arXiv subjects

Shuang Liu

Publications and source records attributed to Shuang Liu.

At least 73 records · Page 4Linked to original sources

arXiv2025

Conformance Testing of Relational DBMS Against SQL Specifications

A Relational Database Management System (RDBMS) is one of the fundamental software that supports a wide range of applications, making it critical to identify bugs within these systems. There has been active research on testing RDBMS, most of which employ crash or use metamorphic relations as the oracle. Although existing approaches can detect bugs in RDBMS, they are far from comprehensively evaluating the RDBMS's correctness (i.e., with respect to the semantics of SQL). In this work, we propose a method to test the semantic conformance of RDBMS i.e., whether its behavior respects the intended semantics of SQL. Specifically, we have formally defined the semantics of SQL and implemented them in Prolog. Then, the Prolog implementation serves as the reference RDBMS, enabling differential testing on existing RDBMS. We applied our approach to four widely-used and thoroughly tested RDBMSs, i.e., MySQL, TiDB, SQLite, and DuckDB. In total, our approach uncovered 19 bugs and 11 inconsistencies, which are all related to violating the SQL specification or missing/unclear specification, thereby demonstrating the effectiveness and applicability of our approach.

cs.DB↗

arXiv2025

The equivalence between Einstein and Jordan frames: a study based on the inflationary magnetogenesis model

The equivalence of the Jordan and Einstein frames has been a subject of considerable interest in the field. In this paper, within the context of $f(R)$ gravity, we explore the inflationary magnetogenesis model, focusing on the magnetic field energy density and its spectrum in both the Jordan and Einstein frames to elucidate the equivalence between these two reference frames. Our analysis reveals that during the inflationary epoch, while the magnetic field exhibits a scale-invariant spectrum in the Einstein frame, it demonstrates a blue spectrum in the Jordan frame. Additionally, we investigate the post-inflationary evolution of the magnetic field's energy density in both frames, uncovering that for scale-invariant spectra in the Einstein frame during inflation, the magnetic field transitions to a blue spectrum, whereas in the Jordan frame, it evolves into a red spectrum. We also establish the conditions under which both frames may exhibit scale-invariant spectra simultaneously during the inflationary period.

astro-ph.CO↗

arXiv2025

Compact dose delivery of laser-accelerated high-energy electron beams towards radiotherapy applications

The use of very high energy electron (VHEE) beams for radiotherapy has been actively studied for over two decades due to their advantageous dose distribution, deep penetration depth and great potential of ultra-high dose-rate irradiation. Recently, laser-plasma wakefield accelerator (LWFA) has emerged as a promising method for the compact generation of VHEE beams, due to its substantially higher accelerating gradients compared to traditional radio-frequency accelerators. However, how to compactly deliver the LWFA-based VHEE beams of relatively large energy spread and create a maximum dose deeply inside the body remains very challenging. In this article, we present a simple dose delivery scheme utilizing only two dipole magnets for LWFA-based VHEE treatment. By adjusting the magnet strengths, the electron beams can be guided along different angular trajectories towards a precise position as deep as 20 cm within a water phantom, creating a maximum dose over the target region and significantly reducing the entrance dose. Supported by Monte Carlo simulations, such a beam delivery approach is demonstrated to be insensitive to the beam energy spread and meanwhile capable of controlling precisely the dose-peak position in both lateral and longitudinal directions. As such, a uniform dose peak can be generated by the weighted sum of VHEE beams that reach different dose-peak depths. These results demonstrate that LWFA-based VHEE beams can be compactly delivered into a deep-seated tumor region in a controllable manner, thus advancing the development of the VHEE radiotherapy towards the practical clinical applications in the near future.

