pwc-archive/papers-with-abstracts huggingface dataset:
Leveraging Contrast Information for Efficient Document Shadow Removal
Authors: Yifan Liu, Jiancheng Huang, Na Liu, Mingfu Yan, Yi Huang, Shifeng Chen
Date: 2025-04-01 00:00:00
Tasks: Document Shadow Removal, Shadow Removal

Document shadows are a major obstacle in the digitization process. Due to the dense information in text and patterns covered by shadows, document shadow removal requires specialized methods. Existing document shadow removal methods, although showing some progress, still rely on additional information such as shadow masks or lack generalization and effectiveness across different shadow scenarios. This often results in incomplete shadow removal or loss of original document content and tones. Moreover, these methods tend to underutilize the information present in the original shadowed document image. In this paper, we refocus our approach on the document images themselves, which inherently contain rich information.We propose an end-to-end document shadow removal method guided by contrast representation, following a coarse-to-fine refinement approach. By extracting document contrast information, we can effectively and quickly locate shadow shapes and positions without the need for additional masks. This information is then integrated into the refined shadow removal process, providing better guidance for network-based removal and feature fusion. Extensive qualitative and quantitative experiments show that our method achieves state-of-the-art performance.
Naming is framing: How cybersecurity's language problems are repeating in AI governance
Authors: Liane Potter
Date: 2025-04-16 00:00:00
Tasks: Hallucination

Language is not neutral; it frames understanding, structures power, and shapes governance. This paper argues that misnomers like cybersecurity and artificial intelligence (AI) are more than semantic quirks; they carry significant governance risks by obscuring human agency, inflating expectations, and distorting accountability. Drawing on lessons from cybersecurity's linguistic pitfalls, such as the 'weakest link' narrative, this paper highlights how AI discourse is falling into similar traps with metaphors like 'alignment,' 'black box,' and 'hallucination.' These terms embed adversarial, mystifying, or overly technical assumptions into governance structures. In response, the paper advocates for a language-first approach to AI governance: one that interrogates dominant metaphors, foregrounds human roles, and co-develops a lexicon that is precise, inclusive, and reflexive. This paper contends that linguistic reform is not peripheral to governance but central to the construction of transparent, equitable, and anticipatory regulatory frameworks.
OmniSVG: A Unified Scalable Vector Graphics Generation Model
Authors: Yiying Yang, Wei Cheng, Sijin Chen, Xianfang Zeng, Jiaxu Zhang, Liao Wang, Gang Yu, Xingjun Ma, Yu-Gang Jiang
Date: 2025-04-08 00:00:00
Tasks: model, Vector Graphics

Scalable Vector Graphics (SVG) is an important image format widely adopted in graphic design because of their resolution independence and editability. The study of generating high-quality SVG has continuously drawn attention from both designers and researchers in the AIGC community. However, existing methods either produces unstructured outputs with huge computational cost or is limited to generating monochrome icons of over-simplified structures. To produce high-quality and complex SVG, we propose OmniSVG, a unified framework that leverages pre-trained Vision-Language Models (VLMs) for end-to-end multimodal SVG generation. By parameterizing SVG commands and coordinates into discrete tokens, OmniSVG decouples structural logic from low-level geometry for efficient training while maintaining the expressiveness of complex SVG structure. To further advance the development of SVG synthesis, we introduce MMSVG-2M, a multimodal dataset with two million richly annotated SVG assets, along with a standardized evaluation protocol for conditional SVG generation tasks. Extensive experiments show that OmniSVG outperforms existing methods and demonstrates its potential for integration into professional SVG design workflows.
Split Federated Learning Empowered Vehicular Edge Intelligence: Concept, Adaptive Design, and Future Directions
Authors: Xianke Qiang; Zheng Chang; Chaoxiong Ye; Timo Hamalainen; Geyong Min
Date: 2025-01-13 00:00:00
Proceeding: IEEE Wireless Communications 2025 1
Tasks: Edge-computing, Federated Learning

