arXiv subjects

Junjie Shi

Publications and source records attributed to Junjie Shi.

At least 19 recordsLinked to original sources

arXiv2026

Deductive Verification for Earliest Deadline First Scheduler Implementations

Real-Time Operating Systems (RTOSes) rely on scheduler implementations to provide predictable task execution. For safety-critical systems, it is therefore not sufficient to reason only about the abstract scheduling policy; the concrete implementation must also preserve the intended scheduling semantics. This is particularly challenging for Earliest Deadline First (EDF) scheduling, because EDF introduces dynamic, deadline-derived priorities that are often realized by reusing kernel infrastructure originally designed for fixed-priority scheduling. In this work, we formalize EDF correctness through three essential properties that any implementation of the Earliest Deadline First (EDF) scheduler must satisfy. Based on these properties, we propose a framework utilizing deductive verification, that applies to any EDF-based scheduler realization. We instantiate the framework in Frama-C/ACSL and apply it to three structurally different EDF scheduler realizations: RTEMS 5, RTEMS 6, and an EDF extension of FreeRTOS.

cs.OS↗

arXiv2026

Deep Bootstrap

In this work, we propose a novel deep bootstrap framework for nonparametric regression based on conditional diffusion models. Specifically, we construct a conditional diffusion model to learn the distribution of the response variable given the covariates. This model is then used to generate bootstrap samples by pairing the original covariates with newly synthesized responses. We reformulate nonparametric regression as conditional sample mean estimation, which is implemented directly via the learned conditional diffusion model. Unlike traditional bootstrap methods that decouple the estimation of the conditional distribution, sampling, and nonparametric regression, our approach integrates these components into a unified generative framework. With the expressive capacity of diffusion models, our method facilitates both efficient sampling from high-dimensional or multimodal distributions and accurate nonparametric estimation. We establish rigorous theoretical guarantees for the proposed method. In particular, we derive optimal end-to-end convergence rates in the Wasserstein distance between the learned and target conditional distributions. Building on this foundation, we further establish the convergence guarantees of the resulting bootstrap procedure. Numerical studies demonstrate the effectiveness and scalability of our approach for complex regression tasks.

stat.ML↗

arXiv2026

REprompt: Prompt Generation for Intelligent Software Development Guided by Requirements Engineering

The rapid development of large language models is transforming software development. Beyond serving as code auto-completion tools in integrated development environments, large language models increasingly function as foundation models within coding agents in vibe-coding scenarios. In such settings, prompts play a central role in agent-based intelligent software development, as they not only guide the behavior of large language models but also serve as carriers of user requirements. Under the dominant conversational paradigm, prompts are typically divided into system prompts and user prompts. System prompts provide high-level instructions to steer model behavior and establish conversational context, while user prompts represent inputs and requirements provided by human users. Despite their importance, designing effective prompts remains challenging, as it requires expertise in both prompt engineering and software engineering, particularly requirements engineering. To reduce the burden of manual prompt construction, numerous automated prompt engineering methods have been proposed. However, most existing approaches neglect the methodological principles of requirements engineering, limiting their ability to generate artifacts that conform to formal requirement specifications in realistic software development scenarios. To address this gap, we propose REprompt, a multi-agent prompt optimization framework guided by requirements engineering. Experiment results demonstrate that REprompt effectively optimizes both system and user prompts by grounding prompt generation in requirements engineering principles.

cs.SE↗

arXiv2025

PubSub-VFL: Towards Efficient Two-Party Split Learning in Heterogeneous Environments via Publisher/Subscriber Architecture

With the rapid advancement of the digital economy, data collaboration between organizations has become a well-established business model, driving the growth of various industries. However, privacy concerns make direct data sharing impractical. To address this, Two-Party Split Learning (a.k.a. Vertical Federated Learning (VFL)) has emerged as a promising solution for secure collaborative learning. Despite its advantages, this architecture still suffers from low computational resource utilization and training efficiency. Specifically, its synchronous dependency design increases training latency, while resource and data heterogeneity among participants further hinder efficient computation. To overcome these challenges, we propose PubSub-VFL, a novel VFL paradigm with a Publisher/Subscriber architecture optimized for two-party collaborative learning with high computational efficiency. PubSub-VFL leverages the decoupling capabilities of the Pub/Sub architecture and the data parallelism of the parameter server architecture to design a hierarchical asynchronous mechanism, reducing training latency and improving system efficiency. Additionally, to mitigate the training imbalance caused by resource and data heterogeneity, we formalize an optimization problem based on participants' system profiles, enabling the selection of optimal hyperparameters while preserving privacy. We conduct a theoretical analysis to demonstrate that PubSub-VFL achieves stable convergence and is compatible with security protocols such as differential privacy. Extensive case studies on five benchmark datasets further validate its effectiveness, showing that, compared to state-of-the-art baselines, PubSub-VFL not only accelerates training by $2 \sim 7\times$ without compromising accuracy, but also achieves a computational resource utilization rate of up to 91.07%.

