arXiv subjects

Yuanbo Zhang

Publications and source records attributed to Yuanbo Zhang.

At least 19 recordsLinked to original sources

arXiv2026

Unequal urban capacities for mobility adaptation under fuel-price shocks

What a city makes reachable depends less on what it contains than on who can still afford to move when travel costs rise. We leverage the 2026 US-Iran oil shock as a natural experiment, applying a hierarchical panel regression discontinuity design to 1.7 trillion point-of-interest visits across 122,000 neighbourhoods in China and the United States. Mobility range declined in nearly three-quarters of neighbourhoods, but responses varied systematically with pre-shock urban conditions. Exposure to energy-intensive travel explained the largest share of modelled heterogeneity in both countries, while adaptive capacity and activity composition further shaped how travel was reorganized. Longer baseline travel intensified contraction, whereas greater car dependence constrained adjustment. Crucially, similar mobility outcomes arose from different processes: some neighbourhoods maintained travel by absorbing higher costs, whereas others appeared structurally locked into travel they could not reorganize. Fuel-price shocks, therefore, act as urban stress tests, revealing which neighbourhoods a city keeps connected.

stat.AP↗

arXiv2026

Spatio-temporal Path Optimization for Stabilizer-Code-Protected Quantum Networks

Quantum Error Correction~(QEC)-protected direct transmission is a fundamental approach to preserve fragile quantum states while they are physically forwarded across noisy quantum networks. When a logical qubit traverses multiple hops, selected QEC-capable nodes may recover the encoded state before it continues along the route. The feasibility and cost of the final transmission strategy therefore depend on how we jointly choose the path, the recovery locations, and the protection schemes. In this paper, we formulate and analyze a cross-layer spatio-temporal path optimization problem for block-style stabilizer-code-protected direct transmission. Our main results include fixed-scheme and flexible-scheme single-flow routing algorithms, as well as a multi-flow routing algorithm. The framework developed in this paper can serve as an algorithmic building block for QEC-aware routing under logical-error and logical-lifetime constraints. Simulations show that it reduces single-flow average routing cost by approximately 25--30\% over Decode-Always and lowers multi-flow throughput-normalized congestion by approximately 28--31\% over Greedy-Assignment.

cs.NI↗

arXiv2026

Signatures of unconventional superconductivity near reentrant and fractional quantum anomalous Hall insulators

Two-dimensional moiré Chern bands provide an exceptional platform for exploring a variety of many-body quantum phases at zero magnetic field within a lattice system. One particular intriguing possibility is that flat Chern bands can, in principle, support exotic superconducting phases together with fractional topological phases. Here, we report the observation of integer and fractional quantum anomalous Hall effects, the reentrant quantum anomalous Hall effect, and superconductivity within the first moiré Chern band of twisted bilayer MoTe2. The superconducting phase emerges from a normal state exhibiting anomalous Hall effects. Our results present the first example of superconductivity emerging within a flat Chern band that simultaneously hosts fractional quantum anomalous effects, a phenomenon never observed in any other systems. Our work expands the understanding of emergent quantum phenomena in moiré Chern bands, and offers a nearly ideal platform for engineering Majorana and parafermion zero modes in gate-controlled hybrid devices.

cond-mat.mes-hall↗

arXiv2026

Twist-angle evolution from valley-polarized fractional topological phases to valley-degenerate superconductivity in twisted bilayer MoTe2

