pwc-archive/papers-with-abstracts huggingface dataset:
A Comprehensive Survey on Image Signal Processing Approaches for Low-Illumination Image Enhancement
Authors: Muhammad Turab
Date: 2025-02-09 00:00:00
Tasks: Deep Learning, Denoising, Image Enhancement

The usage of digital content (photos and videos) in a variety of applications has increased due to the popularity of multimedia devices. These uses include advertising campaigns, educational resources, and social networking platforms. There is an increasing need for high-quality graphic information as people become more visually focused. However, captured images frequently have poor visibility and a high amount of noise due to the limitations of image-capturing devices and lighting conditions. Improving the visual quality of images taken in low illumination is the aim of low-illumination image enhancement. This problem is addressed by traditional image enhancement techniques, which alter noise, brightness, and contrast. Deep learning-based methods, however, have dominated recently made advances in this area. These methods have effectively reduced noise while preserving important information, showing promising results in the improvement of low-illumination images. An extensive summary of image signal processing methods for enhancing low-illumination images is provided in this paper. Three categories are classified in the review for approaches: hybrid techniques, deep learning-based methods, and traditional approaches. Conventional techniques include denoising, automated white balancing, and noise reduction. Convolutional neural networks (CNNs) are used in deep learningbased techniques to recognize and extract characteristics from low-light images. To get better results, hybrid approaches combine deep learning-based methodologies with more conventional methods. The review also discusses the advantages and limitations of each approach and provides insights into future research directions in this field.
GSCE: A Prompt Framework with Enhanced Reasoning for Reliable LLM-driven Drone Control
Authors: Wenhao Wang, Yanyan Li, Long Jiao, Jiawei Yuan
Date: 2025-02-18 00:00:00
Tasks: Code Generation

The integration of Large Language Models (LLMs) into robotic control, including drones, has the potential to revolutionize autonomous systems. Research studies have demonstrated that LLMs can be leveraged to support robotic operations. However, when facing tasks with complex reasoning, concerns and challenges are raised about the reliability of solutions produced by LLMs. In this paper, we propose a prompt framework with enhanced reasoning to enable reliable LLM-driven control for drones. Our framework consists of novel technical components designed using Guidelines, Skill APIs, Constraints, and Examples, namely GSCE. GSCE is featured by its reliable and constraint-compliant code generation. We performed thorough experiments using GSCE for the control of drones with a wide level of task complexities. Our experiment results demonstrate that GSCE can significantly improve task success rates and completeness compared to baseline approaches, highlighting its potential for reliable LLM-driven autonomous drone systems.
Minimizing Instability in Strategy-Proof Matching Mechanism Using A Linear Programming Approach
Authors: Tohya Sugano
Date: 2025-02-18 00:00:00
Tasks: Blocking

In this paper we address the design of matching mechanisms that are strategy-proof and simultaneously as stable as possible. Building on the impossibility result by \cite{Roth1982-cl} for one-to-one matching problems, we formulate an optimization problem that maximizes stability under the constraint of strategy-proofness. In our model the objective is to minimize the degree of instability measured as the sum (or worst-case maximum) of stability violations over all preference profiles. We further introduce the socially important properties of anonymity and symmetry into the formulation. Our computational results show that, for small markets, our optimization approach leads to mechanisms with substantially lower stability violations than RSD. In particular, the optimal mechanism under our formulation exhibits roughly one-third the stability violation of RSD. For deterministic mechanisms in the three-agent case, we also find that any strategy-proof mechanism hvae at least two blocking pairs at the worst case, and we propose an algorithm that attains this lower bound. Finally, we discuss extensions to larger markets and present simulation evidence that our mechanism yields a reduction of approximately $0.25$ blocking pairs on average compared to SD mechanism.
PUGS: Zero-shot Physical Understanding with Gaussian Splatting
Authors: Yinghao Shuai, Ran Yu, Yuantao Chen, Zijian Jiang, Xiaowei Song, Nan Wang, Jv Zheng, Jianzhu Ma, Meng Yang, Zhicheng Wang, Wenbo Ding, Hao Zhao
Date: 2025-02-17 00:00:00
Tasks: Friction

