arXiv subjects

Yao Chen

Publications and source records attributed to Yao Chen.

At least 37 records · Page 2Linked to original sources

arXiv2026

A Workflow for Evaluating Regional Treatment Effect Heterogeneity in Multi-Regional Clinical Trials

Multi-regional clinical trials (MRCTs) enable efficient global drug development by assessing treatment effects across regions within a single protocol. While powered for overall efficacy, MRCTs are typically not designed to provide confirmatory evidence on regional differences, making an assessment of observed regional heterogeneity largely exploratory and susceptible to sampling variability. Despite this challenge, understanding regional heterogeneity remains important for interpretation and regulatory decision-making. This paper proposes a structured, question-driven framework to guide exploratory assessments of regional heterogeneity in MRCTs. We formulate four key questions to clarify the objectives of such analyses and propose a set of statistical methods to address them. Simulation studies evaluate performance under scenarios with no heterogeneity and heterogeneity driven by observed or unobserved treatment effect modifiers, illustrating how a structured approach can support transparent and cautious interpretation.

stat.AP↗

arXiv2026

XtraMAC: An Efficient MAC Architecture for Mixed-Precision LLM Inference on FPGA

The widespread adoption of mixed-precision quantization in large language models (LLMs) has created demand for hardware that can efficiently perform multiply-accumulate (MAC) operations across mixed datatypes and switch datatypes at runtime. Existing FPGA-based MAC solutions fall short due to limitations in fixed-datatype design, inefficient spatial or temporal resource sharing, and poor support for mixed-precision execution. These limitations collectively lead to under-utilization of DSP resources, limiting achievable parallelism and throughput. In this work, we present XtraMAC, a novel MAC architecture that unifies integer, floating-point, and mixed-precision operations within a single, datatype-adaptive microarchitecture. XtraMAC decomposes all supported MAC formats into a shared integer mantissa product with lightweight sign and exponent handling, enabling dynamic operand packing and efficient DSP resource sharing with constant latency and initiation interval of one across all datatypes. Evaluated on an AMD Xilinx U55c FPGA, XtraMAC achieves 1.4-2.0x higher compute density, reduces per-operation LUT, FF, and DSP consumption by 27-51%, and delivers up to 1.9x greater energy efficiency and 1.2x speedup on representative mixed-precision LLM workloads. The implementation of XtraMAC is open-sourced at https://github.com/Xtra-Computing/XtraMAC.

cs.AR↗

arXiv2026

Spin Correlations in Recirculating Multipass Alkali Cells for Advancing Quantum Magnetometry

Multipass cells enable long optical path lengths in compact volumes and are central to quantum technologies such as atomic magnetometers and optical quantum memories. In optical magnetometry, multipass geometries enhance sensitivity by increasing optical depth, reducing photon shot noise, and enabling quantum non-demolition detection. However, in conventional cylindrical multipass cells, Lissajous beam trajectories lead to repeated revisiting and incomplete mirror coverage, limiting effective volume utilization. Here we present a recirculating multipass alkali cell that overcomes these limitations by increasing the active-to-cell volume ratio and minimizing beam spot overlap. We develop an analytical ABCD-matrix model to predict beam trajectories, spot sizes, and astigmatism, validated by Zemax simulations. We further introduce a general analytical framework for spin correlation noise that incorporates astigmatism and spatial intensity distributions. By deriving the spin-noise time-correlation function and spectrum, we show how beam intensity profiles influence spin diffusion noise. Our results demonstrate improved beam coverage, reduced spot overlap, and enhanced spin correlation, particularly for concave mirrors with long focal lengths, while showing that avoiding tightly-focused regions significantly suppresses spin diffusion noise. These findings establish recirculating multipass cells as a practical, high-performance platform for precision atomic sensing and other multipass-cavity-based quantum devices.

quant-ph↗

arXiv2026

Data-Constrained Modeling of Electron Transport and Asymmetric Precipitation in the 2011 August 4 Solar Flare

