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Yongxin Li

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cond-mat.mtrl-sci Machine Learning Methodology

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preprint2026arXiv

Selection Plateau and a Sparsity-Dependent Hierarchy of Pruning Features

We identify a Selection Plateau phenomenon in one-shot neural network pruning: all rank-monotone weight scorers converge to identical accuracy at fixed sparsity, independent of functional form. We propose the Sparsity-Information-Complexity Spectrum (SICS) hypothesis: a sparsity-dependent minimum feature complexity kappa(S) governs plateau escape, with kappa=0 sufficient at low sparsity (S<0.65), kappa=1 dominant at critical sparsity (S~0.7), and kappa=2 necessary at extreme sparsity (S>0.75). On ViT-Small/CIFAR-10, testing nine feature classes across four sparsities, smooth non-monotone features provide +6.6% escape at S=0.7, while only raw features with high-frequency wiggle escape at S=0.8 (+2.6%). A fake non-monotone scorer underperforms the gradient baseline, indicating the requirement is magnitude-independent non-monotonicity. A handcrafted Gaussian bump achieves only +0.006 escape vs. chaos-derived +0.046, indicating rank-alignment is necessary but insufficient. SICS provides a unifying explanation for the performance clustering of diverse pruning methods and suggests that future selection algorithms should adapt feature complexity to target sparsity.

preprint2022arXiv

Estimating Extreme Value Index by Subsampling for Massive Datasets with Heavy-Tailed Distributions

Modern statistical analyses often encounter datasets with massive sizes and heavy-tailed distributions. For datasets with massive sizes, traditional estimation methods can hardly be used to estimate the extreme value index directly. To address the issue, we propose here a subsampling-based method. Specifically, multiple subsamples are drawn from the whole dataset by using the technique of simple random subsampling with replacement. Based on each subsample, an approximate maximum likelihood estimator can be computed. The resulting estimators are then averaged to form a more accurate one. Under appropriate regularity conditions, we show theoretically that the proposed estimator is consistent and asymptotically normal. With the help of the estimated extreme value index, we can estimate high-level quantiles and tail probabilities of a heavy-tailed random variable consistently. Extensive simulation experiments are provided to demonstrate the promising performance of our method. A real data analysis is also presented for illustration purpose.

preprint2020arXiv

Ferroelastic-switching-driven colossal shear strain and piezoelectricity in a hybrid ferroelectric

Materials that can produce large controllable strains are widely used in shape memory devices, actuators and sensors. Great efforts have been made to improve the strain outputs of various material systems. Among them, ferroelastic transitions underpin giant reversible strains in electrically-driven ferro/piezoelectrics and thermally- or magneticallydriven shape memory alloys. However, large-strain ferroelastic switching in conventional ferroelectrics is very challenging while magnetic and thermal controls are not desirable for applications. Here, we demonstrate an unprecedentedly large shear strain up to 21.5 % in a hybrid ferroelectric, C6H5N(CH3)3CdCl3. The strain response is about two orders of magnitude higher than those of top-performing conventional ferroelectric polymers and oxides. It is achieved via inorganic bond switching and facilitated by the structural confinement of the large organic moieties, which prevents the undesired 180-degree polarization switching. Furthermore, Br substitution can effectively soften the bonds and result in giant shear piezoelectric coefficient (d35 ~ 4800 pm/V) in Br-rich end of the solid solution, C6H5N(CH3)3CdBr3xCl3(1-x). The superior electromechanical properties of the compounds promise their potential in lightweight and high energy density devices, and the strategy described here should inspire the development of next-generation piezoelectrics and electroactive materials based on hybrid ferroelectrics.