pwc-archive/papers-with-abstracts huggingface dataset:
MIAdapt: Source-free Few-shot Domain Adaptive Object Detection for Microscopic Images
Authors: Nimra Dilawar, Sara Nadeem, Javed Iqbal, Waqas Sultani, Mohsen Ali
Date: 2025-03-05 00:00:00
Tasks: Domain Adaptation, object-detection, Object Detection, Unsupervised Domain Adaptation

Existing generic unsupervised domain adaptation approaches require access to both a large labeled source dataset and a sufficient unlabeled target dataset during adaptation. However, collecting a large dataset, even if unlabeled, is a challenging and expensive endeavor, especially in medical imaging. In addition, constraints such as privacy issues can result in cases where source data is unavailable. Taking in consideration these challenges, we propose MIAdapt, an adaptive approach for Microscopic Imagery Adaptation as a solution for Source-free Few-shot Domain Adaptive Object detection (SF-FSDA). We also define two competitive baselines (1) Faster-FreeShot and (2) MT-FreeShot. Extensive experiments on the challenging M5-Malaria and Raabin-WBC datasets validate the effectiveness of MIAdapt. Without using any image from the source domain MIAdapt surpasses state-of-the-art source-free UDA (SF-UDA) methods by +21.3% mAP and few-shot domain adaptation (FSDA) approaches by +4.7% mAP on Raabin-WBC. Our code and models will be publicly available.
When Claims Evolve: Evaluating and Enhancing the Robustness of Embedding Models Against Misinformation Edits
Authors: Jabez Magomere, Emanuele La Malfa, Manuel Tonneau, Ashkan Kazemi, Scott Hale
Date: 2025-03-05 00:00:00
Tasks: Domain Generalization, Fact Checking, Misinformation, Retrieval, valid

Online misinformation remains a critical challenge, and fact-checkers increasingly rely on embedding-based methods to retrieve relevant fact-checks. Yet, when debunked claims reappear in edited forms, the performance of these methods is unclear. In this work, we introduce a taxonomy of six common real-world misinformation edits and propose a perturbation framework that generates valid, natural claim variations. Our multi-stage retrieval evaluation reveals that standard embedding models struggle with user-introduced edits, while LLM-distilled embeddings offer improved robustness at a higher computational cost. Although a strong reranker helps mitigate some issues, it cannot fully compensate for first-stage retrieval gaps. Addressing these retrieval gaps, our train- and inference-time mitigation approaches enhance in-domain robustness by up to 17 percentage points and boost out-of-domain generalization by 10 percentage points over baseline models. Overall, our findings provide practical improvements to claim-matching systems, enabling more reliable fact-checking of evolving misinformation.
Minima Possible Weights: A Homogenous Deep Ensemble Method for Cross-Subject Motor Imagery Classification
Authors: Quang Pham Lam Dinh, Isao Nambu
Date: 2025-02-18 00:00:00
Proceeding: IEEE Access 2025 2
Tasks: Brain Computer Interface, Ensemble Learning, Motor Imagery, Test-time Adaptation

Motor Imagery (MI) systems in Brain-Computer Interface (BCI) research provide communication and control solutions for individuals with motor impairments, yet cross-subject classification remains
challenging due to substantial inter-subject variability. In this study, we propose the Minima Possible Weights
(MPW) method, an unsupervised learning approach designed to enhance MI classification through ensemble
deep learning. MPW aggregates predicted probabilities from multiple models and selects the class with the
lowest associated weight for the final prediction. We evaluated MPW against various ensemble learning
and test-time adaptation methods using two benchmark datasets: BCI Competition IV Dataset 2a and
PhysionetMI. Our results indicate that MPW achieves cross-subject classification accuracy of up to 64.75%
on BCI Competition IV Dataset 2a and 66.92% on PhysionetMI. Although the current performance is not
yet sufficient for practical BCI applications, MPW shows potential in reducing calibration time and easing
the burden of adapting models to new subjects.
Robust Variational Contrastive Learning for Partially View-unaligned Clustering
Authors: Changhao He, Hongyuan Zhu, Peng Hu, Xi Peng
Date: 2024-10-28 00:00:00
Proceeding: ACM Multimedia 2024 10
Tasks: Clustering, Contrastive Learning, Deep Clustering, MULTI-VIEW LEARNING, Variational Inference

