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We consider a decentralized wireless network with several source-destination pairs sharing a limited number of orthogonal frequency bands. Sources learn to adapt their transmissions (specifically, their band selection strategy) over time, in a decentralized manner, without sharing information with each other. Sources can only observe the outcome of their own transmissions (i.e., success or collision), having no prior knowledge of the network size or of the transmission strategy of other sources. The goal of each source is to maximize their own throughput while striving for network-wide fairness. We propose a novel fully decentralized Reinforcement Learning (RL)-based solution that achieves fairness without coordination. The proposed Fair Share RL (FSRL) solution combines: (i) state augmentation with a semi-adaptive time reference; (ii) an architecture that leverages risk control and time difference likelihood; and (iii) a fairnessdriven reward structure. We evaluate FSRL in several network settings. Simulation results suggest that, when we compare FSRL with a common baseline RL algorithm from the literature, FSRL can be up to 89.0% fairer (as measured by Jain’s fairness index) in stringent settings with several sources and a single frequency band, and 48.1% fairer on average. 

A gas hydrate assessment at International Ocean Discovery Program Expedition 400 drill sites was conducted using downhole logging and core data. Here, we calculate and present the base of gas hydrate stability zone at Expedition 400 drill sites in Baffin Bay, northwest Greenland. We used data from downhole logs and sediment cores from Sites U1603, U1604, U1607, and U1608 to assess hydrate and did not find evidence for the presence of hydrate. At Site U1606, only core data were acquired that showed a decrease in pore water salinity, potentially indicating the presence of hydrate; however, further confirmation was not possible due to the unavailability of downhole logging data. Because of the limitation of the acquired data at the drill sites, a further assessment to confirm the presence of hydrate was not possible. Although hydrate was not identified at any drill sites, hydrate might still be present in the region. 

Estimating the location of contact is a primary function of artificial tactile sensing apparatuses that perceive the environment through touch. Existing contact localization methods use flat geometry and uniform sensor distributions as a simplifying assumption, limiting their ability to be used on 3D surfaces with variable density sensing arrays. This paper studies contact localization on an artificial skin embedded with mutual capacitance tactile sensors, arranged non-uniformly in an unknown distribution along a semi-conical 3D geometry. A fully connected neural network is trained to localize the touching points on the embedded tactile sensors. The studied online model achieves a localization error of 5.7 ± 3.0 mm. This research contributes a versatile tool and robust solution for contact localization that is ambiguous in shape and internal sensor distribution.