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Title: Representing Mixtures of Word Embeddings with Mixtures of Topic Embeddings
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International Conference on Learning Representations
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Sponsoring Org:
National Science Foundation
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  1. Abstract

    The Raman spectral behavior of N2, CO2, and CH4in ternary N2–CO2–CH4mixtures was studied from 22°C to 200°C and 10 to 500 bars. The peak position of N2in all mixtures is located at lower wavenumbers compared with pure N2at the same pressure (P)–temperature (T) (PT) conditions. The Fermi diad splitting in CO2is greater in the pure system than in the mixtures, and the Fermi diad splitting increases in the mixtures as CO2concentration increases at constantPandT. The peak position of CH4in the mixtures is shifted to higher wavenumbers compared with pure CH4at the samePTconditions. However, the relationship between peak position and CH4mole fraction is more complicated compared with the trends observed with N2and CO2. The relative order of the peak position isotherms of CH4and N2in the mixtures in pressure–peak position space mimics trends in the molar volume of the mixtures in pressure–molar volume space. Relationships between the direction of peak shift of individual components in the mixtures, the relative molar volumes of the mixtures, and the attraction and repulsion forces between molecules are developed. Additionally, the relationship between the peak position of N2in ternary N2–CO2–CH4mixtures with pressure is extended to other N2‐bearing systems to assess similarities in the Raman spectral behavior of N2in various systems.

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  2. null (Ed.)
    Abstract Population analyses of functional connectivity have provided a rich understanding of how brain function differs across time, individual, and cognitive task. An important but challenging task in such population analyses is the identification of reliable features that describe the function of the brain, while accounting for individual heterogeneity. Our work is motivated by two particularly important challenges in this area: first, how can one analyze functional connectivity data over populations of individuals, and second, how can one use these analyses to infer group similarities and differences. Motivated by these challenges, we model population connectivity data as a multilayer network and develop the multi-node2vec algorithm, an efficient and scalable embedding method that automatically learns continuous node feature representations from multilayer networks. We use multi-node2vec to analyze resting state fMRI scans over a group of 74 healthy individuals and 60 patients with schizophrenia. We demonstrate how multilayer network embeddings can be used to visualize, cluster, and classify functional regions of the brain for these individuals. We furthermore compare the multilayer network embeddings of the two groups. We identify significant differences between the groups in the default mode network and salience network—findings that are supported by the triple network model theory of cognitive organization. Our findings reveal that multi-node2vec is a powerful and reliable method for analyzing multilayer networks. Data and publicly available code are available at . 
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