Young Jean Lee’s Performance of Whiteness: Resisting Colorblind Casting Through Theatrical Realism

This paper aims to provide an alternative interpretation of Songs of the Dragons Flying to Heaven (Songs) that incorporates both Asian and white plots through examining Young Jean Lee’s race plays altogether and creating a context for Lee’s double-plot structuring of Songs. Lee is a recognized Korean American playwright leading the Young Jean Lee Theatre Company. Lee is often categorized as an avant-garde playwright who experiments with and introduces new forms of theatre, encouraging the audience to think outside the box. What makes Lee stand out from other experimental playwrights is her skillful exploration of racial issues in Songs of the Dragons Flying to Heaven (2006), The Shipment (2009), and Straight White Men (2014). The paper intends to reveal the significance of Songs in the history of Asian American theatre that has been overlooked since its premiere. The paper argues that through structurally and characterally stereotyping whiteness in nonwhite narratives, Lee dismantles standardization of whiteness as the norm of the society. Hopefully, the analysis would open up possibilities for nonwhite American plays to cast nonwhite Americans in the role of white Americans, which both mirrors previous white-dominated theatre practices in reverse and creates a visualization of whiteness being performed

An Optimized Dynamic Mode Decomposition Model Robust to Multiplicative Noise

Dynamic mode decomposition (DMD) is an efficient tool for decomposing spatio-temporal data into a set of low-dimensional modes, yielding the oscillation frequencies and the growth rates of physically significant modes. In this paper, we propose a novel DMD model that can be used for dynamical systems affected by multiplicative noise. We first derive a maximum a posteriori (MAP) estimator for the data-based model decomposition of a linear dynamical system corrupted by certain multiplicative noise. Applying penalty relaxation to the MAP estimator, we obtain the proposed DMD model whose epigraphical limits are the MAP estimator and the conventional optimized DMD model. We also propose an efficient alternating gradient descent method for solving the proposed DMD model, and analyze its convergence behavior. The proposed model is demonstrated on both the synthetic data and the numerically generated one-dimensional combustor data, and is shown to have superior reconstruction properties compared to state-of-the-art DMD models. Considering that multiplicative noise is ubiquitous in numerous dynamical systems, the proposed DMD model opens up new possibilities for accurate data-based modal decomposition.Comment: 35 pages, 10 figure

A numerically efficient output-only system-identification framework for stochastically forced self-sustained oscillators

Self-sustained oscillations are ubiquitous in nature and engineering. In this paper, we propose a novel output-only system-identification framework for identifying the system parameters of a self-sustained oscillator affected by Gaussian white noise. A Langevin model that characterizes the self-sustained oscillator is postulated, and the corresponding Fokker--Planck equation is derived from stochastic averaging. From the drift and diffusion terms of the Fokker--Planck equation, unknown parameters of the system are identified. We develop a numerically efficient algorithm for enhancing the accuracy of parameter identification. In particular, a modified Levenberg--Marquardt optimization algorithm tailored to output-only system identification is introduced. The proposed framework is demonstrated on both numerical and experimental oscillators with varying system parameters that develop into self-sustained oscillations. The results show that the computational cost required for performing the system identification is dramatically reduced by using the proposed framework. Also, system parameters that were difficult to be extracted with the existing method could be efficiently computed with the system identification method developed in this study. Pertaining to the robustness and computational efficiency of the presented framework, this study can contribute to an accurate and fast diagnosis of dynamical systems under stochastic forcing.Comment: 17 pages, 10 figure

DPPD: Deformable Polar Polygon Object Detection

Regular object detection methods output rectangle bounding boxes, which are unable to accurately describe the actual object shapes. Instance segmentation methods output pixel-level labels, which are computationally expensive for real-time applications. Therefore, a polygon representation is needed to achieve precise shape alignment, while retaining low computation cost. We develop a novel Deformable Polar Polygon Object Detection method (DPPD) to detect objects in polygon shapes. In particular, our network predicts, for each object, a sparse set of flexible vertices to construct the polygon, where each vertex is represented by a pair of angle and distance in the Polar coordinate system. To enable training, both ground truth and predicted polygons are densely resampled to have the same number of vertices with equal-spaced raypoints. The resampling operation is fully differentable, allowing gradient back-propagation. Sparse polygon predicton ensures high-speed runtime inference while dense resampling allows the network to learn object shapes with high precision. The polygon detection head is established on top of an anchor-free and NMS-free network architecture. DPPD has been demonstrated successfully in various object detection tasks for autonomous driving such as traffic-sign, crosswalk, vehicle and pedestrian objects

