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Scaling and mechanism of the propagation speed of turbulent fronts in pipe flow with the Reynolds number has been a long-standing problem in the past decades. Here, we derive an explicit scaling law of the upstream front speed, which approaches to a power-law scaling at high Reynolds numbers and explain the underlying mechanism. Our data show that the average wall distance of low-speed streaks at the tip of the upstream front, where transition occurs, appears to be constant in local wall units in the wide bulk-Reynolds-number range investigated, between 5000 and 60000. By further assuming that the axial propagation of velocity fluctuations at the front tip, resulting from streak instabilities, is dominated by the advection of the local mean flow, the front speed can be derived as an explicit function of the Reynolds number. The derived formula agrees well with the measured speed by front tracking. Our finding reveals the relationship between the structure and speed of a front, which enables to obtain a close approximation of the front speed based on a single velocity field without having to track the front over time.

In mechanism design theory, agents' types are described as their private information, and the designer may reveal some public information to affect agents' types in order to obtain more payoffs. Traditionally, each agent's private type and the public information are represented as a random variable respectively. In this paper, we propose a type-adjustable mechanism where each agent's private type is represented as a function of two parameters, \emph{i.e.}, his intrinsic factor and an external factor. Each agent's intrinsic factor is modeled as a private random variable, and the external factor is modeled as a solution of the designer's optimization problem. If the designer chooses an optimal value of external factor as public information, the type-adjustable mechanism may yield Pareto-optimal outcomes, which let the designer and each agent obtain more expected payoffs than what they would obtain at most in the traditional optimal mechanisms. As a comparison, in an auction with interdependent values, only the seller will benefit from public information which is represented as a random variable. We propose a revised version of revelation principle for type-adjustable Bayesian equilibrium. In the end, we compare the type-adjustable mechanism with other relevant models., Comment: 18 pages

A novel, real-time, mm-Wave radar-based static hand shape classification algorithm and implementation are proposed. The method finds several applications in low cost and privacy sensitive touchless control technology using 60 Ghz radar as the sensor input. As opposed to prior Range-Doppler image based 2D classification solutions, our method converts raw radar data to 3D sparse cartesian point clouds.The demonstrated 3D radar neural network model using deformable transformers significantly surpasses the performance results set by prior methods which either utilize custom signal processing or apply generic convolutional techniques on Range-Doppler FFT images. Experiments are performed on an internally collected dataset using an off-the-shelf radar sensor.

We investigated the side-wall effects on turbulent bands in channel flow at transitional Reynolds numbers by direct numerical simulations using the open source spectral-element code Nektar++. The width-to-height aspect ratio of 50:1 is considered for this study. Our study shows that turbulent bands can survive the collision with the side wall above bulk Reynolds number of Re around 1000 but decay below Re around 975, i.e. the critical Reynolds number should be approximately between the two Reynolds numbers. We also discussed about the underlying mechanism for the decay of the band at low Reynolds numbers and potential effects of larger spanwise channel widths than that considered in our study. The results are informative for experimental studies of channel flow turbulence at transitional Reynolds numbers.

In mechanism design theory, a designer would like to implement a social choice function which specifies her favorite outcome for each possible profile of agents' private types. The revelation principle asserts that if a social choice function can be implemented by a mechanism in equilibrium, then there exists a direct mechanism that can truthfully implement it. This paper aims to propose a failure of the revelation principle. We point out that in any game the format of each agent's strategy is either an informational message or a realistic action, and the action format is very common in many practical cases. The main result is that: For any given social choice function, if the mechanism which implements it has action-format strategies, then "\emph{honest and obedient}" will no longer be the Bayesian Nash equilibrium of the direct mechanism, actually the social choice function can only be implemented "\emph{dishonestly and disobediently}" in Bayesian Nash equilibrium by the direct mechanism. Consequently, the revelation principle fails when the format of each agent's strategy is an action., Comment: 9 pages

Tuning fork experiments at the undergraduate level usually only demonstrate a tuning fork's linear resonance. In this paper, we introduce an experiment that can be used to measure the nonlinear tuning curve of a regular tuning fork. Using double-grating Doppler interferometry, we achieve measurement accuracy within ten microns. With this experiment setup, we observe typical nonlinear behaviors of the tuning fork such as the softening tuning curve and jump phenomena. Our experiment is inexpensive and easy to operate. It provides an integrated experiment for intermediate-level students and a basis for senior research projects., Comment: 11 pages, 5 figures

In the mechanism design theory, a designer would like to implement a desired social choice function which specifies her favorite outcome for each possible profile of all agents' types. Traditionally, the designer may be in a dilemma in the sense that even if she is not satisfied with some outcome with low profit, she has to announce it because she must obey the mechanism designed by herself. In this paper, we investigate a case where the designer can induce each agent to adjust his type in a one-shot mechanism. We propose that for a profitable Bayesian implementable social choice function, the designer may escape from the above-mentioned dilemma by spending the optimal adjustment cost and obtain a higher profit. Finally, we construct an example to show that the designer can breakthrough the limit of expected profit which she can obtain at most in the traditional optimal auction model., Comment: 12 pages