cs.LG↗

arXiv2025

Uniform Diophantine approximation on the plane for $β$-dynamical systems

In this paper, we investigate the two-dimensional uniform Diophantine approximation in $β$-dynamical systems. Let $β_i > 1(i=1,2)$ be real numbers, and let $T_{β_i}$ denote the $β_i$-transformation defined on $[0, 1]$. For each $(x, y) \in[0,1]^2$, we define the asymptotic approximation exponent $$ v_{β_1, β_2}(x, y)=\sup \left\{0 \leq v<\infty: \begin{array}{l} T_{β_1}^n x<β_1^{-n v} \\ T_{β_2}^n y<β_2^{-n v} \end{array} \text { for infinitely many } n \in \mathbb{N}\right\} \text {, } $$ and the uniform approximation exponent $$ \hat{v}_{β_1, β_2}(x, y)=\sup \left\{0 \leq \hat{v}<\infty: \forall~ N \gg 1, \exists 1 \leq n \leq N \text { such that } \begin{array}{l} T_{β_1}^n x < β_1^{-N \hat{v}} \\ T_{β_2}^n y < β_2^{-N \hat{v}} \end{array}\right\} . $$ We calculate the Hausdorff dimension of the intersection $$\left\{(x, y) \in[0,1]^2: \hat{v}_{β_1, β_2}(x, y)=\hat{v} \text { and } v_{β_1, β_2}(x, y)=v\right\}$$ for any $\hat{v}$ and $v$ satisfying $\log _{β_2}{β_1}>\frac{\hat{v}}{v}(1+v)$. As a corollary, we establish a definite formula for the Hausdorff dimension of the level set of the uniform approximation exponent.

math.DS↗

arXiv2025

Rethinking Testing for LLM Applications: Characteristics, Challenges, and a Lightweight Interaction Protocol

Applications of Large Language Models~(LLMs) have evolved from simple text generators into complex software systems that integrate retrieval augmentation, tool invocation, and multi-turn interactions. Their inherent non-determinism, dynamism, and context dependence pose fundamental challenges for quality assurance. This paper decomposes LLM applications into a three-layer architecture: \textbf{\textit{System Shell Layer}}, \textbf{\textit{Prompt Orchestration Layer}}, and \textbf{\textit{LLM Inference Core}}. We then assess the applicability of traditional software testing methods in each layer: directly applicable at the shell layer, requiring semantic reinterpretation at the orchestration layer, and necessitating paradigm shifts at the inference core. A comparative analysis of Testing AI methods from the software engineering community and safety analysis techniques from the AI community reveals structural disconnects in testing unit abstraction, evaluation metrics, and lifecycle management. We identify four fundamental differences that underlie 6 core challenges. To address these, we propose four types of collaborative strategies (\emph{Retain}, \emph{Translate}, \emph{Integrate}, and \emph{Runtime}) and explore a closed-loop, trustworthy quality assurance framework that combines pre-deployment validation with runtime monitoring. Based on these strategies, we offer practical guidance and a protocol proposal to support the standardization and tooling of LLM application testing. We propose a protocol \textbf{\textit{Agent Interaction Communication Language}} (AICL) that is used to communicate between AI agents. AICL has the test-oriented features and is easily integrated in the current agent framework.

cs.SE↗

arXiv2025

Importance Sampling is All You Need: Predict LLM's performance on new benchmark by reusing existing benchmark