Energetic electrons accelerated at coronal reconnection sites during solar flares precipitate into the lower solar atmosphere, generating nonthermal emissions and regulating energy deposition. However, how their transport and precipitation are jointly governed by the three-dimensional (3D) magnetic topology, turbulent scattering, and Coulomb collisions remains unclear. Here, we aim to disentangle these physical processes by using a data-constrained 3D particle transport model for the 2011 August 4 flare. The simulated distribution of precipitated electrons aligns closely with photospheric quasi-separatrix layers and reproduces the observed two-ribbon morphology in 1700~Å. We reveal a strong polarity asymmetry, with the 10~s precipitation fraction about six times higher in the weak positive polarity. This arises primarily from distinct mirror ratios of different polarities under the 3D magnetic configuration and can be understood via a modified escape probability for an asymmetric magnetic bottle. Varying strengths of turbulent scattering lead to a rise-then-fall trend and a pronounced energy dependence in the precipitation fraction. Coulomb collisions globally suppress precipitation, especially at low energies, and further amplify the polarity asymmetry. This integrated modeling framework bridges detailed transport physics to observable flare emissions and advances the development of quantitative models for realistic solar flare events.

astro-ph.SR↗

arXiv2026

Coronal Diagnostics Via Modelling Periodic-Beaded Stripes of Solar Radio Bursts

Using high-resolution data from the Chashan Broadband Solar radio spectrometer at meter wavelengths (CBSm) of the Chinese Meridian Project-Phase II (CMP-II), Li et al. (2025) identified a novel fine spectral structure of solar radio bursts, termed periodic beaded stripes, and proposed a generation mechanism. Here we report additional events and develop a quantitative method to determine the physical conditions in the emission region. Periodic stripes tend to occur in the post-phase of flares and are associated with complex magnetic configurations. They repeat on sub-second timescales and show $\sim$0.1 s bead-like modulations, often accompanied by low-frequency absorptions. Modeling the chained stripes with linear kinetic theory of the double plasma resonance (DPR) instability constrains the source-region magnetic field to 0.2-1.7 G and the plasma density to (1-7) $\times 10^8$ cm $^{-3}$. The former follows the drift of individual stripes, and the latter tracks the overall trend. This study summarizes the key properties of periodic beaded stripes and establishes a quantitative DPR-based framework for coronal diagnostics.

astro-ph.SR↗

arXiv2026

ATTNPO: Attention-Guided Process Supervision for Efficient Reasoning

Large reasoning models trained with reinforcement learning and verifiable rewards (RLVR) achieve strong performance on complex reasoning tasks, yet often overthink, generating redundant reasoning without performance gains. Existing trajectory-level length penalties often fail to effectively shorten reasoning length and degrade accuracy, as they uniformly treat all reasoning steps and lack fine-grained signals to distinguish redundancy from necessity. Meanwhile, process-supervised methods are typically resource-intensive and suffer from inaccurate credit assignment. To address these issues, we propose ATTNPO, a low-overhead process-supervised RL framework that leverages the model's intrinsic attention signals for step-level credit assignment. We first identify a set of special attention heads that naturally focus on essential steps while suppressing redundant ones. By leveraging the attention scores of these heads, We then employ two sub-strategies to mitigate overthinking by discouraging redundant steps while preserving accuracy by reducing penalties on essential steps. Experimental results show that ATTNPO substantially reduces reasoning length while significantly improving performance across 9 benchmarks.

cs.CL↗

arXiv2026

Improving Reasoning Capabilities in Small Models through Mixture-of-Layers Distillation with Stepwise Attention on Key Information

The significant computational demands of large language models have increased interest in distilling reasoning abilities into smaller models via Chain-of-Thought (CoT) distillation. Current CoT distillation methods mainly focus on transferring teacher-generated rationales for complex reasoning to student models. However, they do not adequately explore teachers' dynamic attention toward critical information during reasoning. We find that language models exhibit progressive attention shifts towards key information during reasoning, which implies essential clues for drawing conclusions. Building on this observation and analysis, we introduce a novel CoT distillation framework that transfers the teacher's stepwise attention on key information to the student model. This establishes structured guidance for the student's progressive concentration on key information during reasoning. More importantly, we develop a Mixture of Layers module enabling dynamic alignment that adapts to different layers between the teacher and student. Our method achieves consistent performance improvements across multiple mathematical and commonsense reasoning datasets. To our knowledge, it is the first method to leverage stepwise attention within CoT distillation to improve small model reasoning.

