arXiv subjects

Yunfeng Lu

Publications and source records attributed to Yunfeng Lu.

13 recordsLinked to original sources

arXiv2026

A unified quantum electrical platform for synchronous metrological realization of volt, ohm and ampere

A co-located integration quantum electrical standard is essential to reduce reliance on distributed traceability in high-accuracy metrology, especially for portable and on-site use. Metrologically, realizing any two of voltage, resistance, and current is sufficient, as the third follows from Ohm's law. The combination of Josephson voltage and quantum Hall resistance offers better uncertainty, but conflicts with the tesla-level field for quantum Hall and near-zero field for Josephson operation. Here we report a compact unified platform enabling co-realization of quantum voltage and resistance in a single cryostat near 4 K, with quantum current derived via Ohm's law. A hierarchical magnetic shielding with staged attenuation and spatial confinement allows 6 T and below 50 nT to coexist within 270 mm axial separation with negligible cross-coupling. In integrated operation, the Josephson and quantum Hall subsystems agree with expected quantized values within relative standard uncertainties of 2.6E-9 and 1.4E-8, respectively. Linking them via an improved cryogenic current comparator realizes a 50 {\mu}A quantum current with relative uncertainty of 6.6E-8. These results demonstrate that three basic electrical units can be synchronously realized with superior metrological consistency on a single integrated platform, offering a viable transition from distributed calibration chains toward compact-integrated quantum-based realization.

physics.ins-det↗

arXiv2026

TopFeaRe: Locating Critical State of Adversarial Resilience for Graphs Regarding Topology-Feature Entanglement

Graph adversarial attacks are usually produced from the two perspectives of topology/structure and node feature, both of them represent the paramount characteristics learned by today's deep learning models. Although some defense countermeasures are proposed at present, they fails to disclose the intrinsic reasons why these two aspects necessitate and how they are adequately fused to co-learn the graph representation. Towards this question, we in this paper propose an adversarial defense approach through locating the graph's critical state of adversarial resilience, resorting to the equilibrium-point theory in the discipline of complex dynamic system (CDS). In brief, our work has three novelties: i) Adversarial-Attack Modeling, i.e. map a graph regime into CDS, and use the oscillation of dynamic system to model the behavior of adversarial perturbation; ii) 2D Topology-Feature-Entangled Function Design for Perturbed Graph, i.e. project graph topology and node feature as two characteristic spaces, and define two-dimensional entangled perturbation functions to represent the dynamic variance under adversarial attacks; and iii) Location of Critical State of Adversarial Resilience, i.e. utilize the equilibrium-point theory to locate the graph's critical state of attack resilience resorting to the perturbation-reflected 2D function. Finally, multi-facet experiments on five commonly-used realistic datasets validate the effectiveness of our proposed approach, and the results show our approach can significantly outperform the state-of-the-art baselines under four representative graph adversarial attacks.

cs.CR↗

arXiv2026

CAMA: Exploring Collusive Adversarial Attacks in c-MARL

Cooperative multi-agent reinforcement learning (c-MARL) has been widely deployed in real-world applications, such as social robots, embodied intelligence, UAV swarms, etc. Nevertheless, many adversarial attacks still exist to threaten various c-MARL systems. At present, the studies mainly focus on single-adversary perturbation attacks and white-box adversarial attacks that manipulate agents' internal observations or actions. To address these limitations, we in this paper attempt to study collusive adversarial attacks through strategically organizing a set of malicious agents into three collusive attack modes: Collective Malicious Agents, Disguised Malicious Agents, and Spied Malicious Agents. Three novelties are involved: i) three collusive adversarial attacks are creatively proposed for the first time, and a unified framework CAMA for policy-level collusive attacks is designed; ii) the attack effectiveness is theoretically analyzed from the perspectives of disruptiveness, stealthiness, and attack cost; and iii) the three collusive adversarial attacks are technically realized through agent's observation information fusion, attack-trigger control. Finally, multi-facet experiments on four SMAC II maps are performed, and experimental results showcase the three collusive attacks have an additive adversarial synergy, strengthening attack outcome while maintaining high stealthiness and stability over long horizons. Our work fills the gap for collusive adversarial learning in c-MARL.

