arXiv subjects

Zheng Zhou

Publications and source records attributed to Zheng Zhou.

At least 55 records · Page 3Linked to original sources

arXiv2025

Twisted traces on abelian quantum Higgs and Coulomb branches

We study twisted traces on the quantum Higgs branches $A_{\operatorname{Higgs}}$ of $3d, \mathcal{N}=4$ gauge theories, that is, the quantum Hamiltonian reductions of Weyl algebras. In theories which are good, we define a twisted trace that arises naturally from the correlation functions of the gauge theory. We show that this trace induces an inner product and a short star product on $A_{\operatorname{Higgs}}$. We analyze this trace in the case of an abelian gauge group and show that it has a natural expansion in terms of the twisted traces of Verma modules, confirming a conjecture of the first author and Okazaki. This expansion has a natural interpretation in terms of 3-d mirror symmetry, and we predict that it can be interpreted as an Atiyah-Bott fixed-point formula under the quantum Hikita isomorphism.

hep-th↗

arXiv2025

Measuring Modern Phishing Tactics: A Quantitative Study of Body Obfuscation Prevalence, Co-occurrence, and Filter Impact

Phishing attacks frequently use email body obfuscation to bypass detection filters, but quantitative insights into how techniques are combined and their impact on filter scores remain limited. This paper addresses this gap by empirically investigating the prevalence, co-occurrence patterns, and spam score associations of body obfuscation techniques. Analysing 386 verified phishing emails, we quantified ten techniques, identified significant pairwise co-occurrences revealing strategic layering like the presence of text in images with multipart abuse, and assessed associations with antispam scores using multilinear regression. Text in Image (47.0%), Base64 Encoding (31.2%), and Invalid HTML (28.8%) were highly prevalent. Regression (R${}^2$=0.486, p<0.001) linked Base64 Encoding and Text in Image with significant antispam evasion (p<0.05) in this configuration, suggesting potential bypass capabilities, while Invalid HTML correlated with higher scores. These findings establish a quantitative baseline for complex evasion strategies, underscoring the need for multi-modal defences against combined obfuscation tactics.

cs.CR↗

arXiv2025

CausalDiffTab: Mixed-Type Causal-Aware Diffusion for Tabular Data Generation

Training data has been proven to be one of the most critical components in training generative AI. However, obtaining high-quality data remains challenging, with data privacy issues presenting a significant hurdle. To address the need for high-quality data. Synthesize data has emerged as a mainstream solution, demonstrating impressive performance in areas such as images, audio, and video. Generating mixed-type data, especially high-quality tabular data, still faces significant challenges. These primarily include its inherent heterogeneous data types, complex inter-variable relationships, and intricate column-wise distributions. In this paper, we introduce CausalDiffTab, a diffusion model-based generative model specifically designed to handle mixed tabular data containing both numerical and categorical features, while being more flexible in capturing complex interactions among variables. We further propose a hybrid adaptive causal regularization method based on the principle of Hierarchical Prior Fusion. This approach adaptively controls the weight of causal regularization, enhancing the model's performance without compromising its generative capabilities. Comprehensive experiments conducted on seven datasets demonstrate that CausalDiffTab outperforms baseline methods across all metrics. Our code is publicly available at: https://github.com/Godz-z/CausalDiffTab.

cs.CL↗

arXiv2025

Quantum Simulation of Two-Dimensional $\mathrm{U(1)}$ Gauge Theory in Rydberg and Rydberg-Dressed Atom Arrays

Simulating $\mathrm{U(1)}$ quantum gauge theories with spatial dimension greater than one is of great physical significance yet has not been achieved experimentally. Here we propose a simple realization of $\mathrm{U(1)}$ gauge theory on triangular lattice Rydberg atom arrays. Within experimentally accessible range, we find that the effective model well simulates various aspects of the $\mathrm{U(1)}$ gauge theory, such as emergence of topological sectors, incommensurability, and the deconfined Rokhsar-Kivelson point. Our proposal is easy to implement experimentally and exhibits pronounced quantum dynamics compared with previous proposals realizing $\mathrm{U(1)}$ and $\mathbb Z_2$ gauge theories.