Although multi-view learning has achieved remarkable progress over the past decades, most existing methods implicitly assume that all views (or modalities) are well-aligned. In practice, however, collecting fully aligned views is challenging due to complexities and discordances in time and space, resulting in the Partially View-unaligned Problem (PVP), such as audio-video asynchrony caused by network congestion. While some methods are proposed to align the unaligned views by learning view-invariant representations, almost all of them overlook specific information across different views for complementarity, limiting performance improvement. To address these problems, we propose a robust framework, dubbed VariatIonal ConTrAstive Learning (VITAL), designed to learn both common and specific information simultaneously. To be specific, each data sample is first modeled as a Gaussian distribution in the latent space, where the mean estimates the most probable common information, while the variance indicates view-specific information. Second, by using variational inference, VITAL conducts intra- and inter-view contrastive learning to preserve common and specific semantics in the distribution representations, thereby achieving comprehensive perception. As a result, the common representation (mean) could be used to guide category-level realignment, while the specific representation (variance) complements sample semantic information, thereby boosting overall performance. Finally, considering the abundance of False Negative Pairs (FNPs) generated by unsupervised contrastive learning, we propose a robust loss function that seamlessly incorporates FNP rectification into the contrastive learning paradigm. Empirical evaluations on eight benchmark datasets reveal that VITAL outperforms ten state-of-the-art deep clustering baselines, demonstrating its efficacy in both partially and fully aligned scenarios. The Code is available at https://github.com/He-Changhao/2024-MM-VITAL.
Active Learning for Deep Learning-Based Hemodynamic Parameter Estimation
Authors: Patryk Rygiel, Julian Suk, Kak Khee Yeung, Christoph Brune, Jelmer M. Wolterink
Date: 2025-03-05 00:00:00
Tasks: Active Learning, Deep Learning, parameter estimation, Prognosis

Hemodynamic parameters such as pressure and wall shear stress play an important role in diagnosis, prognosis, and treatment planning in cardiovascular diseases. These parameters can be accurately computed using computational fluid dynamics (CFD), but CFD is computationally intensive. Hence, deep learning methods have been adopted as a surrogate to rapidly estimate CFD outcomes. A drawback of such data-driven models is the need for time-consuming reference CFD simulations for training. In this work, we introduce an active learning framework to reduce the number of CFD simulations required for the training of surrogate models, lowering the barriers to their deployment in new applications. We propose three distinct querying strategies to determine for which unlabeled samples CFD simulations should be obtained. These querying strategies are based on geometrical variance, ensemble uncertainty, and adherence to the physics governing fluid dynamics. We benchmark these methods on velocity field estimation in synthetic coronary artery bifurcations and find that they allow for substantial reductions in annotation cost. Notably, we find that our strategies reduce the number of samples required by up to 50% and make the trained models more robust to difficult cases. Our results show that active learning is a feasible strategy to increase the potential of deep learning-based CFD surrogates.
TAAD: Time-varying adversarial anomaly detection in dynamic graphs
Authors: Guanghua Liu, Jia Zhang, Peng Lv, Chenlong Wang, Huan Wang, Di Wang
Date: 2024-10-14 00:00:00
Proceeding: Information Processing and Management 2024 10
Tasks: Anomaly Detection