Moiré superlattices formed by semiconducting transition metal dichalcogenides (TMDs) provide a highly tunable platform for investigating strongly correlated and topological quantum phases. As a prototypical example, twisted bilayer MoTe2 (tMoTe2) has been shown to host fractional topological phases, such as zero-field fractional Chern insulators (FCIs) exhibiting fractional quantum anomalous Hall (FQAH) effects. However, how these correlated topological phases evolve with twist angle and compete with other quantum phases in tMoTe2 remains largely unexplored. Here we report a systematic transport study of twist-angle-dependent phase diagrams in tMoTe2 across a range of 3.8°-5.78°, revealing an evolution from fractionalized states of matter with spontaneous valley polarization to valley-degenerate superconductivity. At relatively small twist angles, partially-filled Chern bands of tMoTe2 host FQAH states following the Jain sequence, together with signatures of an anomalous composite Fermi liquid at moiré hole filling factor νh = 1/2. Increasing twist angle progressively suppresses fractional topological phases and reconstructs the half-filled Chern band into symmetry-breaking integer Chern insulating states. At νh = 1, we observe a transition from robust integer quantum anomalous Hall (IQAH) insulators at small angles to displacement-field-tuned, topologically trivial correlated insulators at larger angles. Remarkably, at a twist angle of 5.78°, superconductivity emerges adjacent to the correlated insulating phase, with a phase diagram closely resembling that recently reported in twisted bilayer WSe2 (tWSe2). Our results uncover a unified twist-angle-driven phase evolution linking fractional topology, symmetry breaking, magnetic order, and superconductivity, providing new insight into the emergent quantum phenomena in moiré systems.

cond-mat.mes-hall↗

arXiv2026

Unveiling the impact of anti-site defects in magnetic transitions of few-layer MnBi2Te4 by operando heating

As the first experimentally discovered intrinsic magnetic topological insulator, MnBi2Te4 has attracted widespread attentions, providing a unique platform for the exploration of topological quantum phases, such as quantum anomalous Hall effect and axion insulator state. Despite the increasing number of potential factors affecting samples being identified, obtaining the high-quality device performance with desired topological quantum phases remains a challenge. In this work, by comparing the reflective magnetic circular dichroism (RMCD) of crystals with different defect densities that are characterized by atomically resolved scanning tunneling microscopy, we demonstrate that anti-site defects play an essential role in achieving ideal magnetic states. By measuring RMCD hysteresis loops with operando heating, we find that MnBi2Te4 few-layer samples are highly susceptible to thermal impact, even at temperature as low as 45°C. The magnetic behavior of heating-treated samples is akin to that of samples fabricated into devices, revealing the thermal impact on devices as well. Starting from few-layers with ideal layer-dependent magnetic order, thermal heating leads to the convergence of magnetization and transition fields between odd- and even-layers. The observed heating-induced magnetic evolution can serve as a valuable reference for assessing the sample quality or the density of anti-site defects. Our findings not only point out the long-standing hidden factor that arose controversies in MnBi2Te4, but also pave the way for controllably engineering the topological quantum phenomena.

cond-mat.mtrl-sci↗

arXiv2025

GPUnion: Autonomous GPU Sharing on Campus

A pronounced imbalance in GPU resources exists on campus, where some laboratories own underutilized servers while others lack the compute needed for AI research. GPU sharing can alleviate this disparity, while existing platforms typically rely on centralized oversight and persistent allocation models, conflicting with the voluntary and autonomous nature of academic resource ownership. We present GPUnion, a campus-scale GPU sharing platform enabling voluntary participation while preserving full provider autonomy. GPUnion incorporates three core mechanisms: i) container-based task dispatching and execution, ii) resource provider-first architecture, and iii) resilient execution featuring automatic check-pointing and migration. Case studies across multiple campus scenarios demonstrate 30% more GPU utilization improvement, 40% increase in interactive sessions, and 94% successful workload migration during provider departures.

cs.DC↗

arXiv2025

Intrinsic layer polarization and multi-flatband transport in non-centrosymmetric mixed-stacked multilayer graphene

Graphene multilayers exhibit electronic spectra that depend sensitively on both the number of layers and their stacking order. Beyond trilayer graphene, mixed stacking sequences (alternating Bernal and rhombohedral layers) give rise to multiple coexisting low-energy bands. Here we investigate ABCBC-stacked pentalayer graphene, a less-studied non-centrosymmetric mixed sequence. This stacking can be regarded as an ABC (rhombohedral) trilayer on top of an AB (Bernal) bilayer, so its low-energy band structure contains both a cubic band and a parabolic band that hybridize. In transport measurements, we observe an intrinsic band gap at charge neutrality whose magnitude changes asymmetrically under an applied perpendicular displacement field. This behavior reflects the spontaneous layer polarization inherent to the broken inversion symmetry and mirror symmetry. By tuning the displacement field and carrier density, we drive multiple Lifshitz transitions in the Fermi surface topology and realize Landau levels with different degeneracies arising from the multi-flatband system. Remarkably, a v = -6 quantum Hall state emerges at an exceptionally low magnetic field (~20 mT), indicating the interplay between spontaneous symmetry breaking and Berry curvatures. Our results establish mixed-stacked multilayer graphene as a tunable platform with various broken symmetries and multiple flatbands, suitable for exploring emergent correlated electronic states.

