arXiv subjects

Qin Wang

Publications and source records attributed to Qin Wang.

At least 109 records · Page 6Linked to original sources

arXiv2025

FLUXLAYER: High-Performance Design for Cross-chain Fragmented Liquidity

Autonomous Market Makers (AMMs) rely on arbitrage to facilitate passive price updates. Liquidity fragmentation poses a complex challenge across different blockchain networks. This paper proposes FluxLayer, a solution to mitigate fragmented liquidity and capture the maximum extractable value (MEV) in a cross-chain environment. FluxLayer is a three-layer framework that integrates a settlement layer, an intent layer, and an under-collateralised leverage lending vault mechanism. Our evaluation demonstrates that FluxLayer can effectively enhance cross-chain MEV by capturing more arbitrage opportunities, reducing costs, and improving overall liquidity.

q-fin.CP↗

arXiv2025

Mixer-Informer-Based Two-Stage Transfer Learning for Long-Sequence Load Forecasting in Newly Constructed Electric Vehicle Charging Stations

The rapid rise in electric vehicle (EV) adoption demands precise charging station load forecasting, challenged by long-sequence temporal dependencies and limited data in new facilities. This study proposes MIK-TST, a novel two-stage transfer learning framework integrating Mixer, Informer, and Kolmogorov-Arnold Networks (KAN). The Mixer fuses multi-source features, Informer captures long-range dependencies via ProbSparse attention, and KAN enhances nonlinear modeling with learnable activation functions. Pre-trained on extensive data and fine-tuned on limited target data, MIK-TST achieves 4% and 8% reductions in MAE and MSE, respectively, outperforming baselines on a dataset of 26 charging stations in Boulder, USA. This scalable solution enhances smart grid efficiency and supports sustainable EV infrastructure expansion.

eess.SY↗

arXiv2025

A Novel Inverter Control Strategy with Power Decoupling for Microgrid Operations in Grid-Connected and Islanded Modes

Grid-forming, particularly those utilizing droop control and virtual synchronous generators (VSG), can actively regulate the frequency and voltage of microgrid systems, exhibiting dynamic characteristics akin to those of synchronous generators. Although droop control and VSG control each have distinct benefits, neither can fully meet the diverse, dynamic needs of both grid-connected (GC) and islanded (IS) modes. Additionally, the coupling between active and reactive power can negatively impact microgrids' dynamic performance and stability. To solve these problems, this paper introduces a unified dynamic power coupling (UDC) model. This model's active power control loop can be tailored to meet diverse requirements. By implementing a well-designed control loop, the system can harness the advantages of both droop control and VSG control. In islanded mode, the proposed model can provide virtual inertia and damping properties, while in grid-connected mode, the inverter's active power output can follow the changed references without significant overshoot or oscillation. Furthermore, the model incorporates coupling compensation and virtual impedance based on a relative gain array in the frequency domain to facilitate quantitative analysis of power coupling characteristics. This paper outlines a distinct design process for the unified model. Finally, the proposed control method has been validated through simulation.

eess.SY↗

arXiv2025

Hybrid Implementation for Untrusted-node-based Quantum Key Distribution Network

Quantum key distribution (QKD) serves as a cornerstone of secure quantum communication, providing unconditional security grounded in quantum mechanics. While trusted-node networks have facilitated early QKD deployment, their vulnerability to node compromise underscores the need for untrusted-node architectures. Measurement-device-independent QKD (MDI-QKD) and twin-field QKD (TF-QKD) have emerged as leading candidates, addressing security vulnerabilities and extending transmission distances. Despite the wide adoptions in various fiber scaling, no integrated implementation of these two protocols has been demonstrated to date. Here, we present a hybrid system that seamlessly integrates TF-QKD and MDI-QKD into one untrusted-node-based architecture. Utilizing an efficient phase estimation method based on asymmetric interferometers, we convert twin-field global phase tracking to relative phase calibration, allowing near continuous running of both protocols. Experiments demonstrate secure finite-size key rates for sending-or-not-sending QKD and MDI-QKD over fiber distances of 150 to 431 km. The results align with theoretical simulations and show the ability to surpass the absolute repeaterless key capacity. Our work offers an unified framework for deploying multi-protocol QKD networks, laying the foundation for adaptable and scalable quantum infrastructures that can meet a wide range of security and performance needs.

