pwc-archive/papers-with-abstracts huggingface dataset:
Economy and sustainability analysis with a novel modular configurable multi-modal white-box building model
Authors: Haozhen Cheng, Veit Hagenmeyer, Hüseyin K. Çakmak
Date: 2025-03-27 00:00:00

This paper presents a novel modeling approach for building performance simulation, characterized as a white-box model with a high degree of modularity and flexibility, enabling direct integration into complex large-scale energy system co-simulations. The introduced model is described in detail, with a focus on its modular structure, and proposes various configurations that include various building insulation, heating methods, occupancy patterns, and weather data to analyze different scenarios, and the energy consumption, CO2 emissions, and heating costs are compared and analyzed across 36 introduced scenarios. The thermodynamic behavior of the model is shown to be consistent with real-world conditions, and the comparison of the scenarios concludes that the use of heat pumps for indoor heating in well-insulated buildings has significant economic and sustainability benefits, whereas the use of natural gas-fueled boilers is more cost-effective for buildings with low energy ratings.
ProHOC: Probabilistic Hierarchical Out-of-Distribution Classification via Multi-Depth Networks
Authors: Erik Wallin, Fredrik Kahl, Lars Hammarstrand
Date: 2025-03-27 00:00:00
Proceeding: CVPR 2025 1
Tasks: Out of Distribution (OOD) Detection

Out-of-distribution (OOD) detection in deep learning has traditionally been framed as a binary task, where samples are either classified as belonging to the known classes or marked as OOD, with little attention given to the semantic relationships between OOD samples and the in-distribution (ID) classes. We propose a framework for detecting and classifying OOD samples in a given class hierarchy. Specifically, we aim to predict OOD data to their correct internal nodes of the class hierarchy, whereas the known ID classes should be predicted as their corresponding leaf nodes. Our approach leverages the class hierarchy to create a probabilistic model and we implement this model by using networks trained for ID classification at multiple hierarchy depths. We conduct experiments on three datasets with predefined class hierarchies and show the effectiveness of our method. Our code is available at https://github.com/walline/prohoc.
AUTOBargeSim: MATLAB(R) toolbox for the design and analysis of the guidance and control system for autonomous inland vessels
Authors: Abhishek Dhyani, Amirreza Haqshenas Mojaveri, Chengqian Zhang, Dhanika Mahipala, Hoang Anh Tran, Yan-Yun Zhang, Zhongbi Luo, Vasso Reppa
Date: 2025-03-27 00:00:00
Tasks: Collision Avoidance

This paper introduces AUTOBargeSim, a simulation toolbox for autonomous inland vessel guidance and control system design. AUTOBargeSim is developed using MATLAB and provides an easy-to-use introduction to various aspects of autonomous inland navigation, including mapping, modelling, control design, and collision avoidance, through examples and extensively documented code. Applying modular design principles in the simulator structure allows it to be easily modified according to the user's requirements. Furthermore, a GUI interface facilitates a simple and quick execution. Key performance indices for evaluating the performance of the controller and collision avoidance method in confined space are also provided. The current version of AUTOBargeSim attempts to improve reproducibility in the design and simulation of marine systems while serving as a foundation for simulating and evaluating vessel behaviour considering operational, system, and environmental constraints.
Unsupervised Real-World Denoising: Sparsity is All You Need
Authors: Hamadi Chihaoui, Paolo Favaro
Date: 2025-03-27 00:00:00
Tasks: All, Denoising

