A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Improved fMRI activation detection for single-subject studies.
New study shows limits to classifying brain activity from randomized EEG trials.
Many modern databases include personal and sensitive correlated data, such as private information on users connected together in a social network, and measurements of physical activity of single subjects across time. However, differential privacy, the current gold standard in data privacy, does not adequately address p…
In statistical connectomics, the quantitative study of brain networks, estimating the mean of a population of graphs based on a sample is a core problem. Often, this problem is especially difficult because the sample or cohort size is relatively small, sometimes even a single subject. While using the element-wise sampl…
Objective: Joint analysis of multi-subject brain imaging datasets has wide applications in biomedical engineering. In these datasets, some sources belong to all subjects (joint), a subset of subjects (partially-joint), or a single subject (individual). In this paper, this source model is referred to as joint/partially-…
In this work we reduce undersampling artefacts in two-dimensional () golden-angle radial cine cardiac MRI by applying a modified version of the U-net. We train the network on spatio-temporal slices which are previously extracted from the image sequences. We compare our approach to two and a Deep Lear…
Unified framework detects dynamic community structure in brain networks across individuals.
Deep learning model classifies concurrent human interactions from WiFi data with high accuracy.