Application Research of a Deep Learning Model Integrating CycleGAN and YOLO in PCB Infrared Defect Detection

This paper addresses the critical bottleneck of infrared (IR) data scarcity in Printed Circuit Board (PCB) defect detection by proposing a cross-modal data augmentation framework integrating CycleGAN and YOLOv8. Unlike conventional methods relying on paired supervision, we leverage CycleGAN to perform unpaired image-to-image translation, mapping abundant visible-light PCB images into the infrared domain. This generative process synthesizes high-fidelity pseudo-IR samples that preserve the structural semantics of defects while accurately simulating thermal distribution patterns. Subsequently, we construct a heterogeneous training strategy that fuses generated pseudo-IR data with limited real IR samples to train a lightweight YOLOv8 detector. Experimental results demonstrate that this method effectively enhances feature learning under low-data conditions. The augmented detector significantly outperforms models trained on limited real data alone and approaches the performance benchmarks of fully supervised training, proving the efficacy of pseudo-IR synthesis as a robust augmentation strategy for industrial inspection.

SoS: Analysis of Surface over Semantics in Multilingual Text-To-Image Generation

Text-to-image (T2I) models are increasingly employed by users worldwide. However, prior research has pointed to the high sensitivity of T2I towards particular input languages - when faced with languages other than English (i.e., different surface forms of the same prompt), T2I models often produce culturally stereotypical depictions, prioritizing the surface over the prompt's semantics. Yet a comprehensive analysis of this behavior, which we dub Surface-over-Semantics (SoS), is missing. We present the first analysis of T2I models' SoS tendencies. To this end, we create a set of prompts covering 171 cultural identities, translated into 14 languages, and use it to prompt seven T2I models. To quantify SoS tendencies across models, languages, and cultures, we introduce a novel measure and analyze how the tendencies we identify manifest visually. We show that all but one model exhibit strong surface-level tendency in at least two languages, with this effect intensifying across the layers of T2I text encoders. Moreover, these surface tendencies frequently correlate with stereotypical visual depictions.

Press Start to Charge: Videogaming the Online Centralized Charging Scheduling Problem

We study the online centralized charging scheduling problem (OCCSP). In this problem, a central authority must decide, in real time, when to charge dynamically arriving electric vehicles (EVs), subject to capacity limits, with the objective of balancing load across a finite planning horizon. To solve the problem, we first gamify it; that is, we model it as a game where charging blocks are placed within temporal and capacity constraints on a grid. We design heuristic policies, train learning agents with expert demonstrations, and improve them using Dataset Aggregation (DAgger). From a theoretical standpoint, we show that gamification reduces model complexity and yields tighter generalization bounds than vector-based formulations. Experiments across multiple EV arrival patterns confirm that gamified learning enhances load balancing. In particular, the image-to-movement model trained with DAgger consistently outperforms heuristic baselines, vector-based approaches, and supervised learning agents, while also demonstrating robustness in sensitivity analyses. These operational gains translate into tangible economic value. In a real-world case study for the Greater Montréal Area (Québec, Canada) using utility cost data, the proposed methods lower system costs by tens of millions of dollars per year over the prevailing practice and show clear potential to delay costly grid upgrades.

Fair-Eye Net: A Fair, Trustworthy, Multimodal Integrated Glaucoma Full Chain AI System

Glaucoma is a top cause of irreversible blindness globally, making early detection and longitudinal follow-up pivotal to preventing permanent vision loss. Current screening and progression assessment, however, rely on single tests or loosely linked examinations, introducing subjectivity and fragmented care. Limited access to high-quality imaging tools and specialist expertise further compromises consistency and equity in real-world use. To address these gaps, we developed Fair-Eye Net, a fair, reliable multimodal AI system closing the clinical loop from glaucoma screening to follow-up and risk alerting. It integrates fundus photos, OCT structural metrics, VF functional indices, and demographic factors via a dual-stream heterogeneous fusion architecture, with an uncertainty-aware hierarchical gating strategy for selective prediction and safe referral. A fairness constraint reduces missed diagnoses in disadvantaged subgroups. Experimental results show it achieved an AUC of 0.912 (96.7% specificity), cut racial false-negativity disparity by 73.4% (12.31% to 3.28%), maintained stable cross-domain performance, and enabled 3-12 months of early risk alerts (92% sensitivity, 88% specificity). Unlike post hoc fairness adjustments, Fair-Eye Net optimizes fairness as a primary goal with clinical reliability via multitask learning, offering a reproducible path for clinical translation and large-scale deployment to advance global eye health equity.

Attention mechanisms in neural networks

Attention mechanisms represent a fundamental paradigm shift in neural network architectures, enabling models to selectively focus on relevant portions of input sequences through learned weighting functions. This monograph provides a comprehensive and rigorous mathematical treatment of attention mechanisms, encompassing their theoretical foundations, computational properties, and practical implementations in contemporary deep learning systems. Applications in natural language processing, computer vision, and multimodal learning demonstrate the versatility of attention mechanisms. We examine language modeling with autoregressive transformers, bidirectional encoders for representation learning, sequence-to-sequence translation, Vision Transformers for image classification, and cross-modal attention for vision-language tasks. Empirical analysis reveals training characteristics, scaling laws that relate performance to model size and computation, attention pattern visualizations, and performance benchmarks across standard datasets. We discuss the interpretability of learned attention patterns and their relationship to linguistic and visual structures. The monograph concludes with a critical examination of current limitations, including computational scalability, data efficiency, systematic generalization, and interpretability challenges.

