Why is okra so slimy? The plant's signature goo helps it thrive in the heat. Okra gets its slime from a substance called mucilage. Breakthroughs, discoveries, and DIY tips sent six days a week. Okra is one of those vegetables with a polarizing reputation.

The use of recommender systems in the recruitment domain has been labeled as 'high-risk' in recent legislation. As a result, strict requirements regarding explainability and fairness have been put in place to ensure proper treatment of all involved stakeholders. To allow for stakeholder-specific explainability, while also handling highly heterogeneous recruitment data, we propose a novel explainable multi-stakeholder job recommender system using graph neural networks: the Occupational Knowledge-based Recommender using Attention (OKRA). The proposed method is capable of providing both candidate- and company-side recommendations and explanations. We find that OKRA performs substantially better than six baselines in terms of nDCG for two datasets. Furthermore, we find that the tested models show a bias toward candidates and vacancies located in urban areas. Overall, our findings suggest that OKRA provides a balance between accuracy, explainability, and fairness.

Medical imaging is key in modern medicine. From magnetic resonance imaging (MRI) to microscopic imaging for blood cell detection, diagnostic medical imaging reveals vital insights into patient health. To predict diseases or provide individualized therapies, machine learning techniques like kernel methods have been widely used. Nevertheless, there are multiple challenges for implementing kernel methods. Medical image data often originates from various hospitals and cannot be combined due to privacy concerns, and the high dimensionality of image data presents another significant obstacle. While randomised encoding offers a promising direction, existing methods often struggle with a trade-off between accuracy and efficiency. Addressing the need for efficient privacy-preserving methods on distributed image data, we introduce OKRA (Orthonormal K-fRAmes), a novel randomized encoding-based approach for kernel-based machine learning. This technique, tailored for widely used kernel functions, significantly enhances scalability and speed compared to current state-of-the-art solutions. Through experiments conducted on various clinical image datasets, we evaluated model quality, computational performance, and resource overhead. Additionally, our method outperforms comparable approaches

For far too long, sales reps and commercial managers in the pharmaceutical industry have had their responsibilities eroded by wave after wave of IT implementations, each one supposedly making life easier - but doing the exact opposite. The time has come for a more intelligent solution. It's time for technology to empower, not overrule. It's time for sales and marketing managers to regain control. Single black-box'next best actions' which dictate and disempower If you want to re-empower your sales and marketing staff with a smarter generation of technology, enter your details.

I joined the Star Wars fandom late in the game. I grew up in India, where Star Wars is less of a thing, but in 2015 I watched "Star Wars: The Force Awakens" and set out on a quest to watch everything. They can move and talk (though mostly in gibberish) and act intelligently. As we all know, technology is often inspired by movies. Just look at how speech technologies are evolving and inspired by movies like Star Wars.