quant-ph↗

arXiv2025

A new series of 3D CFTs with $\mathrm{Sp}(N)$ global symmetry on fuzzy sphere

The quest to discover new 3D CFTs has been intriguing for physicists. For this purpose, fuzzy sphere reguarlisation that studies interacting quantum systems defined on the lowest Landau level on a sphere has emerged as a powerful tool. In this paper, we discover a series of new CFTs with global symmetry $\mathrm{Sp}(N)$ in the fuzzy sphere models that are closely related to the $\mathrm{SO}(5)$ deconfined phase transition, and are described by a $\mathrm{Sp}(N)/(\mathrm{Sp}(M)\times \mathrm{Sp}(N-M))$ non-linear sigma model with a Wess-Zumino-Witten term. We numerically verify the emergent conformal symmetry by observing the integer-spaced conformal multiplets and studying the finite-size scaling of the conformality. We discuss possible candidates for these newly discovered CFTs, the most plausible ones being Chern-Simons-matter theories which have $N$ flavour of gapless bosons or fermions coupled to a non-Abelian (viz. $\mathrm{Sp}(1)$, $\mathrm{Sp}(2)$, etc.) Chern-Simons gauge field. Our work provides new avenues for studying interacting CFTs in 3D, possibly facilitating the non-perturbative study of critical gauge theories and previously undiscovered CFTs.

hep-th↗

arXiv2025

Can Large Language Models Become Policy Refinement Partners? Evidence from China's Social Security Studies

The rapid development of large language models (LLMs) is reshaping operational paradigms across multidisciplinary domains. LLMs' emergent capability to synthesize policy-relevant insights across disciplinary boundaries suggests potential as decision-support tools. However, their actual performance and suitability as policy refinement partners still require verification through rigorous and systematic evaluations. Our study employs the context-embedded generation-adaptation framework to conduct a tripartite comparison among the American GPT-4o, the Chinese DeepSeek-R1 and human researchers, investigating the capability boundaries and performance characteristics of LLMs in generating policy recommendations for China's social security issues. This study demonstrates that while LLMs exhibit distinct advantages in systematic policy design, they face significant limitations in addressing complex social dynamics, balancing stakeholder interests, and controlling fiscal risks within the social security domain. Furthermore, DeepSeek-R1 demonstrates superior performance to GPT-4o across all evaluation dimensions in policy recommendation generation, illustrating the potential of localized training to improve contextual alignment. These findings suggest that regionally-adapted LLMs can function as supplementary tools for generating diverse policy alternatives informed by domain-specific social insights. Nevertheless, the formulation of policy refinement requires integration with human researchers' expertise, which remains critical for interpreting institutional frameworks, cultural norms, and value systems.

cs.CY↗

arXiv2025

Infrared Emission of Polycyclic Aromatic Hydrocarbon Molecules in Titan: Cyanonaphthalenes

As the only moon in the solar system with a thick atmosphere, Titan is a compelling and enigmatic world containing a complex organic haze. Polycyclic aromatic hydrocarbon (PAH) molecules are believed to play an essential role in the formation of Titan's aerosols and haze layers. The existence of PAHs in Titan's upper atmosphere has been revealed by the detection of the 3.28-micron emission band with Cassini's Visual and Infrared Mapping Spectrometer (VIMS). However, there is little knowledge about the identity, composition, size and abundance of PAH molecules in Titan's atmosphere. Due to its unprecedented sensitivity and spectral coverage and resolution, the advent of the James Webb Space Telescope (JWST) could possibly enable a full characterization of the chemical makeups of Titan's aerosols. In particular, with a much better spectral resolution than Cassini's VIMS, JWST's Near Infrared Spectrograph (and Mid Infrared Instrument) could enable the spectral bands to be better resolved, potentially providing crucial information about which PAHs are really present in Titan's upper atmosphere. To facilitate JWST to search for and identify Titan's PAH molecules, we are performing a systematic study of the photophysics of PAHs in Titan's upper atmosphere. As a pilot study, here we report the infrared emission spectra of vibrationally excited cyanonapthalenes and their ions which are composed of two fused benzene rings and one nitrile (-CN) group. The calculated emission spectra will help JWST to quantitatively determine or place an upper limit on the abundances of cyanonapthalenes in Titan's upper atmosphere.

astro-ph.EP↗

arXiv2025

GaussianCAD: Robust Self-Supervised CAD Reconstruction from Three Orthographic Views Using 3D Gaussian Splatting

