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Qin Li

Qin Li appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

ResearcherUnclaimed source record
math.NANumerical Analysisquant-phmath.APCryptography and Securityphysics.flu-dynMachine Learningmath.QAphysics.acc-phphysics.comp-phhep-thmath-phmath.MPmath.OCastro-ph.SRmath.DGphysics.soc-phastro-ph.IMCell BehaviorComputation and LanguageComputational Engineering, Finance, and ScienceComputer Visioncs.CYDistributed, Parallel, and Cluster Computingeess.ASeess.SYFormal Languages and Automata Theoryhep-exmath.AGmath.CTmath.CVmath.PRphysics.ins-detphysics.plasm-phPopulations and EvolutionSocial and Information NetworksSoundSystems and Control

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Published work

55 published item(s)

preprint2026arXiv

Comparative study of equilibrium and non-equilibrium predictions by different models for a hypersonic cone at high-altitude

Targeting a cone with the half-angle as 10-deg at M = 27 and H = 72 km, simulations were conducted comparatively to analyze the predictions by different equilibrium and non-equilibrium gas models. Following validation and grid studies, systematic comparisons on aerodynamic performance, flow structures, and characteristic distributions were performed. The key findings are: (1) While the overall flow structures are broadly similar, discrepancies exist in the features at the base locations, e.g., the diverse high-temperature distributions. Notably, the vibrational temperatures distribute differently under slip and non-slip boundary conditions near the wall; (2) The equilibrium gas model predicts higher drag coefficient, wall heat flux, and skin friction than those of non-equilibrium models. Predictions also vary among the non-equilibrium models themselves. Specifically, compared to the three-temperature model, the one- and two-temperature models predict larger drag coefficients with the relative difference exceeding 5%. Nevertheless, the results from the three-temperature model with and without slip conditions are largely consistent; (3) The disparities between equilibrium and non-equilibrium characteristics are primarily manifested in the shock layer and wake regions. Within these regions, the overall temperature for the equilibrium gas is lower than that for the non-equilibrium cases, while in the latter specific non-equilibrium features are distinctly exhibited, e.g., the translational-rotational temperature is generally higher than that from the one-temperature model, and the vibrational-electronic temperature shows the opposite trend. Notably, in the slip flow within the three-temperature model, the translational-rotational temperature is higher and, particularly, the vibrational temperature is even larger than counterparts of the non-slip flows near the wall and base center line.

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preprint2026arXiv

Data selection: at the interface of PDE-based inverse problem and randomized linear algebra

All inverse problems rely on data to recover unknown parameters, yet not all data are equally informative. This raises the central question of data selection. A distinctive challenge in PDE-based inverse problems is their inherently infinite-dimensional nature: both the parameter space and the design space are infinite, which greatly complicates the selection process. Somewhat unexpectedly, randomized numerical linear algebra (RNLA), originally developed in very different contexts, has provided powerful tools for addressing this challenge. These methods are inherently probabilistic, with guarantees typically stating that information is preserved with probability at least 1-p when using N randomly selected, weighted samples. Here, the notion of "information" can take different mathematical forms depending on the setting. In this review, we survey the problem of data selection in PDE-based inverse problems, emphasize its unique infinite-dimensional aspects, and highlight how RNLA strategies have been adapted and applied in this context.

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preprint2026arXiv

Modeling Epidemic Dynamics of Mutant Strains with Evolutionary Game-based Vaccination Behavior

The outbreak of mutant strains and vaccination behaviors have been the focus of recent epidemiological research, but most existing epidemic models failed to simultaneously capture viral mutation and consider the complexity and behavioral dynamics of vaccination. To address this gap, we develop an extended SIRS model that distinguishes infections with the original strain and a mutant strain, and explicitly introduces a vaccinated compartment state. At the behavioral level, we employ evolutionary game theory to model individual vaccination decisions, where strategies are determined by both neighbors' choices and the current epidemiological situation. This process corresponds to the time-varying vaccination rate of susceptible individuals transitioning to vaccinated individuals at the epidemic spreading level. We then couple the epidemic and vaccination behavioral processes through the microscopic Markov chain approach (MMCA) and finally investigate the evolutionary dynamics via numerical simulations. The results show that our framework can effectively mitigate outbreaks across different disease scenarios. Sensitivity analysis further reveals that vaccination uptake is most strongly influenced by vaccine cost, efficacy, and perceived risk of side effects. Overall, this behavior-aware modeling framework captures the co-evolution of viral mutation and vaccination behavior, providing quantitative and theoretical support for designing effective public health vaccination policies.

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preprint2026arXiv

Provable imitation learning for control of instability in partially-observed Vlasov--Poisson equations

We consider the stabilization of Vlasov--Poisson plasma dynamics, a central control problem in nuclear fusion. Our focus is the gap between what an ideal controller would use and what experiments can actually observe: while optimal policy may rely on the full phase-space state, practical feedback is typically limited to sparse macroscopic diagnostics. We therefore study imitation learning methods that distill a fully observed expert policy into controllers operating only on macroscopic measurements. We show the stability guarantees of the learned policy, where the error floor depends on the minimal behavior cloning loss achievable under the observation constraints. We further characterize this minimal loss in terms of a notion of entropy that quantifies the complexity of the initial distribution. Our results demonstrates the theoretical feasibility of learning stabilizing feedback policies for kinetic plasma dynamics from macroscopic observations, and exhibits the adaptivity of the learning approach to low-complexity structures. Through extensive numerical experiments, we validate our theory and show that the learned policies can stabilize the system using only macroscopic observations, within a significantly longer time horizon than non-adaptive baseline controllers.

