3,528 research outputs found
Value Discount of Business Groups Surrounding the Asia Financial Crisis: Evidence from Korean Chaebols
Asian Financial Crisis, Business Group, Chaebol, Diversification, Firm Value
Contextual Linear Bandits under Noisy Features: Towards Bayesian Oracles
We study contextual linear bandit problems under uncertainty on features;
they are noisy with missing entries. To address the challenges from the noise,
we analyze Bayesian oracles given observed noisy features. Our Bayesian
analysis finds that the optimal hypothesis can be far from the underlying
realizability function, depending on noise characteristics, which is highly
non-intuitive and does not occur for classical noiseless setups. This implies
that classical approaches cannot guarantee a non-trivial regret bound. We thus
propose an algorithm aiming at the Bayesian oracle from observed information
under this model, achieving regret bound with respect to
feature dimension and time horizon . We demonstrate the proposed
algorithm using synthetic and real-world datasets.Comment: 30 page
Disorder-free sputtering method on graphene
Deposition of various materials onto graphene without causing any disorder is
highly desirable for graphene applications. Especially, sputtering is a
versatile technique to deposit various metals and insulators for spintronics,
and indium tin oxide to make transparent devices. However, the sputtering
process causes damage to graphene because of high energy sputtered atoms. By
flipping the substrate and using a high Ar pressure, we demonstrate that the
level of damage to graphene can be reduced or eliminated in dc, rf, and
reactive sputtering processes
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