arXiv subjects

Tong Zhou

Publications and source records attributed to Tong Zhou.

At least 37 records · Page 2Linked to original sources

arXiv2026

Structure Identification of NDS with Descriptor Subsystems under Asynchronous, Non-Uniform, and Slow-Rate Sampling

This paper extends previous identification method to the asynchronous sampling scenario, enabling the simultaneous handling of asynchronous, non-uniform, and slow-rate sampling conditions. Moving beyond lumped systems, the proposed framework targets the identification of interconnection structure of Networked Dynamic Systems (NDS) with descriptor-form subsystems. In the first stage, right tangential interpolations are estimated from steady-state outputs, allowing all asynchronous samples to be fused into a unified estimator. In the second stage, a left null-space projection is employed to decouple the bilinear dependence between state-related matrices and interconnection parameters, reducing the identification problem to two successive linear estimation problems. The proposed approach eliminates the full-normal-rank transfer matrix assumption required in previous work, while providing theoretical guarantees of mean-square consistency and asymptotic unbiasedness. Numerical results demonstrate that the framework can accurately recover the system structure, even under severe sampling irregularities.

eess.SY↗

arXiv2026

Towards Scalable Braiding: Topological Superconductivity Unlocked under Arbitrary Magnetic Field Directions in Curved Planar Josephson Junctions

The non-Abelian statistics of Majorana zero modes (MZMs) are central to fault-tolerant topological quantum computing. Planar Josephson junctions provide a particularly versatile platform for realizing robust topological superconductivity hosting MZMs over a broad parameter space. However, it is generally believed that such topological superconductivity is restricted to a narrow range of in-plane magnetic field orientations, posing a major obstacle to scalable and noncollinear junction-network architectures. Here, we uncover that the apparent suppression of MZMs under misaligned fields does not arise from the destruction of topological superconductivity itself, but instead originates from emergent shifted bulk states at other momenta that obscure the global excitation gap and MZMs. By introducing spatial modulations along the junction to scatter and gap out these bulk states, we restore a global topological gap and recover MZMs for arbitrary in-plane magnetic field orientations. Remarkably, such modulations can be naturally realized by transforming a straight junction into a curved geometry, rendering the topological gap robust against field misalignment and enabling MZMs survival in complex junction networks. Building on this robustness, we propose a scalable protocol for MZMs braiding and fusion using gate or superconducting-phase control, opening new routes toward scalable topological quantum computing.

cond-mat.supr-con↗

arXiv2026

Symmetry-Driven Unconventional Magnetoelectric Coupling in Perovskite Altermagnets: From Bulk to the Two-Dimensional Limit

The emergence of altermagnets establishes a new paradigm for multiferroics. Unlike conventional multiferroics relying on direct magnetoelectric coupling, multiferroic altermagnets host a crystal-symmetry-mediated magnetoelectric interaction that is intrinsically more efficient and robust. Among candidate material platforms, layered perovskites are particularly appealing owing to their structural diversity and synthetic versatility. However, magnetoelectric properties at the two-dimensional scale remain largely unexplored, hindering their applicability in miniaturized, highly integrated devices. Here, we systematically investigate the dimensional evolution of ferroelectric polarization and magnetism in perovskite systems through symmetry analysis. We demonstrate that altermagnetism can persist in the two-dimensional limit, yet is strongly constrained by the magnetic configuration-with only C-type antiferromagnetic order supporting it. Based on mode-decomposition calculations, we further reveal that symmetry-restricted multimode couplings simultaneously govern ferroelectric polarization and altermagnetic spin splitting. Finally, combined with first-principles calculations, we propose several strategies to lift the magnetic-configuration constraint, extending the range of viable altermagnetic systems. These results underscore the critical role of dimensionality in symmetry-driven magnetoelectric coupling in perovskite altermagnets and pave the way toward next-generation electrically controlled spintronic and multiferroic devices.

cond-mat.mtrl-sci↗

arXiv2026

Non-Hermitian higher-order topological insulators enabled by altermagnet engineering