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preprint2025arXiv

Stability of the reconstruction of the heat reflection coefficient in the phonon transport equation

The reflection coefficient is an important thermal property of materials, especially at the nanoscale, and determining this property requires solving an inverse problem based on macroscopic temperature measurements. In this manuscript, we investigate the stability of this inverse problem to infer the reflection coefficient in the phonon transport equation. We show that the problem becomes ill-posed as the system transitions from the ballistic to the diffusive regime, characterized by the Knudsen number converging to zero. Such a stability estimate clarifies the discrepancy observed in previous studies on the well-posedness of this inverse problem. Furthermore, we quantify the rate at which the stability deteriorates with respect to the Knudsen number and confirm the theoretical result with numerical evidence.

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preprint2024arXiv

Reconstructing the kinetic chemotaxis kernel using macroscopic data: well-posedness and ill-posedness

Bacterial motion is steered by external stimuli (chemotaxis), and the motion described on the mesoscopic scale is uniquely determined by a parameter $K$ that models velocity change response from the bacteria. This parameter is called chemotaxis kernel. In a practical setting, it is inferred by experimental data. We deploy a PDE-constrained optimization framework to perform this reconstruction using velocity-averaged, localized data taken in the interior of the domain. The problem can be well-posed or ill-posed depending on the data preparation and the experimental setup. In particular, we propose one specific design that guarantees numerical reconstructability and local convergence. This design is adapted to the discretization of $K$ in space and decouples the reconstruction of local values of $K$ into smaller cell problems, opening up parallelization opportunities. Numerical evidences support the theoretical findings.

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preprint2022arXiv

High-frequency limit of the inverse scattering problem: asymptotic convergence from inverse Helmholtz to inverse Liouville

We investigate the asymptotic relation between the inverse problems relying on the Helmholtz equation and the radiative transfer equation (RTE) as physical models, in the high-frequency limit. In particular, we evaluate the asymptotic convergence of a generalized version of inverse scattering problem based on the Helmholtz equation, to the inverse scattering problem of the Liouville equation (a simplified version of RTE). The two inverse problems are connected through the Wigner transform that translates the wave-type description on the physical space to the kinetic-type description on the phase space, and the Husimi transform that models data localized both in location and direction. The finding suggests that impinging tightly concentrated monochromatic beams can indeed provide stable reconstruction of the medium, asymptotically in the high-frequency regime. This fact stands in contrast with the unstable reconstruction for the classical inverse scattering problem when the probing signals are plane-waves.

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preprint2022arXiv

Improvements to enhance robustness of third-order scale-independent WENO-Z schemes

Although there are many improvements to WENO3-Z that target the achievement of optimal order in the occurrence of the first-order critical point (CP1), they mainly address resolution performance, while the robustness of schemes is of less concern and lacks understanding accordingly. In light of our analysis considering the occurrence of critical points within grid intervals, we theoretically prove that it is impossible for a scale-independent scheme that has the stencil of WENO3-Z to fulfill the above order achievement, and current scale-dependent improvements barely fulfill the job when CP1 occurs at the middle of the grid cell. In order to achieve scale-independent improvements, we devise new smoothness indicators that increase the error order from 2 to 4 when CP1 occurs and perform more stably. Meanwhile, we construct a new global smoothness indicator that increases the error order from 4 to 5 similarly, through which new nonlinear weights with regard to WENO3-Z are derived and new scale-independents improvements, namely WENO-ZES2 and -ZES3, are acquired. Through 1D scalar and Euler tests, as well as 2D computations, in comparison with typical scale-dependent improvement, the following performances of the proposed schemes are demonstrated: The schemes can achieve third-order accuracy at CP1 no matter its location in the stencil, indicate high resolution in resolving flow subtleties, and manifest strong robustness in hypersonic simulations (e.g., the accomplishment of computations on hypersonic half-cylinder flow with Mach numbers reaching 16 and 19, respectively, as well as essentially non-oscillatory solutions of inviscid sharp double cone flow at M=9.59), which contrasts the comparative WENO3-Z improvement.

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preprint2022arXiv

Kapranov's $L_\infty$ structures, Fedosov's star products, and one-loop exact BV quantizations on Kähler manifolds

We study quantization schemes on a Kähler manifold and relate several interesting structures. We first construct Fedosov's star products on a Kähler manifold $X$ as quantizations of Kapranov's $L_\infty$-algebra structure. Then we investigate the Batalin-Vilkovisky (BV) quantizations associated to these star products. A remarkable feature is that they are all one-loop exact, meaning that the Feynman weights associated to graphs with two or more loops all vanish. This leads to a succinct cochain level formula in de Rham cohomology for the algebraic index.

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preprint2022arXiv

Low-rank approximation for multiscale PDEs

Historically, analysis for multiscale PDEs is largely unified while numerical schemes tend to be equation-specific. In this paper, we propose a unified framework for computing multiscale problems through random sampling. This is achieved by incorporating randomized SVD solvers and manifold learning techniques to numerically reconstruct the low-rank features of multiscale PDEs. We use multiscale radiative transfer equation and elliptic equation with rough media to showcase the application of this framework.

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preprint2022arXiv

Predicting Solar Energetic Particles Using SDO/HMI Vector Magnetic Data Products and a Bidirectional LSTM Network

Solar energetic particles (SEPs) are an essential source of space radiation, which are hazards for humans in space, spacecraft, and technology in general. In this paper we propose a deep learning method, specifically a bidirectional long short-term memory (biLSTM) network, to predict if an active region (AR) would produce an SEP event given that (i) the AR will produce an M- or X-class flare and a coronal mass ejection (CME) associated with the flare, or (ii) the AR will produce an M- or X-class flare regardless of whether or not the flare is associated with a CME. The data samples used in this study are collected from the Geostationary Operational Environmental Satellite's X-ray flare catalogs provided by the National Centers for Environmental Information. We select M- and X-class flares with identified ARs in the catalogs for the period between 2010 and 2021, and find the associations of flares, CMEs and SEPs in the Space Weather Database of Notifications, Knowledge, Information during the same period. Each data sample contains physical parameters collected from the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory. Experimental results based on different performance metrics demonstrate that the proposed biLSTM network is better than related machine learning algorithms for the two SEP prediction tasks studied here. We also discuss extensions of our approach for probabilistic forecasting and calibration with empirical evaluation.

