arXiv subjects

Haowei Chen

Publications and source records attributed to Haowei Chen.

At least 19 recordsLinked to original sources

arXiv2026

Excitons probe intrinsic flat band Mottness in a van der Waals heterostructure

Excitons provide a sensitive optical probe of electronic correlations in nearby two-dimensional materials, yet their coupling to intrinsic flat-band Mott systems remains largely unexplored. Here we combine gate-tunable optical spectroscopy with first-principles calculations to study monolayer WSe$_2$ in direct contact with the van der Waals Mott insulator Nb$_3$Cl$_8$. The gate evolution of WSe$_2$ excitonic resonances reveals signatures of a correlation-reconstructed Mott gap in Nb$_3$Cl$_8$ that is absent from the single-particle band picture. In the electron-doped regime, the WSe$_2$ 2s Rydberg exciton undergoes a multistage evolution and develops into interlayer attractive and repulsive polaron branches, showing that a Rydberg exciton can be dressed by strongly correlated flat-band electrons in an adjacent Mott layer. Under an out-of-plane magnetic field, spin-polarized Nb$_3$Cl$_8$ states further induce valley-selective exciton coupling, producing a strongly enhanced circular polarization of the WSe$_2$ exciton emission. These results extend exciton-based sensing and exciton-polaron physics to intrinsic flat-band Mott materials, providing an optical route to probe and engineer correlation-driven interfacial quasiparticles.

cond-mat.mtrl-sci↗

arXiv2026

External magnetic-field effects on dipolar-particle orbits and critical collisions in Kerr--Bertotti--Robinson spacetime

Strong magnetic fields affect particle dynamics through two distinct channels: gravitational backreaction deforms the spacetime, while direct coupling to an intrinsic magnetic moment depends on the relative orientation of the field and the dipole. We disentangle these effects by studying equatorial orbits and near-horizon collisions of electrically neutral magnetized particles in the exact Kerr--Bertotti--Robinson spacetime. The field-induced geometric deformation shifts turning points and circular-orbit domains and can eliminate a finite effective-potential well together with its bound orbits. Even without direct dipole coupling, it can also offset the Kerr periapsis advance and produce a finite-radius zero-precession orbit. Direct dipole coupling breaks the symmetry under magnetic-field reversal and shifts the radius, energy, and angular momentum of the innermost stable circular orbit in an orientation-dependent manner. The formal ultrarelativistic endpoints of these orbit branches, however, remain fixed by the background geometry and approach circular null orbits. In the Ba\~nados--Silk--West mechanism, both magnetic effects modify finite-radius potential barriers and hence the ability of a critical particle to reach the near-horizon collision region. In the representative nonzero-field cases examined here, an exactly critical particle released from infinity is blocked before reaching an extremal horizon, although a locally admissible collision between critical and usual particles can still produce unbounded center-of-mass energy. Finite dipole coupling shifts the barriers but does not change the leading near-horizon divergence. Near a nonextremal horizon, an exactly critical particle is excluded and the collision energy remains finite.

gr-qc↗

arXiv2026

Realization of Air-Stable Two-Dimensional Superconductor Nb2Pd3Te5 With Quasi-One-Dimensional Pair Density Modulation

Two-dimensional (2D) superconductors provide a fertile platform for exploring reduced-dimensional superconductivity and emergent quantum phenomena. Incorporating quasi-one-dimensional (quasi-1D) structural motifs into 2D superconductors offers a powerful route to engineer strong electronic anisotropy, enabling unconventional superconducting states and anisotropic superconducting transport functionalities. However, such systems remain rarely realized. Here we report the realization of a 2D superconductor Nb2Pd3Te5, exhibiting an intrinsic quasi-1D pair density modulation. Monolayer and bilayer Nb2Pd3Te5 is synthesized via van-der-Waals epitaxy. Using ultralow-temperature scanning tunneling microscopy/spectroscopy, we observe the quasi-1D crystal structure and superconductivity below ~0.6 K with a pronounced quasi-1D pair density modulation. Remarkably, both monolayer and bilayer Nb2Pd3Te5 show strong air stability. Our findings establish atomically 2D Nb2Pd3Te5 as a robust and promising platform for exploring novel low-dimensional quantum phenomena and anisotropy-enabled superconducting devices.

cond-mat.mtrl-sci↗

arXiv2026

Horizon-Evanescent Scalar Clouds from Coupled Rotation and Magnetic Fields around Black Holes

