arXiv subjects

Yuchen Han

Publications and source records attributed to Yuchen Han.

At least 19 recordsLinked to original sources

arXiv2026

Rapid and high-sensitive NV-based microwave field imaging via digital lock-in amplification for on-chip microstrip diagnostics

High-resolution, high-sensitivity microwave (MW) magnetic field imaging is indispensable for non-destructive integrated circuit (IC) testing, radio-frequency device characterization, and spintronic research. Yet, the practical utility of these techniques is severely constrained by the pervasive challenge of isolating weak magnetic signatures from intense optical and electronic noise, which fundamentally limits both acquisition speed and detection sensitivity. Here, we overcome this barrier by introducing a wide-field imaging scheme based on an ensemble of diamond nitrogen-vacancy (NV) centers, synergistically combined with digital lock-in amplification (DLA). By exploiting digital demodulation, the DLA precisely extracts the MW-field response at a specific modulation frequency from background noise (e.g., laser intensity fluctuations), dramatically improving the signal-to-noise ratio (SNR). Consequently, our system attains a magnetic field sensitivity of 126 nT/$\sqrt(Hz)$. Critically, the unprecedented SNR permits a pixel dwell time of under one millisecond, allowing full-field images to be acquired within seconds-more than an order of magnitude faster than state-of-the-art NV-based wide-field techniques. This combination of speed, sensitivity, and micron-scale spatial resolution (1.6 $\mu$m) paves the way for quasi-real-time, non-invasive diagnostics of dynamic MW devices and integrated circuits.

physics.optics↗

arXiv2026

More Rejective, Not More Discriminative: The Unit of Verification in Pre-Execution LLM Oversight

Pre-execution oversight is core to trusted monitoring in AI control: a fallible LLM monitor vets planned actions before irreversible execution. Over-blocking forfeits usefulness and pressures deployers to disable it. Every protocol must fix a unit of verification: how many actions one call reviews. Existing designs take the unit as given; its effect on fallible monitors is unmeasured. Natural traces cannot isolate it: review length co-varies with error type and position. Catch alone misleads: rejecting everything catches everything. Measuring this needs boundary variation alone and a matched clean control. We introduce the twin-prefix framework, which supplies both. Each gold plan yields a prefix with one injected, environment-accepted error and a clean twin differing in one write. Judging each pair at five nested lengths ties verdict changes to the unit alone. Discrimination is scored by pre-registered informedness, catch minus false rejection. Longer review raises catch; false rejection climbs in lockstep. Informedness peaks at one or two actions for all six judges in both domains: longer windows make zero-shot monitors more rejective, not more discriminative. Replaying withheld observations traces the failure largely to observation deprivation. Safety cases should state the unit and co-report the clean series. Our framework is the first controlled, pre-registered instrument for this choice and never reads catch alone. Our calibrated short unit recovers up to 0.95 informedness over eight-action review, and no tested label-blind policy consistently beats it.

cs.AI↗

arXiv2026

On-chip Radio Frequency Maser

Room-temperature solid-state masers offer exceptional frequency selectivity and ultra-low noise for weak-signal detection. However, their reliance on bulky metallic resonators has significantly hindered integration, miniaturization, and extension to lower frequencies. Here, we demonstrate the first on-chip radio-frequency maser operating at room temperature, exploiting optically pumped triplet states of pentacene. The device produces stimulated emission at 106.62 MHz and enables ultra-sensitive microwave magnetic-field detection with a sensitivity of ($\sim 10\,\rm{fT/\sqrt{Hz}}$), functioning simultaneously as a local oscillator and a sensor. By actively controlling microwave dissipation, we achieve efficient regulation of the maser output, revealing a key mechanism for tuning emission in open cavity-free systems. This work extends pentacene-based masers into the radio-frequency regime and establishes a highly integrated on-chip architecture for room-temperature masers, offering a new pathway toward portable quantum devices.

quant-ph↗

arXiv2025

Detecting Axion Dark Matter with an Organic Molecular Maser

We present a novel quantum sensing approach to search for axion-electron interactions around the axion mass of 6 \mueV. In this region, laboratory searches are relatively scarce, and our direct experiment measuring the axion-electron coupling constant reaches the sensitivity of 8 \times 10^{-6} GeV^{-1}. The method, based on an organic molecular maser establishes a proof-of-principle for quantum-enhanced detection, with a corresponding magnetic field sensitivity of 0.85 fT/\sqrt{\rm{Hz}}. The methodology is generic and can be readily extended to other physical systems, further broadening its applicability in quantum sensing and dark matter searches.

hep-ph↗

arXiv2025

When Active Learning Fails, Uncalibrated Out of Distribution Uncertainty Quantification Might Be the Problem

