Anh N. Nhu

In this work, we introduce the Time-Aware World Model (TAWM), a model-based approach that explicitly incorporates temporal dynamics. By conditioning on the time-step size, ∆t, and training over a diverse range of ∆t values – rather than sampling at a fixed time-step – TAWM learns both high- and low-frequency task dynamics across diverse control problems. Grounded in the information-theoretic insight that the optimal sampling rate depends on a system’s underlying dynamics, this time-aware formulation improves both performance and data efficiency. Empirical evaluations show that TAWM consistently outperforms conventional models across varying observation rates in a variety of control tasks, using the same number of training samples and iterations. Our code can be found online at: github.com/anh-nn01/Time-Aware-World-Model.

Spatial variability has been one of the major challenges for large-area crop monitoring and classification with remote sensing. Recent works on deep learning have introduced spatial transformation methods to automatically partition a heterogeneous region into multiple homogeneous sub-regions during the training process. However, the framework is only designed for deep learning and is not available for other models, e.g., decision tree and random forest, which are frequently the models of choice in many crop mapping products. This paper develops a geo-aware random forest (Geo-RF) model to enable new capabilities to automatically recognize spatial variability during training, partition the space, and learn local models. Specifically, Geo-RF can capture spatial partitions with flexible shapes via an efficient bi-partitioning optimization algorithm. Geo-RF also automatically determines the number of partitions needed in a hierarchical manner via statistical tests and builds local RF models along the partitioning process to explicitly address spatial variability and improve classification quality. We used both synthetic and real-world data to evaluate the effectiveness of Geo-RF. First, through the controlled synthetic experiment, Geo-RF demonstrated the ability to capture the artificially-inserted true partition where a different relationship between the inputs and outputs is used. Second, we showed the improvements from Geo-RF using crop classification for five major crops over the contiguous US. The results demonstrated that Geo-RF is able to significantly improve classification performance in sub-regions that are otherwise compromised in a single RF model. For example, the partition around downstream Mississippi for soybean classification led to major improvements for about 0.10-0.25 in F1 scores in the area, and the score increased from 0.57 to 0.82 at certain locations. Similarly, for rice classification, the partition in Arkansas led to F1 scores increasing from 0.59 to 0.88 in local areas. In addition, we evaluated the models under different parameter settings, and the results showed that Geo-RF led to improvements over RF in the vast majority of scenarios (e.g., varying model complexity and training sizes). Computationally, Geo-RF took about one to three times more training time while its execution time during testing was similar to that of RF. Overall, Geo-RF showed the ability to automatically address spatial variability via partitioning optimization, which is an important skill for improving crop classification over heterogeneous geographic areas at large scale. Future research can explore the use of Geo-RF for other geographic regions and applications, interpretable methods to understand the data-driven partitioning, and new designs to further enhance the computational efficiency.

The Neutron star Interior Composition Explorer (NICER) is an International Space Station (ISS)-based Space Telescope developed by NASA and devoted to the study of high-energy X-Ray sources in the universe, including but not limited to neutron stars, pulsars, and black holes in stellar systems and active galactic nuclei (AGN). One prominent problem with NICER observations is the highly variable background spectra, obscuring actual signals of astrophysical sources and negatively affecting scientific analysis of the targets. Therefore, obtaining accurate estimations of the background spectra is crucial to filter the noise and facilitate better scientific discoveries of new astronomical objects. In this paper, we propose the very first Deep Neural Network architecture to model the NICER background spectra variation using information about the spacecraft and telescope associated with each observation. In particular, we develop a BERT-based architecture with tokenizers applied to different groups of features in our tabular dataset. We also introduce an adapted Tabular Deep Residual Network architecture as the predictor following the Transformer modules in our network. We show that our model outperforms the current state-of-the-art background model developed by the NICER team in most evaluation metrics. Finally, we discuss pathways and future work for the deployment of this model on NASA’s next versions of HEASARC Software packages.

While deep learning models have high representation power and promising performances, there is often a lack of evidence to interpret potential reasons behind the predictions, which is a major concern limiting their usability for scientific discovery. We propose a Neural Additive Convolutional Neural Network (NA-CNN) to enhance the interpretability of the model to facilitate scientific discoveries in climate science. To investigate the interpretation quality of NA-CNN, we perform experiments on the El Niño identification task where the ground truth for El Niño patterns is known and can be used for validation. Experiment results show that compared to Spatial Attention and state-of-the-art post-hoc explanation techniques, NA-CNN has higher interpretation precision, remarkably improved physical consistency, and reduced redundancy. These qualities provide an encouraging ground for domain scientists to focus their analysis on potentially relevant patterns and derive laws governing phenomena with unknown physical processes.