K
Kevin K. Lin
Researcher at University of Arizona
Publications - 98
Citations - 1656
Kevin K. Lin is an academic researcher from University of Arizona. The author has contributed to research in topics: Rucaparib & PARP inhibitor. The author has an hindex of 20, co-authored 86 publications receiving 1236 citations. Previous affiliations of Kevin K. Lin include New York University & University of California, Berkeley.
Papers
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Proceedings Article
Train Big, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers
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The Selberg Zeta Function for Convex Co-Compact Schottky Groups
TL;DR: In this article, an upper bound on the Selberg zeta function for a convex co-compact Schottky group acting on the hyperbolic space ℍ� n====== +1 is given, where in strips parallel to the imaginary axis the zeta functions are bounded by exp (C|s|δ) where δ is the dimension of the limit set of the group.
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Phase-amplitude descriptions of neural oscillator models
TL;DR: The phase description that allows one to track the evolution of distance from the cycle as well as phase on cycle is generalised, and the subsequent phase-amplitude description is shown to be very well suited to understanding the response of the oscillator to external stimuli.
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Molecular and clinical determinants of response and resistance to rucaparib for recurrent ovarian cancer treatment in ARIEL2 (Parts 1 and 2)
Elizabeth M. Swisher,Tanya T. Kwan,Amit M. Oza,Anna V. Tinker,Isabelle Ray-Coquard,Ana Oaknin,Robert L. Coleman,Carol Aghajanian,Gottfried E. Konecny,David M. O'Malley,Alexandra Leary,Diane Provencher,Stephen Welch,Lee-may Chen,Andrea E. Wahner Hendrickson,Ling Ma,Prafull Ghatage,R Kristeleit,Oliver Dorigo,Ashan Musafer,Scott H. Kaufmann,Julia A. Elvin,Douglas I. Lin,Setsuko K. Chambers,E. Dominy,Lan Thanh Vo,Sandra Goble,Lara Maloney,Heidi Giordano,Thomas Harding,Alexander Dobrovic,Clare L. Scott,Kevin K. Lin,Iain A. McNeish +33 more
TL;DR: In this paper, a post hoc exploratory biomarker analysis of pre-and post-platinum ARIEL2 samples, RAD51C and RAD51D mutations and high-level BRCA1 promoter methylation predict response to rucaparib.
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Data-based stochastic model reduction for the Kuramoto–Sivashinsky equation
TL;DR: In this paper, the authors proposed a method to solve the problem of energy-efficient wireless sensor networks for the first time, which was later validated by the National Science Foundation and U.S. Department of Energy.