The timely detection of anomalous nodes that can cause significant harm is essential in realworld networks. One challenge for anomaly detection in dynamic graphs is the identification of abnormal nodes at newly emerged moments. Unfortunately, existing methods tend to learn nontransferable features from historical moments that do not generalize well to newly emerged moments. In response to this challenge, we propose Time-varying Adversarial Anomaly Detection (TAAD), a generalizable model to learn transferable features from historical moments, which can transfer prior anomaly knowledge to newly emerged moments. It comprises four components: the feature extractor, the anomaly detector, the time-varying discriminator and the score generator. The time-varying discriminator cooperates with the feature extractor to conduct adversarial training, which decreases the distributional differences in the feature representations of nodes between historical and newly emerged moments to learn transferable features. The score generator measures the distributional differences of feature representations between normal and abnormal nodes, and further learns discriminable features. Extensive experiments conducted with four different datasets present that the proposed TAAD outperforms state-of-the-art methods.
Working memory affects motor, but not perceptual timing
Authors: MohammadAmin Farajzadeh, Mehdi Sanayei
Date: 2024-06-25 00:00:00
Proceeding: bioRxiv 2024 6

Whether different timing tasks utilize the same brain processes is still debated. To address this question, we investigated how working memory affects two different timing tasks: time reproduction and time discrimination. We found that delay intervals led to an overestimation in the reproduction task but did not introduce any bias in the perception of time in the discrimination task. Delay intervals affected the perception of time when subjects had to actively reproduce the perceived interval, but not when subjects were merely recalling the content of working memory. In subsequent Bayesian modeling, we showed that in the reproduction task, subjects updated their measurement of the stimulus on the current trial (likelihood) based on the delay interval, rather than changes in the motor system or updating priors based on the delay interval. Our findings suggest that the brain processes involved in time reproduction and discrimination are not completely overlapping, and that delay intervals in working memory tasks lead to changes in updating the likelihood. This robustness in prior and updates in likelihood provides both stability and sensitivity in the perception of time.
Can Diffusion Models Provide Rigorous Uncertainty Quantification for Bayesian Inverse Problems?
Authors: Evan Scope Crafts, Umberto Villa
Date: 2025-03-04 00:00:00
Tasks: Image Inpainting, Retrieval, Uncertainty Quantification

In recent years, the ascendance of diffusion modeling as a state-of-the-art generative modeling approach has spurred significant interest in their use as priors in Bayesian inverse problems. However, it is unclear how to optimally integrate a diffusion model trained on the prior distribution with a given likelihood function to obtain posterior samples. While algorithms that have been developed for this purpose can produce high-quality, diverse point estimates of the unknown parameters of interest, they are often tested on problems where the prior distribution is analytically unknown, making it difficult to assess their performance in providing rigorous uncertainty quantification. In this work, we introduce a new framework, Bayesian Inverse Problem Solvers through Diffusion Annealing (BIPSDA), for diffusion model based posterior sampling. The framework unifies several recently proposed diffusion model based posterior sampling algorithms and contains novel algorithms that can be realized through flexible combinations of design choices. Algorithms within our framework were tested on model problems with a Gaussian mixture prior and likelihood functions inspired by problems in image inpainting, x-ray tomography, and phase retrieval. In this setting, approximate ground-truth posterior samples can be obtained, enabling principled evaluation of the performance of the algorithms. The results demonstrate that BIPSDA algorithms can provide strong performance on the image inpainting and x-ray tomography based problems, while the challenging phase retrieval problem, which is difficult to sample from even when the posterior density is known, remains outside the reach of the diffusion model based samplers.
Addressing Overprescribing Challenges: Fine-Tuning Large Language Models for Medication Recommendation Tasks
Authors: Zihao Zhao, Chenxiao Fan, Chongming Gao, Fuli Feng, Xiangnan He
Date: 2025-03-05 00:00:00
Tasks: Medical Question Answering, parameter-efficient fine-tuning, Question Answering, Recommendation Systems