Supervised training for real-world denoising presents challenges due to the difficulty of collecting large datasets of paired noisy and clean images. Recent methods have attempted to address this by utilizing unpaired datasets of clean and noisy images. Some approaches leverage such unpaired data to train denoisers in a supervised manner by generating synthetic clean-noisy pairs. However, these methods often fall short due to the distribution gap between synthetic and real noisy images. To mitigate this issue, we propose a solution based on input sparsification, specifically using random input masking. Our method, which we refer to as Mask, Inpaint and Denoise (MID), trains a denoiser to simultaneously denoise and inpaint synthetic clean-noisy pairs. On one hand, input sparsification reduces the gap between synthetic and real noisy images. On the other hand, an inpainter trained in a supervised manner can still accurately reconstruct sparse inputs by predicting missing clean pixels using the remaining unmasked pixels. Our approach begins with a synthetic Gaussian noise sampler and iteratively refines it using a noise dataset derived from the denoiser's predictions. The noise dataset is created by subtracting predicted pseudo-clean images from real noisy images at each iteration. The core intuition is that improving the denoiser results in a more accurate noise dataset and, consequently, a better noise sampler. We validate our method through extensive experiments on real-world noisy image datasets, demonstrating competitive performance compared to existing unsupervised denoising methods.
Multimodal surface defect detection from wooden logs for sawing optimization
Authors: Bořek Reich, Matej Kunda, Fedor Zolotarev, Tuomas Eerola, Pavel Zemčík, Tomi Kauppi
Date: 2025-03-27 00:00:00
Tasks: Defect Detection

We propose a novel, good-quality, and less demanding method for detecting knots on the surface of wooden logs using multimodal data fusion. Knots are a primary factor affecting the quality of sawn timber, making their detection fundamental to any timber grading or cutting optimization system. While X-ray computed tomography provides accurate knot locations and internal structures, it is often too slow or expensive for practical use. An attractive alternative is to use fast and cost-effective log surface measurements, such as laser scanners or RGB cameras, to detect surface knots and estimate the internal structure of wood. However, due to the small size of knots and noise caused by factors, such as bark and other natural variations, detection accuracy often remains low when only one measurement modality is used. In this paper, we demonstrate that by using a data fusion pipeline consisting of separate streams for RGB and point cloud data, combined by a late fusion module, higher knot detection accuracy can be achieved compared to using either modality alone. We further propose a simple yet efficient sawing angle optimization method that utilizes surface knot detections and cross-correlation to minimize the amount of unwanted arris knots, demonstrating its benefits over randomized sawing angles.
Real-time Tracking System with Partially Coupled Sources
Authors: Saeid Sadeghi Vilni, Risto Wichman
Date: 2025-03-27 00:00:00

We consider a pull-based real-time tracking system consisting of multiple partially coupled sources and a sink. The sink monitors the sources in real-time and can request one source for an update at each time instant. The sources send updates over an unreliable wireless channel. The sources are partially coupled, and updates about one source can provide partial knowledge about other sources. We study the problem of minimizing the sum of an average distortion function and a transmission cost. Since the controller is at the sink side, the controller (sink) has only partial knowledge about the source states, and thus, we model the problem as a partially observable Markov decision process (POMDP) and then cast it as a belief-MDP problem. Using the relative value iteration algorithm, we solve the problem and propose a control policy. Simulation results show the proposed policy's effectiveness and superiority compared to a baseline policy.
Combining Graph Attention Networks and Distributed Optimization for Multi-Robot Mixed-Integer Convex Programming
Authors: Viet-Anh Le, Panagiotis Kounatidis, Andreas A. Malikopoulos
Date: 2025-03-27 00:00:00
Tasks: Distributed Optimization, Graph Attention, Motion Planning, Robot Navigation

In this paper, we develop a fast mixed-integer convex programming (MICP) framework for multi-robot navigation by combining graph attention networks and distributed optimization. We formulate a mixed-integer optimization problem for receding horizon motion planning of a multi-robot system, taking into account the surrounding obstacles. To address the resulting multi-agent MICP problem in real time, we propose a framework that utilizes heterogeneous graph attention networks to learn the latent mapping from problem parameters to optimal binary solutions. Furthermore, we apply a distributed proximal alternating direction method of multipliers algorithm for solving the convex continuous optimization problem. We demonstrate the effectiveness of our proposed framework through experiments conducted on a robotic testbed.
DuckSegmentation: A segmentation model based on the AnYue Hemp Duck Dataset
Authors: Ling Feng, Tianyu Xie, Wei Ma, Ruijie Fu, Yingxiao Zhang, Jun Li, Bei Zhou
Date: 2025-03-27 00:00:00
Tasks: Knowledge Distillation, Object Recognition, Segmentation

