Gravitational radiation from Kerr black holes using the Sasaki-Nakamura formalism: Waveforms and fluxes at infinity

Kavli Affiliate: Xian Chen| Summary: In linear perturbation theory for Kerr black holes, there are two equivalent formalisms, namely the Teukolsky and the Sasaki-Nakamura (SN) formalism. Typically, one defaults to the Teukolsky formalism, especially when calculating extreme mass ratio inspiral waveforms, and uses the SN formalism when dealing with extended sources, as it offers superior […]


Continue.. Gravitational radiation from Kerr black holes using the Sasaki-Nakamura formalism: Waveforms and fluxes at infinity

Gravitational radiation from Kerr black holes using the Sasaki-Nakamura formalism: waveforms and fluxes at infinity

Kavli Affiliate: Xian Chen| Summary: In linear perturbation theory for Kerr black holes, there are two equivalent formalisms, namely the Teukolsky and the Sasaki-Nakamura (SN) formalism. Typically, one defaults to the Teukolsky formalism, especially when calculating extreme mass ratio inspiral waveforms, and uses the SN formalism when dealing with extended sources, as it offers superior […]


Continue.. Gravitational radiation from Kerr black holes using the Sasaki-Nakamura formalism: waveforms and fluxes at infinity

Reinforcement Learning Control of Quantum Error Correction

Kavli Affiliate: N. C. Yeh| First 5 Authors: [#item_custom_name[1, [#item_custom_name[2, [#item_custom_name[3, [#item_custom_name[4, [#item_custom_name[5| Summary:The promise of fault-tolerant quantum computing is challenged by environmental drift that relentlessly degrades the quality of quantum operations. The contemporary solution, halting the entire quantum computation for recalibration, is unsustainable for the long runtimes of the future algorithms. We address this […]


Continue.. Reinforcement Learning Control of Quantum Error Correction

Reinforcement Learning Control of Quantum Error Correction

Kavli Affiliate: Peter Ford| First 5 Authors: Volodymyr Sivak, Volodymyr Sivak, , , | Summary:The promise of fault-tolerant quantum computing is challenged by environmental drift that relentlessly degrades the quality of quantum operations. The contemporary solution, halting the entire quantum computation for recalibration, is unsustainable for the long runtimes of the future algorithms. We address […]


Continue.. Reinforcement Learning Control of Quantum Error Correction

Polarization Controlled Supercurrent in Ferroelectric Josephson Junction

Kavli Affiliate: Mazhar Ali | Summary: Josephson junctions are essential devices in superconducting electronics and quantum computing hardware. Here we predict electrical control of the supercurrent in composite superconductor-insulator-ferroelectric-insulator-superconductor (S-I-FE-I-S) Josephson junctions. Inversion symmetry broken by unequal dielectric barrier thicknesses and/or potentials converts ferroelectric polarization reversal into a substantial change of the critical current. With […]


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Scalar-Magnetometer Search for Ultralight Dark Photon Dark Matter with a Single-Site, Two-Sensor Array: A 6-Channel DTFT Likelihood Analysis with Scalar Optically Pumped Magnetometers

Kavli Affiliate: Yevgeny Stadnik | Summary:We report on a laboratory search for ultralight dark photon dark matter using a single-site, two-sensor scalar magnetometer array. The experiment employs two scalar optically pumped magnetometers (OPMs) operated in a differential configuration to suppress common-mode noise and enhance sensitivity to spatially coherent dark photon fields. We analyze 10.5 hours […]


Continue.. Scalar-Magnetometer Search for Ultralight Dark Photon Dark Matter with a Single-Site, Two-Sensor Array: A 6-Channel DTFT Likelihood Analysis with Scalar Optically Pumped Magnetometers

From IDs to Semantics: A Generative Framework for Cross-Domain Recommendation with Adaptive Semantic Tokenization

Kavli Affiliate: Wayne Hu| First 5 Authors: Peiyu Hu, Peiyu Hu, , , | Summary:Cross-domain recommendation (CDR) is crucial for improving recommendation accuracy and generalization, yet traditional methods are often hindered by the reliance on shared user/item IDs, which are unavailable in most real-world scenarios. Consequently, many efforts have focused on learning disentangled representations through […]


Continue.. From IDs to Semantics: A Generative Framework for Cross-Domain Recommendation with Adaptive Semantic Tokenization

Predict-then-Optimize for Seaport Power-Logistics Scheduling: Generalization across Varying Tasks Stream

Kavli Affiliate: Yuchong Tai | First 5 Authors: Chuanqing Pu, Chuanqing Pu, , , | Summary: Power-logistics scheduling in modern seaports typically follow a predict-then-optimize pipeline. To enhance the decision quality of forecasts, decision-focused learning has been proposed, which aligns the training of forecasting models with downstream decision outcomes. However, this end-to-end design inherently restricts […]


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AlignSurvey: A Comprehensive Benchmark for Human Preferences Alignment in Social Surveys

Kavli Affiliate: Biao Huang | Summary:Understanding human attitudes, preferences, and behaviors through social surveys is essential for academic research and policymaking. Yet traditional surveys face persistent challenges, including fixed-question formats, high costs, limited adaptability, and difficulties ensuring cross-cultural equivalence. While recent studies explore large language models (LLMs) to simulate survey responses, most are limited to […]


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Probing the Physical Origin of the Balmer Decrement in the Broad-line Region of Nearby Active Galactic Nuclei via Spectral Variability

Kavli Affiliate: Luis Ho| First 5 Authors: Suyeon Son, Suyeon Son, , , | Summary:To investigate the physical origin of the Balmer decrement in the broad-line region of active galactic nuclei (AGNs), we measure the temporal variability of the fluxes of the broad H$β$ and H$α$ emission lines using multi-epoch spectroscopic data of low-redshift AGNs […]


Continue.. Probing the Physical Origin of the Balmer Decrement in the Broad-line Region of Nearby Active Galactic Nuclei via Spectral Variability