We show that proximity to an altermagnet provides an efficient route to engineering non-Hermitian higher-order topological phases. The proximity-induced altermagnetic order gaps the edge states of a topological insulator, thereby driving a transition from a first-order to a second-order topological phase. When combined with nonreciprocal hopping, the system exhibits both the non-Hermitian skin effect and a hybrid skin-topological effect, whereby first-order edge states and second-order corner states accumulate at selected corners of the lattice. We demonstrate that the spectral winding number of the edge states under cylindrical geometry dictates this corner localization and can be reversed by tuning the altermagnetic order. Consequently, both edge and corner states become directionally controllable. Our results establish altermagnets as a versatile platform for realizing and tuning skin-topological phenomena in non-Hermitian higher-order topological systems.

cond-mat.mes-hall↗

arXiv2025

Defect Engineering for Stabilizing Magnetic and Topological Properties in Mn(Bi1-xSbx)2Te4

MnBi2Te4 is a versatile platform for exploring diverse topological quantum states, yet its potential is hampered by intrinsic antisite defects. While Sb substitution has been employed to tune the Fermi level towards the charge neutral point, it exacerbates the formation of Mn-Sb antisite defects. Here, we address this challenge by combining first-principles calculations with strategic synthesis to systematically investigate and control antisite defects in Mn(Bi1-xSbx)2Te4. Our calculations reveal that increasing antisite defect density progressively destroys the field-forced magnetic Weyl state, eventually driving the system into a trivial magnetic insulator. Motivated by these findings, we develop an optimized chemical vapor transport method, yielding high-quality Mn(Bi1-xSbx)2Te4 crystals with significantly reduced antisite defect density. The emergence of strong Shubnikov-de Haas oscillations in the forced ferromagnetic state and a pronounced anomalous Hall effect near charge neutrality, with opposite signs for n- and p-type samples, confirms the type-II Weyl semimetal nature. These findings underscore the critical role of antisite defects in determining the magnetic and topological properties of Mn(Bi1-xSbx)2Te4 and establish defect engineering via optimized synthesis as a crucial strategy for realizing its exotic magnetic topological states.

cond-mat.mtrl-sci↗

arXiv2025

Altermagnetoelectric Spin Field Effect Transistor

Spin field-effect transistors (SFETs) are promising candidates for low-power spin-based electronics, yet existing realizations that rely on spin-orbit coupling are constrained by limited material choices and short spin-coherence lengths. Here we propose a different operating principle based on multiferroic altermagnets, in which spin splitting is tuned by an electric field through symmetry control rather than conventional spin-orbit physics. Using an effective model combined with quantum transport simulations, we show that the conductance is determined by the degree of matching between the electrically controlled spin texture of the channel and the fixed spin polarization of ferromagnetic contacts, enabling clear ON and OFF states. Remarkably, we also address a long-standing challenge in multiferroic device design: spintronic channels require metallic carriers, whereas ferroelectricity is usually suppressed in metals. We resolve this conflict by imprinting multiferroic altermagnetism into highly conductive materials via the proximity effect. First-principles calculations for graphene on multiferroic vanadium sulfide halides confirm that graphene acquires a ferroelectrically switchable spin splitting while retaining its metallic character. These results establish a practical route to SFET implementation and identify multiferroic altermagnets as a versatile platform for next-generation spintronic devices.

cond-mat.mtrl-sci↗

arXiv2025

OMGSR: You Only Need One Mid-timestep Guidance for Real-World Image Super-Resolution

