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1. Epigenetic tuning of brain signal entropy in emergent human social behavior

   https://doi.org/10.1186/s12916-020-01683-x  

   Puglia, Meghan H.; Krol, Kathleen M.; Missana, Manuela; Williams, Cabell L.; Lillard, Travis S.; Morris, James P.; Connelly, Jessica J.; Grossmann, Tobias (December 2020, BMC Medicine)

   null (Ed.)

   Abstract Background How the brain develops accurate models of the external world and generates appropriate behavioral responses is a vital question of widespread multidisciplinary interest. It is increasingly understood that brain signal variability—posited to enhance perception, facilitate flexible cognitive representations, and improve behavioral outcomes—plays an important role in neural and cognitive development. The ability to perceive, interpret, and respond to complex and dynamic social information is particularly critical for the development of adaptive learning and behavior. Social perception relies on oxytocin-regulated neural networks that emerge early in development. Methods We tested the hypothesis that individual differences in the endogenous oxytocinergic system early in life may influence social behavioral outcomes by regulating variability in brain signaling during social perception. In study 1, 55 infants provided a saliva sample at 5 months of age for analysis of individual differences in the oxytocinergic system and underwent electroencephalography (EEG) while listening to human vocalizations at 8 months of age for the assessment of brain signal variability. Infant behavior was assessed via parental report. In study 2, 60 infants provided a saliva sample and underwent EEG while viewing faces and objects and listening to human speech and water sounds at 4 months of age. Infant behavior was assessed via parental report and eye tracking. Results We show in two independent infant samples that increased brain signal entropy during social perception is in part explained by an epigenetic modification to the oxytocin receptor gene ( OXTR ) and accounts for significant individual differences in social behavior in the first year of life. These results are measure-, context-, and modality-specific: entropy, not standard deviation, links OXTR methylation and infant behavior; entropy evoked during social perception specifically explains social behavior only; and only entropy evoked during social auditory perception predicts infant vocalization behavior. Conclusions Demonstrating these associations in infancy is critical for elucidating the neurobiological mechanisms accounting for individual differences in cognition and behavior relevant to neurodevelopmental disorders. Our results suggest that an epigenetic modification to the oxytocin receptor gene and brain signal entropy are useful indicators of social development and may hold potential diagnostic, therapeutic, and prognostic value. 

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2. Surprise! Predicting Infant Visual Attention in a Socially Assistive Robot Contingent Learning Paradigm

   https://doi.org/10.1109/RO-MAN46459.2019.8956385  

   Klein, Lauren; Itti, Laurent; Smith, Beth A.; Rosales, Marcelo; Nikolaidis, Stefanos; Matarić; Maja J. (October 2019, Ro-Man 2019- International Conference on Robot and Human Interactive Communication)

   Early intervention to address developmental disability in infants has the potential to promote improved outcomes in neurodevelopmental structure and function [1]. Researchers are starting to explore Socially Assistive Robotics (SAR) as a tool for delivering early interventions that are synergistic with and enhance human-administered therapy. For SAR to be effective, the robot must be able to consistently attract the attention of the infant in order to engage the infant in a desired activity. This work presents the analysis of eye gaze tracking data from five 6-8 month old infants interacting with a Nao robot that kicked its leg as a contingent reward for infant leg movement. We evaluate a Bayesian model of lowlevel surprise on video data from the infants’ head-mounted camera and on the timing of robot behaviors as a predictor of infant visual attention. The results demonstrate that over 67% of infant gaze locations were in areas the model evaluated to be more surprising than average. We also present an initial exploration using surprise to predict the extent to which the robot attracts infant visual attention during specific intervals in the study. This work is the first to validate the surprise model on infants; our results indicate the potential for using surprise to inform robot behaviors that attract infant attention during SAR interactions. 

