We study Diffusion Schrödinger Bridge (DSB) models in the context of dynamical astrophysical systems, specifically tackling observational inverse prediction tasks within Giant Molecular Clouds (GMCs) for star formation. We introduce the AstroDSB model, a variant of DSB with the pairwise domain assumption tailored for astrophysical dynamics.

We study Diffusion Schrödinger Bridge (DSB) models in the context of dynamical astrophysical systems, specifically tackling observational inverse prediction tasks within Giant Molecular Clouds (GMCs) for star formation. We introduce the Astro-DSB model, a variant of DSB with the pairwise domain assumption tailored for astrophysical dynamics. By investigating its learning process and prediction performance in both physically simulated data and in real observations (the Taurus B213 data), we present two main takeaways. First, from the astrophysical perspective, our proposed paired DSB method improves interpretability, learning efficiency, and prediction performance over conventional astrostatistical and other machine learning methods. Second, from the generative modeling perspective, probabilistic generative modeling reveals improvements over discriminative pixel-to-pixel modeling in Out-Of-Distribution (OOD) testing cases of physical simulations with unseen initial conditions and different dominant physical processes. Our study expands research into diffusion models beyond the traditional visual synthesis application and provides evidence of the models' learning abilities beyond pure data statistics, paving a path for future physics-aware generative models which can align dynamics between machine learning and real (astro)physical systems.

Deep neural networks (DNNs) have demonstrated remarkable success in medical imaging, yet their real-world deployment remains challenging due to spurious correlations, where models can learn non-clinical features instead of meaningful medical patterns. Existing medical imaging datasets are not designed to systematically study this issue, largely due to restrictive licensing and limited supplementary patient data. To address this gap, we introduce SpurBreast, a curated breast MRI dataset that intentionally incorporates spurious correlations to evaluate their impact on model performance. Analyzing over 100 features involving patient, device, and imaging protocol, we identify two dominant spurious signals: magnetic field strength (a global feature influencing the entire image) and image orientation (a local feature affecting spatial alignment). Through controlled dataset splits, we demonstrate that DNNs can exploit these non-clinical signals, achieving high validation accuracy while failing to generalize to unbiased test data. Alongside these two datasets containing spurious correlations, we also provide benchmark datasets without spurious correlations, allowing researchers to systematically investigate clinically relevant and irrelevant features, uncertainty estimation, adversarial robustness, and generalization strategies.

Magnetic soft continuum robots are capable of bending with remote control in confined space environments, and they have been applied in various bioengineering contexts. As one type of ferromagnetic soft continuums, the Magnetically Induced Metamorphic Materials (MIMMs)-based continuum (MC) exhibits similar bending behaviors. Based on the characteristics of its base material, MC is flexible in modifying unit stiffness and convenient in molding fabrication. However, recent studies on magnetic continuum robots have primarily focused on one or two design parameters, limiting the development of a comprehensive magnetic continuum bending model. In this work, we constructed graded-stiffness MCs (GMCs) and developed a numerical model for GMCs' bending performance, incorporating four key parameters that determine their performance. The simulated bending results were validated with real bending experiments in four different categories: varying magnetic field, cross-section, unit stiffness, and unit length. The graded-stiffness design strategy applied to GMCs prevents sharp bending at the fixed end and results in a more circular curvature. We also trained an expansion model for GMCs' bending performance that is highly efficient and accurate compared to the simulation process. An extensive library of bending prediction for GMCs was built using the trained model.

Geomagnetic navigation leverages the ubiquitous Earth's magnetic signals to navigate missions, without dependence on GPS services or pre-stored geographic maps. It has drawn increasing attention and is promising particularly for long-range navigation into unexplored areas. Current geomagnetic navigation studies are still in the early stages with simulations and computational validations, without concrete efforts to develop cost-friendly test platforms that can empower deployment and experimental analysis of the developed approaches. This paper presents a hardware-in-the-loop simulation testbed to support geomagnetic navigation experimentation. Our testbed is dedicated to synthesizing geomagnetic field environment for the navigation. We develop the software in the testbed to simulate the dynamics of the navigation environment, and we build the hardware to generate the physical magnetic field, which follows and aligns with the simulated environment. The testbed aims to provide controllable magnetic field that can be used to experiment with geomagnetic navigation in labs, thus avoiding real and expensive navigation experiments, e.g., in the ocean, for validating navigation prototypes. We build the testbed with off-the-shelf hardware in an unshielded environment to reduce cost. We also develop the field generation control and hardware parameter optimization for quality magnetic field generation. We conduct a detailed performance analysis to show the quality of the field generation by the testbed, and we report the experimental results on performance indicators, including accuracy, uniformity, stability, and convergence of the generated field towards the target geomagnetic environment.