We show that black-hole rotation and an external magnetic field can jointly generate a qualitatively new class of scalar cloud. Using the Kerr-Bertotti-Robinson geometry as a separable laboratory for magnetized rotating black holes, we study a charged massive scalar field and map the radial Klein-Gordon equation into a one-dimensional Schr\"{o}dinger-like form. The magnetic coupling shifts the near-horizon dispersion relation and realizes a positive horizon gap: a sufficient near-horizon criterion under which the horizon wavenumber becomes purely imaginary in a finite frequency band below the usual kinematic synchronization frequency. In this band the physical horizon boundary condition is no longer a propagating ingoing wave, but a regular exponentially decaying state. This rotation--magnetic-field mechanism quenches the superradiant flux and supports horizon-decaying scalar clouds (Type-II), distinct from the usual synchronized propagating clouds (Type-I). Matched asymptotic expansions and numerical shooting solutions are used to exhibit both branches and their spatial profiles. Thus the Kerr-Bertotti-Robinson solution is not an isolated curiosity, but an explicit realization of a broader positive-gap criterion for stationary bosonic configurations absent in isolated Kerr systems.

gr-qc↗

arXiv2026

UniPET: a universal network for high-quality PET image denoising across varied dose reduction factors

Most existing deep learning-based PET image denoising methods assume a fixed and known dose reduction factor (DRF) for low-dose PET images. However, these methods encounter significant performance degradation when the DRF varies beyond the assumed one in practical applications. To address the challenge posed by varied DRFs, several preliminary studies focus on the task of universal PET image denoising, aiming to train a universal model over low-dose data across DRFs. Nonetheless, these vanilla universal models often struggle with misaligned styles present in different DRF data, leading to the \textit{style elimination issue} with a significant over-smoothing effect. To deal with this issue, we innovatively introduce domain generalization to PET image denoising and propose a universal PET image denoising network (UniPET) to achieve high-quality PET image denoising across diverse DRFs. UniPET comprises two primary innovations: a style alignment network (SAN) and a region-aware learning strategy (RALS). Specifically, SAN utilizes style alignment techniques derived from domain generalization to align and recover styles across different DRFs, ensuring the model's generalizability across various DRFs while effectively preserving styles. Furthermore, to enhance style recovery, RALS distinguishes between flat and stylized regions, exclusively conducting adversarial learning on the latter, thereby more effectively guiding the model's focus towards learning stylized regions. It is demonstrated that our proposed UniPET can adaptively recover different DRF styles and achieve high-quality PET image denoising across DRFs. Comprehensive experiments show that UniPET exhibits comparable performance to individual DRF-specific models at specific DRFs and realizes state-of-the-art performance in universal PET image denoising quantitatively, perceptually, and clinically.

cs.CV↗

arXiv2026

Manipulating Charge Distribution in Moir\'e Superlattices by Light

In ordinary solids, nonlinear optical responses are typically studied in terms of unit-cell averages due to the angstr\"om-scale lattice constants. In contrast, moir\'e superlattices, characterized by a large length scale, unlock an often-overlooked degree of freedom: intra-supercell spatial variations of local observables. Here, we formulate the second-order direct current (DC) charge response in a spatially resolved manner, showing that even uniform optical illumination can drive a static, spatially non-uniform charge redistribution within a supercell. This effect is ubiquitous and cannot be forbidden by any crystalline symmetries. Furthermore, we identify a dominant contribution arising from diverging analytical response coefficients, which leads to linear-in-time growth of the redistribution in the absence of relaxation. This growth is driven by the convergence or divergence of local DC photocurrents. Applying our theory to twisted bilayer MoTe$_2$, we demonstrate strong, highly tunable charge modulation controlled by light intensity and frequency, opening a route to in situ, all-optical control of moir\'e-periodic electrostatic potentials. Our work underscores the importance of intra-cell degrees of freedom, which enable a qualitatively richer class of nonlinear optical responses in moir\'e superlattices.

cond-mat.mtrl-sci↗

arXiv2026

Charged Superradiant Instability of Spherically Symmetric Regular Black Holes in de Sitter Spacetime: Time- and Frequency-Domain Analysis