cond-mat.mes-hall↗

arXiv2025

Cascade of Even-Denominator Fractional Quantum Hall States in Mixed-Stacked Multilayer Graphene

The fractional quantum Hall effect (FQHE), particularly at half-filling of Landau levels, provides a unique window into topological phases hosting non-Abelian excitations. However, experimental platforms simultaneously offering large energy gaps, delicate tunability, and robust non-Abelian signatures remain scarce. Here, we report the observation of a cascade of even-denominator FQH states at filling factors $ν$ = ${-5/2}$, ${-7/2}$, ${-9/2}$, ${-11/2}$, and ${-13/2}$, alongside numerous odd-denominator states in mixed-stacked pentalayer graphene, a previously unexplored system characterized by intertwined quadratic and cubic band dispersions. These even-denominator states, representing the highest filling half-filled states reported so far in the zeroth Landau level (ZLL), emerge from two distinct intra-ZLL and exhibit unprecedented displacement field tunability driven by LL crossings in the hybridized multiband structure. At half fillings, continuous quasiparticle phase transitions between paired FQH states, magnetic Bloch states, and composite Fermi liquids are clearly identified upon tuning external fields. Numerical calculations, revealing characteristic sixfold ground-state degeneracy and chiral graviton spectral analysis, suggest the observed even-denominator FQH states belong to the non-Abelian Moore-Read type. These results establish mixed-stacked multilayer graphene as a rich and versatile crystalline platform for exploring tunable correlated topological phases.

cond-mat.mes-hall↗

arXiv2025

Quantized Topological States and Parity Anomaly in Intrinsic Quantum Anomalous Hall Insulator MnBi2Te4

When thinned down to just a few atomic layers, the layered magnetic topological insulator MnBi2Te4 offers an exceptional platform for exploring a wide range of topological phenomena. In this work, we overcome longstanding challenges in synthesizing high-purity MnBi2Te4 crystals and report the observation of a myriad of quantized topological states in high-quality five-septuple-layer (5-SL) samples under magnetic fields up to 45 Tesla. We show that the nontrivial topology of 5-SL MnBi2Te4, in the presence of Landau quantization, is governed by a generalized topological index rooted in the parity anomaly of Dirac fermions in (2+1) dimensions. The anomaly manifests as an anomalous Landau level, giving rise to gate-tunable helical edge transport. Our results establish high-quality MnBi2Te4 as a robust platform for exploring emergent topological states and for advancing novel quantum device applications.

cond-mat.mes-hall↗

arXiv2025

Synthesizing and Adapting Error Correction Data for Mobile Large Language Model Applications

Error correction is an important capability when applying large language models (LLMs) to facilitate user typing on mobile devices. In this paper, we use LLMs to synthesize a high-quality dataset of error correction pairs to evaluate and improve LLMs for mobile applications. We first prompt LLMs with error correction domain knowledge to build a scalable and reliable addition to the existing data synthesis pipeline. We then adapt the synthetic data distribution to match the mobile application domain by reweighting the samples. The reweighting model is learnt by predicting (a handful of) live A/B test metrics when deploying LLMs in production, given the LLM performance on offline evaluation data and scores from a small privacy-preserving on-device language model. Finally, we present best practices for mixing our synthetic data with other data sources to improve model performance on error correction in both offline evaluation and production live A/B testing.

