159 citations
- Texas A&M UniversityUS57 papers
- Chinese Academy of SciencesCN29 papers
- Institute of Theoretical PhysicsCN29 papers
- Academy of AthensGR27 papers
- Houston Advanced Research CenterUS27 papers
- Mitchell InstituteUS21 papers
- The University of MelbourneAU13 papers
- Eureka ScientificUS12 papers
- Kavli Institute for Theoretical SciencesCN11 papers
- ARC Centre of Excellence for Dark Matter Particle PhysicsAU10 papers
- Massachusetts Institute of TechnologyUS9 papers
- Center for Astrophysics Harvard & SmithsonianUS8 papers
154 papers
Melanoma Classification Through Deep Ensemble Learning and Explainable AI
Wadduwage Shanika Perera, ABM Islam, Van Vung Pham +1
Melanoma is one of the most aggressive and deadliest skin cancers, leading to mortality if not detected and treated in the early stages. Artificial intelligence techniques have rec…
Nuclear and electron scattering by neutrinos and dark matter in condensed systems
James B. Dent, Barry A. Friedman, Jayden L. Newstead +1
Low-threshold dark matter detectors, in particular cryogenic detectors based on dielectric materials, are among the best tools for probing sub-GeV dark matter masses. In the coming…
Direct Collapse Black Hole Candidates from Decaying Dark Matter
Yash Aggarwal, James B. Dent, Philip Tanedo +1
Injecting 1-13.6 eV photons into the early universe can suppress the molecular hydrogen abundance and alter the star formation history dramatically enough to produce direct collaps…
t-channel dark matter at the LHC -- a whitepaper
Chiara Arina, Benjamin Fuks, Luca Panizzi +67
This report, summarising work achieved in the context of the LHC Dark Matter Working Group, investigates the phenomenology of -channel dark matter models, spanning minimal setup…
Correlation Number for Potentials with Entropy Gaps and Cusped Hitchin Representations
Lien-Yung Kao, Giuseppe Martone
We introduce a correlation number for two strictly positive, locally Hölder continuous, independent potentials with strong entropy gaps at infinity on a topologically mixing counta…
Maximum likelihood degree of the -stochastic blockmodel
Cashous Bortner, Jennifer Garbett, Elizabeth Gross +3
Log-linear exponential random graph models are a specific class of statistical network models that have a log-linear representation. This class includes many stochastic blockmodel…