Efficiently and meaningfully estimating prediction uncertainty is important for exploration in active learning campaigns in materials discovery, where samples with high uncertainty are interpreted as containing information missing from the model. In this work, the effect of different uncertainty estimation and calibration methods are evaluated for active learning when using ensembles of ALIGNN, eXtreme Gradient Boost, Random Forest, and Neural Network model architectures. We compare uncertainty estimates from ALIGNN deep ensembles to loss landscape uncertainty estimates obtained for solubility, bandgap, and formation energy prediction tasks. We then evaluate how the quality of the uncertainty estimate impacts an active learning campaign that seeks model generalization to out-of-distribution data. Uncertainty calibration methods were found to variably generalize from in-domain data to out-of-domain data. Furthermore, calibrated uncertainties were generally unsuccessful in reducing the amount of data required by a model to improve during an active learning campaign on out-of-distribution data when compared to random sampling and uncalibrated uncertainties. The impact of poor-quality uncertainty persists for random forest and eXtreme Gradient Boosting models trained on the same data for the same tasks, indicating that this is at least partially intrinsic to the data and not due to model capacity alone. Analysis of the target, in-distribution uncertainty, out-of-distribution uncertainty, and training residual distributions suggest that future work focus on understanding empirical uncertainties in the feature input space for cases where ensemble prediction variances do not accurately capture the missing information required for the model to generalize.

cond-mat.mtrl-sci↗

arXiv2025

A Hubbard exciton fluid in a photo-doped antiferromagnetic Mott insulator

The undoped antiferromagnetic Mott insulator naturally has one charge carrier per lattice site. When it is doped with additional carriers, they are unstable to spin fluctuation-mediated Cooper pairing as well as other unconventional types of charge, spin, and orbital current ordering. Photo-excitation can produce charge carriers in the form of empty (holons) and doubly occupied (doublons) sites that may also exhibit charge instabilities. There is evidence that antiferromagnetic correlations enhance attractive interactions between holons and doublons, which can then form bound pairs known as Hubbard excitons, and that these might self-organize into an insulating Hubbard exciton fluid. However, this out-of-equilibrium phenomenon has not been detected experimentally. Here, we report the transient formation of a Hubbard exciton fluid in the antiferromagnetic Mott insulator Sr$_{2}$IrO$_{4}$ using ultrafast terahertz conductivity. Following photo-excitation, we observe rapid spectral weight transfer from a Drude metallic response to an insulating response. The latter is characterized by a finite energy peak originating from intra-excitonic transitions, whose assignment is corroborated by our numerical simulations of an extended Hubbard model. The lifetime of the peak is short, approximately one picosecond, and scales exponentially with Mott gap size, implying extremely strong coupling to magnon modes.

cond-mat.str-el↗

arXiv2025

Observation of excitons bound by antiferromagnetic correlations

Two-dimensional Mott insulators host antiferromagnetic (AFM) correlations that are predicted to enhance the attractive interaction between empty (holons) and doubly occupied (doublons) sites, creating a novel pathway for exciton formation. However, experimental confirmation of this spin-mediated binding mechanism remains elusive. Leveraging the distinct magnetic critical properties of the Mott antiferromagnets Sr$_2$IrO$_4$ and Sr$_3$Ir$_2$O$_7$, we show using time-resolved THz spectroscopy that excitons only exist at temperatures below where short-range AFM correlation develops. The excitons remain stable up to photodoping densities approaching the predicted excitonic Mott insulator-to-metal transition, revealing a unique robustness against screening. Our results establish the viability of spin-bound excitons and introduce opportunities for excitonic control through magnetic degrees of freedom.

cond-mat.str-el↗

arXiv2025

Nonparametric learning of covariate-based Markov jump processes using RKHS techniques

We propose a novel nonparametric approach for linking covariates to Continuous Time Markov Chains (CTMCs) using the mathematical framework of Reproducing Kernel Hilbert Spaces (RKHS). CTMCs provide a robust framework for modeling transitions across clinical or behavioral states, but traditional multistate models often rely on linear relationships. In contrast, we use a generalized Representer Theorem to enable tractable inference in functional space. For the Frequentist version, we apply normed square penalties, while for the Bayesian version, we explore sparsity inducing spike and slab priors. Due to the computational challenges posed by high-dimensional spaces, we successfully adapt the Expectation Maximization Variable Selection (EMVS) algorithm to efficiently identify the posterior mode. We demonstrate the effectiveness of our method through extensive simulation studies and an application to follicular cell lymphoma data. Our performance metrics include the normalized difference between estimated and true nonlinear transition functions, as well as the difference in the probability of getting absorbed in one the final states, capturing the ability of our approach to predict long-term behaviors.

