High-quality test items are essential for educational assessments, particularly within Item Response Theory (IRT). Traditional validation methods rely on resource-intensive pilot testing to estimate item difficulty and discrimination. More recently, Item-Writing Flaw (IWF) rubrics emerged as a domain-general approach for evaluating test items based on textual features. However, their relationship to IRT parameters remains underexplored. To address this gap, we conducted a study involving over 7,000 multiple-choice questions across various STEM subjects (e.g., math and biology). Using an automated approach, we annotated each question with a 19-criteria IWF rubric and studied relationships to data-driven IRT parameters. Our analysis revealed statistically significant links between the number of IWFs and IRT difficulty and discrimination parameters, particularly in life and physical science domains. We further observed how specific IWF criteria can impact item quality more and less severely (e.g., negative wording vs. implausible distractors). Overall, while IWFs are useful for predicting IRT parameters--particularly for screening low-difficulty MCQs--they cannot replace traditional data-driven validation methods. Our findings highlight the need for further research on domain-general evaluation rubrics and algorithms that understand domain-specific content for robust item validation.

Whole-slide image classification represents a key challenge in computational pathology and medicine. Attention-based multiple instance learning (MIL) has emerged as an effective approach for this problem. However, the effect of attention mechanism architecture on model performance is not well-documented for biomedical imagery. In this work, we compare different methods and implementations of MIL, including deep learning variants. We introduce a new method using higher-dimensional feature spaces for deep MIL. We also develop a novel algorithm for whole-slide image classification where extreme machine learning is combined with attention-based MIL to improve sensitivity and reduce training complexity. We apply our algorithms to the problem of detecting circulating rare cells (CRCs), such as erythroblasts, in peripheral blood. Our results indicate that nonlinearities play a key role in the classification, as removing them leads to a sharp decrease in stability in addition to a decrease in average area under the curve (AUC) of over 4%. We also demonstrate a considerable increase in robustness of the model with improvements of over 10% in average AUC when higher-dimensional feature spaces are leveraged. In addition, we show that extreme learning machines can offer clear improvements in terms of training efficiency by reducing the number of trained parameters by a factor of 5 whilst still maintaining the average AUC to within 1.5% of the deep MIL model. Finally, we discuss options of enriching the classical computing framework with quantum algorithms in the future. This work can thus help pave the way towards more accurate and efficient single-cell diagnostics, one of the building blocks of precision medicine.

Young motorcyclists, particularly those aged 15 to 24 years old, face a heightened risk of severe crashes due to factors such as speeding, traffic violations, and helmet usage. This study aims to identify key factors influencing crash severity by analyzing 10,726 young motorcyclist crashes in Texas from 2017 to 2022. Two advanced tabular deep learning models, ARMNet and MambaNet, were employed, using an advanced resampling technique to address class imbalance. The models were trained to classify crashes into three severity levels, Fatal or Severe, Moderate or Minor, and No Injury. ARMNet achieved an accuracy of 87 percent, outperforming 86 percent of Mambanet, with both models excelling in predicting severe and no injury crashes while facing challenges in moderate crash classification. Key findings highlight the significant influence of demographic, environmental, and behavioral factors on crash outcomes. The study underscores the need for targeted interventions, including stricter helmet enforcement and educational programs customized to young motorcyclists. These insights provide valuable guidance for policymakers in developing evidence-based strategies to enhance motorcyclist safety and reduce crash severity.

