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publications

The pulse: transient fMRI signal increases in subcortical arousal systems during transitions in attention.

Published in NeuroImage, 2021

Studies of attention emphasize cortical circuits for salience monitoring and top-down control. However, subcortical arousal systems have a major influence on dynamic cortical state. We hypothesize that task-related increases in attention begin with a “pulse” in subcortical arousal and cortical attention networks, which may be reflected indirectly through transient fMRI signals…

Recommended citation: Rong Li, Jun Hwan Ryu, Peter Vincent, Max Springer, Dan Kluger, Erik A. Levinsohn, Yu Chen, Huafu Chen and Hal Blumenfeld. (2021). "The pulse: transient fMRI signal increases in subcortical arousal systems during transitions in attention." Neuroimage, in press.

talks

A Machine Learning Approach for Classification of Spike-Wave Discharges in Absence Epilepsy

Published:

RATIONALE: Driving safety is consistently identified as a top concern for people with epilepsy. In young adults who outgrow clinical seizures, the question of whether they should be granted driving privileges remains a challenge for clinicians. We introduce an EEG-based machine learning approach to predict with a minimum false discovery rate whether or not generalized 3-4Hz spike-wave discharges (SWDs) produce impaired behavioral responsiveness.

Analysis of a Learning Based Algorithm for Budget Pacing

Published:

ABSTRACT: In this talk, we discuss analysis of a natural learning algorithm for uniform pacing of advertising budgets, equipped to adapt to varying ad sale platform conditions. On the demand side, advertisers face a fundamental technical challenge in automating bidding in a way that spreads their allotted budget across a given campaign subject to hidden, and potentially dynamic, “spent amount” functions. This automation and calculation must be done in runtime, implying a necessary low computational cost for the high frequency auction rate. Advertisers are additionally expected to exhaust nearly all of their sub-interval (by the hour or minute) budgets to maintain budgeting quotas in the long run. Our study analyzes a simple learning algorithm that adapts to the latent spent amount function of the market and learns the optimal average bidding value for a period of auctions in a small fraction of the total campaign time, allowing for smooth budget pacing in real-time. We prove our algorithm is robust to changes in the auction mechanism, and exhibits a fast convergence to a stable average bidding strategy. The algorithm not only guarantees that budgets are nearly spent in their entirety, but also smoothly paces bidding to prevent early exit from the campaign and a loss of the opportunity to bid on potentially lucrative impressions later in the period.