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Xin-Yi Fan

Publications and source records attributed to Xin-Yi Fan.

2 recordsLinked to original sources

Planted vertex cover problem on regular random graphs and nonmonotonic temperature-dependence in the supercooled region

We introduce a planted vertex cover problem on regular random graphs and study it by the cavity method of statistical mechanics. Different from conventional Ising models, the equilibrium ferromagnetic phase transition of this binary-spin two-body interaction system is discontinuous, as the paramagnetic phase is separated from the ferromagnetic phase by an extensive free energy barrier. The free energy landscape can be distinguished into three different types depending on the two degree parameters of the planted graph. The critical inverse temperatures at which the paramagnetic phase becomes locally unstable towards the ferromagnetic phase ($β_{\textrm{pf}}$) and towards spin glass phases ($β_{\textrm{pg}}$) satisfy $β_{\textrm{pf}} > β_{\textrm{pg}}$, $β_{\textrm{pf}} < β_{\textrm{pg}}$ and $β_{\textrm{pf}} = β_{\textrm{pg}}$, respectively, in these three landscapes. A locally stable anti-ferromagnetic phase emerges in the free energy landscape if $β_{\textrm{pf}} < β_{\textrm{pg}}$. When exploring the free energy landscape by stochastic local search dynamics, we find that in agreement with our theoretical prediction, the first-passage time from the paramagnetic phase to the ferromagnetic phase is nonmonotonic with the inverse temperature. The potential relevance of the planted vertex cover model to supercooled glass-forming liquids is briefly discussed.

cond-mat.stat-mech

Lateral predictive coding revisited: Internal model, symmetry breaking, and response time

Predictive coding is a promising theoretical framework in neuroscience for understanding information transmission and perception. It posits that the brain perceives the external world through internal models and updates these models under the guidance of prediction errors. Previous studies on predictive coding emphasized top-down feedback interactions in hierarchical multi-layered networks but largely ignored lateral recurrent interactions. We perform analytical and numerical investigations in this work on the effects of single-layer lateral interactions. We consider a simple predictive response dynamics and run it on the MNIST dataset of hand-written digits. We find that learning will generally break the interaction symmetry between peer neurons, and that high input correlation between two neurons does not necessarily bring strong direct interactions between them. The optimized network responds to familiar input signals much faster than to novel or random inputs, and it significantly reduces the correlations between the output states of pairs of neurons.

q-bio.NC