Current robotic systems can understand the categories and poses of objects well. But understanding physical properties like mass, friction, and hardness, in the wild, remains challenging. We propose a new method that reconstructs 3D objects using the Gaussian splatting representation and predicts various physical properties in a zero-shot manner. We propose two techniques during the reconstruction phase: a geometry-aware regularization loss function to improve the shape quality and a region-aware feature contrastive loss function to promote region affinity. Two other new techniques are designed during inference: a feature-based property propagation module and a volume integration module tailored for the Gaussian representation. Our framework is named as zero-shot physical understanding with Gaussian splatting, or PUGS. PUGS achieves new state-of-the-art results on the standard benchmark of ABO-500 mass prediction. We provide extensive quantitative ablations and qualitative visualization to demonstrate the mechanism of our designs. We show the proposed methodology can help address challenging real-world grasping tasks. Our codes, data, and models are available at https://github.com/EverNorif/PUGS
Leveraging large language models for structured information extraction from pathology reports
Authors: Jeya Balaji Balasubramanian, Daniel Adams, Ioannis Roxanis, Amy Berrington de Gonzalez, Penny Coulson, Jonas S. Almeida, Montserrat García-Closas
Date: 2025-02-14 00:00:00

Background: Structured information extraction from unstructured histopathology reports facilitates data accessibility for clinical research. Manual extraction by experts is time-consuming and expensive, limiting scalability. Large language models (LLMs) offer efficient automated extraction through zero-shot prompting, requiring only natural language instructions without labeled data or training. We evaluate LLMs' accuracy in extracting structured information from breast cancer histopathology reports, compared to manual extraction by a trained human annotator. Methods: We developed the Medical Report Information Extractor, a web application leveraging LLMs for automated extraction. We developed a gold standard extraction dataset to evaluate the human annotator alongside five LLMs including GPT-4o, a leading proprietary model, and the Llama 3 model family, which allows self-hosting for data privacy. Our assessment involved 111 histopathology reports from the Breast Cancer Now (BCN) Generations Study, extracting 51 pathology features specified in the study's data dictionary. Results: Evaluation against the gold standard dataset showed that both Llama 3.1 405B (94.7% accuracy) and GPT-4o (96.1%) achieved extraction accuracy comparable to the human annotator (95.4%; p = 0.146 and p = 0.106, respectively). While Llama 3.1 70B (91.6%) performed below human accuracy (p <0.001), its reduced computational requirements make it a viable option for self-hosting. Conclusion: We developed an open-source tool for structured information extraction that can be customized by non-programmers using natural language. Its modular design enables reuse for various extraction tasks, producing standardized, structured data from unstructured text reports to facilitate analytics through improved accessibility and interoperability.
Positional Encoding in Transformer-Based Time Series Models: A Survey
Authors: Habib Irani, Vangelis Metsis
Date: 2025-02-17 00:00:00
Tasks: Anomaly Detection, Benchmarking, Survey, Time Series, Time Series Analysis, Time Series Classification

Recent advancements in transformer-based models have greatly improved time series analysis, providing robust solutions for tasks such as forecasting, anomaly detection, and classification. A crucial element of these models is positional encoding, which allows transformers to capture the intrinsic sequential nature of time series data. This survey systematically examines existing techniques for positional encoding in transformer-based time series models. We investigate a variety of methods, including fixed, learnable, relative, and hybrid approaches, and evaluate their effectiveness in different time series classification tasks. Furthermore, we outline key challenges and suggest potential research directions to enhance positional encoding strategies. By delivering a comprehensive overview and quantitative benchmarking, this survey intends to assist researchers and practitioners in selecting and designing effective positional encoding methods for transformer-based time series models.
Label Drop for Multi-Aspect Relation Modeling in Universal Information Extraction
Authors: Lu Yang, Jiajia Li, En Ci, Lefei Zhang, Zuchao Li, Ping Wang
Date: 2025-02-18 00:00:00
Tasks: Relation, Relation Extraction, UIE