quant-ph↗

arXiv2025

On-demand storing time-bin qubit states with optical quantum memory

Quantum memory, serving as a crucial device for storing and releasing quantum states, holds significant importance in long-distance quantum communications. Up to date, quantum memories have been realized in many different systems. However, most of them have complex structures and high costs. Besides, it is not easy to simultaneously achieve both high storage efficiency and fidelity. In this paper, we experimentally demonstrate a low-cost optical quantum memory with high efficiency and high fidelity, by utilizing a butterfly shaped cavity consisting of one polarization beam splitter, two reflecting mirrors and one pockels cell crystal. In order to quantify the quality of the quantum memory, we carry out tomography measurements on the time-bin qubits encoded with weak coherent states after storage for N rounds. The storage efficiency per round can reach up to 95.0\%, and the overall state fidelity can exceed 99.1\%. It thus seems very promising for practical implementation in quantum communication and networks.

quant-ph↗

arXiv2025

BRC20 Snipping Attack

In this paper, we introduce and implement BRC20 sniping attack. Our attack manipulates the BRC20 token transfers in open markets and disrupts the fairness among bidding participants. The long-standing principle of ``highest bidder wins'' is rendered ineffective. Typically, open BRC20 token markets rely on Partially Signed Bitcoin Transactions (PSBT) to broadcast selling intents and wait for buying auctions. Our attack targets the BRC20 buying process (i.e., transfer) by injecting a front-running transaction to complete the full signature of the PSBT. At its core, the attack exploits the mempool's fee-based transaction selection mechanism to snipe the victim transaction, replicate metadata, and front-run the legesmate transaction. This attack applies to platforms using PSBT for BRC20 token transfers, including popular Bitcoin exchanges and marketplaces (e.g., Magic Eden, Unisat, Gate.io, OKX). We implemented and tested the attack on a Bitcoin testnet (regtest), validating its effectiveness through multiple experimental rounds. Results show that the attacker consistently replaces legitimate transactions by submitting higher-fee PSBTs. We have also made responsible disclosures to the mentioned exchanges.

cs.CR↗

arXiv2025

The maximal coarse Baum-Connes conjecture for spaces that admit an A-by-FCE coarse fibration structure

In this paper, we introduce a concept of A-by-FCE coarse fibration structure for metric spaces, which serves as a generalization of the A-by-CE structure for a sequence of group extensions proposed by Deng, Wang, and Yu. We prove that the maximal coarse Baum-Connes conjecture holds for metric spaces with bounded geometry that admit an A-by-FCE coarse fibration structure. As an application, the relative expanders constructed by Arzhantseva and Tessera, as well as the box spaces derived from an ``amenable-by-Haagerup'' group extension, admit the A-by-FCE coarse fibration structure. Consequently, the maximal coarse Baum-Connes conjecture holds for these spaces, which may not admit an FCE structure, i.e. fibred coarse embedding into Hilbert space.

math.KT↗

arXiv2025

MiniMax-01: Scaling Foundation Models with Lightning Attention

We introduce MiniMax-01 series, including MiniMax-Text-01 and MiniMax-VL-01, which are comparable to top-tier models while offering superior capabilities in processing longer contexts. The core lies in lightning attention and its efficient scaling. To maximize computational capacity, we integrate it with Mixture of Experts (MoE), creating a model with 32 experts and 456 billion total parameters, of which 45.9 billion are activated for each token. We develop an optimized parallel strategy and highly efficient computation-communication overlap techniques for MoE and lightning attention. This approach enables us to conduct efficient training and inference on models with hundreds of billions of parameters across contexts spanning millions of tokens. The context window of MiniMax-Text-01 can reach up to 1 million tokens during training and extrapolate to 4 million tokens during inference at an affordable cost. Our vision-language model, MiniMax-VL-01 is built through continued training with 512 billion vision-language tokens. Experiments on both standard and in-house benchmarks show that our models match the performance of state-of-the-art models like GPT-4o and Claude-3.5-Sonnet while offering 20-32 times longer context window. We publicly release MiniMax-01 at https://github.com/MiniMax-AI.

cs.CL↗

arXiv2025

Logic Meets Magic: LLMs Cracking Smart Contract Vulnerabilities

Smart contract vulnerabilities caused significant economic losses in blockchain applications. Large Language Models (LLMs) provide new possibilities for addressing this time-consuming task. However, state-of-the-art LLM-based detection solutions are often plagued by high false-positive rates. In this paper, we push the boundaries of existing research in two key ways. First, our evaluation is based on Solidity v0.8, offering the most up-to-date insights compared to prior studies that focus on older versions (v0.4). Second, we leverage the latest five LLM models (across companies), ensuring comprehensive coverage across the most advanced capabilities in the field. We conducted a series of rigorous evaluations. Our experiments demonstrate that a well-designed prompt can reduce the false-positive rate by over 60%. Surprisingly, we also discovered that the recall rate for detecting some specific vulnerabilities in Solidity v0.8 has dropped to just 13% compared to earlier versions (i.e., v0.4). Further analysis reveals the root cause of this decline: the reliance of LLMs on identifying changes in newly introduced libraries and frameworks during detection.

