After so many announcements of new facility builds in the US, Eli Lilly has made a welcome commitment to a European investment.
Artificial intelligence in oncology: promise, peril, and the future of patient–physician interaction
Artificial intelligence (AI) is increasingly embedded in oncology. While initial technical evaluations emphasize diagnostic accuracy and efficiency, the impact on patient–physician interaction (PPI)—the foundation of trust, communication, comprehension, and shared decision-making—remains underexplored. In this review, we studied the current development of AI technology facing both physicians and patients with a focus in cancer care. Among […]
Healthcare practitioners’ acceptance of using telehealth in the Kingdom of Saudi Arabia: an application of the unified theory of acceptance and use of technology model
IntroductionTelehealth offers several advantages over traditional in-person clinic visits. Despite its potential benefits, some barriers affect the optimal use of telehealth. Understanding healthcare practitioners’ (HCPs) acceptance of telehealth is essential to ensure the successful, high-quality, and safe implementation of telehealth programs. However, a comprehensive, theory-driven understanding of the factors influencing HCPs’ acceptance of telehealth in […]
Arcutis taps ‘90210’ star power to spell out the needs of people with skin conditions
Arcutis Biotherapeutics has forged another celebrity partnership, signing up the actor Tori Spelling and her teenage daughter Stella McDermott to encourage people with inflammatory skin conditions to talk to their healthcare providers.
Insulet campaign takes aim at workplace challenges for people with diabetes
Insulet is taking diabetes awareness into the workplace. Having found 79% of people with diabetes have faced bias or misunderstanding at work, the medtech giant is rolling out a range of resources intended to trigger changes in how workplaces approach the condition.
The Hidden Power of Normalization: Exponential Capacity Control in Deep Neural Networks
arXiv:2511.00958v1 Announce Type: cross Abstract: Normalization methods are fundamental components of modern deep neural networks (DNNs). Empirically, they are known to stabilize optimization dynamics and improve generalization. However, the underlying theoretical mechanism by which normalization contributes to both optimization and generalization remains largely unexplained, especially when using many normalization layers in a DNN architecture. In […]
FeNN-DMA: A RISC-V SoC for SNN acceleration
arXiv:2511.00732v1 Announce Type: cross Abstract: Spiking Neural Networks (SNNs) are a promising, energy-efficient alternative to standard Artificial Neural Networks (ANNs) and are particularly well-suited to spatio-temporal tasks such as keyword spotting and video classification. However, SNNs have a much lower arithmetic intensity than ANNs and are therefore not well-matched to standard accelerators like GPUs and […]
Parameter Interpolation Adversarial Training for Robust Image Classification
arXiv:2511.00836v1 Announce Type: cross Abstract: Though deep neural networks exhibit superior performance on various tasks, they are still plagued by adversarial examples. Adversarial training has been demonstrated to be the most effective method to defend against adversarial attacks. However, existing adversarial training methods show that the model robustness has apparent oscillations and overfitting issues in […]
Region-Aware Reconstruction Strategy for Pre-training fMRI Foundation Model
arXiv:2511.00443v1 Announce Type: cross Abstract: The emergence of foundation models in neuroimaging is driven by the increasing availability of large-scale and heterogeneous brain imaging datasets. Recent advances in self-supervised learning, particularly reconstruction-based objectives, have demonstrated strong potential for pretraining models that generalize effectively across diverse downstream functional MRI (fMRI) tasks. In this study, we explore […]
FlashEVA: Accelerating LLM inference via Efficient Attention
arXiv:2511.00576v1 Announce Type: cross Abstract: Transformer models have revolutionized natural language processing, achieving state-of-the-art performance and demonstrating remarkable scalability. However, their memory demands, particularly due to maintaining full context in memory, pose significant challenges for inference. In this paper, we present FlashEVA, an efficient implementation of EVA (Efficient Attention via Control Variates), and demonstrate how […]