Universal Information Extraction (UIE) has garnered significant attention due to its ability to address model explosion problems effectively. Extractive UIE can achieve strong performance using a relatively small model, making it widely adopted. Extractive UIEs generally rely on task instructions for different tasks, including single-target instructions and multiple-target instructions. Single-target instruction UIE enables the extraction of only one type of relation at a time, limiting its ability to model correlations between relations and thus restricting its capability to extract complex relations. While multiple-target instruction UIE allows for the extraction of multiple relations simultaneously, the inclusion of irrelevant relations introduces decision complexity and impacts extraction accuracy. Therefore, for multi-relation extraction, we propose LDNet, which incorporates multi-aspect relation modeling and a label drop mechanism. By assigning different relations to different levels for understanding and decision-making, we reduce decision confusion. Additionally, the label drop mechanism effectively mitigates the impact of irrelevant relations. Experiments show that LDNet outperforms or achieves competitive performance with state-of-the-art systems on 9 tasks, 33 datasets, in both single-modal and multi-modal, few-shot and zero-shot settings.\footnote{https://github.com/Lu-Yang666/LDNet}
How does a Language-Specific Tokenizer affect LLMs?
Authors: Jean Seo, Jaeyoon Kim, Sungjoo Byun, Hyopil Shin
Date: 2025-02-18 00:00:00

The necessity of language-specific tokenizers intuitively appears crucial for effective natural language processing, yet empirical analyses on their significance and underlying reasons are lacking. This study explores how language-specific tokenizers influence the behavior of Large Language Models predominantly trained with English text data, through the case study of Korean. The research unfolds in two main stages: (1) the development of a Korean-specific extended tokenizer and (2) experiments to compare models with the basic tokenizer and the extended tokenizer through various Next Token Prediction tasks. Our in-depth analysis reveals that the extended tokenizer decreases confidence in incorrect predictions during generation and reduces cross-entropy in complex tasks, indicating a tendency to produce less nonsensical outputs. Consequently, the extended tokenizer provides stability during generation, potentially leading to higher performance in downstream tasks.
FrGNet: A fourier-guided weakly-supervised framework for nuclear instance segmentation
Authors: Peng Ling, Wenxiao Xiong
Date: 2025-02-14 00:00:00
Tasks: Instance Segmentation, Nuclear Segmentation, Semantic Segmentation

Nuclear instance segmentation has played a critical role in pathology image analysis. The main challenges arise from the difficulty in accurately segmenting instances and the high cost of precise mask-level annotations for fully-supervised training.In this work, we propose a fourier guidance framework for solving the weakly-supervised nuclear instance segmentation problem. In this framework, we construct a fourier guidance module to fuse the priori information into the training process of the model, which facilitates the model to capture the relevant features of the nuclear. Meanwhile, in order to further improve the model's ability to represent the features of nuclear, we propose the guide-based instance level contrastive module. This module makes full use of the framework's own properties and guide information to effectively enhance the representation features of nuclear. We show on two public datasets that our model can outperform current SOTA methods under fully-supervised design, and in weakly-supervised experiments, with only a small amount of labeling our model still maintains close to the performance under full supervision.In addition, we also perform generalization experiments on a private dataset, and without any labeling, our model is able to segment nuclear images that have not been seen during training quite effectively. As open science, all codes and pre-trained models are available at https://github.com/LQY404/FrGNet.
Towards Transparent and Accurate Plasma State Monitoring at JET
Authors: Andrin Bürli, Alessandro Pau, Thomas Koller, Olivier Sauter, JET Contributors
Date: 2025-02-14 00:00:00
Tasks: Feature Importance, Multi-Task Learning