Image-to-Image Translation with Generative Adversarial Network for Electrical Resistance Tomography Reconstruction

Electrical tomography techniques have been widely employed for multiphase-flow monitoring owing to their non invasive nature, intrinsic safety, and low cost. Nevertheless, conventional reconstructions struggle to capture fine details, which hampers broader adoption. Motivated by recent advances in deep learning, this study introduces a Pix2Pix generative adversarial network (GAN) to enhance image reconstruction in electrical capacitance tomography (ECT). Comprehensive simulated and experimental databases were established and multiple baseline reconstruction algorithms were implemented. The proposed GAN demonstrably improves quantitative metrics such as SSIM, PSNR, and PMSE, while qualitatively producing high resolution images with sharp boundaries that are no longer constrained by mesh discretization.

Multilingual-To-Multimodal (M2M): Unlocking New Languages with Monolingual Text

Multimodal models excel in English, supported by abundant image-text and audio-text data, but performance drops sharply for other languages due to limited multilingual multimodal resources. Existing solutions rely heavily on machine translation, while advances in multilingual text modeling remain underutilized. We introduce METAL, a lightweight alignment method that learns only a few linear layers using English text alone to map multilingual text embeddings into a multimodal space. Despite its simplicity, METAL matches baseline performance in English (94.9 percent Recall at 10) and achieves strong zero-shot transfer (89.5 percent Recall at 10 averaged across 11 languages, 10 unseen) on XTD text-to-image retrieval. Qualitative t-SNE visualizations show that multilingual embeddings align tightly with multimodal representations, while weight analysis reveals that the transformation reshapes embedding geometry rather than performing trivial rotations. Beyond image-text retrieval, METAL generalizes to audio-text retrieval and cross-lingual text-to-image generation. We release code and checkpoints at https://github.com/m2m-codebase/M2M , as well as multilingual evaluation datasets including MSCOCO Multilingual 30K (https://huggingface.co/datasets/piyushsinghpasi/mscoco-multilingual-30k ), AudioCaps Multilingual (https://huggingface.co/datasets/piyushsinghpasi/audiocaps-multilingual ), and Clotho Multilingual (https://huggingface.co/datasets/piyushsinghpasi/clotho-multilingual ), to facilitate further research.

OpenNavMap: Structure-Free Topometric Mapping via Large-Scale Collaborative Localization

Scalable and maintainable map representations are fundamental to enabling large-scale visual navigation and facilitating the deployment of robots in real-world environments. While collaborative localization across multi-session mapping enhances efficiency, traditional structure-based methods struggle with high maintenance costs and fail in feature-less environments or under significant viewpoint changes typical of crowd-sourced data. To address this, we propose OPENNAVMAP, a lightweight, structure-free topometric system leveraging 3D geometric foundation models for on-demand reconstruction. Our method unifies dynamic programming-based sequence matching, geometric verification, and confidence-calibrated optimization to robust, coarse-to-fine submap alignment without requiring pre-built 3D models. Evaluations on the Map-Free benchmark demonstrate superior accuracy over structure-from-motion and regression baselines, achieving an average translation error of 0.62m. Furthermore, the system maintains global consistency across 15km of multi-session data with an absolute trajectory error below 3m for map merging. Finally, we validate practical utility through 12 successful autonomous image-goal navigation tasks on simulated and physical robots. Code and datasets will be publicly available in https://rpl-cs-ucl.github.io/OpenNavMap_page.

Anomaly Detection by Effectively Leveraging Synthetic Images

Anomaly detection plays a vital role in industrial manufacturing. Due to the scarcity of real defect images, unsupervised approaches that rely solely on normal images have been extensively studied. Recently, diffusion-based generative models brought attention to training data synthesis as an alternative solution. In this work, we focus on a strategy to effectively leverage synthetic images to maximize the anomaly detection performance. Previous synthesis strategies are broadly categorized into two groups, presenting a clear trade-off. Rule-based synthesis, such as injecting noise or pasting patches, is cost-effective but often fails to produce realistic defect images. On the other hand, generative model-based synthesis can create high-quality defect images but requires substantial cost. To address this problem, we propose a novel framework that leverages a pre-trained text-guided image-to-image translation model and image retrieval model to efficiently generate synthetic defect images. Specifically, the image retrieval model assesses the similarity of the generated images to real normal images and filters out irrelevant outputs, thereby enhancing the quality and relevance of the generated defect images. To effectively leverage synthetic images, we also introduce a two stage training strategy. In this strategy, the model is first pre-trained on a large volume of images from rule-based synthesis and then fine-tuned on a smaller set of high-quality images. This method significantly reduces the cost for data collection while improving the anomaly detection performance. Experiments on the MVTec AD dataset demonstrate the effectiveness of our approach.

Multi-Attribute guided Thermal Face Image Translation based on Latent Diffusion Model

Modern surveillance systems increasingly rely on multi-wavelength sensors and deep neural networks to recognize faces in infrared images captured at night. However, most facial recognition models are trained on visible light datasets, leading to substantial performance degradation on infrared inputs due to significant domain shifts. Early feature-based methods for infrared face recognition proved ineffective, prompting researchers to adopt generative approaches that convert infrared images into visible light images for improved recognition. This paradigm, known as Heterogeneous Face Recognition (HFR), faces challenges such as model and modality discrepancies, leading to distortion and feature loss in generated images. To address these limitations, this paper introduces a novel latent diffusion-based model designed to generate high-quality visible face images from thermal inputs while preserving critical identity features. A multi-attribute classifier is incorporated to extract key facial attributes from visible images, mitigating feature loss during infrared-to-visible image restoration. Additionally, we propose the Self-attn Mamba module, which enhances global modeling of cross-modal features and significantly improves inference speed. Experimental results on two benchmark datasets demonstrate the superiority of our approach, achieving state-of-the-art performance in both image quality and identity preservation.