arXiv subjects

Qian Li

Publications and source records attributed to Qian Li.

At least 91 records · Page 5Linked to original sources

arXiv2025

Photonics of topological magnetic textures

Topological textures in magnetically ordered materials are important case studies for fundamental research with promising applications in data science. They can also serve as photonic elements to mold electromagnetic fields endowing them with features inherent to the spin order, as demonstrated analytically and numerically in this work. A self-consistent theory is developed for the interaction of spatially structured electromagnetic fields with non-collinear, topologically non-trivial spin textures. A tractable numerical method is designed and implemented for the calculation of the formed magnetic/photonic textures in the entire simulation space. Numerical illustrations are presented for scattering from point-like singularities, i.e. Bloch points, in the magnetization vector fields, evidencing that the geometry and topology of the magnetic order results in photonic fields that embody orbital angular momentum, chirality as well as magnetoelectric densities. Features of the scattered fields can serve as a fingerprint for the underlying magnetic texture and its dynamics. The findings point to the potential of topological magnetic textures as a route to molding photonic fields.

cond-mat.other↗

arXiv2025

Translating Electrocardiograms to Cardiac Magnetic Resonance Imaging Useful for Cardiac Assessment and Disease Screening: A Multi-Center Study

Cardiovascular diseases (CVDs) are the leading cause of global mortality, necessitating accessible and accurate diagnostic tools. While cardiac magnetic resonance imaging (CMR) provides gold-standard insights into cardiac structure and function, its clinical utility is limited by high cost and complexity. In contrast, electrocardiography (ECG) is inexpensive and widely available but lacks the granularity of CMR. We propose CardioNets, a deep learning framework that translates 12-lead ECG signals into CMR-level functional parameters and synthetic images, enabling scalable cardiac assessment. CardioNets integrates cross-modal contrastive learning and generative pretraining, aligning ECG with CMR-derived cardiac phenotypes and synthesizing high-resolution CMR images via a masked autoregressive model. Trained on 159,819 samples from five cohorts, including the UK Biobank (n=42,483) and MIMIC-IV-ECG (n=164,550), and externally validated on independent clinical datasets (n=3,767), CardioNets achieved strong performance across disease screening and phenotype estimation tasks. In the UK Biobank, it improved cardiac phenotype regression R2 by 24.8% and cardiomyopathy AUC by up to 39.3% over baseline models. In MIMIC, it increased AUC for pulmonary hypertension detection by 5.6%. Generated CMR images showed 36.6% higher SSIM and 8.7% higher PSNR than prior approaches. In a reader study, ECG-only CardioNets achieved 13.9% higher accuracy than human physicians using both ECG and real CMR. These results suggest that CardioNets offers a promising, low-cost alternative to CMR for large-scale CVD screening, particularly in resource-limited settings. Future efforts will focus on clinical deployment and regulatory validation of ECG-based synthetic imaging.

eess.IV↗

arXiv2025

Intracavity-birefringence-enabled soliton states and wavelength control in the (C + L)-band fiber lasers

We demonstrate a compact all-fiber nonlinear-polarization-evolution (NPE) fiber laser capable of outputting wavelength-manipulated multiple laser states in the C + L band. Leveraging the intracavity birefringence-induced filtering effect, without external spectral filters, the laser achieves wavelength-tunable conventional solitons (CSs) and soliton molecules (SMs) with a tuning range of 72.85 nm and 45.54 nm, respectively, by adjusting the polarization states and pump power. Furthermore, wavelength-switchable harmonic mode-locking (over 1 GHz) and dual-wavelength mode-locking can also be achieved in the C + L band. To the best of our knowledge, this is the first experimental realization of wavelength-manipulated multiple soliton states covering the C + L band in a single-fiber-laser oscillator. Two examples of digital multi-letter encoding and transmission are further demonstrated on this platform by means of pump-driven wavelength/soliton states switching. By enabling controllable multi-state solitons and broad spectral tunability within a compact architecture, our work provides a promising source for spectrally diverse and temporally stable pulsed light sources that hold significant promise for applications in microscopy, bioimaging, and LiDAR.

physics.optics↗

arXiv2025

Smart Surveillance: Identifying IoT Device Behaviours using ML-Powered Traffic Analysis

