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Researcher profile

Qin Wang

Qin Wang contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Cryptography and SecurityComputer Visionquant-phArtificial Intelligenceeess.SPMachine Learningmath.KTmath.MGmath.OAComputation and Languagecond-mat.mtrl-scicond-mat.str-elcond-mat.supr-concs.CYDistributed, Parallel, and Cluster Computingeess.ASeess.IVEmerging Technologiesmath-phmath.APmath.GRmath.MPmath.NAMethodologyMultiagent SystemsNumerical Analysisphysics.app-phphysics.flu-dynphysics.opticsq-fin.GNSound

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Published work

38 published item(s)

preprint2026arXiv

Measuring Memecoin Fragility

Memecoins, emerging from internet culture and community-driven narratives, have rapidly evolved into a unique class of crypto assets. Unlike technology-driven cryptocurrencies, their market dynamics are primarily shaped by viral social media diffusion, celebrity influence, and speculative capital inflows. To capture the distinctive vulnerabilities of these ecosystems, we present the first Memecoin Ecosystem Fragility Framework (ME2F). ME2F formalizes memecoin risks in three dimensions: i) Volatility Dynamics Score capturing persistent and extreme price swings together with spillover from base chains; ii) Whale Dominance Score quantifying ownership concentration among top holders; and iii) Sentiment Amplification Score measuring the impact of attention-driven shocks on market stability. We apply ME2F to representative tokens (over 65% market share) and show that fragility is not evenly distributed across the ecosystem. Politically themed tokens such as TRUMP, MELANIA, and LIBRA concentrate the highest risks, combining volatility, ownership concentration, and sensitivity to sentiment shocks. Established memecoins such as DOGE, SHIB, and PEPE fall into an intermediate range. Benchmark tokens ETH and SOL remain consistently resilient due to deeper liquidity and institutional participation. Our findings provide the first ecosystem-level evidence of memecoin fragility and highlight governance implications for enhancing market resilience in the Web3 era.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

SoK: Stablecoins in Retail Payments

Stablecoins have emerged as a rapidly growing digital payment instrument, raising the question of whether blockchain-based settlement can function as a substitute for incumbent card networks in retail payments. This Systematization of Knowledge (SoK) provides a systematic comparison between stablecoin payment arrangements and card networks by situating both within a unified analytical framework. We first map their respective payment infrastructures, participant roles, and transaction lifecycles, highlighting fundamental differences in how authorization, settlement, and recourse are organized. Building on this mapping, we introduce the CLEAR framework, which evaluates retail payment systems across five dimensions: cost, legality, experience, architecture, and reach. Our analysis shows that stablecoins deliver efficient, continuous, and programmable settlement, often compressing rail-level merchant fees and enabling 24/7 value transfer. However, these advantages are accompanied by an inversion of the traditional pricing and risk-allocation structure. Card networks internalize consumer-side frictions through subsidies, standardized liability rules, and post-transaction recourse, thereby supporting mass-market adoption. Stablecoin arrangements, by contrast, externalize transaction fees, error prevention, and dispute resolution to users, intermediaries, and courts, resulting in weaker consumer protection, higher cognitive burden at the point of interaction, and fragmented acceptance. Accordingly, stablecoins exhibit a conditional comparative advantage in closed-loop environments, cross-border corridors, and high-friction payment contexts, but remain structurally disadvantaged as open-loop retail payment instruments.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Swimming with Whales: Analysis of Power Imbalances in Stake-Weighted Governance

Voting methods weighted by stakes are the fundamental governance paradigm in Proof-of-Stake (PoS) blockchains. Such a paradigm is known to be prone to power distortions: a few users possessing large stakes may completely control decision making, even without owning the totality of the stakes. We study this phenomenon through the lens of computational social choice, focusing on the extent of power imbalances in stake-weighted voting when power is quantified using the Penrose-Banzhaf power index. Our work presents both analytical and empirical contributions. Analytically, we demonstrate that while a perfect alignment between power and relative stake ownership is generally unattainable, it can be approximated in expectation under specific conditions. Empirically, using data from a real-world on-chain governance system (Project Catalyst), we provide a more fine-grained understanding of the power imbalances that are likely to occur in current stake-weighted governance systems.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

VIPER Strike: Defeating Visual Reasoning CAPTCHAs via Structured Vision-Language Inference

