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1. 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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2. 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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3. 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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4. Curating for Convergence: Data Stewardship for Interdisciplinary Inquiry

   https://doi.org/10.1353/lib.2023.0007  

   Palmer, Carole L.; Cragin, Melissa H. (August 2023, Library Trends)

   Advances in data infrastructure are often led by disciplinary initiatives aimed at innovation in federation and sharing of data and related research materials. In library and information science (LIS), the data services area has focused on data curation and stewardship to support description and deposit of data for access, reuse, and preservation. At the same time, solutions to societal grand challenges are thought to lie in convergence research, characterized by a problem-focused orientation and deep cross-disciplinary integration, requiring access to highly varied data sources with differing resolutions or scales. We argue that data curation and stewardship work in LIS should expand to foster convergence research based on a robust understanding of the dynamics of disciplinary and interdisciplinary research methods and practices. Highlighting unique contributions by Dr. Linda C. Smith to the field of LIS, we outline how her work illuminates problems that are core to current directions in convergence research. Drawing on advances in data infrastructure in the earth and geosciences and trends in qualitative domains, we emphasize the importance of metastructures and the necessary influence of disciplinary practice on principles, standards, and provisions for ethical use across the evolving data ecosystem. 

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5. Evaluation Metrics for Object Detection for Autonomous Systems

   Badithela Apurva; Wongpiromsarn, Tichakorn; Murray, Richard M. (October 2023, 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))

   This paper studies the evaluation of learning-based object detection models in conjunction with model-checking of formal specifications defined on an abstract model of an autonomous system and its environment. In particular, we define two metrics – proposition-labeled and class-labeled confusion matrices – for evaluating object detection, and we incorporate these metrics to compute the satisfaction probability of system-level safety requirements. While confusion matrices have been effective for comparative evaluation of classification and object detection models, our framework fills two key gaps. First, we relate the performance of object detection to formal requirements defined over downstream high-level planning tasks. In particular, we provide empirical results that show that the choice of a good object detection algorithm, with respect to formal requirements on the overall system, significantly depends on the downstream planning and control design. Secondly, unlike the traditional confusion matrix, our metrics account for variations in performance with respect to the distance between the ego and the object being detected. We demonstrate this framework on a car-pedestrian example by computing the satisfaction probabilities for safety requirements formalized in Linear Temporal Logic (LTL). 

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