1.1k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Kyushu UniversityJP5 papers
- Aix-Marseille UniversitéFR3 papers
- Centre National de la Recherche ScientifiqueFR3 papers
- Heidelberg UniversityDE3 papers
- University of OsloNO3 papers
- A. Alikhanyan National LaboratoryAM2 papers
- AGH University of KrakowPL2 papers
- Alexandru Ioan Cuza UniversityRO2 papers
- Ankara UniversityTR2 papers
- Argonne National LaboratoryUS2 papers
- Aristotle University of ThessalonikiGR2 papers
- Azerbaijan National Academy of SciencesAZ2 papers
6 papers
ALMA Reveals an Explosive Outflow Candidate in IRAS 16119--5048
Yongquan Luo, Jianjun Zhou, Tie Liu +24
We present a multiwavelength study of the massive star formation region IRAS 16119-5048 (I16119) using ALMA ATOMS Band 3 and QUARKS Band 6 observations, complemented by archival AT…
Study of Particle Fluence Effects on Collected Charge and Depletion Voltage of the ATLAS IBL Planar Pixel Sensors
ATLAS Collaboration
After ten years of operation at the LHC, the planar pixel sensors of the innermost barrel layer of the ATLAS Pixel detector have accumulated an average bulk damage fluence in exces…
FIRESTORM I: Stellar Feedback and Gas Kinematics in the Evolved W40 Hub-Filament System
Ming-Kang Lim, Ram K. Yadav, L. K. Dewangan +12
The FIRESTORM project--Feedback-Induced Regions and Emission from Star-forming Tracers of ObseRvable Molecular Gas--has targeted four star-forming regions to quantify the impact of…
An implementation of neural simulation-based inference for parameter estimation in ATLAS
ATLAS Collaboration
Neural simulation-based inference is a powerful class of machine-learning-based methods for statistical inference that naturally handles high-dimensional parameter estimation witho…
Explanation of Machine Learning Models Using Shapley Additive Explanation and Application for Real Data in Hospital
Yasunobu Nohara, Koutarou Matsumoto, Hidehisa Soejima +1
When using machine learning techniques in decision-making processes, the interpretability of the models is important. In the present paper, we adopted the Shapley additive explanat…
\textit{C. elegans} collectively forms dynamical networks
Takuma Sugi, Hiroshi Ito, Masaki Nishimura +1
Understanding physical rules underlying collective motions requires perturbation of controllable parameters in self-propelled particles. However, controlling parameters in animals…