Denoising Diffusion Probabilistic Models (DDPMs) show promising potential in one-step Real-World Image Super-Resolution (Real-ISR). Current one-step Real-ISR methods typically inject the low-quality (LQ) image latent representation at the start or end timestep of the DDPM scheduler. Recent studies have begun to note that the LQ image latent and the pre-trained noisy latent representations are intuitively closer at a mid-timestep. However, a quantitative analysis of these latent representations remains lacking. Considering these latent representations can be decomposed into signal and noise, we propose a method based on the Signal-to-Noise Ratio (SNR) to pre-compute an average optimal mid-timestep for injection. To better approximate the pre-trained noisy latent representation, we further introduce the Latent Representation Refinement (LRR) loss via a LoRA-enhanced VAE encoder. We also fine-tune the backbone of the DDPM-based generative model using LoRA to perform one-step denoising at the average optimal mid-timestep. Based on these components, we present OMGSR, a GAN-based Real-ISR framework that employs a DDPM-based generative model as the generator and a DINOv3-ConvNeXt model with multi-level discriminator heads as the discriminator. We also propose the DINOv3-ConvNeXt DISTS (Dv3CD) loss, which is enhanced for structural perception at varying resolutions. Within the OMGSR framework, we develop OMGSR-S based on SD2.1-base. An ablation study confirms that our pre-computation strategy and LRR loss significantly improve the baseline. Comparative studies demonstrate that OMGSR-S achieves state-of-the-art performance across multiple metrics. Code is available at \hyperlink{Github}{https://github.com/wuer5/OMGSR}.

cs.CV↗

arXiv2025

Parameter Recovery from Tangential Interpolations for Systems with an LFT Structure

This paper investigates how to recover parameters of a linear time invariant system from values and derivatives of its transfer function matrix, along several particular directions at a prescribed set of points in the complex plane, in which system matrices depend on these parameters through a linear fractional transformation. A necessary and sufficient condition is derived for a unique determination of these system parameters, which is expressed by a vector inequality. Under some particular situations, this condition reduces to a full column rank requirement on a constant matrix. Moreover, a method is given to recover system parameters from these values and derivatives, which is expressed by a vector linear equation with some rank constraints, for which various methods exist for finding its solutions. Robustness of the suggested recovery method is also clarified. A numerical example is given to illustrate characteristics of the suggested method, as well as effectiveness of derivative information introduction in parameter recovery, in which natural frequency and damping ratio are to be recovered for a transfer function.

eess.SY↗

arXiv2025

MotivGraph-SoIQ: Integrating Motivational Knowledge Graphs and Socratic Dialogue for Enhanced LLM Ideation

Large Language Models (LLMs) hold substantial potential for accelerating academic ideation but face critical challenges in grounding ideas and mitigating confirmation bias for further refinement. We propose integrating motivational knowledge graphs and socratic dialogue to address these limitations in enhanced LLM ideation (MotivGraph-SoIQ). This novel framework provides essential grounding and practical idea improvement steps for LLM ideation by integrating a Motivational Knowledge Graph (MotivGraph) with a Q-Driven Socratic Ideator. The MotivGraph structurally stores three key node types(problem, challenge and solution) to offer motivation grounding for the LLM ideation process. The Ideator is a dual-agent system utilizing Socratic questioning, which facilitates a rigorous refinement process that mitigates confirmation bias and improves idea quality across novelty, experimental rigor, and motivational rationality dimensions. On the ICLR25 paper topics dataset, MotivGraph-SoIQ exhibits clear advantages over existing state-of-the-art approaches across LLM-based scoring, ELO ranking, and human evaluation metrics.

cs.CL↗

arXiv2025

JUNO 20-inch PMT and electronics system characterization using large pulses of PMT dark counts at the Pan-Asia testing platform

The main goal of the JUNO experiment is to determine the neutrino mass ordering with a 20kt liquid-scintillator detector. The 20-inch PMT and its 1F3 (one for three) electronics are crucial to realize the excellent energy resolution of at least 3% at 1MeV. The knowledge on the PMT and 1F3 electronics response is critical for detector performance understanding. A study of the JUNO 20-inch PMT and 1F3 electronics system characterization is presented using large pulses of PMT dark count at the Pan-Asia testing platform in China. Thanks to its broad amplitude range and high rate, the large pulse signals are also used to investigate the PMT after pulse response.

