pwc-archive/papers-with-abstracts huggingface dataset:
An Explainable Disease Surveillance System for Early Prediction of Multiple Chronic Diseases
Authors: Shaheer Ahmad Khan, Muhammad Usamah Shahid, Ahmad Abdullah, Ibrahim Hashmat, Muddassar Farooq
Date: 2025-01-27 00:00:00
Tasks: Disease Prediction

This study addresses a critical gap in the healthcare system by developing a clinically meaningful, practical, and explainable disease surveillance system for multiple chronic diseases, utilizing routine EHR data from multiple U.S. practices integrated with CureMD's EMR/EHR system. Unlike traditional systems--using AI models that rely on features from patients' labs--our approach focuses on routinely available data, such as medical history, vitals, diagnoses, and medications, to preemptively assess the risks of chronic diseases in the next year. We trained three distinct models for each chronic disease: prediction models that forecast the risk of a disease 3, 6, and 12 months before a potential diagnosis. We developed Random Forest models, which were internally validated using F1 scores and AUROC as performance metrics and further evaluated by a panel of expert physicians for clinical relevance based on inferences grounded in medical knowledge. Additionally, we discuss our implementation of integrating these models into a practical EMR system. Beyond using Shapley attributes and surrogate models for explainability, we also introduce a new rule-engineering framework to enhance the intrinsic explainability of Random Forests.
Multi-View Attention Syntactic Enhanced Graph Convolutional Network for Aspect-based Sentiment Analysis
Authors: Xiang Huang, Hao Peng, Shuo Sun, Zhifeng Hao, Hui Lin, Shuhai Wang
Date: 2025-01-27 00:00:00
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Dependency Parsing, Sentiment Analysis

Aspect-based Sentiment Analysis (ABSA) is the task aimed at predicting the sentiment polarity of aspect words within sentences. Recently, incorporating graph neural networks (GNNs) to capture additional syntactic structure information in the dependency tree derived from syntactic dependency parsing has been proven to be an effective paradigm for boosting ABSA. Despite GNNs enhancing model capability by fusing more types of information, most works only utilize a single topology view of the dependency tree or simply conflate different perspectives of information without distinction, which limits the model performance. To address these challenges, in this paper, we propose a new multi-view attention syntactic enhanced graph convolutional network (MASGCN) that weighs different syntactic information of views using attention mechanisms. Specifically, we first construct distance mask matrices from the dependency tree to obtain multiple subgraph views for GNNs. To aggregate features from different views, we propose a multi-view attention mechanism to calculate the attention weights of views. Furthermore, to incorporate more syntactic information, we fuse the dependency type information matrix into the adjacency matrices and present a structural entropy loss to learn the dependency type adjacency matrix. Comprehensive experiments on four benchmark datasets demonstrate that our model outperforms state-of-the-art methods. The codes and datasets are available at https://github.com/SELGroup/MASGCN.
Evaluating Data Influence in Meta Learning
Authors: Chenyang Ren, Huanyi Xie, Shu Yang, Meng Ding, Lijie Hu, Di Wang
Date: 2025-01-27 00:00:00
Tasks: Bilevel Optimization, Computational Efficiency, Few-Shot Learning, Meta-Learning

As one of the most fundamental models, meta learning aims to effectively address few-shot learning challenges. However, it still faces significant issues related to the training data, such as training inefficiencies due to numerous low-contribution tasks in large datasets and substantial noise from incorrect labels. Thus, training data attribution methods are needed for meta learning. However, the dual-layer structure of mata learning complicates the modeling of training data contributions because of the interdependent influence between meta-parameters and task-specific parameters, making existing data influence evaluation tools inapplicable or inaccurate. To address these challenges, based on the influence function, we propose a general data attribution evaluation framework for meta-learning within the bilevel optimization framework. Our approach introduces task influence functions (task-IF) and instance influence functions (instance-IF) to accurately assess the impact of specific tasks and individual data points in closed forms. This framework comprehensively models data contributions across both the inner and outer training processes, capturing the direct effects of data points on meta-parameters as well as their indirect influence through task-specific parameters. We also provide several strategies to enhance computational efficiency and scalability. Experimental results demonstrate the framework's effectiveness in training data evaluation via several downstream tasks.
Generative AI for Lyapunov Optimization Theory in UAV-based Low-Altitude Economy Networking
Authors: Zhang Liu, Dusit Niyato, Jiacheng Wang, Geng Sun, Lianfen Huang, Zhibin Gao, Xianbin Wang
Date: 2025-01-27 00:00:00
Tasks: Model-based Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Stochastic Optimization

