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  1. Determinant maximization problem gives a general framework that models problems arising in as diverse fields as statistics [Puk06], convex geometry [Kha96], fair allocations [AGSS16], combinatorics [AGV18], spectral graph theory [NST19a], network design, and random processes [KT12]. In an instance of a determinant maximization problem, we are given a collection of vectors U = {v1, . . . , vn} ⊂ Rd , and a goal is to pick a subset S ⊆ U of given vectors to maximize the determinant of the matrix ∑i∈S vivi^T. Often, the set S of picked vectors must satisfy additional combinatorial constraints such as cardinality constraint (|S| ≤ k) or matroid constraint (S is a basis of a matroid defined on the vectors). In this paper, we give a polynomial-time deterministic algorithm that returns a r O(r)-approximation for any matroid of rank r ≤ d. This improves previous results that give e O(r^2)-approximation algorithms relying on e^O(r)-approximate estimation algorithms [NS16, AG17,AGV18, MNST20] for any r ≤ d. All previous results use convex relaxations and their relationship to stable polynomials and strongly log-concave polynomials. In contrast, our algorithm builds on combinatorial algorithms for matroid intersection, which iteratively improve any solution by finding an alternating negative cyclemore »in the exchange graph defined by the matroids. While the det(.) function is not linear, we show that taking appropriate linear approximations at each iteration suffice to give the improved approximation algorithm.« less
    Free, publicly-accessible full text available October 31, 2023
  2. Murphy, B. (Ed.)
    A key form of scientific literacy is being able to leverage the knowledge, practices, and commitments of ethical science to everyday civic matters of social consequence. Learning how to engage in civic life in equity-focused ways needs to be intertwined with learning disciplinary—or transdisciplinary—knowledge and practices. In this article we discuss how an art-science learning program at Science Gallery Dublin in Ireland supported subsequent civic participation by adolescent youth. Using longitudinal case studies of young people, we document how they became agents of change in their homes, schools, and wider communities over several years after participating in the program. This work provides insight into how specific design features of informal learning environments help launch or expand the science-linked identities of youth interested in participation in civic life and social action. These cases also illustrate how to develop educational models that support young people to take informed action toward matters of community and environmental consequence, a key aspect of building a more sustainable and thriving future.
    Free, publicly-accessible full text available January 1, 2023
  3. Free, publicly-accessible full text available April 1, 2023
  4. Abstract Understanding propagation of scintillation light is critical for maximizing the discovery potential of next-generation liquid xenon detectors that use dual-phase time projection chamber technology. This work describes a detailed optical simulation of the DARWIN detector implemented using Chroma, a GPU-based photon tracking framework. To evaluate the framework and to explore ways of maximizing efficiency and minimizing the time of light collection, we simulate several variations of the conventional detector design. Results of these selected studies are presented. More generally, we conclude that the approach used in this work allows one to investigate alternative designs faster and in more detail than using conventional Geant4 optical simulations, making it an attractive tool to guide the development of the ultimate liquid xenon observatory.
    Free, publicly-accessible full text available July 1, 2023
  5. Abstract The XENON collaboration has published stringent limits on specific dark matter – nucleon recoil spectra from dark matter recoiling on the liquid xenon detector target. In this paper, we present an approximate likelihood for the XENON1T 1 t-year nuclear recoil search applicable to any nuclear recoil spectrum. Alongside this paper, we publish data and code to compute upper limits using the method we present. The approximate likelihood is constructed in bins of reconstructed energy, profiled along the signal expectation in each bin. This approach can be used to compute an approximate likelihood and therefore most statistical results for any nuclear recoil spectrum. Computing approximate results with this method is approximately three orders of magnitude faster than the likelihood used in the original publications of XENON1T, where limits were set for specific families of recoil spectra. Using this same method, we include toy Monte Carlo simulation-derived binwise likelihoods for the upcoming XENONnT experiment that can similarly be used to assess the sensitivity to arbitrary nuclear recoil signatures in its eventual 20 t-year exposure.
    Free, publicly-accessible full text available November 1, 2023
  6. Free, publicly-accessible full text available October 1, 2023
  7. Free, publicly-accessible full text available August 1, 2023