pwc-archive/papers-with-abstracts huggingface dataset:
Generalized Semantic Contrastive Learning via Embedding Side Information for Few-Shot Object Detection
Authors: Ruoyu Chen, Hua Zhang, Jingzhi Li, Li Liu, Zhen Huang, Xiaochun Cao
Date: 2025-04-09 00:00:00
Tasks: Contrastive Learning, counterfactual, Counterfactual Explanation, Few-Shot Object Detection, object-detection, Object Detection

The objective of few-shot object detection (FSOD) is to detect novel objects with few training samples. The core challenge of this task is how to construct a generalized feature space for novel categories with limited data on the basis of the base category space, which could adapt the learned detection model to unknown scenarios. However, limited by insufficient samples for novel categories, two issues still exist: (1) the features of the novel category are easily implicitly represented by the features of the base category, leading to inseparable classifier boundaries, (2) novel categories with fewer data are not enough to fully represent the distribution, where the model fine-tuning is prone to overfitting. To address these issues, we introduce the side information to alleviate the negative influences derived from the feature space and sample viewpoints and formulate a novel generalized feature representation learning method for FSOD. Specifically, we first utilize embedding side information to construct a knowledge matrix to quantify the semantic relationship between the base and novel categories. Then, to strengthen the discrimination between semantically similar categories, we further develop contextual semantic supervised contrastive learning which embeds side information. Furthermore, to prevent overfitting problems caused by sparse samples, a side-information guided region-aware masked module is introduced to augment the diversity of samples, which finds and abandons biased information that discriminates between similar categories via counterfactual explanation, and refines the discriminative representation space further. Extensive experiments using ResNet and ViT backbones on PASCAL VOC, MS COCO, LVIS V1, FSOD-1K, and FSVOD-500 benchmarks demonstrate that our model outperforms the previous state-of-the-art methods, significantly improving the ability of FSOD in most shots/splits.
Efficient Deployment of Spiking Neural Networks on SpiNNaker2 for DVS Gesture Recognition Using Neuromorphic Intermediate Representation
Authors: Sirine Arfa, Bernhard Vogginger, Chen Liu, Johannes Partzsch, Mark Schone, Christian Mayr
Date: 2025-09-04 00:00:00
Tasks: Edge-computing, Gesture Recognition, Quantization

Spiking Neural Networks (SNNs) are highly energy-efficient during inference, making them particularly suitable for deployment on neuromorphic hardware. Their ability to process event-driven inputs, such as data from dynamic vision sensors (DVS), further enhances their applicability to edge computing tasks. However, the resource constraints of edge hardware necessitate techniques like weight quantization, which reduce the memory footprint of SNNs while preserving accuracy. Despite its importance, existing quantization methods typically focus on synaptic weights quantization without taking account of other critical parameters, such as scaling neuron firing thresholds.
To address this limitation, we present the first benchmark for the DVS gesture recognition task using SNNs optimized for the many-core neuromorphic chip SpiNNaker2. Our study evaluates two quantization pipelines for fixed-point computations. The first approach employs post training quantization (PTQ) with percentile-based threshold scaling, while the second uses quantization aware training (QAT) with adaptive threshold scaling. Both methods achieve accurate 8-bit on-chip inference, closely approximating 32-bit floating-point performance. Additionally, our baseline SNNs perform competitively against previously reported results without specialized techniques. These models are deployed on SpiNNaker2 using the neuromorphic intermediate representation (NIR). Ultimately, we achieve 94.13% classification accuracy on-chip, demonstrating the SpiNNaker2's potential for efficient, low-energy neuromorphic computing.
Latent Diffusion Autoencoders: Toward Efficient and Meaningful Unsupervised Representation Learning in Medical Imaging
Authors: Gabriele Lozupone, Alessandro Bria, Francesco Fontanella, Frederick J. A. Meijer, Claudio De Stefano, Henkjan Huisman
Date: 2025-04-11 00:00:00
Tasks: Attribute, Computational Efficiency, Counterfactual Explanation, Data Augmentation, Diagnostic, Disentanglement, Image Generation, Linear-Probe Classification, Medical Image Analysis, Medical Image Generation, Missing Values, Representation Learning, SSIM

