arXiv subjects

Wei Gu

Publications and source records attributed to Wei Gu.

At least 19 recordsLinked to original sources

arXiv2026

Conical Defects in JT Gravity from BF Theory: Quantization, Fusion, and Weighted Moduli Spaces

We construct a gauge-invariant BF representative of a conical defect in Jackiw--Teitelboim gravity, with its elliptic direction fixed by the reconstruction of the gravitational variables. In the gravitational sector, the BF observable reproduces the metric defect insertion and its distributional curvature source, and fixes the holonomy around the defect to an elliptic conjugacy class. For a single defect on the disk, the position integral combines with the residual $\mathrm{PSL}(2,\mathbb R)$ quotient, leaving a fixed elliptic sector. The exact boundary quantization of this sector gives the elementary kernel and one-defect disk amplitude directly, without using a matrix model. The same local BF operator describes both sharp and blunt defects. Their difference appears only when several defect positions are integrated. BF source composition makes the fractional deficits additive. Requiring a fused source to remain in the gravitational cone sector gives the admissibility condition for defect collisions, while positivity of the hyperbolic area on each component gives the corresponding stability condition. These conditions coincide with those for Hassett weighted curves. Combining these BF results with the known Hassett compactification and conical Weil--Petersson geometry yields the fixed-order genus-zero contact terms and recovers the cluster expansion of deformed JT gravity. Thus sharp and blunt defects differ through the compactified geometry of their relative positions, rather than through the elementary BF operator.

hep-th↗

arXiv2026

Preconditioner-Based Acceleration Method for Solving EMTP Linear Equations

The computational speed of electromagnetic transient programs (EMTP) is severely limited by both the curse of dimensionality and the ill-conditioned system matrix, which collectively degrade solver performance. However, existing research on EMTP acceleration has largely overlooked the issue of ill-conditioning. This letter presents a first systematic, EMT-oriented investigation of the ill-conditioning of the EMTP admittance matrix by establishing a link between its physical origins and mathematical pathologies, thereby revealing the underlying mechanism by which network topology induces ill-conditioning. Building upon these structural insights, a preconditioner-based strategy is developed that significantly accelerates computation while preserving numerical accuracy. Simulation results demonstrate the outstanding efficiency and robustness of the proposed approach.

eess.SY↗

arXiv2026

Dynamical Generation of the VY Superpotential in $N=1$ SYM: A Higher-Form Perspective

We present a semiclassical account of the Veneziano-Yankielowicz (VY) superpotential in four-dimensional $N=1$ super Yang-Mills theory. Motivated by two-dimensional gauged linear sigma models, where superpotentials arise from vortex dynamics, we reinterpret domain walls as fundamental objects associated with higher-form gauge fields. In this formulation, the vacuum structure is encoded in a compact three-form gauge field, whose four-form flux labels topological sectors. In the presence of charged matter with total charge $N$, these sectors exhibit a natural $\mathbb{Z}_N$ structure, leading to a decomposition into $N$ semiclassical contributions. These contributions arise from Euclidean point-like configurations in the higher-form sector, analogous to fractional instantons. We show that these configurations provide the relevant non-perturbative contributions to the effective superpotential. Integrating out the associated degrees of freedom reproduces the VY superpotential in the infrared. This gives a semiclassical origin of the VY superpotential in terms of higher-form gauge dynamics.

hep-th↗

arXiv2026

Schubert line defects in 3d GLSMs, part II: Partial flag manifolds and parabolic quantum polynomials

