Artificial Neural Networks (ANNs) have revolutionized computer vision with great performance, but their “black-box” nature creates significant challenges in domains requiring transparency, accountability, and regulatory compliance. The opacity of these systems hampers their adoption in critical applications where understanding decision-making processes is essential. Scientists are curious to understand these models’ internal mechanisms and want to… →
Stereo depth estimation plays a crucial role in computer vision by allowing machines to infer depth from two images. This capability is vital for autonomous driving, robotics, and augmented reality applications. Despite advancements in deep learning, many existing stereo-matching models require domain-specific fine-tuning to achieve high accuracy. The challenge lies in developing a model that… →
Modern VLMs struggle with tasks requiring complex visual reasoning, where understanding an image alone is insufficient, and deeper interpretation is needed. While recent advancements in LLMs have significantly improved text-based reasoning, similar progress in the visual domain remains limited. Existing VLMs often fail when required to combine visual and textual cues for logical deductions, highlighting… →
To evaluate the comparative efficacy of Ticagrelor and Clopidogrel in treating patients with coronary heart disease and unstable angina, as well as their effects on serum inflammatory factors, thereby providing a solid foundation for future clinical diagnosis and treatment. The frequency of angina attacks in the Ticagrelor group was lower than in the Clopidogrel group… →
LLMs are widely used for conversational AI, content generation, and enterprise automation. However, balancing performance with computational efficiency is a key challenge in this field. Many state-of-the-art models require extensive hardware resources, making them impractical for smaller enterprises. The demand for cost-effective AI solutions has led researchers to develop models that deliver high performance with… →
Normalization layers have become fundamental components of modern neural networks, significantly improving optimization by stabilizing gradient flow, reducing sensitivity to weight initialization, and smoothing the loss landscape. Since the introduction of batch normalization in 2015, various normalization techniques have been developed for different architectures, with layer normalization (LN) becoming particularly dominant in Transformer models. Their… →
BACKGROUND: Chronic Plantar Fasciitis (CPF) is commonly associated with elevated levels of anxiety and pain catastrophizing. Pain Neuroscience Education (PNE) has shown promise in addressing these psychological components, but high-quality evidence assessing its combined impact with physiotherapy for CPF is limited. →
CONCLUSIONS: The effect of the AD-program was not statistically significant in this RCT. Limitations include that this study was underpowered due to recruitment difficulties during the COVID-19 pandemic. More research on the efficacy and implementation of the AD-program is warranted. →