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Simple Surgical Practice—No Expensive Simulator Required💪🏅 👉High-quality surgical practice does not always require sophisticated simulation models—basic materials can be enough to refine needle handling and running sutures. 👉Spare surgical needles (in the OR!) with Kleenex or simple cloth provide an accessible way to repeatedly practice hand control, spacing, bite... show more
33,172 次观看 • 2 个月前 •via X (Twitter)
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![Robots can now reconstruct 3D scenes in real time from a single RGB camera. [📍 Projects page + paper] No depth sensor. No retraining. 30 FPS. Researchers at the Imperial College London introduced KV-Tracker, a training-free method that makes heavy models like π³ and Depth Anything 3 fast enough for real-time tracking. The idea is simple. These models use global self-attention, which is powerful but computationally expensive. KV-Tracker caches the key and value pairs from selected keyframes and reuses them for new frames. That cache becomes an implicit scene representation. Result: • Up to 30 FPS • 10 to 15x speedup • Accurate 6-DoF tracking on benchmarks like TUM RGB-D and 7-Scenes • Works with monocular RGB only It also supports object-level tracking with masks and allows saving the KV-cache for later reuse. For robotics, this reduces hardware constraints and moves real-time 3D perception closer to practical deployment. Credit to Marwan Taher (Marwan Taher) at Imperial’s Dyson Robotics Lab and many others who contributed to this! 📍 Save projects page + paper for later: Video: ——- if it matters in AI or Robotics you'll read it here first:](https://image.24vids.com/tw-2041062366025031787/ext_tw_video_thumb/2041062352678776832/pu/img/Zpv7-4QTzuOTgs7h.jpg)
![So let's talk about what Rick Jackson really means here, and why it shows he should not be governor. At a press event, Mr. Jackson said that “not having [health] insurance in Georgia does not mean you don’t have healthcare.” It is a common trope used by Republicans to avoid addressing our high uninsurance rates, particularly in Medicaid non-expansion states like ours. What he is likely referring to is EMTALA, a 1980s federal law that states patients who go to the emergency room must be seen and receive "stabilizing care" before discharge. If you show up at an ER bleeding to death, for example, the hospital needs to at least address the cause of the bleeding before sending you on your way. That is not "healthcare" in the sense that most people think of it. That is battlefield triage. EMTALA does not provide for maintenance healthcare--non-emergent treatment of hypertension, or diabetes, or heart disease. EMTALA does not require preventative care, like cancer screenings or annual physicals. EMTALA does not require coverage for medications, prenatal care, mental healthcare, outside of emergencies. EMTALA, essentially, puts into statute a principle that no one should need explained. If someone needs urgent care, you help them, regardless of their insurance status. But it does not cover the costs for that care. Nor does not provide a mechanism for hospitals and providers to recoup the expense of providing that care. What this means in a state like Georgia, with among the highest rates of health uninsurance in the country, is that more uninsured patients will put off preventative and maintenance care because they can't afford it. They will present to the ER only when they have to, sicker than they needed to be, incur the most expensive form of healthcare possible (catastrophic care through the ED), and then be unable to cover those costs out of pocket. Who ends up eating those costs? The providers. The hospitals. The state. The taxpayers. Rick Jackson, the multibillionaire healthcare staffing company CEO who is now running for governor, doesn't think this is a problem for the state he is trying to lead, even as state agencies are being told to tighten their belts and we anticipate revenue shortfalls. This putative healthcare business expert can't see the issue with insisting that he will refuse to expand Medicaid and avail patients of the coverage they need, funded by the federal tax dollars we’ve already been paying for more than a decade. And he wants to tell the rest of us who understand the issue that we're wrong. This man cannot be the next governor of Georgia. Our healthcare system could not survive a Governor Jackson.](https://image.24vids.com/tw-2091219728446439642/amplify_video_thumb/2091219694820712449/img/b1jsdW8QHYubnt8Y.jpg)
