arXiv subjects

Shuyu Chen

Publications and source records attributed to Shuyu Chen.

12 recordsLinked to original sources

arXiv2026

SCTA: An Agentic Framework for Stable and Interpretable Target Gene Discovery from Single-Cell RNA Sequencing

Identifying therapeutic target genes from single-cell RNA sequencing (scRNA-seq) data remains a fundamental challenge in translational biology. Unlike bulk assays, scRNA-seq captures heterogeneous cellular states and rare subpopulations, but this same heterogeneity makes target discovery highly sensitive to analytical choices throughout the pipeline, including preprocessing, cell population selection, differential expression analysis, and downstream biological interpretation. As a result, existing workflows and general-purpose analysis agents often produce unstable or difficult-to-interpret target hypotheses, limiting their reliability for disease-focused discovery. We present SCTA (Single-Cell Target Agent), a decision-centric agentic framework for stable and interpretable target gene discovery from scRNA-seq data. Rather than treating analysis as a single general-purpose reasoning task, SCTA decomposes target discovery into specialized agents aligned with key decision points in the single-cell pipeline and constrains downstream reasoning with structured biological evidence. In a representative ablation study on hereditary chronic pancreatitis, we demonstrate that SCTA's full evidence integration yields the most stable target selection across independent runs among the tested configurations, while recovering biologically coherent, disease-relevant mechanisms validated in prior studies. These results suggest that decision-aware agent orchestration tailored to the structure of single-cell analysis can improve the robustness, interpretability, and practical utility of target discovery in precision medicine.

cs.LG↗

arXiv2026

Horizontal and Longitudinal Comparisons Among AI Subfields: A Bibliometric Perspective

Recent artificial intelligence has developed rapidly with significant interdisciplinary expansion, yet existing studies often treat it as a whole, lacking systematic long-term subfield comparisons and structural analyses, thereby limiting understanding of internal differences and evolutionary mechanisms. To address this gap, we employ bibliometric methods, using expert interviews and indicator screening to construct an analytical framework. Twelve bibliometric indicators are selected across three dimensions: Impact and Dissemination, Collaboration Characteristics, and Author Characteristics. We conduct horizontal and longitudinal analyses of five subfields (AI, CV, ML, NLP, Web\&IR) from 2000 to 2024. Using CSRankings classification and a dataset of 106,622 papers, we apply violin plots, chord diagrams, and sankey diagrams to characterize structural features and evolutionary paths. Results show that these subfields have entered high-intensity knowledge diffusion: academic impact increased, knowledge dissemination accelerated, external disciplinary reliance grown, and knowledge production shifted from closed accumulation to open, interdisciplinary, multi-actor networks. On this basis, subfields exhibit significant structural differentiation: CV leads in academic impact with a task-oriented trajectory; ML shows shrinking industry collaboration but concentrated international collaboration with a relatively dispersed structure; Web\&IR is strongly industry-driven with a stable collaboration network; AI shows continuous growth; NLP remains relatively stable. Overall, this study reveals artificial intelligence evolving from unified diffusion to structural differentiation, constructs an extensible multidimensional framework, and provides a quantitative approach for understanding complex technological field evolution.

cs.DL↗

arXiv2026

Bifrost: A Much Simpler Secure Two-Party Data Join Protocol for Secure Data Analytics

Secure data join enables two parties with vertically distributed data to securely compute the joined table, allowing the parties to perform downstream Secure multi-party computation-based Data Analytics (SDA), such as training machine learning models, based on the joined table. While Circuit-based Private Set Intersection (CPSI) can be used for secure data join, it introduces redundant dummy rows in the joined table, which results in high overhead in the downstream SDA tasks. iPrivJoin addresses this issue but introduces significant communication overhead in the redundancy removal process, as it relies on the cryptographic primitive OPPRF for data encoding and multiple rounds of oblivious shuffles. In this paper, we propose a much simpler secure data join protocol, Bifrost, which outputs (the secret shares of) a redundancy-free joined table. The highlight of Bifrost lies in its simplicity: it builds upon two conceptually simple building blocks, an ECDH-PSI protocol and a two-party oblivious shuffle protocol. The lightweight protocol design allows Bifrost to avoid the need for OPPRF. We also proposed a simple optimization named \textit{dual mapping} that reduces the rounds of oblivious shuffle needed from two to one. Experiments on datasets of up to 100 GB show that Bifrost achieves $2.54 \sim 22.32\times$ speedup and reduces the communication by $84.15\% \sim 88.97\%$ compared to the SOTA redundancy-free secure data join protocol iPrivJoin. Notably, the communication size of Bifrost is nearly equal to the size of the input data. In the two-step SDA pipeline evaluation (secure join and SDA), the redundancy-free property of Bifrost not only avoids the catastrophic error rate blowup in the downstream tasks caused by the dummy rows in the joined table (as introduced in CPSI), but also shows up to $2.80\times$ speed-up in the SDA process with up to $73.15\%$ communication reduction.