Visual Reasoning CAPTCHAs (VRCs) combine visual scenes with natural-language queries that demand compositional inference over objects, attributes, and spatial relations. They are increasingly deployed as a primary defense against automated bots. Existing solvers fall into two paradigms: vision-centric, which rely on template-specific detectors but fail on novel layouts, and reasoning-centric, which leverage LLMs but struggle with fine-grained visual perception. Both lack the generality needed to handle heterogeneous VRC deployments. We present ViPer, a unified attack framework that integrates structured multi-object visual perception with adaptive LLM-based reasoning. ViPer parses visual layouts, grounds attributes to question semantics, and infers target coordinates within a modular pipeline. Evaluated on six major VRC providers (VTT, Geetest, NetEase, Dingxiang, Shumei, Xiaodun), ViPer achieves up to 93.2% success, approaching human-level performance across multiple benchmarks. Compared to prior solvers, GraphNet (83.2%), Oedipus (65.8%), and the Holistic approach (89.5%), ViPer consistently outperforms all baselines. The framework further maintains robustness across alternative LLM backbones (GPT, Grok, DeepSeek, Kimi), sustaining accuracy above 90%. To anticipate defense, we further introduce Template-Space Randomization (TSR), a lightweight strategy that perturbs linguistic templates without altering task semantics. TSR measurably reduces solver (i.e., attacker) performance. Our proposed design suggests directions for human-solvable but machine-resistant CAPTCHAs.

0 citations0 reviewsNo code linkedOpen access
preprint2024arXiv

From Beginner to Expert: Modeling Medical Knowledge into General LLMs

Recently, large language model (LLM) based artificial intelligence (AI) systems have demonstrated remarkable capabilities in natural language understanding and generation. However, these models face a significant challenge when it comes to sensitive applications, such as reasoning over medical knowledge and answering medical questions in a physician-like manner. Prior studies attempted to overcome this challenge by increasing the model size (>100B) to learn more general medical knowledge, while there is still room for improvement in LLMs with smaller-scale model sizes (<100B). In this work, we start from a pre-trained general LLM model (AntGLM-10B) and fine-tune it from a medical beginner towards a medical expert (called AntGLM-Med-10B), which leverages a 3-stage optimization procedure, i.e., general medical knowledge injection, medical domain instruction tuning, and specific medical task adaptation. Our contributions are threefold: (1) We specifically investigate how to adapt a pre-trained general LLM in medical domain, especially for a specific medical task. (2) We collect and construct large-scale medical datasets for each stage of the optimization process. These datasets encompass various data types and tasks, such as question-answering, medical reasoning, multi-choice questions, and medical conversations. (3) Specifically for multi-choice questions in the medical domain, we propose a novel Verification-of-Choice approach for prompting engineering, which significantly enhances the reasoning ability of LLMs. Remarkably, by combining the above approaches, our AntGLM-Med-10B model can outperform the most of LLMs on PubMedQA, including both general and medical LLMs, even when these LLMs have larger model size.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

IronForge: An Open, Secure, Fair, Decentralized Federated Learning

Federated learning (FL) provides an effective machine learning (ML) architecture to protect data privacy in a distributed manner. However, the inevitable network asynchrony, the over-dependence on a central coordinator, and the lack of an open and fair incentive mechanism collectively hinder its further development. We propose \textsc{IronForge}, a new generation of FL framework, that features a Directed Acyclic Graph (DAG)-based data structure and eliminates the need for central coordinators to achieve fully decentralized operations. \textsc{IronForge} runs in a public and open network, and launches a fair incentive mechanism by enabling state consistency in the DAG, so that the system fits in networks where training resources are unevenly distributed. In addition, dedicated defense strategies against prevalent FL attacks on incentive fairness and data privacy are presented to ensure the security of \textsc{IronForge}. Experimental results based on a newly developed testbed FLSim highlight the superiority of \textsc{IronForge} to the existing prevalent FL frameworks under various specifications in performance, fairness, and security. To the best of our knowledge, \textsc{IronForge} is the first secure and fully decentralized FL framework that can be applied in open networks with realistic network and training settings.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A Bott periodicity theorem for $\ell^p$-spaces and the coarse Novikov conjecture at infinity

We formulate and prove a Bott periodicity theorem for an $\ell^p$-space ($1\leq p<\infty$). For a proper metric space $X$ with bounded geometry, we introduce a version of $K$-homology at infinity, denoted by $K_*^{\infty}(X)$, and the Roe algebra at infinity, denoted by $C^*_{\infty}(X)$. Then the coarse assembly map descents to a map from $\lim_{d\to\infty}K_*^{\infty}(P_d(X))$ to $K_*(C^*_{\infty}(X))$, called the coarse assembly map at infinity. We show that to prove the coarse Novikov conjecture, it suffices to prove the coarse assembly map at infinity is an injection. As a result, we show that the coarse Novikov conjecture holds for any metric space with bounded geometry which admits a fibred coarse embedding into an $\ell^p$-space. These include all box spaces of a residually finite hyperbolic group and a large class of warped cones of a compact space with an action by a hyperbolic group.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A Weak Consensus Algorithm and Its Application to High-Performance Blockchain

