The bird is a beautiful silver-gray, and as she dies twitching in the lasernet I'm grateful for two things: First, that she didn't make a sound. Second, that this will be the very last time. They're called corpse doves--because the darkest part of their gray plumage surrounds the lighter part, giving the impression that skeleton faces are peeking out from behind trash cans and bushes--and their crime is having the ability to carry diseases that would be compatible with humans. I open my hand, triggering the display from my imprinted handheld, and record an image to verify the elimination. A ding from my palm lets me know I've reached my quota for the day and, with that, the year. I'm tempted to give this one a send-off, a real burial with holy words and some flowers, but then I hear a pack of streetrats hooting beside me. My city-issued vest is reflective and nanopainted so it projects a slight glow. I don't know if it's to keep us safe like they say, or if it's just that so many of us are ex-cons working court-ordered labor, and civilians want to be able to keep an eye on us. Either way, everyone treats us like we're invisible--everyone except children.

Simultaneous behavioral and electrophysiological recordings call for new methods to reveal the interactions between neural activity and behavior. A milestone would be an interpretable model of the co-variability of spiking activity and behavior across trials. Here, we model a mouse cortical sensory-motor pathway in a tactile detection task reported by licking with a large recurrent spiking neural network (RSNN), fitted to the recordings via gradient-based optimization. We focus specifically on the difficulty to match the trial-to-trial variability in the data. Our solution relies on optimal transport to define a distance between the distributions of generated and recorded trials. The technique is applied to artificial data and neural recordings covering six cortical areas. We find that the resulting RSNN can generate realistic cortical activity and predict jaw movements across the main modes of trial-to-trial variability. Our analysis also identifies an unexpected mode of variability in the data corresponding to task-irrelevant movements of the mouse.

We start by proving statement (ii). We now prove statement (iii). The last constraint is trivially satisfied. This can be easily shown by induction. 's constraint remains equal when Let's pick such a branching Moreover, observe that every edge in B is tight.

We help address these challenges through three contributions. First, we publish a new dataset, EHRSHOT, which contains de-identified structured data from the electronic health records (EHRs) of 6,739 patients from Stanford Medicine.

Large conferences such as NeurIPS and AAAI serve as crossroads of various AI fields, since they attract submissions from a vast number of communities. However, in some cases, this has resulted in a poor reviewing experience for some communities, whose submissions get assigned to less qualified reviewers outside of their communities. An often-advocated solution is to break up any such large conference into smaller conferences, but this can lead to isolation of communities and harm interdisciplinary research.

In large-scale applications, datasets often contain billions of high-dimensional points. Grouping similar data points into clusters is crucial for understanding and organizing datasets.