The modernization of smart farming is a way to improve agricultural production efficiency, and improve the agricultural production environment. Although many large models have achieved high accuracy in the task of object recognition and segmentation, they cannot really be put into use in the farming industry due to their own poor interpretability and limitations in computational volume. In this paper, we built AnYue Shelduck Dateset, which contains a total of 1951 Shelduck datasets, and performed target detection and segmentation annotation with the help of professional annotators. Based on AnYue ShelduckDateset, this paper describes DuckProcessing, an efficient and powerful module for duck identification based on real shelduckfarms. First of all, using the YOLOv8 module designed to divide the mahjong between them, Precision reached 98.10%, Recall reached 96.53% and F1 score reached 0.95 on the test set. Again using the DuckSegmentation segmentation model, DuckSegmentation reached 96.43% mIoU. Finally, the excellent DuckSegmentation was used as the teacher model, and through knowledge distillation, Deeplabv3 r50 was used as the student model, and the final student model achieved 94.49% mIoU on the test set. The method provides a new way of thinking in practical sisal duck smart farming.
consexpressionR: an R package for consensus differential gene expression analysis
Authors: Juliana Costa-Silva, David Menotti, Fabricio M. Lopes
Date: 2025-03-27 00:00:00

Motivation: Bulk RNA-Seq is a widely used method for studying gene expression across a variety of contexts. The significance of RNA-Seq studies has grown with the advent of high-throughput sequencing technologies. Computational methods have been developed for each stage of the identification of differentially expressed genes. Nevertheless, there are few studies exploring the association between different types of methods. In this study, we evaluated the impact of the association of methodologies in the results of differential expression analysis. By adopting two data sets with qPCR data (to gold-standard reference), seven methods were implemented and assessed in R packages (EBSeq, edgeR, DESeq2, limma, SAMseq, NOISeq, and Knowseq), which was performed and assessed separately and in association. The results were evaluated considering the adopted qPCR data. Results: Here, we introduce consexpressionR, an R package that automates differential expression analysis using consensus of at least seven methodologies, producing more assertive results with a significant reduction in false positives. Availability: consexpressionR is an R package available via source code and support are available at GitHub (https://github.com/costasilvati/consexpressionR).
Physics-Informed Neural Network-Based Control for Grid-Forming Converter's Stability Under Overload Conditions
Authors: Abhay Kumar, Dushyant Sharma, Mayukha Pal
Date: 2025-03-27 00:00:00

Grid-forming converters (GFCs) are crucial for frequency and voltage stability in modern power systems. However, their performance under overload conditions remains a challenge. This paper highlights the limitations of existing approaches in managing DC source saturation and AC current limits, emphasizing the need for improved control strategies to ensure system stability. This paper proposes a control strategy based on a physics-informed neural network (PINN) to improve GFC performance under overloaded conditions, effectively preventing switch failures and mitigating DC source saturation. This approach outperforms conventional methods by maintaining stable voltage and frequency, even under significant load increase where traditional droop control alone proves inadequate. The post-disturbance operating point of GFCs remains unchanged using PINN-based control with an improvement of 0.245 Hz in frequency and 0.03 p.u. in active power when compared to an already existing current limitation strategy. Additionally, it reduces peak voltage deviations during transients by 24.14\%, lowers the rate of change of frequency (ROCOF) from 0.02 Hz/s to 0.005 Hz/s, and improves the rate of change of voltage (ROCOV), keeping both within acceptable limits. These improvements significantly enhance system resilience, especially in inertia-less power networks.
Distributed observer-based leak detection in pipe flow with nonlinear friction
Authors: Nils Christian Aars Wilhelmsen, Ole Morten Aamo
Date: 2025-03-27 00:00:00
Tasks: Friction

The problem of leak detection in a pipeline with nonlinear friction is considered. A distributed observer-based method is proposed which applies a linearised, distributed adaptive observer design to the nonlinear model. The methodology is tested in simulations for two different operating points.
HORT: Monocular Hand-held Objects Reconstruction with Transformers
Authors: Zerui Chen, Rolandos Alexandros Potamias, ShiZhe Chen, Cordelia Schmid
Date: 2025-03-27 00:00:00
Tasks: Denoising

