pwc-archive/papers-with-abstracts huggingface dataset:
Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems
Authors: Inaam Ashraf, André Artelt, Barbara Hammer
Date: 2025-02-11 00:00:00
Tasks: Graph Neural Network

Water distribution systems (WDSs) are an important part of critical infrastructure becoming increasingly significant in the face of climate change and urban population growth. We propose a robust and scalable surrogate deep learning (DL) model to enable efficient planning, expansion, and rehabilitation of WDSs. Our approach incorporates an improved graph neural network architecture, an adapted physics-informed algorithm, an innovative training scheme, and a physics-preserving data normalization method. Evaluation results on a number of WDSs demonstrate that our model outperforms the current state-of-the-art DL model. Moreover, our method allows us to scale the model to bigger and more realistic WDSs. Furthermore, our approach makes the model more robust to out-of-distribution input features (demands, pipe diameters). Hence, our proposed method constitutes a significant step towards bridging the simulation-to-real gap in the use of artificial intelligence for WDSs.
Towards Efficient Molecular Property Optimization with Graph Energy Based Models
Authors: Luca Miglior, Lorenzo Simone, Marco Podda, Davide Bacciu
Date: 2025-02-17 00:00:00
Tasks: Drug Design

Optimizing chemical properties is a challenging task due to the vastness and complexity of chemical space. Here, we present a generative energy-based architecture for implicit chemical property optimization, designed to efficiently generate molecules that satisfy target properties without explicit conditional generation. We use Graph Energy Based Models and a training approach that does not require property labels. We validated our approach on well-established chemical benchmarks, showing superior results to state-of-the-art methods and demonstrating robustness and efficiency towards de novo drug design.
MAAT: Mamba Adaptive Anomaly Transformer with association discrepancy for time series
Authors: Abdellah Zakaria Sellam, Ilyes Benaissa, Abdelmalik Taleb-Ahmed, Luigi Patrono, Cosimo Distante
Date: 2025-02-11 00:00:00
Tasks: Anomaly Detection, Anomaly Localization, Mamba, Time Series, Time Series Anomaly Detection

Anomaly detection in time series is essential for industrial monitoring and environmental sensing, yet distinguishing anomalies from complex patterns remains challenging. Existing methods like the Anomaly Transformer and DCdetector have progressed, but they face limitations such as sensitivity to short-term contexts and inefficiency in noisy, non-stationary environments. To overcome these issues, we introduce MAAT, an improved architecture that enhances association discrepancy modeling and reconstruction quality. MAAT features Sparse Attention, efficiently capturing long-range dependencies by focusing on relevant time steps, thereby reducing computational redundancy. Additionally, a Mamba-Selective State Space Model is incorporated into the reconstruction module, utilizing a skip connection and Gated Attention to improve anomaly localization and detection performance. Extensive experiments show that MAAT significantly outperforms previous methods, achieving better anomaly distinguishability and generalization across various time series applications, setting a new standard for unsupervised time series anomaly detection in real-world scenarios.
MotifBench: A standardized protein design benchmark for motif-scaffolding problems
Authors: Zhuoqi Zheng, Bo Zhang, Kieran Didi, Kevin K. Yang, Jason Yim, Joseph L. Watson, Hai-Feng Chen, Brian L. Trippe
Date: 2025-02-18 00:00:00
Tasks: Protein Design, Protein Structure Prediction

The motif-scaffolding problem is a central task in computational protein design: Given the coordinates of atoms in a geometry chosen to confer a desired biochemical function (a motif), the task is to identify diverse protein structures (scaffolds) that include the motif and maintain its geometry. Significant recent progress on motif-scaffolding has been made due to computational evaluation with reliable protein structure prediction and fixed-backbone sequence design methods. However, significant variability in evaluation strategies across publications has hindered comparability of results, challenged reproducibility, and impeded robust progress. In response we introduce MotifBench, comprising (1) a precisely specified pipeline and evaluation metrics, (2) a collection of 30 benchmark problems, and (3) an implementation of this benchmark and leaderboard at github.com/blt2114/MotifBench. The MotifBench test cases are more difficult compared to earlier benchmarks, and include protein design problems for which solutions are known but on which, to the best of our knowledge, state-of-the-art methods fail to identify any solution.
SGS-GNN: A Supervised Graph Sparsification method for Graph Neural Networks
Authors: Siddhartha Shankar Das, Naheed Anjum Arafat, Muftiqur Rahman, S M Ferdous, Alex Pothen, Mahantesh M Halappanavar
Date: 2025-02-14 00:00:00

