A Hybrid CNN-Transformer Model for Heart Disease Prediction Using Life History Data

Kavli Affiliate: Ting Xu | First 5 Authors: Ran Hao, Yanlin Xiang, Junliang Du, Qingyuan He, Jiacheng Hu | Summary: This study proposed a hybrid model of a convolutional neural network (CNN) and a Transformer to predict and diagnose heart disease. Based on CNN’s strength in detecting local features and the Transformer’s high capacity in […]


Continue.. A Hybrid CNN-Transformer Model for Heart Disease Prediction Using Life History Data

Photomapping the electrically coupled networks of the thalamus and cortex

Kavli Affiliate: Kevin Bender | Authors: Mitchell J Vaughn, David S Uygun, Radhika Basheer, Kevin Bender and Julie Haas | Summary: Electrical synapses are expressed ubiquitously across the brain and are crucial components of active neural circuitry and connectomes. Identification of coupled networks in living tissue is limited by technical demands of multiplexed recordings, and […]


Continue.. Photomapping the electrically coupled networks of the thalamus and cortex

Portable transcranial therapeutic ultrasound enhances targeted gene delivery for Parkinson’s disease: from rodent models to non-human primates

Kavli Affiliate: Vincent Ferrera | Authors: Alec J. Batts, Robin Ji, Sua Bae, Fotios N. Tsitsos, Sergio Jiménez-Gambín, Nancy Kwon, Samantha L. Gorman, Deny Tsakri, Rebecca L. Noel, Jonas Bendig, Daniella A. Jimenez, Melody DiBenedetto, Sofia A. Del Castillo, Filimon B. Keleta, James Caicedo, Alexander Romanov, Colleen T. Curley, Yulia Dzhashiashvili, Greglynn D. Walton-Gibbs, Bradley […]


Continue.. Portable transcranial therapeutic ultrasound enhances targeted gene delivery for Parkinson’s disease: from rodent models to non-human primates

The Stochastic Siren: Astrophysical Gravitational-Wave Background Measurements of the Hubble Constant

Kavli Affiliate: Daniel Holz | Summary:We report the first measurement of the Hubble constant $H_0$ using the stochastic gravitational-wave background arising from binary black hole mergers. This astrophysical background is sensitive to the expansion history of the Universe and thus can be used for cosmological parameter inference independently of not only electromagnetic methods, but also […]


Continue.. The Stochastic Siren: Astrophysical Gravitational-Wave Background Measurements of the Hubble Constant

The Stochastic Siren: Astrophysical Gravitational-Wave Background Measurements of the Hubble Constant

Kavli Affiliate: Daniel E. Holz | First 5 Authors: Bryce Cousins, Kristen Schumacher, Adrian Ka-Wai Chung, Colm Talbot, Thomas Callister | Summary: Gravitational waves from individually resolved compact object mergers can be used as standard sirens, offering a novel self-calibrating precision probe of cosmology. While the standard siren method has been well-explored, the gravitational-wave background […]


Continue.. The Stochastic Siren: Astrophysical Gravitational-Wave Background Measurements of the Hubble Constant

Mapping the merging zone of late infall in the AB Aur planet-forming system

Kavli Affiliate: Ruobing Dong | Summary:Late infall events challenge the traditional view that planet formation occurs without external influence. Here we present deep ALMA $^12$CO $J=2-1$ and SO $J_N=5_6-4_5$ observations toward AB Aurigae, a Class II disk system with strong signs of gravitational instability and ongoing planet formation. By applying Keplerian and anti-Keplerian masks, we […]


Continue.. Mapping the merging zone of late infall in the AB Aur planet-forming system

Reweighting and Analysing Event Generator Systematics by Neural Networks on High-Level Features

Kavli Affiliate: Mihoko Nojiri | Summary:The state-of-the-art deep learning (DL) models for jet classification use jet constituent information directly, improving performance tremendously. This draws attention to interpretability, namely, the decision-making process, correlations contributing to the classification, and high-level features (HLFs) representing the difference between signal and background. We address the interpretability issue using a modular […]


Continue.. Reweighting and Analysing Event Generator Systematics by Neural Networks on High-Level Features

Reweighting and Analysing Event Generator Systematics by Neural Networks on High-Level Features

Kavli Affiliate: Mihoko M. Nojiri | First 5 Authors: , , , , | Summary: The state-of-the-art deep learning (DL) models for jet classification use jet constituent information directly, improving performance tremendously. This draws attention to interpretability, namely, the decision-making process, correlations contributing to the classification, and high-level features (HLFs) representing the difference between signal […]


Continue.. Reweighting and Analysing Event Generator Systematics by Neural Networks on High-Level Features

Reweighting and Analysing Event Generator Systematics by Neural Networks on High-Level Features

Kavli Affiliate: Mihoko M. Nojiri | First 5 Authors: Amon Furuichi, Sung Hak Lim, Mihoko M. Nojiri, , | Summary: The state-of-the-art deep learning (DL) models for jet classification use jet constituent information directly, improving performance tremendously. This draws attention to interpretability, namely, the decision-making process, correlations contributing to the classification, and high-level features (HLFs) […]


Continue.. Reweighting and Analysing Event Generator Systematics by Neural Networks on High-Level Features

Global Neutrino Constraints on the Minimal U(1)$_L_μ-L_τ$ Model

Kavli Affiliate: Satoshi Shirai | Summary:We examine the minimal U(1)$_L_μ-L_τ$ gauge model in light of the latest neutrino data, including neutrino oscillations, cosmological observations, direct mass measurements, and neutrinoless double-beta decay. Using the most conservative oscillation data, we find that normal ordering is excluded at approximately the 90% confidence level (CL). Incorporating cosmological constraints from […]


Continue.. Global Neutrino Constraints on the Minimal U(1)$_L_μ-L_τ$ Model