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preprint2021arXiv

Classical limit for the varying-mass Schrödinger equation with random inhomogeneities

The varying-mass Schrödinger equation (VMSE) has been successfully applied to model electronic properties of semiconductor hetero-structures, for example, quantum dots and quantum wells. In this paper, we consider VMSE with small random heterogeneities, and derive a radiative transfer equation as its asymptotic limit. The main tool is to systematically apply the Wigner transform in the classical regime when the rescaled Planck constant $ε\ll 1$, and expand the Wigner equation to proper orders of $ε$. As a proof of concept, we numerically compute both VMSE and its limiting radiative transfer equation, and show that their solutions agree well in the classical regime.

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preprint2021arXiv

Cryptanalysis and improvement of a semi-quantum private comparison protocol based on Bell states

Semi-quantum private comparison (SQPC) allows two participants with limited quantum ability to securely compare the equality of their secrets with the help of a semi-dishonest third party (TP). Recently, Jiang proposed a SQPC protocol based on Bell states (Quantum Inf Process 19(6): 180, 2020) and claimed it is secure. In this paper, we present two types of attack on Jiang's SQPC protocol. In the first type of attack, an outside eavesdropper will make participants accept a wrong result. In the second type of attack, a malicious participant will not only make the other participant accept a wrong result, but also learn the secret of the honest participant. Neither type of attack will be detected. In addition, we propose an improved SQPC protocol that can resist these two types of attack.

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preprint2021arXiv

Improving speech recognition models with small samples for air traffic control systems

In the domain of air traffic control (ATC) systems, efforts to train a practical automatic speech recognition (ASR) model always faces the problem of small training samples since the collection and annotation of speech samples are expert- and domain-dependent task. In this work, a novel training approach based on pretraining and transfer learning is proposed to address this issue, and an improved end-to-end deep learning model is developed to address the specific challenges of ASR in the ATC domain. An unsupervised pretraining strategy is first proposed to learn speech representations from unlabeled samples for a certain dataset. Specifically, a masking strategy is applied to improve the diversity of the sample without losing their general patterns. Subsequently, transfer learning is applied to fine-tune a pretrained or other optimized baseline models to finally achieves the supervised ASR task. By virtue of the common terminology used in the ATC domain, the transfer learning task can be regarded as a sub-domain adaption task, in which the transferred model is optimized using a joint corpus consisting of baseline samples and new transcribed samples from the target dataset. This joint corpus construction strategy enriches the size and diversity of the training samples, which is important for addressing the issue of the small transcribed corpus. In addition, speed perturbation is applied to augment the new transcribed samples to further improve the quality of the speech corpus. Three real ATC datasets are used to validate the proposed ASR model and training strategies. The experimental results demonstrate that the ASR performance is significantly improved on all three datasets, with an absolute character error rate only one-third of that achieved through the supervised training. The applicability of the proposed strategies to other ASR approaches is also validated.

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preprint2021arXiv

On developing piecewise rational mapping with fine regulation capability for WENO schemes

On the idea of mapped WENO-JS scheme, properties of mapping methods are analyzed, uncertainties in mapping development are investigated, and new rational mappings are proposed. Based on our former understandings, i.e. mapping at endpoints {0, 1} tending to identity mapping, an integrated Cm,n condition is summarized for function development. Uncertainties, i.e., whether the mapping at endpoints would make mapped scheme behave like WENO or ENO, whether piecewise implementation would entail numerical instability, and whether WENO3-JS could preserve the third-order at first-order critical points by mapping, are analyzed and clarified. A new piecewise rational mapping with sufficient regulation capability is developed afterwards, where the flatness of mapping around the linear weights and its endpoint convergence toward identity mapping can be coordinated explicitly and simultaneously. Hence, the increase of resolution and preservation of stability can be balanced. Especially, concrete mappings are determined for WENO3,5,7-JS. Numerical cases are tested for the new mapped WENO-JS, which regards numerical stability including that in long time computation, resolution and robustness. In purpose of comparison, some recent mappings such as IM by [App. Math. Comput. 232, 2014:453-468], RM by [J. Sci. Comput. 67, 2016:540-580] and AIM by [J. Comput. Phys. 381, 2019:162-188] are chosen; in addition, some recent WENO-Z type scheme are selected also. Proposed new schemes can preserve optimal orders at corresponding critical points, achieve numerical stability and indicate overall comparative advantages regarding accuracy, resolution and robustness.

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preprint2021arXiv

Semi-classical limit of an inverse problem for the Schrödinger equation

It is a classical derivation that the Wigner equation, derived from the Schrödinger equation that contains the quantum information, converges to the Liouville equation when the rescaled Planck constant $ε\to0$. Since the latter presents the Newton's second law, the process is typically termed the (semi-)classical limit. In this paper, we study the classical limit of an inverse problem for the Schrödinger equation. More specifically, we show that using the initial condition and final state of the Schrödinger equation to reconstruct the potential term, in the classical regime with $ε\to0$, becomes using the initial and final state to reconstruct the potential term in the Liouville equation. This formally bridges an inverse problem in quantum mechanics with an inverse problem in classical mechanics.