This study presents Latent Diffusion Autoencoder (LDAE), a novel encoder-decoder diffusion-based framework for efficient and meaningful unsupervised learning in medical imaging, focusing on Alzheimer disease (AD) using brain MR from the ADNI database as a case study. Unlike conventional diffusion autoencoders operating in image space, LDAE applies the diffusion process in a compressed latent representation, improving computational efficiency and making 3D medical imaging representation learning tractable. To validate the proposed approach, we explore two key hypotheses: (i) LDAE effectively captures meaningful semantic representations on 3D brain MR associated with AD and ageing, and (ii) LDAE achieves high-quality image generation and reconstruction while being computationally efficient. Experimental results support both hypotheses: (i) linear-probe evaluations demonstrate promising diagnostic performance for AD (ROC-AUC: 90%, ACC: 84%) and age prediction (MAE: 4.1 years, RMSE: 5.2 years); (ii) the learned semantic representations enable attribute manipulation, yielding anatomically plausible modifications; (iii) semantic interpolation experiments show strong reconstruction of missing scans, with SSIM of 0.969 (MSE: 0.0019) for a 6-month gap. Even for longer gaps (24 months), the model maintains robust performance (SSIM > 0.93, MSE < 0.004), indicating an ability to capture temporal progression trends; (iv) compared to conventional diffusion autoencoders, LDAE significantly increases inference throughput (20x faster) while also enhancing reconstruction quality. These findings position LDAE as a promising framework for scalable medical imaging applications, with the potential to serve as a foundation model for medical image analysis. Code available at https://github.com/GabrieleLozupone/LDAE
SRVP: Strong Recollection Video Prediction Model Using Attention-Based Spatiotemporal Correlation Fusion
Authors: Yuseon Kim, Kyongseok Park
Date: 2025-04-10 00:00:00
Tasks: Video Prediction

Video prediction (VP) generates future frames by leveraging spatial representations and temporal context from past frames. Traditional recurrent neural network (RNN)-based models enhance memory cell structures to capture spatiotemporal states over extended durations but suffer from gradual loss of object appearance details. To address this issue, we propose the strong recollection VP (SRVP) model, which integrates standard attention (SA) and reinforced feature attention (RFA) modules. Both modules employ scaled dot-product attention to extract temporal context and spatial correlations, which are then fused to enhance spatiotemporal representations. Experiments on three benchmark datasets demonstrate that SRVP mitigates image quality degradation in RNN-based models while achieving predictive performance comparable to RNN-free architectures.
Change Detection Needs Neighborhood Interaction in Transformer
Authors: Hangling Ma, Lingran Zhao, Bingquan Li, Ruiqing Niu, Yueyue Wang
Date: 2023-11-22 00:00:00
Proceeding: remotesensing 2023 11
Tasks: Change Detection

Remote sensing image change detection (CD) is an essential technique for analyzing surface changes from co-registered images of different time periods. The main challenge in CD is to identify the alterations that the user intends to emphasize, while excluding pseudo-changes caused by external factors. Recent advancements in deep learning and image change detection have shown remarkable performance with ConvNet-based and Transformer-based techniques. However, ConvNet-based methods are limited by the local receptive fields of convolutional kernels that cannot effectively capture the change features in spatial–temporal information, while Transformer-based CD models need to be driven by a large amount of data due to the lack of inductive biases, and at the same time need to bear the costly computational complexity brought by self-attention. To address these challenges, we propose a Transformer-based Siamese network structure called BTNIFormer. It incorporates a sparse attention mechanism called Dilated Neighborhood Attention (DiNA), which localizes the attention range of each pixel to its neighboring context. Extensive experiments conducted on two publicly available datasets demonstrate the benefits of our proposed innovation. Compared to the most competitive recent Transformer-based approaches, our method achieves a significant 12.00% improvement in IoU while reducing computational costs by half. This provides a promising solution for further development of the Transformer structure in CD tasks.
Deep Ensembling of Multiband Images for Earth Remote Sensing and Foramnifera Data
Authors: Loris Nanni, Sheryl Brahnam, Matteo Ruta, Daniele Fabris, Martina Boscolo Bacheto, Tommaso Milanello
Date: 2025-04-02 00:00:00
Proceeding: Sensors 2025 4
Tasks: Classification, Ensemble Learning, image-classification, Image Classification, Satellite Image Classification

