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1. Risk Perceptions and Adaptation to Climate Change and Sea-Level Rise: Insights from General Public Opinion Survey in Florida

   https://doi.org/10.1061/(ASCE)WR.1943-5452.0001156  

   Sikder, Abu Hena ; Mozumder, Pallab ( March 2020 , Journal of Water Resources Planning and Management)

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2. Stash: A comprehensive stall-centric characterization of public cloud VMs for distributed deep learning

   Aakash Sharma, Vivek M. ( July 2023 , Applied Electronics)
3. Stash: A comprehensive stall-centric characterization of public cloud VMs for distributed deep learning

   Sharma, Aakash ; Bhasi, Vivek ; Singh, Sonali ; Jain, Rishabh ; Raj, Jashwant ; Mitra, Subrata ; Kandemir, Mahmut Taylan ; Kesidis, George ; Das, Chita ( January 2023 , Proceedings of the International Conference on Distributed Computing Systems)

   Deep neural networks (DNNs) are increasingly popular owing to their ability to solve complex problems such as image recognition, autonomous driving, and natural language processing. Their growing complexity coupled with the use of larger volumes of training data (to achieve acceptable accuracy) has warranted the use of GPUs and other accelerators. Such accelerators are typically expensive, with users having to pay a high upfront cost to acquire them. For infrequent use, users can, instead, leverage the public cloud to mitigate the high acquisition cost. However, with the wide diversity of hardware instances (particularly GPU instances) available in public cloud, it becomes challenging for a user to make an appropriate choice from a cost/performance standpoint. In this work, we try to address this problem by (i) introducing a comprehensive distributed deep learning (DDL) profiler Stash, which determines the various execution stalls that DDL suffers from, and (ii) using Stash to extensively characterize various public cloud GPU instances by running popular DNN models on them. Specifically, it estimates two types of communication stalls, namely, interconnect and network stalls, that play a dominant role in DDL execution time. Stash is implemented on top of prior work, DS-analyzer, that computes only the CPU and disk stalls. Using our detailed stall characterization, we list the advantages and shortcomings of public cloud GPU instances for users to help them make an informed decision(s). Our characterization results indicate that the more expensive GPU instances may not be the most performant for all DNN models and that AWS can sometimes sub-optimally allocate hardware interconnect resources. Specifically, the intra-machine interconnect can introduce communication overheads of up to 90% of DNN training time and the network-connected instances can suffer from up to 5× slowdown compared to training on a single instance. Furthermore, (iii) we also model the impact of DNN macroscopic features such as the number of layers and the number of gradients on communication stalls, and finally, (iv) we briefly discuss a cost comparison with existing work. 

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4. Assaying neural activity of children during video game play in public spaces: a deep learning approach

   https://doi.org/10.1088/1741-2552/ab1876  

   Sujatha Ravindran, Akshay ; Mobiny, Aryan ; Cruz-Garza, Jesus G. ; Paek, Andrew ; Kopteva, Anastasiya ; Contreras Vidal, José L. ( May 2019 , Journal of Neural Engineering)

   Objective. Understanding neural activity patterns in the developing brain remains one of the grand challenges in neuroscience. Developing neural networks are likely to be endowed with functionally important variability associated with the environmental context, age, gender, and other variables. Therefore, we conducted experiments with typically developing children in a stimulating museum setting and tested the feasibility of using deep learning techniques to help identify patterns of brain activity associated with different conditions.Approach. A four-channel dry EEG-based Mobile brain-body imaging data of children at rest and during videogame play (VGP) was acquired at the Children’s Museum of Houston. A data-driven approach based on convolutional neural networks (CNN) was used to describe underlying feature representations in the EEG and their ability to discern task and gender. The variability of the spectral features of EEG during the rest condition as a function of age was also analyzed.Main results. Alpha power (7–13 Hz) was higher during rest whereas theta power (4–7 Hz) was higher during VGP. Beta (13–18 Hz) power was the most significant feature, higher in females, when differentiating between males and females. Using data from both temporoparietal channels to classify between VGP and rest condition, leave-one-subject-out cross-validation accuracy of 67% was obtained. Age-related changes in EEG spectral content during rest were consistent with previous developmental studies conducted in laboratory settings showing an inverse relationship between age and EEG power.Significance. These findings are the first to acquire, quantify and explain brain patterns observed during VGP and rest in freely behaving children in a museum setting using a deep learning framework. The study shows how deep learning can be used as a data driven approach to identify patterns in the data and explores the issues and the potential of conducting experiments involving children in a natural and engaging environment.

    

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5. Rock magnetic variability of quaternary deep-sea sediments from the Bering Sea and their environmental implications

   https://doi.org/10.1016/j.dsr.2021.103487  

   Lund, Steve ; Mortazavi, Emily ; Platzman, Ellen ; Kirby, Matt ; Stoner, Joe ; Okada, Makoto ( June 2021 , Deep Sea Research Part I: Oceanographic Research Papers)