A large number of consensus algorithms have been proposed. However, the requirement of strict consistency limits their wide adoption, especially in high-performance required systems. In this paper, we propose a weak consensus algorithm that only maintains the consistency of relative positions between the messages. We apply this consensus algorithm to construct a high-performance blockchain system, called \textit{Sphinx}. We implement the system with 32k+ lines of code including all components like consensus/P2P/ledger/etc. The evaluations show that Sphinx can reach a peak throughput of 43k TPS (with 8 full nodes), which is significantly faster than current blockchain systems such as Ethereum given the same experimental environment. To the best of our knowledge, we present the first weak consensus algorithm with a fully implemented blockchain system.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Amorphous p-Type Conducting Zn-x Ir Oxide (x > 0.13) Thin Films Deposited by Reactive Magnetron Cosputtering

Zinc-iridium oxide (Zn-Ir-O) thin films have been demonstrated as a p-type conducting material. However, the stability of p-type conductivity with respect to chemical composition or temperature is still unclear. In this study we discuss the local atomic structure and the electrical properties of Zn-Ir-O films in the large Ir concentration range. The films are deposited by reactive DC magnetron co-sputtering at two different substrate temperatures-without intentional heating and at 300 °C. Extended X-ray absorption fine structure (EXAFS) analysis reveals that strongly disordered ZnO4 tetrahedra are the main Zn complexes in Zn-Ir-O films with up to 67.4 at% Ir. As the Ir concentration increases, an effective increase of Ir oxidation state is observed. Reverse Monte Carlo analysis of EXAFS at Zn K-edge shows that the average Zn-O interatomic distance and disorder factor increase with the Ir concentration. We observed that the nano-crystalline w-ZnO structure is preserved in a wider Ir concentration range if the substrate is heated during deposition. At low Ir concentration, the transition from n- to p-type conductivity is observed regardless of the temperature of the substrates. Electrical resistivity decreases exponentially with the Ir concentration in the Zn-Ir-O films.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Coarse embeddings at infinity and generalized expanders at infinity

We introduce a notion of coarse embedding at infinity into Hilbert space for metric spaces, which is a weakening of the notion of fibred coarse embedding and a far generalization of Gromov&#39;s concept of coarse embedding. It turns out that a residually finite group admits a coarse embedding into Hilbert space if and only if one (or equivalently, every) box space of the group admits a coarse embedding at infinity into Hilbert space. Moreover, we introduce a concept of generalized expander at infinity and show that it is an obstruction to coarse embeddability at infinity.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Continual Test-Time Domain Adaptation

Test-time domain adaptation aims to adapt a source pre-trained model to a target domain without using any source data. Existing works mainly consider the case where the target domain is static. However, real-world machine perception systems are running in non-stationary and continually changing environments where the target domain distribution can change over time. Existing methods, which are mostly based on self-training and entropy regularization, can suffer from these non-stationary environments. Due to the distribution shift over time in the target domain, pseudo-labels become unreliable. The noisy pseudo-labels can further lead to error accumulation and catastrophic forgetting. To tackle these issues, we propose a continual test-time adaptation approach~(CoTTA) which comprises two parts. Firstly, we propose to reduce the error accumulation by using weight-averaged and augmentation-averaged predictions which are often more accurate. On the other hand, to avoid catastrophic forgetting, we propose to stochastically restore a small part of the neurons to the source pre-trained weights during each iteration to help preserve source knowledge in the long-term. The proposed method enables the long-term adaptation for all parameters in the network. CoTTA is easy to implement and can be readily incorporated in off-the-shelf pre-trained models. We demonstrate the effectiveness of our approach on four classification tasks and a segmentation task for continual test-time adaptation, on which we outperform existing methods. Our code is available at \url{https://qin.ee/cotta}.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Domain Shift-oriented Machine Anomalous Sound Detection Model Based on Self-Supervised Learning

Thanks to the development of deep learning, research on machine anomalous sound detection based on self-supervised learning has made remarkable achievements. However, there are differences in the acoustic characteristics of the test set and the training set under different operating conditions of the same machine (domain shifts). It is challenging for the existing detection methods to learn the domain shifts features stably with low computation overhead. To address these problems, we propose a domain shift-oriented machine anomalous sound detection model based on self-supervised learning (TranSelf-DyGCN) in this paper. Firstly, we design a time-frequency domain feature modeling network to capture global and local spatial and time-domain features, thus improving the stability of machine anomalous sound detection stability under domain shifts. Then, we adopt a Dynamic Graph Convolutional Network (DyGCN) to model the inter-dependence relationship between domain shifts features, enabling the model to perceive domain shifts features efficiently. Finally, we use a Domain Adaptive Network (DAN) to compensate for the performance decrease caused by domain shifts, making the model adapt to anomalous sound better in the self-supervised environment. The performance of the suggested model is validated on DCASE 2020 task 2 and DCASE 2022 task 2.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Exploring Unfairness on Proof of Authority: Order Manipulation Attacks and Remedies