cs.CL↗

arXiv2026

Sparse Growing Transformer: Training-Time Sparse Depth Allocation via Progressive Attention Looping

Existing approaches to increasing the effective depth of Transformers predominantly rely on parameter reuse, extending computation through recursive execution. Under this paradigm, the network structure remains static along the training timeline, and additional computational depth is uniformly assigned to entire blocks at the parameter level. This rigidity across training time and parameter space leads to substantial computational redundancy during training. In contrast, we argue that depth allocation during training should not be a static preset, but rather a progressively growing structural process. Our systematic analysis reveals a deep-to-shallow maturation trajectory across layers, where high-entropy attention heads play a crucial role in semantic integration. Motivated by this observation, we introduce the Sparse Growing Transformer (SGT). SGT is a training-time sparse depth allocation framework that progressively extends recurrence from deeper to shallower layers via targeted attention looping on informative heads. This mechanism induces structural sparsity by selectively increasing depth only for a small subset of parameters as training evolves. Extensive experiments across multiple parameter scales demonstrate that SGT consistently outperforms training-time static block-level looping baselines under comparable settings, while reducing the additional training FLOPs overhead from approximately 16--20% to only 1--3% relative to a standard Transformer backbone.

cs.CL↗

arXiv2026

CLEAR: Unlocking Generative Potential for Degraded Image Understanding in Unified Multimodal Models

Image degradation from blur, noise, compression, and poor illumination severely undermines multimodal understanding in real-world settings. Unified multimodal models that combine understanding and generation within a single architecture are a natural fit for this challenge, as their generative pathway can model the fine-grained visual structure that degradation destroys. Yet these models fail to leverage their own generative capacity on degraded inputs. We trace this disconnect to two compounding factors: existing training regimes never ask the model to invoke generation during reasoning, and the standard decode-reencode pathway does not support effective joint optimization. We present CLEAR, a framework that connects the two capabilities through three progressive steps: (1) supervised fine-tuning on a degradation-aware dataset to establish the generate-then-answer reasoning pattern; (2) a Latent Representation Bridge that replaces the decode-reencode detour with a direct, optimizable connection between generation and reasoning; (3) Interleaved GRPO, a reinforcement learning method that jointly optimizes text reasoning and visual generation under answer-correctness rewards. We construct MMD-Bench, covering three degradation severity levels across six standard multimodal benchmarks. Experiments show that CLEAR substantially improves robustness on degraded inputs while preserving clean-image performance. Our analysis further reveals that removing pixel-level reconstruction supervision leads to intermediate visual states with higher perceptual quality, suggesting that task-driven optimization and visual quality are naturally aligned.

cs.CV↗

arXiv2026

Comparing methods to assess treatment effect heterogeneity in general parametric regression models

This paper reviews and compares methods to assess treatment effect heterogeneity in the context of parametric regression models. These methods include the standard likelihood ratio tests, bootstrap likelihood ratio tests, and Goeman's global test motivated by testing whether the random effect variance is zero. We place particular emphasis on tests based on the score-residual of the treatment effect and explore different variants of tests in this class. All approaches are compared in a simulation study, and the approach based on residual scores is illustrated in a clinical trial with time-to-event outcome comparing treatment versus placebo. Our findings demonstrate that score-residual based methods provide practical, flexible and reliable tools for exploring treatment effect heterogeneity and treatment effect modifiers, and can provide useful guidance for decision making around treatment effect heterogeneity.

stat.AP↗

arXiv2026

Dynamics of a nonlocal epidemic model with a new free boundary condition, part 1: Spreading-vanishing dichotomy