To achieve ubiquitous intelligence in future vehicular networks, artificial intelligence (AI) is essential for extracting valuable insights from vehicular data to enhance AI-driven services. By integrating AI technologies into vehicular edge computing (VEC) platforms, which provide essential storage, computing, and network resources, vehicular edge intelligence (VEI) can be fully realized. Traditional centralized learning, as one of the enabling technologies for VEI, places significant strain on network bandwidth while also increasing latency and privacy concerns. Nowadays, distributed machine learning methods, such as federated learning (FL), split learning (SL), and split federated learning (SFL), are widely applied in vehicular networks to support VEI. However, these methods still face significant challenges due to the mobility and constrained resources inherent in vehicular networks. In this article, we first provide an overview of the system architecture, performance metrics, and challenges associated with VEI design. Then, the adaptive design of SFL, namely adaptive split federated learning (ASFL) is introduced. The proposed ASFL scheme dynamically adapts the cut layer selection process and operates in parallel, optimizing both communication and computation efficiency while improving model performance under non-IID data distribution. Finally, we highlight future research directions to shed the light on the efficient design of SFL.
All-atom Diffusion Transformers: Unified generative modelling of molecules and materials
Authors: Chaitanya K. Joshi, Xiang Fu, Yi-Lun Liao, Vahe Gharakhanyan, Benjamin Kurt Miller, Anuroop Sriram, Zachary W. Ulissi
Date: 2025-03-05 00:00:00
Tasks: All, Unconditional Crystal Generation, Unconditional Molecule Generation

Diffusion models are the standard toolkit for generative modelling of 3D atomic systems. However, for different types of atomic systems - such as molecules and materials - the generative processes are usually highly specific to the target system despite the underlying physics being the same. We introduce the All-atom Diffusion Transformer (ADiT), a unified latent diffusion framework for jointly generating both periodic materials and non-periodic molecular systems using the same model: (1) An autoencoder maps a unified, all-atom representations of molecules and materials to a shared latent embedding space; and (2) A diffusion model is trained to generate new latent embeddings that the autoencoder can decode to sample new molecules or materials. Experiments on QM9 and MP20 datasets demonstrate that jointly trained ADiT generates realistic and valid molecules as well as materials, exceeding state-of-the-art results from molecule and crystal-specific models. ADiT uses standard Transformers for both the autoencoder and diffusion model, resulting in significant speedups during training and inference compared to equivariant diffusion models. Scaling ADiT up to half a billion parameters predictably improves performance, representing a step towards broadly generalizable foundation models for generative chemistry. Open source code: https://github.com/facebookresearch/all-atom-diffusion-transformer
Federated Semi-Supervised Multi-Task Learning to Detect COVID-19 and Lungs Segmentation Marking Using Chest Radiography Images and Raspberry Pi Devices: An Internet of Medical Things Application
Authors: Mahbub Ul Alam, Rahim Rahmani
Date: 2021-07-24 00:00:00
Proceeding: Sensors 2021 7
Tasks: Federated Learning, Multi-Task Learning, Segmentation, Transfer Learning

nternet of Medical Things (IoMT) provides an excellent opportunity to investigate better automatic medical decision support tools with the effective integration of various medical equipment and associated data. This study explores two such medical decision-making tasks, namely COVID-19 detection and lung area segmentation detection, using chest radiography images. We also explore different cutting-edge machine learning techniques, such as federated learning, semi-supervised learning, transfer learning, and multi-task learning to explore the issue. To analyze the applicability of computationally less capable edge devices in the IoMT system, we report the results using Raspberry Pi devices as accuracy, precision, recall, 𝐹𝑠𝑐𝑜𝑟𝑒
 for COVID-19 detection, and average dice score for lung segmentation detection tasks. We also publish the results obtained through server-centric simulation for comparison. The results show that Raspberry Pi-centric devices provide better performance in lung segmentation detection, and server-centric experiments provide better results in COVID-19 detection. We also discuss the IoMT application-centric settings, utilizing medical data and decision support systems, and posit that such a system could benefit all the stakeholders in the IoMT domain.
FedSepsis: A Federated Multi-Modal Deep Learning-Based Internet of Medical Things Application for Early Detection of Sepsis from Electronic Health Records Using Raspberry Pi and Jetson Nano Devices
Authors: Mahbub Ul Alam, Rahim Rahmani
Date: 2023-01-14 00:00:00
Proceeding: Sensors 2023 1
Tasks: CPU, Federated Learning