Medication recommendation systems have garnered attention within healthcare for their potential to deliver personalized and efficacious drug combinations based on patient's clinical data. However, existing methodologies encounter challenges in adapting to diverse Electronic Health Records (EHR) systems and effectively utilizing unstructured data, resulting in limited generalization capabilities and suboptimal performance. Recently, interest is growing in harnessing Large Language Models (LLMs) in the medical domain to support healthcare professionals and enhance patient care. Despite the emergence of medical LLMs and their promising results in tasks like medical question answering, their practical applicability in clinical settings, particularly in medication recommendation, often remains underexplored. In this study, we evaluate both general-purpose and medical-specific LLMs for medication recommendation tasks. Our findings reveal that LLMs frequently encounter the challenge of overprescribing, leading to heightened clinical risks and diminished medication recommendation accuracy. To address this issue, we propose Language-Assisted Medication Recommendation (LAMO), which employs a parameter-efficient fine-tuning approach to tailor open-source LLMs for optimal performance in medication recommendation scenarios. LAMO leverages the wealth of clinical information within clinical notes, a resource often underutilized in traditional methodologies. As a result of our approach, LAMO outperforms previous state-of-the-art methods by over 10% in internal validation accuracy. Furthermore, temporal and external validations demonstrate LAMO's robust generalization capabilities across various temporal and hospital contexts. Additionally, an out-of-distribution medication recommendation experiment demonstrates LAMO's remarkable accuracy even with medications outside the training data.
AHCPTQ: Accurate and Hardware-Compatible Post-Training Quantization for Segment Anything Model
Authors: Wenlun Zhang, Shimpei Ando, Kentaro Yoshioka
Date: 2025-03-05 00:00:00
Tasks: Instance Segmentation, Quantization, Semantic Segmentation

The Segment Anything Model (SAM) has demonstrated strong versatility across various visual tasks. However, its large storage requirements and high computational cost pose challenges for practical deployment. Post-training quantization (PTQ) has emerged as an effective strategy for efficient deployment, but we identify two key challenges in SAM that hinder the effectiveness of existing PTQ methods: the heavy-tailed and skewed distribution of post-GELU activations, and significant inter-channel variation in linear projection activations. To address these challenges, we propose AHCPTQ, an accurate and hardware-efficient PTQ method for SAM. AHCPTQ introduces hardware-compatible Hybrid Log-Uniform Quantization (HLUQ) to manage post-GELU activations, employing log2 quantization for dense small values and uniform quantization for sparse large values to enhance quantization resolution. Additionally, AHCPTQ incorporates Channel-Aware Grouping (CAG) to mitigate inter-channel variation by progressively clustering activation channels with similar distributions, enabling them to share quantization parameters and improving hardware efficiency. The combination of HLUQ and CAG not only enhances quantization effectiveness but also ensures compatibility with efficient hardware execution. For instance, under the W4A4 configuration on the SAM-L model, AHCPTQ achieves 36.6% mAP on instance segmentation with the DINO detector, while achieving a 7.89x speedup and 8.64x energy efficiency over its floating-point counterpart in FPGA implementation.
Enabling Fast, Accurate, and Efficient Real-Time Genome Analysis via New Algorithms and Techniques
Authors: Can Firtina
Date: 2025-03-04 00:00:00
Tasks: Computational Efficiency

The advent of high-throughput sequencing technologies has revolutionized genome analysis by enabling the rapid and cost-effective sequencing of large genomes. Despite these advancements, the increasing complexity and volume of genomic data present significant challenges related to accuracy, scalability, and computational efficiency. These challenges are mainly due to various forms of unwanted and unhandled variations in sequencing data, collectively referred to as noise. In this dissertation, we address these challenges by providing a deep understanding of different types of noise in genomic data and developing techniques to mitigate the impact of noise on genome analysis. First, we introduce BLEND, a noise-tolerant hashing mechanism that quickly identifies both exactly matching and highly similar sequences with arbitrary differences using a single lookup of their hash values. Second, to enable scalable and accurate analysis of noisy raw nanopore signals, we propose RawHash, a novel mechanism that effectively reduces noise in raw nanopore signals and enables accurate, real-time analysis by proposing the first hash-based similarity search technique for raw nanopore signals. Third, we extend the capabilities of RawHash with RawHash2, an improved mechanism that 1) provides a better understanding of noise in raw nanopore signals to reduce it more effectively and 2) improves the robustness of mapping decisions. Fourth, we explore the broader implications and new applications of raw nanopore signal analysis by introducing Rawsamble, the first mechanism for all-vs-all overlapping of raw signals using hash-based search. Rawsamble enables the construction of de novo assemblies directly from raw signals without basecalling, which opens up new directions and uses for raw nanopore signal analysis.
A Novel Multi-Criteria Local Latin Hypercube Refinement System for Commutation Angle Improvement in IPMSMs
Authors: Pedram Asef, Mouloud Denai, Johannes J. H. Paulides, Bruno Ricardo Marques, Andrew Lapthorn
Date: 2025-03-05 00:00:00

