arXiv subjects

Wei Luo

Publications and source records attributed to Wei Luo.

At least 55 records · Page 3Linked to original sources

arXiv2025

Anomagic: Crossmodal Prompt-driven Zero-shot Anomaly Generation

We propose Anomagic, a zero-shot anomaly generation method that produces semantically coherent anomalies without requiring any exemplar anomalies. By unifying both visual and textual cues through a crossmodal prompt encoding scheme, Anomagic leverages rich contextual information to steer an inpainting-based generation pipeline. A subsequent contrastive refinement strategy enforces precise alignment between synthesized anomalies and their masks, thereby bolstering downstream anomaly detection accuracy. To facilitate training, we introduce AnomVerse, a collection of 12,987 anomaly-mask-caption triplets assembled from 13 publicly available datasets, where captions are automatically generated by multimodal large language models using structured visual prompts and template-based textual hints. Extensive experiments demonstrate that Anomagic trained on AnomVerse can synthesize more realistic and varied anomalies than prior methods, yielding superior improvements in downstream anomaly detection. Furthermore, Anomagic can generate anomalies for any normal-category image using user-defined prompts, establishing a versatile foundation model for anomaly generation.

cs.CV↗

arXiv2025

TED++: Submanifold-Aware Backdoor Detection via Layerwise Tubular-Neighbourhood Screening

As deep neural networks power increasingly critical applications, stealthy backdoor attacks, where poisoned training inputs trigger malicious model behaviour while appearing benign, pose a severe security risk. Many existing defences are vulnerable when attackers exploit subtle distance-based anomalies or when clean examples are scarce. To meet this challenge, we introduce TED++, a submanifold-aware framework that effectively detects subtle backdoors that evade existing defences. TED++ begins by constructing a tubular neighbourhood around each class's hidden-feature manifold, estimating its local ``thickness'' from a handful of clean activations. It then applies Locally Adaptive Ranking (LAR) to detect any activation that drifts outside the admissible tube. By aggregating these LAR-adjusted ranks across all layers, TED++ captures how faithfully an input remains on the evolving class submanifolds. Based on such characteristic ``tube-constrained'' behaviour, TED++ flags inputs whose LAR-based ranking sequences deviate significantly. Extensive experiments are conducted on benchmark datasets and tasks, demonstrating that TED++ achieves state-of-the-art detection performance under both adaptive-attack and limited-data scenarios. Remarkably, even with only five held-out examples per class, TED++ still delivers near-perfect detection, achieving gains of up to 14\% in AUROC over the next-best method. The code is publicly available at https://github.com/namle-w/TEDpp.

cs.LG↗

arXiv2025

DP-TTA: Test-time Adaptation for Transient Electromagnetic Signal Denoising via Dictionary-driven Prior Regularization

Transient Electromagnetic (TEM) method is widely used in various geophysical applications, providing valuable insights into subsurface properties. However, time-domain TEM signals are often submerged in various types of noise. While recent deep learning-based denoising models have shown strong performance, these models are mostly trained on simulated or single real-world scenario data, overlooking the significant differences in noise characteristics from different geographical regions. Intuitively, models trained in one environment often struggle to perform well in new settings due to differences in geological conditions, equipment, and external interference, leading to reduced denoising performance. To this end, we propose the Dictionary-driven Prior Regularization Test-time Adaptation (DP-TTA). Our key insight is that TEM signals possess intrinsic physical characteristics, such as exponential decay and smoothness, which remain consistent across different regions regardless of external conditions. These intrinsic characteristics serve as ideal prior knowledge for guiding the TTA strategy, which helps the pre-trained model dynamically adjust parameters by utilizing self-supervised losses, improving denoising performance in new scenarios. To implement this, we customized a network, named DTEMDNet. Specifically, we first use dictionary learning to encode these intrinsic characteristics as a dictionary-driven prior, which is integrated into the model during training. At the testing stage, this prior guides the model to adapt dynamically to new environments by minimizing self-supervised losses derived from the dictionary-driven consistency and the signal one-order variation. Extensive experimental results demonstrate that the proposed method achieves much better performance than existing TEM denoising methods and TTA methods.