The concept of the Internet of Medical Things brings a promising option to utilize various electronic health records stored in different medical devices and servers to create practical but secure clinical decision support systems. To achieve such a system, we need to focus on several aspects, most notably the usability aspect of deploying it using low-end devices. This study introduces one such application, namely FedSepsis, for the early detection of sepsis using electronic health records. We incorporate several cutting-edge deep learning techniques for the prediction and natural-language processing tasks. We also explore the multimodality aspect for the better use of electronic health records. A secure distributed machine learning mechanism is essential to building such a practical internet of medical things application. To address this, we analyze two federated learning techniques. Moreover, we use two different kinds of low-computational edge devices, namely Raspberry Pi and Jetson Nano, to address the challenges of using such a system in a practical setting and report the comparisons. We report several critical system-level information about the devices, namely CPU utilization, disk utilization, process CPU threads in use, process memory in use (non-swap), process memory available (non-swap), system memory utilization, temperature, and network traffic. We publish the prediction results with the evaluation metrics area under the receiver operating characteristic curve, the area under the precision–recall curve, and the earliness to predict sepsis in hours. Our results show that the performance is satisfactory, and with a moderate amount of devices, the federated learning setting results are similar to the single server-centric setting. Multimodality provides the best results compared to any single modality in the input features obtained from the electronic health records. Generative adversarial neural networks provide a clear superiority in handling the sparsity of electronic health records. Multimodality with the generative adversarial neural networks provides the best result: the area under the precision–recall curve is 96.55%, the area under the receiver operating characteristic curve is 99.35%, and earliness is 4.56 h. FedSepsis suggests that incorporating such a concept together with low-end computational devices could be beneficial for all the medical sector stakeholders and should be explored further.
Exploring LRP and Grad-CAM visualization to interpret multi-label-multi-class pathology prediction using chest radiography
Authors: Mahbub Ul Alam, Rahim Rahmani
Date: 2022-08-31 00:00:00
Proceeding: 2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS) 2022 8
Tasks: Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

The area of interpretable deep neural networks has received increased attention in recent years due to the need for transparency in various fields, including medicine, healthcare, stock market analysis, compliance with legislation, and law. Layer-wise Relevance Propagation (LRP) and Gradient-weighted Class Activation Mapping (Grad-CAM) are two widely used algorithms to interpret deep neural networks. In this work, we investigated the applicability of these two algorithms in the sensitive application area of interpreting chest radiography images. In order to get a more nuanced and balanced outcome, we use a multi-label classification-based dataset and analyze the model prediction by visualizing the outcome of LRP and Grad-CAM on the chest radiography images. The results show that LRP provides more granular heatmaps than Grad-CAM when applied to the CheXpert dataset classification model. We posit that this is due to the inherent construction difference of these algorithms (LRP is layer-wise accumulation, whereas Grad-CAM focuses primarily on the final sections in the model's architecture). Both can be useful for understanding the classification from a micro or macro level to get a superior and interpretable clinical decision support system.
Thousand Voices of Trauma: A Large-Scale Synthetic Dataset for Modeling Prolonged Exposure Therapy Conversations
Authors: Suhas BN, Andrew M. Sherrill, Rosa I. Arriaga, Chris W. Wiese, Saeed Abdullah
Date: 2025-04-16 00:00:00
Tasks: Privacy Preserving