This paper investigates the long-time dynamics of a nonlocal epidemic model with free boundaries, where a pathogen with density $u(t,x)$ and the infected humans with density $v(t,x)$ evolve according to a reaction-diffusion system with nonlocal diffusion over a one dimensional interval $[g(t), h(t)]$, which represents the epidemic region expanding through its boundaries $x=g(t)$ and $x=h(t)$, known as free boundaries. Such a model with free boundary conditions based on those of Cao et al. \cite{fb27} was considered by several works. Inspired by recent works of Feng et al. \cite{fb20} and Long et al. \cite{fb5}, we propose a new free boundary condition, where the expansion rate of the epidemic region, determined by $h'(t)$ and $g'(t)$, is proportional to a linear combination of the outward flux of the pathogen $u$ through the range boundary (as in \cite{fb27}) and the weighted total population of infected individuals $v$ within the region (as in \cite{fb5}). We prove that the system under this new free boundary condition is well-posed, and its long-time dynamical behavior is characterized by a spreading-vanishing dichotomy. Moreover, we obtain sharp criteria for this dichotomy, including a sharp threshold in terms of the initial data $(u_0,v_0)$; and by studying a related eigenvalue problem, we also find a sharp threshold in terms of the diffusion rate, which complements related results in Nguyen and Vo \cite{fb7}. This is Part $1$ of a two part series. In Part $2$, we will determine the spreading speed of the model when spreading occurs, and for some typical classes of kernel functions, we will obtain the precise rates of accelerated spreading.

math.AP↗

arXiv2026

Using Individualized Treatment Effects to Assess Treatment Effect Heterogeneity

Assessing treatment effect heterogeneity (TEH) in clinical trials is crucial, as it provides insights into the variability of treatment responses among patients, influencing important decisions related to drug development. Furthermore, it can lead to personalized medicine by tailoring treatments to individual patient characteristics. This paper introduces novel methodologies for assessing treatment effects using the individual treatment effect as a basis. To estimate this effect, we use a Double Robust (DR) learner to infer a pseudo-outcome that reflects the causal contrast. This pseudo-outcome is then used to perform three objectives: (1) a global test for heterogeneity, (2) ranking covariates based on their influence on effect modification, and (3) providing estimates of the individualized treatment effect. We compare our DR-learner with various alternatives and competing methods in a simulation study, and also use it to assess heterogeneity in a pooled analysis of five Phase III trials in psoriatic arthritis. By integrating these methods with the recently proposed WATCH workflow (Workflow to Assess Treatment Effect Heterogeneity in Drug Development for Clinical Trial Sponsors), we provide a robust framework for analyzing TEH, offering insights that enable more informed decision-making in this challenging area.

stat.AP↗

arXiv2026

Cohesive Group Discovery in Interaction Graphs under Explicit Density Constraints

Discovering cohesive groups is a fundamental primitive in graph-based recommender systems, underpinning tasks such as social recommendation, bundle discovery, and community-aware modeling. In interaction graphs, cohesion is often modeled as the $γ$-quasi-clique, an induced subgraph whose internal edge density meets a user-defined threshold $γ$. This formulation provides explicit control over within-group connectivity while accommodating the sparsity inherent in real-world data. This paper presents EDQC, an effective framework for cohesive group discovery under explicit density constraints. EDQC leverages a lightweight energy diffusion process to rank vertices for localizing promising candidate regions. Guided by this ranking, the framework extracts and refines a candidate subgraph to ensure the output strictly satisfies the target density requirement. Extensive experiments on 75 real-world graphs across varying density thresholds demonstrate that EDQC identifies the largest mean $γ$-quasi-cliques in the vast majority of cases, achieving lower variance than the state-of-the-art methods while maintaining competitive runtime. Furthermore, statistical analysis confirms that EDQC significantly outperforms the baselines, underscoring its robustness and practical utility for cohesive group discovery in graph-based recommender systems.

cs.SI↗

arXiv2026

RidgeWalker: Perfectly Pipelined Graph Random Walks on FPGAs

Graph Random Walks (GRWs) offer efficient approximations of key graph properties and have been widely adopted in many applications. However, GRW workloads are notoriously difficult to accelerate due to their strong data dependencies, irregular memory access patterns, and imbalanced execution behavior. While recent work explores FPGA-based accelerators for GRWs, existing solutions fall far short of hardware potential due to inefficient pipelining and static scheduling. This paper presents RidgeWalker, a high-performance GRW accelerator designed for datacenter FPGAs. The key insight behind RidgeWalker is that the Markov property of GRWs allows decomposition into stateless, fine-grained tasks that can be executed out-of-order without compromising correctness. Building on this, RidgeWalker introduces an asynchronous pipeline architecture with a feedback-driven scheduler grounded in queuing theory, enabling perfect pipelining and adaptive load balancing. We prototype RidgeWalker on datacenter FPGAs and evaluated it across a range of GRW algorithms and real-world graph datasets. Experimental results demonstrate that RidgeWalker achieves an average speedup of 7.0x over state-of-the-art FPGA solutions and 8.1x over GPU solutions, with peak speedups of up to 71.0x and 22.9x, respectively. The source code is publicly available at https://github.com/Xtra-Computing/RidgeWalker.