physics.med-ph↗

arXiv2025

Tuning Magnetism of Metal-Organic Framework by Different Types of Pressure

The properties of metal-organic frameworks (MOFs) are expected to be sensitive to external pressures because of their inherently flexible structures. Although pressure-driven structural transitions have been intensively studied, the influence of pressure on magnetism has been less exploited for MOFs. Especially, the efficiency of applied pressure may strongly depend on the pressure-transmitting medium (PTM) which may have a complex interaction with MOFs. Here, we report the distinctive effects of different types of pressure, including isotropic hydrostatic pressure, quasi-hydrostatic pressure, and uniaxial pressure, on the anisotropic magnetism of the perovskite MOF, [CH3NH3][Co(COOH)3]. It is found that the hydrostatic pressure has the minimal effect whereas the uniaxial pressure has the highest efficiency in tuning magnetization and magnetic anisotropy of the MOF. First-principles calculations reveal that the applied low pressures (less than 11 kbar) do not induce notable lattice distortion in the framework or the superexchange path. Instead, the modulation of hydrogen bonds is identified as a critical factor for pressure tuning of magnetization and anisotropy. These findings underscore the potential applications of directional pressure in precisely controlling the magnetic and electronic properties of MOFs.

cond-mat.mtrl-sci↗

arXiv2025

Provable Robust Overfitting Mitigation in Wasserstein Distributionally Robust Optimization

Wasserstein distributionally robust optimization (WDRO) optimizes against worst-case distributional shifts within a specified uncertainty set, leading to enhanced generalization on unseen adversarial examples, compared to standard adversarial training which focuses on pointwise adversarial perturbations. However, WDRO still suffers fundamentally from the robust overfitting problem, as it does not consider statistical error. We address this gap by proposing a novel robust optimization framework under a new uncertainty set for adversarial noise via Wasserstein distance and statistical error via Kullback-Leibler divergence, called the Statistically Robust WDRO. We establish a robust generalization bound for the new optimization framework, implying that out-of-distribution adversarial performance is at least as good as the statistically robust training loss with high probability. Furthermore, we derive conditions under which Stackelberg and Nash equilibria exist between the learner and the adversary, giving an optimal robust model in certain sense. Finally, through extensive experiments, we demonstrate that our method significantly mitigates robust overfitting and enhances robustness within the framework of WDRO.

cs.LG↗

arXiv2025

TxnSails: Achieving Serializable Transaction Scheduling with Self-Adaptive Isolation Level Selection

Achieving the serializable isolation level, regarded as the gold standard for transaction processing, is costly. Recent studies reveal that adjusting specific query patterns within a workload can still achieve serializability even at lower isolation levels. Nevertheless, these studies typically overlook the trade-off between the performance advantages of lower isolation levels and the overhead required to maintain serializability, potentially leading to suboptimal isolation level choices that fail to maximize performance. In this paper, we present TxnSails, a middle-tier solution designed to achieve serializable scheduling with self-adaptive isolation level selection. First, TxnSails incorporates a unified concurrency control algorithm that achieves serializability at lower isolation levels with minimal additional overhead. Second, TxnSails employs a deep learning method to characterize the trade-off between the performance benefits and overhead associated with lower isolation levels, thus predicting the optimal isolation level. Finally, TxnSails implements a cross-isolation validation mechanism to ensure serializability during real-time isolation level transitions. Extensive experiments demonstrate that TxnSails outperforms state-of-the-art solutions by up to 26.7x and PostgreSQL's serializable isolation level by up to 4.8x.

cs.DB↗

arXiv2025

Stronger sum uncertainty relations for non-Hermitian operators

The uncertainty relations (URs) of two arbitrary Hermitian and non-Hermitian incompatible operators represented by the product of variances have been confirmed theoretically and experimentally in various physical systems. However, the lower bound of the product uncertainty inequality can be null even for two non-commuting operators, i.e., a trivial case. Therefore, for two incompatible operators over the measured system state, the associated URs regarding the sum of variances are valid in a state-dependent manner, and the lower bound is guaranteed to be nontrivial. Although the sum URs formulated for Hermitian and unitary operators have been affirmed, the general forms for arbitrary non-Hermitian operators have not yet been investigated. This study presents the sum URs for non-Hermitian operators acting on system states using an appropriate Hilbert-space metric. The compatible forms of our sum inequalities with the conventional quantum mechanics are also provided via the G-metric formalism. Concrete examples illustrate the validity of the proposed sum URs in both PT-symmetric and PT-broken phases. The developed methods and results can help give an in-depth understanding of the usefulness of G-metric formalism in non-Hermitian quantum mechanics and the sum URs of incompatible operators within.