cs.CR↗

arXiv2025

Improved finite-size analysis for measurement-device-independent quantum digital signature

Quantum digital signatures (QDS), based on the principles of quantum mechanics, provide information-theoretic security, ensuring the integrity, authenticity, and non-repudiation of data transmission. With present QDS protocols, measurement-device-independent QDS (MDI-QDS) can resist all attacks on detections, yet it suffers from finite-size effect. In this work, we present and compare three parameter estimation models for finite-size analysis of two-decoy MDI-QDS. The first model is a commonly used model in previous schemes, and we propose two new models to improve the performance. Subsequently, we perform numerical simulations to evaluate the performance of the three models. The results demonstrate that the proposed methods are less affected by finite-size effect, thereby effectively enhancing the signature rate. This work contributes to the practical development of QDS.

quant-ph↗

arXiv2024

Privacy in Foundation Models: A Conceptual Framework for System Design

AI and its relevant technologies, including machine learning, deep learning, chatbots, virtual assistants, and others, are currently undergoing a profound transformation of development and organizational processes within companies. Foundation models present both significant challenges and incredible opportunities. In this context, ensuring the quality attributes of foundation model-based systems is of paramount importance, and with a particular focus on the challenging issue of privacy due to the sensitive nature of the data and information involved. However, there is currently a lack of consensus regarding the comprehensive scope of both technical and non-technical issues that the privacy evaluation process should encompass. Additionally, there is uncertainty about which existing methods are best suited to effectively address these privacy concerns. In response to this challenge, this paper introduces a novel conceptual framework that integrates various responsible AI patterns from multiple perspectives, with the specific aim of safeguarding privacy.

cs.CR↗

arXiv2024

Equivariant Representation Learning for Augmentation-based Self-Supervised Learning via Image Reconstruction

Augmentation-based self-supervised learning methods have shown remarkable success in self-supervised visual representation learning, excelling in learning invariant features but often neglecting equivariant ones. This limitation reduces the generalizability of foundation models, particularly for downstream tasks requiring equivariance. We propose integrating an image reconstruction task as an auxiliary component in augmentation-based self-supervised learning algorithms to facilitate equivariant feature learning without additional parameters. Our method implements a cross-attention mechanism to blend features learned from two augmented views, subsequently reconstructing one of them. This approach is adaptable to various datasets and augmented-pair based learning methods. We evaluate its effectiveness on learning equivariant features through multiple linear regression tasks and downstream applications on both artificial (3DIEBench) and natural (ImageNet) datasets. Results consistently demonstrate significant improvements over standard augmentation-based self-supervised learning methods and state-of-the-art approaches, particularly excelling in scenarios involving combined augmentations. Our method enhances the learning of both invariant and equivariant features, leading to more robust and generalizable visual representations for computer vision tasks.

cs.CV↗

arXiv2024

Exploring the Market Dynamics of Liquid Staking Derivatives (LSDs)

Staking has emerged as a crucial concept following Ethereum's transition to Proof-of-Stake consensus. The introduction of Liquid Staking Derivatives (LSDs) has effectively addressed the illiquidity issue associated with solo staking, gaining significant market attention. This paper analyzes the LSD market dynamics from the perspectives of both liquidity takers (LTs) and liquidity providers (LPs). We first quantify the price discrepancy between the LSD primary and secondary markets. Then we investigate and empirically measure how LTs can leverage such discrepancy to exploit arbitrage opportunities, unveiling the potential barriers to LSD arbitrages. In addition, we evaluate the financial profit and losses experienced by LPs who supply LSDs for liquidity provision. Our results show that 66% of LSD liquidity positions generate returns lower than those from simply holding the corresponding LSDs.

cs.CR↗

arXiv2024

Integrating Hierarchical Semantic into Iterative Generation Model for Entailment Tree Explanation

Manifestly and logically displaying the line of reasoning from evidence to answer is significant to explainable question answering (QA). The entailment tree exhibits the lines structurally, which is different from the self-explanation principle in large-scale language models. Existing methods rarely consider the semantic association of sentences between and within hierarchies within the tree structure, which is prone to apparent mistakes in combinations. In this work, we propose an architecture of integrating the Hierarchical Semantics of sentences under the framework of Controller-Generator (HiSCG) to explain answers. The HiSCG designs a hierarchical mapping between hypotheses and facts, discriminates the facts involved in tree constructions, and optimizes single-step entailments. To the best of our knowledge, We are the first to notice hierarchical semantics of sentences between the same layer and adjacent layers to yield improvements. The proposed method achieves comparable performance on all three settings of the EntailmentBank dataset. The generalization results on two out-of-domain datasets also demonstrate the effectiveness of our method.

