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From Occurrence to Consequence: A Comprehensive Data-driven Analysis of Building Fire Risk

  • Building fires pose a persistent threat to life, property, and infrastructure.
  • A data-driven framework analyzes U.S. fire risks by integrating over one million fire incident reports with various datasets.
  • Vulnerable communities face higher fire risks due to socioeconomic disparities and outdated or vacant buildings.
  • Targeted interventions, such as mandating fire safety systems and providing subsidies, can enhance fire prevention and protect vulnerable groups.

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Validating Emergency Department Admission Predictions Based on Local Data Through MIMIC-IV

  • This study validates hospital admission prediction models initially developed using a small local dataset from a Greek hospital.
  • Using the comprehensive MIMIC-IV dataset, five algorithms were evaluated for prediction: Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Random Forest (RF), Recursive Partitioning and Regression Trees (RPART), and Support Vector Machines (SVM Radial).
  • Random Forest (RF) demonstrated superior performance, achieving an Area Under the Receiver Operating Characteristic Curve (AUC-ROC) of 0.9999, sensitivity of 0.9997, and specificity of 0.9999 when applied to the MIMIC-IV data.
  • These findings highlight the robustness of Random Forest in handling complex datasets for admission prediction and provide actionable insights for improving Emergency Department (ED) management strategies.

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Hierarchical Adaptive Expert for Multimodal Sentiment Analysis

  • Multimodal sentiment analysis is crucial for understanding human emotions across various communication channels.
  • A novel framework called Hierarchical Adaptive Expert for Multimodal Sentiment Analysis (HAEMSA) is proposed to address the challenge of effectively integrating modality-shared and modality-specific information.
  • HAEMSA utilizes an evolutionary optimization approach, cross-modal knowledge transfer, and multi-task learning to capture both global and local modality representations.
  • Extensive experiments demonstrate that HAEMSA outperforms previous methods, achieving improved accuracy and reducing mean absolute error on multiple benchmark datasets.

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Why Representation Engineering Works: A Theoretical and Empirical Study in Vision-Language Models

  • Representation Engineering (RepE) is a powerful paradigm for enhancing AI transparency.
  • In Vision-Language Models (VLMs), RepE can address challenges related to visual input overriding factual linguistic knowledge.
  • Theoretical framework explaining stability of neural activity in VLMs is developed using principal eigenvector.
  • This work transforms RepE into a structured theoretical framework, opening new directions for improving AI robustness, fairness, and transparency.

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PowerGNN: A Topology-Aware Graph Neural Network for Electricity Grids

  • The paper introduces a topology-aware Graph Neural Network (GNN) framework for predicting power system states in electricity grids with high renewable integration.
  • The GNN model utilizes a graph-based representation of the power network, capturing both spatial and temporal correlations in system dynamics.
  • It outperforms baseline approaches, achieving substantial improvements in predictive accuracy with average RMSEs of 0.13 to 0.17 across all predicted variables.
  • The results highlight the potential of topology-aware learning for scalable and robust power system forecasting in future grids with high renewable penetration.

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Zero-Shot LLMs in Human-in-the-Loop RL: Replacing Human Feedback for Reward Shaping

  • Reinforcement learning often faces challenges with reward misalignment, where agents optimize for given rewards but fail to exhibit the desired behaviors.
  • To address these issues, the proposed approach utilizes zero-shot, off-the-shelf large language models (LLMs) for reward shaping in continuous control tasks.
  • The LLM-HFBF framework is introduced to identify and correct biases in human feedback while incorporating it into the reward shaping process.
  • Empirical experiments demonstrate that the proposed approach reduces reliance on potentially biased human guidance and maintains high reinforcement learning performance.

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A Spatial-temporal Deep Probabilistic Diffusion Model for Reliable Hail Nowcasting with Radar Echo Extrapolation

  • A Spatial-temporal Deep Probabilistic Diffusion Model called SteamCast has been introduced for hail nowcasting with radar echo extrapolation.
  • The model is trained on historical reanalysis archive from Yan'an Meteorological Bureau in China.
  • SteamCast provides 30-minute nowcasts at 6-minute intervals for a single radar reflectivity variable, across 9 different vertical angles, with high resolution.
  • The model delivers competitive results compared to other deep learning-based models for hail nowcasting.

