Scientific computing has benefited from using operator networks to enhance or replace numerical computation for the purpose of simulation and forecasting on a wide array of applications to include computational fluid dynamics and weather forecasting [3]. The two primary neural operators that demonstrated immediate success are the deep operator network (DeepONet) [4] based on the universal approximation theorem in [5], and the Fourier neural operator (FNO) [6]. The basic DeepONet approximates the operator by applying a weighted sum to the product of each of the transformed outputs from two FNN sub-networks. The upper sub-network, or branch net, is applied to the input functions while the lower trunk net is applied to the querying locations of the output function. In contrast, the FNO is a particular type of Neural Operator network [7], which accepts only input functions (not querying locations for the output) and applies a global transformation on the function input via a more intricate architecture. Motivated by fundamental solutions to partial differential equations (PDEs), the FNO network sums the output of an integral kernel transformation to the input function with the output of a linear transformation. The sum is then passed through a non-linear activation function. To accelerate the integral kernel transformation, the FNO applies a Fourier transform (FT) to the input data, with the FT of the integral kernel assumed as trainable parameters.

The rapid advancements in machine learning have made its application to anomalous diffusion analysis both essential and inevitable. This review systematically introduces the integration of machine learning techniques for enhanced analysis of anomalous diffusion, focusing on two pivotal aspects: single trajectory characterization via machine learning and representation learning of anomalous diffusion. We extensively compare various machine learning methods, including both classical machine learning and deep learning, used for the inference of diffusion parameters and trajectory segmentation. Additionally, platforms such as the Anomalous Diffusion Challenge that serve as benchmarks for evaluating these methods are highlighted. On the other hand, we outline three primary strategies for representing anomalous diffusion: the combination of predefined features, the feature vector from the penultimate layer of neural network, and the latent representation from the autoencoder, analyzing their applicability across various scenarios. This investigation paves the way for future research, offering valuable perspectives that can further enrich the study of anomalous diffusion and advance the application of artificial intelligence in statistical physics and biophysics.

Artificial Intelligence (AI) is transforming diverse societal domains, raising critical questions about its risks and benefits and the misalignments between public expectations and academic visions. This study examines how the general public (N=1110) -- people using or being affected by AI -- and academic AI experts (N=119) -- people shaping AI development -- perceive AI's capabilities and impact across 71 scenarios, including sustainability, healthcare, job performance, societal divides, art, and warfare. Participants evaluated each scenario on four dimensions: expected probability, perceived risk and benefit, and overall sentiment (or value). The findings reveal significant quantitative differences: experts anticipate higher probabilities, perceive lower risks, report greater utility, and express more favorable sentiment toward AI compared to the non-experts. Notably, risk-benefit tradeoffs differ: the public assigns risk half the weight of benefits, while experts assign it only a third. Visual maps of these evaluations highlight areas of convergence and divergence, identifying potential sources of public concern. These insights offer actionable guidance for researchers and policymakers to align AI development with societal values, fostering public trust and informed governance.

Graph Neural Networks (GNNs) have emerged as a promising tool to handle data exhibiting an irregular structure. However, most GNN architectures perform well on homophilic datasets, where the labels of neighboring nodes are likely to be the same. In recent years, an increasing body of work has been devoted to the development of GNN architectures for heterophilic datasets, where labels do not exhibit this low-pass behavior. In this work, we create a new graph in which nodes are connected if they share structural characteristics, meaning a higher chance of sharing their labels, and then use this new graph in the GNN architecture. To do this, we compute the k-nearest neighbors graph according to distances between structural features, which are either (i) role-based, such as degree, or (ii) global, such as centrality measures. Experiments show that the labels are smoother in this newly defined graph and that the performance of GNN architectures improves when using this alternative structure.

The National Institute of Health defines Sudden Cardiac Arrest (SCA) as a moment when the heart is not beating sufficiently to maintain perfusion due to the heart's electrical or mechanical failure [1]. SCA is the leading cause of death among youth athletes -- a focus group that has a heightened risk of SCA -- with 1 in 16,000 young athletes and 1 in 5200 athletes at the elite level afflicted yearly [1, 2]. For youth athletes, the primary cause of SCA is hypertrophic cardiomyopathy (HCM) in the U.S. and arrhythmogenic right ventricular cardiomyopathy (ARVC) in Europe. SCA may also result from coronary artery disease, Long QT Syndrome, Myocarditis, Wolff-Parkinson-White syndrome, and dilated cardiomyopathy [1-4]. Figure 1 provides a comprehensive list of significant predictors of SCA [5]. While these disorders do not always lead to instances of SCA, they present a substantial increase in the chance of SCA events, which is further amplified by the innate risk of sports participation [6-9]. Concerningly, the current 14-point questionnaire pre-participation evaluation (PPE) recommended by the American Heart Association (AHA) is ineffective at detecting risk factors with poor sensitivity and specificity of 18.8% and 68.0%