We investigate the superradiant instability of Ay\'on-Beato-Garc\'ia-de Sitter (ABG-dS) black holes under massless charged scalar perturbations using both time-domain evolutions and frequency-domain computations. We show that the instability occurs only for the spherically symmetric mode with $\ell=0$, whereas asymptotically flat ABG black holes remain stable in the massless limit, which underscores the essential role of the cosmological horizon in providing a confining boundary. We further study the dependence of the growth rate on the cosmological constant $\Lambda$, the scalar charge $q$, and the black hole charge $Q$, finding that it reaches a maximum at intermediate values of $\Lambda$ and $q$ and increases monotonically with $Q$. Compared with Reissner-Nordstr\"om-de Sitter black holes, ABG-dS black holes exhibit distinct instability characteristics due to the modified electrostatic potential induced by nonlinear electrodynamics.

gr-qc↗

arXiv2026

Electronic procrystalline state in moire structures

Solid state materials can display varieties of atomic structural orders ranging from crystalline to amorphous, underlying their properties and diverse functionalities. Procrystal has emerged as a new category of solids, featuring a long-range ordered lattice framework tiled with disordered atomic or molecular structures on the lattice sites, arousing great interest due to its novel structural and physical properties. However, the electronic analogue of a procrystal, dubbed as an electronic procrystalline (EPC) state, has never been experimentally observed. Here, we report the observation of an EPC state in a moire superstructure formed between a monolayer metallic NiTe2 and a superconductor NbSe2 with incommensurate lattice wavevectors. The observed EPC state exhibits a long-range periodic charge modulation at the moire scale inlaid with short-range irregular orders within each moire cell. Strikingly, the short-range charge orders inside the moire unit cells have proximately root3*root3 quasi-period, which is absent in pristine NiTe2. Intriguingly, the EPC order is also observed in the superconducting state of the moire superstructure. Furthermore, the emergent EPC state and short-range charge order, coexisting with the proximity induced superconductivity, can be precisely modulated with the thickness of NiTe2. Our findings uncover the potential of moire platform for understanding and tuning novel correlated quantum phases with this exotic procrystalline order.

cond-mat.mtrl-sci↗

arXiv2025

Electric Penrose process in spherically symmetric regular black holes with and without a cosmological constant

We investigate the electric Penrose process in Ay\'{o}n-Beato-Garc\'{i}a (ABG) black holes, both in the presence and absence of a cosmological constant, presenting, to the best of our knowledge, the first such analysis within the context of regular black holes. Our study systematically examines the effects of black hole charge and the cosmological constant on the formation of negative-energy states and the efficiency of energy extraction. Compared to Reissner-Nordstr\"{o}m (RN) black holes, ABG black holes exhibit a significantly larger negative-energy region, enabling the electric Penrose process to operate at larger distances from the event horizon and achieve higher energy extraction efficiency. This enhancement is particularly pronounced near the event horizon, where the performance gap widens with increasing black hole charge. Notably, even for astrophysically realistic values of charge and cosmological constant that approach vanishingly small values, distinct differences persist, yielding a maximum efficiency ratio of approximately $23/8$. These results suggest that, in realistic astrophysical scenarios, ABG black holes can accelerate charged particles more efficiently and serve as more powerful engines for energy extraction than their RN counterparts.

gr-qc↗

arXiv2025

All-in-One Medical Image Restoration with Latent Diffusion-Enhanced Vector-Quantized Codebook Prior

All-in-one medical image restoration (MedIR) aims to address multiple MedIR tasks using a unified model, concurrently recovering various high-quality (HQ) medical images (e.g., MRI, CT, and PET) from low-quality (LQ) counterparts. However, all-in-one MedIR presents significant challenges due to the heterogeneity across different tasks. Each task involves distinct degradations, leading to diverse information losses in LQ images. Existing methods struggle to handle these diverse information losses associated with different tasks. To address these challenges, we propose a latent diffusion-enhanced vector-quantized codebook prior and develop \textbf{DiffCode}, a novel framework leveraging this prior for all-in-one MedIR. Specifically, to compensate for diverse information losses associated with different tasks, DiffCode constructs a task-adaptive codebook bank to integrate task-specific HQ prior features across tasks, capturing a comprehensive prior. Furthermore, to enhance prior retrieval from the codebook bank, DiffCode introduces a latent diffusion strategy that utilizes the diffusion model's powerful mapping capabilities to iteratively refine the latent feature distribution, estimating more accurate HQ prior features during restoration. With the help of the task-adaptive codebook bank and latent diffusion strategy, DiffCode achieves superior performance in both quantitative metrics and visual quality across three MedIR tasks: MRI super-resolution, CT denoising, and PET synthesis.