Controlling and monitoring plasma within a tokamak device is complex and challenging. Plasma off-normal events, such as disruptions, are hindering steady-state operation. For large devices, they can even endanger the machine's integrity and it represents in general one of the most serious concerns for the exploitation of the tokamak concept for future power plants. Effective plasma state monitoring carries the potential to enable an understanding of such phenomena and their evolution which is crucial for the successful operation of tokamaks. This paper presents the application of a transparent and data-driven methodology to monitor the plasma state in a tokamak. Compared to previous studies in the field, supervised and unsupervised learning techniques are combined. The dataset consisted of 520 expert-validated discharges from JET. The goal was to provide an interpretable plasma state representation for the JET operational space by leveraging multi-task learning for the first time in the context of plasma state monitoring. When evaluated as disruption predictors, a sequence-based approach showed significant improvements compared to the state-based models. The best resulting network achieved a promising cross-validated success rate when combined with a physical indicator and accounting for nearby instabilities. Qualitative evaluations of the learned latent space uncovered operational and disruptive regions as well as patterns related to learned dynamics and global feature importance. The applied methodology provides novel possibilities for the definition of triggers to switch between different control scenarios, data analysis, and learning as well as exploring latent dynamics for plasma state monitoring. It also showed promising quantitative and qualitative results with warning times suitable for avoidance purposes and distributions that are consistent with known physical mechanisms.
Data-Efficient Limited-Angle CT Using Deep Priors and Regularization
Authors: Ilmari Vahteristo, Zhi-Song Liu, Andreas Rupp
Date: 2025-02-17 00:00:00
Tasks: Computed Tomography (CT)

Reconstructing an image from its Radon transform is a fundamental computed tomography (CT) task arising in applications such as X-ray scans. In many practical scenarios, a full 180-degree scan is not feasible, or there is a desire to reduce radiation exposure. In these limited-angle settings, the problem becomes ill-posed, and methods designed for full-view data often leave significant artifacts. We propose a very low-data approach to reconstruct the original image from its Radon transform under severe angle limitations. Because the inverse problem is ill-posed, we combine multiple regularization methods, including Total Variation, a sinogram filter, Deep Image Prior, and a patch-level autoencoder. We use a differentiable implementation of the Radon transform, which allows us to use gradient-based techniques to solve the inverse problem. Our method is evaluated on a dataset from the Helsinki Tomography Challenge 2022, where the goal is to reconstruct a binary disk from its limited-angle sinogram. We only use a total of 12 data points--eight for learning a prior and four for hyperparameter selection--and achieve results comparable to the best synthetic data-driven approaches.
Probabilistic neural operators for functional uncertainty quantification
Authors: Christopher Bülte, Philipp Scholl, Gitta Kutyniok
Date: 2025-02-18 00:00:00
Tasks: Uncertainty Quantification, Weather Forecasting

Neural operators aim to approximate the solution operator of a system of differential equations purely from data. They have shown immense success in modeling complex dynamical systems across various domains. However, the occurrence of uncertainties inherent in both model and data has so far rarely been taken into account\textemdash{}a critical limitation in complex, chaotic systems such as weather forecasting. In this paper, we introduce the probabilistic neural operator (PNO), a framework for learning probability distributions over the output function space of neural operators. PNO extends neural operators with generative modeling based on strictly proper scoring rules, integrating uncertainty information directly into the training process. We provide a theoretical justification for the approach and demonstrate improved performance in quantifying uncertainty across different domains and with respect to different baselines. Furthermore, PNO requires minimal adjustment to existing architectures, shows improved performance for most probabilistic prediction tasks, and leads to well-calibrated predictive distributions and adequate uncertainty representations even for long dynamical trajectories. Implementing our approach into large-scale models for physical applications can lead to improvements in corresponding uncertainty quantification and extreme event identification, ultimately leading to a deeper understanding of the prediction of such surrogate models.
Dimension reduction methods, persistent homology and machine learning for EEG signal analysis of Interictal Epileptic Discharges
Authors: Annika Stiehl, Stefan Geißelsöder, Nicole Ille, Fabienne Anselstetter, Harald Bornfleth, Christian Uhl
Date: 2025-02-18 00:00:00
Tasks: Dimensionality Reduction, EEG, Topological Data Analysis

