Zobrazeno 1 - 10
of 1 381
pro vyhledávání: '"Tao Lü"'
Publikováno v:
Shipin gongye ke-ji, Vol 45, Iss 11, Pp 187-194 (2024)
The objective of this study was to optimize the extraction process of total sterols from wild Pyracantha fortuneana leaves (WPFL), and explore their antioxidant and whitening activities. The yield of total sterols (WPFLTPS) was used as indicator, and
Externí odkaz:
https://doaj.org/article/3adbcd34e0c6495a9ea3d558ad2e0bbc
Identifying causal relations is crucial for a variety of downstream tasks. In additional to observational data, background knowledge (BK), which could be attained from human expertise or experiments, is usually introduced for uncovering causal relati
Externí odkaz:
http://arxiv.org/abs/2407.15259
Publikováno v:
工程科学学报, Vol 42, Iss 11, Pp 1411-1421 (2020)
Technologies for the emission and control of pollutants have been widely applied in coal-fired power plants in China to control the emissions of SO2, NOx, and particulate matter (PM). SO2 and NOx belong to the pollutants of major elements, with PM pe
Externí odkaz:
https://doaj.org/article/c250adff161a451c84ee0c290dd4fa2b
Publikováno v:
Chemosensors, Vol 10, Iss 11, p 472 (2022)
Minor elements significantly influence the properties of stainless steel. In this study, a laser-induced breakdown spectroscopy (LIBS) technique combined with a back-propagation artificial intelligence network (BP-ANN) was used to detect nickel (Ni),
Externí odkaz:
https://doaj.org/article/68aca36567144a1fadad9b164529fe8d
Publikováno v:
Metals, Vol 11, Iss 3, p 446 (2021)
Molecular dynamics simulations were performed to study the evolution of single crystal copper with and without a nanovoid (located at the middle of crystal with a diameter of ~2.9 nm) when loaded with shock waves of different velocities. The simulati
Externí odkaz:
https://doaj.org/article/9b44c6c81c4f4d1c9e3c7a208b4b5de1
Neuro-symbolic hybrid systems are promising for integrating machine learning and symbolic reasoning, where perception models are facilitated with information inferred from a symbolic knowledge base through logical reasoning. Despite empirical evidenc
Externí odkaz:
http://arxiv.org/abs/2308.10487
Deep neural networks usually perform poorly when the training dataset suffers from extreme class imbalance. Recent studies found that directly training with out-of-distribution data (i.e., open-set samples) in a semi-supervised manner would harm the
Externí odkaz:
http://arxiv.org/abs/2206.08802
Adversarial training, originally designed to resist test-time adversarial examples, has shown to be promising in mitigating training-time availability attacks. This defense ability, however, is challenged in this paper. We identify a novel threat mod
Externí odkaz:
http://arxiv.org/abs/2201.13329
Learning with noisy labels is a practically challenging problem in weakly supervised learning. In the existing literature, open-set noises are always considered to be poisonous for generalization, similar to closed-set noises. In this paper, we empir
Externí odkaz:
http://arxiv.org/abs/2106.10891
In addition to high accuracy, robustness is becoming increasingly important for machine learning models in various applications. Recently, much research has been devoted to improving the model robustness by training with noise perturbations. Most exi
Externí odkaz:
http://arxiv.org/abs/2103.14824