stat.ME↗

arXiv2025

Effect of Selection Format on LLM Performance

This paper investigates a critical aspect of large language model (LLM) performance: the optimal formatting of classification task options in prompts. Through an extensive experimental study, we compared two selection formats -- bullet points and plain English -- to determine their impact on model performance. Our findings suggest that presenting options via bullet points generally yields better results, although there are some exceptions. Furthermore, our research highlights the need for continued exploration of option formatting to drive further improvements in model performance.

cs.CL↗

arXiv2025

Optimizing Speech Multi-View Feature Fusion through Conditional Computation

Recent advancements have highlighted the efficacy of self-supervised learning (SSL) features in various speech-related tasks, providing lightweight and versatile multi-view speech representations. However, our study reveals that while SSL features expedite model convergence, they conflict with traditional spectral features like FBanks in terms of update directions. In response, we propose a novel generalized feature fusion framework grounded in conditional computation, featuring a gradient-sensitive gating network and a multi-stage dropout strategy. This framework mitigates feature conflicts and bolsters model robustness to multi-view input features. By integrating SSL and spectral features, our approach accelerates convergence and maintains performance on par with spectral models across multiple speech translation tasks on the MUSTC dataset.

eess.AS↗

arXiv2024

Ultra-sensitive solid-state organic molecular microwave quantum receiver

High-accuracy microwave sensing is widely demanded in various fields, ranging from cosmology to microwave quantum technology. Quantum receivers based on inorganic solid-state spin systems are promising candidates for such purpose because of the stability and compatibility, but their best sensitivity is currently limited to a few pT/$\sqrt{\rm{Hz}}$. Here, by utilising an enhanced readout scheme with the state-of-the-art solid-state maser technology, we develop a robust microwave quantum receiver functioned by organic molecular spins at ambient conditions. Owing to the maser amplification, the sensitivity of the receiver achieves 6.14 $\pm$ 0.17 fT/$\sqrt{\rm{Hz}}$ which exceeds three orders of magnitude than that of the inorganic solid-state quantum receivers. The heterodyne detection without additional local oscillators improves bandwidth of the receiver and allows frequency detection. The scheme can be extended to other solid-state spin systems without complicated control pulses and thus enables practical applications such as electron spin resonance spectroscopy, dark matter searches, and astronomical observations.

quant-ph↗

arXiv2024

Generalizable Whole Slide Image Classification with Fine-Grained Visual-Semantic Interaction

Whole Slide Image (WSI) classification is often formulated as a Multiple Instance Learning (MIL) problem. Recently, Vision-Language Models (VLMs) have demonstrated remarkable performance in WSI classification. However, existing methods leverage coarse-grained pathogenetic descriptions for visual representation supervision, which are insufficient to capture the complex visual appearance of pathogenetic images, hindering the generalizability of models on diverse downstream tasks. Additionally, processing high-resolution WSIs can be computationally expensive. In this paper, we propose a novel "Fine-grained Visual-Semantic Interaction" (FiVE) framework for WSI classification. It is designed to enhance the model's generalizability by leveraging the interaction between localized visual patterns and fine-grained pathological semantics. Specifically, with meticulously designed queries, we start by utilizing a large language model to extract fine-grained pathological descriptions from various non-standardized raw reports. The output descriptions are then reconstructed into fine-grained labels used for training. By introducing a Task-specific Fine-grained Semantics (TFS) module, we enable prompts to capture crucial visual information in WSIs, which enhances representation learning and augments generalization capabilities significantly. Furthermore, given that pathological visual patterns are redundantly distributed across tissue slices, we sample a subset of visual instances during training. Our method demonstrates robust generalizability and strong transferability, dominantly outperforming the counterparts on the TCGA Lung Cancer dataset with at least 9.19% higher accuracy in few-shot experiments. The code is available at: https://github.com/ls1rius/WSI_FiVE.

cs.CV↗

arXiv2023

Topological Directional Coupler

Interferometers and beam splitters are fundamental building blocks for photonic neuromorphic and quantum computing machinery. In waveguide-based photonic integrated circuits, beam-splitting is achieved with directional couplers that rely on transition regions where the waveguides are adiabatically bent to suppress back-reflection. We present a novel, compact approach to introducing guided mode coupling. By leveraging multimodal domain walls between microwave topological photonic crystals, we use the photonic-spin-conservation to suppress back-reflection while relaxing the topological protection of the valley degree of freedom to implement tunable beam splitting. Rapid advancements in chip-scale topological photonics suggest that the proposed simultaneous utilization of multiple topological degrees of freedom could benefit the development of novel photonic computing platforms.