cs.AR↗

arXiv2025

Towards DM-free search for Fast Radio Bursts with Machine Learning -- I. An implementation on multibeam data

Searching for fleeting radio transients like fast radio bursts (FRBs) with wide-field radio telescopes has become a common challenge in data-intensive science. Conventional algorithms normally cost enormous time to seek candidates by finding the correct dispersion measures, of which the process is so-called dedispersion. Here we present a novel scheme to identify FRB signals from raw data without dedispersion using Machine Learning (ML). Under the data environment for multibeam receivers, we train the EfficientNet model and achieve both exceeding 92% accuracy and precision in FRB recognition. We find that the searching efficiency can be significantly enhanced without the procedure of dedispersion compared with conventional softwares like TransientX and presto. Specifically, the impact of radio frequency interference (RFI) for single-beam and multibeam data has been investigated, and we find ML can naturally mitigate RFI under the multibeam environment. Finally, we validate the trained model on actual data from the current FRB surveys carried out by the Five-hundred-meter Aperture Spherical radio Telescope, which provides considerable potential for real implementation in the future.

astro-ph.IM↗

arXiv2025

Solar jet-induced perturbation propagating through coronal loops and in-loop electron beam transport indicated by type II and type N radio bursts

Solar type II radio bursts are commonly attributed to coronal shocks driven by coronal mass ejections (CMEs). However, some metric type II bursts have occasionally been reported to occur in the absence of a CME and to be associated with weak solar activities. This study aims to identify the driver of the coronal shock in this kind of type II event. We investigate a high-frequency metric type II burst with clear band splitting, observed simultaneously by the Chashan Broadband Solar radio spectrograph and the Nançay Radioheliograph. It is associated with a C3.1-class flare and a small-scale jet, but without a detectable CME in the coronagraphs. The type II burst is preceded by multiple type III bursts, one of which exhibits characteristics of a type N burst. The type II burst source is associated with the jet-induced perturbation front propagating through nearby closed loops at a speed of $\sim$880 km s$^{-1}$, rather than the much slower jet front. This suggests that the disturbance initiated by the jet can convert to a shock wave within low Alfvénic coronal loops, providing the necessary conditions for electron acceleration and subsequent radio emission. Our findings offer new insights into the formation mechanism of high-frequency type II bursts associated with weak flares and jets.

astro-ph.SR↗

arXiv2025

HLStrans: Dataset for C-to-HLS Hardware Code Synthesis

High-Level Synthesis (HLS) enables hardware design from C/C++ kernels but requires extensive transformations, such as restructuring code, inserting pragmas, adapting data types, and repairing non-synthesizable constructs, to achieve efficient FPGA implementations. While large language models (LLMs) show promise in automating these transformations, progress has been limited by the absence of large-scale, well-structured datasets. Existing HLS datasets focus primarily on resource estimation, lack paired C and HLS examples with testbenches, and cover only a narrow set of optimizations. We introduce HLStrans, the first benchmark-scale dataset for LLM-driven C-to-HLS synthesis. HLStrans contains over 124K paired C and HLS programs for real-world applications, with full testbenches and synthesis-based annotations of latency and resource usage. The dataset systematically captures five categories of transformations and is enriched by an automated augmentation pipeline combining LLMs, Monte Carlo Tree Search (MCTS), and Design Space Exploration (DSE). We benchmark state-of-the-art LLMs on HLStrans, demonstrating that retrieval and fine-tuning significantly improve success rates and performance.

cs.AR↗

arXiv2025

Evolution of Cybersecurity Subdisciplines: A Science of Science Study

The science of science is an emerging field that studies the practice of science itself. We present the first study of the cybersecurity discipline from a science of science perspective. We examine the evolution of two comparable interdisciplinary communities in cybersecurity: the Symposium on Usable Privacy and Security (SOUPS) and Financial Cryptography and Data Security (FC).

cs.CR↗