cs.CL↗

arXiv2024

Empowering Visual Artists with Tokenized Digital Assets with NFTs

The Non-Fungible Tokens (NFTs) has the transformative impact on the visual arts industry by examining the nexus between empowering art practices and leveraging blockchain technology. First, we establish the context for this study by introducing some basic but critical technological aspects and affordances of the blockchain domain. Second, we revisit the creative practices involved in producing traditional artwork, covering various types, production processes, trading, and monetization methods. Third, we introduce and define the key fundamentals of the blockchain ecosystem, including its structure, consensus algorithms, smart contracts, and digital wallets. Fourth, we narrow the focus to NFTs, detailing their history, mechanics, lifecycle, and standards, as well as their application in the art world. In particular, we outline the key processes for minting and trading NFTs in various marketplaces and discuss the relevant market dynamics and pricing. We also consider major security concerns, such as wash trading, to underscore some of the central cybersecurity issues facing this domain. Finally, we conclude by considering future research directions, emphasizing improvements in user experience, security, and privacy. Through this innovative research overview, which includes input from creative industry and cybersecurity sdomain expertise, we offer some new insights into how NFTs can empower visual artists and reshape the wider copyright industries.

cs.CR↗

arXiv2024

SoK: Bitcoin Layer Two (L2)

We present the first Systematization of Knowledge (SoK) on constructing Layer Two (L2) solutions for Bitcoin. We carefully examine a representative subset of ongoing Bitcoin L2 solutions (40 out of 335 extensively investigated cases) and provide a concise yet impactful identification of six classic design patterns through two approaches (i.e., modifying transactions \& creating proofs). Notably, we are the first to incorporate the inscription technology (emerged in mid-2023), along with a series of related innovations. We further establish a reference framework that serves as a baseline criterion ideally suited for evaluating the security aspects of Bitcoin L2 solutions, and which can also be extended to broader L2 applications. We apply this framework to evaluate each of the projects we investigated. We find that the inscription-based approaches introduce new functionality (i.e., programability) to Bitcoin systems, whereas existing proof-based solutions primarily address scalability challenges. Our security analysis reveals new attack vectors targeting data/state (availability, verification), assets (withdrawal, recovery), and users (disputes, censorship).

cs.CR↗

arXiv2024

An Architectural Error Metric for CNN-Oriented Approximate Multipliers

As a potential alternative for implementing the large number of multiplications in convolutional neural networks (CNNs), approximate multipliers (AMs) promise both high hardware efficiency and accuracy. However, the characterization of accuracy and design of appropriate AMs are critical to an AM-based CNN (AM-CNN). In this work, the generation and propagation of errors in an AM-CNN are analyzed by considering the CNN architecture. Based on this analysis, a novel AM error metric is proposed to evaluate the accuracy degradation of an AM-CNN, denoted as the architectural mean error (AME). The effectiveness of the AME is assessed in VGG and ResNet on CIFAR-10, CIFAR-100, and ImageNet datasets. Experimental results show that AME exhibits a strong correlation with the accuracy of AM-CNNs, outperforming the other AM error metrics. To predict the accuracy of AM-CNNs, quadratic regression models are constructed based on the AME; the predictions show an average of 3% deviation from the ground-truth values. Compared with a GPU-based simulation, the AME-based prediction is about $10^{6}\times$ faster.

cs.AR↗

arXiv2024

Molecular Beam Epitaxy Growth and Doping Modulation of Topological Semimetal NiTe$_2$

In this study, high-quality thin films of the topological semimetal phase NiTe$_2$ were prepared using molecular beam epitaxy (MBE) technique, confirmed through X-ray diffraction with pronounced Laue oscillations. Electrical transport experiments reveal that thick films have properties similar to bulk materials. By employing co-deposition, we introduced either magnetic or non-magnetic elements during the growth of thinner films, significantly altering their electrical properties. Notably, magnetic element Cr induces long-range ferromagnetic ordering, leading to the observation of significant anomalous Hall effect in NiTe2 thin films. The Hall conductivity remains nearly constant well below the Curie temperature, indicating the correlation with the intrinsic topological nature of the band structure. Theoretical first principles band calculations support the generation of the Weyl semimetal state in the material through magnetic doping. These findings pave the way for exploring more magnetic Weyl semimetal materials and related low-dimensional quantum devices based on topological semimetal materials.

cond-mat.mtrl-sci↗