arXiv subjects

Jianfei Sun

Publications and source records attributed to Jianfei Sun.

11 recordsLinked to original sources

arXiv2026

Trigger the Straggler: Load Hijack on Mixture-of-Experts LLMs

Expert parallelism (EP) is a common strategy for serving large Mixture-of-Experts (MoE) models across multiple GPUs by distributing experts among devices. Router decisions then determine both which experts process each token and which GPUs execute the resulting work. This procedure exposes a supply-chain attack surface in the serving schedule. We introduce Load Hijack, in which a malicious model provider modifies only a checkpoint's router weights, distributes the poisoned checkpoint, and retains a private trigger. When the trigger appears, the poisoned router concentrates token-to-expert assignments on experts co-located on one GPU. The resulting load makes that GPU a straggler and forces peer devices to wait, while routing on ordinary inputs remains near the clean reference. We find this conditional behavior difficult to achieve because an objective that rewards target-expert use on triggered inputs can also bias ordinary-input routing toward the same experts. To resolve this conflict, Load Hijack employs a three-stage optimization procedure that produces strong trigger-dependent concentration while keeping ordinary-input routing close to the clean reference. Across three MoE families and four corpora, Load Hijack directs 92.3% to 95.6% of triggered token assignments to the target experts. In live EP serving, triggered traffic produces 1.43x the time-to-first-token and 0.86x the throughput measured under ordinary traffic. These results show that poisoned routers can act as trigger-controlled device schedulers and motivate checkpoint audits of routing and runtime load.

cs.CR↗

arXiv2026

Unsupervised Deformable Image Registration with Local-Global Attention and Image Decomposition

Deformable image registration is a critical technology in medical image analysis, with broad applications in clinical practice such as disease diagnosis, multi-modal fusion, and surgical navigation. Traditional methods often rely on iterative optimization, which is computationally intensive and lacks generalizability. Recent advances in deep learning have introduced attention-based mechanisms that improve feature alignment, yet accurately registering regions with high anatomical variability remains challenging. In this study, we proposed a novel unsupervised deformable image registration framework, LGANet++, which employs a novel local-global attention mechanism integrated with a unique technique for feature interaction and fusion to enhance registration accuracy, robustness, and generalizability. We evaluated our approach using five publicly available datasets, representing three distinct registration scenarios: cross-patient, cross-time, and cross-modal CT-MR registration. The results demonstrated that our approach consistently outperforms several state-of-the-art registration methods, improving registration accuracy by 1.39% in cross-patient registration, 0.71% in cross-time registration, and 6.12% in cross-modal CT-MR registration tasks. These results underscore the potential of LGANet++ to support clinical workflows requiring reliable and efficient image registration. The source code is available at https://github.com/huangzyong/LGANet-Registration.

eess.IV↗

arXiv2025

VulnRepairEval: An Exploit-Based Evaluation Framework for Assessing Large Language Model Vulnerability Repair Capabilities

The adoption of Large Language Models (LLMs) for automated software vulnerability patching has shown promising outcomes on carefully curated evaluation sets. Nevertheless, existing datasets predominantly rely on superficial validation methods rather than exploit-based verification, leading to overestimated performance in security-sensitive applications. This paper introduces VulnRepairEval, an evaluation framework anchored in functional Proof-of-Concept (PoC) exploits. Our framework delivers a comprehensive, containerized evaluation pipeline that enables reproducible differential assessment, where repair success requires the original exploit to fail execution against the modified code. The benchmark construction involved extensive data curation: we processed over 400 CVEs and approximately 2,500 potential sources to extract a collection of authentic vulnerability instances (23 Python CVEs) amenable to automated testing with working PoCs. Through VulnRepairEval, we conduct a comprehensive evaluation of 12 popular LLMs and observe a significant performance deficit: even the top-performing model successfully addresses merely 5/23 instances (about 21.7%), exposing critical weaknesses in security-focused applications. Our failure analysis reveals that most unsuccessful attempts stem from imprecise vulnerability identification and patches containing syntactic or semantic errors. Enhanced prompting strategies and multi-agent approaches yield minimal improvements, with overall effectiveness remaining largely unaffected. This work contributes a stringent, practical evaluation framework for LLM-driven vulnerability remediation and underscores the necessity for assessment protocols that authentically reflect real-world exploitation scenarios.