cs.LG↗

arXiv2024

Neural Search Space in Gboard Decoder

Gboard Decoder produces suggestions by looking for paths that best match input touch points on the context aware search space, which is backed by the language Finite State Transducers (FST). The language FST is currently an N-gram language model (LM). However, N-gram LMs, limited in context length, are known to have sparsity problem under device model size constraint. In this paper, we propose \textbf{Neural Search Space} which substitutes the N-gram LM with a Neural Network LM (NN-LM) and dynamically constructs the search space during decoding. Specifically, we integrate the long range context awareness of NN-LM into the search space by converting its outputs given context, into the language FST at runtime. This involves language FST structure redesign, pruning strategy tuning, and data structure optimizations. Online experiments demonstrate improved quality results, reducing Words Modified Ratio by [0.26\%, 1.19\%] on various locales with acceptable latency increases. This work opens new avenues for further improving keyboard decoding quality by enhancing neural LM more directly.

cs.CL↗

arXiv2024

Prompt Public Large Language Models to Synthesize Data for Private On-device Applications

Pre-training on public data is an effective method to improve the performance for federated learning (FL) with differential privacy (DP). This paper investigates how large language models (LLMs) trained on public data can improve the quality of pre-training data for the on-device language models trained with DP and FL. We carefully design LLM prompts to filter and transform existing public data, and generate new data to resemble the real user data distribution. The model pre-trained on our synthetic dataset achieves relative improvement of 19.0% and 22.8% in next word prediction accuracy compared to the baseline model pre-trained on a standard public dataset, when evaluated over the real user data in Gboard (Google Keyboard, a production mobile keyboard application). Furthermore, our method achieves evaluation accuracy better than or comparable to the baseline during the DP FL fine-tuning over millions of mobile devices, and our final model outperforms the baseline in production A/B testing. Our experiments demonstrate the strengths of LLMs in synthesizing data close to the private distribution even without accessing the private data, and also suggest future research directions to further reduce the distribution gap.

cs.LG↗

arXiv2024

Proofread: Fixes All Errors with One Tap

The impressive capabilities in Large Language Models (LLMs) provide a powerful approach to reimagine users' typing experience. This paper demonstrates Proofread, a novel Gboard feature powered by a server-side LLM in Gboard, enabling seamless sentence-level and paragraph-level corrections with a single tap. We describe the complete system in this paper, from data generation, metrics design to model tuning and deployment. To obtain models with sufficient quality, we implement a careful data synthetic pipeline tailored to online use cases, design multifaceted metrics, employ a two-stage tuning approach to acquire the dedicated LLM for the feature: the Supervised Fine Tuning (SFT) for foundational quality, followed by the Reinforcement Learning (RL) tuning approach for targeted refinement. Specifically, we find sequential tuning on Rewrite and proofread tasks yields the best quality in SFT stage, and propose global and direct rewards in the RL tuning stage to seek further improvement. Extensive experiments on a human-labeled golden set showed our tuned PaLM2-XS model achieved 85.56\% good ratio. We launched the feature to Pixel 8 devices by serving the model on TPU v5 in Google Cloud, with thousands of daily active users. Serving latency was significantly reduced by quantization, bucket inference, text segmentation, and speculative decoding. Our demo could be seen in \href{https://youtu.be/4ZdcuiwFU7I}{Youtube}.

cs.CL↗

arXiv2024

Dephasing of Strong-Field-Driven Floquet States Revealed by Time- and Spectrum-Resolved Quantum-Path Interferometry

Floquet engineering, while a powerful tool for ultrafast quantum-state manipulation, faces challenges under strong-field conditions, as recent high harmonic generation studies unveil exceptionally short dephasing times. In this study, using time- and spectrum-resolved quantum-path interferometry, we investigate the dephasing mechanisms of terahertz-driven excitons. Our results reveal a dramatic increase in exciton dephasing rate beyond a threshold field strength, indicating exciton dissociation as the primary dephasing mechanism. Importantly, we demonstrate long dephasing times of strong-field-dressed excitons, supporting coherent strong-field manipulation of quantum materials.

