Zobrazeno 1 - 10
of 1 195
pro vyhledávání: '"Pierre Martin"'
Autor:
Jacques Dupuy, Edwin Fouché, Céline Noirot, Pierre Martin, Charline Buisson, Françoise Guéraud, Fabrice Pierre, Cécile Héliès-Toussaint
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-15 (2024)
Abstract Cancer-derived cell lines are useful tools for studying cellular metabolism and xenobiotic toxicity, but they are not suitable for modeling the biological effects of food contaminants or natural biomolecules on healthy colonic epithelial cel
Externí odkaz:
https://doaj.org/article/023824247d6240e299b44555656c3982
Publikováno v:
Carbon Capture Science & Technology, Vol 11, Iss , Pp 100185- (2024)
CCS (Carbon Capture and Storage) is a major technology aiming to reduce greenhouse gases and reduce carbon footprint. In these applications, Oil Country Tubular Goods (OCTG) and associated premium connections are used to inject industrial CO2 into st
Externí odkaz:
https://doaj.org/article/2ca9295d25d741a2bcd403cbe1b1c6b5
Publikováno v:
IEEE Access, Vol 12, Pp 107017-107045 (2024)
The significant success of the Internet of Things in facilitating connections among consumer devices has led to an evident inclination towards connecting devices within industrial environments, commonly known as the Industrial Internet of Things (IIo
Externí odkaz:
https://doaj.org/article/3d2341ed009847f0a07a866b578d6330
Autor:
Alexandra Sala, James M. Cameron, Paul M. Brennan, Emma J. Crosbie, Tom Curran, Ewan Gray, Pierre Martin-Hirsch, David S. Palmer, Ihtesham U. Rehman, Nicholas J. W. Rattray, Matthew J. Baker
Publikováno v:
Journal of Experimental & Clinical Cancer Research, Vol 42, Iss 1, Pp 1-15 (2023)
Abstract The advances in cancer research achieved in the last 50 years have been remarkable and have provided a deeper knowledge of this disease in many of its conceptual and biochemical aspects. From viewing a tumor as a ‘simple’ aggregate of mu
Externí odkaz:
https://doaj.org/article/580b7a572fd543ac8ff0b8e90d364bf2
Autor:
Maximilian Dreher, Pierre Martin Dombrowski, Matthias Wolfgang Tripp, Niels Münster, Ulrich Koert, Gregor Witte
Publikováno v:
Nature Communications, Vol 14, Iss 1, Pp 1-9 (2023)
Structuring organic films is of scientific and technological interest. Here, the authors use partially fluorinated organic molecules exhibiting strong intermolecular interactions to form extended 2D molecular nanosheets and control their shape throug
Externí odkaz:
https://doaj.org/article/93c744c97b8e48dab4986440ab5a5dad
Autor:
Eugenio Bianchi, Pierre Martin-Dussaud
Publikováno v:
Universe, Vol 10, Iss 4, p 181 (2024)
The metric field of general relativity is almost fully determined by its causal structure. Yet, in spin foam models of quantum gravity, the role played by the causal structure is still largely unexplored. The goal of this paper is to clarify how caus
Externí odkaz:
https://doaj.org/article/630a72c2dc684642b7ef4c064fc211ad
This paper investigates the use of the ASTD language for ensemble anomaly detection in data logs. It uses a sliding window technique for continuous learning in data streams, coupled with updating learning models upon the completion of each window to
Externí odkaz:
http://arxiv.org/abs/2411.07272
Autor:
Pierre Martin
Publikováno v:
Archéologie Médiévale, Vol 51, Pp 317-319 (2021)
Externí odkaz:
https://doaj.org/article/996e904bf03444db97166b9d203ffbe4
Autor:
Masakuna, Jordan F., Nkashama, DJeff Kanda, Soltani, Arian, Frappier, Marc, Tardif, Pierre-Martin, Kabanza, Froduald
Training data sets intended for unsupervised anomaly detection, typically presumed to be anomaly-free, often contain anomalies (or contamination), a challenge that significantly undermines model performance. Most robust unsupervised anomaly detection
Externí odkaz:
http://arxiv.org/abs/2408.07718
Autor:
Nkashama, D'Jeff K., Félicien, Jordan Masakuna, Soltani, Arian, Verdier, Jean-Charles, Tardif, Pierre-Martin, Frappier, Marc, Kabanza, Froduald
Deep learning (DL) has emerged as a crucial tool in network anomaly detection (NAD) for cybersecurity. While DL models for anomaly detection excel at extracting features and learning patterns from data, they are vulnerable to data contamination -- th
Externí odkaz:
http://arxiv.org/abs/2407.08838