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preprint2020arXiv

A low-rank Schwarz method for radiative transport equation with heterogeneous scattering coefficient

Random sampling has been used to find low-rank structure and to build fast direct solvers for multiscale partial differential equations of various types. In this work, we design an accelerated Schwarz method for radiative transfer equations that makes use of approximate local solution maps constructed offline via a random sampling strategy. Numerical examples demonstrate the accuracy, robustness, and efficiency of the proposed approach.

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preprint2020arXiv

An efficient and provably secure arbitrated quantum signature scheme

In this paper, an efficient arbitrated quantum signature scheme is proposed by combining quantum cryptographic techniques and some ideas in classical cryptography. In the presented scheme, the signatory and the receiver can share a long-term secret key with the arbitrator by utilizing the key together with a random number. While in previous quantum signature schemes, the key shared between the signatory and the arbitrator or between the receiver and the arbitrator could be used only once, and thus each time when a signatory needs to sign, the signatory and the receiver have to obtain a new key shared with the arbitrator through a quantum key distribution protocol. Detailed theoretical analysis shows that the proposed scheme is efficient and provably secure.

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preprint2020arXiv

Machine Learning in Heliophysics and Space Weather Forecasting: A White Paper of Findings and Recommendations

The authors of this white paper met on 16-17 January 2020 at the New Jersey Institute of Technology, Newark, NJ, for a 2-day workshop that brought together a group of heliophysicists, data providers, expert modelers, and computer/data scientists. Their objective was to discuss critical developments and prospects of the application of machine and/or deep learning techniques for data analysis, modeling and forecasting in Heliophysics, and to shape a strategy for further developments in the field. The workshop combined a set of plenary sessions featuring invited introductory talks interleaved with a set of open discussion sessions. The outcome of the discussion is encapsulated in this white paper that also features a top-level list of recommendations agreed by participants.

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preprint2020arXiv

Non-recurrent Traffic Congestion Detection with a Coupled Scalable Bayesian Robust Tensor Factorization Model

Non-recurrent traffic congestion (NRTC) usually brings unexpected delays to commuters. Hence, it is critical to accurately detect and recognize the NRTC in a real-time manner. The advancement of road traffic detectors and loop detectors provides researchers with a large-scale multivariable temporal-spatial traffic data, which allows the deep research on NRTC to be conducted. However, it remains a challenging task to construct an analytical framework through which the natural spatial-temporal structural properties of multivariable traffic information can be effectively represented and exploited to better understand and detect NRTC. In this paper, we present a novel analytical training-free framework based on coupled scalable Bayesian robust tensor factorization (Coupled SBRTF). The framework can couple multivariable traffic data including traffic flow, road speed, and occupancy through sharing a similar or the same sparse structure. And, it naturally captures the high-dimensional spatial-temporal structural properties of traffic data by tensor factorization. With its entries revealing the distribution and magnitude of NRTC, the shared sparse structure of the framework compasses sufficiently abundant information about NRTC. While the low-rank part of the framework, expresses the distribution of general expected traffic condition as an auxiliary product. Experimental results on real-world traffic data show that the proposed method outperforms coupled Bayesian robust principal component analysis (coupled BRPCA), the rank sparsity tensor decomposition (RSTD), and standard normal deviates (SND) in detecting NRTC. The proposed method performs even better when only traffic data in weekdays are utilized, and hence can provide more precise estimation of NRTC for daily commuters.

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preprint2020arXiv

On diffusive scaling in acousto-optic imaging

Acousto-optic imaging (AOI) is a hybrid imaging process. By perturbing the to-be-reconstructed tissues with acoustic waves, one introduces the interaction between the acoustic and optical waves, leading to a more stable reconstruction of the optical properties. The mathematical model was described in [25], with the radiative transfer equation serving as the forward model for the optical transport. In this paper we investigate the stability of the reconstruction. In particular, we are interested in how the stability depends on the Knudsen number, Kn, a quantity that measures the intensity of the scattering effect of photon particles in a media. Our analysis shows that as Kn decreases to zero, photons scatter more frequently, and since information is lost, the reconstruction becomes harder. To counter this effect, devices need to be constructed so that laser beam is highly concentrated. We will give a quantitative error bound, and explicitly show that such concentration has an exponential dependence on Kn. Numerical evidence will be provided to verify the proof.

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preprint2020arXiv

PAC Model Checking of Black-Box Continuous-Time Dynamical Systems

In this paper we present a novel model checking approach to finite-time safety verification of black-box continuous-time dynamical systems within the framework of probably approximately correct (PAC) learning. The black-box dynamical systems are the ones, for which no model is given but whose states changing continuously through time within a finite time interval can be observed at some discrete time instants for a given input. The new model checking approach is termed as PAC model checking due to incorporation of learned models with correctness guarantees expressed using the terms error probability and confidence. Based on the error probability and confidence level, our approach provides statistically formal guarantees that the time-evolving trajectories of the black-box dynamical system over finite time horizons fall within the range of the learned model plus a bounded interval, contributing to insights on the reachability of the black-box system and thus on the satisfiability of its safety requirements. The learned model together with the bounded interval is obtained by scenario optimization, which boils down to a linear programming problem. Three examples demonstrate the performance of our approach.

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preprint2020arXiv

Random Sampling and Efficient Algorithms for Multiscale PDEs

We describe a numerical framework that uses random sampling to efficiently capture low-rank local solution spaces of multiscale PDE problems arising in domain decomposition. In contrast to existing techniques, our method does not rely on detailed analytical understanding of specific multiscale PDEs, in particular, their asymptotic limits. We present the application of the framework on two examples --- a linear kinetic equation and an elliptic equation with rough media. On these two examples, this framework achieves the asymptotic preserving property for the kinetic equations and numerical homogenization for the elliptic equations.