cond-mat.mes-hall↗

arXiv2023

Data-driven fixed-point tuning for truncated realized variations

Many methods for estimating integrated volatility and related functionals of semimartingales in the presence of jumps require specification of tuning parameters for their use in practice. In much of the available theory, tuning parameters are assumed to be deterministic and their values are specified only up to asymptotic constraints. However, in empirical work and in simulation studies, they are typically chosen to be random and data-dependent, with explicit choices often relying entirely on heuristics. In this paper, we consider novel data-driven tuning procedures for the truncated realized variations of a semimartingale with jumps based on a type of random fixed-point iteration. Being effectively automated, our approach alleviates the need for delicate decision-making regarding tuning parameters in practice and can be implemented using information regarding sampling frequency alone. We demonstrate our methods can lead to asymptotically efficient estimation of integrated volatility and exhibit superior finite-sample performance compared to popular alternatives in the literature.

math.ST↗

arXiv2023

Modality Adaption or Regularization? A Case Study on End-to-End Speech Translation

Pre-training and fine-tuning is a paradigm for alleviating the data scarcity problem in end-to-end speech translation (E2E ST). The commonplace "modality gap" between speech and text data often leads to inconsistent inputs between pre-training and fine-tuning. However, we observe that this gap occurs in the early stages of fine-tuning, but does not have a major impact on the final performance. On the other hand, we find that there has another gap, which we call the "capacity gap": high resource tasks (such as ASR and MT) always require a large model to fit, when the model is reused for a low resource task (E2E ST), it will get a sub-optimal performance due to the over-fitting. In a case study, we find that the regularization plays a more important role than the well-designed modality adaption method, which achieves 29.0 for en-de and 40.3 for en-fr on the MuST-C dataset. Code and models are available at https://github.com/hannlp/TAB.

cs.CL↗

arXiv2022

Efficient Integrated Volatility Estimation in the Presence of Infinite Variation Jumps via Debiased Truncated Realized Variations

Statistical inference for stochastic processes based on high-frequency observations has been an active research area for more than two decades. One of the most well-known and widely studied problems has been the estimation of the quadratic variation of the continuous component of an It\^o semimartingale with jumps. Several rate- and variance-efficient estimators have been proposed in the literature when the jump component is of bounded variation. However, to date, very few methods can deal with jumps of unbounded variation. By developing new high-order expansions of the truncated moments of a locally stable L\'evy process, we propose a new rate- and variance-efficient volatility estimator for a class of It\^o semimartingales whose jumps behave locally like those of a stable L\'evy process with Blumenthal-Getoor index $Y\in (1,8/5)$ (hence, of unbounded variation). The proposed method is based on a two-step debiasing procedure for the truncated realized quadratic variation of the process and can also cover the case $Y<1$. Our Monte Carlo experiments indicate that the method outperforms other efficient alternatives in the literature in the setting covered by our theoretical framework.

econ.EM↗

arXiv2022

Efficient Volatility Estimation for L\'evy Processes with Jumps of Unbounded Variation

Statistical inference for stochastic processes based on high-frequency observations has been an active research area for more than a decade. One of the most well-known and widely studied problems is that of estimation of the quadratic variation of the continuous component of an It\^o semimartingale with jumps. Several rate- and variance-efficient estimators have been proposed in the literature when the jump component is of bounded variation. However, to date, very few methods can deal with jumps of unbounded variation. By developing new high-order expansions of the truncated moments of a L\'evy process, we construct a new rate- and variance-efficient estimator for a class of L\'evy processes of unbounded variation, whose small jumps behave like those of a stable L\'evy process with Blumenthal-Getoor index less than $8/5$. The proposed method is based on a two-step debiasing procedure for the truncated realized quadratic variation of the process. Our Monte Carlo experiments indicate that the method outperforms other efficient alternatives in the literature in the setting covered by our theoretical framework.

econ.EM↗

arXiv2021

Mode-selective Single-dipole Excitation and Controlled Routing of Guided Waves in a Multi-mode Topological Waveguide

Topology-linked binary degrees of freedom of guided waves have been used to expand the channel capacity of and to ensure robust transmission through photonic waveguides. However, selectively exciting optical modes associated with the desired degree of freedom is challenging and typically requires spatially extended sources or filters. Both approaches are incompatible with the ultimate objective of developing compact mode-selective sources powered by single emitters. In addition, the implementation of highly desirable functionalities, such as controllable distribution of guided modes between multiple detectors, becomes challenging in highly-compact devices due to photon loss to reflections. Here, we demonstrate that a linearly-polarized dipole-like source can selectively excite a topologically robust edge mode with the desired valley degree of freedom. Reflection-free routing of valley-polarized edge modes into two spatially-separated detectors with reconfigurable splitting ratios is also presented. An optical implementation of such a source will have the potential to broaden the applications of topological photonic devices.

physics.optics↗