Lyapunov optimization theory has recently emerged as a powerful mathematical framework for solving complex stochastic optimization problems by transforming long-term objectives into a sequence of real-time short-term decisions while ensuring system stability. This theory is particularly valuable in unmanned aerial vehicle (UAV)-based low-altitude economy (LAE) networking scenarios, where it could effectively address inherent challenges of dynamic network conditions, multiple optimization objectives, and stability requirements. Recently, generative artificial intelligence (GenAI) has garnered significant attention for its unprecedented capability to generate diverse digital content. Extending beyond content generation, in this paper, we propose a framework integrating generative diffusion models with reinforcement learning to address Lyapunov optimization problems in UAV-based LAE networking. We begin by introducing the fundamentals of Lyapunov optimization theory and analyzing the limitations of both conventional methods and traditional AI-enabled approaches. We then examine various GenAI models and comprehensively analyze their potential contributions to Lyapunov optimization. Subsequently, we develop a Lyapunov-guided generative diffusion model-based reinforcement learning framework and validate its effectiveness through a UAV-based LAE networking case study. Finally, we outline several directions for future research.
Online Housing Market
Authors: Julien Lesca
Date: 2025-01-27 00:00:00

This paper studies an online variant of the celebrated housing market problem, where each agent has a single house and seeks to exchange it for another based on her preferences. In this online setting, agents may arrive and depart at any time, meaning that not all agents are present on the housing market simultaneously. I extend the well known serial dictatorship and Gale s top trading cycle mechanisms to this online scenario, aiming to retain their desirable properties such as Pareto efficiency, individual rationality, and strategy proofness. These extensions also seek to prevent agents from strategically delaying their arrival or advancing their departure. I demonstrate that achieving all of these properties simultaneously is impossible in the online context, and I present several variants that achieve different subsets of these properties.
Evidential Physics-Informed Neural Networks
Authors: Hai Siong Tan, Kuancheng Wang, Rafe McBeth
Date: 2025-01-27 00:00:00
Tasks: Uncertainty Quantification

We present a novel class of Physics-Informed Neural Networks that is formulated based on the principles of Evidential Deep Learning, where the model incorporates uncertainty quantification by learning parameters of a higher-order distribution. The dependent and trainable variables of the PDE residual loss and data-fitting loss terms are recast as functions of the hyperparameters of an evidential prior distribution. Our model is equipped with an information-theoretic regularizer that contains the Kullback-Leibler divergence between two inverse-gamma distributions characterizing predictive uncertainty. Relative to Bayesian-Physics-Informed-Neural-Networks, our framework appeared to exhibit higher sensitivity to data noise, preserve boundary conditions more faithfully and yield empirical coverage probabilities closer to nominal ones. Toward examining its relevance for data mining in scientific discoveries, we demonstrate how to apply our model to inverse problems involving 1D and 2D nonlinear differential equations.
A Data-Centric Approach: Dimensions of Visual Complexity and How to find Them
Authors: Karahan Sarıtaş, Tingke Shen, Surabhi S Nath, Peter Dayan
Date: 2025-01-27 00:00:00