Reconstructing hand-held objects in 3D from monocular images remains a significant challenge in computer vision. Most existing approaches rely on implicit 3D representations, which produce overly smooth reconstructions and are time-consuming to generate explicit 3D shapes. While more recent methods directly reconstruct point clouds with diffusion models, the multi-step denoising makes high-resolution reconstruction inefficient. To address these limitations, we propose a transformer-based model to efficiently reconstruct dense 3D point clouds of hand-held objects. Our method follows a coarse-to-fine strategy, first generating a sparse point cloud from the image and progressively refining it into a dense representation using pixel-aligned image features. To enhance reconstruction accuracy, we integrate image features with 3D hand geometry to jointly predict the object point cloud and its pose relative to the hand. Our model is trained end-to-end for optimal performance. Experimental results on both synthetic and real datasets demonstrate that our method achieves state-of-the-art accuracy with much faster inference speed, while generalizing well to in-the-wild images.
ALADIN-$β$: A Distributed Optimization Algorithm for Solving MPCC Problems
Authors: Yifei Wang, Shuting Wu, Genke Yang, Jian Chu, Xu Du
Date: 2025-03-27 00:00:00
Tasks: Distributed Optimization

Mathematical Programs with Complementarity Constraints (MPCC) are critical in various real-world applications but notoriously challenging due to non-smoothness and degeneracy from complementarity constraints. The $\ell_1$-Exact Penalty-Barrier enhanced \texttt{IPOPT} improves performance and robustness by introducing additional inequality constraints and decision variables. However, this comes at the cost of increased computational complexity due to the higher dimensionality and additional constraints introduced in the centralized formulation. To mitigate this, we propose a distributed structure-splitting reformulation that decomposes these inequality constraints and auxiliary variables into independent sub-problems. Furthermore, we introduce Augmented Lagrangian Alternating Direction Inexact Newton (ALADIN)-$\beta$, a novel approach that integrates the $\ell_1$-Exact Penalty-Barrier method with ALADIN to efficiently solve the distributed reformulation. Numerical experiments demonstrate that even without a globalization strategy, the proposed distributed approach achieves fast convergence while maintaining high precision.
Distributed Forgetting-factor Regret-based Online Optimization over Undirected Connected Networks
Authors: Lipo Mo, Jianjun Li, Min Zuo, Lei Wang
Date: 2025-03-27 00:00:00

The evaluation of final-iteration tracking performance is a formidable obstacle in distributed online optimization algorithms. To address this issue, this paper proposes a novel evaluation metric named distributed forgetting-factor regret (DFFR). It incorporates a weight into the loss function at each iteration, which progressively reduces the weights of historical loss functions while enabling dynamic weights allocation across optimization horizon. Furthermore, we develop two distributed online optimization algorithms based on DFFR over undirected connected networks: the Distributed Online Gradient-free Algorithm for bandit-feedback problems and the Distributed Online Projection-free Algorithm for high-dimensional problems. Through theoretical analysis, we derive the upper bounds of DFFR for both algorithms and further prove that under mild conditions, DFFR either converges to zero or maintains a tight upper bound as iterations approach infinity. Experimental simulation demonstrates the effectiveness of the algorithms and the superior performance of DFFR.
Zero-Shot Visual Concept Blending Without Text Guidance
Authors: Hiroya Makino, Takahiro Yamaguchi, Hiroyuki Sakai
Date: 2025-03-27 00:00:00
Tasks: Image Generation, Style Transfer