![Vector Database by Hand ✍️ Vector databases are revolutionizing how we search and analyze complex data. They have become the backbone of Retrieval Augmented Generation (#RAG). How do vector databases work? [1] Given ↳ A dataset of three sentences, each has 3 words (or tokens) ↳ In practice, a dataset may contain millions or billions of sentences. The max number of tokens may be tens of thousands (e.g., 32,768 mistral-7b). Process "how are you" [2] 🟨 Word Embeddings ↳ For each word, look up corresponding word embedding vector from a table of 22 vectors, where 22 is the vocabulary size. ↳ In practice, the vocabulary size can be tens of thousands. The word embedding dimensions are in the thousands (e.g., 1024, 4096) [3] 🟩 Encoding ↳ Feed the sequence of word embeddings to an encoder to obtain a sequence of feature vectors, one per word. ↳ Here, the encoder is a simple one layer perceptron (linear layer + ReLU) ↳ In practice, the encoder is a transformer or one of its many variants. [4] 🟩 Mean Pooling ↳ Merge the sequence of feature vectors into a single vector using "mean pooling" which is to average across the columns. ↳ The result is a single vector. We often call it "text embeddings" or "sentence embeddings." ↳ Other pooling techniques are possible, such as CLS. But mean pooling is the most common. [5] 🟦 Indexing ↳ Reduce the dimensions of the text embedding vector by a projection matrix. The reduction rate is 50% (4->2). ↳ In practice, the values in this projection matrix is much more random. ↳ The purpose is similar to that of hashing, which is to obtain a short representation to allow faster comparison and retrieval. ↳ The resulting dimension-reduced index vector is saved in the vector storage. [6] Process "who are you" ↳ Repeat [2]-[5] [7] Process "who am I" ↳ Repeat [2]-[5] Now we have indexed our dataset in the vector database. [8] 🟥 Query: "am I you" ↳ Repeat [2]-[5] ↳ The result is a 2-d query vector. [9] 🟥 Dot Products ↳ Take dot product between the query vector and database vectors. They are all 2-d. ↳ The purpose is to use dot product to estimate similarity. ↳ By transposing the query vector, this step becomes a matrix multiplication. [10] 🟥 Nearest Neighbor ↳ Find the largest dot product by linear scan. ↳ The sentence with the highest dot product is "who am I" ↳ In practice, because scanning billions of vectors is slow, we use an Approximate Nearest Neighbor (ANN) algorithm like the Hierarchical Navigable Small Worlds (HNSW).](https://image.24vids.com/tw-1795076707386360227/ext_tw_video_thumb/1795076436019023872/pu/img/Ne2RjvPwQ1ouSr32.jpg)
![MiniMax H3 Character Introduction Prompt: Use @[char ref] as the sole character reference. Preserve the exact identity, face, body proportions, hairstyle, outfit, colors, materials and overall silhouette of the character throughout the entire video. Do not redesign, simplify or replace any defining visual features. Create a cinematic character introduction focused on presence, silhouette, attitude and controlled motion. 0–4s Begin with a close shot of a defining lower-body or detail element such as boots, shoes, feet, hands, clothing hem or an important accessory. The character enters frame or settles into position. The camera slowly tracks upward while hair, clothing and secondary elements move naturally in the wind or environment. 4–8s Reveal more of the body with a medium or medium-wide shot from the back, side or three-quarter angle. The character stands in a calm, composed way inside the environment. The camera makes a smooth orbit, arc or lateral move to gradually reveal the character’s face and silhouette. 8–12s Move into a tight cinematic portrait or upper-body shot. The character performs one subtle signature action that fits their personality, such as lifting the chin, turning the head, adjusting clothing, brushing hair aside, opening a hand, looking toward camera, or shifting posture. Keep the motion minimal and intentional. The expression should match the character’s vibe. 12–15s End with a strong full-body hero shot that clearly presents the entire design and silhouette. Use a low-angle, eye-level or slightly dramatic framing depending on the character’s personality. The character settles into a natural final pose and holds it confidently for a clean final reveal. VISUAL DIRECTION Premium cinematic presentation. Match the visual medium and rendering style of @[char ref]. Emphasize clean silhouette, elegant staging, subtle secondary motion, believable hair and cloth movement, strong composition, atmospheric depth and polished lighting. The scene should feel like a high-end anime, game or film character introduction. CAMERA Use a clear progression from detail reveal to partial reveal to face reveal to full-body hero reveal. Camera movement should be smooth, controlled and intentional. Avoid chaotic motion. ENVIRONMENT Place the character in a fitting environment that supports their identity and mood. The background should enhance the character without distracting from them.](https://image.24vids.com/tw-2086412223061135392/amplify_video_thumb/2086412141729402880/img/8hxZX-hc394yGe7P.jpg)
