Dual Prompt Tuning for Domain-Aware Federated Learning

Kavli Affiliate: Feng Wang | First 5 Authors: Guoyizhe Wei, Feng Wang, Anshul Shah, Rama Chellappa, | Summary: Federated learning is a distributed machine learning paradigm that allows multiple clients to collaboratively train a shared model with their local data. Nonetheless, conventional federated learning algorithms often struggle to generalize well due to the ubiquitous domain […]


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A surprisingly high number of dual active galactic nuclei in the early Universe

Kavli Affiliate: Roberto Maiolino | First 5 Authors: Michele Perna, Santiago Arribas, Isabella Lamperti, Chiara Circosta, Elena Bertola | Summary: Merger events can trigger gas accretion onto supermassive black holes (SMBHs) sitting at the centre of galaxies, and form close pairs of active galactic nuclei (AGN). The fraction of AGN in pairs gives key information […]


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Fe-TAMLs as a new class of small molecule peroxidase probes for correlated light and electron microscopy

Kavli Affiliate: Mark Ellisman | Authors: Stephen R Adams, Mason R Mackey, Ranjan Ramachandra, Thomas J Deerinck, Guillaume A Castillon, Sebastien Phan, Junru Hu, Daniela Boassa, John T Ngo and Mark H Ellisman | Summary: We introduce Fe-TAML, a small molecule-based peroxidase as a versatile new member of the correlated fluorescence and electron microscopy toolkit. […]


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Decoding transcriptomic signatures of Cysteine String Protein alpha-mediated synapse maintenance

Kavli Affiliate: Sreeganga Chandra | Authors: Na Wang, Biqing Zhu, Mary Alice Allnutt, Rosalie M Grijalva, Hongyu Zhao and Sreeganga S Chandra | Summary: Synapse maintenance is essential for generating functional circuitry and decrement in this process is a hallmark of neurodegenerative disease. While we are beginning to understand the basis of synapse formation, much […]


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Subrelativistic Alternating Phase Focusing Dielectric Laser Accelerators

Kavli Affiliate: Robert L. Byer | First 5 Authors: Payton Broaddus, Thilo Egenolf, Dylan S. Black, Melanie Murillo, Clarisse Woodahl | Summary: We demonstrate a silicon-based electron accelerator that uses laser optical near fields to both accelerate and confine electrons over extended distances. Two dielectric laser accelerator (DLA) designs were tested, each consisting of two […]


Continue.. Subrelativistic Alternating Phase Focusing Dielectric Laser Accelerators

Sub-relativistic Alternating Phase Focusing Dielectric Laser Accelerators

Kavli Affiliate: Robert L. Byer | First 5 Authors: Payton Broaddus, Thilo Egenolf, Dylan S. Black, Melanie Murillo, Clarisse Woodahl | Summary: We demonstrate a silicon-based electron accelerator that uses laser optical near fields to both accelerate and confine electrons over extended distances. Two dielectric laser accelerators (DLA) designs were tested, each consisting of two […]


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Primordial non-Gaussianities with weak lensing: Information on non-linear scales in the Ulagam full-sky simulations

Kavli Affiliate: Chihway Chang | First 5 Authors: Dhayaa Anbajagane, Chihway Chang, Hayden Lee, Marco Gatti, | Summary: Primordial non-Gaussianities (PNGs) are signatures in the density field that encode particle physics processes from the inflationary epoch. Such signatures have been extensively studied using the Cosmic Microwave Background, through constraining the amplitudes, $f^{X}_{rm NL}$, with future […]


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A Neural Scaling Law from Lottery Ticket Ensembling

Kavli Affiliate: Max Tegmark | First 5 Authors: Ziming Liu, Max Tegmark, , , | Summary: Neural scaling laws (NSL) refer to the phenomenon where model performance improves with scale. Sharma & Kaplan analyzed NSL using approximation theory and predict that MSE losses decay as $N^{-alpha}$, $alpha=4/d$, where $N$ is the number of model parameters, […]


Continue.. A Neural Scaling Law from Lottery Ticket Ensembling

A Neural Scaling Law from Lottery Ticket Ensembling

Kavli Affiliate: Max Tegmark | First 5 Authors: Ziming Liu, Max Tegmark, , , | Summary: Neural scaling laws (NSL) refer to the phenomenon where model performance improves with scale. Sharma & Kaplan analyzed NSL using approximation theory and predict that MSE losses decay as $N^{-alpha}$, $alpha=4/d$, where $N$ is the number of model parameters, […]


Continue.. A Neural Scaling Law from Lottery Ticket Ensembling