The automatic reconstruction of 3D computer-aided design (CAD) models from CAD sketches has recently gained significant attention in the computer vision community. Most existing methods, however, rely on vector CAD sketches and 3D ground truth for supervision, which are often difficult to be obtained in industrial applications and are sensitive to noise inputs. We propose viewing CAD reconstruction as a specific instance of sparse-view 3D reconstruction to overcome these limitations. While this reformulation offers a promising perspective, existing 3D reconstruction methods typically require natural images and corresponding camera poses as inputs, which introduces two major significant challenges: (1) modality discrepancy between CAD sketches and natural images, and (2) difficulty of accurate camera pose estimation for CAD sketches. To solve these issues, we first transform the CAD sketches into representations resembling natural images and extract corresponding masks. Next, we manually calculate the camera poses for the orthographic views to ensure accurate alignment within the 3D coordinate system. Finally, we employ a customized sparse-view 3D reconstruction method to achieve high-quality reconstructions from aligned orthographic views. By leveraging raster CAD sketches for self-supervision, our approach eliminates the reliance on vector CAD sketches and 3D ground truth. Experiments on the Sub-Fusion360 dataset demonstrate that our proposed method significantly outperforms previous approaches in CAD reconstruction performance and exhibits strong robustness to noisy inputs.

cs.CV↗

arXiv2025

Constraining the Milky Way's Dispersion Measure Using FRB and X-ray Data

The dispersion measures (DMs) of fast radio bursts (FRBs) are a valuable tool for probing the baryonic content of the intergalactic and the circumgalactic medium of the intervening galaxies along the sightlines. However, interpreting the DMs is complicated by the contributions from the hot gas in and around our Milky Way. This study examines the relationship between DM_MW, derived from localized FRBs, and the Galaxy's hot gas, using X-ray absorption and emission data from O VII and O VIII. We find evidence for a positive correlation between DM_MW and O VII absorption, reflecting contributions from both the disk and halo components. This conclusion is supported by two lines of evidence: (1) No correlation between DM_MW and O VII/O VIII emission, which primarily traces dense disk regions; and (2) the comparison with electron density models, where DM_MW aligns with models that incorporate both disk and halo components but significantly exceeds predictions from pure disk-only models, emphasizing the halo's role. Furthermore, the lack of correlation with O VIII absorption suggests that the primary temperature of the Galaxy's hot gas is likely around 2 x 10^6 K or less, as traced by O VII absorption, while gas at higher temperatures (~3 x 10^6 K to 5 x 10^6 K) is present but less abundant. Our findings provide insights into the Milky Way's gas distribution and improve DM_MW estimates for future cosmological studies.

astro-ph.GA↗

arXiv2025

Iterative Tree Analysis for Medical Critics

Large Language Models (LLMs) have been widely adopted across various domains, yet their application in the medical field poses unique challenges, particularly concerning the generation of hallucinations. Hallucinations in open-ended long medical text manifest as misleading critical claims, which are difficult to verify due to two reasons. First, critical claims are often deeply entangled within the text and cannot be extracted based solely on surface-level presentation. Second, verifying these claims is challenging because surface-level token-based retrieval often lacks precise or specific evidence, leaving the claims unverifiable without deeper mechanism-based analysis. In this paper, we introduce a novel method termed Iterative Tree Analysis (ITA) for medical critics. ITA is designed to extract implicit claims from long medical texts and verify each claim through an iterative and adaptive tree-like reasoning process. This process involves a combination of top-down task decomposition and bottom-up evidence consolidation, enabling precise verification of complex medical claims through detailed mechanism-level reasoning. Our extensive experiments demonstrate that ITA significantly outperforms previous methods in detecting factual inaccuracies in complex medical text verification tasks by 10%. Additionally, we will release a comprehensive test set to the public, aiming to foster further advancements in research within this domain.

cs.CL↗

arXiv2024

Minor-merger induced star formation rejuvenation in an elliptical radio-loud quasar host, 3C 59

We report a rare case where an elliptical radio-loud quasar host, 3C 59, rejuvenates star formation activity through minor mergers with its nearby satellite galaxies. The inferred star formation history of 3C 59 shows significant star formation rejuvenation within the past 500 Myr, before which remains rather quiescent for most of the cosmic time. Three nearest satellite galaxies of 3C 59 exhibit significant morphological disturbances, and two of them present strong tidal tails pointing towards 3C 59. In addition, all the satellite galaxies within a projected distance of 200 kpc show low star formation activities. They also have systematically lower effective radius ($R_{\rm e}$) than local late-type galaxies, while 3C 59 has significantly larger $R_{\rm e}$ than both early- and late-type galaxies. All these features suggest that ongoing minor mergers between 3C 59 and its nearby satellites could be causing gas to flow into 3C 59, which induces the star formation rejuvenation, and possibly also triggers the quasar activity. The enormous power from the large-scale radio jet of 3C 59 may in turn help keep the halo hot, prevent gas cooling, and further reduce star formation in its satellite galaxies. These results provide important insights into the mass and size growth of central galaxies and star formation quenching of satellite galaxies in galaxy groups.