Proof of Authority (PoA) is a type of permissioned consensus algorithm with a fixed committee. PoA has been widely adopted by communities and industries due to its better performance and faster finality. In this paper, we explore the \textit{unfairness} issue existing in the current PoA implementations. We have investigated 2,500+ \textit{in the wild} projects and selected 10+ as our main focus (covering Ethereum, Binance smart chain, etc.). We have identified two types of order manipulation attacks to separately break the transaction-level (a.k.a. transaction ordering) and the block-level (sealer position ordering) fairness. Both of them merely rely on honest-but-\textit{profitable} sealer assumption without modifying original settings. We launch these attacks on the forked branches under an isolated environment and carefully evaluate the attacking scope towards different implementations. To date (as of Nov 2021), the potentially affected PoA market cap can reach up to $681,087$ million USD. Besides, we further dive into the source code of selected projects, and accordingly, propose our recommendation for the fix. To the best of knowledge, this work provides the first exploration of the \textit{unfairness} issue in PoA algorithms.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Exploring Web3 From the View of Blockchain

Web3 is the most hyped concept from 2020 to date, greatly motivating the prosperity of the Internet of Value and Metaverse. However, no solid evidence stipulates the exact definition, criterion, or standard in the sense of such a buzzword. To fill the gap, we aim to clarify the term in this work. We narrow down the connotation of Web3 by separating it from high-level controversy argues and, instead, focusing on its protocol, architecture, and evaluation from the perspective of blockchain fields. Specifically, we have identified all potential architectural design types and evaluated each of them by employing the scenario-based architecture evaluation method. The evaluation shows that existing applications are neither secure nor adoptable as claimed. Meanwhile, we also discuss opportunities and challenges surrounding the Web3 space and answer several prevailing questions from communities. A primary result is that Web3 still relies on traditional internet infrastructure, not as independent as advocated. This report, as of June 2022, provides the first strict research on Web3 in the view of blockchain. We hope that this work would provide a guide for the development of future Web3 services.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Formal Security Analysis on dBFT Protocol of NEO

NEO is one of the top public chains worldwide. We focus on its backbone consensus protocol, called delegated Byzantine Fault Tolerance (dBFT). The dBFT protocol has been adopted by a variety of blockchain systems such as ONT. dBFT claims to guarantee the security when no more than $f = \lfloor \frac{n}{3} \rfloor$ nodes are Byzantine, where $n$ is the total number of consensus participants. However, we identify attacks to break the claimed security. In this paper, we show our results by providing a security analysis on its dBFT protocol. First, we evaluate NEO&#39;s source code and formally present the procedures of dBFT via the state machine replication (SMR) model. Next, we provide a theoretical analysis with two example attacks. These attacks break the security of dBFT with no more than $f$ nodes. Then, we provide recommendations on how to fix the system against the identified attacks. The suggested fixes have been accepted by the NEO official team. Finally, we further discuss the reasons causing such issues, the relationship with current permissioned blockchain systems, and the scope of potential influence.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Frontrunning Block Attack in PoA Clique: A Case Study

As a fundamental technology of decentralized finance (DeFi), blockchain&#39;s ability to maintain a distributed fair ledger is threatened by manipulation of block/transaction order. In this paper, we propose a frontrunning block attack against the Clique-based Proof of Authority (PoA) algorithms. Our attack can frontrun blocks from honest in-turn sealers by breaking the proper order of leader selection. By falsifying the priority parameters (both \textit{difficulty} and \textit{delay time}), a malicious out-of-turn sealer can always successfully occupy the leader position and produce advantageous blocks that may contain profitable transactions. As a typical instance, we apply our attack to a mature Clique-engined project, HPB (\$3,058,901, as of April 2022). Experimental results demonstrate the effectiveness and feasibility. Then, we further recommend fixes that make identity checks effective. Our investigation and suggestion have been submitted to its official team and got their approval. We believe this work can act as, at least, a warning case for Clique variants to avoid repeating these design mistakes.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

How Do Smart Contracts Benefit Security Protocols?

Smart contracts have recently been adopted by many security protocols. However, existing studies lack satisfactory theoretical support on how contracts benefit security protocols. This paper aims to give a systematic analysis of smart contract (SC)-based security protocols to fulfill the gap of unclear arguments and statements. We firstly investigate \textit{state of the art studies} and establish a formalized model of smart contract protocols with well-defined syntax and assumptions. Then, we apply our formal framework to two concrete instructions to explore corresponding advantages and desirable properties. Through our analysis, we abstract three generic properties (\textit{non-repudiation, non-equivocation, and non-frameability}) and accordingly identify two patterns. (1) a smart contract can be as an autonomous subscriber to assist the trusted third party (TTP); (2) a smart contract can replace traditional TTP. To the best of our knowledge, this is the first study to provide in-depth discussions of SC-based security protocols from a strictly theoretical perspective.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Multi-agent Actor-Critic with Time Dynamical Opponent Model

