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This paper presents acoustic propulsion in air by synthesis jets produced by ultrasounds. Various ultrasonic air-borne propellers have been fabricated on 0.37-mm-thick commercial card piezoelectric speakers (APS2513S-T-R, 25.2 × 16.6 × 0.37 mm3 in size), and studied, with the propulsion force measured through a precision weight scale, as the orifice size, thickness, spacing between orifices, and number (in the orifice array) are varied. Also varied is the orifice depth profile, as the fabrication processes for the orifices produce varying profiles. Strongest acoustic propulsion of 5.4 mg is obtained at 66 kHz (far beyond audible range) with 14 × 14 orificemore »Free, publicly-accessible full text available June 5, 2023
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Developing suitable approximate models for analyzing and simulating complex nonlinear systems is practically important. This paper aims at exploring the skill of a rich class of nonlinear stochastic models, known as the conditional Gaussian nonlinear system (CGNS), as both a cheap surrogate model and a fast preconditioner for facilitating many computationally challenging tasks. The CGNS preserves the underlying physics to a large extent and can reproduce intermittency, extreme events, and other non-Gaussian features in many complex systems arising from practical applications. Three interrelated topics are studied. First, the closed analytic formulas of solving the conditional statistics provide an efficient andmore »Free, publicly-accessible full text available May 17, 2023
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Viscous shocks are a particular type of extreme event in nonlinear multiscale systems, and their representation requires small scales. Model reduction can thus play an essential role in reducing the computational cost for the prediction of shocks. Yet, reduced models typically aim to approximate large-scale dominating dynamics, which do not resolve the small scales by design. To resolve this representation barrier, we introduce a new qualitative characterization of the space–time locations of shocks, named the “shock trace,” via a space–time indicator function based on an empirical resolution-adaptive threshold. Unlike exact shocks, the shock traces can be captured within the representationmore »Free, publicly-accessible full text available April 4, 2023
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The problems of identifying the slow component (e.g., for weather forecast initialization) and of characterizing slow–fast interactions are central to geophysical fluid dynamics. In this study, the related rectification problem of slow manifold closures is addressed when breakdown of slow-to-fast scales deterministic parameterizations occurs due to explosive emergence of fast oscillations on the slow, geostrophic motion. For such regimes, it is shown on the Lorenz 80 model that if 1) the underlying manifold provides a good approximation of the optimal nonlinear parameterization that averages out the fast variables and 2) the residual dynamics off this manifold is mainly orthogonal tomore »Free, publicly-accessible full text available November 23, 2022
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Free, publicly-accessible full text available September 1, 2022
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Free, publicly-accessible full text available December 28, 2022
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The element of repetition in cyberbullying behavior has directed recent computational studies toward detecting cyberbullying based on a social media session. In contrast to a single text, a session may consist of an initial post and an associated sequence of comments. Yet, emerging efforts to enhance the performance of session-based cyberbullying detection have largely overlooked unintended social biases in existing cyberbullying datasets. For example, a session containing certain demographic-identity terms (e.g., “gay” or “black”) is more likely to be classified as an instance of cyberbullying. In this paper, we first show evidence of such bias in models trained on sessionsmore »Free, publicly-accessible full text available August 1, 2022
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Cyberbullying, identified as intended and repeated online bullying behavior, has become increasingly prevalent in the past few decades. Despite the significant progress made thus far, the focus of most existing work on cyberbullying detection lies in the independent content analysis of different comments within a social media session. We argue that such leading notions of analysis suffer from three key limitations: they overlook the temporal correlations among different comments; they only consider the content within a single comment rather than the topic coherence across comments; they remain generic and exploit limited interactions between social media users. In this work, wemore »