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The 1968 discovery of the Crab and Vela pulsars in their respective supernova remnants (SNRs) confirmed Baade and Zwicky's 1934 prediction that supernovae form neutron stars. Observations of Pulsar Wind Nebulae (PWNe), particularly with the…
We present a new modeling tool for planetary nebulae, based on 3D photoionization calculations. Our goal is to show that all the information provided by observations, regarding kinematics and morphology, have to be consistently accounted…
This work presents a new detailed study on the energy-dependent variation in the X-ray polarisation of the Crab Pulsar Wind Nebula (PWN), obtained using data from the Imaging X-ray Polarimetry Explorer (IXPE). For the entire PWN, we…
Deterministic hydrological models with uncertain, but inferred-to-be-time-invariant parameters typically show time-dependent model structural errors. Such errors can occur if a hydrological process is active in certain time periods in…
Manufacturing processes are often perturbed by drifts in the environment and wear in the system, requiring control re-tuning even in the presence of repetitive operations. This paper presents an iterative learning framework for automatic…
We present a significantly-improved data-driven global weather forecasting framework using a deep convolutional neural network (CNN) to forecast several basic atmospheric variables on a global grid. New developments in this framework…
Pulsar Wind nebulae (PWNe), are among the most efficient particle accelerators in the Universe, however understanding the physical conditions and the magnetic geometry in their inner region has always proved elusive. X-ray polarization…
Transformers are crucial for reliable and efficient power system operations, particularly in supporting the integration of renewable energy. Effective monitoring of transformer health is critical to maintain grid stability and performance.…
Partial-wave analyses (PWA) are an essential tool for studying resonance structures in decays with hadronic multi-body final states. For several years, more model-independent approaches to such analyses have been used for various decay…
We develop a learning-based algorithm for the control of autonomous systems governed by unknown, nonlinear dynamics to satisfy user-specified spatio-temporal tasks expressed as signal temporal logic specifications. Most existing algorithms…
The number of known PWNe has recently increased considerably, and the majority of them are now middle-age objects. Recent studies have shown a clear correlation of both X-ray luminosity and size with the PWN age, but fail in providing a…
The segmentation module which precisely outlines the nodules is a crucial step in a computer-aided diagnosis(CAD) system. The most challenging part of such a module is how to achieve high accuracy of the segmentation, especially for the…
Partial differential equations (PDEs) play a crucial role in studying a vast number of problems in science and engineering. Numerically solving nonlinear and/or high-dimensional PDEs is often a challenging task. Inspired by the traditional…
Conditional Neural Processes (CNPs; Garnelo et al., 2018a) are meta-learning models which leverage the flexibility of deep learning to produce well-calibrated predictions and naturally handle off-the-grid and missing data. CNPs scale to…
We study contextual chance-constrained programming under decision-dependent uncertainty. In this setting, a decision not only needs to satisfy constraints but also alters the distribution of uncertain outcomes. This dependency makes the…
Autopilots for fixed-wing aircraft are typically designed based on linearized aerodynamic models consisting of stability and control derivatives obtained from wind-tunnel testing. The resulting local controllers are then pieced together…
We present evidence of wavelength-dependent variations within the infrared, optical, and ultraviolet pulse profile peaks of the Crab Nebula pulsar. The leading and trailing edge half-width half-maxima of the peaks display clear differences…
Cancer detection and prognosis relies heavily on medical imaging, particularly CT and PET scans. Deep Neural Networks (DNNs) have shown promise in tumor segmentation by fusing information from these modalities. However, a critical…
During the last decades, international attempts have been made to develop realistic space weather prediction tools aiming to forecast the conditions on the Sun and in the interplanetary environment. These efforts have led to the development…
Piecewise affine (PWA) systems are widely used to model highly nonlinear behaviors such as contact dynamics in robot locomotion and manipulation. Existing control techniques for PWA systems have computational drawbacks, both in offline…