In multi-agent reinforcement learning, multiple agents learn simultaneously while interacting with a common environment and each other. Since the agents adapt their policies during learning, not only the behavior of a single agent becomes non-stationary, but also the environment as perceived by the agent. This renders it particularly challenging to perform policy improvement. In this paper, we propose to exploit the fact that the agents seek to improve their expected cumulative reward and introduce a novel \textit{Time Dynamical Opponent Model} (TDOM) to encode the knowledge that the opponent policies tend to improve over time. We motivate TDOM theoretically by deriving a lower bound of the log objective of an individual agent and further propose \textit{Multi-Agent Actor-Critic with Time Dynamical Opponent Model} (TDOM-AC). We evaluate the proposed TDOM-AC on a differential game and the Multi-agent Particle Environment. We show empirically that TDOM achieves superior opponent behavior prediction during test time. The proposed TDOM-AC methodology outperforms state-of-the-art Actor-Critic methods on the performed experiments in cooperative and \textbf{especially} in mixed cooperative-competitive environments. TDOM-AC results in a more stable training and a faster convergence.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Multi-grating design for integrated single-atom trapping, manipulation, and readout

An on-chip multi-grating device is proposed to interface single-atoms and integrated photonic circuits, by guiding and focusing lasers to the area with ~10um above the chip for trapping, state manipulation, and readout of single Rubidium atoms. For the optical dipole trap, two 850 nm laser beams are diffracted and overlapped to form a lattice of single-atom dipole trap, with the diameter of optical dipole trap around 2.7um. Similar gratings are designed for guiding 780 nm probe laser to excite and also collect the fluorescence of 87Rb atoms. Such a device provides a compact solution for future applications of single atoms, including the single photon source, single-atom quantum register, and sensor.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

On sampling Kaczmarz-Motzkin methods for solving large-scale nonlinear systems

In this paper, for solving large-scale nonlinear equations we propose a nonlinear sampling Kaczmarz-Motzkin (NSKM) method. Based on the local tangential cone condition and the Jensen&#39;s inequality, we prove convergence of our method with two different assumptions. Then, for solving nonlinear equations with the convex constraints we present two variants of the NSKM method: the projected sampling Kaczmarz-Motzkin (PSKM) method and the accelerated projected sampling Kaczmarz-Motzkin (APSKM) method. With the use of the nonexpansive property of the projection and the convergence of the NSKM method, the convergence analysis is obtained. Numerical results show that the NSKM method with the sample of the suitable size outperforms the nonlinear randomized Kaczmarz (NRK) method in terms of calculation times. The APSKM and PSKM methods are practical and promising for the constrained nonlinear problem.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Revisiting Deep Semi-supervised Learning: An Empirical Distribution Alignment Framework and Its Generalization Bound

In this work, we revisit the semi-supervised learning (SSL) problem from a new perspective of explicitly reducing empirical distribution mismatch between labeled and unlabeled samples. Benefited from this new perspective, we first propose a new deep semi-supervised learning framework called Semi-supervised Learning by Empirical Distribution Alignment (SLEDA), in which existing technologies from the domain adaptation community can be readily used to address the semi-supervised learning problem through reducing the empirical distribution distance between labeled and unlabeled data. Based on this framework, we also develop a new theoretical generalization bound for the research community to better understand the semi-supervised learning problem, in which we show the generalization error of semi-supervised learning can be effectively bounded by minimizing the training error on labeled data and the empirical distribution distance between labeled and unlabeled data. Building upon our new framework and the theoretical bound, we develop a simple and effective deep semi-supervised learning method called Augmented Distribution Alignment Network (ADA-Net) by simultaneously adopting the well-established adversarial training strategy from the domain adaptation community and a simple sample interpolation strategy for data augmentation. Additionally, we incorporate both strategies in our ADA-Net into two exiting SSL methods to further improve their generalization capability, which indicates that our new framework provides a complementary solution for solving the SSL problem. Our comprehensive experimental results on two benchmark datasets SVHN and CIFAR-10 for the semi-supervised image recognition task and another two benchmark datasets ModelNet40 and ShapeNet55 for the semi-supervised point cloud recognition task demonstrate the effectiveness of our proposed framework for SSL.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

SoK: TEE-assisted Confidential Smart Contract

The blockchain-based smart contract lacks privacy since the contract state and instruction code are exposed to the public. Combining smart-contract execution with Trusted Execution Environments (TEEs) provides an efficient solution, called TEE-assisted smart contracts, for protecting the confidentiality of contract states. However, the combination approaches are varied, and a systematic study is absent. Newly released systems may fail to draw upon the experience learned from existing protocols, such as repeating known design mistakes or applying TEE technology in insecure ways. In this paper, we first investigate and categorize the existing systems into two types: the layer-one solution and layer-two solution. Then, we establish an analysis framework to capture their common lights, covering the desired properties (for contract services), threat models, and security considerations (for underlying systems). Based on our taxonomy, we identify their ideal functionalities and uncover the fundamental flaws and reasons for the challenges in each specification design. We believe that this work would provide a guide for the development of TEE-assisted smart contracts, as well as a framework to evaluate future TEE-assisted confidential contract systems.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Stability Approach to Regularization Selection for Reduced-Rank Regression