Social media platforms are becoming the foundations of social interactions including messaging and opinion expression. In this regard, Sentiment Analysis techniques focus on providing solutions to ensure the retrieval and analysis of generated data including sentiments, emotions, and discussed topics. International competitions such as the International Workshop on Semantic Evaluation (SemEval) have attracted many researchers and practitioners with a special research interest in building sentiment analysis systems. In our work, we study top-ranking systems for each SemEval edition during the 2013-2021 period, a total of 658 teams participated in these editions with increasing interest over years. We analyze the proposed systems marking the evolution of research trends with a focus on the main components of sentiment analysis systems including data acquisition, preprocessing, and classification. Our study shows an active use of preprocessing techniques, an evolution of features engineering and word representation from lexicon-based approaches to word embeddings, and the dominance of neural networks and transformers over the classification phase fostering the use of ready-to-use models. Moreover, we provide researchers with insights based on experimented systems which will allow rapid prototyping of new systems and help practitioners build for future SemEval editions.

Electroencephalography (EEG) serves as an essential diagnostic tool in neurology; however, its accurate manual interpretation is a time-intensive process that demands highly specialized expertise, which remains relatively scarce and not consistently accessible. To address these limitations, the implementation of automated pre-screening and analysis systems for EEG data holds considerable promise. Advances in self-supervised learning made it possible to pre-train complex deep learning architectures on large volumes of unlabeled EEG data to learn generalizable representations, that can later be used to enhance performance on multiple tasks while needing less downstream data. In the present paper, we introduce BioSerenity-E1, the first of a family of self-supervised foundation models for clinical EEG applications that combines spectral tokenization with masked prediction to achieve state-of-the-art performance across relevant diagnostic tasks. The two-phase self-supervised pretraining framework initially acquires compressed EEG representations via a transformer-based VQ-VAE architecture designed to reconstruct log-multitaper spectral projections, then implements extensive (70% block) masked token prediction to force the model to learn complex spatiotemporal dependencies in EEG signals. BioSerenity-E1 achieves strong performance across three clinical tasks, either in line or above state-of-the-art methods: seizure detection (AUROC = 0.926, Sensitivity = 0.909), normal/abnormal classification (AUPRC = 0.970 on proprietary data; 0.910 on TUH-Abnormal), and multiclass pathology differentiation on unbalanced data (Weighted F1 = 0.730). The utility of BioSerenity-E1 is further confirmed in low-data regimes scenarios, showing clear improvements in AUPRC (from +2% to 17%) when trained on less than 10% of the available data.

Islands are diversity hotspots and vulnerable to environmental degradation, climate variations, land use changes and societal crises. These factors can exhibit interactive impacts on ecosystem services. The study reviewed a large number of papers on the climate change-islands-ecosystem services topic worldwide. Potential inclusion of land use changes and other drivers of impacts on ecosystem services were sequentially also recorded. The study sought to investigate the impacts of climate change, land use change, and other non-climatic driver changes on island ecosystem services. Explanatory variables examined were divided into two categories: environmental variables and methodological ones. Environmental variables include sea zone geographic location, ecosystem, ecosystem services, climate, land use, other driver variables, Methodological variables include consideration of policy interventions, uncertainty assessment, cumulative effects of climate change, synergistic effects of climate change with land use change and other anthropogenic and environmental drivers, and the diversity of variables used in the analysis. Machine learning and statistical methods were used to analyze their effects on island ecosystem services. Negative climate change impacts on ecosystem services are better quantified by land use change or other non-climatic driver variables than by climate variables. The synergy of land use together with climate changes is modulating the impact outcome and critical for a better impact assessment. Analyzed together, there is little evidence of more pronounced for a specific sea zone, ecosystem, or ecosystem service. Climate change impacts may be underestimated due to the use of a single climate variable deployed in most studies. Policy interventions exhibit low classification accuracy in quantifying impacts indicating insufficient efficacy or integration in the studies.