We construct Schubert line defects in the 3d $\mathcal{N}=2$ supersymmetric gauged linear sigma model (GLSM) with target space a partial flag manifold $X={\rm Fl}({\boldsymbol{k}};n)$, generalizing our construction for complete flag manifolds given in a companion paper arXiv:2512.19802 (part I). In the context of the 3d GLSM/quantum K-theory correspondence, the Schubert line defects are constructed as 1d $\mathcal{N}=2$ supersymmetric gauge theories coupled to the 3d field theory, and they flow to objects supported on Schubert varieties $X_w \subseteq X$ in the quantum K-theory. The flavored Witten index of the 1d defect is expected to compute the Chern character of $[\mathcal{O}_w]$ -- more precisely, it gives us a polynomial representative of the Schubert class in the quantum K-theory ring. We give strong evidence for this claim by showing in examples that the Witten indices of Schubert defects indeed reproduce a recently-defined set of polynomials that represent the Schubert classes in the Whitney presentation, which we call the parabolic Whitney polynomials. Moreover, upon using the quantum ring relations, we can convert these polynomials into seemingly new polynomials in the Toda presentation, which we call the parabolic quantum Grothendieck polynomials. These new polynomials specialize to known polynomials in various limits, including to the quantum Grothendieck polynomials in the case of the complete flag. In the 2d limit, our construction also realizes the Schubert classes $[X_w]$ in the quantum cohomology ring of the partial flag manifold, and the parabolic quantum Grothendieck polynomials then reduce to previously known parabolic quantum Schubert polynomials.

hep-th↗

arXiv2025

Schubert line defects in 3d GLSMs, part I: Complete flag manifolds and quantum Grothendieck polynomials

We construct new half-BPS line defects in 3d $\mathcal{N}=2$ supersymmetric quiver gauge theories whose Higgs branches are complete flag manifolds $X = {\rm Fl}(n)$. Upon circle compactification, the bulk theory flows to a non-linear sigma model (NLSM) with target space $X$ and the line defects flow to objects supported on Schubert varieties $X_w \subseteq X$. These Schubert line defects form an important basis of the quantum K-theory of $X$. They are realized as $\mathcal{N}=2$ supersymmetric quantum mechanics (SQM) quivers coupled to the 3d gauge theory. We show that the insertion of the Schubert line defect restricts the target space of the 3d gauged linear sigma model (GLSM) to the Schubert variety $X_w$, with the 1d degrees of freedom physically realizing a Bott--Samelson resolution of $X_w$. Moreover, we verify in examples that the 1d flavored Witten index of the quiver SQM reproduces the (equivariant) Chern character of the structure sheaf $\mathcal{O}_{X_w}$ as a (double) quantum Grothendieck polynomial, generalizing previous results for $X$ a Grassmannian manifold. Our construction thus provides a more direct realization of the 3d GLSM/quantum K-theory correspondence for complete flag manifolds. Finally, in the small-circle limit, we obtain a 0d-2d coupled system that realizes the Schubert classes $[X_w]$ in the quantum cohomology ring of $X$.

hep-th↗

arXiv2025

On Atiyah-Segal axioms for Witten-type TQFTs

In this paper, we propose a new definition of the trace-map bordism within the Atiyah-Segal framework for Witten-type TQFTs constructed from the topological twist of mass-gapped theories. We demonstrate that these Witten-type TQFTs are unitary under this revised definition and conjecture the self-consistency of the modified bordism category.

hep-th↗

arXiv2025

MAJIC: Markovian Adaptive Jailbreaking via Iterative Composition of Diverse Innovative Strategies

Large Language Models (LLMs) have exhibited remarkable capabilities but remain vulnerable to jailbreaking attacks, which can elicit harmful content from the models by manipulating the input prompts. Existing black-box jailbreaking techniques primarily rely on static prompts crafted with a single, non-adaptive strategy, or employ rigid combinations of several underperforming attack methods, which limits their adaptability and generalization. To address these limitations, we propose MAJIC, a Markovian adaptive jailbreaking framework that attacks black-box LLMs by iteratively combining diverse innovative disguise strategies. MAJIC first establishes a ``Disguise Strategy Pool'' by refining existing strategies and introducing several innovative approaches. To further improve the attack performance and efficiency, MAJIC formulate the sequential selection and fusion of strategies in the pool as a Markov chain. Under this formulation, MAJIC initializes and employs a Markov matrix to guide the strategy composition, where transition probabilities between strategies are dynamically adapted based on attack outcomes, thereby enabling MAJIC to learn and discover effective attack pathways tailored to the target model. Our empirical results demonstrate that MAJIC significantly outperforms existing jailbreak methods on prominent models such as GPT-4o and Gemini-2.0-flash, achieving over 90\% attack success rate with fewer than 15 queries per attempt on average.