The reduced-rank regression model is a popular model to deal with multivariate response and multiple predictors, and is widely used in biology, chemometrics, econometrics, engineering, and other fields. In the reduced-rank regression modelling, a central objective is to estimate the rank of the coefficient matrix that represents the number of effective latent factors in predicting the multivariate response. Although theoretical results such as rank estimation consistency have been established for various methods, in practice rank determination still relies on information criterion based methods such as AIC and BIC or subsampling based methods such as cross validation. Unfortunately, the theoretical properties of these practical methods are largely unknown. In this paper, we present a novel method called StARS-RRR that selects the tuning parameter and then estimates the rank of the coefficient matrix for reduced-rank regression based on the stability approach. We prove that StARS-RRR achieves rank estimation consistency, i.e., the rank estimated with the tuning parameter selected by StARS-RRR is consistent to the true rank. Through a simulation study, we show that StARS-RRR outperforms other tuning parameter selection methods including AIC, BIC, and cross validation as it provides the most accurate estimated rank. In addition, when applied to a breast cancer dataset, StARS-RRR discovers a reasonable number of genetic pathways that affect the DNA copy number variations and results in a smaller prediction error than the other methods with a random-splitting process.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Towards Interpretable Video Super-Resolution via Alternating Optimization

In this paper, we study a practical space-time video super-resolution (STVSR) problem which aims at generating a high-framerate high-resolution sharp video from a low-framerate low-resolution blurry video. Such problem often occurs when recording a fast dynamic event with a low-framerate and low-resolution camera, and the captured video would suffer from three typical issues: i) motion blur occurs due to object/camera motions during exposure time; ii) motion aliasing is unavoidable when the event temporal frequency exceeds the Nyquist limit of temporal sampling; iii) high-frequency details are lost because of the low spatial sampling rate. These issues can be alleviated by a cascade of three separate sub-tasks, including video deblurring, frame interpolation, and super-resolution, which, however, would fail to capture the spatial and temporal correlations among video sequences. To address this, we propose an interpretable STVSR framework by leveraging both model-based and learning-based methods. Specifically, we formulate STVSR as a joint video deblurring, frame interpolation, and super-resolution problem, and solve it as two sub-problems in an alternate way. For the first sub-problem, we derive an interpretable analytical solution and use it as a Fourier data transform layer. Then, we propose a recurrent video enhancement layer for the second sub-problem to further recover high-frequency details. Extensive experiments demonstrate the superiority of our method in terms of quantitative metrics and visual quality.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Both qubits of the singlet state can be steered simultaneously by multiple independent observers via sequential measurement

Quantum correlation is a fundamental property which distinguishes quantum systems from classical ones, and it is also a fragile resource under projective measurement. Recently, it has been shown that a subsystem in entangled pairs can share nonlocality with multiple observers in sequence. Here we present a new steering scenario where both subsystems are accessible by multiple observers. And it is found that the two qubits in singlet state can be simultaneously steered by two sequential observers, respectively.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Measurement-device-independent quantum key distribution with insecure sources

Measurement-device-independent quantum key distribution (MDI-QKD) can eliminate all detector side-channel loopholes and has shown excellent performance in long-distance secret keys sharing. Conventional security proofs, however, require additional assumptions on sources and that can be compromised through uncharacterized side channels in practice. Here, we present a general formalism based on reference technique to prove the security of MDI-QKD against any possible sources imperfection and/or side channels. With this formalism, we investigate the asymptotic performance of single-photon sources without any extra assumptions on the state preparations. Our results highlight the importance of transmitters&#39; security.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Twin-field quantum key distribution with discrete-phase-randomized sources

Thanks to the single-photon interference at a third untrusted party, the twin-field quantun key distribution (TF-QKD) protocol and its variants can beat the well-known rate-loss bound without quantum repeaters, and related experiments have been implemented recently. Generally, quantum states in these schemes should be randomly switched between the code mode and test mode. To adopt the standard decoy-state method, phases of coherent state sources in the test mode are assumed to be continuously randomized. However, such a crucial assumption cannot be well satisfied in experimental implementations. In this paper, to bridge the gap between theory and practice, we propose a TF-QKD variant with discrete-phase-randomized sources both in the code mode and test mode, and prove its security against collective attacks. Our simulation results indicate that, with only a small number of discrete phases, the performance of discrete-phase-randomized sources can overcome the rate-loss bound and approach that of continuous-phase-randomized sources.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

280-km experimental demonstration of quantum digital signature with one decoy state

Quantum digital signature (QDS) guarantee the unforgeability, nonrepudiation and transferability of signature messages with information-theoretical security, and hence has attracted much attention recently. However, most previous implementations of QDS showed relatively low signature rates or/and short transmission distance. In this paper, we report a proof-of-principle phase-encoding QDS demonstration using only one decoy state. Firstly, such method avoids the modulation of vacuum state, thus reducing experimental complexity and random number consumption. Moreover, incorporating with low-loss asymmetric Mach-Zehnder interferometers and real-time polarization calibration technique, we have successfully achieved higher signature rate, e.g., 0.98 bit/s at 103 km, and to date a record-breaking transmission distance over 280-km installed fibers. Our work represents a significant step towards real-world applications of QDS.