cs.CR↗

arXiv2025

IDCloak: A Practical Secure Multi-party Dataset Join Framework for Vertical Privacy-preserving Machine Learning

Vertical privacy-preserving machine learning (vPPML) enables multiple parties to train models on their vertically distributed datasets while keeping datasets private. In vPPML, it is critical to perform the secure dataset join, which aligns features corresponding to intersection IDs across datasets and forms a secret-shared and joint training dataset. However, existing methods for this step could be impractical due to: (1) they are insecure when they expose intersection IDs; or (2) they rely on a strong trust assumption requiring a non-colluding auxiliary server; or (3) they are limited to the two-party setting. This paper proposes IDCloak, the first practical secure multi-party dataset join framework for vPPML that keeps IDs private without a non-colluding auxiliary server. IDCloak consists of two protocols: (1) a circuit-based multi-party private set intersection protocol (cmPSI), which obtains secret-shared flags indicating intersection IDs via an optimized communication structure combining OKVS and OPRF; (2) a secure multi-party feature alignment protocol, which obtains the secret-shared and joint dataset using secret-shared flags, via our proposed efficient secure shuffle protocol. Experiments show that: (1) compared to the state-of-the-art secure two-party dataset join framework (iPrivjoin), IDCloak demonstrates higher efficiency in the two-party setting and comparable performance when the party number increases; (2) compared to the state-of-the-art cmPSI protocol under honest majority, our proposed cmPSI protocol provides a stronger security guarantee (dishonest majority) while improving efficiency by up to $7.78\times$ in time and $8.73\times$ in communication sizes; (3) our proposed secure shuffle protocol outperforms the state-of-the-art shuffle protocol by up to $138.34\times$ in time and $132.13\times$ in communication sizes.

cs.CR↗

arXiv2024

Acoustic Blackbody Absorption: Transcending Causality Limits through Instability-Induced Softness

By coupling unstable components, we demonstrate a novel approach that reduces static modulus to zero, eliminating causality-imposed absorption limitations in acoustics. Our heuristic model simulations achieve ultra-broadband absorption over 99% for wavelengths greater than 132 times the absorber thickness. Theoretical analysis further proves this strategy can approach ideal blackbody behavior with infinitesimal thickness. These findings suggest fundamental physical laws no longer prevent true blackbody absorption realization; the only remaining obstacle is the material limitations.

cond-mat.mtrl-sci↗

arXiv2024

GenoTEX: An LLM Agent Benchmark for Automated Gene Expression Data Analysis

Recent advancements in machine learning have significantly improved the identification of disease-associated genes from gene expression datasets. However, these processes often require extensive expertise and manual effort, limiting their scalability. Large Language Model (LLM)-based agents have shown promise in automating these tasks due to their increasing problem-solving abilities. To support the evaluation and development of such methods, we introduce GenoTEX, a benchmark dataset for the automated analysis of gene expression data. GenoTEX provides analysis code and results for solving a wide range of gene-trait association problems, encompassing dataset selection, preprocessing, and statistical analysis, in a pipeline that follows computational genomics standards. The benchmark includes expert-curated annotations from bioinformaticians to ensure accuracy and reliability. To provide baselines for these tasks, we present GenoAgent, a team of LLM-based agents that adopt a multi-step programming workflow with flexible self-correction, to collaboratively analyze gene expression datasets. Our experiments demonstrate the potential of LLM-based methods in analyzing genomic data, while error analysis highlights the challenges and areas for future improvement. We propose GenoTEX as a promising resource for benchmarking and enhancing automated methods for gene expression data analysis. The benchmark is available at https://github.com/Liu-Hy/GenoTEX.

cs.LG↗

arXiv2024

Analytical Modeling of Acoustic Exponential Materials and Physical Mechanism of Broadband Anti-Reflection

Spatially exponential distributions of material properties are ubiquitous in many natural and engineered systems, from the vertical distribution of the atmosphere to acoustic horns and anti-reflective coatings. These media seamlessly interface different impedances, enhancing wave transmission and reducing internal reflections. This work advances traditional transfer matrix theory by integrating analytical solutions for acoustic exponential materials, which possess exponential density and/or bulk modulus, offering a more accurate predictive tool and revealing the physical mechanism of broadband anti-reflection for sound propagation in such non-uniform materials. Leveraging this method, we designed an acoustic dipole array that effectively mimics exponential mass distribution. Through experiments with precisely engineered micro-perforated plates, we demonstrate an ultra-low reflection rate of about 0.86% across a wide frequency range from 420 Hz to 10,000 Hz. Our modified transfer matrix approach underpins the design of exponential materials, and our layering strategy for stacking acoustic dipoles suggests a pathway to more functional gradient acoustic metamaterials.