The advancement of AI systems for mental health support is hindered by limited access to therapeutic conversation data, particularly for trauma treatment. We present Thousand Voices of Trauma, a synthetic benchmark dataset of 3,000 therapy conversations based on Prolonged Exposure therapy protocols for Post-traumatic Stress Disorder (PTSD). The dataset comprises 500 unique cases, each explored through six conversational perspectives that mirror the progression of therapy from initial anxiety to peak distress to emotional processing. We incorporated diverse demographic profiles (ages 18-80, M=49.3, 49.4% male, 44.4% female, 6.2% non-binary), 20 trauma types, and 10 trauma-related behaviors using deterministic and probabilistic generation methods. Analysis reveals realistic distributions of trauma types (witnessing violence 10.6%, bullying 10.2%) and symptoms (nightmares 23.4%, substance abuse 20.8%). Clinical experts validated the dataset's therapeutic fidelity, highlighting its emotional depth while suggesting refinements for greater authenticity. We also developed an emotional trajectory benchmark with standardized metrics for evaluating model responses. This privacy-preserving dataset addresses critical gaps in trauma-focused mental health data, offering a valuable resource for advancing both patient-facing applications and clinician training tools.
Image Enhancement Based on Histogram-Guided Multiple Transformation Function Estimation
Authors: Jaemin Park, An Gia Vien, Thuy Thi Pham, HanUl Kim, Chul Lee
Date: 2024-10-08 00:00:00
Proceeding: IEEE Transactions on Consumer Electronics 2024 10
Tasks: Image Enhancement, Image-to-Image Translation, Low-Light Image Enhancement, Underwater Image Restoration

Although recent deep learning-based algorithms have achieved significant performance improvements in various image enhancement tasks, most approaches have been developed using image-to-image translation, which is challenging to interpret and analyze the enhancement processes. Several attempts have been made to use the image-to-transformation function approach for better interpretability; however, they often fail to generate complex color mappings, degrading image quality. In this work, we develop a novel transformation function-based algorithm that estimates multiple transformation functions with different properties by exploiting both the spatial and statistical characteristics of the input image to describe complex color mapping. First, we extract the image features that capture spatial information, considering their channel correlations. Next, we estimate multiple transformation functions utilizing a cross-attention block to capture the relevance between spatial and statistical information in the input image and its histogram, respectively. We then estimate the weight maps indicating the pixel-wise contribution of each transformation function by exploiting the spatial correlation between the input and transformed images obtained by each transformation function. Finally, we obtain an enhanced image by taking the weighted sum of the transformed images and the corresponding weight maps. Experimental results demonstrate that the proposed algorithm outperforms state-of-the-art algorithms on various image enhancement tasks.
A New Approach for Segmentation and Quantification of Cells or Nanoparticles
Authors: ZhenZhou Wang
Date: 2016-03-14 00:00:00
Proceeding: IEEE Transactions on Industrial Informatics 2016 3
Tasks: Cell Segmentation, Segmentation

With the rapid development of microscopy imaging technology, the requirement for robust segmentation and quantification of cells or nanoparticles increases greatly. It remains challenging due to the diversity of the cell or nanoparticle types, the arbitrary shapes, and the large numbers of cells or nanoparticles. The most existing methods are only capable of segmenting some specific types of cells or nanoparticles. In this paper, we propose a more versatile approach that is capable of segmenting a variety of cells or nanoparticles. It consists of five parts: 1) automatic gradient image formation; 2) automatic threshold selection; 3) manual calibration of the threshold selection method for each specific type of cell or nanoparticle images; 4) manual determination of the segmentation cases for each specific type of cell or nanoparticle images; and 5) automatic quantification by iterative morphological erosion. After the parameter, N is calibrated and the segmentation case is determined manually for each specific type of cell or nanoparticle images with one or several typical images; only parts 1), 2), and 5) are needed for the rest of processing and they are automatic. The proposed approach is tested with different types of cell and nanoparticle images. Experimental results verified its effectiveness.
Intent-aware Diffusion with Contrastive Learning for Sequential Recommendation
Authors: Yuanpeng Qu, Hajime Nobuhara
Date: 2025-04-22 00:00:00
Tasks: Contrastive Learning, Data Augmentation, Sequential Recommendation