cs.CV↗

arXiv2025

LLM-Friendly Knowledge Representation for Customer Support

We propose a practical approach by integrating Large Language Models (LLMs) with a framework designed to navigate the complexities of Airbnb customer support operations. In this paper, our methodology employs a novel reformatting technique, the Intent, Context, and Action (ICA) format, which transforms policies and workflows into a structure more comprehensible to LLMs. Additionally, we develop a synthetic data generation strategy to create training data with minimal human intervention, enabling cost-effective fine-tuning of our model. Our internal experiments (not applied to Airbnb products) demonstrate that our approach of restructuring workflows and fine-tuning LLMs with synthetic data significantly enhances their performance, setting a new benchmark for their application in customer support. Our solution is not only cost-effective but also improves customer support, as evidenced by both accuracy and manual processing time evaluation metrics.

cs.AI↗

arXiv2025

Hidden integer quantum ferroelectricity in chiral Tellurium

Ferroelectricity is a cornerstone of functional materials research, enabling diverse technologies from non-volatile memory to optoelectronics. Recently, type-I integer quantum ferroelectricity (IQFE), unconstrained by symmetry, has been proposed and experimentally demonstrated; however, as it arises from ionic displacements of an integer lattice vector, the initial and final states are macroscopically indistinguishable, rendering the physical properties unchanged. Here, we propose for the first time the nontrivial counterpart (i.e., type-II IQFE) where the polarization difference between the initial and final states is quantized but the macroscopical properties differ. We further demonstrate the existence of type-II IQFE in bulk chiral tellurium. In few-layer tellurium, the total polarization remains nearly quantized, composed of a bulk-inherited quantum component and a small surface-induced contribution. Molecular dynamics simulations reveal surface-initiated, layer-by-layer switching driven by reduced energy barriers, explaining why ferroelectricity was observed experimentally in few-layer tellurium, but not in bulk tellurium yet. Interestingly, the chirality of the initial and final states in bulk tellurium is opposite, suggesting a novel way to control structural chirality with electric field in chiral photonics and nonvolatile ferroelectric memory devices.

cond-mat.mtrl-sci↗

arXiv2025

IntSR: An Integrated Generative Framework for Search and Recommendation

Generative recommendation has emerged as a promising paradigm, demonstrating remarkable results in both academic benchmarks and industrial applications. However, existing systems predominantly focus on unifying retrieval and ranking while neglecting the integration of search and recommendation (S&R) tasks. What makes search and recommendation different is how queries are formed: search uses explicit user requests, while recommendation relies on implicit user interests. As for retrieval versus ranking, the distinction comes down to whether the queries are the target items themselves. Recognizing the query as central element, we propose IntSR, an integrated generative framework for S&R. IntSR integrates these disparate tasks using distinct query modalities. It also addresses the increased computational complexity associated with integrated S&R behaviors and the erroneous pattern learning introduced by a dynamically changing corpus. IntSR has been successfully deployed across various scenarios in Amap, leading to substantial improvements in digital asset's GMV(+9.34%), POI recommendation's CTR(+2.76%), and travel mode suggestion's ACC(+7.04%).

cs.IR↗

arXiv2025

Recent Advances in Unconventional Ferroelectrics and Multiferroics

Emerging ferroic materials may pave a new way to next-generation nanoelectronic and spintronic devices due to their interesting physical properties. Here, we systematically review unconventional ferroelectric systems, from Hf-based and elementary ferroelectrics to stacking ferroelectricity, polar metallicity, fractional quantum ferroelectricity, wurtzite-type ferroelectricity, and freestanding membranes ferroelectricity. Moreover, multiferroic materials are reviewed, particularly the interplay between novel magnetic states and ferroelectricity, as well as ferrovalley-ferroelectric coupling. Finally, we conclude by discussing current challenges and future opportunities in this field.

cond-mat.mtrl-sci↗

arXiv2025

MindEye-OmniAssist: A Gaze-Driven LLM-Enhanced Assistive Robot System for Implicit Intention Recognition and Task Execution

A promising effective human-robot interaction in assistive robotic systems is gaze-based control. However, current gaze-based assistive systems mainly help users with basic grasping actions, offering limited support. Moreover, the restricted intent recognition capability constrains the assistive system's ability to provide diverse assistance functions. In this paper, we propose an open implicit intention recognition framework powered by Large Language Model (LLM) and Vision Foundation Model (VFM), which can process gaze input and recognize user intents that are not confined to predefined or specific scenarios. Furthermore, we implement a gaze-driven LLM-enhanced assistive robot system (MindEye-OmniAssist) that recognizes user's intentions through gaze and assists in completing task. To achieve this, the system utilizes open vocabulary object detector, intention recognition network and LLM to infer their full intentions. By integrating eye movement feedback and LLM, it generates action sequences to assist the user in completing tasks. Real-world experiments have been conducted for assistive tasks, and the system achieved an overall success rate of 41/55 across various undefined tasks. Preliminary results show that the proposed method holds the potential to provide a more user-friendly human-computer interaction interface and significantly enhance the versatility and effectiveness of assistive systems by supporting more complex and diverse task.