Haptic devices have shown to be valuable in supplementing surgical training, especially when providing haptic feedback based on user performance metrics such as wrench applied by the user on the tool. However, current 6-axis force/torque sensors are prohibitively expensive. This paper presents the design and calibration of a low-cost, six-axis force/torque sensor specially designed for laparoscopic haptic training applications. The proposed design uses Hall-effect sensors to measure the change in the position of magnets embedded in a silicone layer that results from an applied wrench to the device. Preliminary experimental validation demonstrates that these sensors can achieve an accuracy of 0.45 N and 0.014 Nm, and a theoretical XY range of +/-50N, Z range of +/-20N, and torque range of +/-0.2Nm. This study indicates that the proposed low-cost 6-axis force/torque sensor can accurately measure user force and provide useful feedback during laparoscopic training on a haptic device.

Studying the sun's outer atmosphere is challenging due to its complex magnetic fields impacting solar activities. Magnetohydrodynamics (MHD) simulations help model these interactions but are extremely time-consuming (usually on a scale of days). Our research applies the Fourier Neural Operator (FNO) to accelerate the coronal magnetic field modeling, specifically, the Bifrost MHD model. We apply Tensorized FNO (TFNO) to generate solutions from partial differential equations (PDEs) over a 3D domain efficiently. TFNO's performance is compared with other deep learning methods, highlighting its accuracy and scalability. Physics analysis confirms that TFNO is reliable and capable of accelerating MHD simulations with high precision. This advancement improves efficiency in data handling, enhances predictive capabilities, and provides a better understanding of magnetic topologies.

This paper presents the method for the localization and grabbing of the long straight conductor based only on the magnetic field generated by the alternating current flowing through the conductor. The method uses two magnetometers mounted on the robot arm end-effector for localization. This location is then used to determine needed robot movement in order to grab the conductor. The method was tested in the laboratory conditions using the Schunk LWA 4P 6-axis robot arm.

Solar activity is usually caused by the evolution of solar magnetic fields. Magnetic field parameters derived from photospheric vector magnetograms of solar active regions have been used to analyze and forecast eruptive events such as solar flares and coronal mass ejections. Unfortunately, the most recent solar cycle 24 was relatively weak with few large flares, though it is the only solar cycle in which consistent time-sequence vector magnetograms have been available through the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO) since its launch in 2010. In this paper, we look into another major instrument, namely the Michelson Doppler Imager (MDI) on board the Solar and Heliospheric Observatory (SOHO) from 1996 to 2010. The data archive of SOHO/MDI covers more active solar cycle 23 with many large flares. However, SOHO/MDI data only has line-of-sight (LOS) magnetograms. We propose a new deep learning method, named MagNet, to learn from combined LOS magnetograms, Bx and By taken by SDO/HMI along with H-alpha observations collected by the Big Bear Solar Observatory (BBSO), and to generate vector components Bx' and By', which would form vector magnetograms with observed LOS data. In this way, we can expand the availability of vector magnetograms to the period from 1996 to present. Experimental results demonstrate the good performance of the proposed method. To our knowledge, this is the first time that deep learning has been used to generate photospheric vector magnetograms of solar active regions for SOHO/MDI using SDO/HMI and H-alpha data.

ABSTRACT Obtaining high-quality magnetic and velocity fields through Stokes inversion is crucial in solar physics. In this paper, we present a new deep learning method, named Stacked Deep Neural Networks (SDNN), for inferring line-of-sight (LOS) velocities and Doppler widths from Stokes profiles collected by the Near InfraRed Imaging Spectropolarimeter (NIRIS) on the 1.6 m Goode Solar Telescope (GST) at the Big Bear Solar Observatory (BBSO). The training data of SDNN is prepared by a Milne-Eddington (ME) inversion code used by BBSO. We quantitatively assess SDNN, comparing its inversion results with those obtained by the ME inversion code and related machine learning (ML) algorithms such as multiple support vector regression, multilayer perceptrons and a pixel-level convolutional neural network. Major findings from our experimental study are summarized as follows. First, the SDNN-inferred LOS velocities are highly correlated to the ME-calculated ones with the Pearson product-moment correlation coefficient being close to 0.9 on average. Second, SDNN is faster, while producing smoother and cleaner LOS velocity and Doppler width maps, than the ME inversion code. Third, the maps produced by SDNN are closer to ME's maps than those from the related ML algorithms, demonstrating the better learning capability of SDNN than the ML algorithms. Finally, comparison between the inversion results of ME and SDNN based on GST/NIRIS and those from the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory in flare-prolific active region NOAA 12673 is presented. We also discuss extensions of SDNN for inferring vector magnetic fields with empirical evaluation. INTRODUCTION Obtaining high-quality magnetic and velocity fields through Stokes inversion is crucial in solar physics (Nejezchleba 1998; Maurya & Ambastha 2010; Bobra & Couvidat 2015).