Understanding how humans perceive visual complexity is a key area of study in visual cognition. Previous approaches to modeling visual complexity have often resulted in intricate, difficult-to-interpret solutions that employ numerous features or sophisticated deep learning architectures. While these complex models achieve high performance on specific datasets, they often sacrifice interpretability, making it challenging to understand the factors driving human perception of complexity. A recent model based on image segmentations showed promise in addressing this challenge; however, it presented limitations in capturing structural and semantic aspects of visual complexity. In this paper, we propose viable and effective features to overcome these shortcomings. Specifically, we develop multiscale features for the structural aspect of complexity, including the Multiscale Sobel Gradient (MSG), which captures spatial intensity variations across scales, and Multiscale Unique Colors (MUC), which quantifies image colorfulness by indexing quantized RGB values. We also introduce a new dataset SVG based on Visual Genome to explore the semantic aspect of visual complexity, obtaining surprise scores based on the element of surprise in images, which we demonstrate significantly contributes to perceived complexity. Overall, we suggest that the nature of the data is fundamental to understanding and modeling visual complexity, highlighting the importance of both structural and semantic dimensions in providing a comprehensive, interpretable assessment. The code for our analysis, experimental setup, and dataset will be made publicly available upon acceptance.
Adaptive Width Neural Networks
Authors: Federico Errica, Henrik Christiansen, Viktor Zaverkin, Mathias Niepert, Francesco Alesiani
Date: 2025-01-27 00:00:00

For almost 70 years, researchers have mostly relied on hyper-parameter tuning to select the width of neural networks' layers. This paper challenges the status quo by introducing an easy-to-use technique to learn an unbounded width of a neural network's layer during training. The technique does not rely on alternate optimization nor hand-crafted gradient heuristics; rather, it jointly optimizes the width and the parameters of each layer via simple backpropagation. We apply the technique to a broad range of data domains such as tables, images, text, sequences, and graphs, showing how the width adapts to the task's difficulty. The method imposes a soft ordering of importance among neurons, by which it also is possible to truncate the trained network at virtually zero cost, achieving a smooth trade-off between performance and compute resources in a structured way. Alternatively, one can dynamically compress the network with no performance degradation. In light of recent foundation models trained on large datasets, believed to require billions of parameters and where hyper-parameter tuning is unfeasible due to humongous training costs, our approach stands as a viable alternative for width learning.
Boli: A dataset for understanding stuttering experience and analyzing stuttered speech
Authors: Ashita Batra, Mannas narang, Neeraj Kumar Sharma, Pradip K Das
Date: 2025-01-27 00:00:00
Tasks: Image Description

There is a growing need for diverse, high-quality stuttered speech data, particularly in the context of Indian languages. This paper introduces Project Boli, a multi-lingual stuttered speech dataset designed to advance scientific understanding and technology development for individuals who stutter, particularly in India. The dataset constitutes (a) anonymized metadata (gender, age, country, mother tongue) and responses to a questionnaire about how stuttering affects their daily lives, (b) captures both read speech (using the Rainbow Passage) and spontaneous speech (through image description tasks) for each participant and (c) includes detailed annotations of five stutter types: blocks, prolongations, interjections, sound repetitions and word repetitions. We present a comprehensive analysis of the dataset, including the data collection procedure, experience summarization of people who stutter, severity assessment of stuttering events and technical validation of the collected data. The dataset is released as an open access to further speech technology development.
Optimizing Sentence Embedding with Pseudo-Labeling and Model Ensembles: A Hierarchical Framework for Enhanced NLP Tasks
Authors: Ziwei Liu, Qi Zhang, Lifu Gao
Date: 2025-01-27 00:00:00
Tasks: Data Augmentation, Pseudo Label, Sentence, Sentence Embedding, Sentence-Embedding, Sentence Embeddings