The classification of multiband images captured by advanced sensors, such as satellite-mounted imaging systems, is a critical task in remote sensing and environmental monitoring. These sensors provide high-dimensional data that encapsulate a wealth of spectral and spatial information, enabling detailed analyses of the Earth’s surface features. However, the complexity of these data poses significant challenges for accurate and efficient classification. Our study describes and highlights methods for creating ensembles of neural networks for handling multiband images. Two applications are illustrated in this work: (1) satellite image classification tested on the EuroSAT and LCZ42 datasets and (2) a species-level identification of planktic foraminifera. Multichannel images are fed into an ensemble of Convolutional Neural Networks (CNNs) (ResNet50, MobileNetV2, and DenseNet201), where each network is trained using three channels obtained from the multichannel images, and two custom networks (one based on ResNet50 and the other one based on attention) where the input is a multiband image. The ensemble learning framework harnesses these variations to improve classification accuracy, surpassing other advanced methods. The proposed system, implemented in MATLAB 2024b and PyTorch 2.6, is shown to achieve higher classification accuracy than those of human experts for species-level identification of planktic foraminifera (>92% vs. 83%) and state-of-the-art performance on the tested planktic foraminifera, the EuroSAT and LCZ42 datasets.
A Comparative Study of Recommender Systems under Big Data Constraints
Authors: Arimondo Scrivano
Date: 2025-04-11 00:00:00
Tasks: Recommendation Systems

Recommender Systems (RS) have become essential tools in a wide range of digital services, from e-commerce and streaming platforms to news and social media. As the volume of user-item interactions grows exponentially, especially in Big Data environments, selecting the most appropriate RS model becomes a critical task. This paper presents a comparative study of several state-of-the-art recommender algorithms, including EASE-R, SLIM, SLIM with ElasticNet regularization, Matrix Factorization (FunkSVD and ALS), P3Alpha, and RP3Beta. We evaluate these models according to key criteria such as scalability, computational complexity, predictive accuracy, and interpretability. The analysis considers both their theoretical underpinnings and practical applicability in large-scale scenarios. Our results highlight that while models like SLIM and SLIM-ElasticNet offer high accuracy and interpretability, they suffer from high computational costs, making them less suitable for real-time applications. In contrast, algorithms such as EASE-R and RP3Beta achieve a favorable balance between performance and scalability, proving more effective in large-scale environments. This study aims to provide guidelines for selecting the most appropriate recommender approach based on specific Big Data constraints and system requirements.
MixDiT: Accelerating Image Diffusion Transformer Inference with Mixed-Precision MX Quantization
Authors: Daeun Kim, Jinwoo Hwang, Changhun Oh, Jongse Park
Date: 2025-04-11 00:00:00
Tasks: Image Generation, Quantization

Diffusion Transformer (DiT) has driven significant progress in image generation tasks. However, DiT inferencing is notoriously compute-intensive and incurs long latency even on datacenter-scale GPUs, primarily due to its iterative nature and heavy reliance on GEMM operations inherent to its encoder-based structure. To address the challenge, prior work has explored quantization, but achieving low-precision quantization for DiT inferencing with both high accuracy and substantial speedup remains an open problem. To this end, this paper proposes MixDiT, an algorithm-hardware co-designed acceleration solution that exploits mixed Microscaling (MX) formats to quantize DiT activation values. MixDiT quantizes the DiT activation tensors by selectively applying higher precision to magnitude-based outliers, which produce mixed-precision GEMM operations. To achieve tangible speedup from the mixed-precision arithmetic, we design a MixDiT accelerator that enables precision-flexible multiplications and efficient MX precision conversions. Our experimental results show that MixDiT delivers a speedup of 2.10-5.32 times over RTX 3090, with no loss in FID.
CDM-QTA: Quantized Training Acceleration for Efficient LoRA Fine-Tuning of Diffusion Model
Authors: Jinming Lu, Minghao She, Wendong Mao, Zhongfeng Wang
Date: 2025-04-08 00:00:00
Tasks: Image Generation