The proliferation of Internet of Things (IoT) devices has grown exponentially in recent years, introducing significant security challenges. Accurate identification of the types of IoT devices and their associated actions through network traffic analysis is essential to mitigate potential threats. By monitoring and analysing packet flows between IoT devices and connected networks, anomalous or malicious behaviours can be detected. Existing research focuses primarily on device identification within local networks using methods such as protocol fingerprinting and wireless frequency scanning. However, these approaches are limited in their ability to monitor or classify IoT devices externally. To address this gap, we investigate the use of machine learning (ML) techniques, specifically Random Forest (RF), Multilayer Perceptron (MLP), and K-Nearest Neighbours (KNN), in conjunction with targeted network traffic monitoring to classify IoT device types and their actions. We constructed a testbed comprising an NPAT-enabled router and a diverse set of IoT devices, including smart cameras, controller hubs, home appliances, power controllers, and streaming devices. Experimental results demonstrate that IoT device and action recognition is feasible using our proposed ML-driven approach, with the RF classifier achieving the highest accuracy of 91%, while the MLP recorded the lowest accuracy at 56%. Notably, all device categories were successfully classified except for certain actions associated with security cameras, underscoring both the potential and the limitations of the proposed method.

cs.CR↗

arXiv2025

Efficient Turing Machine Simulation with Transformers

Constant bit-size Transformers are known to be Turing complete, but existing constructions require $Ω(s(n))$ chain-of-thought (CoT) steps per simulated Turing machine (TM) step, leading to impractical reasoning lengths. In this paper, we significantly reduce this efficiency gap by proving that any $(t(n),s(n))$-bounded multi-tape TM can be simulated by a constant bit-size Transformer with an optimal $O(s(n))$-long context window and only $O(s(n)^c)$ CoT steps per TM step, where $c>0$ can be made arbitrarily small by letting the Transformers' head-layer product sufficiently large. In addition, our construction shows that sparse attention with fixed geometric offsets suffices for efficient universal computation. Our proof leverages multi-queue TMs as a bridge. The main technical novelty is a more efficient simulation of multi-tape TMs by synchronous multi-queue TMs, improving both time and space complexity under stricter model assumptions.

cs.CC↗

arXiv2025

On-axis absorption and scattering of charged massive scalar waves by Kerr-Newman black-bounce spacetime

We investigate the absorption and scattering of charged massive scalar waves by the Kerr-Newman black-bounce spacetime when the waves are incident along the rotation axis. Our findings indicate that a faster (slower) rotating spacetime or a more repulsive (attractive) electric force tends to reduce (increase) the absorption cross section and results in larger (smaller) angular widths of the scattered wave oscillations. We find that the rotation parameter exerts a suppressive influence on superradiance, which contrasts with the enhancing effect of the repulsive electric force. It is worth mentioning that the regularization parameter $k$ is found to modify the absorption or scattering cross sections only weakly, but can cause a noticeable reduction of superradiance. To further clarify the role of the parameters in superradiance, we study the energy extraction efficiency in the electric Penrose process. For particles moving along the rotation axis, we find that the influence of the parameters ($a, q, k$) on this efficiency is consistent with their effects on superradiance. We also discuss potential astrophysical applications, showing that particles in this process could be accelerated to ultrahigh energies in realistic environments, and could therefore be used to constrain black hole parameters. For the effect of field mass, it is found that a heavier scalar field leads to a larger absorption cross section and a wider interference fringe of the differential scattering cross section. When superradiance happens, i.e., the absorption cross section becomes negative, it is also found that the differential scattering cross section only changes smoothly, with no apparent qualitative feature showing up.

gr-qc↗

arXiv2025

Scattering of charged massive scalar waves by Kerr-Newman black holes

The scattering of charged massive scalar waves by Kerr-Newman black holes, with incidence along the equatorial plane, is investigated in this work. The differential scattering cross section is computed using the partial wave method, with the forward divergence handled via the series reduction technique. For the first time, we systematically examine the influence of the black hole charge, electromagnetic interactions, and field mass on the equatorial cross section. Our results reveal that regardless of whether the electromagnetic interaction is present or not, the frame-dragging effect shifts the glory away from the exact backward direction and can place interference minima there, contrasting with the on-axis scattering case. The average scattered flux intensity at the medium to large scattering angles exhibits a large enhancement as the Lorentz attraction or field mass increases, particularly in the slowly rotating regime, with the enhancement being frequency-dependent. When superradiance occurs, we observe that the cross section in the prograde scattering angles ($\sim 135^{\circ} < ϕ< 270^{\circ}$) increases as the black hole spin increases, due to enhanced prograde partial wave contributions. Meanwhile, the superradiant scattering cross section increases in all (except the forward) directions when the Lorentz force becomes more repulsive. These findings highlight unique equatorial-plane signatures of charged, rotating spacetimes, distinguishing them from prior on-axis analyses.