cs.CV↗

arXiv2025

Solving Formal Math Problems by Decomposition and Iterative Reflection

General-purpose Large Language Models (LLMs) have achieved remarkable success in intelligence, performing comparably to human experts on complex reasoning tasks such as coding and mathematical reasoning. However, generating formal proofs in specialized languages like Lean 4 remains a significant challenge for these models, limiting their application in complex theorem proving and automated verification. Current approaches typically require specializing models through fine-tuning on dedicated formal corpora, incurring high costs for data collection and training. In this work, we introduce \textbf{Delta Prover}, an agent-based framework that orchestrates the interaction between a general-purpose LLM and the Lean 4 proof environment. Delta Prover leverages the reflection and reasoning capabilities of general-purpose LLMs to interactively construct formal proofs in Lean 4, circumventing the need for model specialization. At its core, the agent integrates two novel, interdependent components: an algorithmic framework for reflective decomposition and iterative proof repair, and a custom Domain-Specific Language (DSL) built upon Lean 4 for streamlined subproblem management. \textbf{Delta Prover achieves a state-of-the-art 95.9\% success rate on the miniF2F-test benchmark, surpassing all existing approaches, including those requiring model specialization.} Furthermore, Delta Prover exhibits a significantly stronger test-time scaling law compared to standard Best-of-N proof strategies. Crucially, our findings demonstrate that general-purpose LLMs, when guided by an effective agentic structure, possess substantial untapped theorem-proving capabilities. This presents a computationally efficient alternative to specialized models for robust automated reasoning in formal environments.

cs.AI↗

arXiv2025

Band-spin-valley coupled exciton physics in antiferromagnetic MnPS$_3$

The introduction of intrinsic magnetic order in two-dimensional (2D) semiconductors offers great opportunities for investigating correlated excitonic phenomena. Here, we employ full-spinor GW plus Bethe-Salpeter equation methodology to reveal rich exciton physics in a prototypical 2D N\'{e}el-type antiferromagnetic semiconductor MnPS$_3$, enabled by the interplay among inverted dispersion of the second valence band, spin-valley coupling and magnetic order. The negative hole mass increases the reduced mass of the lowest-energy bright exciton, leading to exchange splitting enhancement of the bright exciton relative to band-edge dark exciton. Notably, such splitting couples with spontaneous valley polarization to generate distinct excitonic fine structure between $K$ and $-K$ valleys, which dictate distinct relaxation behaviors. Crucially, magnetic order transition from N\'{e}el antiferromagnetic to ferromagnetic state induces significant quasiparticle band structure reconstruction and excitonic transitions modification, with low-energy optical excitations being exclusively contributed by majority-spin channel. These findings establish 2D antiferromagnetic semiconductors as an intriguing platform to study band-spin-valley coupled exciton physics.

cond-mat.mtrl-sci↗

arXiv2024

Deep Band Crossings Enhanced Nonlinear Optical Effects

Nonlinear optical (NLO) effects in materials with band crossings have attracted significant research interests due to the divergent band geometric quantities around these crossings. Most current research has focused on band crossings between the valence and conduction bands. However, such crossings are absent in insulators, which are more relevant for NLO applications. In this work, we demonstrate that NLO effects can be significantly enhanced by band crossings within the valence or conduction bands, which we designate as "deep band crossings" (DBCs). As an example, in two dimensions, we show that shift conductivity can be substantially enhanced or even divergent due to a mirror-protected "deep Dirac nodal point". In three dimensions, we propose GeTe as an ideal material where shift conductivity is enhanced by "deep Dirac nodal lines". The ubiquity of this enhancement is further confirmed by high-throughput calculations. Other types of DBCs and NLO effects are also discussed. By manipulating band crossings between arbitrary bands, our work offers a simple, practical, and universal way to greatly enhance NLO effects.

