arXiv:2603.16377v1 Announce Type: cross Abstract: Chronological age predictors often fail to achieve out-of-distribution (OOD) gen- eralization due to exogenous attributes such as race, gender, or tissue. Learning an invariant representation with respect to those attributes is therefore essential to improve OOD generalization and prevent overly optimistic results. In predic- tive settings, these attributes motivate bias […]
Bayesian Inference in Epidemic Modelling: A Beginner’s Guide
arXiv:2603.15175v2 Announce Type: replace-cross Abstract: This lecture note provides a self-contained introduction to Bayesian inference and Markov Chain Monte Carlo (MCMC) methods for parameter estimation in epidemic models. Using the classical Susceptible-Infectious-Recovered (SIR) compartmental model as a running example, we derive the likelihood function from first principles, specify priors on the transmission and recovery parameters, […]
RSGen: Enhancing Layout-Driven Remote Sensing Image Generation with Diverse Edge Guidance
arXiv:2603.15484v2 Announce Type: replace-cross Abstract: Diffusion models have significantly mitigated the impact of annotated data scarcity in remote sensing (RS). Although recent approaches have successfully harnessed these models to enable diverse and controllable Layout-to-Image (L2I) synthesis, they still suffer from limited fine-grained control and fail to strictly adhere to bounding box constraints. To address these […]
Controlling Fish Schools via Reinforcement Learning of Virtual Fish Movement
arXiv:2603.16384v1 Announce Type: cross Abstract: This study investigates a method to guide and control fish schools using virtual fish trained with reinforcement learning. We utilize 2D virtual fish displayed on a screen to overcome technical challenges such as durability and movement constraints inherent in physical robotic agents. To address the lack of detailed behavioral models […]
Physics-Informed Neural Systems for the Simulation of EUV Electromagnetic Wave Diffraction from a Lithography Mask
arXiv:2603.15584v2 Announce Type: replace-cross Abstract: Physics-informed neural networks (PINNs) and neural operators (NOs) for solving the problem of diffraction of Extreme Ultraviolet (EUV) electromagnetic waves from contemporary lithography masks are presented. A novel hybrid Waveguide Neural Operator (WGNO) is introduced, based on a waveguide method with its most computationally expensive components replaced by a neural […]
Multi-Agent Reinforcement Learning Counteracts Delayed CSI in Multi-Satellite Systems
arXiv:2603.16470v1 Announce Type: cross Abstract: The integration of satellite communication networks with next-generation (NG) technologies is a promising approach towards global connectivity. However, the quality of services is highly dependant on the availability of accurate channel state information (CSI). Channel estimation in satellite communications is challenging due to the high propagation delay between terrestrial users […]
DynamicGate MLP Conditional Computation via Learned Structural Dropout and Input Dependent Gating for Functional Plasticity
arXiv:2603.16367v1 Announce Type: cross Abstract: Dropout is a representative regularization technique that stochastically deactivates hidden units during training to mitigate overfitting. In contrast, standard inference executes the full network with dense computation, so its goal and mechanism differ from conditional computation, where the executed operations depend on the input. This paper organizes DynamicGate-MLP into a […]
Data-driven generalized perimeter control: Z”urich case study
arXiv:2603.16599v1 Announce Type: cross Abstract: Urban traffic congestion is a key challenge for the development of modern cities, requiring advanced control techniques to optimize existing infrastructures usage. Despite the extensive availability of data, modeling such complex systems remains an expensive and time consuming step when designing model-based control approaches. On the other hand, machine learning […]
Analyzing Error Sources in Global Feature Effect Estimation
arXiv:2603.15057v2 Announce Type: replace-cross Abstract: Global feature effects such as partial dependence (PD) and accumulated local effects (ALE) plots are widely used to interpret black-box models. However, they are only estimates of true underlying effects, and their reliability depends on multiple sources of error. Despite the popularity of global feature effects, these error sources are […]
Boundary effects in biological planar networks: pentagonsdominate Pyropia marginal cells
arXiv:2503.18855v3 Announce Type: replace Abstract: The topological and geometrical features at the boundary zone of planar polygonal networks remain poorly understood. Based on observations and mathematical proofs, we propose that marginal cells in the thalli of Pyropia haitanensis, a two-dimensional (2D) biological polygonal network, have an average edge number of approximately five. We demonstrate that […]
$D^3$-RSMDE: 40$times$ Faster and High-Fidelity Remote Sensing Monocular Depth Estimation
arXiv:2603.16362v1 Announce Type: cross Abstract: Real-time, high-fidelity monocular depth estimation from remote sensing imagery is crucial for numerous applications, yet existing methods face a stark trade-off between accuracy and efficiency. Although using Vision Transformer (ViT) backbones for dense prediction is fast, they often exhibit poor perceptual quality. Conversely, diffusion models offer high fidelity but at […]
Binding Free Energies without Alchemy
arXiv:2603.12253v2 Announce Type: replace Abstract: Absolute Binding Free Energy (ABFE) methods are among the most accurate computational techniques for predicting protein-ligand binding affinities, but their utility is limited by the need for many simulations of alchemically modified intermediate states. We propose Direct Binding Free Energy (DBFE), an end-state ABFE method in implicit solvent that requires […]