Testing for spectral index variations in polarised CMB foregrounds

Kavli Affiliate: George Efstathiou | First 5 Authors: Roger de Belsunce, Steven Gratton, George Efstathiou, , | Summary: We present a Bayesian parametric component separation method for polarised microwave sky maps. We solve jointly for the primary cosmic microwave background (CMB) signal and the main Galactic polarised foreground components. For the latter, we consider electron-synchrotron […]


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Improved Constraints on Cosmic Birefringence from the WMAP and Planck Cosmic Microwave Background Polarization Data

Kavli Affiliate: Eiichiro Komatsu | First 5 Authors: Johannes R. Eskilt, Eiichiro Komatsu, , , | Summary: The observed pattern of linear polarization of the cosmic microwave background (CMB) photons is a sensitive probe of physics violating parity symmetry under inversion of spatial coordinates. A new parity-violating interaction might have rotated the plane of linear […]


Continue.. Improved Constraints on Cosmic Birefringence from the WMAP and Planck Cosmic Microwave Background Polarization Data

Improved Constraints on Cosmic Birefringence from the WMAP and Planck Cosmic Microwave Background Polarization Data

Kavli Affiliate: Eiichiro Komatsu | First 5 Authors: Johannes R. Eskilt, Eiichiro Komatsu, , , | Summary: The observed pattern of linear polarization of the cosmic microwave background (CMB) photons is a sensitive probe of physics violating parity symmetry under inversion of spatial coordinates. A new parity-violating interaction might have rotated the plane of linear […]


Continue.. Improved Constraints on Cosmic Birefringence from the WMAP and Planck Cosmic Microwave Background Polarization Data

On Consistency in Graph Neural Network Interpretation

Kavli Affiliate: Xiang Zhang | First 5 Authors: Tianxiang Zhao, Dongsheng Luo, Xiang Zhang, Suhang Wang, | Summary: Uncovering rationales behind predictions of graph neural networks (GNNs) has received increasing attention over recent years. Instance-level GNN explanation aims to discover critical input elements, like nodes or edges, that the target GNN relies upon for making […]


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A Model Predictive Control Functional Continuous Time Bayesian Network for Self-Management of Multiple Chronic Conditions

Kavli Affiliate: Jing Wang | First 5 Authors: Syed Hasib Akhter Faruqui, Adel Alaeddini, Jing Wang, Susan P Fisher-Hoch, Joseph B Mccormick | Summary: Multiple chronic conditions (MCC) are one of the biggest challenges of modern times. The evolution of MCC follows a complex stochastic process that is influenced by a variety of risk factors, […]


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Keywords and Instances: A Hierarchical Contrastive Learning Framework Unifying Hybrid Granularities for Text Generation

Kavli Affiliate: Feng Wang | First 5 Authors: Mingzhe Li, XieXiong Lin, Xiuying Chen, Jinxiong Chang, Qishen Zhang | Summary: Contrastive learning has achieved impressive success in generation tasks to militate the "exposure bias" problem and discriminatively exploit the different quality of references. Existing works mostly focus on contrastive learning on the instance-level without discriminating […]


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Measurement of $Br(H to γγ)$ in $e^{+}e^{-}$ collider at 240GeV in CEPC

Kavli Affiliate: Feng Wang | First 5 Authors: Fangyi Guo, Yaquan Fang, Gang Li, Xinchou Lou, Feng Wang | Summary: This note presents the prospects of measuring $Br(Hto gammagamma)$ of the Standard Model Higgs boson at the future Circular Electron-Positron Collider (CEPC) baseline detector (CEPC-v4) in $e^{+}e^{-}$ collisions at $sqrt{s}$ = 240 GeV in $e^{+}e^{-} […]


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On odd number of fermion zero modes on solitons in quantum field theory and string/M theory

Kavli Affiliate: Yuji Tachikawa | First 5 Authors: Yotaro Sato, Yuji Tachikawa, Taizan Watari, , | Summary: We argue that having an odd number of Majorana fermion zero modes on a dynamical point-like soliton signifies an inconsistency in a theory with 3+1 and higher dimensions. We check this statement in a couple of examples in […]


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A large deviation principle for the stochastic heat equation with general rough noise

Kavli Affiliate: Ran Wang | First 5 Authors: Ruinan Li, Ran Wang, Beibei Zhang, , | Summary: We study Freidlin-Wentzell’s large deviation principle for one dimensional nonlinear stochastic heat equation driven by a Gaussian noise: $$frac{partial u^varepsilon(t,x)}{partial t} = frac{partial^2 u^varepsilon(t,x)}{partial x^2}+sqrt{varepsilon} sigma(t, x, u^varepsilon(t,x))dot{W}(t,x),quad t> 0,, xinmathbb{R},$$ where $dot W$ is white in time […]


Continue.. A large deviation principle for the stochastic heat equation with general rough noise

Large deviation principle for stochastic heat equation with general rough noise

Kavli Affiliate: Ran Wang | First 5 Authors: Ruinan Li, Ran Wang, Beibei Zhang, , | Summary: We study Freidlin-Wentzell’s large deviation principle for one dimensional nonlinear stochastic heat equation driven by a Gaussian noise: $$frac{partial u^varepsilon(t,x)}{partial t} = frac{partial^2 u^varepsilon(t,x)}{partial x^2}+sqrt{varepsilon} sigma(t, x, u^varepsilon(t,x))dot{W}(t,x),quad t> 0,, xinmathbb{R},$$ where $dot W$ is white in time […]


Continue.. Large deviation principle for stochastic heat equation with general rough noise