cond-mat.mes-hall↗

arXiv2024

Region Attention Transformer for Medical Image Restoration

Transformer-based methods have demonstrated impressive results in medical image restoration, attributed to the multi-head self-attention (MSA) mechanism in the spatial dimension. However, the majority of existing Transformers conduct attention within fixed and coarsely partitioned regions (\text{e.g.} the entire image or fixed patches), resulting in interference from irrelevant regions and fragmentation of continuous image content. To overcome these challenges, we introduce a novel Region Attention Transformer (RAT) that utilizes a region-based multi-head self-attention mechanism (R-MSA). The R-MSA dynamically partitions the input image into non-overlapping semantic regions using the robust Segment Anything Model (SAM) and then performs self-attention within these regions. This region partitioning is more flexible and interpretable, ensuring that only pixels from similar semantic regions complement each other, thereby eliminating interference from irrelevant regions. Moreover, we introduce a focal region loss to guide our model to adaptively focus on recovering high-difficulty regions. Extensive experiments demonstrate the effectiveness of RAT in various medical image restoration tasks, including PET image synthesis, CT image denoising, and pathological image super-resolution. Code is available at \href{https://github.com/Yaziwel/Region-Attention-Transformer-for-Medical-Image-Restoration.git}{https://github.com/RAT}.

eess.IV↗

arXiv2024

All-In-One Medical Image Restoration via Task-Adaptive Routing

Although single-task medical image restoration (MedIR) has witnessed remarkable success, the limited generalizability of these methods poses a substantial obstacle to wider application. In this paper, we focus on the task of all-in-one medical image restoration, aiming to address multiple distinct MedIR tasks with a single universal model. Nonetheless, due to significant differences between different MedIR tasks, training a universal model often encounters task interference issues, where different tasks with shared parameters may conflict with each other in the gradient update direction. This task interference leads to deviation of the model update direction from the optimal path, thereby affecting the model's performance. To tackle this issue, we propose a task-adaptive routing strategy, allowing conflicting tasks to select different network paths in spatial and channel dimensions, thereby mitigating task interference. Experimental results demonstrate that our proposed \textbf{A}ll-in-one \textbf{M}edical \textbf{I}mage \textbf{R}estoration (\textbf{AMIR}) network achieves state-of-the-art performance in three MedIR tasks: MRI super-resolution, CT denoising, and PET synthesis, both in single-task and all-in-one settings. The code and data will be available at \href{https://github.com/Yaziwel/All-In-One-Medical-Image-Restoration-via-Task-Adaptive-Routing.git}{https://github.com/Yaziwel/AMIR}.

cs.CV↗

arXiv2024

Giant and controllable nonlinear magneto-optical effects in two-dimensional magnets

The interplay of polarization and magnetism in materials with light can create rich nonlinear magneto-optical (NLMO) effects, and the recent discovery of two-dimensional (2D) van der Waals magnets provides remarkable control over NLMO effects due to their superb tunability. Here, based on first-principles calculations, we reported giant NLMO effects in CrI3-based 2D magnets, including a dramatic change of second-harmonics generation (SHG) polarization direction (90 degrees) and intensity (on/off switch) under magnetization reversal, and a 100% SHG circular dichroism effect. We further revealed that these effects could not only be used to design ultra-thin multifunctional optical devices, but also to detect subtle magnetic orderings. Remarkably, we analytically derived conditions to achieve giant NLMO effects and propose general strategies to realize them in 2D magnets. Our work not only uncovers a series of intriguing NLMO phenomena, but also paves the way for both fundamental research and device applications of ultra-thin NLMO materials.

cond-mat.mtrl-sci↗

arXiv2024

Graph Machine Learning based Doubly Robust Estimator for Network Causal Effects

We address the challenge of inferring causal effects in social network data. This results in challenges due to interference -- where a unit's outcome is affected by neighbors' treatments -- and network-induced confounding factors. While there is extensive literature focusing on estimating causal effects in social network setups, a majority of them make prior assumptions about the form of network-induced confounding mechanisms. Such strong assumptions are rarely likely to hold especially in high-dimensional networks. We propose a novel methodology that combines graph machine learning approaches with the double machine learning framework to enable accurate and efficient estimation of direct and peer effects using a single observational social network. We demonstrate the semiparametric efficiency of our proposed estimator under mild regularity conditions, allowing for consistent uncertainty quantification. We demonstrate that our method is accurate, robust, and scalable via an extensive simulation study. We use our method to investigate the impact of Self-Help Group participation on financial risk tolerance.

cs.LG↗

arXiv2023

Self-complementary (Pseudo-)Split Graphs

We are concerned with split graphs and pseudo-split graphs whose complements are isomorphic to themselves. These special subclasses of self-complementary graphs are actually the core of self-complementary graphs. Indeed, we show that all self-complementary graphs with forcibly self-complementary degree sequences are pseudo-split graphs. We also give formulas to calculate the number of self-complementary (pseudo-)split graphs of a given order, and show that Trotignon's conjecture holds for all self-complementary split graphs.

math.CO↗