Recognizing specific events in medical data requires trained personnel. To aid the classification, machine learning algorithms can be applied. In this context, medical records are usually high-dimensional, although a lower dimension can also reflect the dynamics of the signal. In this study, electroencephalogram data with Interictal Epileptic Discharges (IEDs) are investigated. First, the dimensions are reduced using Dynamical Component Analysis (DyCA) and Principal Component Analysis (PCA), respectively. The reduced data are examined using topological data analysis (TDA), specifically using a persistent homology algorithm. The persistent homology results are used for targeted feature generation. The features are used to train and evaluate a Support Vector Machine (SVM) to distinguish IEDs from background activities.
X-IL: Exploring the Design Space of Imitation Learning Policies
Authors: Xiaogang Jia, Atalay Donat, Xi Huang, Xuan Zhao, Denis Blessing, Hongyi Zhou, Han A. Wang, Hanyi Zhang, Qian Wang, Rudolf Lioutikov, Gerhard Neumann
Date: 2025-02-17 00:00:00
Tasks: Imitation Learning, Mamba

Designing modern imitation learning (IL) policies requires making numerous decisions, including the selection of feature encoding, architecture, policy representation, and more. As the field rapidly advances, the range of available options continues to grow, creating a vast and largely unexplored design space for IL policies. In this work, we present X-IL, an accessible open-source framework designed to systematically explore this design space. The framework's modular design enables seamless swapping of policy components, such as backbones (e.g., Transformer, Mamba, xLSTM) and policy optimization techniques (e.g., Score-matching, Flow-matching). This flexibility facilitates comprehensive experimentation and has led to the discovery of novel policy configurations that outperform existing methods on recent robot learning benchmarks. Our experiments demonstrate not only significant performance gains but also provide valuable insights into the strengths and weaknesses of various design choices. This study serves as both a practical reference for practitioners and a foundation for guiding future research in imitation learning.
Simulation of roof snow loads based on a multi-layer snowmelt model: Impact of building heat transfer
Authors: Zhou Xuanyi, Chen Heng, Wu Yue, Zhang Tiange
Date: 2024-04-16 00:00:00
Proceeding: Building Simulation 2024 4

To investigate the impact of building heat transfer on roof snow loads, roof snow loads and snow  load thermal coefficients from 61 Chinese sites over a period of 50 years are simulated based on  basic meteorological data such as temperature, humidity, wind speed, and precipitation, and a  multi-layer snowmelt model considering the building heat transfer. Firstly, the accuracy of the  multi-layer snowmelt model is validated using the data of observed ground snow load and roof  snow melting tests. The relationship between meteorological conditions, snow cover characteristics,  and thermal coefficients of snow loads in three representative sites is then studied. Furthermore,  the characteristics of thermal coefficients in each zone are analyzed by combining them with the  statistical results of meteorological data from 1960 to 2010, and the equations of thermal coefficients  in different zones on indoor temperatures and roof heat transfer coefficients are fitted separately.  Finally, the equations in this paper are compared with the thermal coefficients in the main snow  load codes. The results indicate that the snowmelt model using basic meteorological data can  effectively provide samples of roof snow loads. In the cold zone where the snow cover lasts for a  long time and does not melt easily, the thermal coefficients of the snow loads on the heating  buildings are lower than those in the warm zone due to the long-term influence of the heat from  inside the buildings. Thermal coefficients are negatively correlated with indoor temperatures and  roof heat transfer coefficients. When the indoor temperature is too low or the roof insulation is  good, the roof snow load may exceed the ground snow load. The thermal coefficients for heated  buildings in the main snow load codes are more conservative than those calculated in this paper,  and the thermal coefficients for buildings with lower indoor temperatures tend to be smaller.