Modeling Interdependent and Periodic Real-World Action Sequences

Mobile health applications, including those that track activities such as exercise, sleep, and diet, are becoming widely used. Accurately predicting human actions is essential for targeted recommendations that could improve our health and for personalization of these applications. However, making such predictions is extremely difficult due to the complexities of human behavior, which consists of a large number of potential actions that vary over time, depend on each other, and are periodic. Previous work has not jointly modeled these dynamics and has largely focused on item consumption patterns instead of broader types of behaviors such as eating, commuting or exercising. In this work, we develop a novel statistical model for Time-varying, Interdependent, and Periodic Action Sequences. Our approach is based on personalized, multivariate temporal point processes that model time-varying action propensities through a mixture of Gaussian intensities. Our model captures short-term and long-term periodic interdependencies between actions through Hawkes process-based self-excitations. We evaluate our approach on two activity logging datasets comprising 12 million actions taken by 20 thousand users over 17 months. We demonstrate that our approach allows us to make successful predictions of future user actions and their timing. Specifically, our model improves predictions of actions, and their timing, over existing methods across multiple datasets by up to 156%, and up to 37%, respectively. Performance improvements are particularly large for relatively rare and periodic actions such as walking and biking, improving over baselines by up to 256%. This demonstrates that explicit modeling of dependencies and periodicities in real-world behavior enables successful predictions of future actions, with implications for modeling human behavior, app personalization, and targeting of health interventions.Comment: Accepted at WWW 201

Multiwavelength study of RX J2015.6+3711: a magnetic cataclysmic variable with a 2-hr spin period

The X-ray source RX J2015.6+3711 was discovered by ROSAT in 1996 and recently proposed to be a cataclysmic variable (CV). Here we report on an XMM-Newton observation of RX J2015.6+3711 performed in 2014, where we detected a coherent X-ray modulation at a period of 7196+/-11 s, and discovered other significant (>6sigma) small-amplitude periodicities which we interpret as the CV spin period and the sidebands of a possible ~12 hr periodicity, respectively. The 0.3-10 keV spectrum can be described by a power law (Gamma = 1.15+/-0.04) with a complex absorption pattern, a broad emission feature at 6.60+/-0.01 keV, and an unabsorbed flux of (3.16+/-0.05)x10^{-12} erg/s/cm^2. We observed a significant spectral variability along the spin phase, which can be ascribed mainly to changes in the density of a partial absorber and the power law normalization. Archival X-ray observations carried out by the Chandra satellite, and two simultaneous X-ray and UV/optical pointings with Swift, revealed a gradual fading of the source in the soft X-rays over the last 13 years, and a rather stable X-ray-to-optical flux ratio (F_X/F_V ~1.4-1.7). Based on all these properties, we identify this source with a magnetic CV, most probably of the intermediate polar type. The 2 hr spin period makes RX J2015.6+3711 the second slowest rotator of the class, after RX J0524+4244 ("Paloma", P_spin~2.3 hr). Although we cannot unambiguously establish the true orbital period with these observations, RX J2015.6+3711 appears to be a key system in the evolution of magnetic CVs.Comment: 11 pages, 8 figures, accepted for publication on MNRA

Acoustically driven cortical delta oscillations underpin prosodic chunking

Oscillation-based models of speech perception postulate a cortical computational principle by which decoding is performed within a window structure derived by a segmentation process. Segmentation of syllable-size chunks is realized by a theta oscillator. We provide evidence for an analogous role of a delta oscillator in the segmentation of phrase-sized chunks. We recorded Magnetoencephalography (MEG) in humans, while participants performed a target identification task. Random-digit strings, with phrase-long chunks of two digits, were presented at chunk rates of 1.8 Hz or 2.6 Hz, inside or outside the delta frequency band (defined here to be 0.5 - 2 Hz). Strong periodicities were elicited by chunk rates inside of delta in superior, middle temporal areas and speech-motor integration areas. Periodicities were diminished or absent for chunk rates outside delta, in line with behavioral performance. Our findings show that prosodic chunking of phrase-sized acoustic segments is correlated with acoustic-driven delta oscillations, expressing anatomically specific patterns of neuronal periodicities