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Yixin Guan

Publications and source records attributed to Yixin Guan.

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1/9 Magnetization Plateau in a Classical Kagome Ising Ferromagnet with Competing Further-Neighbor Interactions

The two-dimensional kagome lattice is a paradigmatic platform for exploring geometrically frustrated magnetism. While the nearest-neighbor ferromagnetic Ising model on this lattice is theoretically trivial, competing further-neighbor interactions can reintroduce severe frustration. In this work, we systematically investigate a classical kagome Ising model with ferromagnetic nearest-neighbor (J1) and antiferromagnetic second- (J2) and third-neighbor (J3) couplings using simulated annealing Monte Carlo methods. We demonstrate that while J2 couplings merely suppress the conventional ferromagnetic order, the inclusion of J3 fundamentally reconstructs the low-temperature phase diagram. This extended geometric frustration stabilizes a novel ordered phase characterized by a robust 1/9 magnetization plateau and a massively enlarged 3 by 3 magnetic supercell. Crucially, this fractional ordered phase manifests as a stability plateau in the phase diagram, where its critical temperature becomes nearly independent of the coupling strength J3. We also calculate the corresponding static spin structure factor, revealing a distinct Z6-symmetric reciprocal-space signature for experimental identification. Our findings reveal that complex fractional magnetic orders can emerge purely from classical geometric frustration induced by competing extended interactions, providing a distinct mechanism for understanding fractionally ordered states in real frustrated magnets.

cond-mat.str-el

Rethinking "Risk" in Algorithmic Systems Through A Computational Narrative Analysis of Casenotes in Child-Welfare

Risk assessment algorithms are being adopted by public sector agencies to make high-stakes decisions about human lives. Algorithms model "risk" based on individual client characteristics to identify clients most in need. However, this understanding of risk is primarily based on easily quantifiable risk factors that present an incomplete and biased perspective of clients. We conducted a computational narrative analysis of child-welfare casenotes and draw attention to deeper systemic risk factors that are hard to quantify but directly impact families and street-level decision-making. We found that beyond individual risk factors, the system itself poses a significant amount of risk where parents are over-surveilled by caseworkers and lack agency in decision-making processes. We also problematize the notion of risk as a static construct by highlighting the temporality and mediating effects of different risk, protective, systemic, and procedural factors. Finally, we draw caution against using casenotes in NLP-based systems by unpacking their limitations and biases embedded within them.

cs.HC