gr-qc↗

arXiv2025

Can Visual Input Be Compressed? A Visual Token Compression Benchmark for Large Multimodal Models

Large multimodal models (LMMs) often suffer from severe inference inefficiency due to the large number of visual tokens introduced by image encoders. While recent token compression methods, such as pruning and merging, have shown promise in reducing redundancy, their evaluation remains fragmented and inconsistent. In this work, we present UniPruneBench, a unified and extensible benchmark for visual token pruning in multimodal LLMs. UniPruneBench provides standardized protocols across six ability dimensions and ten datasets, covering ten representative compression algorithms and three families of LMMs (LLaVA-v1.5, Intern-VL3, and Qwen2.5-VL). Beyond task accuracy, it incorporates system-level metrics such as runtime and prefilling latency to provide a holistic view. Our experiments uncover several key findings: (1) random pruning is a surprisingly strong baseline, (2) no single method consistently outperforms others across scenarios, (3) pruning sensitivity varies significantly across tasks, with OCR being most vulnerable, and (4) pruning ratio is the dominant factor governing performance degradation. We believe UniPruneBench will serve as a reliable foundation for future research on efficient multimodal modeling.

cs.CV↗

arXiv2025

Strain and twist angle driven electronic structure evolution in twisted bilayer graphene

In twisted bilayer graphene (TBG) devices, local strains frequently coexist and intertwine with the twist-angle-dependent moiré superlattice, significantly influencing the electronic properties of TBG, yet their combined effects remain incompletely understood. Here, using low-temperature scanning tunneling microscopy, we study a TBG device exhibiting both a continuous twist-angle gradient from 0.35° to 1.30° and spatially varying strain fields, spanning the first (1.1°), second (0.5°) and third (0.3°) magic angles. We visualize the evolution of flat and remote bands in energy and real space with atomic resolution. Near the first magic angle, we discover an anomalous spectral weight transfer between the two flat band peaks, signifying the role of strain and electronic correlations, as further evidenced by an unusual spatial dispersion of these peaks within a moiré unit cell. In contrast, remote band peak energy offers a strain-insensitive indicator of the local twist angle. Structural analysis further reveals non-negligible shear strain across the sample. All observations are quantitatively reproduced by a continuum model that incorporates heterostrain and a self-consistent Hartree potential, revealing the critical but unexplored role of shear strain in shaping the low-energy electronic landscape of TBG.

cond-mat.mes-hall↗

arXiv2025

In-plane polar domains enhanced energy storage

Relaxor ferroelectric thin films are recognized for their ultrahigh power density, rendering them highly promising for energy storage applications in electrical and electronic systems. However, achieving high energy storage performance with chemically homogeneous, environmentally friendly and compositionally stable materials remains challenging. In this work, we present a design of dielectrics with high energy storage performance via an in-plane polar domains incorporating polar nanoregions mechanism. Guided by phase-field simulations, we synthesized La/Si co-doping BaTiO3 solid-solution thin films with high chemical homogeneity to realize high energy storage performance. Given that, we achieve a high energy density of 203.7J/cm3 and an energy efficiency of approximately 80% at an electric field of 6.15MV/cm. This mechanism holds significant promise for the design of next-generation high-performance dielectric materials for energy storage and other advanced functional materials.

cond-mat.mtrl-sci↗

arXiv2025

EEMS: Edge-Prompt Enhanced Medical Image Segmentation Based on Learnable Gating Mechanism