cs.SE↗

arXiv2025

Secure Resource Allocation via Constrained Deep Reinforcement Learning

The proliferation of Internet of Things (IoT) devices and the advent of 6G technologies have introduced computationally intensive tasks that often surpass the processing capabilities of user devices. Efficient and secure resource allocation in serverless multi-cloud edge computing environments is essential for supporting these demands and advancing distributed computing. However, existing solutions frequently struggle with the complexity of multi-cloud infrastructures, robust security integration, and effective application of traditional deep reinforcement learning (DRL) techniques under system constraints. To address these challenges, we present SARMTO, a novel framework that integrates an action-constrained DRL model. SARMTO dynamically balances resource allocation, task offloading, security, and performance by utilizing a Markov decision process formulation, an adaptive security mechanism, and sophisticated optimization techniques. Extensive simulations across varying scenarios, including different task loads, data sizes, and MEC capacities, show that SARMTO consistently outperforms five baseline approaches, achieving up to a 40% reduction in system costs and a 41.5% improvement in energy efficiency over state-of-the-art methods. These enhancements highlight SARMTO's potential to revolutionize resource management in intricate distributed computing environments, opening the door to more efficient and secure IoT and edge computing applications.

cs.LG↗

arXiv2024

An Efficient Privacy-aware Split Learning Framework for Satellite Communications

In the rapidly evolving domain of satellite communications, integrating advanced machine learning techniques, particularly split learning, is crucial for enhancing data processing and model training efficiency across satellites, space stations, and ground stations. Traditional ML approaches often face significant challenges within satellite networks due to constraints such as limited bandwidth and computational resources. To address this gap, we propose a novel framework for more efficient SL in satellite communications. Our approach, Dynamic Topology Informed Pruning, namely DTIP, combines differential privacy with graph and model pruning to optimize graph neural networks for distributed learning. DTIP strategically applies differential privacy to raw graph data and prunes GNNs, thereby optimizing both model size and communication load across network tiers. Extensive experiments across diverse datasets demonstrate DTIP's efficacy in enhancing privacy, accuracy, and computational efficiency. Specifically, on Amazon2M dataset, DTIP maintains an accuracy of 0.82 while achieving a 50% reduction in floating-point operations per second. Similarly, on ArXiv dataset, DTIP achieves an accuracy of 0.85 under comparable conditions. Our framework not only significantly improves the operational efficiency of satellite communications but also establishes a new benchmark in privacy-aware distributed learning, potentially revolutionizing data handling in space-based networks.

cs.LG↗

arXiv2023

Giant magnetocaloric effect and hysteresis loss in Mn$_x$Fe$_{2-x}$P$_{0.5}$Si$_{0.5}$ ($x$ = 0.7-1.2) microwires at ambient temperatures

Magnetocaloric microwires are very promising for energy-efficient magnetic refrigeration in micro electromechanical systems (MEMS) and nano electromechanical systems (NEMS). Creating microwires that exhibit large magnetocaloric effects around room temperature represents an important but challenging task. Here, we report a tunable giant magnetocaloric effect around room temperature in Mn$_x$Fe$_{2-x}$P$_{0.5}$Si$_{0.5}$ ($x$ = 0.7-1.2) microwires by utilizing a melt-extraction technique paired with thermal treatment and chemical engineering. The isothermal magnetic entropy change DeltaSiso and Curie temperature (TC) can be tuned by adjusting the Mn/Fe ratio. The TC varies from 351 to 190 K as x increases from 0.8 to 1.2. Among the compositions investigated, the x = 0.9 sample shows the largest value of DeltaSiso = 18.3 J kg$^{-1}$ K$^{-1}$ for a field change of 5 T around 300 K. After subtracting magnetic hysteresis loss, a large refrigerant capacity of ~284.6 J kg$^{-1}$ is achieved. Our study paves a new pathway for the design of novel magnetocaloric microwires for active magnetic refrigeration at ambient temperatures.

physics.app-ph↗

arXiv2023

Advanced magnetocaloric microwires: What does the future hold?

Magnetic refrigeration (MR) based on the magnetocaloric effect (MCE) is a promising alternative to conventional vapor compression refrigeration techniques. The cooling efficiency of a magnetic refrigerator depends on its refrigeration capacity and operation frequency. Existing refrigerators possess limited cooling efficiency due to the low operating frequency (around tens of Hz). Theory predicts that reducing geometrical effects can increase the operation frequency by reducing the relaxation time of a magnetic material. As compared to other shapes, magnetocaloric wires transfer heat most effectively to a surrounding environment, due to their enhanced surface area. The wire shape also yields a good mechanical response, reducing the relaxation time and consequently increasing the operation frequency of the cooling device. Experiments have validated the theoretical predictions. By assembling microwires with different magnetocaloric properties and Curie temperatures into a laminate structure, a table-like magnetocaloric bed can be created and used as an active cooling device for micro-electro-mechanical system (MEMS) and nano-electro-mechanical system (NEMS). This paper assesses recent progress in the development of magnetocaloric microwires and sheds light on the important factors affecting the magnetocaloric behavior and cooling efficiency in microwire systems. Challenges, opportunities, and strategies regarding the development of advanced magnetocaloric microwires are also discussed.