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preprint2020arXiv

Experimental Three-State Measurement-Device-Independent Quantum Key Distribution with Uncharacterized Sources

The measurement-device-independent quantum key distribution (MDI-QKD) protocol plays an important role in quantum communications due to its high level of security and practicability. It can be immune to all side-channel attacks directed on the detecting devices. However, the protocol still contains strict requirements during state preparation in most existing MDI-QKD schemes, e.g., perfect state preparation or perfectly characterized sources, which are very hard to realize in practice. In this letter, we investigate uncharacterized MDI-QKD by utilizing a three-state method, greatly reducing the finite-size effect. The only requirement for state preparation is that the state are prepared in a bidimensional Hilbert space. Furthermore, a proof-of-principle demonstration over a 170 km transmission distance is achieved, representing the longest transmission distance under the same security level on record.

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preprint2020arXiv

Global Solutions of a Two-Dimensional Riemann Problem for the Pressure Gradient System

We are concerned with a two-dimensional ($2$-D) Riemann problem for compressible flows modeled by the pressure gradient system that is a $2$-D hyperbolic system of conservation laws. The Riemann initial data consist of four constant states in four sectorial regions such that two shock waves and two vortex sheets are generated between the adjacent states. This Riemann problem can be reduced to a boundary value problem in the self-similar coordinates with the Riemann initial data as its asymptotic boundary data, along with two sonic circles determined by the Riemann initial data, for a nonlinear system of mixed-composite type. The solutions keep the four constant states and four planar waves outside the outer sonic circle. The two shocks keep planar until they meet the outer sonic circle at two different points and then generate a diffracted shock to be expected to connect these two points, whose exact location is {\it apriori} unknown which is regarded as a free boundary. Then the $2$-D Riemann problem can be reformulated as a free boundary problem, in which the diffracted transonic shock is the one-phase free boundary to connect the two points, while the other part of the outer sonic circle forms the part of the fixed boundary of the problem. We establish the global existence of a solution of the free boundary problem, as well as the $C^{0,1}$--regularity of both the diffracted shock across the two points and the solution across the outer sonic boundary which is optimal. One of the key observations here is that the diffracted transonic shock can not intersect with the inner sonic circle in the self-similar coordinates. As a result, this $2$-D Riemann problem is solved globally, whose solution contains two vortex sheets and one global $2$-D shock connecting the two original shocks generated by the Riemann data.

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preprint2020arXiv

Missing-Class-Robust Domain Adaptation by Unilateral Alignment for Fault Diagnosis

Domain adaptation aims at improving model performance by leveraging the learned knowledge in the source domain and transferring it to the target domain. Recently, domain adversarial methods have been particularly successful in alleviating the distribution shift between the source and the target domains. However, these methods assume an identical label space between the two domains. This assumption imposes a significant limitation for real applications since the target training set may not contain the complete set of classes. We demonstrate in this paper that the performance of domain adversarial methods can be vulnerable to an incomplete target label space during training. To overcome this issue, we propose a two-stage unilateral alignment approach. The proposed methodology makes use of the inter-class relationships of the source domain and aligns unilaterally the target to the source domain. The benefits of the proposed methodology are first evaluated on the MNIST$\rightarrow$MNIST-M adaptation task. The proposed methodology is also evaluated on a fault diagnosis task, where the problem of missing fault types in the target training dataset is common in practice. Both experiments demonstrate the effectiveness of the proposed methodology.

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preprint2020arXiv

Nanoscale Microscopy Images Colorization Using Neural Networks

Microscopy images are powerful tools and widely used in the majority of research areas, such as biology, chemistry, physics and materials fields by various microscopies (scanning electron microscope (SEM), atomic force microscope (AFM) and the optical microscope, et al.). However, most of the microscopy images are colorless due to the unique imaging mechanism. Though investigating on some popular solutions proposed recently about colorizing images, we notice the process of those methods are usually tedious, complicated, and time-consuming. In this paper, inspired by the achievement of machine learning algorithms on different science fields, we introduce two artificial neural networks for gray microscopy image colorization: An end-to-end convolutional neural network (CNN) with a pre-trained model for feature extraction and a pixel-to-pixel neural style transfer convolutional neural network (NST-CNN), which can colorize gray microscopy images with semantic information learned from a user-provided colorful image at inference time. The results demonstrate that our algorithm not only can colorize the microscopy images under complex circumstances precisely but also make the color naturally according to the training of a massive number of nature images with proper hue and saturation.