Fine-tuning large diffusion models for custom applications demands substantial power and time, which poses significant challenges for efficient implementation on mobile devices. In this paper, we develop a novel training accelerator specifically for Low-Rank Adaptation (LoRA) of diffusion models, aiming to streamline the process and reduce computational complexity. By leveraging a fully quantized training scheme for LoRA fine-tuning, we achieve substantial reductions in memory usage and power consumption while maintaining high model fidelity. The proposed accelerator features flexible dataflow, enabling high utilization for irregular and variable tensor shapes during the LoRA process. Experimental results show up to 1.81x training speedup and 5.50x energy efficiency improvements compared to the baseline, with minimal impact on image generation quality.
Control Co-Design Under Uncertainty for Offshore Wind Farms: Optimizing Grid Integration, Energy Storage, and Market Participation
Authors: Himanshu Sharma, Wei Wang, Bowen Huang, Buxin She, Thiagarajan Ramachandaran
Date: 2025-04-11 00:00:00
Tasks: Decision Making, energy management

Offshore wind farms (OWFs) are set to significantly contribute to global decarbonization efforts. Developers often use a sequential approach to optimize design variables and market participation for grid-integrated offshore wind farms. However, this method can lead to sub-optimal system performance, and uncertainties associated with renewable resources are often overlooked in decision-making. This paper proposes a control co-design approach, optimizing design and control decisions for integrating OWFs into the power grid while considering energy market and primary frequency market participation. Additionally, we introduce optimal sizing solutions for energy storage systems deployed onshore to enhance revenue for OWF developers over time. This framework addresses uncertainties related to wind resources and energy prices. We analyze five U.S. west-coast offshore wind farm locations and potential interconnection points, as identified by the Bureau of Ocean Energy Management (BOEM). Results show that optimized control co-design solutions can increase market revenue by 3.2\% and provide flexibility in managing wind resource uncertainties.
Variational quantum and neural quantum states algorithms for the linear complementarity problem
Authors: Saibal De, Oliver Knitter, Rohan Kodati, Paramsothy Jayakumar, James Stokes, Shravan Veerapaneni
Date: 2025-04-10 00:00:00

Variational quantum algorithms (VQAs) are promising hybrid quantum-classical methods designed to leverage the computational advantages of quantum computing while mitigating the limitations of current noisy intermediate-scale quantum (NISQ) hardware. Although VQAs have been demonstrated as proofs of concept, their practical utility in solving real-world problems -- and whether quantum-inspired classical algorithms can match their performance -- remains an open question. We present a novel application of the variational quantum linear solver (VQLS) and its classical neural quantum states-based counterpart, the variational neural linear solver (VNLS), as key components within a minimum map Newton solver for a complementarity-based rigid body contact model. We demonstrate using the VNLS that our solver accurately simulates the dynamics of rigid spherical bodies during collision events. These results suggest that quantum and quantum-inspired linear algebra algorithms can serve as viable alternatives to standard linear algebra solvers for modeling certain physical systems.
Enterprise-Grade Security for the Model Context Protocol (MCP): Frameworks and Mitigation Strategies
Authors: Vineeth Sai Narajala, Idan Habler
Date: 2025-04-11 00:00:00