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preprint2020arXiv

Security Improvements of Several Basic Quantum Private Query Protocols with O(log N) Communication Complexity

New quantum private database (with N elements) query protocols are presented and analyzed. Protocols preserve O(logN) communication complexity of known protocols for the same task, but achieve several significant improvements in security, especially concerning user privacy. For example, the randomized form of our protocol has a cheat-sensitive property - it allows the user to detect a dishonest database with a nonzero probability, while the phase-encoded private query protocols for the same task do not have such a property. Moreover, when the database performs the computational basis measurement, a particular projective measurement which can cause a significant loss of user privacy in the previous private query protocols with O(logN) communication complexity, at most half of the user privacy could leak to such a database in our protocol, while in the QPQ protocol, the entire user privacy could leak out. In addition, it is proved here that for large N, the user could detect a cheating via the computational basis measurement, with a probability close to 1/2 using O(\sqrt{N}) special queries. Finally, it is shown here, for both forms of our protocol, basic and randomized, how a dishonest database has to act in case it could not learn user's queries.

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preprint2020arXiv

Structured random sketching for PDE inverse problems

For an overdetermined system $\mathsf{A}\mathsf{x} \approx \mathsf{b}$ with $\mathsf{A}$ and $\mathsf{b}$ given, the least-square (LS) formulation $\min_x \, \|\mathsf{A}\mathsf{x}-\mathsf{b}\|_2$ is often used to find an acceptable solution $\mathsf{x}$. The cost of solving this problem depends on the dimensions of $\mathsf{A}$, which are large in many practical instances. This cost can be reduced by the use of random sketching, in which we choose a matrix $\mathsf{S}$ with fewer rows than $\mathsf{A}$ and $\mathsf{b}$, and solve the sketched LS problem $\min_x \, \|\mathsf{S}(\mathsf{A} \mathsf{x}-\mathsf{b})\|_2$ to obtain an approximate solution to the original LS problem. Significant theoretical and practical progress has been made in the last decade in designing the appropriate structure and distribution for the sketching matrix $\mathsf{S}$. When $\mathsf{A}$ and $\mathsf{b}$ arise from discretizations of a PDE-based inverse problem, tensor structure is often present in $\mathsf{A}$ and $\mathsf{b}$. For reasons of practical efficiency, $\mathsf{S}$ should be designed to have a structure consistent with that of $\mathsf{A}$. Can we claim similar approximation properties for the solution of the sketched LS problem with structured $\mathsf{S}$ as for fully-random $\mathsf{S}$? We give estimates that relate the quality of the solution of the sketched LS problem to the size of the structured sketching matrices, for two different structures. Our results are among the first known for random sketching matrices whose structure is suitable for use in PDE inverse problems.

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preprint2019arXiv

Applications of Kinetic Tools to Inverse Transport Problems

We show that the inverse problems for a class of kinetic equations can be solved by classical tools in PDE analysis including energy estimates and the celebrated averaging lemma. Using these tools, we give a unified framework for the reconstruction of the absorption coefficient for transport equations in the subcritical and critical regimes. Moreover, we apply this framework to obtain, to the best of our knowledge, the first result in a nonlinear setting. We also extend the result of recovering the scattering coefficient in [14] from 3D to 2D convex domains.

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preprint2017arXiv

Batalin-Vilkovisky quantization and the algebraic index

Into a geometric setting, we import the physical interpretation of index theorems via semi-classical analysis in topological quantum field theory. We develop a direct relationship between Fedosov's deformation quantization of a symplectic manifold X and the BV quantization of a one-dimensional sigma model with target X. This model is a quantum field theory of AKSZ type and is quantized rigorously using Costello's homotopic theory of effective renormalization. We show that Fedosov's Abelian connections on the Weyl bundle produce solutions to the effective quantum master equation. Moreover, BV integration produces a natural trace map on the deformation quantized algebra. This formulation allows us to exploit a (rigorous) localization argument in quantum field theory to deduce the algebraic index theorem via semi-classical analysis, i.e., one-loop Feynman diagram computations.

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preprint2016arXiv

A convergent method for linear half-space kinetic equations

We give a unified proof for the well-posedness of a class of linear half-space equations with general incoming data and construct a Galerkin method to numerically resolve this type of equations in a systematic way. Our main strategy in both analysis and numerics includes three steps: adding damping terms to the original half-space equation, using an inf-sup argument and even-odd decomposition to establish the well-posedness of the damped equation, and then recovering solutions to the original half-space equation. The proposed numerical methods for the damped equation is shown to be quasi-optimal and the numerical error of approximations to the original equation is controlled by that of the damped equation. This efficient solution to the half-space problem is useful for kinetic-fluid coupling simulations.

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preprint2016arXiv

A sparse decomposition of low rank symmetric positive semi-definite matrices

Suppose that $A \in \mathbb{R}^{N \times N}$ is symmetric positive semidefinite with rank $K \le N$. Our goal is to decompose $A$ into $K$ rank-one matrices $\sum_{k=1}^K g_k g_k^T$ where the modes $\{g_{k}\}_{k=1}^K$ are required to be as sparse as possible. In contrast to eigen decomposition, these sparse modes are not required to be orthogonal. Such a problem arises in random field parametrization where $A$ is the covariance function and is intractable to solve in general. In this paper, we partition the indices from 1 to $N$ into several patches and propose to quantify the sparseness of a vector by the number of patches on which it is nonzero, which is called patch-wise sparseness. Our aim is to find the decomposition which minimizes the total patch-wise sparseness of the decomposed modes. We propose a domain-decomposition type method, called intrinsic sparse mode decomposition (ISMD), which follows the "local-modes-construction + patching-up" procedure. The key step in the ISMD is to construct local pieces of the intrinsic sparse modes by a joint diagonalization problem. Thereafter a pivoted Cholesky decomposition is utilized to glue these local pieces together. Optimal sparse decomposition, consistency with different domain decomposition and robustness to small perturbation are proved under the so called regular-sparse assumption (see Definition 1.2). We provide simulation results to show the efficiency and robustness of the ISMD. We also compare the ISMD to other existing methods, e.g., eigen decomposition, pivoted Cholesky decomposition and convex relaxation of sparse principal component analysis [25] and [40].