physics.app-ph↗

arXiv2023

Topological Magnetoresistance of Magnetic Skyrmionic Bubbles

Magnetic skyrmions offer promising prospects for constructing future energy-efficient and high-density information technology, leading to extensive explorations of new skyrmionic materials recently. The topological Hall effect has been widely adopted as a distinctive marker of skyrmion emergence. Alternately, here we propose a novel signature of skyrmion state by quantitatively investigating the magnetoresistance (MR) induced by skyrmionic bubbles in CeMn2Ge2. An intriguing finding was revealed: the anomalous MR measured at different temperatures can be normalized into a single curve, regardless of sample thickness. This behavior can be accurately reproduced by the recent chiral spin textures MR model. Further analysis of the MR anomaly allowed us to quantitatively examine the effective magnetic fields of various scattering channels. Remarkably, the analyses, combined with the Lorentz transmission electronic microscopy results, indicate that the in-plane scattering channel with triplet exchange interactions predominantly governs the magnetotransport in the Bloch-type skyrmionic bubble state. Our results not only provide insights into the quantum correction on MR induced by skyrmionic bubble phase, but also present an electrical probing method for studying chiral spin texture formation, evolution and their topological properties, which opens up exciting possibilities for identifying new skyrmionic materials and advancing the methodology for studying chiral spin textures.

cond-mat.mtrl-sci↗

arXiv2022

Design of Core-Shell Structured Magnetic Microwires with Desirable Properties for Multifunctional Applications

Amorphous Co-rich microwires with excellent soft magnetic and mechanical properties produced by melt-extraction technique are emerging as a multifunctional material for a variety of applications ranging from ultrasensitive magnetic field sensors to structural health self-monitoring composites. There is a pressing need for enhancing these properties to make the microwires practical for integration into new technologies. Conventional heat treatments at temperature below crystallization may improve the magnetic softness of an as-quenched amorphous wire, but usually deteriorate the good mechanical characteristic of the wire due to crystallization. To overcome this, we propose a new approach that utilizes the advantages of a multi-step Joule current annealing method to design novel (nanocrystal, amorphous)/amorphous core/shell structures directly from as-quenched amorphous microwires. These results show that the density and size of nanocrystals in the core can be optimized by controlling the Joule current intensity, resulting in the large enhancement of soft magnetic and giant magneto-impedance properties, while the amorphous shell preserves the excellent mechanical strength of the microwire. This study also provides a new pathway for the design of novel core/shell structures directly from rapidly quenched amorphous magnetic materials that are currently exploited in high frequency transformers, sensing and cooling devices.

cond-mat.mtrl-sci↗

arXiv2021

Magnetic and magnetocaloric properties of melt-extracted Mn1.26Fe0.60P0.48Si0.52 microwires

The polycrystalline Mn1.26Fe0.60P0.48Si0.52 microwires were successfully fabricated for the first time by the melt-extraction technique, and their magnetic and magnetocaloric properties were investigated systematically. The structural analysis shows that the microwires possess a hexagonal phase with Fe2P type, with a homogeneous composition distribution. Magnetometry measurements show that the microwires undergo a weak first-order magnetic phase transition at a temperature of 142 K. The maximum magnetic entropy change of the microwires reaches 4.64 Jkg-1K-1 for a field change of 5 T. These low-cost Mn1.26Fe0.60P0.48Si0.52 microwires are promising for active magnetic refrigeration in the liquid nitrogen temperature range.

physics.app-ph↗

arXiv2021

A perspective magnetic bed comprising Gd alloy multi-microwires for energy-efficient magnetic refrigeration

We have designed a new magnetic bed structure with desirable table-like magnetocaloric effect (MCE) by using three kinds of soft ferromagnetic Gd-Al-Co microwire arrays with different Curie temperatures ($T_C$). The $T_C$ interval of these three wires is ~10 K and the designed new structure named Sample A. This sample shows a smooth table-like magnetic entropy change ($ΔS_M$) at high applied field change ($μ_0 ΔH=5 T$) ranging from ~92 K to ~107 K. The maximum entropy change ($-ΔS_M^{\rm max}$) and refrigerant capacity (RC) for Sample A at $μ_0 ΔH=5 T$ are calculated to be ~9.42 Jkg$^{-1}$K$^{-1}$ and ~676 Jkg$^{-1}$. The calculated curves of $-ΔS_M(T)$ and the corresponding experimental data match well with each other, suggesting that the desirable magnetocaloric properties of the microwire arrays can be designed. Simulation shows that the RC values of the designed systems increase when increasing the interval of $T_C$. The table-like MCE and the enhanced heat-transfer efficiency due to the enhanced surface areas of the microwires make this newly designed magnetic bed very promising for use in energy-efficient magnetic refrigerators.

physics.app-ph↗