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3. Fairness and Delay in Heterogeneous Half- and Full-Duplex Wireless Networks

   https://doi.org/10.1109/ACSSC.2018.8645117  

   Chen, Tingjun; Diakonikolas, Jelena; Ghaderi, Javad; Zussman, Gil (October 2018, Conference record - Asilomar Conference on Signals, Systems, & Computers)

   Full-duplex (FD) wireless is an attractive communication paradigm with high potential for improving network capacity and reducing delay in wireless networks. Despite significant progress on the physical layer development, the challenges associated with developing medium access control (MAC) protocols for heterogeneous networks composed of both legacy half-duplex (HD) and emerging FD devices have not been fully addressed. In [1], we focused on the design and performance evaluation of scheduling algorithms for heterogeneous HD-FD networks and presented the distributed Hybrid-Greedy Maximal Scheduling (H-GMS) algorithm. H-GMS combines the centralized Greedy Maximal Scheduling (GMS) and a distributed queue-based random-access mechanism, and is throughput-optimal. In this paper, we analyze the delay performance of H-GMS by deriving two lower bounds on the average queue length. We also evaluate the fairness and delay performance of H-GMS via extensive simulations. We show that in heterogeneous HD-FD networks, H-GMS achieves$$16-30\times$$ better delay performance and improves fairness between FD and HD users by up to 50% compared with the fully decentralized Q-CSMA algorithm. 

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4. Fairness and Delay in Heterogeneous Half- and Full-Duplex Wireless Networks

   Tingjun Chen, Jelena Diakonikolasy (January 2018, Asilomar Conference on Signals, Systems, and Computers)

   Abstract—Full-duplex (FD) wireless is an attractive commu- nication paradigm with high potential for improving network capacity and reducing delay in wireless networks. Despite sig- nificant progress on the physical layer development, the chal- lenges associated with developing medium access control (MAC) protocols for heterogeneous networks composed of both legacy half-duplex (HD) and emerging FD devices have not been fully addressed. In [1], we focused on the design and performance evaluation of scheduling algorithms for heterogeneous HD-FD networks and presented the distributed Hybrid-Greedy Maximal Scheduling (H-GMS) algorithm. H-GMS combines the central- ized Greedy Maximal Scheduling (GMS) and a distributed queue- based random-access mechanism, and is throughput-optimal. In this paper, we analyze the delay performance of H-GMS by deriving two lower bounds on the average queue length. We also evaluate the fairness and delay performance of H-GMS via extensive simulations. We show that in heterogeneous HD-FD networks, H-GMS achieves 16–30× better delay performance and improves fairness between FD and HD users by up to 50% compared with the fully decentralized Q-CSMA algorithm. 

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5. Hybrid Scheduling in Heterogeneous Half- and Full-Duplex Wireless Networks

   https://doi.org/1801.01108v2  

   Tingjun Chen, Jelena Diakonikolas (January 2018, ArXiv.org)

   Full-duplex (FD) wireless is an attractive communication paradigm with high potential for improving network capacity and reducing delay in wireless networks. Despite significant progress on the physical layer development, the challenges associated with developing medium access control (MAC) protocols for heterogeneous networks composed of both legacy half-duplex (HD) and emerging FD devices have not been fully addressed. Therefore, we focus on the design and performance evaluation of scheduling algorithms for infrastructure-based heterogeneous HD-FD networks (composed of HD and FD users). We first show that centralized GreedyMaximal Scheduling (GMS) is throughput-optimal in heterogeneous HD-FD networks. We propose the Hybrid-GMS (H-GMS) algorithm, a distributed implementation of GMS that combines GMS and a queue-based random-access mechanism. We prove that H-GMS is throughputoptimal. Moreover, we analyze the delay performance of H-GMS by deriving lower bounds on the average queue length. We further demonstrate the benefits of upgrading HD nodes to FD nodes in terms of throughput gains for individual nodes and the whole network. Finally, we evaluate the performance of HGMS and its variants in terms of throughput, delay, and fairness between FD and HD users via extensive simulations. We show that in heterogeneous HD-FD networks, H-GMS achieves 16–30× better delay performance and improves fairness between HD and FD users by up to 50% compared with the fully decentralized Q-CSMA algorithm. 

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