Contrastive learning has proven effective in training sequential recommendation models by incorporating self-supervised signals from augmented views. Most existing methods generate multiple views from the same interaction sequence through stochastic data augmentation, aiming to align their representations in the embedding space. However, users typically have specific intents when purchasing items (e.g., buying clothes as gifts or cosmetics for beauty). Random data augmentation used in existing methods may introduce noise, disrupting the latent intent information implicit in the original interaction sequence. Moreover, using noisy augmented sequences in contrastive learning may mislead the model to focus on irrelevant features, distorting the embedding space and failing to capture users' true behavior patterns and intents. To address these issues, we propose Intent-aware Diffusion with contrastive learning for sequential Recommendation (InDiRec). The core idea is to generate item sequences aligned with users' purchasing intents, thus providing more reliable augmented views for contrastive learning. Specifically, InDiRec first performs intent clustering on sequence representations using K-means to build intent-guided signals. Next, it retrieves the intent representation of the target interaction sequence to guide a conditional diffusion model, generating positive views that share the same underlying intent. Finally, contrastive learning is applied to maximize representation consistency between these intent-aligned views and the original sequence. Extensive experiments on five public datasets demonstrate that InDiRec achieves superior performance compared to existing baselines, learning more robust representations even under noisy and sparse data conditions.
SUPRA: Subspace Parameterized Attention for Neural Operator on General Domains
Authors: Zherui Yang, Zhengyang Xue, Ligang Liu
Date: 2025-04-22 00:00:00
Tasks: Computational Efficiency

Neural operators are efficient surrogate models for solving partial differential equations (PDEs), but their key components face challenges: (1) in order to improve accuracy, attention mechanisms suffer from computational inefficiency on large-scale meshes, and (2) spectral convolutions rely on the Fast Fourier Transform (FFT) on regular grids and assume a flat geometry, which causes accuracy degradation on irregular domains. To tackle these problems, we regard the matrix-vector operations in the standard attention mechanism on vectors in Euclidean space as bilinear forms and linear operators in vector spaces and generalize the attention mechanism to function spaces. This new attention mechanism is fully equivalent to the standard attention but impossible to compute due to the infinite dimensionality of function spaces. To address this, inspired by model reduction techniques, we propose a Subspace Parameterized Attention (SUPRA) neural operator, which approximates the attention mechanism within a finite-dimensional subspace. To construct a subspace on irregular domains for SUPRA, we propose using the Laplacian eigenfunctions, which naturally adapt to domains' geometry and guarantee the optimal approximation for smooth functions. Experiments show that the SUPRA neural operator reduces error rates by up to 33% on various PDE datasets while maintaining state-of-the-art computational efficiency.
Recent Advances and Future Directions in Extended Reality (XR): Exploring AI-Powered Spatial Intelligence
Authors: Baichuan Zeng
Date: 2025-04-22 00:00:00
Tasks: Mixed Reality

Extended Reality (XR), encompassing Augmented Reality (AR), Virtual Reality (VR) and Mixed Reality (MR), is a transformative technology bridging the physical and virtual world and it has diverse potential which will be ubiquitous in the future. This review examines XR's evolution through foundational framework - hardware ranging from monitors to sensors and software ranging from visual tasks to user interface; highlights state of the art (SOTA) XR products with the comparison and analysis of performance based on their foundational framework; discusses how commercial XR devices can support the demand of high-quality performance focusing on spatial intelligence. For future directions, attention should be given to the integration of multi-modal AI and IoT-driven digital twins to enable adaptive XR systems. With the concept of spatial intelligence, future XR should establish a new digital space with realistic experience that benefits humanity. This review underscores the pivotal role of AI in unlocking XR as the next frontier in human-computer interaction.