cs.CR↗

arXiv2025

A Nakayama result for the quantum K theory of homogeneous spaces

We prove that the ideal of relations in the (equivariant) quantum K ring of a homogeneous space is generated by quantizations of each of the generators of the ideal in the classical (equivariant) K ring. This extends to quantum K theory a result of Siebert and Tian in quantum cohomology. We illustrate this technique in the case of the quantum K ring of partial flag manifolds, using a set of quantum K Whitney relations conjectured by the authors, and recently proved by Huq-Kuruvilla.

math.AG↗

arXiv2025

Alleviating CoD in Renewable Energy Profile Clustering Using an Optical Quantum Computer

The traditional clustering problem of renewable energy profiles is typically formulated as a combinatorial optimization that suffers from the Curse of Dimensionality (CoD) on classical computers. To address this issue, this paper first proposed a kernel-based quantum clustering method. More specifically, the kernel-based similarity between profiles with minimal intra-group distance is encoded into the ground-state of the Hamiltonian in the form of an Ising model. Then, this NP-hard problem can be reformulated into a Quadratic Unconstrained Binary Optimization (QUBO), which a Coherent Ising Machine (CIM) can naturally solve with significant improvement over classical computers. The test results from a real optical quantum computer verify the validity of the proposed method. It also demonstrates its ability to address CoD in an NP-hard clustering problem.

quant-ph↗

arXiv2025

On mixed 't Hooft anomalies of emergent symmetries

In this paper, we investigate the dynamical constraints imposed on the UV theory when it develops an emergent symmetry in the infrared with mixed 't Hooft anomalies. We demonstrate that, under certain conditions, the UV theory must contain non-genuine operators. Our primary examples illustrating this phenomenon are 2D gauged linear sigma models and 3D Chern-Simons-matter theories. Through this analysis, we establish connections between different classes of topological quantum field theories and propose a correspondence between quantum cohomologies of distinct target spaces.

hep-th↗

arXiv2025

Open and Sustainable AI: challenges, opportunities and the road ahead in the life sciences (October 2025 -- Version 2)

Artificial intelligence (AI) has recently seen transformative breakthroughs in the life sciences, expanding possibilities for researchers to interpret biological information at an unprecedented capacity, with novel applications and advances being made almost daily. In order to maximise return on the growing investments in AI-based life science research and accelerate this progress, it has become urgent to address the exacerbation of long-standing research challenges arising from the rapid adoption of AI methods. We review the increased erosion of trust in AI research outputs, driven by the issues of poor reusability and reproducibility, and highlight their consequent impact on environmental sustainability. Furthermore, we discuss the fragmented components of the AI ecosystem and lack of guiding pathways to best support Open and Sustainable AI (OSAI) model development. In response, this perspective introduces a practical set of OSAI recommendations directly mapped to over 300 components of the AI ecosystem. Our work connects researchers with relevant AI resources, facilitating the implementation of sustainable, reusable and transparent AI. Built upon life science community consensus and aligned to existing efforts, the outputs of this perspective are designed to aid the future development of policy and structured pathways for guiding AI implementation.

cs.AI↗

arXiv2025

Integrating Building Thermal Flexibility Into Distribution System: A Privacy-Preserved Dispatch Approach

