Understanding people flow in a city (urban dynamics) is of great importance in urban planning, emergency management, and commercial activity. With the spread of smart devices, many studies on urban dynamics modeling with mobility logs have been conducted. It is predictive analysis, not analysis of the past, that enables various applications contributing to a more prosperous society. To deal with the non-linear effects on urban dynamics from external factors, such as day of the week, national holiday, or weather, we propose a low-rank bilinear Poisson regression model, for a novel and flexible representation of urban dynamics predictive analysis. The results obtained from an experiment with one year's worth of mobility records suggest the high prediction accuracy of the proposed model. We also introduce the following applications: regional event detection via irregularities, visualization of urban dynamics corresponding to urban demographics, and extraction of urban demographics of unknown point of interests.

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Cancer cells are highly heterogeneous to adapt to extreme tumor microenvironments (TMEs). TMEs challenge cancer cells via hypoxia, nutrition starvation, and acidic pH, promoting invasion and metastasis concomitant with genetic, epigenetic, and metabolic alterations. Metabolic adaptation to an extreme TME could allow cancer cells to evade cell death and immune responses, as well as resulting in drug resistance, recurrence, and poor patient prognosis. Therefore, elucidation of the metabolic adaptation of malignant cancer cells within TMEs is necessary, however, most are still elusive. Recently, adaptation of cancer cells within the TME can be analyzed via cell–cell interactions at the single‐cell level. In addition, information into organelle–organelle interactions has recently been obtained. These cell–cell, and organelle–organelle interactions demonstrate the potential as new cancer therapy targets, as they play essential roles in the metabolic adaptation of cancer cells to the TME. In this manuscript, we review (1) metabolic adaptations within tumor microenvironments through (2) cell‐to‐cell, and (3) organelle–organelle metabolic interactions.