cs.LG↗

arXiv2025

FastFHE: Packing-Scalable and Depthwise-Separable CNN Inference Over FHE

The deep learning (DL) has been penetrating daily life in many domains, how to keep the DL model inference secure and sample privacy in an encrypted environment has become an urgent and increasingly important issue for various security-critical applications. To date, several approaches have been proposed based on the Residue Number System variant of the Cheon-Kim-Kim-Song (RNS-CKKS) scheme. However, they all suffer from high latency, which severely limits the applications in real-world tasks. Currently, the research on encrypted inference in deep CNNs confronts three main bottlenecks: i) the time and storage costs of convolution calculation; ii) the time overhead of huge bootstrapping operations; and iii) the consumption of circuit multiplication depth. Towards these three challenges, we in this paper propose an efficient and effective mechanism FastFHE to accelerate the model inference while simultaneously retaining high inference accuracy over fully homomorphic encryption. Concretely, our work elaborates four unique novelties. First, we propose a new scalable ciphertext data-packing scheme to save the time and storage consumptions. Second, we work out a depthwise-separable convolution fashion to degrade the computation load of convolution calculation. Third, we figure out a BN dot-product fusion matrix to merge the ciphertext convolutional layer with the batch-normalization layer without incurring extra multiplicative depth. Last but not least, we adopt the low-degree Legendre polynomial to approximate the nonlinear smooth activation function SiLU under the guarantee of tiny accuracy error before and after encrypted inference. Finally, we execute multi-facet experiments to verify the efficiency and effectiveness of our proposed approach.

cs.CR↗

arXiv2025

DTKG: Dual-Track Knowledge Graph-Verified Reasoning Framework for Multi-Hop QA

Multi-hop reasoning for question answering (QA) plays a critical role in retrieval-augmented generation (RAG) for modern large language models (LLMs). The accurate answer can be obtained through retrieving relational structure of entities from knowledge graph (KG). Regarding the inherent relation-dependency and reasoning pattern, multi-hop reasoning can be in general classified into two categories: i) parallel fact-verification multi-hop reasoning question, i.e., requiring simultaneous verifications of multiple independent sub-questions; and ii) chained multi-hop reasoning questions, i.e., demanding sequential multi-step inference with intermediate conclusions serving as essential premises for subsequent reasoning. Currently, the multi-hop reasoning approaches singly employ one of two techniques: LLM response-based fact verification and KG path-based chain construction. Nevertheless, the former excels at parallel fact-verification but underperforms on chained reasoning tasks, while the latter demonstrates proficiency in chained multi-hop reasoning but suffers from redundant path retrieval when handling parallel fact-verification reasoning. These limitations deteriorate the efficiency and accuracy for multi-hop QA tasks. To address this challenge, we propose a novel dual-track KG verification and reasoning framework DTKG, which is inspired by the Dual Process Theory in cognitive science. Specifically, DTKG comprises two main stages: the Classification Stage and the Branch Processing Stage.

cs.AI↗

arXiv2025

RGMem: Renormalization Group-inspired Memory Evolution for Language Agents

Personalized and continuous interactions are critical for LLM-based conversational agents, yet finite context windows and static parametric memory hinder the modeling of long-term, cross-session user states. Existing approaches, including retrieval-augmented generation and explicit memory systems, primarily operate at the fact level, making it difficult to distill stable preferences and deep user traits from evolving and potentially conflicting dialogues.To address this challenge, we propose RGMem, a self-evolving memory framework inspired by the renormalization group (RG) perspective on multi-scale organization and emergence. RGMem models long-term conversational memory as a multi-scale evolutionary process: episodic interactions are transformed into semantic facts and user insights, which are then progressively integrated through hierarchical coarse-graining, thresholded updates, and rescaling into a dynamically evolving user profile.By explicitly separating fast-changing evidence from slow-varying traits and enabling non-linear, phase-transition-like dynamics, RGMem enables robust personalization beyond flat retrieval or static summarization. Extensive experiments on the LOCOMO and PersonaMem benchmarks demonstrate that RGMem consistently outperforms SOTA memory systems, achieving stronger cross-session continuity and improved adaptation to evolving user preferences. Code is available at https://github.com/fenhg297/RGMem

cs.AI↗

arXiv2025

SifterNet: A Generalized and Model-Agnostic Trigger Purification Approach

Aiming at resisting backdoor attacks in convolution neural networks and vision Transformer-based large model, this paper proposes a generalized and model-agnostic trigger-purification approach resorting to the classic Ising model. To date, existing trigger detection/removal studies usually require to know the detailed knowledge of target model in advance, access to a large number of clean samples or even model-retraining authorization, which brings the huge inconvenience for practical applications, especially inaccessible to target model. An ideal countermeasure ought to eliminate the implanted trigger without regarding whatever the target models are. To this end, a lightweight and black-box defense approach SifterNet is proposed through leveraging the memorization-association functionality of Hopfield network, by which the triggers of input samples can be effectively purified in a proper manner. The main novelty of our proposed approach lies in the introduction of ideology of Ising model. Extensive experiments also validate the effectiveness of our approach in terms of proper trigger purification and high accuracy achievement, and compared to the state-of-the-art baselines under several commonly-used datasets, our SiferNet has a significant superior performance.

