Understanding factors contributing to premature mortality is critical for public health planning. This study examines the relationships between premature death rates and multiple risk factors across several Texas counties, utilizing EPA air quality data, Census information, and county health records from recent years. We analyze the impact of air quality (PM2.5 levels), socioeconomic factors (median household income), and health conditions (COPD prevalence) through statistical analysis and modeling techniques. Results reveal COPD prevalence as a strong predictor of premature death rates, with higher prevalence associated with a substantial increase in years of potential life lost. While socioeconomic factors show a significant negative correlation, air quality demonstrates more complex indirect relationships. These findings emphasize the need for integrated public health interventions that prioritize key health conditions while addressing underlying socioeconomic disparities.

The question of whether AI substitutes or complements human work is central to debates on the future of work. This paper examines the impact of AI on skill demand and compensation in the U.S. economy, analysing 12 million online job vacancies from 2018 to 2023. It investigates internal effects (within-job substitution and complementation) and external effects (across occupations, industries, and regions). Our findings reveal a significant increase in demand for AI-complementary skills, such as digital literacy, teamwork, and resilience, alongside rising wage premiums for these skills in AI roles like Data Scientist. Conversely, substitute skills, including customer service and text review, have declined in both demand and value within AI-related positions. Examining external effects, we find a notable rise in demand for complementary skills in non-AI roles linked to the growth of AI-related jobs in specific industries or regions. At the same time, there is a moderate decline in non-AI roles requiring substitute skills. Overall, AI's complementary effect is up to 50% larger than its substitution effect, resulting in net positive demand for skills. These results, replicated for the UK and Australia, highlight AI's transformative impact on workforce skill requirements. They suggest reskilling efforts should prioritise not only technical AI skills but also complementary skills like ethics and digital literacy.

Artificial intelligence (AI) has emerged as a powerful tool to enhance decision-making and optimize treatment protocols in in vitro fertilization (IVF). In particular, AI shows significant promise in supporting decision-making during the ovarian stimulation phase of the IVF process. This review evaluates studies focused on the applications of AI combined with medical imaging in ovarian stimulation, examining methodologies, outcomes, and current limitations. Our analysis of 13 studies on this topic reveals that, reveal that while AI algorithms demonstrated notable potential in predicting optimal hormonal dosages, trigger timing, and oocyte retrieval outcomes, the medical imaging data utilized predominantly came from two-dimensional (2D) ultrasound which mainly involved basic quantifications, such as follicle size and number, with limited use of direct feature extraction or advanced image analysis techniques. This points to an underexplored opportunity where advanced image analysis approaches, such as deep learning, and more diverse imaging modalities, like three-dimensional (3D) ultrasound, could unlock deeper insights. Additionally, the lack of explainable AI (XAI) in most studies raises concerns about the transparency and traceability of AI-driven decisions - key factors for clinical adoption and trust. Furthermore, many studies relied on single-center designs and small datasets, which limit the generalizability of their findings. This review highlights the need for integrating advanced imaging analysis techniques with explainable AI methodologies, as well as the importance of leveraging multicenter collaborations and larger datasets. Addressing these gaps has the potential to enhance ovarian stimulation management, paving the way for efficient, personalized, and data-driven treatment pathways that improve IVF outcomes.

The Knowledge Tracing (KT) task focuses on predicting a learner's future performance based on the historical interactions. The knowledge state plays a key role in learning process. However, considering that the knowledge state is influenced by various learning factors in the interaction process, such as the exercises similarities, responses reliability and the learner's learning state. Previous models still face two major limitations. First, due to the exercises differences caused by various complex reasons and the unreliability of responses caused by guessing behavior, it is hard to locate the historical interaction which is most relevant to the current answered exercise. Second, the learning state is also a key factor to influence the knowledge state, which is always ignored by previous methods. To address these issues, we propose a new method named Learning State Enhanced Knowledge Tracing (LSKT). Firstly, to simulate the potential differences in interactions, inspired by Item Response Theory~(IRT) paradigm, we designed three different embedding methods ranging from coarse-grained to fine-grained views and conduct comparative analysis on them. Secondly, we design a learning state extraction module to capture the changing learning state during the learning process of the learner. In turn, with the help of the extracted learning state, a more detailed knowledge state could be captured. Experimental results on four real-world datasets show that our LSKT method outperforms the current state-of-the-art methods.

Despite advances in AI's performance and interpretability, AI advisors can undermine experts' decisions and increase the time and effort experts must invest to make decisions. Consequently, AI systems deployed in high-stakes settings often fail to consistently add value across contexts and can even diminish the value that experts alone provide. Beyond harm in specific domains, such outcomes impede progress in research and practice, underscoring the need to understand when and why different AI advisors add or diminish value. To bridge this gap, we stress the importance of assessing the value AI advice brings to real-world contexts when designing and evaluating AI advisors. Building on this perspective, we characterize key pillars -- pathways through which AI advice impacts value -- and develop a framework that incorporates these pillars to create reliable, personalized, and value-adding advisors. Our results highlight the need for system-level, value-driven development of AI advisors that advise selectively, are tailored to experts' unique behaviors, and are optimized for context-specific trade-offs between decision improvements and advising costs. They also reveal how the lack of inclusion of these pillars in the design of AI advising systems may be contributing to the failures observed in practical applications.

