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Free, publicly-accessible full text available May 20, 2023
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Free, publicly-accessible full text available January 1, 2023
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Free, publicly-accessible full text available November 1, 2022
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While contrastive approaches of self-supervised learning (SSL) learn representations by minimizing the distance between two augmented views of the same data point (positive pairs) and maximizing views from different data points (negative pairs), recent \emph{non-contrastive} SSL (e.g., BYOL and SimSiam) show remarkable performance {\it without} negative pairs, with an extra learnable predictor and a stop-gradient operation. A fundamental question rises: why they do not collapse into trivial representation? In this paper, we answer this question via a simple theoretical study and propose a novel approach, \ourmethod{}, that \emph{directly} sets the linear predictor based on the statistics of its inputs, rathermore »
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Small, low-cost IoT devices are typically equipped with only a single, low-quality antenna, significantly limiting communication range and link quality. In particular, these antennas are typically linearly polarized and therefore susceptible to polarization mismatch, which can easily cause 10-15 dBm of link loss on communication to and from such devices. In this work, we highlight this under-appreciated issue and propose the augmentation of IoT deployment environments with programmable, RF-sensitive surfaces made of metamaterials. Our smart meta-surface mitigates polarization mismatch by rotating the polarization of signals that pass through or reflect off the surface. We integrate our metasurface into an IoTmore »
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Free, publicly-accessible full text available June 1, 2023