The inherent thermal storage capacity of buildings brings considerable thermal flexibility to the heating/cooling loads, which are promising demand response resources for power systems. It is widely believed that integrating the thermal flexibility of buildings into the distribution system can improve the operating economy and reliability of the system. However, the private information of the buildings needs to be transferred to the distribution system operator (DSO) to achieve a coordinated optimization, bringing serious privacy concerns to users. Given this issue, we propose a novel privacy-preserved optimal dispatch approach for the distribution system incorporating buildings. Using it, the DSO can exploit the thermal flexibility of buildings without accessing their private information, such as model parameters and indoor temperature profiles. Specifically, we first develop an optimal dispatch model for the distribution system integrating buildings, which can be extended to other storage-like flexibility resources. Second, we reveal that the privacy-preserved integration of buildings is a joint privacy preservation problem for both parameters and state variables and then design a privacy-preserved algorithm based on transformation-based encryption, constraint relaxation, and constraint extension techniques. Besides, we implement a detailed privacy analysis for the proposed method, considering both semi-honest adversaries and external eavesdroppers. Case studies demonstrate the accuracy, privacy-preserved performance, and computational efficiency of the proposed method.

eess.SY↗

arXiv2025

Fast Critical Clearing Time Calculation for Power Systems with Synchronous and Asynchronous Generation

The increasing penetration of renewables is replacing traditional synchronous generation in modern power systems with low-inertia asynchronous converter-interfaced generators (CIGs). This penetration threatens the dynamic stability of the modern power system. To assess the latter, we resort to the critical clearing time (CCT) as a stability index, which is typically computed through a large number of time-domain simulations. This is especially true for CIG-embedded power systems, where the complexity of the model is further increased. To alleviate the computing burden, we developed a trajectory sensitivity-based method for assessing the CCT in power systems with synchronous and asynchronous generators. This allows us to obtain the CCT cost-effectively. The simulation results reveal the excellent performance of the proposed method.

eess.SY↗

arXiv2025

On the Data-Driven Modeling of Price-Responsive Flexible Loads: Formulation and Algorithm

The flexible loads in power systems, such as interruptible and transferable loads, are critical flexibility resources for mitigating power imbalances. Despite their potential, accurate modeling of these loads is a challenging work and has not received enough attention, limiting their integration into operational frameworks. To bridge this gap, this paper develops a data-driven identification theory and algorithm for price-responsive flexible loads (PRFLs). First, we introduce PRFL models that capture both static and dynamic decision mechanisms governing their response to electricity price variations. Second, We develop a data-driven identification framework that explicitly incorporates forecast and measurement errors. Particularly, we give a theoretical analysis to quantify the statistical impact of such noise on parameter estimation. Third, leveraging the bilevel structure of the identification problem, we propose a Bayesian optimization-based algorithm that features the scalability to large sample sizes and the ability to offer posterior differentiability certificates as byproducts. Numerical tests demonstrate the effectiveness and superiority of the proposed approach.

eess.SY↗

arXiv2025

Dynamic Energy Flow Analysis of Integrated Electricity and Gas Systems: A Semi-Analytical Approach

Ensuring the safe and reliable operation of integrated electricity and gas systems (IEGS) requires dynamic energy flow (DEF) simulation tools that achieve high accuracy and computational efficiency. However, the inherent strong nonlinearity of gas dynamics and its bidirectional coupling with power grids impose significant challenges on conventional numerical algorithms, particularly in computational efficiency and accuracy. Considering this, we propose a novel non-iterative semi-analytical algorithm based on differential transformation (DT) for DEF simulation of IEGS. First, we introduce a semi-discrete difference method to convert the partial differential algebraic equations of the DEF model into ordinary differential algebraic equations to resort to the DT. Particularly, by employing spatial central difference and numerical boundary extrapolation, we effectively avoid the singularity issue of the DT coefficient matrix. Second, we propose a DT-based semi-analytical solution method, which can yield the solution of the DEF model by recursion. Finally, simulation results demonstrate the superiority of the proposed method.

eess.SY↗

arXiv2025

Matrix Low-dimensional Qubit Casting Based Quantum Electromagnetic Transient Network Simulation Program