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Uncertainty Weighted Gradients for Model Calibration

  • Model calibration is important for accurate predictions in deep neural networks.
  • Existing loss functions, like focal loss, fail to achieve optimal calibration performance.
  • A new loss framework is proposed, addressing misalignment and uncertainty estimation issues.
  • Extensive experiments show the proposed method achieves state-of-the-art performance.

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Harnessing Mixed Features for Imbalance Data Oversampling: Application to Bank Customers Scoring

  • This study proposes an oversampling strategy called MGS-GRF for rare event detection in binary classification on tabular data.
  • MGS-GRF is designed to handle mixed features (continuous and categorical variables) and exhibits coherence and association properties.
  • The method uses a kernel density estimator and locally estimated full-rank covariances to generate continuous features, while categorical features are drawn from the original samples through a generalized random forest.
  • Experimental results show that MGS-GRF outperforms other synthetic procedures in terms of predictive performances, as evaluated on both simulated and real-world datasets.

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MoRE-LLM: Mixture of Rule Experts Guided by a Large Language Model

  • The paper introduces a method called Mixture of Rule Experts guided by a Large Language Model (MoRE-LLM) to align machine learning models with human domain knowledge.
  • MoRE-LLM combines a data-driven black-box model with knowledge extracted from a Large Language Model (LLM) to enable domain knowledge-aligned and transparent predictions.
  • The Mixture of Rule Experts (MoRE) generates local rule-based surrogates during training and utilizes them for the classification task, while the LLM enhances domain knowledge alignment and provides context to the rules.
  • The proposed method ensures interpretability and is evaluated on various datasets, comparing its performance with interpretable and non-interpretable baselines.

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Reasoning Beyond Limits: Advances and Open Problems for LLMs

  • Recent generative reasoning breakthroughs have transformed how large language models (LLMs) tackle complex problems.
  • Techniques such as inference-time scaling, reinforcement learning, supervised fine-tuning, and distillation have improved reasoning capabilities of LLM models.
  • A comprehensive analysis of the top 27 LLM models released between 2023 and 2025 is presented.
  • Key challenges in advancing LLM capabilities, including improving multi-step reasoning, overcoming limitations in chained tasks, and enhancing long-context retrieval are discussed.

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RBFleX-NAS: Training-Free Neural Architecture Search Using Radial Basis Function Kernel and Hyperparameter Detection

  • RBFleX-NAS is a training-free Neural Architecture Search (NAS) framework that utilizes the Radial Basis Function (RBF) kernel and a detection algorithm.
  • Conventional NAS techniques require extensive training for evaluating candidate networks.
  • RBFleX-NAS outperforms other training-free NAS methods in terms of top-1 accuracy and search time.
  • RBFleX-NAS introduces NAFBee, an extended activation design space, for improved activation function exploration.

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A Methodology to extract Geo-Referenced Standard Routes from AIS Data

  • This study proposes a methodology to analyze route between maritime points of interest and extract geo-referenced standard routes from raw AIS data.
  • The methodology involves segmenting AIS data into distinct routes using a finite state machine (FSM) and aggregating the segments based on departure and destination ports.
  • Iterative density-based clustering is used to model the routes, with clustering parameters assigned manually and extended to the entire dataset using linear regression.
  • The unsupervised approach has been tested on a six-year AIS dataset covering the Arctic region and the Europe, Middle East, North Africa areas, proving effective in extracting standard routes with less than 5% outliers.

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Cyborg Data: Merging Human with AI Generated Training Data

  • Automated scoring systems used in large-scale assessment traditionally require a large quantity of hand-scored data for accurate predictions.
  • Generative Large Language Models can generalize to new tasks with little to no data but still need fine-tuning.
  • The proposed model distillation pipeline, named 'Cyborg Data', combines human and machine-scored responses in training.
  • Student models trained on 'Cyborg Data' achieve performance similar to training on the entire dataset, using only 10% of the original hand-scored data.

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ShieldAgent: Shielding Agents via Verifiable Safety Policy Reasoning

  • ShieldAgent is a guardrail agent designed to enforce safety policy compliance for other autonomous agents.
  • It constructs a safety policy model by extracting verifiable rules from policy documents and generates a shielding plan.
  • ShieldAgent-Bench, a dataset with 3K safety-related pairs of agent instructions and action trajectories, is introduced.
  • Experiments show that ShieldAgent outperforms prior methods, achieving high precision and efficiency in safeguarding agents.

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