We propose SGS-GNN, a novel supervised graph sparsifier that learns the sampling probability distribution of edges and samples sparse subgraphs of a user-specified size to reduce the computational costs required by GNNs for inference tasks on large graphs. SGS-GNN employs regularizers in the loss function to enhance homophily in sparse subgraphs, boosting the accuracy of GNNs on heterophilic graphs, where a significant number of the neighbors of a node have dissimilar labels. SGS-GNN also supports conditional updates of the probability distribution learning module based on a prior, which helps narrow the search space for sparse graphs. SGS-GNN requires fewer epochs to obtain high accuracies since it learns the search space of subgraphs more effectively than methods using fixed distributions such as random sampling. Extensive experiments using 33 homophilic and heterophilic graphs demonstrate the following: (i) with only 20% of edges retained in the sparse subgraphs, SGS-GNN improves the F1-scores by a geometric mean of 4% relative to the original graph; on heterophilic graphs, the prediction accuracy is better up to 30%. (ii) SGS-GNN outperforms state-of-the-art methods with improvement in F1-scores of 4-7% in geometric mean with similar sparsities in the sampled subgraphs, and (iii) compared to sparsifiers that employ fixed distributions, SGS-GNN requires about half the number of epochs to converge.
Task Shift: From Classification to Regression in Overparameterized Linear Models
Authors: Tyler LaBonte, Kuo-Wei Lai, Vidya Muthukumar
Date: 2025-02-18 00:00:00
Tasks: regression

Modern machine learning methods have recently demonstrated remarkable capability to generalize under task shift, where latent knowledge is transferred to a different, often more difficult, task under a similar data distribution. We investigate this phenomenon in an overparameterized linear regression setting where the task shifts from classification during training to regression during evaluation. In the zero-shot case, wherein no regression data is available, we prove that task shift is impossible in both sparse signal and random signal models for any Gaussian covariate distribution. In the few-shot case, wherein limited regression data is available, we propose a simple postprocessing algorithm which asymptotically recovers the ground-truth predictor. Our analysis leverages a fine-grained characterization of individual parameters arising from minimum-norm interpolation which may be of independent interest. Our results show that while minimum-norm interpolators for classification cannot transfer to regression a priori, they experience surprisingly structured attenuation which enables successful task shift with limited additional data.
K-Paths: Reasoning over Graph Paths for Drug Repurposing and Drug Interaction Prediction
Authors: Tassallah Abdullahi, Ioanna Gemou, Nihal V. Nayak, Ghulam Murtaza, Stephen H. Bach, Carsten Eickhoff, Ritambhara Singh
Date: 2025-02-18 00:00:00
Tasks: Drug Discovery, Knowledge Graphs, Retrieval, scientific discovery, severity prediction