In modern power systems, the integration of converter-interfaced generations requires the development of electromagnetic transient network simulation programs (EMTP) that can capture rapid fluctuations. However, as the power system scales, the EMTP's computing complexity increases exponentially, leading to a curse of dimensionality that hinders its practical application. Facing this challenge, quantum computing offers a promising approach for achieving exponential acceleration. To realize this in noisy intermediate-scale quantum computers, the variational quantum linear solution (VQLS) was advocated because of its robustness against depolarizing noise. However, it suffers data inflation issues in its preprocessing phase, and no prior research has applied quantum computing to high-frequency switching EMT networks.To address these issues, this paper first designs the matrix low-dimension qubit casting (MLQC) method to address the data inflation problem in the preprocessing of the admittance matrix for VQLS in EMT networks. Besides, we propose a real-only quantum circuit reduction method tailored to the characteristics of the EMT network admittance matrices. Finally, the proposed quantum EMTP algorithm (QEMTP) has been successfully verified for EMT networks containing a large number of high-frequency switching elements.

quant-ph↗

arXiv2024

A Risk-Averse Just-In-Time Scheme for Learning-Based Operation of Microgrids with Coupled Electricity-Hydrogen-Ammonia under Uncertainties

This paper proposes a Risk-Averse Just-In-Time (RAJIT) operation scheme for Ammonia-Hydrogen-based Micro-Grids (AHMGs) to boost electricity-hydrogen-ammonia coupling under uncertainties. First, an off-grid AHMG model is developed, featuring a novel multi-mode ammonia synthesis process and a hydrogen-ammonia dual gas turbine with tunable feed-in ratios. Subsequently, a state-behavior mapping strategy linking hydrogen storage levels with the operation modes of ammonia synthesis is established to prevent cost-ineffective shutdowns. The proposed model substantially improves operational flexibility but results in a challenging nonlinear fractional program. Based upon this model, a data-driven RAJIT scheme is developed for the real-time rolling optimization of AHMGs. Unlike conventional one-size-fits-all schemes using one optimization method throughout, the data driven RAJIT intelligently switches between cost-effective deterministic optimization and risk-averse online-learning distributionally robust optimization depending on actual risk profiles, thus capitalizing on the respective strengths of these two optimization methods. To facilitate the solution of the resulting nonlinear program, we develop an equivalent-reformulation-based solution methodology by leveraging a constraint-tightening technique. Numerical simulations demonstrate that the proposed scheme guarantees safety and yields an overall cost reduction up to 14.6% compared with several state-of-the-art methods.

eess.SY↗

arXiv2024

Quantify Gas-to-Power Fault Propagation Speed:A Semi-Implicit Simulation Approach

Relying heavily on the secure supply of natural gas, the modern clean electric power systems are prone to the gas disturbances induced by the inherent rupture and leakage faults. For the first time, this paper studies the cross-system propagation speed of these faults using a simulation-based approach. Firstly, we establish the differential algebraic equation models of the rupture and leakage faults respectively. The boundary conditions at the fault locations are derived using the method of characteristics. Secondly, we propose utilizing a semi-implicit approach to perform post-fault simulations. The approach, based on the stiffly-accurate Rosenbrock scheme, possesses the implicit numerical stability and explicit computation burdens. Therefore, the high-dimensional and multi-time-scale stiff models can be solved in an efficient and robust way. Thirdly, to accurately locate the simulation events, which can not be predicted a priori, we propose a critical-time-location strategy based on the continuous Runge-Kutta approach. In case studies, we verified the accuracy and the efficiency superiority of the proposed simulation approach. The impacts of gas faults on gas and power dynamics were investigated by simulation, where the critical events were identified accurately. We found that the fault propagation speed mainly depends on the fault position and is influenced by the pipe frictions. The bi-directional coupling between gas and power may lead to cascading failures.

eess.SY↗