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Video 🎥 🦷 Deep Margin Elevation made predictable Learn how to manage deep proximal margins with a step-by-step DME technique using everX Flow. Elevating subgingival margins to a more accessible level improves isolation, adhesion, and overall restorative outcomes. By using fibre-reinforced composite as a dentine replacement, you can enhance... show more

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15,798 次观看 • 2 个月前 •via X (Twitter)

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Build and customize complex AI applications with a flexible framework in this new short course, Building AI Applications with Haystack. Created in collaboration with deepset, makers of Haystack, and taught by Tuana, who is the developer relations lead for Haystack at deepset. Generative AI technology is changing rapidly and it can be challenging to integrate APIs from different LLMs, vector databases, and various tools such as web search. In this course, you will learn how to use the Haystack framework to make your development process more modular, allowing you to manage complexity and focus more on building your application. In detail, you’ll: - Build a RAG pipeline using Haystack’s main building blocks – components, pipelines, and document stores. - Create custom components in your pipeline by building a Hacker News summarizer that extends your app’s ability to access APIs. - Use conditional routing to create a branching pipeline with a fallback to web search mechanism when the LLM does not have the necessary context to respond to the user's query. - Build a self-reflecting agent for named entity recognition that loops using an output validator custom component. - Create a chat agent using OpenAI's function-calling capabilities which allow you to provide Haystack pipelines as tools to the LLM, enhancing that agent's capabilities. By the end of this course, you will learn a high-level orchestration framework that can help make your applications flexible, extendible, and maintainable, even as the technology stack changes, new user needs arise, and you add new features to your application. Please sign up here:

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The Asian Squat Test Step 1 - Drop down into a full squat with heels on ground Step 2 - Hold for 60-120 seconds Step 3 - Monitor foot angle & how the body feels This is a signal of ankle, knee, and hip mobility and leg strength, strongly correlated with independence, fall risk, and longevity as you get older. If you can hold this squat for a minute or more, you've got healthy hips and ankles. If you can't, start by holding onto a pillar and gently lower yourself into a squat position. Aim for 3–5 minutes total per day in short holds (30–60 seconds), and build up gradually.

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Everything I do as First Minister is driven by a deep commitment to our young people, to create a future where they can thrive and achieve their dreams. It was a real joy to visit the amazing pupils and staff at St. Mary’s Dunnamore, and to spend time with the brilliant P7s as they get ready to take their next big step to secondary school. To every young person going into secondary school, always believe in yourself. There’s nothing you can’t achieve. Dream big. The future is yours. 👏🏻

Everything I do as First Minister is driven by a deep commitment to our young people, to create a future where they can thrive and achieve their dreams. It was a real joy to visit the amazing pupils and staff at St. Mary’s Dunnamore, and to spend time with the brilliant P7s as they get ready to take their next big step to secondary school. To every young person going into secondary school, always believe in yourself. There’s nothing you can’t achieve. Dream big. The future is yours. 👏🏻

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Diffusion has shown great promise for generating robot **actions**, can it act as a **world model** to generate the future conditioned on actions? In our work led by han qi Haocheng Yin and in collaboration with Yilun Du, we show a **controllable** action-conditioned video diffusion model can produce photorealistic and (near) physics-accurate future predictions. This ability strengthens the policy via: - ranking different action proposals and selecting the best, or - **visual** trajectory optimization by optimizing the action proposals using gradient ascent. Learn more about Generative Predictive Control (GPC) at:

Diffusion has shown great promise for generating robot actions, can it act as a world model to generate the future conditioned on actions? In our work led by han qi Haocheng Yin and in collaboration with Yilun Du, we show a controllable action-conditioned video diffusion model can produce photorealistic and (near) physics-accurate future predictions. This ability strengthens the policy via: - ranking different action proposals and selecting the best, or - visual trajectory optimization by optimizing the action proposals using gradient ascent. Learn more about Generative Predictive Control (GPC) at:

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38,434 次观看 • 1 年前