On the Role of Pre-trained Language Models in Word Ordering: A Case Study with BART

Word ordering is a constrained language generation task taking unordered words as input. Existing work uses linear models and neural networks for the task, yet pre-trained language models have not been studied in word ordering, let alone why they help. We use BART as an instance and show its effectiveness in the task. To explain why BART helps word ordering, we extend analysis with probing and empirically identify that syntactic dependency knowledge in BART is a reliable explanation. We also report performance gains with BART in the related partial tree linearization task, which readily extends our analysis.Comment: COLING 202

Discriminating between Babcock-Leighton-type solar dynamo models by torsional oscillations

The details of the dynamo process in the Sun are an important aspect of research in solar-terrestrial physics and astrophysics. The surface part of the dynamo can be constrained by direct observations, but the subsurface part lacks direct observational constraints. The torsional oscillations, a small periodic variation of the Sun's rotation with the solar cycle, are thought to result from the Lorentz force of the cyclic magnetic field generated by the dynamo. In this study, we aim to discriminate between three Babcock-Leighton (BL) dynamo models by comparing the zonal acceleration of the three models with the observed one. The property that the poleward and equatorward branches of the torsional oscillations originate from about $\pm 55^\circ$ latitudes with their own migration time periods serves as an effective discriminator that could constrain the configuration of the magnetic field in the convection zone. The toroidal field, comprising poleward and equatorward branches separated at about $\pm 55^\circ$ latitudes can generate the two branches of the torsional oscillations. The alternating acceleration and deceleration bands in time is the other property of the torsional oscillations that discriminate between the dynamo models. To reproduce this property, the phase difference between the radial ($B_{r}$) and toroidal ($B_{\phi}$) components of the magnetic field near the surface should be about $\pi/2$.Comment: 11 pages, 4 figures, accepted for publication in Ap

Modeling effects of starspots on stellar magnetic cycles

Observations show that faster-rotating stars tend to have stronger magnetic activity and shorter magnetic cycles. The cyclical magnetic activity of the Sun and stars is believed to be driven by the dynamo process. The success of the Babcock-Leighton (BL) dynamo in understanding the solar cycle suggests an important role that starspots could play in stellar magnetic cycles. We aim at extending the BL mechanism to solar-mass stars with various rotation rates and explore the effects of emergence properties of starspots in latitudes and tilt angles on stellar magnetic cycles. We adopt a kinematic BL-type dynamo model operating in the bulk of the convection zone. The profiles of the large-scale flow fields are from the mean-field hydrodynamical model for various rotators. The BL source term in the model is constructed based on the rotation dependence of starspots emergence. That is, faster rotators have starspots at higher latitudes with larger tilt angles.Faster rotators have poloidal flux appearing closer to about $\pm55^\circ$ latitudes, where the toroidal field generation efficiency is the strongest because of the strongest latitudinal differential rotation there. It takes a shorter time for faster rotators to transport the surface poloidal field from their emergence latitude to the $\pm 55^\circ$ latitudes of efficient $\Omega$-effect thus shortening their magnetic cycles. The faster rotators operate in a more supercritical regime due to a stronger BL $\alpha$-effect relating to the tilt angles, which leads to stronger saturated magnetic fields and a coupling of the poloidal field between two hemispheres more difficult. Thus the magnetic field parity shifts from the hemispherically asymmetric mixed mode to quadrupole, and further to dipole when a star spins down. The emergence of starspots plays an essential role in the large-scale stellar dynamo.Comment: 11 pages, 10 figures, accepted by A&

Experiments and Models of Thermo-Induced Shape Memory Polymers

Recent advances in experiments and models of thermo-induced shape memory polymers (TSMPs) were reviewed. Some important visco-elastic and visco-plastic features, such as rate-dependent and temperature-dependent stress-strain curves and nonuniform temperature distribution were experimentally investigated, and the interaction between the mechanical deformation and the internal heat generation was discussed. The influences of loading rate and peak strain on the shape memory effect (SME) and shape memory degeneration of TSMPs were revealed under monotonic and cyclic thermo-mechanical loadings, respectively. Based on experimental observations, the capability of recent developed visco-elastic and visco-plastic models for predicting the SME was evaluated, and the thermo-mechanically coupled models were used to reasonably predict the thermo-mechanical responses of TSMPs

PiML Toolbox for Interpretable Machine Learning Model Development and Diagnostics

PiML (read $\pi$-ML, /`pai`em`el/) is an integrated and open-access Python toolbox for interpretable machine learning model development and model diagnostics. It is designed with machine learning workflows in both low-code and high-code modes, including data pipeline, model training and tuning, model interpretation and explanation, and model diagnostics and comparison. The toolbox supports a growing list of interpretable models (e.g. GAM, GAMI-Net, XGB1/XGB2) with inherent local and/or global interpretability. It also supports model-agnostic explainability tools (e.g. PFI, PDP, LIME, SHAP) and a powerful suite of model-agnostic diagnostics (e.g. weakness, reliability, robustness, resilience, fairness). Integration of PiML models and tests to existing MLOps platforms for quality assurance are enabled by flexible high-code APIs. Furthermore, PiML toolbox comes with a comprehensive user guide and hands-on examples, including the applications for model development and validation in banking. The project is available at https://github.com/SelfExplainML/PiML-Toolbox