arXiv subjects

Yuchao Jiang

Publications and source records attributed to Yuchao Jiang.

7 recordsLinked to original sources

arXiv2026

SiMOS quantum-dot spin qubits enabled by extreme-ultraviolet lithography

The realization of large-scale silicon quantum processors requires spin qubits compatible with advanced semiconductor manufacturing technologies, demanding lithographic processes that combine nanometer-scale precision with exceptional uniformity. Although the highest-performing silicon spin qubits demonstrated to date have relied on electron-beam (e-beam) lithography, its serial exposure process limits reproducibility studies and wafer-scale fabrication. Here, we demonstrate high-performance silicon metal-oxide-semiconductor (SiMOS) spin qubits fabricated using extreme-ultraviolet (EUV) lithography in a 300 mm semiconductor pilot line. We report wafer-scale quantum-dot uniformity metrics, including 100 % room-temperature gate-to-gate leakage yield and sub-nanometer control of critical gate dimensions. We characterize four double-dot systems realized in two triple-quantum-dot devices. Gate set tomography (GST) reveals consistently high fidelities across all four systems, with values up to 99.8 % for SPAM, 99.9 % for single-qubit gates, and 99.1 % for two-qubit gates. The devices exhibit highly reproducible exchange turn-on characteristics of 10-13 dec/V, indicating high fabrication uniformity enabled by EUV patterning. These results establish EUV lithography as a viable manufacturing technology for quantum processors based on high-fidelity SiMOS spin qubits.

cond-mat.mes-hall↗

arXiv2026

Nested Atoms Model with Application to Clustering Big Population-Scale Single-Cell Data

We consider the problem of clustering nested or hierarchical data, where observations are grouped and there are both group-level and observation-level variables. In our motivating OneK1K dataset, observations consist of single-cell RNA-sequencing (scRNA-seq) data from 982 individuals (groups), totaling 1.27 million cells (observations), along with individual-specific genotype data. This type of data would enable the identification of cell types and the investigation of how genetic variations among individuals influence differences in cell-type profiles. Our goal, therefore, is to jointly cluster cells and individuals to capture the heterogeneity across both levels using cell-specific gene expressions as well as individual-specific genotypes. However, existing grouped clustering methods do not incorporate group-level variables, thereby limiting their ability to capture the heterogeneity of genotypes in our motivating application. To address this, we propose the Nested Atoms Model (NAM), a new Bayesian nonparametric approach that enables the desired two-layered clustering, accounting for both group-level and observation-level variables. To scale NAM for high-dimensional data, we develop a fast variational Bayesian inference algorithm. Simulations show that NAM outperforms existing methods that ignore group-level variables. Applied to the OneK1K dataset, NAM identifies clusters of genetically similar individuals with homogeneous cell-type profiles. The resulting cell clusters align with known immune cell types based on differential gene expression, underscoring the ability of NAM to capture nested heterogeneity and provide biologically meaningful insights.

stat.ME↗

arXiv2026

Multivariable Bidirectional Mendelian Randomization via Bayesian Directed Cyclic Graphical Models with Correlated Errors

Mendelian randomization (MR) is a pivotal tool in genetics, genomics, and epidemiology, leveraging genetic variants as instrumental variables to infer causal relationships between exposures and outcomes. Traditional MR methods, while powerful, often rely on stringent assumptions such as the absence of feedback loops, which are frequently violated in complex biological networks. In addition, many popular MR approaches focus on only two variables (i.e., one exposure and one outcome), whereas our motivating applications of gene regulatory networks have many variables. In this article, we introduce a novel Bayesian framework for multivariable MR that concurrently addresses unmeasured confounding and feedback loops. Central to our approach is a sparse conditional cyclic graphical model with a sparse error variance-covariance matrix. Two structural priors are employed to enable the modeling and inference of causal relationships as well as latent confounding structures. Our method is designed to operate effectively with summary-level data, facilitating its application in contexts where individual-level data are inaccessible, e.g., due to privacy concerns. It can also account for horizontal pleiotropy, under which we establish the sufficient identifiability conditions. Through extensive simulations and applications to the GTEx and OneK1K data, we demonstrate the superior performance of our approach in recovering biologically plausible causal relationships in the presence of possible feedback loops and unmeasured confounding. Using posterior samples, we further quantify uncertainty in inferred network motifs by computing their posterior probabilities. The R package MR.RGM that implements the proposed method is available on CRAN (https://cran.r-project.org/package=MR.RGM).

