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We present CrazyJoystick, a flyable handheld joystick allowing seamless interaction methods to change between joystick and hand-tracking while displaying on-demand haptic feedback in extended reality (XR). Our system comprises a quadrotor that can autonomously approach the user when needed, addressing the limitations of conventional handheld and wearable devices that require continuous carrying throughout interactions. CrazyJoystick dynamically reallocates all thrust for haptic rendering during stationary states, eliminating the need to hover while delivering feedback. A customized cage allows users to grasp the device and interact with virtual objects, receiving 3.5 degree-of-freedom feedback. This novel transition method allows us to harvest the aerial mobility from multi-rotor based haptic devices, while having high force-to-weight ratios from being handheld during interaction. This paper describes the design and implementation of CrazyJoystick, evaluates its force and torque performance, and usability of the system in three VR applications. Our evaluation of torque rendering found that users can perceive the direction with an accuracy of 92.2%. User studies further indicated that the system significantly improves presence in VR environments. Participants found on-demand haptic feedback intuitive and enjoyable, emphasizing the potential of CrazyJoystick to redefine immersive interactions in XR through portable and adaptive feedback mechanisms. 

To fully capitalize on the unique properties of 2D materials, cost-effective techniques for producing high-quality 2D flakes at scale are crucial. In this work, we show that dry ball-milling, a commonly used powder-processing technique, can be effectively and efficiently upgraded into an automated exfoliation technique. It is done by adding polymer as adhesives into a ball mill to mimic the well-known tape exfoliation process, which is known to produce 2D flakes with the highest quality but is limited by its extremely low efficiency on large-scale production. Seventeen types of commonly seen polymers, including both artificial and natural ones, have been examined as additives to dry ball-mill hexagonal boron nitride. A parallel comparison between different additives identifies low-cost natural polymers such as starch as promising dry ball-mill additives to produce ultrathin flakes with the largest aspect ratio. The mechanical, thermal, and surface properties of the polymers are proposed as key features that simultaneously determine the exfoliation efficiency, and their ranking of importance in the mechanical exfoliation process is revealed using a machine learning model. Finally, the potential of the polymer-assisted ball-mill exfoliation method as a universal way to produce ultra-thin 2D nanosheets is also demonstrated. 

ABSTRACT The prediction of extreme events in time series is a fundamental problem arising in many financial, scientific, engineering, and other applications. We begin by establishing a general Neyman–Pearson‐type characterization of optimal extreme event predictors in terms of density ratios. This yields new insights and several closed‐form optimal extreme event predictors for additive models. These results naturally extend to time series, where we study optimal extreme event prediction for both light‐ and heavy‐tailed autoregressive and moving average models. Using a uniform law of large numbers for ergodic time series, we establish the asymptotic optimality of an empirical version of the optimal predictor for autoregressive models. Using multivariate regular variation, we obtain an expression for the optimal extremal precision in heavy‐tailed infinite moving averages, which provides theoretical bounds on the ability to predict extremes in this general class of models. We address the important problem of predicting solar flares by applying our theory and methodology to a state‐of‐the‐art time series consisting of solar soft x‐ray flux measurements. Our results demonstrate the success and limitations in solar flare forecasting of long‐memory autoregressive models and long‐range‐dependent, heavy‐tailed FARIMA models. 

Abstract This paper presents a new statistical method that enables the use of systematic errors in the maximum-likelihood regression of integer-count Poisson data to a parametric model. The method is primarily aimed at the characterization of the goodness-of-fit statistic in the presence of the over-dispersion that is induced by sources of systematic error, and is based on a quasi-maximum-likelihood method that retains the Poisson distribution of the data. We show that the Poisson deviance, which is the usual goodness-of-fit statistic and that is commonly referred to in astronomy as the Cash statistics, can be easily generalized in the presence of systematic errors, under rather general conditions. The method and the associated statistics are first developed theoretically, and then they are tested with the aid of numerical simulations and further illustrated with real-life data from astronomical observations. The statistical methods presented in this paper are intended as a simple general-purpose framework to include additional sources of uncertainty for the analysis of integer-count data in a variety of practical data analysis situations.