arXiv subjects

Jiaqi Tian

Publications and source records attributed to Jiaqi Tian.

5 recordsLinked to original sources

arXiv2026

A d-Electron Heavy-Fermion-Like Superconductor with Frustration-Induced Flat Bands

Heavy-fermion superconductors are mostly associated with f-electron materials with Kondo lattices, while known d-electron heavy-fermion-like systems are often linked to orbital-selective local moments, Hund-metal physics, or a charge-density-wave mechanism. Here we report Mo4PtGa17, a noncentrosymmetric itinerant d-electron superconductor with a geometrically frustrated breathing-pyrochlore Mo lattice. Thermodynamic, transport and NMR measurements reveal heavy-fermion-like behavior superconductivity and dominant ferromagnetic spin fluctuations near a ferromagnetic instability. Theoretical calculations identify nearly flat bands, van Hove singularities and Kramers nodal lines near the Fermi energy, derived intrinsically from Mo-4d states and are robust against on-site electronic correlations. These results suggest that the geometrically frustrated lattice in Mo4PtGa17 generates an intriguing electronic structure that enhances the density of states, spin susceptibility and quasiparticle mass. Mo4PtGa17 therefore identifies a unique route to heavy-fermion-like superconductivity in d-electron materials through geometrical frustration, different from the previously reported systems.

cond-mat.str-el↗

arXiv2026

Cluster-breaking and reconfiguration effects in $_Λ^{12}\rm{B}$ hypernucleus

We investigate the cluster-breaking effect and spatial distribution of negative-parity states in the $_Λ^{12}\rm{B}$ hypernucleus using the Hyper-Brink model with cluster-breaking(CB-Hyper-Brink) optimized via Control Neural Network (Ctrl.NN). The results demonstrate that the inclusion of cluster-breaking is essential for accurately reproducing the observed low-lying energy levels and for making reliable predictions of the Hoyle-analog state 1-4 in $_Λ^{12}\rm{B}$. Cluster-breaking manifests as strong spin-orbit correlations and the dissolution of ideal cluster configurations, as revealed by the analysis of one-body spin-orbit operator expectation values and the spatial overlap with projected cluster bases. The interplay between short-range repulsion and intermediate-range attraction in the Lambda N interaction induces the cluster reconfiguration effect, which is characterized by the coexistence of Lambda-alpha and Lambda-triton correlations; this reconfiguration effect leads to a modest stabilization and shrinkage of cluster structures. The variation in electric quadrupole transition strengths, B(E2), between the ground and Hoyle-analog states serves as a sensitive probe for the degree of cluster-breaking, providing direct evidence for its physical relevance. These findings highlight the crucial role of cluster-breaking in characterizing the hypernuclear structure and offer a comprehensive framework for understanding the interplay between clustering and shell-model dynamics in hypernuclei.

nucl-th↗

arXiv2025

Hypernuclear cluster states of $_Λ^{12}\rm{B}$ Unveiled through Neural Network-Driven Microscopic Calculation

We investigate the hypernuclear cluster states of $_Λ^{12}\mathrm{B}$ using a neural-network-driven microscopic model. We extend the Control Neural Networks (Ctrl.NN) method and systematically calculate the positive-parity spectrum of $_Λ^{12}\mathrm{B}$. By incorporating $sd$-shell excitations and parity-coupling effects into the $_Λ^{12}\mathrm{B}$ hypernuclear system, we reveal structural changes, including clustering effects and new configurations such as isosceles-triangle and $α$-$t$-$α$ linear-chain structures. Furthermore, by comparing with experimental data, we identify that many peaks ($\#$6 and $\#$8) can be interpreted as $p_Λ$ dominant states, which is consistent with shell-model predictions. Notably, based on our analysis of the excited states of $_Λ^{12}\mathrm{B}$, we propose possible candidates for previously unexplained or controversial experimental peaks.

nucl-th↗

arXiv2023

Analyze the Robustness of Classifiers under Label Noise

This study explores the robustness of label noise classifiers, aiming to enhance model resilience against noisy data in complex real-world scenarios. Label noise in supervised learning, characterized by erroneous or imprecise labels, significantly impairs model performance. This research focuses on the increasingly pertinent issue of label noise's impact on practical applications. Addressing the prevalent challenge of inaccurate training data labels, we integrate adversarial machine learning (AML) and importance reweighting techniques. Our approach involves employing convolutional neural networks (CNN) as the foundational model, with an emphasis on parameter adjustment for individual training samples. This strategy is designed to heighten the model's focus on samples critically influencing performance.

cs.LG↗

arXiv2023

Analyze the robustness of three NMF algorithms (Robust NMF with L1 norm, L2-1 norm NMF, L2 NMF)

Non-negative matrix factorization (NMF) and its variants have been widely employed in clustering and classification tasks (Long, & Jian , 2021). However, noises can seriously affect the results of our experiments. Our research is dedicated to investigating the noise robustness of non-negative matrix factorization (NMF) in the face of different types of noise. Specifically, we adopt three different NMF algorithms, namely L1 NMF, L2 NMF, and L21 NMF, and use the ORL and YaleB data sets to simulate a series of experiments with salt-and-pepper noise and Block-occlusion noise separately. In the experiment, we use a variety of evaluation indicators, including root mean square error (RMSE), accuracy (ACC), and normalized mutual information (NMI), to evaluate the performance of different NMF algorithms in noisy environments. Through these indicators, we quantify the resistance of NMF algorithms to noise and gain insights into their feasibility in practical applications.

cs.LG↗