stat.ME↗

arXiv2025

DiverseClaire: Simulating Students to Improve Introductory Programming Course Materials for All CS1 Learners

Although CS programs are booming, introductory courses like CS1 still adopt a one-size-fits-all formats that can exacerbate cognitive load and discourage learners with autism, ADHD, dyslexia and other neurological conditions. These call for compassionate pedagogies and Universal Design For Learning (UDL) to create learning environments and materials where cognitive diversity is welcomed. To address this, we introduce DiverseClaire a pilot study, which simulates students including neurodiverse profiles using LLMs and diverse personas. By leveraging Bloom's Taxonomy and UDL, DiverseClaire compared UDL-transformed lecture slides with traditional formats. To evaluate DiverseClaire controlled experiments, we used the evaluation metric the average score. The findings revealed that the simulated neurodiverse students struggled with learning due to lecture slides that were in inaccessible formats. These results highlight the need to provide course materials in multiple formats for diverse learner preferences. Data from our pilot study will be made available to assist future CS1 instructors.

cs.CY↗

arXiv2025

The Ranking Blind Spot: Decision Hijacking in LLM-based Text Ranking

Large Language Models (LLMs) have demonstrated strong performance in information retrieval tasks like passage ranking. Our research examines how instruction-following capabilities in LLMs interact with multi-document comparison tasks, identifying what we term the "Ranking Blind Spot", a characteristic of LLM decision processes during comparative evaluation. We analyze how this ranking blind spot affects LLM evaluation systems through two approaches: Decision Objective Hijacking, which alters the evaluation goal in pairwise ranking systems, and Decision Criteria Hijacking, which modifies relevance standards across ranking schemes. These approaches demonstrate how content providers could potentially influence LLM-based ranking systems to affect document positioning. These attacks aim to force the LLM ranker to prefer a specific passage and rank it at the top. Malicious content providers can exploit this weakness, which helps them gain additional exposure by attacking the ranker. In our experiment, We empirically show that the proposed attacks are effective in various LLMs and can be generalized to multiple ranking schemes. We apply these attack to realistic examples to show their effectiveness. We also found stronger LLMs are more vulnerable to these attacks. Our code is available at: https://github.com/blindspotorg/RankingBlindSpot

cs.IR↗

arXiv2025

The impact of process steps on nearly ideal subthreshold slope in 300-mm compatible InGaZnO TFT

While we demonstrate a back-gated (BG) amorphous Indium-Gallium-Zinc-Oxide (a-IGZO) transistors with a nearly ideal subthreshold slope (SS) ~ 60 mV/dec. However, SS degrades when a top-gated (TG) configuration is implemented. The energy distribution of traps inferred from temperature-dependent (T = 4 K - 300 K) and multi-frequency (f = 1 kHz - 100 kHz) admittance measurements, reveals a much higher trap density in TG devices. By analyzing the impact of each process step and conducting forming gas anneal (FGA) experiments, we reveal the role of hydrogen in the deterioration of the SS in the IGZO-based transistors.

physics.app-ph↗

arXiv2025

Aggregating Dependent Signals with Heavy-Tailed Combination Tests

Combining dependent p-values poses a long-standing challenge in statistical inference, particularly when aggregating findings from multiple methods to enhance signal detection. Recently, p-value combination tests based on regularly varying-tailed distributions, such as the Cauchy combination test and harmonic mean p-value, have attracted attention for their robustness to unknown dependence. This paper provides a theoretical and empirical evaluation of these methods under an asymptotic regime where the number of p-values is fixed and the global test significance level approaches zero. We examine two types of dependence among the p-values. First, when p-values are pairwise asymptotically independent, such as with bivariate normal test statistics with no perfect correlation, we prove that these combination tests are asymptotically valid. However, they become equivalent to the Bonferroni test as the significance level tends to zero for both one-sided and two-sided p-values. Empirical investigations suggest that this equivalence can emerge at moderately small significance levels. Second, under pairwise quasi-asymptotic dependence, such as with bivariate t-distributed test statistics, our simulations suggest that these combination tests can remain valid and exhibit notable power gains over Bonferroni, even as the significance level diminishes. These findings highlight the potential advantages of these combination tests in scenarios where p-values exhibit substantial dependence. Our simulations also examine how test performance depends on the support and tail heaviness of the underlying distributions.

stat.ME↗