Graphdiyne as a high-capacity lithium ion battery anode material

Using the first-principles calculations, we explored the feasibility of using graphdiyne, a 2D layer of sp and sp2 hybrid carbon networks, as lithium ion battery anodes. We found that the composite of the Li-intercalated multilayer ??-graphdiyne was C6Li7.31 and that the calculated voltage was suitable for the anode. The practical specific/volumetric capacities can reach up to 2719 mAh g-1/2032 mAh cm-3, much greater than the values of ???372 mAh g-1/???818 mAh cm -3, ???1117 mAh g-1/???1589 mAh cm-3, and ???744 mAh g-1 for graphite, graphynes, and ??-graphdiyne, respectively. Our calculations suggest that multilayer ??-graphdiyne can serve as a promising high-capacity lithium ion battery anode.open3

Asking Clarification Questions to Handle Ambiguity in Open-Domain QA

Ambiguous questions persist in open-domain question answering, because formulating a precise question with a unique answer is often challenging. Previously, Min et al. (2020) have tackled this issue by generating disambiguated questions for all possible interpretations of the ambiguous question. This can be effective, but not ideal for providing an answer to the user. Instead, we propose to ask a clarification question, where the user's response will help identify the interpretation that best aligns with the user's intention. We first present CAMBIGNQ, a dataset consisting of 5,654 ambiguous questions, each with relevant passages, possible answers, and a clarification question. The clarification questions were efficiently created by generating them using InstructGPT and manually revising them as necessary. We then define a pipeline of tasks and design appropriate evaluation metrics. Lastly, we achieve 61.3 F1 on ambiguity detection and 40.5 F1 on clarification-based QA, providing strong baselines for future work.Comment: 15 pages, 4 figure

Analyzing Norm Violations in Live-Stream Chat

Toxic language, such as hate speech, can deter users from participating in online communities and enjoying popular platforms. Previous approaches to detecting toxic language and norm violations have been primarily concerned with conversations from online forums and social media, such as Reddit and Twitter. These approaches are less effective when applied to conversations on live-streaming platforms, such as Twitch and YouTube Live, as each comment is only visible for a limited time and lacks a thread structure that establishes its relationship with other comments. In this work, we share the first NLP study dedicated to detecting norm violations in conversations on live-streaming platforms. We define norm violation categories in live-stream chats and annotate 4,583 moderated comments from Twitch. We articulate several facets of live-stream data that differ from other forums, and demonstrate that existing models perform poorly in this setting. By conducting a user study, we identify the informational context humans use in live-stream moderation, and train models leveraging context to identify norm violations. Our results show that appropriate contextual information can boost moderation performance by 35\%.Comment: 17 pages, 8 figures, 15 table

Biopsychological traits of Sasang typology based on Sasang personality questionnaire and body mass index

BACKGROUND: The purpose of present study was to examine biological and psychological characteristics of people according to the Sasang typology, which is popular in Korea. We evaluated the Sasang Personality Questionnaire (SPQ) as a measure of temperament, and Body Mass Index (BMI) as a measure of the somatic properties of each Sasang type. METHODS: Subjects were 2506 (877 males, 1629 females) outpatients between the ages of 20 through 70 who requested traditional medical assessment and treatment in Korea. The structural validity of the SPQ was examined and its correlation with BMI was analyzed. The SPQ and BMI measures of each Sasang type across age and gender were presented and their differences were analyzed with Analysis of Variance. RESULTS: Confirmatory factor analysis and path analysis identified an acceptable three-factor structure of the SPQ measuring differences in individual’s behavior, emotion, and cognition. SPQ scores (29.71 ± 1.00, 28.29 ± 0.19 and 26.14 ± 0.22) and BMI scores (22.92 ± 0.09, 25.56 ± 0.10 and 21.44 ± 0.10) were significantly (p < 0.001) different among So-Yang, Tae-Eum and So-Eum Sasang types, respectively. CONCLUSIONS: The results showed that the SPQ and BMI is a reliable measure for quantifying the biopsychological characteristics of each types, and useful for guiding personalized and type-specific treatment with medical herbs and acupuncture

Analysis of Skin Humidity Variation Between Sasang Types

The purpose of this study was to examine the relationship between variations in skin humidity (SH) induced by perspiration across Sasang types and to identify novel and effective Sasang classification factors. We also analyzed the responses of each Sasang type to sweating-related QSCC II items. The results revealed a significant difference in SH across gender and significant differences in SH before and after perspiration between Tae-Eum and So-Eum men. In addition, Tae-Eum women showed significant differences in SH compared with women classified as another Sasang type. Furthermore, evaluation of the items related to sweating in the QSCC II and their relationship to each constitution revealed a significant difference between Tae-Eum and other Sasang types. Overall, the results of this study indicate that there is a distinct SH difference following perspiration between Tae-Eum and other Sasang types. Such findings may aid in Sasang typology diagnostic testing with the support of further sophisticated clinical studies