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1. Mitigation of Scheduling Violations in Time-Sensitive Networking using Deep Deterministic Policy Gradient

   https://doi.org/10.1145/3472735.3473385  

   Zhou, Boyang; Cheng, Liang (August 2021, FlexNets '21: Proceedings of the 4th FlexNets Workshop on Flexible Networks Artificial Intelligence Supported Network Flexibility and Agility)

   null (Ed.)

   Time-Sensitive Networking (TSN) is designed for real-time applications, usually pertaining to a set of Time-Triggered (TT) data flows. TT traffic generally requires low packet loss and guaranteed upper bounds on end-to-end delay. To guarantee the end-to-end delay bounds, TSN uses Time-Aware Shaper (TAS) to provide deterministic service to TT flows. Each frame of TT traffic is scheduled a specific time slot at each switch for its transmission. Several factors may influence frame transmissions, which then impact the scheduling in the whole network. These factors may cause frames sent in wrong time slots, namely misbehaviors. To mitigate the occurrence of misbehaviors, we need to find proper scheduling for the whole network. In our research, we use a reinforcement-learning model, which is called Deep Deterministic Policy Gradient (DDPG), to find the suitable scheduling. DDPG is used to model the uncertainty caused by the transmission-influencing factors such as time-synchronization errors. Compared with the state of the art, our approach using DDPG significantly decreases the number of misbehaviors in TSN scenarios studied and improves the delay performance of the network. 

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2. Deep Reinforcement Learning with Uncertain Data for Real-Time Stormwater System Control and Flood Mitigation

   https://doi.org/10.3390/w12113222  

   Saliba, Sami M.; Bowes, Benjamin D.; Adams, Stephen; Beling, Peter A.; Goodall, Jonathan L. (November 2020, Water)

   null (Ed.)

   Flooding in many areas is becoming more prevalent due to factors such as urbanization and climate change, requiring modernization of stormwater infrastructure. Retrofitting standard passive systems with controllable valves/pumps is promising, but requires real-time control (RTC). One method of automating RTC is reinforcement learning (RL), a general technique for sequential optimization and control in uncertain environments. The notion is that an RL algorithm can use inputs of real-time flood data and rainfall forecasts to learn a policy for controlling the stormwater infrastructure to minimize measures of flooding. In real-world conditions, rainfall forecasts and other state information are subject to noise and uncertainty. To account for these characteristics of the problem data, we implemented Deep Deterministic Policy Gradient (DDPG), an RL algorithm that is distinguished by its capability to handle noise in the input data. DDPG implementations were trained and tested against a passive flood control policy. Three primary cases were studied: (i) perfect data, (ii) imperfect rainfall forecasts, and (iii) imperfect water level and forecast data. Rainfall episodes (100) that caused flooding in the passive system were selected from 10 years of observations in Norfolk, Virginia, USA; 85 randomly selected episodes were used for training and the remaining 15 unseen episodes served as test cases. Compared to the passive system, all RL implementations reduced flooding volume by 70.5% on average, and performed within a range of 5%. This suggests that DDPG is robust to noisy input data, which is essential knowledge to advance the real-world applicability of RL for stormwater RTC. 

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3. 3-D SAR imaging for multistatic GPR

   https://doi.org/10.1117/12.2519430  

   Pereira, Mauricio; Zhang, Yu; Huston, Dryver; Xia, Tian; Dhar, Nibir K.; Dutta, Achyut K.; Babu, Sachidananda R. (May 2019, SPIE Defense + Commercial Sensing, 2019, Proceedings Volume 10980, Image Sensing Technologies: Materials, Devices, Systems, and Applications VI; 109801D (2019))

   Ground penetrating radar (GPR) is a remote geophysical sensing method that has been applied in the localization of underground utilities, bridge deck survey, localization of landmines, mapping of terrain for aid in driverless cars, etc. Multistatic GPR can deliver a faster survey, wider spatial coverage, and multiple viewpoints of the subsurface. However, because of the transmit and receive antennas spatial offset, formation of 3D GPR image by simple stacking of the acquired A-scans is inaccurate. Also, averaging of different receivers data may lead to destructive interference of back-scattered waves due to different time delays implied by the spatial offset, so averaging does not lead to higher SNR in general. Furthermore, the energy back-scattered by scatter points are spread in hyperbolas in the GPR raw data. Migration or imaging algorithms are employed to increase SNR by focusing the hyperbolas. This focusing process also leads to better accuracy in target localization. In this paper, a computationally efficient synthetic aperture radar (SAR) imaging algorithm that properly integrates multistatic GPR data in both ground and air-coupled cases is presented. The algorithm is successfully applied on two synthetic datasets. 

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4. Scan-Centric, Frequency-Based Method for Characterizing Peaks from Direct Injection Fourier transform Mass Spectrometry Experiments

   https://doi.org/10.3390/metabo12060515  

   Flight, R.M.; Mitchell, J.M.; Moseley, H.N.B. (January 2022, Metabolites)

   We present a novel, scan-centric method for characterizing peaks from direct injection multi-scan Fourier transform mass spectra of complex samples that utilizes frequency values derived directly from the spacing of raw m/z points in spectral scans. Our peak characterization method utilizes intensity-independent noise removal and normalization of scan-level data to provide a much better fit of relative intensity to natural abundance probabilities for low abundance isotopologues that are not present in all of the acquired scans. Moreover, our method calculates both peak- and scan-specific statistics incorporated within a series of quality control steps that are designed to robustly derive peak centers, intensities, and intensity ratios with their scan-level variances. These cross-scan characterized peaks are suitable for use in our previously published peak assignment methodology, Small Molecule Isotope Resolved Formula Enumeration (SMIRFE) 

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5. Functional connectivity uniqueness and variability? Linkages with cognitive and psychiatric problems in children

   https://doi.org/10.1038/s44220-023-00151-8  

   Fu, Zening; Liu, Jingyu; Salman, Mustafa S; Sui, Jing; Calhoun, Vince D (December 2023, Nature Mental Health)

   Abstract Brain functional connectivity (FC) derived from functional magnetic resonance imaging has been serving as a potential ‘fingerprint’ for adults. However, cross-scan variation of FC can be substantial and carries biological information, especially during childhood. Here we performed a large-scale cross-sectional analysis on cross-scan FC stability and its associations with a diverse range of health measures in children. Functional network connectivity (FNC) was extracted via a hybrid independent component analysis framework on 9,071 participants and compared across four scans. We found that FNC can identify a given child from a large group with high accuracy (maximum >94%) and replicated the results across multiple scans. We then performed a linear mixed-effects model to investigate how cross-scan FNC stability was predictive of children’s behaviour. Although we could not find strong relationships between FNC stability and children’s behaviour, we observed significant but small associations between them (maximumr = 0.1070), with higher stability correlated with better cognitive performance, longer sleep duration and less psychotic expression. Via a multivariate analysis method, we captured larger effects between FNC stability and children’s cognitive performance (maximumr = 0.2932), which further proved the relevance of FNC stability to neurocognitive development. Overall, our findings show that a child’s connectivity profile is not only intrinsic but also exhibits reliable variability across scans, regardless of brain growth and development. Cross-scan connectivity stability may serve as a valuable neuroimaging feature to draw inferences on early cognitive and psychiatric behaviours in children. 

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