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  1. Abstract Background

    In this paper, we consider the applicability of the customer journey framework from retailing as a driver for urban informatics at individual scales within urban science. The customer journey considers shopper experiences in the context of shopping paths, retail service spaces, and touch-points that draw them into contact. Around this framework, retailers have developed sophisticated data science for observation, identification, and measurement of customers in the context of their shopping behavior. This knowledge supports broad data-driven understanding of customer experiences in physical spaces, economic spaces of decision and choice, persuasive spaces of advertising and branding, and inter-personal spaces of customer-staff interaction.

    Method

    We review the literature on pedestrian and high street retailing, and on urban informatics. We investigate whether the customer journey could be usefully repurposed for urban applications. Specifically, we explore the potential use of the customer journey framework for producing new insight into pedestrian behavior, where a sort of empirical hyperopia has long abounded because data are always in short supply.

    Results

    Our review addresses how the customer journey might be used as a structure for examining how urban walkers come into contact with the built environment, how people actively and passively sense and perceive ambient city life as they move, howmore »pedestrians make sense of urban context, and how they use this knowledge to build cognition of city streetscapes. Each of these topics has relevance to walking studies specifically, but also to urban science more generally. We consider how retailing might reciprocally benefit from urban science perspectives, especially in extending the reach of retailers' insight beyond store walls, into the retail high streets from which they draw custom.

    Conclusion

    We conclude that a broad set of theoretical frameworks, data collection schemes, and analytical methodologies that have advanced retail data science closer and closer to individual-level acumen might be usefully applied to accomplish the same in urban informatics. However, we caution that differences between retailers’ and urban scientists’ viewpoints on privacy presents potential controversy.

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  2. Free, publicly-accessible full text available October 1, 2023
  3. Free, publicly-accessible full text available November 1, 2023
  4. ABSTRACT

    We present 2D hydrodynamical simulations of the transition of a protoplanetary nebula (PPN) to a planetary nebula for central stars in binary systems that have undergone a common-envelope event. After 1000 yr of magnetically driven dynamics (PPN phase), a line-driven stellar wind is introduced into the computational domain and the expansion of the nebula is simulated for another 10 000 yr, including the effects of stellar photoionization. In this study we consider central stars with main sequence (final) masses of 1 (0.569) and 2.5 (0.677) M⊙, together with a 0.6-M⊙ main-sequence companion. Extremely bipolar, narrow-waisted PPNe result in bipolar planetary nebulae, while the rest of the shapes mainly evolve into elliptical planetary nebulae. The initial magnetic field’s effects on the collimated structures, such as jets, tend to disappear in most of the cases, leaving behind the remnants of those features in only a few cases. Equatorial zones fragmented mainly by photoionization (1-M⊙progenitors), result in ‘necklace’ structures made of cometary clumps aligned with the radiation field. On the other hand, fragmentation by photoionization and shocked wind (2.5-M⊙progenitors) give rise to the formation of multiple clumps in the latitudinal direction, which remain within the lobes, close to the center, which are immersed and surroundedmore »by hot shocked gas, not necessarily aligned with the radiation field. These results reveal that the fragmentation process has a dependence on the stellar-mass progenitor. This fragmentation is made possible by the distribution of gas in the previous post-common-envelope PPN as sculpted by the action of the jets.

