Dr. Steven Fernandes and his research team proposed a novel confidence metric called the attribution-based confidence (ABC) metric for deep neural networks (DNNs). The ABC metric characterizes whether the output of a DNN on an input can be trusted. DNNs are known to be brittle on inputs outside their training distribution and are hence susceptible to adversarial attacks.

Dr. John J. Sunderland, PhD, MBA
Professor of Radiology-Division of Nuclear Medicine
Carver College of Medicine
University of Iowa

Abstract: The use of radioactive decay and their particulate and gamma-ray emissions in medical imaging and therapy dates back to the late 1930’s with the use of radioactive Iodine. Use of nuclear medicine expanded substantially in the 1960’s with the advent of the gamma camera, and then scientific excitement was boosted again with the invention of positron emission tomography (PET scanning) in the late 70’s. These nuclear technologies demonstrated the ability not to image the anatomy (like x-rays, CT, and later MRI), but to image the actual molecular biochemical underpinnings of diseases, like cancer (the Warburg Effect – look it up!), heart disease, and Alzheimer’s disease.

Clinical use of PET imaging began is the early 1990’s. Creighton University had one of the first clinical PET facilities in the US, opening in 1991 on Dorcas Street, complete with its own cyclotron used to produce radioactive 18F, 11C, 13N, 15O. But challenges to Medicare and insurance reimbursement coupled with regulatory complexities, mostly from FDA, resulted in slow growth, and even stagnation of the field.

Beginning around 2012, through advances in radiation detector technology, computing power, corporate investment, and infrastructure building, nuclear imaging and in particular, radiopharmaceutical therapy have taken off into one of the fastest growing segments of medicine today.

Brownie Friday will be held this Friday, October 4th in the Physics club room at 3. Feel free to make brownies to share or just come and enjoy the brownies!

You are invited to Physnic 2024!
Food, beverages, and dessert will be provided. This is a great way to meet other physics majors,
faculty, and like minded people. We look forward to seeing you there!

Guest Speaker: Dr. David Meyer, Professor of Mathematics at the University of California, San Diego

Quantum computing offers the possibility of efficient solutions to problems that may be classically intractable. Recently, these problems have included sampling from a probability distribution and inferring a relation from partial information (e.g., a recommender system), both of which fall into the class of data science or machine learning problems. An important characteristic of such problems and their solutions is the model for the data generating process, specifically whether it is classical or quantum. In the second part of the talk, we’ll analyze some data from a simple human behavior, answering survey questions, and discuss whether or not to model it quantum mechanically. I will (re)introduce all the quantum mechanics necessary for our analysis.

Public Lecture by Dr. Bharat Ratra, Distinguished Professor of Physics at Kansas State University.

Dark energy is the leading candidate for the mechanism that is responsible for causing the cosmological expansion to accelerate. Dr. Bharat Ratra will describe the astronomical data which persuade cosmologists that (as yet undetected) dark energy and dark matter are by far the main components of the energy budget of the universe at the present time. He will review how these observations have led to the development of a quantitative "standard" model of cosmology that describes the evolution of the universe from an early epoch of inflation to the complex hierarchy of structure seen today. In this non-technical talk, he will also discuss the basic physics, and the history of ideas, on which this model is based.