The commutation angle is defined as the angle between the fundamental of the motor phase current and the fundamental of the back-EMF. It can be utilised to provide a compensating effect in IPMSMs. This is due to the reluctance torque component being dependent on the commutation angle of the phase current even before entering the extended speed range. A real-time maximum torque per current and voltage strategy is demonstrated to find the trajectory and optimum commutation angles, gamma, where the level of accuracy depends on the application and available computational speed. A magnet volume reduction using a novel multi-criteria local Latin hypercube refinement (MLHR) sampling system is also presented to improve the optimisation process. The proposed new technique minimises the magnet mass to motor torque density whilst maintaining a similar phase current level. A mapping of gamma allows the determination of the optimum angles, as shown in this paper. The 3rd generation Toyota Prius IPMSM is considered as the reference motor, where the rotor configuration is altered to allow for an individual assessment.
Navigating Intelligence: A Survey of Google OR-Tools and Machine Learning for Global Path Planning in Autonomous Vehicles
Authors: Alexandre Benoit, Pedram Asef
Date: 2025-03-05 00:00:00
Tasks: Autonomous Vehicles, Q-Learning, Traveling Salesman Problem

We offer a new in-depth investigation of global path planning (GPP) for unmanned ground vehicles, an autonomous mining sampling robot named ROMIE. GPP is essential for ROMIE's optimal performance, which is translated into solving the traveling salesman problem, a complex graph theory challenge that is crucial for determining the most effective route to cover all sampling locations in a mining field. This problem is central to enhancing ROMIE's operational efficiency and competitiveness against human labor by optimizing cost and time. The primary aim of this research is to advance GPP by developing, evaluating, and improving a cost-efficient software and web application. We delve into an extensive comparison and analysis of Google operations research (OR)-Tools optimization algorithms. Our study is driven by the goal of applying and testing the limits of OR-Tools capabilities by integrating Reinforcement Learning techniques for the first time. This enables us to compare these methods with OR-Tools, assessing their computational effectiveness and real-world application efficiency. Our analysis seeks to provide insights into the effectiveness and practical application of each technique. Our findings indicate that Q-Learning stands out as the optimal strategy, demonstrating superior efficiency by deviating only 1.2% on average from the optimal solutions across our datasets.
A Predict-Then-Optimize Customer Allocation Framework for Online Fund Recommendation
Authors: Xing Tang, Yunpeng Weng, Fuyuan Lyu, Dugang Liu, Xiuqiang He
Date: 2025-03-05 00:00:00

With the rapid growth of online investment platforms, funds can be distributed to individual customers online. The central issue is to match funds with potential customers under constraints. Most mainstream platforms adopt the recommendation formulation to tackle the problem. However, the traditional recommendation regime has its inherent drawbacks when applying the fund-matching problem with multiple constraints. In this paper, we model the fund matching under the allocation formulation. We design PTOFA, a Predict-Then-Optimize Fund Allocation framework. This data-driven framework consists of two stages, i.e., prediction and optimization, which aim to predict expected revenue based on customer behavior and optimize the impression allocation to achieve the maximum revenue under the necessary constraints, respectively. Extensive experiments on real-world datasets from an industrial online investment platform validate the effectiveness and efficiency of our solution. Additionally, the online A/B tests demonstrate PTOFA's effectiveness in the real-world fund recommendation scenario.