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1. 30th Annual Computational Neuroscience Meeting: CNS*2021–Meeting Abstracts

   Handy, G.; Borisyuk, A. (December 2021, Journal of computational neuroscience)

   Bojak, I.; Linster, C. (Ed.)
2. Keep Meeting Summaries on Topic: Abstractive Multi-Modal Meeting Summarization

   Li, Manling; Zhang, Lingyu; Radke, Richard J.; Ji, Heng (July 2019, 57th Conference of the Association for Computational Linguistics)

   Transcripts of natural, multi-person meetings differ significantly from documents like news articles, which can make Natural Language Generation models generate unfocused summaries. We develop an abstractive meeting summarizer from both videos and audios of meeting recordings. Specifically, we propose a multi-modal hierarchical attention mechanism across three levels: topic segment, utterance and word. To narrow down the focus into topically-relevant segments, we jointly model topic segmentation and summarization. In addition to traditional textual features, we introduce new multi-modal features derived from visual focus of attention, based on the assumption that an utterance is more important if its speaker receives more attention. Experiments show that our model significantly outperforms the state-of-the-art with both BLEU and ROUGE measures. 

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3. 2022 Annual Meeting

   https://doi.org/10.1785/0220220087  

   DUGDA, M.; KASSA, A. B.; POUCHARD, L.; DIRES, E.; MCDANIEL, L. (April 2022, Seismological Research Letters)

   Caplan-Auerbach, Jackie; Schmidt, D. (Ed.)

   Title: Efficient Access and Manipulation of Big Seismic Data from Disparate Sources Seismological data recordings have been growing exponentially in the last three decades. For instance, the data archive at the IRIS Data Management Center (DMC) grew from less than 10 Tebibytes in 1992 to greater than 750 Tebibytes today (in 2022). In addition to such big data archives, retrieving and merging data from various disparate seismic sources also creates big data which will enable obtaining higher-resolution seismic images and understanding phenomena such as earthquake cycles. Moreover, recent progress in geosciences with the application of AI/ML using big data has shown the potential of discovering patterns that were not previously recognized. However, aggregating large seismic datasets introduces its own challenges. Some of these challenges arise from the fact that many data centers have their own way of distributing data, and the format of the data and metadata are different in many cases. The objective of this investigation is the development of data access and manipulation tools for retrieval, merging, processing, and the management of big seismic data from disparate seismic data sources. We develop a free, open-source, direct data accessing, gathering, and processing software toolbox for disparate sources using Python. Aggregating data from different data centers will enable us to investigate the seismic structure beneath a region of interest at a higher resolution by merging the seismic databases. Such a merged dataset can be applied on studies around the boundaries between countries if those countries have different networks. One boundary region of geologic interest to exemplify the benefits of aggregated seismic datasets from different networks in two countries is the southern side of the Rio Grande Rift including the bordering areas between the US and Mexico. Previous more detailed seismic studies on Rio Grande were conducted mostly on the US side of the Rift Valley. 

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4. Oral Abstracts: European Glia Meeting 2021

   https://doi.org/10.1002/glia.24035  

   Hines, JH (July 2021, Glia)

   Oral Presentation Title: Evolution of the oligodendrocyte cell type and adaptive myelination phenotype (S10-03) 

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5. Abstracts From the 2018 Annual Meeting

   https://doi.org/10.1177/1545968318817151  

   Augenstein, T; Washabaugh, E; Remy, C; Krishnan, C (November 2018, Neurorehabilitation and Neural Repair)

   Muscle weakness and loss of independent joint control are the 2 most common neuromotor impairments after stroke. While there are a number of approaches to improve poststroke muscle weakness, there are currently no rehabilitation strategies that directly target a patient’s inability to match and independently activate the normal patterned muscle coordination strategies, or “muscle synergies.” Our goal is to develop an EMG-based controller for retraining healthy muscle synergies in patients with stroke-related disabilities. The controller can be integrated into rehabilitation robots for their ability to structure the robot’s force output based on input EMG activity. However, developing such a controller would require a clear understanding of the relationship between the applied force from a rehabilitation robot and the resulting changes to a patient’s muscle synergies. Therefore, this study was performed to quantify how the muscle synergies of horizontal planar-reaching are affected by direction of an applied force at the end-effector (ie, hand). A 2 DOF, 10 muscle model was developed in MATLAB using parameters obtained from the OpenSim (version 3.3) open source software system. Simulation experiments were then performed in MATLAB to investigate the relationship between the applied force and the resulting muscle synergies. The simulated event was composed of several trials of the same righthanded, planar, multidirectional reaching task from 0° (to the right) to 360°. Each trial applied a different steering force direction at the subject’s hand, varying from −45° to 45° relative to the reaching direction. The simulation trials were also validated by evaluating the EMG patterns of a healthy subject when performing the same reaching task with varying steering force directions. For the 0° steering force trials, the muscle synergies and their activation timings were extracted using nonnegative matrix factorization (NMF). For all other trials, the synergy matrix was fixed and the activation timings were extracted from the product of the EMG of that trial and the pseudo-inverse of the synergy matrix from the 0° steering force trial. By fixing the synergy matrix in the trials with steering forces, we can directly track activation changes of a certain synergy as steering force is varied. For both simulation and experimental trials, circular statistics revealed a linear relationship between changes in steering force direction and principal direction of synergy activation. These results suggest that the activation of a synergy can be controlled directly by the direction of an applied steering force. This has relevant implications in synergy-based controller design because a computer can easily manipulate a patient’s muscle synergies and track the changes while avoiding the computational expense of NMF. In addition, similar analysis could be used to extract the relationship between applied forces and changes in synergies for other types of motion. 

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