astro-ph.GA↗

arXiv2024

Joint-Optimized Unsupervised Adversarial Domain Adaptation in Remote Sensing Segmentation with Prompted Foundation Model

Unsupervised Domain Adaptation for Remote Sensing Semantic Segmentation (UDA-RSSeg) addresses the challenge of adapting a model trained on source domain data to target domain samples, thereby minimizing the need for annotated data across diverse remote sensing scenes. This task presents two principal challenges: (1) severe inconsistencies in feature representation across different remote sensing domains, and (2) a domain gap that emerges due to the representation bias of source domain patterns when translating features to predictive logits. To tackle these issues, we propose a joint-optimized adversarial network incorporating the "Segment Anything Model (SAM) (SAM-JOANet)" for UDA-RSSeg. Our approach integrates SAM to leverage its robust generalized representation capabilities, thereby alleviating feature inconsistencies. We introduce a finetuning decoder designed to convert SAM-Encoder features into predictive logits. Additionally, a feature-level adversarial-based prompted segmentor is employed to generate class-agnostic maps, which guide the finetuning decoder's feature representations. The network is optimized end-to-end, combining the prompted segmentor and the finetuning decoder. Extensive evaluations on benchmark datasets, including ISPRS (Potsdam/Vaihingen) and CITY-OSM (Paris/Chicago), demonstrate the effectiveness of our method. The results, supported by visualization and analysis, confirm the method's interpretability and robustness. The code of this paper is available at https://github.com/CV-ShuchangLyu/SAM-JOANet.

cs.CV↗

arXiv2024

BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation

Dataset Distillation (DD) is an emerging technique that compresses large-scale datasets into significantly smaller synthesized datasets while preserving high test performance and enabling the efficient training of large models. However, current research primarily focuses on enhancing evaluation accuracy under limited compression ratios, often overlooking critical security concerns such as adversarial robustness. A key challenge in evaluating this robustness lies in the complex interactions between distillation methods, model architectures, and adversarial attack strategies, which complicate standardized assessments. To address this, we introduce BEARD, an open and unified benchmark designed to systematically assess the adversarial robustness of DD methods, including DM, IDM, and BACON. BEARD encompasses a variety of adversarial attacks (e.g., FGSM, PGD, C&W) on distilled datasets like CIFAR-10/100 and TinyImageNet. Utilizing an adversarial game framework, it introduces three key metrics: Robustness Ratio (RR), Attack Efficiency Ratio (AE), and Comprehensive Robustness-Efficiency Index (CREI). Our analysis includes unified benchmarks, various Images Per Class (IPC) settings, and the effects of adversarial training. Results are available on the BEARD Leaderboard, along with a library providing model and dataset pools to support reproducible research. Access the code at BEARD.

cs.CV↗

arXiv2024

Studying the 3d Ising surface CFTs on the fuzzy sphere

Boundaries not only are fundamental elements in nearly all realistic physical systems, but also greatly enrich the structure of quantum field theories. In this paper, we demonstrate that conformal field theory (CFT) with a boundary, known as surface CFT in three dimensions, can be studied with the setup of fuzzy sphere. We consider the example of surface criticality of the 3D Ising CFT. We propose two schemes by cutting a boundary in the orbital space or the real space to realise the ordinary and the normal surface CFTs on the fuzzy sphere. We obtain the operator spectra through state-operator correspondence. We observe integer spacing of the conformal multiplets, and thus provide direct evidence of conformal symmetry. We identify the ordinary surface primary $o$, the displacement operator $\mathrm{D}$ and their conformal descendants and extract their scaling dimensions. We also study the one-point and two-point correlation functions and extract the bulk-to-surface OPE coefficients, some of which are reported for the first time. In addition, using the overlap of the bulk CFT state and the polarised state, we calculate the boundary central charges of the 3D Ising surface CFTs non-perturbatively. Other conformal data obtained in this way also agrees with prior methods.

hep-th↗

arXiv2024

Does the Order of Fine-tuning Matter and Why?