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  5. Understanding the intrinsic patterns of human brain is important to make inferences about the mind and brain-behavior association. Electrophysiological methods (i.e. MEG/EEG) provide direct measures of neural activity without the effect of vascular confounds. The blood oxygenated level-dependent (BOLD) signal of functional MRI (fMRI) reveals the spatial and temporal brain activity across different brain regions. However, it is unclear how to associate the high temporal resolution Electrophysiological measures with high spatial resolution fMRI signals. Here, we present a novel interpretable model for coupling the structure and function activity of brain based on heterogeneous contrastive graph representation. The proposed method is able to link manifest variables of the brain (i.e. MEG, MRI, fMRI and behavior performance) and quantify the intrinsic coupling strength of different modal signals. The proposed method learns the heterogeneous node and graph representations by contrasting the structural and temporal views through the mind to multimodal brain data. The first experiment with 1200 subjects from Human connectome Project (HCP) shows that the proposed method outperforms the existing approaches in predicting individual gender and enabling the location of the importance of brain regions with sex difference. The second experiment associates the structure and temporal views between the low-level sensory regionsmore »and high-level cognitive ones. The experimental results demonstrate that the dependence of structural and temporal views varied spatially through different modal variants. The proposed method enables the heterogeneous biomarkers explanation for different brain measurements.« less
    Free, publicly-accessible full text available September 15, 2023
  6. Brain large-scale dynamics is constrained by the heterogeneity of intrinsic anatomical substrate. Little is known how the spatiotemporal dynamics adapt for the heterogeneous structural connectivity (SC). Modern neuroimaging modalities make it possible to study the intrinsic brain activity at the scale of seconds to minutes. Diffusion magnetic resonance imaging (dMRI) and functional MRI reveals the large-scale SC across different brain regions. Electrophysiological methods (i.e. MEG/EEG) provide direct measures of neural activity and exhibits complex neurobiological temporal dynamics which could not be solved by fMRI. However, most of existing multimodal analytical methods collapse the brain measurements either in space or time domain and fail to capture the spatio-temporal circuit dynamics. In this paper, we propose a novel spatio-temporal graph Transformer model to integrate the structural and functional connectivity in both spatial and temporal domain. The proposed method learns the heterogeneous node and graph representation via contrastive learning and multi-head attention based graph Transformer using multimodal brain data (i.e. fMRI, MRI, MEG and behavior performance). The proposed contrastive graph Transformer representation model incorporates the heterogeneity map constrained by T1-to-T2-weighted (T1w/T2w) to improve the model fit to structurefunction interactions. The experimental results with multimodal resting state brain measurements demonstrate the proposed method couldmore »highlight the local properties of large-scale brain spatio-temporal dynamics and capture the dependence strength between functional connectivity and behaviors. In summary, the proposed method enables the complex brain dynamics explanation for different modal variants.« less
    Free, publicly-accessible full text available September 15, 2023
  7. The transformation and transmission of brain stimuli reflect the dynamical brain activity in space and time. Compared with functional magnetic resonance imaging (fMRI), magneto- or electroencephalography (M/EEG) fast couples to the neural activity through generated magnetic fields. However, the MEG signal is inhomogeneous throughout the whole brain, which is affected by the signal-to-noise ratio, the sensors’ location and distance. Current non-invasive neuroimaging modalities such as fMRI and M/EEG excel high resolution in space or time but not in both. To solve the main limitations of current technique for brain activity recording, we propose a novel recurrent memory optimization approach to predict the internal behavioral states in space and time. The proposed method uses Optimal Polynomial Projections to capture the long temporal history with robust online compression. The training process takes the pairs of fMRI and MEG data as inputs and predicts the recurrent brain states through the Siamese network. In the testing process, the framework only uses fMRI data to generate the corresponding neural response in space and time. The experimental results with Human connectome project (HCP) show that the predicted signal could reflect the neural activity with high spatial resolution as fMRI and high temporal resolution as MEG signal.more »The experimental results demonstrate for the first time that the proposed method is able to predict the brain response in both milliseconds and millimeters using only fMRI signal.« less
    Free, publicly-accessible full text available September 15, 2023
  8. Free, publicly-accessible full text available November 21, 2023
  9. Free, publicly-accessible full text available December 1, 2023
  10. We explore the dynamics of a nanoscale doubly clamped beam that is under high tension, immersed in a viscous fluid, and driven externally by a spatially varying drive force. We develop a theoretical description that is valid for all possible values of tension, includes the motion of the higher modes of the beam, and accounts for a harmonic force that is applied over a limited spatial region of the beam near its ends. We compare our theoretical predictions with experimental measurements for a nanoscale beam that is driven electrothermally and immersed in air and water. The theoretical predictions show good agreement with experiments, and the validity of a simplified string approximation is demonstrated.
    Free, publicly-accessible full text available July 21, 2023