physics.ins-det↗

arXiv2025

Emergent Multiferroic Altermagnets and Spin Control via Noncollinear Molecular Polarization

Altermagnets, with spin splitting and vanishing magnetization, have been attributed to many fascinating phenomena and potential applications. In particular, integrating ferroelectricity with altermagnetism to enable magnetoelectric coupling and electric control of spin has drawn significant attention. However, its experimental realization and precise spin manipulation remain elusive. Here, by focusing on molecular ferroelectrics, the first discovered ferroelectrics renowned for their highly controllable molecular polarizations and structural flexibility, we reveal that these obstacles can be removed by an emergent multiferroic altermagnets with tunable spin polarization in a large class of fabricated organic materials. Using a symmetry-based design and a tight-binding model, we uncover the underlying mechanism of such molecular ferroelectric altermagnets and demonstrate how noncollinear molecular polarization can switch the spin polarization on and off and even reverse its sign. From the first-principles calculations, we verify the feasibility of these materials in a series of well-established hybrid organic-inorganic perovskites and metal-organic frameworks. Our findings bridge molecular ferroelectrics and altermagnetic spintronics, highlighting an unexplored potential of multifunctional organic multiferroics.

cond-mat.mtrl-sci↗

arXiv2025

Two-dimensional magnetic tunnel p-n junctions for low-power electronics

For decades, semiconductors and their heterostructures have underpinned both fundamental and applied research across all areas of electronics. Two-dimensional, 2D (atomically thin) semiconductors have now the potential to push further the miniaturization of electronic components, enabling the development of more efficient electronics. Here, we report on a giant anomalous zero-bias spin voltage in magnetic tunnel junctions based on 2D materials. The generation, manipulation and detection of electron spin across a nanometer-thick magnetic tunnel junction do not require any applied bias. It is achieved by exploiting high-quality ferromagnetic/semiconductor interfaces and the asymmetric diffusion of spin-up/spin-down electrons across a semiconductor p-n junction. The large spin-voltage signal exceeds 30,000% and is far greater than the highest magnetoresistance signals reported to date. Our findings reveal unexplored opportunities to transform and amplify spin information for low-power electronics.

cond-mat.mes-hall↗

arXiv2025

A Microlocal Theory for Zariski-Constructible Sheaves on Rigid Analytic Varieties

We develop a microlocal theory, in the sense of Kashiwara-Schapira, for Zariski-constructible sheaves on rigid analytic varieties. We define and study monodromic sheaves, the monodromic Fourier transform, specialisation, microlocalisation, micro-hom, and singular support in this context. Some questions and conjectures are formulated in the end. The appendix contains infinity-categorical characterisations of monodromic sheaves.

math.AG↗

arXiv2025

Ferroelastic Altermagnetism

Synergizing altermagnetism and other ferroic orders, such as ferroelectric switchable altermagnetism [Phys. Rev. Lett. 134, 106801 (2025) and ibid. 106802 (2025)], offers an effective route to achieve nonvolatile switching of altermagnetic spin splitting. In this work, by synergizing altermagnetism and ferroelasticity, we propose the concept of ferroelastic altermagnets in which the ferroelastic crystal reorientation can drive multistate nonvolatile switching of the altermagnetic spin splitting via altermagnetoelastic effect. Using monolayers RuF4 and CuF2 as material candidates, we demonstrate 2-state and 3-state altermagnetic spin splitting switching as driven by ferroelastic strain states. Transport calculation shows that multistate spin conductivities can be ferroelastically encoded in an ferroelastic altermagnet, thus suggesting the potential of ferroelastic altermagnetic as nonvolatile nanomechanical spin switches. The proposed concept of ferroelastic altermagnetism enriches the emerging landscape of multiferroic altermagnetism, paving a way towards altermagnetic-based straintronic device applications.

cond-mat.mtrl-sci↗

arXiv2025

Interaction Identification of a Heterogeneous NDS with Quadratic-Bilinear Subsystems