Medical image segmentation is vital for diagnosis, treatment planning, and disease monitoring but is challenged by complex factors like ambiguous edges and background noise. We introduce EEMS, a new model for segmentation, combining an Edge-Aware Enhancement Unit (EAEU) and a Multi-scale Prompt Generation Unit (MSPGU). EAEU enhances edge perception via multi-frequency feature extraction, accurately defining boundaries. MSPGU integrates high-level semantic and low-level spatial features using a prompt-guided approach, ensuring precise target localization. The Dual-Source Adaptive Gated Fusion Unit (DAGFU) merges edge features from EAEU with semantic features from MSPGU, enhancing segmentation accuracy and robustness. Tests on datasets like ISIC2018 confirm EEMS's superior performance and reliability as a clinical tool.

cs.CV↗

arXiv2025

Practical 1-um GHz fiber comb on silica-based platform

We present a fully stabilized 1-GHz Yb-fiber laser frequency comb built on silica substrates, utilizing "optical cubes" to house all optical components, ensuring long-term stability and practical operation. Both the femtosecond laser and f-to-2f interferometer are constructed to silica bricks, with a compact footprint of 290 mm * 250 mm, and a total weight of 1.8 kg. This system provides a stable repetition rate, offset frequency, and a supercontinuum spanning 460-1560 nm without requiring amplification. The carrier-envelop offset frequency exhibits exceptional in-loop stability, with a fractional frequency instability of 3.07* 10^(-18) at a 1 second averaging time, improving to 2.12*10^(-20) at a 10,000 second, maintaining uninterrupted operation for over 60 hours. This work demonstrates a high-performance GHz fiber-based frequency comb, paving the way for applications beyond laboratory environments, including dual-comb spectroscopy, astronomical spectrograph calibration, and portable optical clocks.

physics.optics↗

arXiv2025

Constant Bit-size Transformers Are Turing Complete

We prove that any Turing machine running on inputs of arbitrary length can be simulated by a constant bit-size transformer, as long as the context window is sufficiently long. This improves previous works, which require scaling up either the model's precision or the number of parameters on longer inputs. Furthermore, we prove that the complexity class SPACE$[s(n)]$ exactly characterizes the expressive power of a constant bit-size transformer with a context window of length $s(n)$. Our approach relies on simulating Post machines, a Turing-complete computational model. Post machines can be modeled as automata equipped with a queue, exhibiting computational behaviors naturally aligned with those of transformers. The behavioral similarity between transformers and Post machines may offer new insights into the mechanisms underlying the reasoning abilities of transformers.

cs.CC↗

arXiv2025

DENet: Dual-Path Edge Network with Global-Local Attention for Infrared Small Target Detection

Infrared small target detection is crucial for remote sensing applications like disaster warning and maritime surveillance. However, due to the lack of distinctive texture and morphological features, infrared small targets are highly susceptible to blending into cluttered and noisy backgrounds. A fundamental challenge in designing deep models for this task lies in the inherent conflict between capturing high-resolution spatial details for minute targets and extracting robust semantic context for larger targets, often leading to feature misalignment and suboptimal performance. Existing methods often rely on fixed gradient operators or simplistic attention mechanisms, which are inadequate for accurately extracting target edges under low contrast and high noise. In this paper, we propose a novel Dual-Path Edge Network that explicitly addresses this challenge by decoupling edge enhancement and semantic modeling into two complementary processing paths. The first path employs a Bidirectional Interaction Module, which uses both Local Self-Attention and Global Self-Attention to capture multi-scale local and global feature dependencies. The global attention mechanism, based on a Transformer architecture, integrates long-range semantic relationships and contextual information, ensuring robust scene understanding. The second path introduces the Multi-Edge Refiner, which enhances fine-grained edge details using cascaded Taylor finite difference operators at multiple scales. This mathematical approach, along with an attention-driven gating mechanism, enables precise edge localization and feature enhancement for targets of varying sizes, while effectively suppressing noise. Our method provides a promising solution for precise infrared small target detection and localization, combining structural semantics and edge refinement in a unified framework.

