Enhancing molecular design efficiency: Uniting language models and generative networks with genetic algorithms [0.03%]
增强分子设计效率：利用遗传算法结合语言模型和生成网络
Debsindhu Bhowmik,Pei Zhang,Zachary Fox et al.
Debsindhu Bhowmik et al.
This study examines the effectiveness of generative models in drug discovery, material science, and polymer science, aiming to overcome constraints associated with traditional inverse design methods relying on heuristic rules. Generative mo...
Data-driven evaluation of electric vehicle energy consumption for generalizing standard testing to real-world driving [0.03%]
数据驱动的电动汽车能耗评估：从标准测试推广到实际驾驶
Xinmei Yuan,Jiangbiao He,Yutong Li et al.
Xinmei Yuan et al.
Standard energy-consumption testing, providing the only publicly available quantifiable measure of battery electric vehicle (BEV) energy consumption, is crucial for promoting transparency and accountability in the electrified automotive ind...
An evaluation of synthetic data augmentation for mitigating covariate bias in health data [0.03%]
评估合成数据增强在健康数据中缓解协变量偏差方面的效果
Lamin Juwara,Alaa El-Hussuna,Khaled El Emam
Lamin Juwara
Data bias is a major concern in biomedical research, especially when evaluating large-scale observational datasets. It leads to imprecise predictions and inconsistent estimates in standard regression models. We compare the performance of co...
Optimal shrinkage denoising breaks the noise floor in high-resolution diffusion MRI [0.03%]
最优收缩去噪可使高分辨率弥散MRI突破噪声底线
Khoi Huynh,Wei-Tang Chang,Ye Wu et al.
Khoi Huynh et al.
The spatial resolution attainable in diffusion magnetic resonance (MR) imaging is inherently limited by noise. The weaker signal associated with a smaller voxel size, especially at a high level of diffusion sensitization, is often buried un...
A comprehensive benchmark for COVID-19 predictive modeling using electronic health records in intensive care [0.03%]
基于电子健康记录的重症监护新冠预测模型全面评测基准标准研究
Junyi Gao,Yinghao Zhu,Wenqing Wang et al.
Junyi Gao et al.
The COVID-19 pandemic highlighted the need for predictive deep-learning models in health care. However, practical prediction task design, fair comparison, and model selection for clinical applications remain a challenge. To address this, we...
Meera A Desai,Irene V Pasquetto,Abigail Z Jacobs et al.
Meera A Desai et al.
Alongside an explosion in research and development related to large language models, there has been a concomitant rise in the creation of pretraining datasets-massive collections of text, typically scraped from the web. Drawing on the field...
Leticia Márquez-Magaña
Leticia Márquez-Magaña
For science to be socially transformative it must be anti-deficit, meaning it must oppose efforts aimed at correcting perceived deficiencies in individuals. Instead, asset-based approaches are needed that recognize and value cultural streng...
Federated learning for multi-omics: A performance evaluation in Parkinson's disease [0.03%]
多组学的联合学习：在帕金森病中的性能评估
Benjamin P Danek,Mary B Makarious,Anant Dadu et al.
Benjamin P Danek et al.
While machine learning (ML) research has recently grown more in popularity, its application in the omics domain is constrained by access to sufficiently large, high-quality datasets needed to train ML models. Federated learning (FL) represe...
Meet the authors: Georgios Rizos, Jenna L. Lawson, and Björn W. Schuller [0.03%]
作者简介：Georgios Rizos, Jenna L. Lawson, 和 Björn W. Schuller
Georgios Rizos,Jenna L Lawson,Björn W Schuller
Georgios Rizos
In their recent publication in Patterns, the authors proposed a methodology based on sample-free Bayesian neural networks and label smoothing to improve both predictive and calibration performance on animal call detection. Such approaches h...
Propagating variational model uncertainty for bioacoustic call label smoothing [0.03%]
生物声学叫声标签平滑的变分模型不确定性传播
Georgios Rizos,Jenna Lawson,Simon Mitchell et al.
Georgios Rizos et al.
Along with propagating the input toward making a prediction, Bayesian neural networks also propagate uncertainty. This has the potential to guide the training process by rejecting predictions of low confidence, and recent variational Bayesi...