Strong kinetic-inductance Kerr nonlinearity with titanium nitride nanowires

Kavli Affiliate: Mohammad Mirhosseini | First 5 Authors: Chaitali Joshi, Wenyuan Chen, Henry G. LeDuc, Peter K. Day, Mohammad Mirhosseini | Summary: Thin films of disordered superconductors such as titanium nitride (TiN) exhibit large kinetic inductance (KI), high critical temperature, and large quality factors at the single-photon level. KI nonlinearity can be exploited as an […]


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Estimating Cosmological Constraints from Galaxy Cluster Abundance using Simulation-Based Inference

Kavli Affiliate: Brian Nord | First 5 Authors: Moonzarin Reza, Yuanyuan Zhang, Brian Nord, Jason Poh, Aleksandra Ciprijanovic | Summary: Inferring the values and uncertainties of cosmological parameters in a cosmology model is of paramount importance for modern cosmic observations. In this paper, we use the simulation-based inference (SBI) approach to estimate cosmological constraints from […]


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Consistent Quality Oriented Rate Control in HEVC via Balancing Intra and Inter Frame Coding

Kavli Affiliate: Wei Gao | First 5 Authors: Wei Gao, Qiuping Jiang, Ronggang Wang, Siwei Ma, Ge Li | Summary: Consistent quality oriented rate control in video coding has attracted much more attention. However, the existing efforts only focus on decreasing variations between every two adjacent frames, but neglect coding trade-off problem between intra and […]


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Testing the key role of the stellar mass-halo mass relation in galaxy merger rates and morphologies via DECODE, a novel Discrete statistical sEmi-empiriCal mODEl

| First 5 authors: [#feed_custom_author[1]], [#feed_custom_author[2]], [#feed_custom_author[3]], [#feed_custom_author[4]], [#feed_custom_author[5]] | Summary: The relative roles of mergers and star formation in regulating galaxy growth are still a matter of intense debate. We here present our DECODE, a new Discrete statistical sEmi-empiriCal mODEl specifically designed to predict rapidly and efficiently, in a full cosmological context, galaxy assembly […]


Continue.. Testing the key role of the stellar mass-halo mass relation in galaxy merger rates and morphologies via DECODE, a novel Discrete statistical sEmi-empiriCal mODEl

Improved Polarization Calibration of the BICEP3 CMB Polarimeter at the South Pole

Kavli Affiliate: Chao-Lin Kuo | First 5 Authors: J. Cornelison, C. Vergès, P. A. R. Ade, Z. Ahmed, M. Amiri | Summary: The BICEP3 Polarimeter is a small aperture, refracting telescope, dedicated to the observation of the Cosmic Microwave Background (CMB) at 95GHz. It is designed to target degree angular scale polarization patterns, in particular […]


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AlphaVC: High-Performance and Efficient Learned Video Compression

Kavli Affiliate: Jing Wang | First 5 Authors: Yibo Shi, Yunying Ge, Jing Wang, Jue Mao, | Summary: Recently, learned video compression has drawn lots of attention and show a rapid development trend with promising results. However, the previous works still suffer from some criticial issues and have a performance gap with traditional compression standards […]


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Polarizations of Gravitational Waves in the Bumblebee Gravity Model

Kavli Affiliate: Lijing Shao | First 5 Authors: Dicong Liang, Rui Xu, Xuchen Lu, Lijing Shao, | Summary: Lorentz violation modifies the dispersion relation of gravitational waves (GWs), and induces birefringence and anisotropy in propagation. Our study shows that Lorentz violation can also activate multiple polarizations of GWs. We use the gauge invariants to investigate […]


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Random Quantum Circuits

Kavli Affiliate: Matthew P. A. Fisher | First 5 Authors: Matthew P. A. Fisher, Vedika Khemani, Adam Nahum, Sagar Vijay, | Summary: Quantum circuits — built from local unitary gates and local measurements — are a new playground for quantum many-body physics and a tractable setting to explore universal collective phenomena far-from-equilibrium. These models have […]


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Content-oriented learned image compression

Kavli Affiliate: Jing Wang | First 5 Authors: Meng Li, Shangyin Gao, Yihui Feng, Yibo Shi, Jing Wang | Summary: In recent years, with the development of deep neural networks, end-to-end optimized image compression has made significant progress and exceeded the classic methods in terms of rate-distortion performance. However, most learning-based image compression methods are […]


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