cs.RO↗

arXiv2025

Crystalline electric field excitations in Weyl semimetal \textit{R}AlSi (\textit{R} = Ce, Pr and Nd)

The rare earth intermetallic system \textit{R}Al\textit{X} (\textit{R} = rare earth elements, \textit{X} = Si and Ge) is known to be a promising candidate of magnetic Weyl semimetal. Due to the complex interactions between the rare earth elements and surrounding atoms, as well as hybridization with itinerant electrons, this family likely possesses highly intriguing and novel magnetic structures and thus exhibits dynamic behaviors. We systematically probe polycrystalline samples of \textit{R}AlSi (\textit{R} = La, Ce, Pr and Nd) combining inelastic neutron scattering (INS), heat capacity and magnetic susceptibility measurements. The INS measurements identify well-resolved crystalline electric field (CEF) excitations at 19.2 and 24.9 meV in CeAlSi, at 5.4 meV in PrAlSi, and at 2.5 and 4.2 meV in NdAlSi. We analyzed the INS data using the corresponding CEF models and determined the CEF parameters and ground state wave functions of \textit{R}AlSi (\textit{R} = Ce, Pr and Nd). Our results suggest strong single-ion anisotropy in their ground states: $|\pm3/2\rangle$ (94.5\%) in CeAlSi, $|\pm3\rangle$ (99.2\%) in PrAlSi, and $|\pm9/2\rangle$ (76.2\%) in NdAlSi. Notably, the weaker anisotropy and strong exchange interactions in NdAlSi promote competing magnetic orders and CEF splitting at low temperature, contrasting with the robust CEF levels in magnetic states of CeAlSi and PrAlSi.

cond-mat.str-el↗

arXiv2025

Global Supply Chain Reallocation and Shift under Triple Crises: A U.S.-China Perspective

US-China trade tensions, the COVID-19 pandemic, and the Russia-Ukraine conflict have disrupted and reshaped global supply chains. Existing studies caution that these tensions may not meaningfully reduce U.S. dependence on China-linked supply chains. This study examines the drivers of this unmet reallocation under overlapping geopolitical and public health disruptions. It investigates how these shocks jointly reconfigured bilateral trade and global value chain (GVC) participation and positioning among the U.S., China, and major trading partners during 2016-2023. Using monthly bilateral trade data across all sectors and multi-regional input-output tables for GVC decomposition, we combine a multi-period event-study with structural analysis to evaluate trade-flow disruptions and shifts in participation and functional positioning within GVCs. We find that China's exports remained robust, expanded across global markets, and sustained a rise in GVC participation, becoming more embedded in upstream segments through increased intermediate shipments to Asia and Europe. Meanwhile, U.S. imports increasingly shifted toward "China+1" partners, especially ASEAN, whose trade structures remain closely tied to Chinese upstream supply chains. These strengthening triangular relationships reveal how global reallocation and GVCs have evolved around the U.S. and China across successive shocks. Based on the evidence, we propose a supply chain resilience framework defined by three interacting dimensions: the level of GVC participation, the functional position within the value chain, and a country's capacity to re-couple in the post-shock landscape, conditioned by market diversification, economic complexity, and institutional capability. These findings carry significant implications for trade policy and industrial strategy in an era of geopolitical and geoeconomic fragmentation.

econ.GN↗

arXiv2025

Test-Time Model Adaptation for Quantized Neural Networks

Quantizing deep models prior to deployment is a widely adopted technique to speed up inference for various real-time applications, such as autonomous driving. However, quantized models often suffer from severe performance degradation in dynamic environments with potential domain shifts and this degradation is significantly more pronounced compared with their full-precision counterparts, as shown by our theoretical and empirical illustrations. To address the domain shift problem, test-time adaptation (TTA) has emerged as an effective solution by enabling models to learn adaptively from test data. Unfortunately, existing TTA methods are often impractical for quantized models as they typically rely on gradient backpropagation--an operation that is unsupported on quantized models due to vanishing gradients, as well as memory and latency constraints. In this paper, we focus on TTA for quantized models to improve their robustness and generalization ability efficiently. We propose a continual zeroth-order adaptation (ZOA) framework that enables efficient model adaptation using only two forward passes, eliminating the computational burden of existing methods. Moreover, we propose a domain knowledge management scheme to store and reuse different domain knowledge with negligible memory consumption, reducing the interference of different domain knowledge and fostering the knowledge accumulation during long-term adaptation. Experimental results on three classical architectures, including quantized transformer-based and CNN-based models, demonstrate the superiority of our methods for quantized model adaptation. On the quantized W6A6 ViT-B model, our ZOA is able to achieve a 5.0\% improvement over the state-of-the-art FOA on ImageNet-C dataset. The source code is available at https://github.com/DengZeshuai/ZOA.