The Model Context Protocol (MCP), introduced by Anthropic, provides a standardized framework for artificial intelligence (AI) systems to interact with external data sources and tools in real-time. While MCP offers significant advantages for AI integration and capability extension, it introduces novel security challenges that demand rigorous analysis and mitigation. This paper builds upon foundational research into MCP architecture and preliminary security assessments to deliver enterprise-grade mitigation frameworks and detailed technical implementation strategies. Through systematic threat modeling and analysis of MCP implementations and analysis of potential attack vectors, including sophisticated threats like tool poisoning, we present actionable security patterns tailored for MCP implementers and adopters. The primary contribution of this research lies in translating theoretical security concerns into a practical, implementable framework with actionable controls, thereby providing essential guidance for the secure enterprise adoption and governance of integrated AI systems.
Understanding the Impact of Data Domain Extraction on Synthetic Data Privacy
Authors: Georgi Ganev, Meenatchi Sundaram Muthu Selva Annamalai, Sofiane Mahiou, Emiliano De Cristofaro
Date: 2025-04-11 00:00:00

Privacy attacks, particularly membership inference attacks (MIAs), are widely used to assess the privacy of generative models for tabular synthetic data, including those with Differential Privacy (DP) guarantees. These attacks often exploit outliers, which are especially vulnerable due to their position at the boundaries of the data domain (e.g., at the minimum and maximum values). However, the role of data domain extraction in generative models and its impact on privacy attacks have been overlooked. In this paper, we examine three strategies for defining the data domain: assuming it is externally provided (ideally from public data), extracting it directly from the input data, and extracting it with DP mechanisms. While common in popular implementations and libraries, we show that the second approach breaks end-to-end DP guarantees and leaves models vulnerable. While using a provided domain (if representative) is preferable, extracting it with DP can also defend against popular MIAs, even at high privacy budgets.
EO-VLM: VLM-Guided Energy Overload Attacks on Vision Models
Authors: Minjae Seo, Myoungsung You, Junhee Lee, Jaehan Kim, Hwanjo Heo, Jintae Oh, Jinwoo Kim
Date: 2025-04-11 00:00:00
Tasks: Autonomous Driving, GPU, Language Modeling, Language Modelling

Vision models are increasingly deployed in critical applications such as autonomous driving and CCTV monitoring, yet they remain susceptible to resource-consuming attacks. In this paper, we introduce a novel energy-overloading attack that leverages vision language model (VLM) prompts to generate adversarial images targeting vision models. These images, though imperceptible to the human eye, significantly increase GPU energy consumption across various vision models, threatening the availability of these systems. Our framework, EO-VLM (Energy Overload via VLM), is model-agnostic, meaning it is not limited by the architecture or type of the target vision model. By exploiting the lack of safety filters in VLMs like DALL-E 3, we create adversarial noise images without requiring prior knowledge or internal structure of the target vision models. Our experiments demonstrate up to a 50% increase in energy consumption, revealing a critical vulnerability in current vision models.
The More is not the Merrier: Investigating the Effect of Client Size on Federated Learning
Authors: Eleanor Wallach, Sage Siler, Jing Deng
Date: 2025-04-11 00:00:00
Tasks: Edge-computing, Federated Learning

Federated Learning (FL) has been introduced as a way to keep data local to clients while training a shared machine learning model, as clients train on their local data and send trained models to a central aggregator. It is expected that FL will have a huge implication on Mobile Edge Computing, the Internet of Things, and Cross-Silo FL. In this paper, we focus on the widely used FedAvg algorithm to explore the effect of the number of clients in FL. We find a significant deterioration of learning accuracy for FedAvg as the number of clients increases. To address this issue for a general application, we propose a method called Knowledgeable Client Insertion (KCI) that introduces a very small number of knowledgeable clients to the MEC setting. These knowledgeable clients are expected to have accumulated a large set of data samples to help with training. With the help of KCI, the learning accuracy of FL increases much faster even with a normal FedAvg aggregation technique. We expect this approach to be able to provide great privacy protection for clients against security attacks such as model inversion attacks. Our code is available at https://github.com/Eleanor-W/KCI_for_FL.