cs.LG↗

arXiv2023

Contrastive Prompt Learning-based Code Search based on Interaction Matrix

Code search aims to retrieve the code snippet that highly matches the given query described in natural language. Recently, many code pre-training approaches have demonstrated impressive performance on code search. However, existing code search methods still suffer from two performance constraints: inadequate semantic representation and the semantic gap between natural language (NL) and programming language (PL). In this paper, we propose CPLCS, a contrastive prompt learning-based code search method based on the cross-modal interaction mechanism. CPLCS comprises:(1) PL-NL contrastive learning, which learns the semantic matching relationship between PL and NL representations; (2) a prompt learning design for a dual-encoder structure that can alleviate the problem of inadequate semantic representation; (3) a cross-modal interaction mechanism to enhance the fine-grained mapping between NL and PL. We conduct extensive experiments to evaluate the effectiveness of our approach on a real-world dataset across six programming languages. The experiment results demonstrate the efficacy of our approach in improving semantic representation quality and mapping ability between PL and NL.

cs.SE↗

arXiv2022

Predicting Li-ion Battery Cycle Life with LSTM RNN

Efficient and accurate remaining useful life prediction is a key factor for reliable and safe usage of lithium-ion batteries. This work trains a long short-term memory recurrent neural network model to learn from sequential data of discharge capacities at various cycles and voltages and to work as a cycle life predictor for battery cells cycled under different conditions. Using experimental data of first 60 - 80 cycles, our model achieves promising prediction accuracy on test sets of around 80 samples.

cs.LG↗

arXiv2015

An analytical algorithm for 3D magnetic field mapping of a watt balance magnet

A yoke-based permanent magnet, which has been employed in many watt balances at national metrology institutes, is supposed to generate strong and uniform magnetic field in an air gap in the radial direction. However, in reality the fringe effect due to the finite height of the air gap will introduce an undesired vertical magnetic component to the air gap, which should either be measured or modeled towards some optimizations of the watt balance. A recent publication, i.e., {\it Metrologia} 52(4) 445 [1], presented a full field mapping method, which in theory will supply useful information for profile characterization and misalignment analysis. This article is an additional material of [1], which develops a different analytical algorithm to represent the 3D magnetic field of a watt balance magnet based on only one measurement for the radial magnetic flux density along the vertical direction, $B_r(z)$. The new algorithm is based on the electromagnetic nature of the magnet, which has a much better accuracy.

physics.ins-det↗

arXiv2015

Coils and the Electromagnet Used in the Joule Balance at the NIM

In the joule balance developed at National Institute of Metrology (NIM), the dynamic phase of a watt balance is replaced by the mutual inductance measurement in an attempt to provide an alternative method for the kg redefinition. But for this method a rather large current in the exciting coil, is needed to offer the necessary magnetic field in the force weighing phase, and the coil heating becomes an important uncertainty source. To reduce coil heating, a new coil system, in which a ferromagnetic material is used to increase the magnetic field was designed recently. But adopting the ferromagnetic material brings the difficulty from the nonlinear characteristic of material. This problem can be removed by measuring the magnetic flux linkage difference of the suspended coil at two vertical positions directly to replace the mutual inductance parameter. Some systematic effects of this magnet are discussed.

physics.ins-det↗

arXiv2014

The Improvement of Joule Balance NIM-1 and the Design of New Joule Balance NIM-2

The development of the joule balance method to measure the Planck constant, in support of the redefinition of the kilogram, has been going on at the National Institute of Metrology of China (NIM) since 2007. The first prototype has been built to check the feasibility of the principle. In 2011, the relative uncertainty of the Planck constant measurement at NIM is $7.7\times10^{-5}$. Self-heating of the coils, swing of the coil, are the main uncertainty contributions. Since 2012, some improvements have been made to reduce these uncertainties. The relative uncertainty of the joule balance is reduced to $7.2\times10^{-6}$ at present. The Planck constant measured with the joule balance is $h=6.6261041(470)\times10^{-34}$Js. The relative difference between the determined h and the CODATA2010 recommendation value is $5\times10^{-6}$. Further improvements are still being carried out on the NIM-1 apparatus. At the same time, the design and construction of a brand new and compact joule balance NIM-2 are also in progress and presented here.

physics.ins-det↗

arXiv2014

The NIM Inertial Mass Measurement Project

An inertial mass measurement project, which is expected to precisely measure the Planck constant, $h$, for possible comparisons with known gravitational mass measurement projects, e.g., the watt balance and the Avogadro project, is being carried out at the National Institute of Metrology, China. The principle, apparatus, and experimental investigations of the inertial mass measurement are presented. The prototype of the experiment and the Planck constant with relative uncertainty of several parts in $10^{4}$ have been achieved for principle testing.

physics.ins-det↗