arXiv subjects

Ran Peng

Publications and source records attributed to Ran Peng.

5 recordsLinked to original sources

arXiv2026

Visualizing Symmetry Broken Chern Insulators and their Quantum Melting

In the presence of a magnetic field, electronic states of moir\'e quantum materials develop a Hofstadter spectrum that provides a unique setting for studying the interplay between band topology and strong electron-electron interaction. Using scanning tunneling microscopy, we study Hofstadter's states in bilayer graphene aligned with hexagonal BN and directly visualize the formation of interaction-driven symmetry breaking Chern insulators. Our measurements reveal the formation of phases that double, triple or quadruple the moir\'e unit cell at fractional filling of the Hofstadter bands, as well as states with complex intra-unit-cell wave functions. We visualize two distinct quantum phenomena in different Chern states, including quantum melting driven by the appearance and proliferation of topological defects, and a quantum transition co-occurring with phase competition and separation.

cond-mat.mes-hall↗

arXiv2026

Beyond Variance: Prompt-Efficient RLVR via Rare-Event Amplification and Bidirectional Pairing

Reinforcement learning with verifiable rewards (RLVR) is effective for training large language models on deterministic outcome reasoning tasks. Prior work shows RLVR works with few prompts, but prompt selection is often based only on training-accuracy variance, leading to unstable optimization directions and weaker transfer. We revisit prompt selection from a mechanism-level view and argue that an effective minibatch should provide both (i) a reliable positive anchor and (ii) explicit negative learning signals from rare failures. Based on this principle, we propose \emph{positive--negative pairing}: at each update, we sample a hard-but-solvable $q^{+}$ and an easy-but-brittle prompt $q^{-}$(high success rate but not perfect), characterized by low and high empirical success rates under multiple rollouts. We further introduce Weighted GRPO, which reweights binary outcomes at the pair level and uses group-normalized advantages to amplify rare successes on $q^{+}$ into sharp positive guidance while turning rare failures on $q^{-}$ into strong negative penalties. This bidirectional signal provides informative learning feedback for both successes and failures, improving sample efficiency without suppressing exploration. On Qwen2.5-Math-7B, a single paired minibatch per update consistently outperforms a GRPO baseline that selects two prompts via commonly used variance-based selection heuristics: AIME~2025 Pass@8 improves from 16.8 to 22.2, and AMC23 Pass@64 from 94.0 to 97.0, while remaining competitive with large-scale RLVR trained from a pool of 1209 training prompts. Similar gains are observed on Qwen2.5-Math-7B-Instruct.

cs.LG↗

arXiv2023

A Polarization and Radiomics Feature Fusion Network for the Classification of Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma

Classifying hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) is a critical step in treatment selection and prognosis evaluation for patients with liver diseases. Traditional histopathological diagnosis poses challenges in this context. In this study, we introduce a novel polarization and radiomics feature fusion network, which combines polarization features obtained from Mueller matrix images of liver pathological samples with radiomics features derived from corresponding pathological images to classify HCC and ICC. Our fusion network integrates a two-tier fusion approach, comprising early feature-level fusion and late classification-level fusion. By harnessing the strengths of polarization imaging techniques and image feature-based machine learning, our proposed fusion network significantly enhances classification accuracy. Notably, even at reduced imaging resolutions, the fusion network maintains robust performance due to the additional information provided by polarization features, which may not align with human visual perception. Our experimental results underscore the potential of this fusion network as a powerful tool for computer-aided diagnosis of HCC and ICC, showcasing the benefits and prospects of integrating polarization imaging techniques into the current image-intensive digital pathological diagnosis. We aim to contribute this innovative approach to top-tier journals, offering fresh insights and valuable tools in the fields of medical imaging and cancer diagnosis. By introducing polarization imaging into liver cancer classification, we demonstrate its interdisciplinary potential in addressing challenges in medical image analysis, promising advancements in medical imaging and cancer diagnosis.

eess.IV↗

arXiv2021

Phonons in magic-angle twisted bilayer graphene

Magic-angle twisted bilayer graphene (TBG) has attracted significant interest recently due to the discoveries of diverse correlated and topological states in this system. Despite the extensive research on the electron-electron interaction effects and topological properties of the electrons, the phonons of magic-angle TBG are relatively less explored. In this work, we study the phonon properties in magic-angle TBG based on \textit{ab} \textit{initio} deep potential molecular dynamics. We have calculated phonon band structures and density of states at the magic angle, and have systematically analyzed the phonon eigenmodes at high-symmetry points in the moir\'e Brillouin zone. In particular, at the moir\'e $\Gamma$ point, we have discovered a number of soft modes which can exhibit dipolar-like, stripe-like, and octupolar-like vibrational patterns within the moir\'e supercell, as well as some "vortical" modes with nonzero curl in real space. At the moir\'e $K$/$K'$ points, there are time-reversal breaking chiral phonon modes with nonzero local phonon polarizations. We have further studied the phonon effects on the electronic structures by freezing certain soft phonon modes. We find that if a soft "stripe" phonon mode at moir\'e $\Gamma$ point is assumed to be frozen, the system would exhibit a charge order which naturally explains the recent observations from scanning tunnelling microscopy. Moreover, there are also low-frequency $C_{2z}$-breaking modes at moir\'e $\Gamma$ point, which would gap out the Dirac points at the charge neutrality point once these modes get frozen. This provides a new perspective to the origin of correlated insulator state at the charge neutrality point.

cond-mat.mes-hall↗

arXiv2021

Topological piezoelectric response in moir\'e graphene systems

We theoretically study the piezoelectric effects in moir\`e graphene systems. Since the strain couples to the electrons in the system as a pseudo vector potential, which has opposite signs for the $K$ and $K'$ valleys of graphene, its effects on the two valleys with opposite Chern numbers do not cancel out, but adds up. As a result, some components of the piezoelectric tensor in these systems, which typically have non-trivial topology in their flat bands, are nearly quantized in terms of the valley Chern numbers. Such a conclusion is verified by numerical calculations of the in-plane piezoelectric response of hBN-aligned twisted bilayer graphene, twisted bilayer-monolayer graphene, and twisted double bilayer graphene systems using both continuum model and atomistic tight-binding model. We find that by tuning the vertical displacement field and/or twist angle, which may induce gap closures between the flat bands and remote bands in these systems, plateau shapes of the piezoelectric response are obtained, with abrupt jumps across the topological phase transitions. We propose that such nearly quantized piezoelectric response may serve as a direct experimental probe for the valley Chern numbers of the flat bands in moir\'e graphene systems.

cond-mat.mes-hall↗