A single-electron transistor incorporated as part of a nanomechanical resonator represents an extreme limit of electron-phonon coupling. While it allows for fast and sensitive electromechanical measurements, it also introduces backaction forces from electron tunnelling which randomly perturb the mechanical state. Despite the stochastic nature of this backaction, under conditions of strong coupling it is predicted to create self-sustaining coherent mechanical oscillations. Here, we verify this prediction using time-resolved measurements of a vibrating carbon nanotube transistor. This electromechanical oscillator has intriguing similarities with a laser. The single-electron transistor, pumped by an electrical bias, acts as a gain medium while the resonator acts as a phonon cavity. Despite the unconventional operating principle, which does not involve stimulated emission, we confirm that the output is coherent, and demonstrate other laser behaviour including injection locking and frequency narrowing through feedback. arXiv:1903.04474v1 [cond-mat.mes-hall]
Abstract-Crowdsourcing systems allocate tasks to a group of workers over the Internet, which have become an effective paradigm for human-powered problem solving such as image classification, optical character recognition and proofreading. In this paper, we focus on incentivizing crowd workers to label a set of binary tasks under strict budget constraint. We properly profile the tasks' difficulty levels and workers' quality in crowdsourcing systems, where the collected labels are aggregated with sequential Bayesian approach. To stimulate workers to undertake crowd labeling tasks, the interaction between workers and the platform is modeled as a reverse auction. We reveal that the platform utility maximization could be intractable, for which an incentive mechanism that determines the winning bid and payments with polynomial-time computation complexity is developed. Moreover, we theoretically prove that our mechanism is truthful, individually rational and budget feasible. Through extensive simulations, we demonstrate that our mechanism utilizes budget efficiently to achieve high platform utility with polynomial computation complexity.
Abstract-Indoor localization has been an active research field for decades, where the received signal strength (RSS) fingerprinting based methodology is widely adopted and induces many important localization techniques such as the recently proposed one building the fingerprint database with crowdsourcing. While efforts have been dedicated to improve the accuracy and efficiency of localization, the fundamental limits of RSS fingerprinting based methodology itself is still unknown in a theoretical perspective. In this paper, we present a general probabilistic model to shed light on a fundamental question: how good the RSS fingerprinting based indoor localization can achieve? Concretely, we present the probability that a user can be localized in a region with certain size, given the RSS fingerprints submitted to the system. We reveal the interaction among the localization accuracy, the reliability of location estimation and the number of measurements in the RSS fingerprinting based location determination. Moreover, we present the optimal fingerprints reporting strategy that can achieve the best accuracy for given reliability and the number of measurements, which provides a design guideline for the RSS fingerprinting based indoor localization facilitated by crowdsourcing paradigm.
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