quant-ph↗

arXiv2025

On principal eigenvalues of linear time-periodic parabolic systems: symmetric mutation case

The paper is concerned with the effect of the spatio-temporal heterogeneity on the principal eigenvalue of some linear time-periodic parabolic system. Various asymptotic behaviors of the principal eigenvalue and its monotonicity, as a function of the diffusion rate and frequency, are first derived. In particular, some singular behaviors of the principal eigenvalues are observed when both diffusion rate and frequency approach zero, with some scalar time-periodic Hamilton-Jacobi equation as the limiting equation. Furthermore, we completely classify the topological structures of the level sets for the principal eigenvalues in the plane of frequency and diffusion rate. Our results not only generalize most of the findings in [S. Liu and Y. Lou, J. Funct. Anal., 282 (2022), 109338] for scalar periodic-parabolic operators, but also reveal more rich global information, for time-periodic parabolic systems, on the dependence of the principal eigenvalues upon the spatio-temporal heterogeneity.

math.AP↗

arXiv2024

A first measurement of galaxy merger rate increasing in dynamically colder protoclusters at cosmic noon

The process of galaxy cluster formation likely leaves an imprint on the properties of its individual member galaxies. Understanding this process is essential for uncovering the evolutionary connections between galaxies and cosmic structures. Here we study a sample of ten protoclusters at z~2-3 in different dynamical states that we estimate based on spectroscopic data of their members. We combine the dynamical information with HST imaging to measure galaxy sizes and pair fractions. Our analysis reveals a clear anti-correlation between the velocity dispersion of the protocluster and its galaxy pair fractions (indicative of merger rates). The velocity dispersion also anti-correlates with the dispersion in size among of the member galaxies. These correlations may be explained by protoclusters in colder dynamical states maintaining a velocity dispersion and galaxy number density that boosts galaxy mergers, which in turn contributes to the structural expansion and compaction of galaxies. Our findings offer constraints for cosmological models regarding the evolution of galaxy morphology across different stages in the assembly of protoclusters.

astro-ph.GA↗

arXiv2024

GeoTP: Latency-aware Geo-Distributed Transaction Processing in Database Middlewares (Extended Version)

The widespread adoption of database middleware for supporting distributed transaction processing is prevalent in numerous applications, with heterogeneous data sources deployed across national and international boundaries. However, transaction processing performance significantly drops due to the high network latency between the middleware and data sources and the long lock contention span, where transactions may be blocked while waiting for the locks held by concurrent transactions. In this paper, we propose GeoTP, a latency-aware geo-distributed transaction processing approach in database middlewares. GeoTP incorporates three key techniques to enhance geo-distributed transaction performance. First, we propose a decentralized prepare mechanism, which diminishes the requirement of network round trips for distributed transactions. Second, we design a latency-aware scheduler to minimize the lock contention span by strategically postponing the lock acquisition time point. Third, heuristic optimizations are proposed for the scheduler to reduce the lock contention span further. We implemented GeoTP on Apache Shardingsphere, a state-of-the-art middleware, and extended it into Apache ScalarDB. Experimental results on YCSB and TPC-C demonstrate that GeoTP achieves up to 17.7x performance improvement over Shardingsphere.

