The complexity of classifying continuous t-norms up to isomorphism

Kavli Affiliate: Yi Zhou | First 5 Authors: Jialiang He, Lili Shen, Yi Zhou, , | Summary: It is shown that the isomorphism relation between continuous t-norms is Borel bireducible with the relation of order isomorphism between linear orders on the set of natural numbers, and therefore, it is Borel bireducible with every Borel complete […]


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Coherent Information Phase Transition in a Noisy Quantum Circuit

Kavli Affiliate: Jing Wang | First 5 Authors: Dongheng Qian, Jing Wang, , , | Summary: Coherent information quantifies the transmittable quantum information through a channel and is directly linked to the channel’s quantum capacity. In the context of dynamical purification transitions, scrambling dynamics sustain extensive and positive coherent information at low measurement rates, but […]


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Modular invariance groups and defect McKay-Thompson series

Kavli Affiliate: Jeffrey A. Harvey | First 5 Authors: Harry Fosbinder-Elkins, Jeffrey A. Harvey, , , | Summary: It has been known since 1992 that the McKay-Thompson series $T_g(q)$ of the Moonshine module form Hauptmoduln for genus zero subgroups of $SL(2, mathbb{R})$. In 2021, Lin and Shao constructed a series analogous to the McKay-Thompson series […]


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Galaxies Lighting Up: Discovery of Seventy New Turn-on Changing-look Quasars

Kavli Affiliate: Linhua Jiang | First 5 Authors: Qian Yang, Paul J. Green, Xue-Bing Wu, Michael Eracleous, Linhua Jiang | Summary: "Changing-look quasars" (CLQs), discovered less than a decade ago, show dramatic, rapid changes in optical/UV continuum and broad line emission. The majority of CLQs have been found dimming as "turn-off" CLQs because most selection […]


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Polyunsaturated fatty acid-derived lipid mediator networks characterize COVID-19 severity and risk for critical illness

Kavli Affiliate: Martin Lindquist | Authors: Maria Papadaki, Eleftherios Pavlos, Marc Dubourdeau, Vincent Bailif, Kamal Badirou, Ioanna-Evdokia Galani, Dimitris Papelis, Natalia Kamperi, Vasiliki Triantafyllia, Lena Siouti, Maria Salagianni, Maria Manioudaki, Nikolaos Paschalidis, Giannis Vatsellas, Evangelia Koukaki, Vasiliki Rapti, Dimitris Thanos, Nikoletta Rovina, Garyfallia Poulakou, Aurelie Cobat, Jean-Laurent Casanova, Constantin Tamvakopoulos and Evangelos Andreakos | Summary: […]


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Effects of connectivity hyperalignment (CHA) on estimated brain network properties: from coarse-scale to fine-scale

Kavli Affiliate: Martin Lindquist | Authors: Farzad V. Farahani, Mary Beth Nebel, Tor Wager and Martin A. Lindquist | Summary: Recent gains in functional magnetic resonance imaging (fMRI) studies have been driven by increasingly sophisticated statistical and computational techniques and the ability to capture brain data at finer spatial and temporal resolution. These advances allow […]


Continue.. Effects of connectivity hyperalignment (CHA) on estimated brain network properties: from coarse-scale to fine-scale

Spin Excitation Continuum in the Exactly Solvable Triangular-Lattice Spin Liquid CeMgAl11O19

Kavli Affiliate: Leon Balents | First 5 Authors: Bin Gao, Tong Chen, Chunxiao Liu, Mason L. Klemm, Shu Zhang | Summary: In magnetically ordered insulators, elementary quasiparticles manifest as spin waves – collective motions of localized magnetic moments propagating through the lattice – observed via inelastic neutron scattering. In effective spin-1/2 systems where geometric frustrations […]


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Spin-dependent exotic interactions

Kavli Affiliate: Yevgeny V. Stadnik | First 5 Authors: Lei Cong, Wei Ji, Pavel Fadeev, Filip Ficek, Min Jiang | Summary: Novel interactions beyond the four known fundamental forces in nature (electromagnetic, gravitational, strong and weak interactions), may arise due to "new physics" beyond the standard model, manifesting as a "fifth force". This review is […]


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Towards a Unified Benchmark and Framework for Deep Learning-Based Prediction of Nuclear Magnetic Resonance Chemical Shifts

Kavli Affiliate: Feng Wang | First 5 Authors: Fanjie Xu, Wentao Guo, Feng Wang, Lin Yao, Hongshuai Wang | Summary: The study of structure-spectrum relationships is essential for spectral interpretation, impacting structural elucidation and material design. Predicting spectra from molecular structures is challenging due to their complex relationships. Herein, we introduce NMRNet, a deep learning […]


Continue.. Towards a Unified Benchmark and Framework for Deep Learning-Based Prediction of Nuclear Magnetic Resonance Chemical Shifts