Sentence embedding tasks are important in natural language processing (NLP), but improving their performance while keeping them reliable is still hard. This paper presents a framework that combines pseudo-label generation and model ensemble techniques to improve sentence embeddings. We use external data from SimpleWiki, Wikipedia, and BookCorpus to make sure the training data is consistent. The framework includes a hierarchical model with an encoding layer, refinement layer, and ensemble prediction layer, using ALBERT-xxlarge, RoBERTa-large, and DeBERTa-large models. Cross-attention layers combine external context, and data augmentation techniques like synonym replacement and back-translation increase data variety. Experimental results show large improvements in accuracy and F1-score compared to basic models, and studies confirm that cross-attention and data augmentation make a difference. This work presents an effective way to improve sentence embedding tasks and lays the groundwork for future NLP research.
D-PLS: Decoupled Semantic Segmentation for 4D-Panoptic-LiDAR-Segmentation
Authors: Maik Steinhauser, Laurenz Reichardt, Nikolas Ebert, Oliver Wasenmüller
Date: 2025-01-27 00:00:00
Tasks: Instance Segmentation, Segmentation, Semantic Segmentation

This paper introduces a novel approach to 4D Panoptic LiDAR Segmentation that decouples semantic and instance segmentation, leveraging single-scan semantic predictions as prior information for instance segmentation. Our method D-PLS first performs single-scan semantic segmentation and aggregates the results over time, using them to guide instance segmentation. The modular design of D-PLS allows for seamless integration on top of any semantic segmentation architecture, without requiring architectural changes or retraining. We evaluate our approach on the SemanticKITTI dataset, where it demonstrates significant improvements over the baseline in both classification and association tasks, as measured by the LiDAR Segmentation and Tracking Quality (LSTQ) metric. Furthermore, we show that our decoupled architecture not only enhances instance prediction but also surpasses the baseline due to advancements in single-scan semantic segmentation.
Transfer of Knowledge through Reverse Annealing: A Preliminary Analysis of the Benefits and What to Share
Authors: Eneko Osaba, Esther Villar-Rodriguez
Date: 2025-01-27 00:00:00
Tasks: Benchmarking, Transfer Learning

Being immersed in the NISQ-era, current quantum annealers present limitations for solving optimization problems efficiently. To mitigate these limitations, D-Wave Systems developed a mechanism called Reverse Annealing, a specific type of quantum annealing designed to perform local refinement of good states found elsewhere. Despite the research activity around Reverse Annealing, none has theorized about the possible benefits related to the transfer of knowledge under this paradigm. This work moves in that direction and is driven by experimentation focused on answering two key research questions: i) is reverse annealing a paradigm that can benefit from knowledge transfer between similar problems? and ii) can we infer the characteristics that an input solution should meet to help increase the probability of success? To properly guide the tests in this paper, the well-known Knapsack Problem has been chosen for benchmarking purposes, using a total of 34 instances composed of 14 and 16 items.
Beyond In-Distribution Performance: A Cross-Dataset Study of Trajectory Prediction Robustness
Authors: Yue Yao, Daniel Goehring, Joerg Reichardt
Date: 2025-01-27 00:00:00
Tasks: Data Augmentation, Inductive Bias, Trajectory Prediction

We study the Out-of-Distribution (OoD) generalization ability of three SotA trajectory prediction models with comparable In-Distribution (ID) performance but different model designs. We investigate the influence of inductive bias, size of training data and data augmentation strategy by training the models on Argoverse 2 (A2) and testing on Waymo Open Motion (WO) and vice versa. We find that the smallest model with highest inductive bias exhibits the best OoD generalization across different augmentation strategies when trained on the smaller A2 dataset and tested on the large WO dataset. In the converse setting, training all models on the larger WO dataset and testing on the smaller A2 dataset, we find that all models generalize poorly, even though the model with the highest inductive bias still exhibits the best generalization ability. We discuss possible reasons for this surprising finding and draw conclusions about the design and test of trajectory prediction models and benchmarks.
CrySPAI: A new Crystal Structure Prediction Software Based on Artificial Intelligence
Authors: Zongguo Wang, Ziyi Chen, Yang Yuan, Yangang Wang
Date: 2025-01-27 00:00:00