With the proliferation of images in online content, language-guided image retrieval (LGIR) has emerged as a research hotspot over the past decade, encompassing a variety of subtasks with diverse input forms. While the development of large multimodal models (LMMs) has significantly facilitated these tasks, existing approaches often address them in isolation, requiring the construction of separate systems for each task. This not only increases system complexity and maintenance costs, but also exacerbates challenges stemming from language ambiguity and complex image content, making it difficult for retrieval systems to provide accurate and reliable results. To this end, we propose ImageScope, a training-free, three-stage framework that leverages collective reasoning to unify LGIR tasks. The key insight behind the unification lies in the compositional nature of language, which transforms diverse LGIR tasks into a generalized text-to-image retrieval process, along with the reasoning of LMMs serving as a universal verification to refine the results. To be specific, in the first stage, we improve the robustness of the framework by synthesizing search intents across varying levels of semantic granularity using chain-of-thought (CoT) reasoning. In the second and third stages, we then reflect on retrieval results by verifying predicate propositions locally, and performing pairwise evaluations globally. Experiments conducted on six LGIR datasets demonstrate that ImageScope outperforms competitive baselines. Comprehensive evaluations and ablation studies further confirm the effectiveness of our design.

Recent advances in test-time scaling have shown promising results in improving Large Language Models (LLMs) performance through strategic computation allocation during inference. While this approach has demonstrated strong performance improvements in logical and mathematical reasoning tasks, its application to natural language generation (NLG), especially summarization, has yet to be explored. Multi-Document Summarization (MDS) is a challenging task that focuses on extracting and synthesizing useful information from multiple lengthy documents. Unlike reasoning tasks, MDS requires a more nuanced approach to prompt design and ensemble, as there is no "best" prompt to satisfy diverse summarization requirements. To address this, we propose a novel framework that leverages inference-time scaling for this task. Precisely, we take prompt ensemble approach by leveraging various prompt to first generate candidate summaries and then ensemble them with an aggregator to produce a refined summary. We also introduce two new evaluation metrics: Consistency-Aware Preference (CAP) score and LLM Atom-Content-Unit (ACU) score, to enhance LLM's contextual understanding while mitigating its positional bias. Extensive experiments demonstrate the effectiveness of our approach in improving summary quality while identifying and analyzing the scaling boundaries in summarization tasks.

Seven genes have been linked to particularly fast ageing of the brain – but 13 drugs and supplements might reduce their effects. The activity of many genes contributes to the difference between our actual age and the biological age of our brains, defined by how old our cells indicate we are, which creates what is known as a brain age gap. Restoring the brain's mitochondria could slow ageing and end dementia To find genes that accelerate brain ageing and widen this gap, Zhengxing Huang at Zhejiang University in China and his colleagues trained a deep-learning model called 3D-ViT on some medical records and used others to check it gave accurate responses. They then used it to analyse data from nearly 39,000 people who had health, genetic and lifestyle information, along with biological samples, stored in the UK Biobank. These participants were 64 years old, on average, and about half were women.

This paper presents a constraint-guided deep learning framework for developing physically consistent health indicators in bearing prognostics and health management. Conventional data-driven methods often lack physical plausibility, while physics-based models are limited by incomplete system knowledge. To address this, we integrate domain knowledge into deep learning using constraints to enforce monotonicity, bound output values between 1 and 0 (representing healthy to failed states), and ensure consistency between signal energy trends and health indicator estimates. This eliminates the need for complex loss term balancing. We implement constraint-guided gradient descent within an autoencoder architecture, creating a constrained autoencoder. However, the framework is adaptable to other architectures. Using time-frequency representations of accelerometer signals from the Pronostia dataset, our constrained model generates smoother, more reliable degradation profiles compared to conventional methods, aligning with expected physical behavior. Performance is assessed using three metrics: trendability, robustness, and consistency. Compared to a conventional baseline, the constrained model improves all three. Another baseline, incorporating monotonicity via a soft-ranking loss function, outperforms in trendability but falls short in robustness and consistency. An ablation study confirms that the monotonicity constraint enhances trendability, the boundary constraint ensures consistency, and the energy-health consistency constraint improves robustness. These findings highlight the effectiveness of constraint-guided deep learning in producing reliable, physically meaningful health indicators, offering a promising direction for future prognostic applications.