose verification is crucial for ensuring the quality of ART plans while the patient is positioned on the treatment couch. However, traditional dose verification algor...
Application of kernel principal component analysis for optical vector atomic magnetometry [0.03%]
核主成分分析在光学向量原子磁探测中的应用研究
James A McKelvy,Irina Novikova,Eugeniy E Mikhailov et al.
James A McKelvy et al.
Vector atomic magnetometers that incorporate electromagnetically induced transparency (EIT) allow for precision measurements of magnetic fields that are sensitive to the directionality of the observed field by virtue of fundamental physics....
GPU optimization techniques to accelerate optiGAN-a particle simulation GAN [0.03%]
基于GPU优化的加速粒子模拟生成对抗网络方法
Anirudh Srikanth,Carlotta Trigila,Emilie Roncali
Anirudh Srikanth
The demand for specialized hardware to train AI models has increased in tandem with the increase in the model complexity over the recent years. Graphics processing unit (GPU) is one such hardware that is capable of parallelizing operations ...
WATUNet: a deep neural network for segmentation of volumetric sweep imaging ultrasound [0.03%]
WATUNet：用于分割体积扫描成像超声的深度神经网络
Donya Khaledyan,Thomas J Marini,Avice O&#x;Connell et al.
Donya Khaledyan et al.
Limited access to breast cancer diagnosis globally leads to delayed treatment. Ultrasound, an effective yet underutilized method, requires specialized training for sonographers, which hinders its widespread use. Volume sweep imaging (VSI) i...
Data-driven modeling of noise time series with convolutional generative adversarial networks [0.03%]
基于卷积生成对抗网络的噪声时间序列数据驱动建模方法研究
Adam Wunderlich,Jack Sklar
Adam Wunderlich
Random noise arising from physical processes is an inherent characteristic of measurements and a limiting factor for most signal processing and data analysis tasks. Given the recent interest in generative adversarial networks (GANs) for dat...
Recipes for when physics fails: recovering robust learning of physics informed neural networks [0.03%]
物理失效时的配方：恢复基于物理信息的神经网络的稳健学习方法
Chandrajit Bajaj,Luke McLennan,Timothy Andeen et al.
Chandrajit Bajaj et al.
Physics-informed neural networks (PINNs) have been shown to be effective in solving partial differential equations by capturing the physics induced constraints as a part of the training loss function. This paper shows that a PINN can be sen...