With the rapid advancement of large language models , code generation has become a key benchmark for evaluating LLM capabilities. However, existing benchmarks face two major challenges: (1) the escalating cost of constructing high-quality test suites and reference solutions, and (2) the increasing risk of data contamination, which undermines the reliability of benchmark-based evaluations. In this paper, we propose BIS, a prompt-centric evaluation framework that enables ground-truth-free prediction of LLM performance on code generation tasks. Rather than executing generated code, BIS estimates performance metrics by analyzing the prompt distribution alone. Built on importance sampling theory and implemented using Importance Weighted Autoencoders, our method reweights samples from existing annotated benchmarks to estimate performance on new, unseen benchmarks. To stabilize the estimation, we introduce weight truncation strategies and compute marginal expectations across the fitted distributions. BIS serves as a complementary tool that supports benchmark development and validation under constrained resources, offering actionable and quick feedback for prompt selection and contamination assessment. We conduct extensive experiments involving 8,000 evaluation points across 4 CodeLlama models and 9 diverse benchmarks. Our framework achieves an average absolute prediction error of 1.1% for code correctness scores, with best- and worst-case errors of 0.3% and 1.9%, respectively. It also generalizes well to other metrics, attaining average absolute errors of 2.15% for pass@1. These results demonstrate the reliability and broad applicability of BIS, which can significantly reduce the cost and effort of benchmarking LLMs in code-related tasks.

cs.AI↗

arXiv2024

Spin-layer coupling in altermagnets multilayer: a design principle for spintronics

The discovery of collinear symmetric-compensated altermagnets (AM) with intrinsic spin splitting provides a route towards energy-efficient and ultrafast device applications. Here, using first-principles calculations and symmetry analysis, we propose a series of AM Cr2SX (X=O, S, Se) monolayer and explore the spin splitting in Cr2SX multilayer. A general design principle for realizing the spin-layer coupling in odd/even-layer is mapped out based on the comprehensive analysis of spin group symmetry. The spin splitting behavior related with the MzUt, Mz and ML symmetries in AM multilayer can be significantly modulated by magnetic orders, crystal symmetry and external perpendicular gate field (Ez). Due to the spin-compensated bands of sublayers linked by overall Mz and interlayers ML symmetries, the Cr2S2 odd-layer exhibits the unique coexistence of spin splitting and spin degeneracy at high symmetric paths and X/Y valley, respectively. Furthermore, owing to the higher priority of overall ML symmetry compared to interlayers ML symmetry in AM even-layer, the spin-layer coupling of AM multilayer shows strong odd/even-layer dependence. Our work not only offer a new direction for manipulating spin splitting, but also greatly enrich the research on AM monolayer and multilayer.

physics.app-ph↗

arXiv2024

Piezoelectric Manipulation and Engineering for Layertronics in Two-Dimensional Materials

The electronic transport characteristics of two-dimensional (2D) systems have widespread application prospects in the fabrication of multifunctional nanodevices. However, the current research for basic transport phenomena, such as anomalous valley Hall effect (AVHE) and piezoelectric response, is limited to discrete discussion. Here, we theoretically propose a valley-piezoelectricity coupling strategy beyond the existing paradigm to realize AVHE and layer Hall effect (LHE) in ferrovalley (FV) systems, and its essential principle can be extended to general valleytronic materials. Through first-principles calculations, we demonstrate that the large polarized electric field of 2.8*106 (1.67*107) V/m can be induced by 0.1% uniaxial strain in FV 2H-LaHF (1T-LaHF) monolayers. In addition, the microscopic mechanism of interlayer antiferromagnetic (AFM) state of 2H-LaHF bilayer is uncovered by the spin Hamiltonian and super-superexchange (SSE) interaction. Our findings pave the way for new explorations of valley Hall-related effect involving piezoelectricity.

cond-mat.mes-hall↗

arXiv2024

Possible way to achieve anomalous valley Hall effect by tunable intrinsic piezoelectric polarization in FeO$_2$SiGeN$_2$ monolayer

Valley-related multiple Hall effect and piezoelectric response are novel transport characteristics in low-dimensional system, however few studies have reported their coexistence in a single system as well as their coupling relationships. By first-principles calculations, we propose a multifunctional Janus semiconductor, i.e. FeO$_2$SiGeN$_2$ monolayer with large valley polarization of about 120 meV and in-plane piezoelectric polarization with d11 of -0.714.03 pm/V. The magnetic anisotropy energy can be significantly regulated by electronic correlation strength and strain, which can be attributed to the change of competition relationship about Fe-3d-resolved magnetic anisotropy energy brought about by external regulatory means. Electronic correlation strength can induce phase transitions in Janus FeO$_2$SiGeN$_2$ monolayer from ferrovalley to quantum anomalous Hall phase, while the half-valley metallic state as the boundary of the phase transition can gererate 100% spin- and valley polarization. The related phase transition mechanism is analyzed based on the two-band strained kp model. The presence of piezoelectric strain coefficients d11 in valleytronic material makes the coupling between charge degrees of freedom and valley degrees of freedom possible, and the intrinsic electric field caused by the in-plane piezoelectric response provide the way to realize piezoelectric anomalous valley Hall effect. This work may pave a way to find a new member of materials with valley-related multiple Hall effect and stimulate further experimental works related to valleytronics and piezotronics.

