Technology-based interventions for Autism Spectrum Disorder (ASD) are frequently justified on the grounds that digital tools “increase engagement” and “enhance motivation.” However, across domains such as robot-assisted therapy, immersive environments (virtual and augmented reality), and ICT-based educational applications, outcomes labeled as engagement are often derived from observable indicators including gaze, time-on-task, interaction duration, task adherence, […]
Deerfield trots into pharma sales software space with CRM-enhancing tool
Deerfield Group has launched software designed to help pharma brand marketing teams keep sales reps on message.
Merck’s new AI commercial strategy ‘reimagining engagement with HCPs’
Merck & Co.’s $1 billion deal with Google Cloud is seeking to bolster its AI credentials—and the U.S. Big Pharma has some big plans for its commercial teams.
Cognitive Alignment At No Cost: Inducing Human Attention Biases For Interpretable Vision Transformers
arXiv:2604.20027v1 Announce Type: cross Abstract: For state-of-the-art image understanding, Vision Transformers (ViTs) have become the standard architecture but their processing diverges substantially from human attentional characteristics. We investigate whether this cognitive gap can be shrunk by fine-tuning the self-attention weights of Google’s ViT-B/16 on human saliency fixation maps. To isolate the effects of semantically relevant […]
Is Four Enough? Automated Reasoning Approaches and Dual Bounds for Condorcet Dimensions of Elections
arXiv:2604.19851v1 Announce Type: cross Abstract: In an election where $n$ voters rank $m$ candidates, a Condorcet winning set is a committee of $k$ candidates such that for any outside candidate, a majority of voters prefer some committee member. Condorcet’s paradox shows that some elections admit no Condorcet winning sets with a single candidate (i.e., $k=1$), […]
Generalization and Membership Inference Attack a Practical Perspective
arXiv:2604.19936v1 Announce Type: cross Abstract: With the emergence of new evaluation metrics and attack methodologies for Membership Inference Attacks (MIA), it becomes essential to reevaluate previously accepted assumptions. In this paper, we revisit the longstanding debate regarding the correlation between MIA success rates and model generalization using an empirical approach. We focused on employing augmentation […]
Enhancing ASR Performance in the Medical Domain for Dravidian Languages
arXiv:2604.19797v1 Announce Type: cross Abstract: Automatic Speech Recognition (ASR) for low-resource Dravidian languages like Telugu and Kannada faces significant challenges in specialized medical domains due to limited annotated data and morphological complexity. This work proposes a novel confidence-aware training framework that integrates real and synthetic speech data through a hybrid confidence mechanism combining static perceptual […]
Co-Located Tests, Better AI Code: How Test Syntax Structure Affects Foundation Model Code Generation
arXiv:2604.19826v1 Announce Type: cross Abstract: AI coding assistants increasingly generate code alongside tests. How developers structure test code, whether inline with the implementation or in separate blocks, has traditionally been a matter of testing philosophy. We investigate whether this choice affects AI code generation quality. We conduct a large-scale empirical study (830+ generated files, 12 […]
MambaLiteUNet: Cross-Gated Adaptive Feature Fusion for Robust Skin Lesion Segmentation
arXiv:2604.20286v1 Announce Type: cross Abstract: Recent segmentation models have demonstrated promising efficiency by aggressively reducing parameter counts and computational complexity. However, these models often struggle to accurately delineate fine lesion boundaries and texture patterns essential for early skin cancer diagnosis and treatment planning. In this paper, we propose MambaLiteUNet, a compact yet robust segmentation framework […]
Adaptive Conformal Anomaly Detection with Time Series Foundation Models for Signal Monitoring
arXiv:2604.20122v1 Announce Type: cross Abstract: We propose a post-hoc adaptive conformal anomaly detection method for monitoring time series that leverages predictions from pre-trained foundation models without requiring additional fine-tuning. Our method yields an interpretable anomaly score directly interpretable as a false alarm rate (p-value), facilitating transparent and actionable decision-making. It employs weighted quantile conformal prediction […]
Automated Detection of Dosing Errors in Clinical Trial Narratives: A Multi-Modal Feature Engineering Approach with LightGBM
arXiv:2604.19759v1 Announce Type: new Abstract: Clinical trials require strict adherence to medication protocols, yet dosing errors remain a persistent challenge affecting patient safety and trial integrity. We present an automated system for detecting dosing errors in unstructured clinical trial narratives using gradient boosting with comprehensive multi-modal feature engineering. Our approach combines 3,451 features spanning traditional […]
Towards Secure Logging: Characterizing and Benchmarking Logging Code Security Issues with LLMs
arXiv:2604.20211v1 Announce Type: cross Abstract: Logging code plays an important role in software systems by recording key events and behaviors, which are essential for debugging and monitoring. However, insecure logging practices can inadvertently expose sensitive information or enable attacks such as log injection, posing serious threats to system security and privacy. Prior research has examined […]