Results 51 to 60 of about 352,249 (316)
Anomaly Detection with Partially Observed Anomalies [PDF]
Companion of the The Web Conference 2018 on The Web Conference 2018 - WWW '18, 2018 In this paper, we consider the problem of anomaly detection. Previous studies mostly deal with this task in either supervised or unsupervised manner according to whether label information is available. However, there always exists settings which are different from the two standard manners.
Ya-Lin Zhang +4 more
openaire +1 more source
Anchorage‐independent and faster growth in clonal population from UV‐irradiated NER‐deficient cells
FEBS Open Bio, EarlyView.UV‐irradiated cells expressing a DDB2 mutant protein unable to interact with PCNA (DDB2PCNA‐) form clones able to grow without anchorage. Different experimental approaches reveal heterogeneity in cell cycle regulation and drug response within these clones, emphasizing the crucial role of the DDB2‐PCNA interaction in preventing cellular transformation ...
Paola Perucca +6 more
wiley +1 more source
Anomaly Detection by Robust Statistics
, 2017 Real data often contain anomalous cases, also known as outliers. These may spoil the resulting analysis but they may also contain valuable information. In either case, the ability to detect such anomalies is essential.
Hubert, Mia, Rousseeuw, Peter J.
core +1 more source
FEBS Open Bio, EarlyView.Time‐resolved X‐ray solution scattering captures how proteins change shape in real time under near‐native conditions. This article presents a practical workflow for light‐triggered TR‐XSS experiments, from data collection to structural refinement. Using a calcium‐transporting membrane protein as an example, the approach can be broadly applied to study ...
Fatemeh Sabzian‐Molaei +3 more
wiley +1 more source
Comparative Analysis of Anomaly Detection Techniques Using Generative Adversarial Network
Sir Syed University Research Journal of Engineering and Technology, 2023 Anomaly detection in a piece of data is a challenging task. Researchers use different approaches to classify data as anomalous. These include traditional, supervised, unsupervised, and semi-supervised techniques.
Imran Ullah Khan +4 more
doaj
Importance Weighted Adversarial Discriminative Transfer for Anomaly Detection [PDF]
, 2021 Cangning Fan +7 more
openalex +1 more source
Adversarially Learned Anomaly Detection on CMS Open Data: re-discovering the top quark
, 2020 We apply an Adversarially Learned Anomaly Detection (ALAD) algorithm to the problem of detecting new physics processes in proton-proton collisions at the Large Hadron Collider.
Cerri, Olmo +5 more
core
Unsupervised Anomaly Detection with Unlabeled Data Using Clustering [PDF]
, 2005 Intrusions pose a serious security risk in a network environment. New intrusion types, of which detection systems are unaware, are the most difficult to detect.
Abdullah, Abdul Hanan +2 more
core
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective To delineate specific in vivo white matter pathology in neuronal intranuclear inclusion disease (NIID) using diffusion spectrum imaging (DSI) and define its clinical relevance. Methods DSI was performed on 42 NIID patients and 38 matched controls.
Kaiyan Jiang +10 more
wiley +1 more source
Hyperspectral Anomaly Detection Method Based on Low Rank Total Variation Regu-larization
Jisuanji kexue yu tansuo, 2020 Hyperspectral remote sensing technology provides abundant spectral information for exploring objects and supplies a better data source for anomaly detection.
XU Chao, ZHAN Tianming
doaj +1 more source