This paper attacks time-domain identification for interaction parameters of a heterogeneous networked dynamic system (NDS), with each of its subsystems being described by a continuous-time descriptor quadratic-bilinear time-invariant (QBTI) model. The obtained results can also be applied to parameter estimations for a lumped QBTI system. No restrictions are put on the sampling rate. Explicit formulas are derived respectively for the transient and steady-state responses of the NDS, provided that the probing signal is generated by a linear time invariant (LTI) system. Some relations have been derived between the NDS steady-state response and its frequency domain input-output mappings. These relations reveal that the value of some NDS associated generalized TFMs can in principle be estimated at almost any interested point of the imaginary axis from time-domain input-output experimental data, as well as its derivatives and a right tangential interpolation along an arbitrary direction. Based on these relations, an estimation algorithm is suggested respectively for the parameters of the NDS and the values of these generalized TFMs. A numerical example is included to illustrate characteristics of the suggested estimation algorithms.

cs.MA↗

arXiv2025

Knowledge-Aware Diverse Reranking for Cross-Source Question Answering

This paper presents Team Marikarp's solution for the SIGIR 2025 LiveRAG competition. The competition's evaluation set, automatically generated by DataMorgana from internet corpora, encompassed a wide range of target topics, question types, question formulations, audience types, and knowledge organization methods. It offered a fair evaluation of retrieving question-relevant supporting documents from a 15M documents subset of the FineWeb corpus. Our proposed knowledge-aware diverse reranking RAG pipeline achieved first place in the competition.

cs.CL↗

arXiv2025

Identification of LFT Structured Descriptor Systems with Slow and Non-uniform Sampling

Time domain identification is studied in this paper for parameters of a continuous-time multi-input multi-output descriptor system, with these parameters affecting system matrices through a linear fractional transformation. Sampling is permitted to be slow and non-uniform, and there are no necessities to satisfy the Nyquist frequency restrictions. This model can be used to describe the behaviors of a networked dynamic system, and the obtained results can be straightforwardly applied to an ordinary state-space model, as well as a lumped system. An explicit formula is obtained respectively for the transient and steady-state responses of the system stimulated by an arbitrary signal. Some relations have been derived between the system steady-state response and its transfer function matrix (TFM), which reveal that the value of a TFM at almost any interested point, as well as its derivatives and a right tangential interpolation along an arbitrary direction, can in principle be estimated from input-output experimental data. Based on these relations, an estimation algorithm is suggested respectively for the parameters of the descriptor system and the values of its TFM. Their properties like asymptotic unbiasedness, consistency, etc., are analyzed. A simple numerical example is included to illustrate characteristics of the suggested estimation algorithms.

cs.MA↗

arXiv2025

ASP2LJ : An Adversarial Self-Play Laywer Augmented Legal Judgment Framework

Legal Judgment Prediction (LJP) aims to predict judicial outcomes, including relevant legal charge, terms, and fines, which is a crucial process in Large Language Model(LLM). However, LJP faces two key challenges: (1)Long Tail Distribution: Current datasets, derived from authentic cases, suffer from high human annotation costs and imbalanced distributions, leading to model performance degradation. (2)Lawyer's Improvement: Existing systems focus on enhancing judges' decision-making but neglect the critical role of lawyers in refining arguments, which limits overall judicial accuracy. To address these issues, we propose an Adversarial Self-Play Lawyer Augmented Legal Judgment Framework, called ASP2LJ, which integrates a case generation module to tackle long-tailed data distributions and an adversarial self-play mechanism to enhance lawyers' argumentation skills. Our framework enables a judge to reference evolved lawyers' arguments, improving the objectivity, fairness, and rationality of judicial decisions. Besides, We also introduce RareCases, a dataset for rare legal cases in China, which contains 120 tail-end cases. We demonstrate the effectiveness of our approach on the SimuCourt dataset and our RareCases dataset. Experimental results show our framework brings improvements, indicating its utilization. Our contributions include an integrated framework, a rare-case dataset, and publicly releasing datasets and code to support further research in automated judicial systems.

cs.CL↗