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preprint2016arXiv

Energy-spread measurement of triple-pulse electron beams based on the magnetic dispersion principle

The energy-spread of the triple-pulse electron beam generated by the Dragon-II linear induction accelerator is measured using the method of energy dispersion in the magnetic field. A sector magnet is applied for energy analyzing of the electron beam, which has a bending radius of 300 mm and a deflection angle of 90 degrees. For each pulse, both the time-resolved and the integral images of the electron position at the output port of the bending beam line are recorded by a streak camera and a CCD camera, respectively. Experimental results demonstrate an energy-spread of less than +-2.0% for the electron pulses. The cavity voltage waveforms obtained by different detectors are also analyzed for comparison.

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preprint2016arXiv

Exploring the locally low dimensional structure in solving random elliptic PDEs

We propose a stochastic multiscale finite element method (StoMsFEM) to solve random elliptic partial differential equations with a high stochastic dimension. The key idea is to simultaneously upscale the stochastic solutions in the physical space for all random samples and explore the low stochastic dimensions of the stochastic solution within each local patch. We propose two effective methods to achieve this simultaneous local upscaling. The first method is a high order interpolation method in the stochastic space that explores the high regularity of the local upscaled quantities with respect to the random variables. The second method is a reduced-order method that explores the low rank property of the multiscale basis functions within each coarse grid patch. Our complexity analysis shows that compared with the standard FEM on a fine grid, the StoMsFEM can achieve computational saving in the order of $(H/h)^{d}/(\log(H/h))^k$, where $H/h$ is the ratio between the coarse and the fine gird sizes, $d$ is the physical dimension and $k$ is the local stochastic dimension. Several numerical examples are presented to demonstrate the accuracy and effectiveness of the proposed methods. In the high contrast example, we observe a factor of 2000 speed-up.

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preprint2016arXiv

On free-stream preservation in stationary grids for arbitrary linear upwind schemes

In order to improve the application maturity of high-order difference schemes, the free-stream preservation property, whose importance has been widely recognized in recent years, has been developed into a focus of study.. In past literatures, only central schemes are considered to be suitable for free-stream preservation. In this study, the methodology for arbitrary linear schemes to achieve the property is investigated. First, derivations of grid metric by Thomas, Lombard and Neier are reviewed, through which linear schemes for the metric and unsplit flux could attain the property by the proof of Vinokur and Yee firstly. In practical applications, flux splittings are usually at presence and therefore the direct use of upwind schemes seems difficult to fulfill free-stream preservation. To overcome the difficulty, two attempts are made: firstly, a central-scheme-decomposition is worked out, through which a central difference scheme is derived to approximate the first-order partial derivative in metric evaluations; secondly, treatments are proposed for flux splitting and a concrete example is presented. Through these two attempts, the linear upwind node schemes can achieve the property. For half-node or mixed type schemes, interpolations should be used to derive variables at half nodes. As a result, a directionally consistent interpolation is proposed, which is shown to be necessary in order to avoid violation of the metric identity and free-stream preservation. Two numerical problems are also tested, i.e., the free-stream and vortex preservation on wavy, largely randomized and triangular grids. Numerical results validate aforementioned theoretical outcomes; especially, simulations on the triangular grids indicate the methods discussed in this study, which are typical algorithms on structured grids, has the application potential for problems on unstructured meshes.

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preprint2016arXiv

Private reputation retrieval in public - a privacy-aware announcement scheme for VANETs

An announcement scheme is a system that facilitates vehicles to broadcast road-related information in vehicular ad hoc networks (VANETs) in order to improve road safety and efficiency. Here we propose a new cryptographic primitive for public updating of reputation score based on the Boneh-Boyen-Shacham short group signature scheme. This allows private reputation score retrieval without a secure channel. Using this we devise a privacy-aware announcement scheme using reputation systems which is reliable, auditable and robust.

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preprint2016arXiv

Semiquantum key distribution with secure delegated quantum computation

Semiquantum key distribution allows a quantum party to share a random key with a "classical" party who only can prepare and measure qubits in the computational basis or reorder some qubits when he has access to a quantum channel. In this work, we present a protocol where a secret key can be established between a quantum user and an almost classical user who only needs the quantum ability to access quantum channels, by securely delegating quantum computation to a quantum server. We show the proposed protocol is robust even when the delegated quantum server is a powerful adversary, and is experimentally feasible with current technology. As one party of our protocol is the most quantum-resource efficient, it can be more practical and significantly widen the applicability scope of quantum key distribution.

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preprint2015arXiv

A macro-level model for investigating the effect of directional bias on network coverage

Random walks have been proposed as a simple method of efficiently searching, or disseminating information throughout, communication and sensor networks. In nature, animals (such as ants) tend to follow correlated random walks, i.e., random walks that are biased towards their current heading. In this paper, we investigate whether or not complementing random walks with directional bias can decrease the expected discovery and coverage times in networks. To do so, we develop a macro-level model of a directionally biased random walk based on Markov chains. By focussing on regular, connected networks, the model allows us to efficiently calculate expected coverage times for different network sizes and biases. Our analysis shows that directional bias can significantly reduce coverage time, but only when the bias is below a certain value which is dependent on the network size.

