Net, a new vision framework that not only segments crops and weeds at the pixel level but also predicts vegetation beyond the camera’s current field of view.
Stacker on MSN
Beyond generic: Why Gen Z’s desire for custom experiences makes luxury gifting a retention edge
Compartés reports Gen Z favors personalized gifting over generic items, valuing authenticity and emotional connection in ...
Ultrafiltration membranes used in pharmaceutical manufacturing and other industrial processes have long relied on separating ...
The Brighterside of News on MSN
New light-based computer chip revolutionizes AI — processes data millions of times faster
As artificial intelligence grows more powerful, so does its appetite for speed and energy. The quest for faster, smarter systems has driven researchers to an unlikely ally—light itself.
Bump scaling is pushing defect inspection to the limit. What comes next and why it matters.
CT analysis of tablet microstructure reveals critical insights into disintegration and dissolution, driving improvements in ...
Studies reveal how the Lunit SCOPE® suite identifies immunotherapy-responsive subtypes, standardizes immune phenotyping, and accelerates antibody-based target discovery ...
In doing so, it aims to address the growing shortage of radiology professionals in Europe and to relieve the burden on clinical staff. Radiology in Europe faces major challenges. Over the past 25 ...
Removing friction in the giving process and employing strategic nudges can boost your organization’s fundraising revenue.
The Brighterside of News on MSN
AI microscope autonomously performs tasks like a scientist, only much faster
Within a modest engineering laboratory at Duke University, a new type of researcher is quietly at work next to an optical microscope. This new researcher has no need for coffee, does not become tired, ...
Abstract: The basis of numerous computer vision applications is image segmentation, which entails dividing an image into coherent areas. In this study, different methods that are explored for image ...
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