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preprint2020arXiv

Off-Policy Reinforcement Learning for Efficient and Effective GAN Architecture Search

In this paper, we introduce a new reinforcement learning (RL) based neural architecture search (NAS) methodology for effective and efficient generative adversarial network (GAN) architecture search. The key idea is to formulate the GAN architecture search problem as a Markov decision process (MDP) for smoother architecture sampling, which enables a more effective RL-based search algorithm by targeting the potential global optimal architecture. To improve efficiency, we exploit an off-policy GAN architecture search algorithm that makes efficient use of the samples generated by previous policies. Evaluation on two standard benchmark datasets (i.e., CIFAR-10 and STL-10) demonstrates that the proposed method is able to discover highly competitive architectures for generally better image generation results with a considerably reduced computational burden: 7 GPU hours. Our code is available at https://github.com/Yuantian013/E2GAN.

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preprint2020arXiv

Potential, Challenges and Future Directions for Deep Learning in Prognostics and Health Management Applications

Deep learning applications have been thriving over the last decade in many different domains, including computer vision and natural language understanding. The drivers for the vibrant development of deep learning have been the availability of abundant data, breakthroughs of algorithms and the advancements in hardware. Despite the fact that complex industrial assets have been extensively monitored and large amounts of condition monitoring signals have been collected, the application of deep learning approaches for detecting, diagnosing and predicting faults of complex industrial assets has been limited. The current paper provides a thorough evaluation of the current developments, drivers, challenges, potential solutions and future research needs in the field of deep learning applied to Prognostics and Health Management (PHM) applications.

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preprint2020arXiv

Security Analysis on Tangle-based Blockchain through Simulation

The Tangle-based structure becomes one of the most promising solutions when designing DAG-based blockchain systems. The approach improves the scalability by directly confirming multiple transactions in parallel instead of single blocks in linear. However, the performance gain may bring potential security risks. In this paper, we construct three types of attacks with comprehensive evaluations, namely parasite attack (PS), double spending attack (DS), and hybrid attack (HB). To achieve that, we deconstruct the Tangle-based projects (e.g. IOTA) and abstract the main components to rebuild a simple but flexible network for the simulation. Then, we informally define three smallest actions to build up the attack strategies layer by layer. Based on that, we provide analyses to evaluate different types of attacks. To the best of our knowledge, this is the first study to provide a comprehensive security analysis of Tangle-based blockchains.

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preprint2020arXiv

The de Hass-van Alphen quantum oscillations in a three-dimensional Dirac semimetal TiSb2

We have used the de Hass-van Alphen (dHvA) effect to investigate the Fermi surface of high-quality crystalline TiSb2, which unveiled a nontrivial topologic nature by analyzing the dHvA quantum oscillations. Moreover, our analysis on the quantum oscillation frequencies associated with nonzero Berry phase when the magnetic field is parallel to both of the ab-plane and c-axis of TiSb2 finds that the Fermi surface topology has a three-dimensional (3D) feature. The results are supported by the first-principle calculations which revealed a symmetry-protected Dirac point appeared along the Γ-Z high symmetry line near the Fermi level. On the (001) surface, the bulk Dirac points are found to project onto the -Γ point with nontrivial surface states. Our finding will substantially enrich the family of 3D Dirac semimetals which are useful for topological applications.

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preprint2019arXiv

Bulk Fermi surface of the layered superconductor TaSe3 with three-dimensional strong topological insulator state

High magnetic field transport measurements and ab initio calculations on the layered superconductor TaSe3 have provided compelling evidences for the existence of a three-dimensional strong topological insulator state. Longitudinal magnetotransport measurements up to ~ 33 T unveiled striking Shubnikov-de Hass oscillations with two fundamental frequencies at 100 T and 175 T corresponding to a nontrivial electron Fermi pocket at the B point and a nontrivial hole Fermi pocket at the Γ point respectively in the Brillouin zone. However, calculations revealed one more electron pocket at the B point, which was not detected by the magnetotransport measurements, presumably due to the limited carrier momentum relaxation time. Angle dependent quantum oscillations by rotating the sample with respect to the magnetic field revealed clear changes in the two fundamental frequencies, indicating anisotropic electronic Fermi pockets. The ab initio calculations gave the topological Z2 invariants of (1; 100) and revealed a single Dirac cone on the (1 0 -1) surface at the X point with helical spin texture at a constant-energy contour, suggesting a strong topological insulator state. The results demonstrate TaSe3 an excellent platform to study the interplay between topological phase and superconductivity and a promising system for the exploration of topological superconductivity.

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