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1. Design for metrology for freeform optics manufacturing

   https://doi.org/10.1016/j.procir.2019.04.255  

   Venditti, K; Evans, C; Falaggis, K; Blum, A; Chaudhuri, R; Goodsell, J; Rolland, J (April 2019, CIRP)

   Freeform optical surfaces offer significant design opportunities but pose new challenges in metrology and manufacturing. Evolution in optics manufacturing processes have changed the surface spatial frequencies that must be measured. Optical surface definition is expected to be with respect to fiducials and datums which must be realizable at all stages of manufacture; uncertainty in that realization becomes important in some cases. Concurrent engineering is required, but appropriate data has not been collated for use by optical designers. One approach to providing such data is described. 

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2. 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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3. 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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4. Principles for AI Education for Elementary Grades Students

   https://doi.org/10.1145/3502717.3532143  

   Ottenbreit-Leftwich, Anne; Glazewski, Krista; Jeon, Minji; Jantaraweragul, Katie; Hmelo-Silver, Cindy; Scribner, Adam; Lee, Seung; Mott, Bradford; Lester, James (July 2022, 27th ACM Conference on Innovation and Technology in Computer Science Education)

   AI is beginning to transform every aspect of society. With the dramatic increases in AI, K-12 students need to be prepared to understand AI. To succeed as the workers, creators, and innovators of the future, students must be introduced to core concepts of AI as early as elementary school. However, building a curriculum that introduces AI content to K-12 students present significant challenges, such as connecting to prior knowledge, and developing curricula that are meaningful for students and possible for teachers to teach. To lay the groundwork for elementary AI education, we conducted a qualitative study into the design of AI curricular approaches with elementary teachers and students. Interviews with elementary teachers and students suggests four design principles for creating an effective elementary AI curriculum to promote uptake by teachers. This example will present the co-designed curriculum with teachers (PRIMARYAI) and describe how these four elements were incorporated into real-world problem-based learning scenarios. 

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5. Methods for Robot Behavior Adaptation for Cognitive Neurorehabilitation

   Kubota, Alyssa; Riek, Laurel D. (January 2021, Annual review of control robotics and autonomous systems)

   null (Ed.)

   An estimated 11% of adults report experiencing some form of cognitive decline which may be associated with conditions such as stroke or dementia, and can impact their memory, cognition, behavior, and physical abilities. While there are no known pharmacological treatments for many of these conditions, behavioral treatments such as cognitive training can prolong the independence of people with cognitive impairments. These treatments teach metacognitive strategies to compensate for memory difficulties in their everyday lives. Personalizing these treatments to suit the preferences and goals of an individual is critical to improving their engagement and sustainment, as well as maximizing the treatment’s effectiveness. Robots have great potential to facilitate these training regimens and support people with cognitive impairments, their caregivers, and clinicians. This article examines how robots can adapt their behavior to be personalized to an individual in the context of cognitive neurorehabilitation. We provide an overview of existing robots being used to support neurorehabilitation, and identify key principles to working in this space. We then examine state-of-the-art technical approaches to enabling longitudinal behavioral adaptation. To conclude, we discuss our recent work on enabling social robots to automatically adapt their behavior and explore open challenges for longitudinal behavior adaptation. This work will help guide the robotics community as they continue to provide more engaging, effective, and personalized interactions between people and robots. 

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