We propose a novel, zero-shot image generation technique called "Visual Concept Blending" that provides fine-grained control over which features from multiple reference images are transferred to a source image. If only a single reference image is available, it is difficult to isolate which specific elements should be transferred. However, using multiple reference images, the proposed approach distinguishes between common and unique features by selectively incorporating them into a generated output. By operating within a partially disentangled Contrastive Language-Image Pre-training (CLIP) embedding space (from IP-Adapter), our method enables the flexible transfer of texture, shape, motion, style, and more abstract conceptual transformations without requiring additional training or text prompts. We demonstrate its effectiveness across a diverse range of tasks, including style transfer, form metamorphosis, and conceptual transformations, showing how subtle or abstract attributes (e.g., brushstroke style, aerodynamic lines, and dynamism) can be seamlessly combined into a new image. In a user study, participants accurately recognized which features were intended to be transferred. Its simplicity, flexibility, and high-level control make Visual Concept Blending valuable for creative fields such as art, design, and content creation, where combining specific visual qualities from multiple inspirations is crucial.
On Tensor-based Polynomial Hamiltonian Systems
Authors: Shaoxuan Cui, Guofeng Zhang, Hildeberto Jardon-Kojakhmetov, Ming Cao
Date: 2025-03-27 00:00:00
Tasks: tensor algebra

It is known that a linear system with a system matrix A constitutes a Hamiltonian system with a quadratic Hamiltonian if and only if A is a Hamiltonian matrix. This provides a straightforward method to verify whether a linear system is Hamiltonian or whether a given Hamiltonian function corresponds to a linear system. These techniques fundamentally rely on the properties of Hamiltonian matrices. Building on recent advances in tensor algebra, this paper generalizes such results to a broad class of polynomial systems. As the systems of interest can be naturally represented in tensor forms, we name them tensor-based polynomial systems. Our main contribution is that we formally define Hamiltonian cubical tensors and characterize their properties. Crucially, we demonstrate that a tensor-based polynomial system is a Hamiltonian system with a polynomial Hamiltonian if and only if all associated system tensors are Hamiltonian cubical tensors-a direct parallel to the linear case. Additionally, we establish a computationally tractable stability criterion for tensor-based polynomial Hamiltonian systems. Finally, we validate all theoretical results through numerical examples and provide a further intuitive discussion.
Delving Deep into Semantic Relation Distillation
Authors: Zhaoyi Yan, KangJun Liu, Qixiang Ye
Date: 2025-03-27 00:00:00
Tasks: Knowledge Distillation, Model Compression, Relation, Superpixels

Knowledge distillation has become a cornerstone technique in deep learning, facilitating the transfer of knowledge from complex models to lightweight counterparts. Traditional distillation approaches focus on transferring knowledge at the instance level, but fail to capture nuanced semantic relationships within the data. In response, this paper introduces a novel methodology, Semantics-based Relation Knowledge Distillation (SeRKD), which reimagines knowledge distillation through a semantics-relation lens among each sample. By leveraging semantic components, \ie, superpixels, SeRKD enables a more comprehensive and context-aware transfer of knowledge, which skillfully integrates superpixel-based semantic extraction with relation-based knowledge distillation for a sophisticated model compression and distillation. Particularly, the proposed method is naturally relevant in the domain of Vision Transformers (ViTs), where visual tokens serve as fundamental units of representation. Experimental evaluations on benchmark datasets demonstrate the superiority of SeRKD over existing methods, underscoring its efficacy in enhancing model performance and generalization capabilities.
On Learning-Based Traffic Monitoring With a Swarm of Drones
Authors: Marko Maljkovic, Nikolas Geroliminis
Date: 2025-03-27 00:00:00

Efficient traffic monitoring is crucial for managing urban transportation networks, especially under congested and dynamically changing traffic conditions. Drones offer a scalable and cost-effective alternative to fixed sensor networks. However, deploying fleets of low-cost drones for traffic monitoring poses challenges in adaptability, scalability, and real-time operation. To address these issues, we propose a learning-based framework for decentralized traffic monitoring with drone swarms, targeting the uneven and unpredictable distribution of monitoring needs across urban areas. Our approach introduces a semi-decentralized reinforcement learning model, which trains a single Q-function using the collective experience of the swarm. This model supports full scalability, flexible deployment, and, when hardware allows, the online adaptation of each drone's action-selection mechanism. We first train and evaluate the model in a synthetic traffic environment, followed by a case study using real traffic data from Shenzhen, China, to validate its performance and demonstrate its potential for real-world applications in complex urban monitoring tasks.