physics.optics↗

arXiv2023

Federated Learning of Gboard Language Models with Differential Privacy

We train language models (LMs) with federated learning (FL) and differential privacy (DP) in the Google Keyboard (Gboard). We apply the DP-Follow-the-Regularized-Leader (DP-FTRL)~\citep{kairouz21b} algorithm to achieve meaningfully formal DP guarantees without requiring uniform sampling of client devices. To provide favorable privacy-utility trade-offs, we introduce a new client participation criterion and discuss the implication of its configuration in large scale systems. We show how quantile-based clip estimation~\citep{andrew2019differentially} can be combined with DP-FTRL to adaptively choose the clip norm during training or reduce the hyperparameter tuning in preparation for training. With the help of pretraining on public data, we train and deploy more than twenty Gboard LMs that achieve high utility and $ρ-$zCDP privacy guarantees with $ρ\in (0.2, 2)$, with two models additionally trained with secure aggregation~\citep{bonawitz2017practical}. We are happy to announce that all the next word prediction neural network LMs in Gboard now have DP guarantees, and all future launches of Gboard neural network LMs will require DP guarantees. We summarize our experience and provide concrete suggestions on DP training for practitioners.

cs.LG↗

arXiv2023

Private Federated Learning in Gboard

This white paper describes recent advances in Gboard(Google Keyboard)'s use of federated learning, DP-Follow-the-Regularized-Leader (DP-FTRL) algorithm, and secure aggregation techniques to train machine learning (ML) models for suggestion, prediction and correction intelligence from many users' typing data. Gboard's investment in those privacy technologies allows users' typing data to be processed locally on device, to be aggregated as early as possible, and to have strong anonymization and differential privacy where possible. Technical strategies and practices have been established to allow ML models to be trained and deployed with meaningfully formal DP guarantees and high utility. The paper also looks ahead to how technologies such as trusted execution environments may be used to further improve the privacy and security of Gboard's ML models.

cs.CR↗

arXiv2023

Magnetic field stabilized Wigner crystal states in a graphene moiré superlattice

Wigner crystals are predicted as the crystallization of the dilute electron gas moving in a uniform background when the electron-electron Coulomb energy dominates the kinetic energy. The Wigner crystal has previously been observed in the ultraclean two-dimensional electron gas (2DEG) present on the surface of liquid helium and in semiconductor quantum wells at high magnetic field. More recently, Wigner crystals have also been reported in WS2/WSe2 moiré heterostructures. ABC-stacked trilayer graphene on boron nitride (ABC-TLG/hBN) moiré superlattices provide a unique tunable platform to explore Wigner crystal states where the electron correlation can be controlled by electric and magnetic field. Here we report the observation of magnetic field stabilized Wigner crystal states in a ABC-TLG/hBN moiré superlattice. We show that correlated insulating states emerge at multiple fractional and integer fillings corresponding to v = 1/3, 2/3, 1, 4/3, 5/3 and 2 electrons per moiré lattice site under a magnetic field. These correlated insulating states can be attributed to generalized Mott states for the integer fillings (v = 1, 2) and generalized Wigner crystal states for the fractional fillings (v = 1/3, 2/3, 4/3, 5/3). The generalized Wigner crystal states are stabilized by a vertical magnetic field, and they are strongest at one magnetic flux quantum per three moiré superlattices. The correlated insulating states at v = 2 persists up to 30 Tesla, which can be described by a Mott-Hofstadter transition at high magnetic field. The tunable Mott and Wigner crystal states in the ABC-TLG/hBN highlight the opportunities to discover new correlated quantum phases due to the interplay between the magnetic field and moiré flatbands.

cond-mat.mes-hall↗

arXiv2023

Flat Chern Band From Twisted Bilayer MnBi$_2$Te$_4$

We construct a continuum model for the Moiré superlattice of twisted bilayer MnBi$_2$Te$_4$, and study the band structure of the bilayer in both ferromagnetic (FM) and antiferromagnetic (AFM) phases. We find the system exhibits highly tunable Chern bands with Chern number up to $3$. We show that a twist angle of $1^\circ$ turns the highest valence band into a flat band with Chern number $\pm1$ that is isolated from all other bands in both FM and AFM phases. This result provides a promising platform for realizing time-reversal breaking correlated topological phases, such as fractional Chern insulator and $p+ip$ topological superconductor. In addition, our calculation indicates that the twisted stacking facilitates the emergence of quantum anomalous Hall effect in MnBi$_2$Te$_4$.

cond-mat.mes-hall↗