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1. Recruiting Teachers for VolsTeach for Appalachia

   Kim, N.; Hodge, L.; King, S. (July 2021, Southeastern Robert Noyce Conference)

   null (Ed.)

   This presentation poster summarizes the recruitment efforts, insights gained, and lessons learned through the VolsTeach for Appalachia project that focuses on recruiting and preparing community college students in becoming STEM teachers in East Tennessee. 

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2. 2023 roadmap for materials for quantum technologies

   https://doi.org/10.1088/2633-4356/aca3f2  

   Becher, Christoph; Gao, Weibo; Kar, Swastik; Marciniak, Christian D.; Monz, Thomas; Bartholomew, John G.; Goldner, Philippe; Loh, Huanqian; Marcellina, Elizabeth; Goh, Kuan Eng Johnson; et al (January 2023, Materials for Quantum Technology)

   Abstract Quantum technologies are poised to move the foundational principles of quantum physics to the forefront of applications. This roadmap identifies some of the key challenges and provides insights on material innovations underlying a range of exciting quantum technology frontiers. Over the past decades, hardware platforms enabling different quantum technologies have reached varying levels of maturity. This has allowed for first proof-of-principle demonstrations of quantum supremacy, for example quantum computers surpassing their classical counterparts, quantum communication with reliable security guaranteed by laws of quantum mechanics, and quantum sensors uniting the advantages of high sensitivity, high spatial resolution, and small footprints. In all cases, however, advancing these technologies to the next level of applications in relevant environments requires further development and innovations in the underlying materials. From a wealth of hardware platforms, we select representative and promising material systems in currently investigated quantum technologies. These include both the inherent quantum bit systems and materials playing supportive or enabling roles, and cover trapped ions, neutral atom arrays, rare earth ion systems, donors in silicon, color centers and defects in wide-band gap materials, two-dimensional materials and superconducting materials for single-photon detectors. Advancing these materials frontiers will require innovations from a diverse community of scientific expertise, and hence this roadmap will be of interest to a broad spectrum of disciplines. 

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3. Meeting the Moment: Opportunities and Limitations for for Genetically Engineered Microbes for Bioremediation

   https://doi.org/10.38126/JSPG260201  

   Brewer, Avery M; George, Dalton R (August 2025, Journal of Science Policy & Governance)

   As anthropogenic compounds are released into the environment at unprecedented rates, there is an ever-growing need for robust remediation strategies. Bioremediation, a method of immobilizing or transforming contaminants, is cost-competitive, environmentally friendly, and effective. With the global bioremediation market anticipated to grow by $8.29 billion between 2023 and 2028, this method of reducing pollutants represents a rapidly expanding sector of the bioeconomy. Millions of tons of pollutants now contaminate soil and groundwater, posing severe threats to human and environmental health. At the same time, as contaminants of emerging concern such as microplastics, pharmaceuticals, pesticides, and per- and polyfluorinated alkyl substances (PFAS) resist treatment with naturally occurring organisms, it may be useful to expand bioremediation’s toolkit to include genetically engineered microbes for bioremediation (GEMBs). There has been long-standing interest in developing GEMBs to enable faster remediation times and address a wider range of contaminants. Despite decades of investigation and development of GEMBs, none have been commercialized to date. Historically, the perceived need for GEMBs has not been sufficient to overcome the investment and risk in the context of an uncertain regulatory environment and a paucity of fundamental knowledge of GEMBs. However, as industries, environments, and human health experience disruptions from increasingly recalcitrant, widespread, and hazardous contaminants, the value proposition of GEMBs is more compelling than ever before. The contemporary challenges with managing environmental contamination coupled with advances in genetic engineering methods and renewed interest from researchers, developers, and policymakers signal an opportunity to realize the potential of GEMBs. To support safe and efficient development, characterization, and commercialization of GEMBs as a means of urgently addressing environmental contamination, we propose clarifying and restructuring the risk assessment process for GEMBs, establishing an interagency coordination office, collaboratively addressing critical knowledge gaps, and leveraging public-private partnerships. 

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4. Reason for Future, Act for Now: A Principled Architecture for Autonomous LLM Agents

   Liu, Zhihan; Hu, Hao; Zhang, Shenao; Guo, Hongyi; Ke, Shuqi; Liu, Boyi; Wang, Zhaoran (October 2024, ICML)

   Large language models (LLMs) demonstrate impressive reasoning abilities, but translating reasoning into actions in the real world remains challenging. In particular, it is unclear how to complete a given task provably within a minimum number of interactions with the external environment, e.g., through an internal mechanism of reasoning. To this end, we propose the first framework with provable regret guarantees to orchestrate reasoning and acting, which we call “reason for future, act for now” (RAFA). Specifically, we design a prompt template for reasoning that learns from the memory buffer and plans a future trajectory over a long horizon (“reason for future”). At each step, the LLM agent takes the initial action of the planned trajectory (“act for now”), stores the collected feedback in the memory buffer, and reinvokes the reasoning routine to replan the future trajectory from the new state. The key idea is to cast reasoning in LLMs as learning and planning in Bayesian adaptive Markov decision processes (MDPs). Correspondingly, we prompt LLMs with the memory buffer to estimate the unknown environment (learning) and generate an optimal trajectory for multiple future steps that maximize a value function (planning). The learning and planning subroutines are performed in an “incontext” manner to emulate the actor-critic update for MDPs. Our theoretical analysis establishes a √T regret, while our experimental validation demonstrates superior empirical performance. Here, T denotes the number of online interactions. 

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5. Algorithms for Computing Closest Points for Segments

   https://doi.org/10.4230/LIPIcs.STACS.2024.58  

   Wang, Haitao (March 2024, Schloss Dagstuhl – Leibniz-Zentrum für Informatik)

   Beyersdorff, Olaf; Kanté, Mamadou Moustapha; Kupferman, Orna; Lokshtanov, Daniel (Ed.)

   Given a set P of n points and a set S of n segments in the plane, we consider the problem of computing for each segment of S its closest point in P. The previously best algorithm solves the problem in n^{4/3}2^{O(log^*n)} time [Bespamyatnikh, 2003] and a lower bound (under a somewhat restricted model) Ω(n^{4/3}) has also been proved. In this paper, we present an O(n^{4/3}) time algorithm and thus solve the problem optimally (under the restricted model). In addition, we also present data structures for solving the online version of the problem, i.e., given a query segment (or a line as a special case), find its closest point in P. Our new results improve the previous work. 

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