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preprint2015arXiv

Assessing Google Correlate Queries for Influenza H1N1 Surveillance in Asian Developing Countries

So far, Google Trend data have been used for influenza surveillance in many European and American countries; however, there are few attempts to apply the low-cost surveillance method in Asian developing countries. To investigate the correlation between the search trends and the influenza activity in Asia, we examined the Google query data of four Asian developing countries.

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preprint2015arXiv

Diffusion approximations and domain decomposition method of linear transport equations: asymptotics and numerics

In this paper we construct numerical schemes to approximate linear transport equations with slab geometry by diffusion equations. We treat both the case of pure diffusive scaling and the case where kinetic and diffusive scalings coexist. The diffusion equations and their data are derived from asymptotic and layer analysis which allows general scattering kernels and general data. We apply the half-space solver in [20] to resolve the boundary layer equation and obtain the boundary data for the diffusion equation. The algorithms are validated by numerical experiments and also by error analysis for the pure diffusive scaling case.

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preprint2015arXiv

Experimental comparison of various techniques for spot size measurement of high-energy x-ray source

In the experiment of flash-radiography, the quality of acquired image strongly depends on the focal size of the x-ray source spot. A variety of techniques based on imaging of pinhole, slit and rollbar are adopted to measure the focal spot size of the Dragon-I linear induction accelerator. The image of pinhole provides a two-dimensional distribution of the x-ray spot, while those of slit and rollbar give a line-spread distribution and an edge-spread distribution, respectively. The spot size characterized by full-width at half-maximum and that characterized by the LANL definition are calculated for comparison.

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preprint2015arXiv

Experimental observation of spatial jitters of a triple-pulse x-ray source based on the pinhole imaging technique

In high-energy flash radiography, scattered photons will degrade the acquiring image, which limits the resolving power of the interface and density of the dense object. The application of large anti-scatter grid is capable of remarkably decreasing scattered photons, whereas requires a very stable source position in order to reduce the loss of signal photons in the grid structure. The pinhole imaging technique is applied to observe spatial jitters of a triple-pulse radiographic source produced by a linear induction accelerator. Numerical simulations are taken to analyze the performance of the imaging technique with same or close parameters of the pinhole object and experimental alignment Experiments are carried out to observe spatial jitters of the source between different measurements. Deviations of the source position between different pulses are also measured in each experiment.

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preprint2015arXiv

Half-space Kinetic Equations with General Boundary Conditions

We study half-space linear kinetic equations with general boundary conditions that consist of both given incoming data and various type of reflections, extending our previous work [LLS14] on half-space equations with incoming boundary conditions. As in [LLS14], the main technique is a damping adding-removing procedure. We establish the well-posedness of linear (or linearized) half-space equations with general boundary conditions and quasi-optimality of the numerical scheme. The numerical method is validated by examples including a two-species transport equation, a multi-frequency transport equation, and the linearized BGK equation in 2D velocity space.

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preprint2015arXiv

Spot size measurement of flash-radiography source utilizing the pinhole imaging method

The spot size of the x-ray source is a key parameter of a flash-radiography facility, which is usually quoted as an evaluation of the resolving power. The pinhole imaging technique is applied to measure the spot size of the Dragon-I linear induction accelerator, by which a two-dimensional spatial distribution of the source spot is obtained. Experimental measurements are performed to measure the spot image when the transportation and focusing of the electron beam is tuned by adjusting the currents of solenoids in the downstream section. The spot size of full-width at half maximum and that defined from the spatial frequency at half peak value of the modulation transfer function are calculated and discussed.

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preprint2014arXiv

A multi-band semiclassical model for surface hopping quantum dynamics

In the paper we derive a semiclassical model for surface hopping allowing quantum dynamical non-adiabatic transition between different potential energy surfaces in which cases the classical Born-Oppenheimer approximation breaks down. The model is derived using the Wigner transform and Weyl quantization, and the central idea is to evolve the entire Wigner matrix rather than just the diagonal entries as was done previously in the adiabatic case. The off-diagonal entries of the Wigner matrix suitably describe the non-adiabatic transition, such as the Berry connection, for avoided crossings. We study the numerical approximation issues of the model, and then conduct numerical experiments to validate the model.

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preprint2014arXiv

Numerical and Experimental Studies on the Separation Topology of the MVG Controlled Flow at M=2.5

In this paper, the implicitly implemented LES method and fifth order bandwidth optimized WENO scheme are used to make comprehensive studies on the separation topology of the MVG controlled flow at M=2.5 and Reθ=5760. Experiments are also made to verify the prediction of the computation. Analyses are conducted on three categories of the topology: the surface separation, cross-section separation and the three dimensional structure of the vortices. A complete description about the separation topology and a series of new findings are obtained. Among them, a pair of spiral point is first predicted by the computation and verified by the experiment. A corresponding new vortex model with 7 vortex tubes is presented also.

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preprint2014arXiv

Numerical Study of Mechanism of U-shaped Vortex Formation

This paper illustrates the mechanism of U-shaped vortex formation which is found both by experiment and DNS. The main goal of this paper is to explain how the U-shaped vortex is formed and further develops. According to the results obtained by our direct numerical simulation with high order accuracy, the U-shaped vortex is part of the coherent vortex structure and is actually the tertiary streamwise vortices induced by the secondary vortices. The new finding is quite different from existing theories which describe that the U-shaped vortex is newly formed as the head of young turbulence spot and finally break down to small pieces. In addition, we find that the U-shaped vortex has the same vorticity sign as the original λ-shaped vortex tube legs and serves as a second neck to supply vorticity to the ringlike vortex when the original vortex tube is stretched and multiple rings are generated.