cs.CV↗

arXiv2025

A Comprehensive Survey for Real-World Industrial Defect Detection: Challenges, Approaches, and Prospects

Industrial defect detection is vital for upholding product quality across contemporary manufacturing systems. As the expectations for precision, automation, and scalability intensify, conventional inspection approaches are increasingly found wanting in addressing real-world demands. Notable progress in computer vision and deep learning has substantially bolstered defect detection capabilities across both 2D and 3D modalities. A significant development has been the pivot from closed-set to open-set defect detection frameworks, which diminishes the necessity for extensive defect annotations and facilitates the recognition of novel anomalies. Despite such strides, a cohesive and contemporary understanding of industrial defect detection remains elusive. Consequently, this survey delivers an in-depth analysis of both closed-set and open-set defect detection strategies within 2D and 3D modalities, charting their evolution in recent years and underscoring the rising prominence of open-set techniques. We distill critical challenges inherent in practical detection environments and illuminate emerging trends, thereby providing a current and comprehensive vista of this swiftly progressing field.

cs.CV↗

arXiv2025

Design and construction of the multiplexing cold neutron spectrometer BOYA with double-column Rowland focusing analyzers

Developing neutron spectrometers with higher counting efficiency has been an essential pursuit in neutron instrumentation. In this work, we present BOYA, a multiplexing cold neutron spectrometers designed and implemented at the China Advanced Research Reactor. Equipped with 34 angular analyzing channels spanning 119°, each containing 5 inelastic channels and 1 diffraction channel, BOYA enhances the measurement efficiency by two orders of magnitude over a traditional triple-axis spectrometer. To optimize both intensity and energy resolution, innovative double-column Rowland focusing analyzers have been developed. By filling the crystal gaps in the traditional Rowland focusing geometry, our design enhances the neutron beam coverage without introducing appreciable double-scattering. Our commissioning results on vanadium and MnWO4 have confirmed the success of the design, establishing BOYA as a successful multiplexing instrument for neutron spectroscopy.

physics.ins-det↗

arXiv2025

Neural Architecture Search generated Phase Retrieval Net for Real-time Off-axis Quantitative Phase Imaging

In off-axis Quantitative Phase Imaging (QPI), artificial neural networks have been recently applied for phase retrieval with aberration compensation and phase unwrapping. However, the involved neural network architectures are largely unoptimized and inefficient with low inference speed, which hinders the realization of real-time imaging. Here, we propose a Neural Architecture Search (NAS) generated Phase Retrieval Net (NAS-PRNet) for accurate and fast phase retrieval. NAS-PRNet is an encoder-decoder style neural network, automatically found from a large neural network architecture search space through NAS. By modifying the differentiable NAS scheme from SparseMask, we learn the optimized skip connections through gradient descent. Specifically, we implement MobileNet-v2 as the encoder and define a synthesized loss that incorporates phase reconstruction loss and network sparsity loss. NAS-PRNet has achieved high-fidelity phase retrieval by achieving a peak Signal-to-Noise Ratio (PSNR) of 36.7 dB and a Structural SIMilarity (SSIM) of 86.6% as tested on interferograms of biological cells. Notably, NAS-PRNet achieves phase retrieval in only 31 ms, representing 15x speedup over the most recent Mamba-UNet with only a slightly lower phase retrieval accuracy.

eess.IV↗

arXiv2025

Perspective of high-speed Mach-Zehnder modulators based on nonlinear optics and complex band structures