cs.CV↗

arXiv2025

Practice on Long Behavior Sequence Modeling in Tencent Advertising

Long-sequence modeling has become an indispensable frontier in recommendation systems for capturing users' long-term preferences. However, user behaviors within advertising domains are inherently sparse, posing a significant barrier to constructing long behavioral sequences using data from a single advertising domain alone. This motivates us to collect users' behaviors not only across diverse advertising scenarios, but also beyond the boundaries of the advertising domain into content domains-thereby constructing unified commercial behavior trajectories. This cross-domain or cross-scenario integration gives rise to the following challenges: (1) feature taxonomy gaps between distinct scenarios and domains, (2) inter-field interference arising from irrelevant feature field pairs, and (3) target-wise interference in temporal and semantic patterns when optimizing for different advertising targets. To address these challenges, we propose several practical approaches within the two-stage framework for long-sequence modeling. In the first (search) stage, we design a hierarchical hard search method for handling complex feature taxonomy hierarchies, alongside a decoupled embedding-based soft search to alleviate conflicts between attention mechanisms and feature representation. In the second (sequence modeling) stage, we introduce: (a) Decoupled Side Information Temporal Interest Networks (TIN) to mitigate inter-field conflicts; (b) Target-Decoupled Positional Encoding and Target-Decoupled SASRec to address target-wise interference; and (c) Stacked TIN to model high-order behavioral correlations. Deployed in production on Tencent's large-scale advertising platforms, our innovations delivered significant performance gains: an overall 4.22% GMV lift in WeChat Channels and an overall 1.96% GMV increase in WeChat Moments.

cs.IR↗

arXiv2025

Every Keystroke You Make: A Tech-Law Measurement and Analysis of Event Listeners for Wiretapping

The privacy community has a long track record of investigating emerging types of web tracking techniques. Recent work has focused on compliance of web trackers with new privacy laws such as Europe's GDPR and California's CCPA. Despite the growing body of research documenting widespread lack of compliance with new privacy laws, there is a lack of robust enforcement. Different from prior work, we conduct a tech-law analysis to map decades-old U.S. laws about interception of electronic communications--so-called wiretapping--to web tracking. Bridging the tech-law gap for older wiretapping laws is important and timely because, in cases where legal harm to privacy is proven, they can provide statutory private right of action, are at the forefront of recent privacy enforcement, and could ultimately lead to a meaningful change in the web tracking landscape. In this paper, we focus on a particularly invasive tracking technique: the use of JavaScript event listeners by third-party trackers for real-time keystroke interception on websites. We use an instrumented web browser to crawl a sample of the top-million websites to investigate the use of event listeners that aligns with the criteria for wiretapping, according to U.S. wiretapping law at the federal level and in California. We find evidence that 38.52% websites installed third-party event listeners to intercept keystrokes, and that at least 3.18% websites transmitted intercepted information to a third-party server, which aligns with the criteria for wiretapping. We further find evidence that the intercepted information such as email addresses typed into form fields are used for unsolicited email marketing. Beyond our work that maps the intersection between technical measurement and U.S. wiretapping law, additional future legal research is required to determine when the wiretapping observed in our paper passes the threshold for illegality.

cs.CR↗

arXiv2025

Novel constants based on the generalization of Von Neumann-Jordan constant

We introduce a new geometric constant based on a generalization of the parallelogram law, and study its properties as well as some relationships with other well-known geometric constants. A sufficient condition for normal structure is presented. Next, we introduce a constant and calculate its value in a specific space. Furthermore, we introduce another new constant and investigate some of its basic properties.

math.FA↗

arXiv2025

BCRNet: Enhancing Landmark Detection in Laparoscopic Liver Surgery via Bezier Curve Refinement

Laparoscopic liver surgery, while minimally invasive, poses significant challenges in accurately identifying critical anatomical structures. Augmented reality (AR) systems, integrating MRI/CT with laparoscopic images based on 2D-3D registration, offer a promising solution for enhancing surgical navigation. A vital aspect of the registration progress is the precise detection of curvilinear anatomical landmarks in laparoscopic images. In this paper, we propose BCRNet (Bezier Curve Refinement Net), a novel framework that significantly enhances landmark detection in laparoscopic liver surgery primarily via the Bezier curve refinement strategy. The framework starts with a Multi-modal Feature Extraction (MFE) module designed to robustly capture semantic features. Then we propose Adaptive Curve Proposal Initialization (ACPI) to generate pixel-aligned Bezier curves and confidence scores for reliable initial proposals. Additionally, we design the Hierarchical Curve Refinement (HCR) mechanism to enhance these proposals iteratively through a multi-stage process, capturing fine-grained contextual details from multi-scale pixel-level features for precise Bezier curve adjustment. Extensive evaluations on the L3D and P2ILF datasets demonstrate that BCRNet outperforms state-of-the-art methods, achieving significant performance improvements. Code will be available.

cs.CV↗