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Full-Text Articles in Physical Sciences and Mathematics
The European Commission And Ai: Guidelines, Acts And Plans Impacting The Teaching Of Ai And Teaching With Ai, Keith Quille, Brett A. Becker, Lidia Vidal-Meliá
The European Commission And Ai: Guidelines, Acts And Plans Impacting The Teaching Of Ai And Teaching With Ai, Keith Quille, Brett A. Becker, Lidia Vidal-Meliá
Academic Posters Collection
Recent developments, guidelines, and acts by the European Commission have started to frame policy for AI and related areas such as ML and data, not only for the broader community, but in the context of education specifically. This poster presents a succinct overview of these developments. Specifically, we look to bring together all publications that might impact the teaching of AI (for example, teacher expectations in the coming years around AI competencies) and publications that affect the use of AI in the classroom. We mean using tools and systems that incorporate both ‘Good Old Fashioned’ AI and those that can …
A Machine Learning Approach To Denoising Particle Detector Observations In Nuclear Physics, Polykarpos Thomadakis, Angelos Angelopoulos, Gagik Gavalian, Nikos Chrisochoides
A Machine Learning Approach To Denoising Particle Detector Observations In Nuclear Physics, Polykarpos Thomadakis, Angelos Angelopoulos, Gagik Gavalian, Nikos Chrisochoides
College of Sciences Posters
With the evolution in detector technologies and electronic components used in the Nuclear Physics field, experimental setups become larger and more complex. Faster electronics enable particle accelerator experiments to run with higher beam intensity, providing more interactions per time and more particles per interaction. However, the increased beam intensities present a challenge to particle detectors because of the higher amount of noise and uncorrelated signals. Higher noise levels lead to a more challenging particle reconstruction process by increasing the number of combinatorics to analyze and background signals to eliminate. On the other hand, increasing the beam intensity can provide physics …
Human Interaction With Fake News, Autumn Woodson, Sampath Jayarathna (Mentor)
Human Interaction With Fake News, Autumn Woodson, Sampath Jayarathna (Mentor)
Computer & Information Science: Research Experiences for Undergraduates in Disinformation Detection and Analytics
No abstract provided.
Fake Review Detection, Michael Husk, Faryaneh Poursardar (Mentor)
Fake Review Detection, Michael Husk, Faryaneh Poursardar (Mentor)
Computer & Information Science: Research Experiences for Undergraduates in Disinformation Detection and Analytics
No abstract provided.
Bayesian And Related Methods: Techniques Based On Bayes' Theorem, Mehmet Vurkaç
Bayesian And Related Methods: Techniques Based On Bayes' Theorem, Mehmet Vurkaç
Systems Science Friday Noon Seminar Series
Bayes' theorem is a simple algebraic consequence of conditional probability. Yet, its consequences are critical to philosophy, society, and technology. Starting from its simple derivation, we will show how its interpretation in terms of base rates (priors) and class-conditional likelihoods illuminates everyday problems in medicine and law, and provides signal processing, communications, machine learning, model selection, and other applications of statistics with powerful classification and estimation tools. Next, we will briefly examine some of the ways in which this theorem can be adopted to include multiple attributes, contexts, hypotheses, and levels of risk. Methods derived from or related to Bayes’ …
Hardware Acceleration Of Inference Computing: The Numenta Htm Algorithm, Dan Hammerstrom
Hardware Acceleration Of Inference Computing: The Numenta Htm Algorithm, Dan Hammerstrom
Systems Science Friday Noon Seminar Series
In this presentation I will describe the latest version of the Numenta HTM Cortical Learning Algorithm and why it is interesting for doing research into radical new computer architectures. Then I will discuss the hardware acceleration research we are doing, and briefly look at some preliminary applications development.
On The Effect Of Criticality And Topology On Learning In Random Boolean Networks, Alireza Goudarzi
On The Effect Of Criticality And Topology On Learning In Random Boolean Networks, Alireza Goudarzi
Systems Science Friday Noon Seminar Series
Random Boolean networks (RBN) are discrete dynamical systems composed of N automata with a binary state, each of which interacts with other automata in the network. RBNs were originally introduced as simplified models of gene regulation. In this presentation, I will present recent work done conjointly with Natali Gulbahce (UCSF), Thimo Rohlf (MPI, CNRS), and Christof Teuscher (PSU). We extend the study of learning in feedforward Boolean networks to random Boolean networks (RBNs) and systematically explore the relationship between the learning capability, the network topology, the system size N, the training sample T, and the complexity of the computational task. …