Crystal structure predictions based on the combination of first-principles calculations and machine learning have achieved significant success in materials science. However, most of these approaches are limited to predicting specific systems, which hinders their application to unknown or unexplored domains. In this paper, we present CrySPAI, a crystal structure prediction package developed using artificial intelligence (AI) to predict energetically stable crystal structures of inorganic materials given their chemical compositions. The software consists of three key modules, an evolutionary optimization algorithm (EOA) that searches for all possible crystal structure configurations, density functional theory (DFT) that provides the accurate energy values for these structures, and a deep neural network (DNN) that learns the relationship between crystal structures and their corresponding energies. To optimize the process across these modules, a distributed framework is implemented to parallelize tasks, and an automated workflow has been integrated into CrySPAI for seamless execution. This paper reports the development and implementation of AI AI-based CrySPAI Crystal Prediction Software tool and its unique features.
Intelligent Code Embedding Framework for High-Precision Ransomware Detection via Multimodal Execution Path Analysis
Authors: Levi Gareth, Maximilian Fairbrother, Peregrine Blackwood, Lucasta Underhill, Benedict Ruthermore
Date: 2025-01-27 00:00:00

Modern threat landscapes continue to evolve with increasing sophistication, challenging traditional detection methodologies and necessitating innovative solutions capable of addressing complex adversarial tactics. A novel framework was developed to identify ransomware activity through multimodal execution path analysis, integrating high-dimensional embeddings and dynamic heuristic derivation mechanisms to capture behavioral patterns across diverse attack variants. The approach demonstrated high adaptability, effectively mitigating obfuscation strategies and polymorphic characteristics often employed by ransomware families to evade detection. Comprehensive experimental evaluations revealed significant advancements in precision, recall, and accuracy metrics compared to baseline techniques, particularly under conditions of variable encryption speeds and obfuscated execution flows. The framework achieved scalable and computationally efficient performance, ensuring robust applicability across a range of system configurations, from resource-constrained environments to high-performance infrastructures. Notable findings included reduced false positive rates and enhanced detection latency, even for ransomware families employing sophisticated encryption mechanisms. The modular design allowed seamless integration of additional modalities, enabling extensibility and future-proofing against emerging threat vectors. Quantitative analyses further highlighted the system's energy efficiency, emphasizing its practicality for deployment in environments with stringent operational constraints. The results underline the importance of integrating advanced computational techniques and dynamic adaptability to safeguard digital ecosystems from increasingly complex threats.
SpatialVLA: Exploring Spatial Representations for Visual-Language-Action Model
Authors: Delin Qu, Haoming Song, Qizhi Chen, Yuanqi Yao, Xinyi Ye, Yan Ding, Zhigang Wang, Jiayuan Gu, Bin Zhao, Dong Wang, Xuelong Li
Date: 2025-01-27 00:00:00
Tasks: Robot Manipulation

In this paper, we claim that spatial understanding is the keypoint in robot manipulation, and propose SpatialVLA to explore effective spatial representations for the robot foundation model. Specifically, we introduce Ego3D Position Encoding to inject 3D information into the input observations of the visual-language-action model, and propose Adaptive Action Grids to represent spatial robot movement actions with adaptive discretized action grids, facilitating learning generalizable and transferrable spatial action knowledge for cross-robot control. SpatialVLA is first pre-trained on top of a vision-language model with 1.1 Million real-world robot episodes, to learn a generalist manipulation policy across multiple robot environments and tasks. After pre-training, SpatialVLA is directly applied to perform numerous tasks in a zero-shot manner. The superior results in both simulation and real-world robots demonstrate its advantage of inferring complex robot motion trajectories and its strong in-domain multi-task generalization ability. We further show the proposed Adaptive Action Grids offer a new and effective way to fine-tune the pre-trained SpatialVLA model for new simulation and real-world setups, where the pre-learned action grids are re-discretized to capture robot-specific spatial action movements of new setups. The superior results from extensive evaluations demonstrate the exceptional in-distribution generalization and out-of-distribution adaptation capability, highlighting the crucial benefit of the proposed spatial-aware representations for generalist robot policy learning. All the details and codes will be open-sourced.