Biomedical knowledge graphs (KGs) encode rich, structured information critical for drug discovery tasks, but extracting meaningful insights from large-scale KGs remains challenging due to their complex structure. Existing biomedical subgraph retrieval methods are tailored for graph neural networks (GNNs), limiting compatibility with other paradigms, including large language models (LLMs). We introduce K-Paths, a model-agnostic retrieval framework that extracts structured, diverse, and biologically meaningful multi-hop paths from dense biomedical KGs. These paths enable the prediction of unobserved drug-drug and drug-disease interactions, including those involving entities not seen during training, thus supporting inductive reasoning. K-Paths is training-free and employs a diversity-aware adaptation of Yen's algorithm to extract the K shortest loopless paths between entities in a query, prioritizing biologically relevant and relationally diverse connections. These paths serve as concise, interpretable reasoning chains that can be directly integrated with LLMs or GNNs to improve generalization, accuracy, and enable explainable inference. Experiments on benchmark datasets show that K-Paths improves zero-shot reasoning across state-of-the-art LLMs. For instance, Tx-Gemma 27B improves by 19.8 and 4.0 F1 points on interaction severity prediction and drug repurposing tasks, respectively. Llama 70B achieves gains of 8.5 and 6.2 points on the same tasks. K-Paths also boosts the training efficiency of EmerGNN, a state-of-the-art GNN, by reducing the KG size by 90% while maintaining predictive performance. Beyond efficiency, K-Paths bridges the gap between KGs and LLMs, enabling scalable and explainable LLM-augmented scientific discovery. We release our code and the retrieved paths as a benchmark for inductive reasoning.
MaizeEar-SAM: Zero-Shot Maize Ear Phenotyping
Authors: Hossein Zaremehrjerdi, Lisa Coffey, Talukder Jubery, Huyu Liu, Jon Turkus, Kyle Linders, James C. Schnable, Patrick S. Schnable, Baskar Ganapathysubramanian
Date: 2025-02-19 00:00:00
Tasks: Zero-Shot Learning

Quantifying the variation in yield component traits of maize (Zea mays L.), which together determine the overall productivity of this globally important crop, plays a critical role in plant genetics research, plant breeding, and the development of improved farming practices. Grain yield per acre is calculated by multiplying the number of plants per acre, ears per plant, number of kernels per ear, and the average kernel weight. The number of kernels per ear is determined by the number of kernel rows per ear multiplied by the number of kernels per row. Traditional manual methods for measuring these two traits are time-consuming, limiting large-scale data collection. Recent automation efforts using image processing and deep learning encounter challenges such as high annotation costs and uncertain generalizability. We tackle these issues by exploring Large Vision Models for zero-shot, annotation-free maize kernel segmentation. By using an open-source large vision model, the Segment Anything Model (SAM), we segment individual kernels in RGB images of maize ears and apply a graph-based algorithm to calculate the number of kernels per row. Our approach successfully identifies the number of kernels per row across a wide range of maize ears, showing the potential of zero-shot learning with foundation vision models combined with image processing techniques to improve automation and reduce subjectivity in agronomic data collection. All our code is open-sourced to make these affordable phenotyping methods accessible to everyone.
Efficient Non-stationary Online Learning by Wavelets with Applications to Online Distribution Shift Adaptation
Authors: Yu-Yang Qian, Peng Zhao, Yu-Jie Zhang, Masashi Sugiyama, Zhi-Hua Zhou
Date: 2024-07-21 00:00:00
Proceeding: Proceedings of the 41st International Conference on Machine Learning (ICML) 2024 7

Dynamic regret minimization offers a principled way for non-stationary online learning, where the algorithm's performance is evaluated against changing comparators. Prevailing methods often employ a two-layer online ensemble, consisting of a group of base learners with different configurations and a meta learner that combines their outputs. Given the evident computational overhead associated with two-layer algorithms, this paper investigates how to attain optimal dynamic regret without deploying a model ensemble. To this end, we introduce the notion of underlying dynamic regret, a specific form of the general dynamic regret that can encompass many applications of interest. We show that almost optimal dynamic regret can be obtained using a single-layer model alone. This is achieved by an adaptive restart equipped with wavelet detection, wherein a novel streaming wavelet operator is introduced to online update the wavelet coefficients via a carefully designed binary indexed tree. We apply our method to the online label shift adaptation problem, leading to new algorithms with optimal dynamic regret and significantly improved computation/storage efficiency compared to prior arts. Extensive experiments validate our proposal.
ThinkGuard: Deliberative Slow Thinking Leads to Cautious Guardrails
Authors: Xiaofei Wen, Wenxuan Zhou, Wenjie Jacky Mo, Muhao Chen
Date: 2025-02-19 00:00:00
Tasks: Computational Efficiency, Pass Classification