This study explores how the Rashomon effect influences variable importance in the context of student demographics used for academic outcomes prediction. Our research follows the way machine learning algorithms are employed in Educational Data Mining, focusing on highlighting the so-called Rashomon effect. The study uses the Rashomon set of simple-yet-accurate models trained using decision trees, random forests, light GBM, and XGBoost algorithms with the Open University Learning Analytics Dataset. We found that the Rashomon set improves the predictive accuracy by 2-6%. Variable importance analysis revealed more consistent and reliable results for binary classification than multiclass classification, highlighting the complexity of predicting multiple outcomes. Key demographic variables imd_band and highest_education were identified as vital, but their importance varied across courses, especially in course DDD. These findings underscore the importance of model choice and the need for caution in generalizing results, as different models can lead to different variable importance rankings. The codes for reproducing the experiments are available in the repository: https://anonymous.4open.science/r/JEDM_paper-DE9D.

Hand preference and degree of handedness (DoH) are two different aspects of human behavior which are often confused to be one. DoH is a person's inherent capability of the brain; affected by nature and nurture. In this study, we used dominant and non-dominant handwriting traits to assess DoH for the first time, on 43 subjects of three categories- Unidextrous, Partially Unidextrous, and Ambidextrous. Features extracted from the segmented handwriting signals called strokes were used for DoH quantification. Davies Bouldin Index, Multilayer perceptron, and Convolutional Neural Network (CNN) were used for automated grading of DoH. The outcomes of these methods were compared with the widely used DoH assessment questionnaires from Edinburgh Inventory (EI). The CNN based automated grading outperformed other computational methods with an average classification accuracy of 95.06% under stratified 10-fold cross-validation. The leave-one-subject-out strategy on this CNN resulted in a test individual's DoH score which was converted into a 4-point score. Around 90% of the obtained scores from all the implemented computational methods were found to be in accordance with the EI scores under 95% confidence interval. Automated grading of degree of handedness using handwriting signals can provide more resolution to the Edinburgh Inventory scores. This could be used in multiple applications concerned with neuroscience, rehabilitation, physiology, psychometry, behavioral sciences, and forensics.

Accurate origin-destination (OD) passenger flow prediction is crucial for enhancing metro system efficiency, optimizing scheduling, and improving passenger experiences. However, current models often fail to effectively capture the asynchronous departure characteristics of OD flows and underutilize the inflow and outflow data, which limits their prediction accuracy. To address these issues, we propose CSP-AIT-Net, a novel spatiotemporal graph attention framework designed to enhance OD flow prediction by incorporating asynchronous inflow tracking and advanced station semantics representation. Our framework restructures the OD flow prediction paradigm by first predicting outflows and then decomposing OD flows using a spatiotemporal graph attention mechanism. To enhance computational efficiency, we introduce a masking mechanism and propose asynchronous passenger flow graphs that integrate inflow and OD flow with conservation constraints. Furthermore, we employ contrastive learning to extract high-dimensional land use semantics of metro stations, enriching the contextual understanding of passenger mobility patterns. Validation of the Shanghai metro system demonstrates improvement in short-term OD flow prediction accuracy over state-of-the-art methods. This work contributes to enhancing metro operational efficiency, scheduling precision, and overall system safety.

Many challenges in science and engineering, such as drug discovery and communication network design, involve optimizing complex and expensive black-box functions across vast search spaces. Thus, it is essential to leverage existing data to avoid costly active queries of these black-box functions. To this end, while Offline Black-Box Optimization (BBO) is effective for deterministic problems, it may fall short in capturing the stochasticity of real-world scenarios. To address this, we introduce Stochastic Offline BBO (SOBBO), which tackles both black-box objectives and uncontrolled uncertainties. We propose two solutions: for large-data regimes, a differentiable surrogate allows for gradient-based optimization, while for scarce-data regimes, we directly estimate gradients under conservative field constraints, improving robustness, convergence, and data efficiency. Numerical experiments demonstrate the effectiveness of our approach on both synthetic and real-world tasks.

Background: Explainability in phishing detection model can support a further solution of phishing attack mitigation by increasing trust and understanding how phishing can be detected. Objective: The aims of this study to determine and best recommendation to apply an approach which has several components with abilities to fulfil the critical needs Methods: A methodology starting with analyzing both black-box and white-box models to get the pros and cons specifically in phishing detection. The conclusion of the analysis will be validated by experiment using a set of well-known algorithms and public phishing datasets. Experimental metrics covers 3 measurements such as predictive accuracy and explainability metrics. Conclusion: Both models are comparable in terms of interpretability and consistency, with room for improvement in diverse datasets. EBM as an example of white-box model is generally better suited for applications requiring explainability and actionable insights. Finally, each model, white-box and black-box model has positive and negative aspects both for performance metric and for explainable metric. It is important to consider the objective of model usage.