To improve the performance on a target task, researchers have fine-tuned language models with an intermediate task before the target task of interest. However, previous works have focused on the pre-trained language models and downstream tasks in Natural Language Processing (NLP) and considered only one intermediate task. The effect of fine-tuning multiple intermediate tasks and their ordering on target task performance has not been fully explored in Software Engineering. In this study, we perform the first empirical study on analyzing the impact of task ordering on target task performance. Experimental results show that there is an impact of task ordering on target task performance by up to 6% of performance gain and up to 4% of performance loss. To explain such an impact, we consider a variety of potential factors, including the characteristics of dataset (syntactic similarity and semantic similarity analysis, dataset size), model (probing task and attention analysis), and task (task affinity analysis). Our study provides Software Engineering researchers and practitioners with insights into the effect of task orderings and how to select the one that is cost-effective while achieving the best performance gain.

cs.SE↗

arXiv2024

Uhlmann quench and geometric dynamic quantum phase transition of mixed states

Dynamic quantum phase transitions (DQPT) following quantum quenches exhibit singular behavior of the overlap between the initial and evolved states. Here we present a formalism to incorporate a geometric phase into quench dynamics of mixed quantum states, a process named the Uhlmann quench, based on the Uhlmann parallel transport. To overcome the incompatibility between the Uhlmann parallel-transport condition and Hamiltonian dynamics, we formulate the evolution of purification of the density matrix in a form which not only respects the dynamics according to the density matrix but also follows the Uhlmann parallel-transport condition to generate a geometric phase after a quantum quench. For cyclic processes exemplified by a spin-1/2 system, geometric DQPTs (GDQPTs) can emerge with both singular behavior in the dynamic analogue of the free energy and jumps of the geometric phase. Moreover, the Uhlmann phase reflecting the holonomy is generated at the end of each cycle. The Uhlmann quench thus paves the way for investigating the interplay between quantum dynamics and geometric processes in mixed states.

quant-ph↗

arXiv2024

MVPatch: More Vivid Patch for Adversarial Camouflaged Attacks on Object Detectors in the Physical World

Recent studies have shown that Adversarial Patches (APs) can effectively manipulate object detection models. However, the conspicuous patterns often associated with these patches tend to attract human attention, posing a significant challenge. Existing research has primarily focused on enhancing attack efficacy in the physical domain while often neglecting the optimization of stealthiness and transferability. Furthermore, applying APs in real-world scenarios faces major challenges related to transferability, stealthiness, and practicality. To address these challenges, we introduce generalization theory into the context of APs, enabling our iterative process to simultaneously enhance transferability and refine visual correlation with realistic images. We propose a Dual-Perception-Based Framework (DPBF) to generate the More Vivid Patch (MVPatch), which enhances transferability, stealthiness, and practicality. The DPBF integrates two key components: the Model-Perception-Based Module (MPBM) and the Human-Perception-Based Module (HPBM), along with regularization terms. The MPBM employs ensemble strategy to reduce object confidence scores across multiple detectors, thereby improving AP transferability with robust theoretical support. Concurrently, the HPBM introduces a lightweight method for achieving visual similarity, creating natural and inconspicuous adversarial patches without relying on additional generative models. The regularization terms further enhance the practicality of the generated APs in the physical domain. Additionally, we introduce naturalness and transferability scores to provide an unbiased assessment of APs. Extensive experimental validation demonstrates that MVPatch achieves superior transferability and a natural appearance in both digital and physical domains, underscoring its effectiveness and stealthiness.

cs.CR↗

arXiv2024

Local geometry and quantum geometric tensor of mixed states

The quantum geometric tensor (QGT) is a fundamental concept for characterizing the local geometry of quantum states. After casting the geometry of pure quantum states and extracting the QGT, we generalize the geometry to mixed quantum states via the density matrix and its purification. The gauge-invariant QGT of mixed states is derived, whose real and imaginary parts are the Bures metric and the Uhlmann form, respectively. In contrast to the imaginary part of the pure-state QGT that is proportional to the Berry curvature, the Uhlmann form vanishes identically for ordinary physical processes. Moreover, there exists a Pythagorean-like equation that links different local distances and reflect the underlying fibration. The Bures metric of mixed states is shown to reduce to the corresponding Fubini-Study metric of the ground state as temperature approaches zero, establishing a correspondence despite the different underlying fibrations. We also present two examples with contrasting local geometries and discuss experimental implications.

quant-ph↗