cs.DB↗

arXiv2024

The Road to Trust: Building Enclaves within Confidential VMs

Integrity is critical for maintaining system security, as it ensures that only genuine software is loaded onto a machine. Although confidential virtual machines (CVMs) function within isolated environments separate from the host, it is important to recognize that users still encounter challenges in maintaining control over the integrity of the code running within the trusted execution environments (TEEs). The presence of a sophisticated operating system (OS) raises the possibility of dynamically creating and executing any code, making user applications within TEEs vulnerable to interference or tampering if the guest OS is compromised. To address this issue, this paper introduces NestedSGX, a framework which leverages virtual machine privilege level (VMPL), a recent hardware feature available on AMD SEV-SNP to enable the creation of hardware enclaves within the guest VM. Similar to Intel SGX, NestedSGX considers the guest OS untrusted for loading potentially malicious code. It ensures that only trusted and measured code executed within the enclave can be remotely attested. To seamlessly protect existing applications, NestedSGX aims for compatibility with Intel SGX by simulating SGX leaf functions. We have also ported the SGX SDK and the Occlum library OS to NestedSGX, enabling the use of existing SGX toolchains and applications in the system. Performance evaluations show that context switches in NestedSGX take about 32,000 -- 34,000 cycles, approximately $1.9\times$ -- $2.1\times$ higher than that of Intel SGX. NestedSGX incurs minimal overhead in most real-world applications, with an average overhead below 2% for computation and memory intensive workloads and below 15.68% for I/O intensive workloads.

cs.CR↗

arXiv2024

Computational Ghost Imaging with Low-Density Parity-Check Code

Ghost imaging (GI) is a high-resolution imaging technology that has been a subject of interest to many fields in the past 20 years. Most GI researchers focus on the reconstruction of signal under-sampling, nevertheless, how to use information redundancy to improve the result's belief in a complex environment has hardly been studied. Motivated by this, we propose a computational GI system based on the low-density parity-check (LDPC) coded radiation field by exploiting the signal redundancy. The non-ideal factors generated within the imaging process can be eliminated by setting up the matching fading channel model. We have derived the analytical lower bound on the bit error rate for the proposed LDPC-coded GI system. The effectiveness and performance of the LDPC-coded GI system are further validated through numerical and experiment results.

eess.SP↗

arXiv2024

Formalizing UML State Machines for Automated Verification -- A Survey

The Unified Modeling Language (UML) is a standard for modeling dynamic systems. UML behavioral state machines are used for modeling the dynamic behavior of object-oriented designs. The UML specification, maintained by the Object Management Group (OMG), is documented in natural language (in contrast to formal language). The inherent ambiguity of natural languages may introduce inconsistencies in the resulting state machine model. Formalizing UML state machine specification aims at solving the ambiguity problem and at providing a uniform view to software designers and developers. Such a formalization also aims at providing a foundation for automatic verification of UML state machine models, which can help to find software design vulnerabilities at an early stage and reduce the development cost. We provide here a comprehensive survey of existing work from 1997 to 2021 related to formalizing UML state machine semantics for the purpose of conducting model checking at the design stage.

cs.SE↗

arXiv2024

Generalization Bound and New Algorithm for Clean-Label Backdoor Attack

The generalization bound is a crucial theoretical tool for assessing the generalizability of learning methods and there exist vast literatures on generalizability of normal learning, adversarial learning, and data poisoning. Unlike other data poison attacks, the backdoor attack has the special property that the poisoned triggers are contained in both the training set and the test set and the purpose of the attack is two-fold. To our knowledge, the generalization bound for the backdoor attack has not been established. In this paper, we fill this gap by deriving algorithm-independent generalization bounds in the clean-label backdoor attack scenario. Precisely, based on the goals of backdoor attack, we give upper bounds for the clean sample population errors and the poison population errors in terms of the empirical error on the poisoned training dataset. Furthermore, based on the theoretical result, a new clean-label backdoor attack is proposed that computes the poisoning trigger by combining adversarial noise and indiscriminate poison. We show its effectiveness in a variety of settings.