physics.app-ph↗

arXiv2023

LC-NeRF: Local Controllable Face Generation in Neural Randiance Field

3D face generation has achieved high visual quality and 3D consistency thanks to the development of neural radiance fields (NeRF). Recently, to generate and edit 3D faces with NeRF representation, some methods are proposed and achieve good results in decoupling geometry and texture. The latent codes of these generative models affect the whole face, and hence modifications to these codes cause the entire face to change. However, users usually edit a local region when editing faces and do not want other regions to be affected. Since changes to the latent code affect global generation results, these methods do not allow for fine-grained control of local facial regions. To improve local controllability in NeRF-based face editing, we propose LC-NeRF, which is composed of a Local Region Generators Module and a Spatial-Aware Fusion Module, allowing for local geometry and texture control of local facial regions. Qualitative and quantitative evaluations show that our method provides better local editing than state-of-the-art face editing methods. Our method also performs well in downstream tasks, such as text-driven facial image editing.

cs.CV↗

arXiv2020

Acoustic Metal

Metal reflects electromagnetic waves because of the large conductivity that is responsible for dissipation. During which the waves undergo a 180$^\circ$ phase change that is independent of the frequency. There is no counterpart material for acoustic waves. Here we show that by using an array of acoustic resonators with a designed high-density dissipative component, an "acoustic metal" can be realised that strongly couples with sound over a wide frequency range not otherwise attainable by conventional means. In particular, we show the acoustic Faraday cage effect that when used as a ring covering an air duct, 99% of the noise can be blocked without impeding the airflow. We further delineate the underlying volume requirement for an acoustic metal based on the constraint of the causality principle. Our findings complement the missing properties of acoustic materials and pave the way to the strong wave-material couplings that are critical for the applications as high-performance audio devices.

cond-mat.mtrl-sci↗

arXiv2016

Optimal Sound Absorbing Structures

Causal nature of the acoustic response, for any materials or structures, dictates an inequality that relates the absorption spectrum of the sample to its thickness. We present a general recipe for constructing sound-absorbing structures that can attain near-equality for the causal relation with very high absorption performance; such structures are denoted optimal. Our strategy involves using carefully designed acoustic metamaterials as backing to a thin layer of conventional sound absorbing material, e.g., acoustic sponge. By using this design approach, we have realized a 12 cm-thick structure that exhibits broadband, near-perfect flat absorption spectrum starting at around 400 Hz. From the causal relation, the calculated minimum sample thickness is 11.5 cm for the observed absorption spectrum. We present the theory that underlies such absorption performance, involving the evanescent waves and their interaction with a dissipative medium, and show the excellent agreement with the experiment.

physics.class-ph↗

arXiv2016

High-flux water desalination with interfacial salt sieving effect in nanoporous carbon composite membranes

Nanoporous carbon composite membranes, comprising a layer of porous carbon fiber structures with an average channel width of 30-60 nm grown on a porous ceramic substrate, are found to exhibit robust desalination effect with high freshwater flux. In three different membrane processes of vacuum membrane distillation, reverse osmosis and forward osmosis, the carbon composite membrane showed 100% salt rejection with 3.5 to 20 times higher freshwater flux compared to existing polymeric membranes. Thermal accounting experiments found that at least 80% of the freshwater pass through the carbon composite membrane with no phase change. Molecular dynamics simulations revealed a unique salt rejection mechanism. When seawater is interfaced with either vapor or the surface of carbon, one to three interfacial atomic layers contain no salt ions. Below the liquid entry pressure, the salt solution is stopped at the openings to the porous channels and forms a meniscus, while the surface layer of freshwater can feed the surface diffusion flux that is fast-transported on the surfaces of the carbon fibers, driven by the chemical potential gradient. As the surface-transported water does not involve a phase change, hence that component involves no energy expenditure in the form of latent heat.

cond-mat.mtrl-sci↗

arXiv2013

Coalescence of Pickering emulsion droplets induced by an electric field

Combining high-speed photography with electric current measurement, we investigate the electrocoalescence of Pickering emulsion droplets. Under high enough electric field, the originally-stable droplets coalesce via two distinct approaches: normal coalescence and abnormal coalescence. In the normal coalescence, a liquid bridge grows continuously and merges two droplets together, similar to the classical picture. In the abnormal coalescence, however, the bridge fails to grow indefinitely; instead it breaks up spontaneously due to the geometric constraint from particle shells. Such connecting-then-breaking cycles repeat multiple times, until a stable connection is established. In depth analysis indicates that the defect size in particle shells determines the exact merging behaviors: when the defect size is larger than a critical size around the particle diameter, normal coalescence will show up; while abnormal coalescence will appear for coatings with smaller defects.

cond-mat.soft↗