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  1. Flexible work arrangement policies provide employees the flexibility to manage their work and personal lives. Despite various efforts of public organizations, struggles to simplify or integrate work and private life demands continue, resulting in employees’ lower satisfaction, higher stress, higher turnover, and lower productivity. Our study focuses on the social environments of individual employees by investigating how social networks affect individuals’ efforts to balance work and life in a higher education setting. Using a 2011 NSF-funded national survey of Science, Technology, Engineering and Math (STEM) faculty in US universities, we examine the impact of multiplexity in social networks on work-life balance (WLB) and how the impact varies by gender. Our regression results suggest that the impact of relational multiplexity on WLB follows an inverse U-shape pattern and varies by gender. In particular, multiplex ties that individuals socialize outside their work can affect their WLB and the impact varies by gender.

     
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  2. Free, publicly-accessible full text available September 30, 2024
  3. Free, publicly-accessible full text available July 23, 2024
  4. We provide a concise review of the exponentially convergent multiscale finite element method (ExpMsFEM) for efficient model reduction of PDEs in heterogeneous media without scale separation and in high-frequency wave propagation. The ExpMsFEM is built on the non-overlapped domain decomposition in the classical MsFEM while enriching the approximation space systematically to achieve a nearly exponential convergence rate regarding the number of basis functions. Unlike most generalizations of the MsFEM in the literature, the ExpMsFEM does not rely on any partition of unity functions. In general, it is necessary to use function representations dependent on the right-hand side to break the algebraic Kolmogorov n-width barrier to achieve exponential convergence. Indeed, there are online and offline parts in the function representation provided by the ExpMsFEM. The online part depends on the right-hand side locally and can be computed in parallel efficiently. The offline part contains basis functions that are used in the Galerkin method to assemble the stiffness matrix; they are all independent of the right-hand side, so the stiffness matrix can be used repeatedly in multi-query scenarios. 
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  5. When young children create, they are exploring their emerging skills. And when young children reflect, they are transforming their learning experiences. Yet early childhood play environments often lack toys and tools to scaffold reflection. In this work, we design a stuffed animal robot to converse with young children and prompt creative reflection through open-ended storytelling. We also contribute six design goals for child-robot interaction design. In a hybrid Wizard of Oz study, 33 children ages 4-5 years old across 10 U.S. states engaged in creative play then conversed with a stuffed animal robot to tell a story about their creation. By analyzing children’s story transcripts, we discover four approaches that young children use when responding to the robot’s reflective prompting: Imaginative, Narrative Recall, Process-Oriented, and Descriptive Labeling. Across these approaches, we find that open-ended child-robot interaction can integrate personally meaningful reflective storytelling into diverse creative play practices. 
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  6. null (Ed.)
    Building a sketch of an n-by-n empirical kernel matrix is a common approach to accelerate the computation of many kernel methods. In this paper, we propose a unified framework of constructing sketching methods in kernel ridge regression (KRR), which views the sketching matrix S as an accumulation of m rescaled sub-sampling matrices with independent columns. Our framework incorporates two commonly used sketching methods, sub-sampling sketches (known as the Nyström method) and sub-Gaussian sketches, as special cases with m=1 and m=infinity respectively. Under the new framework, we provide a unified error analysis of sketching approximation and show that our accumulation scheme improves the low accuracy of sub-sampling sketches when certain incoherence characteristic is high, and accelerates the more accurate but computationally heavier sub-Gaussian sketches. By optimally choosing the number m of accumulations, we show that a best trade-off between computational efficiency and statistical accuracy can be achieved. In practice, the sketching method can be as efficiently implemented as the sub-sampling sketches, as only minor extra matrix additions are needed. Our empirical evaluations also demonstrate that the proposed method may attain the accuracy close to sub-Gaussian sketches, while is as efficient as sub-sampling-based sketches. 
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  7. null (Ed.)
    Nyström approximation is a fast randomized method that rapidly solves kernel ridge regression (KRR) problems through sub-sampling the n-by-n empirical kernel matrix appearing in the objective function. However, the performance of such a sub-sampling method heavily relies on correctly estimating the statistical leverage scores for forming the sampling distribution, which can be as costly as solving the original KRR. In this work, we propose a linear time (modulo poly-log terms) algorithm to accurately approximate the statistical leverage scores in the stationary-kernel-based KRR with theoretical guarantees. Particularly, by analyzing the first-order condition of the KRR objective, we derive an analytic formula, which depends on both the input distribution and the spectral density of stationary kernels, for capturing the non-uniformity of the statistical leverage scores. Numerical experiments demonstrate that with the same prediction accuracy our method is orders of magnitude more efficient than existing methods in selecting the representative sub-samples in the Nyström approximation. 
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  8. null (Ed.)