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Human-generated Spatial-Temporal Data (HSTD), represented as trajectory sequences, has undergone a data revolution, thanks to advances in mobile sensing, data mining, and AI. Previous studies have revealed the effectiveness of employing attention mechanisms to analyze massive HSTD. However, traditional attention models face challenges when managing lengthy and noisy trajectories as their computation comes with large memory overheads. Furthermore, attention scores within HSTD trajectories are sparse (i.e., most of the scores are zeros), and clustered with varying lengths (i.e., consecutive tokens clustered with similar scores). To address these challenges, we introduce an innovative strategy named Memory-efficient Trajectory Attention (MeTA). We leverage complicated spatial-temporal features (e.g., traffic speed, proximity to PoIs) and design an innovative feature-based trajectory partition technique to shrink trajectory length. Additionally, we present a learnable dynamic sorting mechanism, with which attention is only computed between sub-trajectories that have prominent correlations. Empirical validations using real-world HSTD demonstrate that our approach not only yields competitive results but also significantly lowers memory usage compared with state-of-the-art methods. Our approach presents innovative solutions for memory-efficient trajectory attention, offering valuable insights for handling HSTD efficiently. 

Abstract The recharge oscillator (RO) is a simple mathematical model of the El Niño Southern Oscillation (ENSO). In its original form, it is based on two ordinary differential equations that describe the evolution of equatorial Pacific sea surface temperature and oceanic heat content. These equations make use of physical principles that operate in nature: (a) the air‐sea interaction loop known as the Bjerknes feedback, (b) a delayed oceanic feedback arising from the slow oceanic response to winds within the equatorial band, (c) state‐dependent stochastic forcing from fast wind variations known as westerly wind bursts (WWBs), and (d) nonlinearities such as those related to deep atmospheric convection and oceanic advection. These elements can be combined at different levels of RO complexity. The RO reproduces ENSO key properties in observations and climate models: its amplitude, dominant timescale, seasonality, and warm/cold phases amplitude asymmetry. We discuss the RO in the context of timely research questions. First, the RO can be extended to account for ENSO pattern diversity (with events that either peak in the central or eastern Pacific). Second, the core RO hypothesis that ENSO is governed by tropical Pacific dynamics is discussed from the perspective of influences from other basins. Finally, we discuss the RO relevance for studying ENSO response to climate change, and underline that accounting for ENSO diversity, nonlinearities, and better links of RO parameters to the long term mean state are important research avenues. We end by proposing important RO‐based research problems. 

The importance of machine learning (ML) in scientific discovery is growing. In order to prepare the next generation for a future dominated by data and artificial intelligence, we need to study how ML can improve K-12 students’ scientific discovery in STEM learning and how to assist K-12 teachers in designing ML-based scientific discovery (SD) learning activities. This study proposes research ideas and provides initial findings on the relationship between different ML components and young learners’ scientific investigation behaviors. Results show that cluster analysis is promising for supporting pattern interpretation and scientific communication behaviors. The levels of cognitive complexity are associated with different ML-powered SD and corresponding learning support is needed. The next steps include a further co-design study between K-12 STEM teachers and ML experts and a plan for collecting and analyzing data to further understand the connection between ML and SD. 

Objective: This study examined factors that were associated with the effectiveness of pre-existing household emergency plans during the 2011 EF5 Joplin and EF4 Tuscaloosa tornadoes. We focused on whether discussing with family members helped increase the plan’s effectiveness. Methods: A telephone survey based on random sampling was conducted in 2012 with 1006 respondents in both cities. Each city experienced huge losses, injuries, and casualties. The working sample included 494 respondents who had a household emergency plan in place before these tornadoes. Results: Multinomial logistic regression showed that discussing with family members increased the helpfulness of the plan in Joplin, where people had not experienced tornadoes frequently and were less prepared for tornadoes relative to residents in Tuscaloosa. Conclusions: This study provides empirical evidence on the importance of encouraging family involvement when making household emergency plans, especially in places that are less prepared for disasters than those that are better prepared. Key Words: disaster preparedness, household emergency plan, tornado, vulnerability 

An unsupervised image-to-image translation (UI2I) task deals with learning a mapping between two domains without paired images. While existing UI2I methods usually require numerous unpaired images from different domains for training, there are many scenarios where training data is quite limited. In this paper, we argue that even if each domain contains a single image, UI2I can still be achieved. To this end, we propose TuiGAN, a generative model that is trained on only two unpaired images and amounts to one-shot unsupervised learning. With TuiGAN, an image is translated in a coarse-to-fine manner where the generated image is gradually refined from global structures to local details. We conduct extensive experiments to verify that our versatile method can outperform strong baselines on a wide variety of UI2I tasks. Moreover, TuiGAN is capable of achieving comparable performance with the state-of-the-art UI2I models trained with sufficient data. 

We present the first measurement of cosmic-ray fluxes of ${}^6\mathrm{Li}$and ${}^7\mathrm{Li}$isotopes in the rigidity range from 1.9 to 25 GV. The measurements are based on $9.7\times {10}^5$ ${}^6\mathrm{Li}$and $1.04\times {10}^6$ ${}^7\mathrm{Li}$nuclei collected by the Alpha Magnetic Spectrometer on the International Space Station from May 2011 to October 2023. We observe that over the entire rigidity range the ${}^6\mathrm{Li}$and ${}^7\mathrm{Li}$fluxes exhibit nearly identical time variations and, above $\sim 4\mathrm{GV}$, the time variations of ${}^6\mathrm{Li}$, ${}^7\mathrm{Li}$, He, Be, B, C, N, and O fluxes are identical. Above $\sim 7\mathrm{GV}$, we find an identical rigidity dependence of the ${}^6\mathrm{Li}$and ${}^7\mathrm{Li}$fluxes. This shows that they are both produced by collisions of heavier cosmic-ray nuclei with the interstellar medium and, in particular, excludes the existence of a sizable primary component in the ${}^7\mathrm{Li}$flux. Published by the American Physical Society2025