cond-mat.mes-hall↗

arXiv2024

TCDformer-based Momentum Transfer Model for Long-term Sports Prediction

Accurate sports prediction is a crucial skill for professional coaches, which can assist in developing effective training strategies and scientific competition tactics. Traditional methods often use complex mathematical statistical techniques to boost predictability, but this often is limited by dataset scale and has difficulty handling long-term predictions with variable distributions, notably underperforming when predicting point-set-game multi-level matches. To deal with this challenge, this paper proposes TM2, a TCDformer-based Momentum Transfer Model for long-term sports prediction, which encompasses a momentum encoding module and a prediction module based on momentum transfer. TM2 initially encodes momentum in large-scale unstructured time series using the local linear scaling approximation (LLSA) module. Then it decomposes the reconstructed time series with momentum transfer into trend and seasonal components. The final prediction results are derived from the additive combination of a multilayer perceptron (MLP) for predicting trend components and wavelet attention mechanisms for seasonal components. Comprehensive experimental results show that on the 2023 Wimbledon men's tournament datasets, TM2 significantly surpasses existing sports prediction models in terms of performance, reducing MSE by 61.64% and MAE by 63.64%.

cs.LG↗

arXiv2024

Eliminating Timing Anomalies in Scheduling Periodic Segmented Self-Suspending Tasks with Release Jitter

Ensuring timing guarantees for every individual tasks is critical in real-time systems. Even for periodic tasks, providing timing guarantees for tasks with segmented self-suspending behavior is challenging due to timing anomalies, i.e., the reduction of execution or suspension time of some jobs increases the response time of another job. The release jitter of tasks can add further complexity to the situation, affecting the predictability and timing guarantees of real-time systems. The existing worst-case response time analyses for sporadic self-suspending tasks are only over-approximations and lead to overly pessimistic results. In this work, we address timing anomalies without compromising the worst-case response time (WCRT) analysis when scheduling periodic segmented self-suspending tasks with release jitter. We propose two treatments: segment release time enforcement and segment priority modification, and prove their effectiveness in eliminating timing anomalies. Our evaluation demonstrates that the proposed treatments achieve higher acceptance ratios in terms of schedulability compared to state-of-the-art scheduling algorithms. Additionally, we implement the segment-level fixed-priority scheduling mechanism on RTEMS and verify the validity of our segment priority modification treatment. This work expands our previous conference publication at the 29th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS 2023), which considers only periodic segmented self-suspending tasks without release jitter.

cs.DC↗

arXiv2024

PASSION: Towards Effective Incomplete Multi-Modal Medical Image Segmentation with Imbalanced Missing Rates

Incomplete multi-modal image segmentation is a fundamental task in medical imaging to refine deployment efficiency when only partial modalities are available. However, the common practice that complete-modality data is visible during model training is far from realistic, as modalities can have imbalanced missing rates in clinical scenarios. In this paper, we, for the first time, formulate such a challenging setting and propose Preference-Aware Self-diStillatION (PASSION) for incomplete multi-modal medical image segmentation under imbalanced missing rates. Specifically, we first construct pixel-wise and semantic-wise self-distillation to balance the optimization objective of each modality. Then, we define relative preference to evaluate the dominance of each modality during training, based on which to design task-wise and gradient-wise regularization to balance the convergence rates of different modalities. Experimental results on two publicly available multi-modal datasets demonstrate the superiority of PASSION against existing approaches for modality balancing. More importantly, PASSION is validated to work as a plug-and-play module for consistent performance improvement across different backbones. Code is available at https://github.com/Jun-Jie-Shi/PASSION.

cs.CV↗

arXiv2024

FedMLP: Federated Multi-Label Medical Image Classification under Task Heterogeneity