Optical modulators are essential building blocks for high-capacity optical communication and massively parallel computing. Among all types of optical modulators, travelling-wave Mach-Zehnder modulators (TW-MZMs) featuring high speed and efficiency are widely used, and have been developed on a variety of integrated material platforms. Existing methods to design and simulate TW-MZMs so far strongly rely on the peculiar material properties, and thus inevitably involve complicated electrical-circuit models. As a result, these methods diverge significantly. In addition, they become increasingly inefficient and inaccurate for TW-MZMs with extending length and levitating modulation speed, posing formidable challenges for millimeter-wave and terahertz operation. Here, we present an innovative perspective to understand and analyze high-speed TW-MZMs. Our perspective leverages nonlinear optics and complex band structures of RF photonic crystals, and is thus entirely electromagnetic-wave-based. Under this perspective, we showcase the design, optoelectronic simulation and experimental validation of high-speed TW-MZMs based on Si and LiNbO$_3$, and further demonstrate unambiguous advantages in simplicity, accuracy and efficiency over conventional methods. Our approach can essentially be applied to nearly any integrated material platform, including those based on semiconductors and electro-absorption materials. With high-frequency electrode designs and optoelectronic co-simulation, our approach facilitates the synergy and convergence of electronics and photonics, and offers a viable route to constructing future high-speed millimeter-wave and terahertz photonics and quantum systems.

physics.optics↗

arXiv2025

Exploring Spatial Context: A Comprehensive Bibliography of GWR and MGWR

Local spatial models such as Geographically Weighted Regression (GWR) and Multiscale Geographically Weighted Regression (MGWR) serve as instrumental tools to capture intrinsic contextual effects through the estimates of the local intercepts and behavioral contextual effects through estimates of the local slope parameters. GWR and MGWR provide simple implementation yet powerful frameworks that could be extended to various disciplines that handle spatial data. This bibliography aims to serve as a comprehensive compilation of peer-reviewed papers that have utilized GWR or MGWR as a primary analytical method to conduct spatial analyses and acts as a useful guide to anyone searching the literature for previous examples of local statistical modeling in a wide variety of application fields.

stat.ME↗

arXiv2025

Exploring Intrinsic Normal Prototypes within a Single Image for Universal Anomaly Detection

Anomaly detection (AD) is essential for industrial inspection, yet existing methods typically rely on ``comparing'' test images to normal references from a training set. However, variations in appearance and positioning often complicate the alignment of these references with the test image, limiting detection accuracy. We observe that most anomalies manifest as local variations, meaning that even within anomalous images, valuable normal information remains. We argue that this information is useful and may be more aligned with the anomalies since both the anomalies and the normal information originate from the same image. Therefore, rather than relying on external normality from the training set, we propose INP-Former, a novel method that extracts Intrinsic Normal Prototypes (INPs) directly from the test image. Specifically, we introduce the INP Extractor, which linearly combines normal tokens to represent INPs. We further propose an INP Coherence Loss to ensure INPs can faithfully represent normality for the testing image. These INPs then guide the INP-Guided Decoder to reconstruct only normal tokens, with reconstruction errors serving as anomaly scores. Additionally, we propose a Soft Mining Loss to prioritize hard-to-optimize samples during training. INP-Former achieves state-of-the-art performance in single-class, multi-class, and few-shot AD tasks across MVTec-AD, VisA, and Real-IAD, positioning it as a versatile and universal solution for AD. Remarkably, INP-Former also demonstrates some zero-shot AD capability. Code is available at:https://github.com/luow23/INP-Former.

cs.CV↗

arXiv2025

INP-Former++: Advancing Universal Anomaly Detection via Intrinsic Normal Prototypes and Residual Learning

Anomaly detection (AD) is essential for industrial inspection and medical diagnosis, yet existing methods typically rely on ``comparing'' test images to normal references from a training set. However, variations in appearance and positioning often complicate the alignment of these references with the test image, limiting detection accuracy. We observe that most anomalies manifest as local variations, meaning that even within anomalous images, valuable normal information remains. We argue that this information is useful and may be more aligned with the anomalies since both the anomalies and the normal information originate from the same image. Therefore, rather than relying on external normality from the training set, we propose INP-Former, a novel method that extracts Intrinsic Normal Prototypes (INPs) directly from the test image. Specifically, we introduce the INP Extractor, which linearly combines normal tokens to represent INPs. We further propose an INP Coherence Loss to ensure INPs can faithfully represent normality for the testing image. These INPs then guide the INP-guided Decoder to reconstruct only normal tokens, with reconstruction errors serving as anomaly scores. Additionally, we propose a Soft Mining Loss to prioritize hard-to-optimize samples during training. INP-Former achieves state-of-the-art performance in single-class, multi-class, and few-shot AD tasks across MVTec-AD, VisA, and Real-IAD, positioning it as a versatile and universal solution for AD. Remarkably, INP-Former also demonstrates some zero-shot AD capability. Furthermore, we propose a soft version of the INP Coherence Loss and enhance INP-Former by incorporating residual learning, leading to the development of INP-Former++. The proposed method significantly improves detection performance across single-class, multi-class, semi-supervised, few-shot, and zero-shot settings.

cs.CV↗