The increasing use of electronic health records (EHRs) for real-world evidence (RWE) studies is hindered by substantial heterogeneity in data collection practices and local coding schemes across healthcare providers. Data standardization—particularly the mapping of locally defined medical concepts to standardized vocabularies—is therefore a critical but labour-intensive step, traditionally relying on extensive manual review by clinical […]
Mobile health apps for older adults: real-world evidence on engagement and medication adherence
IntroductionA rapidly aging global population is placing increasing strain on healthcare systems. Digital health (mHealth) applications may support older adults in managing chronic conditions and adhering to medication, yet this population is often underrepresented in research. This study aimed to investigate engagement, retention, and adherence among adults aged ≥65 years using the Perx Health mobile […]
HELM: Harness-Enhanced Long-horizon Memory for Vision-Language-Action Manipulation
arXiv:2604.18791v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models fail systematically on long-horizon manipulation tasks despite strong short-horizon performance. We show that this failure is not resolved by extending context length alone in the current reactive execution setting; instead, it stems from three recurring execution-loop deficiencies: the memory gap, the verification gap, and the recovery gap. […]
Conjuring Semantic Similarity
arXiv:2410.16431v4 Announce Type: replace Abstract: The semantic similarity between sample expressions measures the distance between their latent ‘meaning’. These meanings are themselves typically represented by textual expressions. We propose a novel approach whereby the semantic similarity among textual expressions is based not on other expressions they can be rephrased as, but rather based on the […]
REVEAL: Multimodal Vision-Language Alignment of Retinal Morphometry and Clinical Risks for Incident AD and Dementia Prediction
arXiv:2604.18757v1 Announce Type: cross Abstract: The retina provides a unique, noninvasive window into Alzheimer’s disease (AD) and dementia, capturing early structural changes through morphometric features, while systemic and lifestyle risk factors reflect well-established contributors to disease susceptibility long before clinical symptom onset. However, current retinal analysis frameworks typically model imaging and risk factors separately, limiting […]
Adaptive MSD-Splitting: Enhancing C4.5 and Random Forests for Skewed Continuous Attributes
arXiv:2604.19722v1 Announce Type: cross Abstract: The discretization of continuous numerical attributes remains a persistent computational bottleneck in the induction of decision trees, particularly as dataset dimensions scale. Building upon the recently proposed MSD-Splitting technique — which bins continuous data using the empirical mean and standard deviation to dramatically improve the efficiency and accuracy of the […]
FASE : A Fairness-Aware Spatiotemporal Event Graph Framework for Predictive Policing
arXiv:2604.18644v1 Announce Type: cross Abstract: Predictive policing systems that allocate patrol resources based solely on predicted crime risk can unintentionally amplify racial disparities through feedback driven data bias. We present FASE, a Fairness Aware Spatiotemporal Event Graph framework, which integrates spatiotemporal crime prediction with fairness constrained patrol allocation and a closed loop deployment feedback simulator. […]
LASER: Learning Active Sensing for Continuum Field Reconstruction
arXiv:2604.19355v1 Announce Type: cross Abstract: High-fidelity measurements of continuum physical fields are essential for scientific discovery and engineering design but remain challenging under sparse and constrained sensing. Conventional reconstruction methods typically rely on fixed sensor layouts, which cannot adapt to evolving physical states. We propose LASER, a unified, closed-loop framework that formulates active sensing as […]
Sessa: Selective State Space Attention
arXiv:2604.18580v2 Announce Type: replace-cross Abstract: Modern sequence modeling is dominated by two families: Transformers, whose self-attention can access arbitrary elements of the visible sequence, and structured state-space models, which propagate information through an explicit recurrent state. These mechanisms face different limitations on long contexts: when attention is diffuse, the influence of individual tokens is diluted […]
Dual Triangle Attention: Effective Bidirectional Attention Without Positional Embeddings
arXiv:2604.18603v1 Announce Type: new Abstract: Bidirectional transformers are the foundation of many sequence modeling tasks across natural, biological, and chemical language domains, but they are permutation-invariant without explicit positional embeddings. In contrast, unidirectional attention inherently encodes positional information through its triangular mask, enabling models to operate without positional embeddings altogether. Here, we introduce Dual Triangle […]
Bootstrapping Code Translation with Weighted Multilanguage Exploration
arXiv:2601.03512v2 Announce Type: replace-cross Abstract: Code translation across multiple programming languages is essential yet challenging due to two vital obstacles: scarcity of parallel data paired with executable test oracles, and optimization imbalance when handling diverse language pairs. We propose BootTrans, a bootstrapping method that resolves both obstacles. Its key idea is to leverage the functional […]
Quantum AI for Cancer Diagnostic Biomarker Discovery
arXiv:2604.18621v1 Announce Type: new Abstract: Quantum machine learning offers a promising new paradigm for computational biology by leveraging quantum mechanical principles to enhance cancer classification, biomarker discovery, and bioinformatics diagnostics. In this study, we apply QML to identify subtype specific biomarkers for lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), the two predominant forms […]