Cross-silo federated learning (FL) enables decentralized organizations to collaboratively train models while preserving data privacy and has made significant progress in medical image classification. One common assumption is task homogeneity where each client has access to all classes during training. However, in clinical practice, given a multi-label classification task, constrained by the level of medical knowledge and the prevalence of diseases, each institution may diagnose only partial categories, resulting in task heterogeneity. How to pursue effective multi-label medical image classification under task heterogeneity is under-explored. In this paper, we first formulate such a realistic label missing setting in the multi-label FL domain and propose a two-stage method FedMLP to combat class missing from two aspects: pseudo label tagging and global knowledge learning. The former utilizes a warmed-up model to generate class prototypes and select samples with high confidence to supplement missing labels, while the latter uses a global model as a teacher for consistency regularization to prevent forgetting missing class knowledge. Experiments on two publicly-available medical datasets validate the superiority of FedMLP against the state-of-the-art both federated semi-supervised and noisy label learning approaches under task heterogeneity. Code is available at https://github.com/szbonaldo/FedMLP.

cs.LG↗

arXiv2023

An exponentially shrinking problem

The Jarník-Besicovitch theorem is a fundamental result in metric number theory which gives the Hausdorff dimension for limsup sets. We investigate a related problem of estimating the Hausdorff dimension of a liminf set. Let $h>0, τ\geq 1$, and for any $j\geq 1$ define the integer sequence $q_{j+1}=q_j^h$. We prove the Hausdorff dimension of the set $$Λ^\bftheta_d(τ)=\left\{\xx\in[0, 1)^d: \|q_jx_i-θ_i\|<q_j^{-τ} \ \text{for all } j\geq 1, i=1,2,\cdots,d\right\},$$ where $\left\|\star\right\|$ denotes the distance to the nearest integer and $\bftheta\in [0, 1)^d$ is fixed. We also give some heuristics for the Hausdorff dimension of the corresponding multiplicative set $$\MM_d^\bftheta(τ)=\left\{\xx\in[0, 1)^d:\prod_{i=1}^d \|q_jx_i-θ_i\|<q_j^{-τ} \ \text{for all } j\geq 1\right\}.$$

math.NT↗

arXiv2023

Improving Semiconductor Device Modeling for Electronic Design Automation by Machine Learning Techniques

The semiconductors industry benefits greatly from the integration of Machine Learning (ML)-based techniques in Technology Computer-Aided Design (TCAD) methods. The performance of ML models however relies heavily on the quality and quantity of training datasets. They can be particularly difficult to obtain in the semiconductor industry due to the complexity and expense of the device fabrication. In this paper, we propose a self-augmentation strategy for improving ML-based device modeling using variational autoencoder-based techniques. These techniques require a small number of experimental data points and does not rely on TCAD tools. To demonstrate the effectiveness of our approach, we apply it to a deep neural network-based prediction task for the Ohmic resistance value in Gallium Nitride devices. A 70% reduction in mean absolute error when predicting experimental results is achieved. The inherent flexibility of our approach allows easy adaptation to various tasks, thus making it highly relevant to many applications of the semiconductor industry.

cs.LG↗

arXiv2022

Binaural Rendering of Ambisonic Signals by Neural Networks

Binaural rendering of ambisonic signals is of broad interest to virtual reality and immersive media. Conventional methods often require manually measured Head-Related Transfer Functions (HRTFs). To address this issue, we collect a paired ambisonic-binaural dataset and propose a deep learning framework in an end-to-end manner. Experimental results show that neural networks outperform the conventional method in objective metrics and achieve comparable subjective metrics. To validate the proposed framework, we experimentally explore different settings of the input features, model structures, output features, and loss functions. Our proposed system achieves an SDR of 7.32 and MOSs of 3.83, 3.58, 3.87, 3.58 in quality, timbre, localization, and immersion dimensions.

cs.SD↗

arXiv2022

Neural Sound Field Decomposition with Super-resolution of Sound Direction

Sound field decomposition predicts waveforms in arbitrary directions using signals from a limited number of microphones as inputs. Sound field decomposition is fundamental to downstream tasks, including source localization, source separation, and spatial audio reproduction. Conventional sound field decomposition methods such as Ambisonics have limited spatial decomposition resolution. This paper proposes a learning-based Neural Sound field Decomposition (NeSD) framework to allow sound field decomposition with fine spatial direction resolution, using recordings from microphone capsules of a few microphones at arbitrary positions. The inputs of a NeSD system include microphone signals, microphone positions, and queried directions. The outputs of a NeSD include the waveform and the presence probability of a queried position. We model the NeSD systems respectively with different neural networks, including fully connected, time delay, and recurrent neural networks. We show that the NeSD systems outperform conventional Ambisonics and DOANet methods in sound field decomposition and source localization on speech, music, and sound events datasets. Demos are available at https://www.youtube.com/watch?v=0GIr6doj3BQ.

cs.SD↗