Tag: Computer science

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Automating Observations with Talmo Pereira

Episode 2 of Princeton Insights: The Highlights

In this episode of The Highlights, we're joined by Talmo Pereira, a Ph.D. candidate in the Department of Neuroscience. Pereira holds a Porter Ogden Jacobus Fellowship, one of the highest graduate honors given by the University. We discuss the ups and downs of grad school and how the software he is developing, Social LEAP Estimates Animal Poses (SLEAP), is working to unite neuroscience, ecology, and computer science.

RESOURCES:

Princeton Insights coverage: A computational model for automated tracking of socially-interacting animals.

Original Paper: SLEAP: Multi-animal pose tracking.

Coverage by the Daily Princetonian.

CREDITS:

This episode of The Highlights was produced under the 145th Managing Board of the Daily Princetonian in partnership with Princeton Insights. Talmo Pereira is a Ph.D. candidate in the Department of Neuroscience. He can be reached at talmo@princeton.edu.

Written/Hosted by Thiago Tarraf Varella GS and Andy Jones GS.

Produced by Isabel Rodrigues.

Original Insights Coverage by Andy Jones GS.

Subscribe to The Highlights on Spotify, Podcast Addict, or wherever you get your podcasts! And subscribe to the Daily Princetonian to get the latest news at Princeton!

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Using Doppler radar to detect and track hidden objects

Review written by Aishwarya Mandyam (COS, G1)

The prevalence of self-driving or partially self-driving cars is increasingly within our sights. However, before these vehicles can make their mainstream debut, it’s important that consumers are ensured of their safety. In particular, these cars should be able to “see” and “act” like human drivers. As such, a self-driving car’s collision and error detection system must effectively reason about what it cannot see. A big concern with self-driving cars is the question of safety; we expect that a human driver has sufficient intuition to avoid pedestrians and stationary objects. Naturally, we would expect self-driving cars to exhibit the same level of caution for avoiding collisions. In a new paper out of Professor Felix Heide’s group in the Princeton COS department in collaboration with Mercedes Benz, Scheiner et al. introduce a method that uses Doppler radar to enable cars to see around corners. Scheiner et al.’s method to track hidden objects allows for a more effective error detection method that can make these vehicles better suited to operating in the real world where safety is a primary concern. 

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The false duality of habitual versus goal-directed behavior

Written by Anika Maskara ‘23 & Thiago Tarraf Varella (PSY GS)

It is common in popular culture to imagine human decision making as a clash of two distinct choices. There is a “good option” and a “bad option,” an angel or a devil sitting on our shoulders. Like many dichotomies, though, that view of decision making is misleading. It is true that research suggests we have two different decision-making systems that sometimes disagree about which action to take, but neither is better or worse than the other; they simply use different algorithms to help us decide what to do.

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How can human uncertainty improve machine image classification?

Review written by Shanka Subhra Mondal (ELE)

In recent years, machine learning (ML) has become commonplace in our software and devices. Its applications are varied, ranging from finance and marketing to healthcare and computer vision. ML already has the ability to out-perform humans on many tasks, such as video game competitions and image/object recognition, to name a few. At a high-level, ML comprises a set of algorithms that rely heavily on data (training data) to make decisions about another set of data (testing data) that it has not previously encountered. One of the sub-fields of computer vision where machine learning has proven particularly useful is in image classification, where the goal is to categorize objects in a given image. While this task might sound easy for humans, it can be challenging for an algorithm, particularly when the picture is blurred, not properly illuminated, or noisy. Robust image classification is not an easy task.

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Learning to control the brain

Review written by Eleni Papadoyannis (PNI)

How do humans control a complex system like the brain? Over the years, neuroscientists have discovered numerous methods to do exactly that. Applying chemicals, such as muscimol, can drive inhibition to shut down a brain region. Alternatively, shining light can selectively activate certain cell types through the photo-sensitive protein channelrhodopsin. Sending electrical impulses via electrodes in deep brain stimulation (DBS) can also control regional activity in humans. Causal manipulation of the brain not only offers incredible insight into hypotheses relating neural activity to behavior, but also serves as a clinical tool. Electrical and magnetic stimulation methods have been used as therapies for treating patients with a variety of diseases and disorders, such as using DBS to control motor disruption in Parkinson’s. A major limitation with many stimulation methods, however, is that the protocol is static while the brain is plastic—over time, brain responses to stimulation may no longer elicit what was intended as the brain naturally changes. 

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