We propose a novel concept of operations using optimal planning methods and machine learning (ML) to collect spaceborne data that is unprecedented for monitoring wildfires, process it to create new or enhanced products in the context of wildfire danger or spread monitoring, and assimilate them to improve existing, wildfire decision support tools delivered to firefighters within latency appropriate for time-critical applications. The concept is studied with respect to NASA's CYGNSS Mission, a constellation of passive microwave receivers that measure specular GNSS-R reflections despite clouds and smoke. Our planner uses a Mixed Integer Program formulation to schedule joint observation data collection and downlink for all satellites. Optimal solutions are found quickly that collect 98-100% of available observation opportunities. ML-based fire predictions that drive the planner objective are greater than 40% more correlated with ground truth than existing state-of-art. The presented case study on the TX Smokehouse Creek fire in 2024 and LA fires in 2025 represents the first high-resolution data collected by CYGNSS of active fires. Creation of Burnt Area Maps (BAM) using ML on data from active fires and BAM assimilation into NASA's Weather Research and Forecasting Model using neural nets to broadcast fire spread are novel outcomes. BAM and CYGNSS obtained soil moisture are integrated for the first time into USGS fire danger maps. Inclusion of CYGNSS data in ML-based burn predictions boosts accuracy by 13%, and inclusion of high-resolution data boosts ML recall by another 15%. The proposed workflow has an expected latency of 6-30h, improving on the current delivery time of multiple days. All components in the proposed concept are shown to be computationally scalable and globally generalizable, with sustainability considerations such as edge efficiency and low latency on small devices.

Now, let us derive the differential equation that gives a solution to the variational problem. This condition yields the Euler-Lagrange equation, d dt @ L @ q = @ L @q . Here, we derive the Noether's learning dynamics by applying Noether's theorem to the A general form of the Noether's theorem relates the dynamics of Noether By evaluating the right hand side of Eq. 23, we get e Now, we harness the covariant property of the Lagrangian formulation, i.e., it preserves the form Plugging this expression obtained from the steady-state condition of Eq.27 Here, we ignore the inertia term in Eq. 16, assuming that the mass (learning rate) is finite but small All the experiments were run using the PyTorch code base. We used Tiny ImageNet dataset to generate all the empirical figures in this work. The key hyperparameters we used are listed with each figure.

Combining these two properties, we introduce the reward-free deployment efficiency setting, a new paradigm for RL research.

Code is released at https://github.com/yuelinan/DARE .

A multi-armed bandit (MAB) problem is one of the classic models of sequential decision making (Auer et al., 2002; Agrawal and Goyal, 2012, 2013a).

Semi-supervised semantic segmentation requires the model to effectively propagate the label information from limited annotated images to unlabeled ones. A challenge for such a per-pixel prediction task is the large intra-class variation, i.e., regions belonging to the same class may exhibit a very different appearance even in the same

In the following, we review related work from graph kernels, GNNs, and theory. More recently, graph kernels' developments have emphasized scalability, focusing on Notable instances of this architecture include, e.g., [ Sato et al. studied the limits of GNNs when applied to combinatorial problems. Finally, there exists a new line of work focusing on extending GNNs to hypergraphs, see, e.g., [ We briefly describe the Weisfeiler-Leman algorithm and, along the way, introduce our notation. Let k be a fixed positive integer. The successive refinement steps are also called rounds or iterations .

Hence, the running time of each iteration does not take the sparsity of a given graph into account.