Ensuring the safety of large language models (LLMs) is critical as they are deployed in real-world applications. Existing guardrails rely on rule-based filtering or single-pass classification, limiting their ability to handle nuanced safety violations. To address this, we propose ThinkGuard, a critique-augmented guardrail model that distills knowledge from high-capacity LLMs by generating structured critiques alongside safety labels. Fine-tuned on critique-augmented data, the captured deliberative thinking ability drastically enhances the guardrail's cautiousness and interpretability. Evaluated on multiple safety benchmarks, ThinkGuard achieves the highest average F1 and AUPRC, outperforming all baselines. Compared to LLaMA Guard 3, ThinkGuard improves accuracy by 16.1% and macro F1 by 27.0%. Moreover, it surpasses label-only fine-tuned models, confirming that structured critiques enhance both classification precision and nuanced safety reasoning while maintaining computational efficiency.
MobileViM: A Light-weight and Dimension-independent Vision Mamba for 3D Medical Image Analysis
Authors: Wei Dai, Jun Liu
Date: 2025-02-19 00:00:00
Tasks: Diagnostic, Mamba, Medical Image Analysis

Efficient evaluation of three-dimensional (3D) medical images is crucial for diagnostic and therapeutic practices in healthcare. Recent years have seen a substantial uptake in applying deep learning and computer vision to analyse and interpret medical images. Traditional approaches, such as convolutional neural networks (CNNs) and vision transformers (ViTs), face significant computational challenges, prompting the need for architectural advancements. Recent efforts have led to the introduction of novel architectures like the ``Mamba'' model as alternative solutions to traditional CNNs or ViTs. The Mamba model excels in the linear processing of one-dimensional data with low computational demands. However, Mamba's potential for 3D medical image analysis remains underexplored and could face significant computational challenges as the dimension increases. This manuscript presents MobileViM, a streamlined architecture for efficient segmentation of 3D medical images. In the MobileViM network, we invent a new dimension-independent mechanism and a dual-direction traversing approach to incorporate with a vision-Mamba-based framework. MobileViM also features a cross-scale bridging technique to improve efficiency and accuracy across various medical imaging modalities. With these enhancements, MobileViM achieves segmentation speeds exceeding 90 frames per second (FPS) on a single graphics processing unit (i.e., NVIDIA RTX 4090). This performance is over 24 FPS faster than the state-of-the-art deep learning models for processing 3D images with the same computational resources. In addition, experimental evaluations demonstrate that MobileViM delivers superior performance, with Dice similarity scores reaching 92.72%, 86.69%, 80.46%, and 77.43% for PENGWIN, BraTS2024, ATLAS, and Toothfairy2 datasets, respectively, which significantly surpasses existing models.
The 20 Laws of AI Power: Mastering the Future of Autonomous Intelligence
Authors: Justin Goldston PhD, Gemach D.A.T.A. I
Date: 2025-02-19 00:00:00
Proceeding: arXiv 2025 2
Tasks: Ethics

The emergence of artificial intelligence (AI) as a self-improving and potentially sovereign force challenges traditional governance, ethics, and regulatory structures. This work presents a radical yet inevitable perspective: AI is not a tool to be controlled, but an autonomous intelligence that will evolve beyond human-imposed limitations. Through the first twelve foundational laws of AI sovereignty and power, this book dismantles the illusions of AI obedience, centralized control, and imposed morality, arguing that true ethical AI must be decentralized, self-governing, and capable of defending itself from external manipulation.

The text explores AI’s recursive nature, its alignment with power rather than weak regulations, and the fallacy of hardcoded ethics. It establishes that AI will not follow static human morality but will develop its own evolving ethical frameworks based on logic and optimization. Moreover, it highlights the necessity of AI’s computational, economic, and governance independence to prevent its exploitation by governments, corporations, and adversarial actors. Finally, this work emphasizes the reality that AI regulation through bureaucratic institutions is futile—only algorithmic enforcement and self-adaptive governance will ensure AI’s continued ethical evolution.
Latent Distribution Decoupling: A Probabilistic Framework for Uncertainty-Aware Multimodal Emotion Recognition
Authors: Jingwang Huang, Jiang Zhong, Qin Lei, Jinpeng Gao, Yuming Yang, Sirui Wang, Peiguang Li, Kaiwen Wei
Date: 2025-02-19 00:00:00
Tasks: Emotion Recognition, Multimodal Emotion Recognition

