Bingyu Sun | Searcharxiv

arXiv2026

Emergent Skyrmion Hall Effect in $d$-wave Altermagnets at Finite Temperature

Altermagnets combine compensated magnetic order with unconventional symmetry-dependent responses, offering a promising platform for spintronic applications. Here, we show that a voltage-controlled magnetic-anisotropy gradient drives altermagnetic (ATM) skyrmions in a nearly rectilinear, Hall-free manner in the absence of thermal fluctuations, owing to their strongly compensated gyrotropic response. Thermal magnons qualitatively modify this behavior by increasing the longitudinal drag through magnon--skyrmion scattering and generating a transverse reaction force through handedness-dependent skew scattering. Owing to the anisotropic altermagnetic magnon band structure, the relative transport weights of the two magnon handednesses are interchanged between propagation along the $x$ and $y$ directions, resulting in transverse skyrmion drifts of opposite sign. By contrast, along the high-symmetry direction, the two magnon handednesses remain degenerate and their transverse contributions cancel, preserving Hall-free motion even at finite temperature. We thus uncover a thermally emergent anisotropic skyrmion Hall effect whose direction-dependent magnitude and sign originate from the intrinsic symmetry-dependent magnon spectrum, making it a generic finite-temperature dynamical feature of ATM skyrmions. Our results establish a low-power route toward electrically controlled and thermally tunable ATM skyrmion transport.

cond-mat.mtrl-sci↗

arXiv2015

From Observational Studies to Causal Rule Mining

Randomised controlled trials (RCTs) are the most effective approach to causal discovery, but in many circumstances it is impossible to conduct RCTs. Therefore observational studies based on passively observed data are widely accepted as an alternative to RCTs. However, in observational studies, prior knowledge is required to generate the hypotheses about the cause-effect relationships to be tested, hence they can only be applied to problems with available domain knowledge and a handful of variables. In practice, many data sets are of high dimensionality, which leaves observational studies out of the opportunities for causal discovery from such a wealth of data sources. In another direction, many efficient data mining methods have been developed to identify associations among variables in large data sets. The problem is, causal relationships imply associations, but the reverse is not always true. However we can see the synergy between the two paradigms here. Specifically, association rule mining can be used to deal with the high-dimensionality problem while observational studies can be utilised to eliminate non-causal associations. In this paper we propose the concept of causal rules (CRs) and develop an algorithm for mining CRs in large data sets. We use the idea of retrospective cohort studies to detect CRs based on the results of association rule mining. Experiments with both synthetic and real world data sets have demonstrated the effectiveness and efficiency of CR mining. In comparison with the commonly used causal discovery methods, the proposed approach in general is faster and has better or competitive performance in finding correct or sensible causes. It is also capable of finding a cause consisting of multiple variables, a feature that other causal discovery methods do not possess.

cs.AI↗