# Sitemap

A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

## Our GALLER-E

Human x DALL-E Collab

## Our GALLER-E

Human x DALL-E Collab

## Markdown

This is a page not in th emain menu

## Future Blog Post

Published:

This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

## Blog Post number 4

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

## Blog Post number 3

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

## Blog Post number 2

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

## Blog Post number 1

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

## 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.

## Analysis of a Learning Based Algorithm for Budget Pacing

Published in arXiv, 2022

In this paper, we analyze 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…

Recommended citation: MohammadTaghi Hajiaghayi and Max Springer. (2022). "Analysis of a Learning Based Algorithm for Budget Pacing" arXiv, preprint.

## A Machine-Learning Approach for Predicting Impaired Consciousness in Absence Epilepsy

Published in Annals of Clinical and Translational Neurology, 2022

Behavior during 3–4 Hz spike-wave discharges (SWDs) in absence epilepsy can vary from obvious behavioral arrest to no detectible deficits. Knowing if behavior is impaired is crucial for clinical care but may be difficult to determine without specialized behavioral testing, often inaccessible in practice. We aimed to develop a pure electroencephalography (EEG)-based machine-learning method to predict SWD-related behavioral impairment. Our classification goals were 100% predictive value, with no behaviorally impaired SWDs misclassified as spared; and maximal sensitivity. First, using labeled data with known behavior (130 SWDs in 34 patients), we extracted EEG time, frequency domain, and common spatial pattern features and applied support vector machines and linear discriminant analysis to classify SWDs as spared or impaired. We evaluated 32 classification models, optimized with 10-fold cross-validation. We then generalized these models to unlabeled data (220 SWDs in 41 patients), where behavior during individual SWDs was not known, but observers reported the presence of clinical seizures. For labeled data, the best classifier achieved 100% spared predictive value and 93% sensitivity. The best classifier on the unlabeled data achieved 100% spared predictive value, but with a lower sensitivity of 35%, corresponding to a conservative classification of 8 patients out of 23 as free of clinical seizures. Our findings demonstrate the feasibility of machine learning to predict impaired behavior during SWDs based on EEG features. With additional validation and optimization in a larger data sample, applications may include EEG-based prediction of driving safety, treatment adjustment, and insight into mechanisms of impaired consciousness in absence seizures.

Recommended citation: Springer, M. et al (2022), A machine-learning approach for predicting impaired consciousness in absence epilepsy. Ann Clin Transl Neurol. https://doi.org/10.1002/acn3.51647

## Online Algorithms for the Santa Claus Problem

Published in NeurIPS 2022, 2022

The Santa Claus problem is a fundamental problem in fair division: the goal is to partition a set of heterogeneous items among heterogeneous agents so as to maximize the minimum value of items received by any agent. In this paper, we study the online version of this problem where the items are not known in advance and have to be assigned to agents as they arrive over time. If the arrival order of items is arbitrary, then no good assignment exists in the worst case. However, we show that even for arbitrary items, if their arrival order is random, then for any $\varepsilon > 0$, we can obtain a competitive ratio of $1-\varepsilon$ when the optimal assignment gives value at least $\Omega(\log n / \varepsilon^2)$ to every agent. We also show that this result is almost tight: namely, if the optimal solution has value at most $C \ln n / \varepsilon$ for some constant $C$, then there is no $(1-\varepsilon)$-competitive algorithm even with random arrival order.

Recommended citation: Hajiaghayi. et al (2022), Online Algorithms for the Santa Claus Problem (NeurIPS 2022)

## Driving Safety in Patients with Generalized SWD but No Clinical Seizures: Evaluation with a Realistic Driving Simulator

Published:

See the poster here.

## 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.