The prevailing educational methods predominantly rely on traditional classroom instruction or online delivery, often limiting the teachers' ability to engage effectively with all the students simultaneously. A more intrinsic method of evaluating student attentiveness during lectures can enable the educators to tailor the course materials and their teaching styles in order to better meet the students' needs. The aim of this paper is to enhance teaching quality in real time, thereby fostering a higher student engagement in the classroom activities. By monitoring the students' electroencephalography (EEG) signals and employing machine learning algorithms, this study proposes a comprehensive solution for addressing this challenge. Machine learning has emerged as a powerful tool for simplifying the analysis of complex variables, enabling the effective assessment of the students' concentration levels based on specific parameters. However, the real-time impact of machine learning models necessitates a careful consideration as their deployment is concerned. This study proposes a machine learning-based approach for predicting the level of students' comprehension with regard to a certain topic. A browser interface was introduced that accesses the values of the system's parameters to determine a student's level of concentration on a chosen topic. The deployment of the proposed system made it necessary to address the real-time challenges faced by the students, consider the system's cost, and establish trust in its efficacy. This paper presents the efforts made for approaching this pertinent issue through the implementation of innovative technologies and provides a framework for addressing key considerations for future research directions.

Background and Objective: Radiation pneumonitis (RP) is a side effect of thoracic radiation therapy. Recently, Machine learning (ML) models enhanced with radiomic and dosiomic features provide better predictions by incorporating spatial information beyond DVHs. However, to improve the clinical decision process, we propose to use uncertainty quantification (UQ) to improve the confidence in model prediction. This study evaluates the impact of post hoc UQ methods on the discriminative performance and calibration of ML models for RP prediction. Methods: This study evaluated four ML models: logistic regression (LR), support vector machines (SVM), extreme gradient boosting (XGB), and random forest (RF), using radiomic, dosiomic, and dosimetric features to predict RP. We applied UQ methods, including Patt scaling, isotonic regression, Venn-ABERS predictor, and Conformal Prediction, to quantify uncertainty. Model performance was assessed through Area Under the Receiver Operating Characteristic curve (AUROC), Area Under the Precision-Recall Curve (AUPRC), and Adaptive Calibration Error (ACE) using Leave-One-Out Cross-Validation (LOO-CV). Results: UQ methods enhanced predictive performance, particularly for high-certainty predictions, while also improving calibration. Radiomic and dosiomic features increased model accuracy but introduced calibration challenges, especially for non-linear models like XGB and RF. Performance gains from UQ methods were most noticeable at higher certainty thresholds. Conclusion: Integrating UQ into ML models with radiomic and dosiomic features improves both predictive accuracy and calibration, supporting more reliable clinical decision-making. The findings emphasize the value of UQ methods in enhancing applicability of predictive models for RP in healthcare settings.

Supporting the health and well-being of dynamic populations around the world requires governmental agencies, organizations and researchers to understand and reason over complex relationships between human behavior and local contexts in order to identify high-risk groups and strategically allocate limited resources. Traditional approaches to these classes of problems often entail developing manually curated, task-specific features and models to represent human behavior and the natural and built environment, which can be challenging to adapt to new, or even, related tasks. To address this, we introduce a Population Dynamics Foundation Model (PDFM) that aims to capture the relationships between diverse data modalities and is applicable to a broad range of geospatial tasks. We first construct a geo-indexed dataset for postal codes and counties across the United States, capturing rich aggregated information on human behavior from maps, busyness, and aggregated search trends, and environmental factors such as weather and air quality. We then model this data and the complex relationships between locations using a graph neural network, producing embeddings that can be adapted to a wide range of downstream tasks using relatively simple models. We evaluate the effectiveness of our approach by benchmarking it on 27 downstream tasks spanning three distinct domains: health indicators, socioeconomic factors, and environmental measurements. The approach achieves state-of-the-art performance on all 27 geospatial interpolation tasks, and on 25 out of the 27 extrapolation and super-resolution tasks. We combined the PDFM with a state-of-the-art forecasting foundation model, TimesFM, to predict unemployment and poverty, achieving performance that surpasses fully supervised forecasting. The full set of embeddings and sample code are publicly available for researchers.

Stochastic Gradient Descent (SGD) with gradient clipping is a powerful technique for enabling differentially private optimization. Although prior works extensively investigated clipping with a constant threshold, private training remains highly sensitive to threshold selection, which can be expensive or even infeasible to tune. This sensitivity motivates the development of adaptive approaches, such as quantile clipping, which have demonstrated empirical success but lack a solid theoretical understanding. This paper provides the first comprehensive convergence analysis of SGD with quantile clipping (QC-SGD). We demonstrate that QC-SGD suffers from a bias problem similar to constant-threshold clipped SGD but show how this can be mitigated through a carefully designed quantile and step size schedule. Our analysis reveals crucial relationships between quantile selection, step size, and convergence behavior, providing practical guidelines for parameter selection. We extend these results to differentially private optimization, establishing the first theoretical guarantees for DP-QC-SGD. Our findings provide theoretical foundations for widely used adaptive clipping heuristic and highlight open avenues for future research.

In this article, we introduce \textsf{LASER} (Locally Adaptive Smoothing Estimator for Regression), a computationally efficient locally adaptive nonparametric regression method that performs variable bandwidth local polynomial regression. We prove that it adapts (near-)optimally to the local H\"{o}lder exponent of the underlying regression function \texttt{simultaneously} at all points in its domain. Furthermore, we show that there is a single ideal choice of a global tuning parameter under which the above mentioned local adaptivity holds. Despite the vast literature on nonparametric regression, instances of practicable methods with provable guarantees of such a strong notion of local adaptivity are rare. The proposed method achieves excellent performance across a broad range of numerical experiments in comparison to popular alternative locally adaptive methods.