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Because of their open enrollment policies, community colleges serve large numbers of students whose K–12 education has left them unready to succeed in college-level courses. Colleges typically use test scores such as the ACT to place students into appropriate course levels. However, the use of test-based measures alone has been criticized as being an incomplete measure of students’ ability. As a result, most colleges have begun to use high school grade point average (HSGPA) to supplement placement policy. We examine one such reform enacted across the 16 colleges in the Kentucky Community & Technical College System (KCTCS). We find that the number of students deemed college ready upon entrance to KCTCS substantially rose after HSGPA was added and that HSGPA is a stronger predictor of passing gateway courses than ACT scores. Newly college ready students based on HSGPA but not ACT scores generally had weaker first-year outcomes than students who met ACT college readiness benchmarks, but the gaps in gateway course enrollment and completion narrowed during the study period. However, we are unable to attribute the narrowing of these gaps solely to the use of HSGPA because of other concurrent developmental education reforms. 

We describe the development of a one-credit course to promote AI literacy at The University of Texas at Austin. In response to a call for the rapid deployment of class to serve a broad audience in Fall of 2023, we designed a 14-week seminar-style course that incorporated an interdisciplinary group of speakers who lectured on topics ranging from the fundamentals of AI to societal concerns including disinformation and employment. University students, faculty, and staff, and even community members outside of the University, were invited to enroll in this online offering: The Essentials of AI for Life and Society. We collected feedback from course participants through weekly reflections and a final survey. Satisfyingly, we found that attendees reported gains in their AI literacy. We sought critical feedback through quantitative and qualitative analysis, which uncovered challenges in designing a course for this general audience. We utilized the course feedback to design a three-credit version of the course that is being offered in Fall of 2024. The lessons we learned and our plans for this new iteration may serve as a guide to instructors designing AI courses for a broad audience. 

Abstract High‐intensity long‐duration continuous auroral electrojet (AE) activity (HILDCAA) events are associated with intensification of relativistic electron fluxes in the inner magnetosphere. The physical mechanisms of this intensification are not well established yet. We study observations by the Time History of Events and Macroscale Interactions during Substorms (THEMIS) spacecraft in the near earth plasma sheet at radial distances of 10 Earth radii, at the transition region between tail and dipole‐like magnetic configurations, referred to as the nightside transition region (NTR), during a HILDCAA event. The observations revealed recurrent dipolarizations accompanied by plasma flow vortices, impulsive electric field enhancements, and increases in electron fluxes at energies of 100 keV up to 1 MeV. Electron pitch angle (PA) distributions at THEMIS showed field‐aligned flux enhancements at energies of 100 keV. This indicates a Fermi‐type energization. Arguably, electrons gain energy up to MeV via repetitive bouncing through the acceleration region. Energization of ions was insignificant which led to 1. We suggest that the increased ratio leads to a local increase of the Hall conductivity in the conjugate ionosphere, which causes ionospheric current intensification and strong , consistent with observations. 

The remarkable performance of large language models (LLMs) in generation tasks has enabled practitioners to leverage publicly available models to power custom applications, such as chatbots and virtual assistants. However, the data used to train or fine-tune these LLMs is often undisclosed, allowing an attacker to compromise the data and inject backdoors into the models. In this paper, we develop a novel inference time defense, named CLEANGEN, to mitigate backdoor attacks for generation tasks in LLMs. CLEANGEN is a lightweight and effective decoding strategy that is compatible with the state-of-the-art (SOTA) LLMs. Our insight behind CLEANGEN is that compared to other LLMs, back doored LLMs assign significantly higher probabilities to tokens representing the attacker-desired contents. These discrepancies in token probabilities enable CLEANGEN to identify suspicious tokens favored by the attacker and replace them with tokens generated by another LLM that is not compromised by the same attacker, thereby avoiding generation of attacker-desired content. We evaluate CLEANGEN against five SOTA backdoor attacks. Our results show that CLEANGEN achieves lower attack success rates (ASR) compared to five SOTA baseline defenses for all five backdoor attacks. Moreover, LLMs deploying CLEANGEN maintain helpfulness in their responses when serving benign user queries with minimal added computational overhead. 

The performance of sensor arrays in sensing and wireless communications improves with more elements, but this comes at the cost of increased energy consumption and hardware expense. This work addresses the challenge of selecting k sensor elements from a set of m to optimize a generic Quality-of-Service metric. Evaluating all possible sensor subsets is impractical, leading to prior solutions using convex relaxations, greedy algorithms, and supervised learning approaches. The current paper proposes a new framework that employs deep generative modeling, treating sensor selection as a deterministic Markov Decision Process where sensor subsets of size k arise as terminal states. Generative Flow Networks (GFlowNets) are employed to model an action distribution conditioned on the state. Sampling actions from the aforementioned distribution ensures that the probability of arriving at a terminal state is proportional to the performance of the corresponding subset. Applied to a standard sensor selection scenario, the developed approach outperforms popular methods which are based on convex optimization and greedy algorithms. Finally, a multiobjective formulation of the proposed approach is adopted and applied on the sparse antenna array design for Integrated Sensing and Communication (ISAC) systems. The multiobjective variation is shown to perform well in managing the trade-off between radar and communication performance.