cs.LG↗

arXiv2024

Principal eigenvalue for some elliptic operators with large drift: Neumann boundary conditions

The paper is concerned with the principal eigenvalue of some linear elliptic operators with drift in two dimensional space. We provide a refined description of the asymptotic behavior for the principal eigenvalue as the drift rate approaches infinity. Under some non-degeneracy assumptions, our results illustrate that these asymptotic behaviors are completely determined by some connected components in the omega-limit set of the system of ordinary differential equations associated with the drift term, which includes stable fixed points, stable limit cycles, hyperbolic saddles connecting homoclinic orbits, and families of closed orbits. Some discussions on degenerate cases are also included.

math.AP↗

arXiv2024

Theoretical investigation of the relations between quantum decoherence and weak-to-strong measurement transition

This paper delves into the crucial aspects of pointer-induced quantum decoherence and the transition between von Neumann's projective strong measurement and Aharonov's weak measurement. Both phenomena significantly impact the dynamical understanding of quantum measurement processes. Specifically, we focus on the interplay between quantum decoherence and the transition from weak to strong measurement by deducing and comparing the quantum decoherence and weak-to-strong measurement transition factors within a general model and using the well-known Stern-Gerlach experiment as an illustrative example. Our findings reveal that both phenomena can be effectively characterized by a universal transition factor intricately linked to the coupling between the system and the measurement apparatus. The analysis presented can clarify the mechanism behind the relations of quantum decoherence to the weak measurement and weak-to-strong measurement transition.

quant-ph↗

arXiv2024

Pressure tuning of hydrogen bond ordering in the metal-organic framework [(CH3)2NH2]Mn(HCOO)3

The influence of pressure on the hydrogen bond ordering in the perovskite metal-organic framework [(CH3)2NH2]Mn(HCOO)3 has been investigated by dielectric, pyroelectric adn magnetic measurements in a piston-cylinder cell. Under ambient pressure the ordering of hydrogen bonds takes place at TC = 188 K and induces a first-order ferroelectric phase transition. With increasing pressure to p = 3.92 kbar, the order-disorder transition shifts to a lower temperature and retains the first-order ferroelectric nature. However, under higher pressures, the ordering process of hydrogen bonds is split into two transitions: a broad antiferroelectric transition at high temperature and a first-order ferroelectric transition at low temperature. With increasing pressure, the antiferroelectric phase is enhanced whereas the ferroelectric phase is greatly suppressed, which implies that compression of the perovskite framework favors antiparallel arrangement of the hydrogen bonds. The canted anti-ferromagnetic transition was almost unchanged when pressure up to 10.85 kbar. Our study demonstrated that the perovskite metal-organic frameworks are more sensitive to external pressure than conventional perovskite oxides so that their electric properties can be easily tuned by pressure.

cond-mat.mtrl-sci↗

arXiv2024

Resonant Quantum Magnetodielectric Effect in Multiferroic Metal-Organic Framework [CH3NH3]Co(HCOO)3

We report the observation of both resonant quantum tunneling of magnetization (RQTM) and resonant quantum magnetodielectric (RQMD) effect in the perovskite multiferroic metal-organic framework [CH3NH3]Co(HCOO)3. An intrinsic magnetic phase separation emerges at low temperatures due to hydrogen-bond-modified long range super-exchange interaction, leading to the coexistence of canted antiferromagnetic order and single-ion magnet. Subsequently, a stair-shaped magnetic hysteresis loop along the [101] direction characterizing the RQTM appears below the magnetic blocking temperature. More interestingly, the magnetic field dependence of dielectric permittivity exhibits pronounced negative peaks at the critical fields corresponding to the RQTM, a phenomenon termed the RQMD effect which enables electrical detection of the RQTM. These intriguing properties make the multiferroic metal-organic framework a promising candidate for solid-state quantum computing.

cond-mat.mtrl-sci↗