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preprint2014arXiv

On the B-twisted topological sigma model and Calabi-Yau geometry

We provide a rigorous perturbative quantization of the B-twisted topological sigma model via a first order quantum field theory on derived mapping space in the formal neighborhood of constant maps. We prove that the first Chern class of the target manifold is the obstruction to the quantization via Batalin-Vilkovisky formalism. When the first Chern class vanishes, i.e. on Calabi-Yau manifolds, the factorization algebra of observables gives rise to the expected topological correlation functions in the B-model. We explain a twisting procedure to generalize to the Landau-Ginzburg case, and show that the resulting topological correlations coincide with Vafa's residue formula.

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preprint2013arXiv

Asymptotic-preserving exponential methods for the quantum Boltzmann equation with high-order accuracy

In this paper we develop high-order asymptotic-preserving methods for the spatially inhomogeneous quantum Boltzmann equation. We follow the work in Li and Pareschi, where asymptotic preserving exponential Runge-Kutta methods for the classical inhomogeneous Boltzmann equation were constructed. A major difficulty here is related to the non Gaussian steady states characterizing the quantum kinetic behavior. We show that the proposed schemes work with high-order accuracy uniformly in time for all Planck constants ranging from classical regime to quantum regime, and all Knudsen numbers ranging from kinetic regime to fluid regime. Computational results are presented for both Bose gas and Fermi gas.

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preprint2013arXiv

Cardy algebras and sewing constraints, II

This is the part II of a two-part work started in arXiv:0807.3356 [math.QA]. In part I, Cardy algebras were studied, a notion which arises from the classification of genus-0,1 open-closed rational conformal field theories. In this part, we prove that a Cardy algebra also satisfies the higher genus factorisation and modular-invariance properties formulated in arXiv:hep-th/0612306 in terms of the notion of a solution to the sewing constraints. We present the proof by showing that the latter notion, which is defined as a monoidal natural transformation, can be expressed in terms of generators and relations, which correspond exactly to the defining data and axioms of a Cardy algebra.

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preprint2013arXiv

On the existence of quantum signature for quantum messages

Quantum signature (QS) is used to authenticate the identity of the originator, ensure data integrity and provide non-repudiation service with unconditional security. Depending on whether a trusted third party named arbitrator is involved or not, QS is classified as arbitrated QS and true QS. This paper studies existence problem about the two kinds of QS and contributes to two points: 1) a basic framework is provided to analyze the possibility of arbitrated QS on signing quantum messages; 2) disagreement between the impossibility of true QS and an existing true QS scheme is solved.

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preprint2012arXiv

Exponential Runge-Kutta schemes for inhomogeneous Boltzmann equations with high order of accuracy

We consider the development of exponential methods for the robust time discretization of space inhomogeneous Boltzmann equations in stiff regimes. Compared to the space homogeneous case, or more in general to the case of splitting based methods, studied in Dimarco Pareschi (SIAM J. Num. Anal. 2011) a major difficulty is that the local Maxwellian equilibrium state is not constant in a time step and thus needs a proper numerical treatment. We show how to derive asymptotic preserving (AP) schemes of arbitrary order and in particular using the Shu-Osher representation of Runge-Kutta methods we explore the monotonicity properties of such schemes, like strong stability preserving (SSP) and positivity preserving. Several numerical results confirm our analysis.

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preprint2012arXiv

On the security of arbitrated quantum signature schemes

Due to potential capability of providing unconditional security, arbitrated quantum signature (AQS) schemes, whose implementation depends on the participation of a trusted third party, received intense attention in the past decade. Recently, some typical AQS schemes were cryptanalyzed and improved. In this paper, we analyze security property of some AQS schemes and show that all the previous AQS schemes, no matter original or improved, are still insecure in the sense that the messages and the corresponding signatures can be exchanged among different receivers, allowing the receivers to deny accepting the signature of an appointed message. Some further improvement methods on the AQS schemes are also discussed.

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preprint2011arXiv

Breaking a chaotic image encryption algorithm based on perceptron model

Recently, a chaotic image encryption algorithm based on perceptron model was proposed. The present paper analyzes security of the algorithm and finds that the equivalent secret key can be reconstructed with only one pair of known-plaintext/ciphertext, which is supported by both mathematical proof and experiment results. In addition, some other security defects are also reported.

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preprint2011arXiv

On the impossibility of non-static quantum bit commitment between two parties

Recently, Choi \emph{et al}. proposed an assumption on Mayers-Lo-Chau (MLC) no-go theorem that the state of the entire quantum system is invariable to both participants before the unveiling phase. This means that the theorem is only applicable to static quantum bit commitment (QBC). This paper find that the assumption is unnecessary and the MLC no-go theorem can be applied to not only static QBC, but also non-static one. A non-static QBC protocol proposed by Choi \emph{et al.} is briefly reviewed and analyzed to work as a supporting example. In addition, a novel way to prove the impossibility of the two kinds of QBC is given.

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preprint2010arXiv

Sharing a quantum secret without a trusted party

In a conventional quantum (k,n) threshold scheme, a trusted party shares a secret quantum state with n participants such that any k of those participants can cooperate to recover the original secret, while fewer than k participants obtain no information about the secret. In this paper we show how to construct a quantum (k,n) threshold scheme without the assistance of a trusted party, who generates and distributes shares among the participants. Instead, each participant chooses his private state and contributes the same to the determination of the final secret quantum state.

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preprint2009arXiv

Semi-quantum secret sharing using entangled states

Secret sharing is a procedure for sharing a secret among a number of participants such that only the qualified subsets of participants have the ability to reconstruct the secret. Even in the presence of eavesdropping, secret sharing can be achieved when all the members are quantum. So what happens if not all the members are quantum? In this paper we propose two semi-quantum secret sharing protocols using maximally entangled GHZ-type states in which quantum Alice shares a secret with two classical parties, Bob and Charlie, in a way that both parties are sufficient to obtain the secret, but one of them cannot. The presented protocols are also showed to be secure against eavesdropping.

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