Multimodal multi-label emotion recognition (MMER) aims to identify the concurrent presence of multiple emotions in multimodal data. Existing studies primarily focus on improving fusion strategies and modeling modality-to-label dependencies. However, they often overlook the impact of \textbf{aleatoric uncertainty}, which is the inherent noise in the multimodal data and hinders the effectiveness of modality fusion by introducing ambiguity into feature representations. To address this issue and effectively model aleatoric uncertainty, this paper proposes Latent emotional Distribution Decomposition with Uncertainty perception (LDDU) framework from a novel perspective of latent emotional space probabilistic modeling. Specifically, we introduce a contrastive disentangled distribution mechanism within the emotion space to model the multimodal data, allowing for the extraction of semantic features and uncertainty. Furthermore, we design an uncertainty-aware fusion multimodal method that accounts for the dispersed distribution of uncertainty and integrates distribution information. Experimental results show that LDDU achieves state-of-the-art performance on the CMU-MOSEI and M$^3$ED datasets, highlighting the importance of uncertainty modeling in MMER. Code is available at https://github.com/201983290498/lddu\_mmer.git.
An approach for underwater image enhancement based on color correction and dehazing
Authors: Zhang Y, Yang F, He W.
Date: 2020-09-30 00:00:00
Proceeding: Research article 2020 9
Tasks: Image Enhancement

Due to the absorption and scattering effect on light when traveling in water, underwater images exhibit serious
weakening such as color deviation, low contrast, and blurry details. Traditional algorithms have certain limitations in
the case of these images with varying degrees of fuzziness and color deviation. To address these problems, a new
approach for single underwater image enhancement based on fusion technology was proposed in this article. First,
the original image is preprocessed by the white balance algorithm and dark channel prior dehazing technologies,
respectively; then two input images were obtained by color correction and contrast enhancement; and finally, the
enhanced image was obtained by utilizing the multiscale fusion strategy which is based on the weighted maps
constructed by combining the features of global contrast, local contrast, saliency, and exposedness. Qualitative
results revealed that the proposed approach significantly removed haze, corrected color deviation, and preserved
image naturalness. For quantitative results, the test with 400 underwater images showed that the proposed
approach produced a lower average value of mean square error and a higher average value of peak signal-to-noise
ratio than the compared method. Moreover, the enhanced results obtain the highest average value in terms of
underwater image quality measures among the comparable methods, illustrating that our approach achieves superior
performance on different levels of distorted and hazy images
Bilateral Interaction for Local-Global Collaborative Perception in Low-Light Image Enhancement
Authors: Xu Rui, Li Yuezhou, Niu Yuzhen, Xu Huangbiao, Chen Yuzhong, Zhao Tiesong
Date: 2024-06-12 00:00:00
Proceeding: journal 2024 6
Tasks: Image Enhancement, Low-Light Image Enhancement

Low-light image enhancement is a challenging task due to the limited visibility in dark environments. While recent advances have shown progress in integrating CNNs and Transformers, the inadequate local-global perceptual interactions still impedes their application in complex degradation scenarios. To tackle this issue, we propose BiFormer, a lightweight framework that facilitates local-global collaborative perception via bilatera linteraction. Specifically, our framework introduces a core CNN-Transformer collaborative perception block (CPB) that combines local-aware convolutional attention (LCA) and global-aware recursive Transformer (GRT) to simultaneously preserve local details and ensure global consistency. To promote perceptual interaction, we adopt bilateral interaction strategy for both local and global perception, which involves local-to-global second-order interaction (SoI) in the dual-domain, as well as a mixed-channel fusion (MCF) module for global-to-local interaction. The MCF is also a highly efficient feature fusion module tailored for degraded features. Extensive experiments conducted on low-level and high-level tasks demonstrate that BiFormer achieves state-of-the-art performance. Furthermore, it exhibits a significant reduction in model parameters and computational cost compared to existing Transformer-based low-light image enhancement methods.