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Seo Kang-joon was born Lee Seung-hwan in Gunpo, South Korea. In the second year of secondary school, Seo went to review abroad in Malaysia for one year and seven months and returned to Korea in the primary yr of high school. After his debut, he enrolled at the Faculty of Performing Arts of Dong Seoul College. Seo labored half-time as a model before his debut. Seo took on bit parts in various television series, earlier than making his appearing debut as with a supporting position Agreement in the English language SBS' drama The Suspicious Housekeeper. Best New Actor award on the seventh Korea Drama Awards. He has then appeared in tv dramas What Happens to My Family? In 2014, Seo joined the SBS selection present Roommate as a cast member. He also appeared on Law of the Jungle as a visitor. Seo's breakout function got here in 2016 as a proficient pianist in the school romance drama Cheese within the Trap. The same 12 months, he starred in his first leading function in tvN drama Entourage, the Korean remake of the American tv sequence of the identical name.

We obtained a dog! She's each essentially the most great and most annoying thing I've ever encountered. It's exhausting work and she needs too much of coaching, but she's come a long way in the time we've had her. 2026 will bring extra training periods, and hopefully more success. As I write this she's befriended the folks at the table subsequent to me within the café, and doing her finest poor orphan impression in the hopes of a chunk of their sausage sandwiches. I took a step again from social media in the latter half of the year, not for any specific reason, but it surely just wasn't making me glad. I'm keeping up with folks's RSS feeds, but in all probability missing out on what people are up to by not checking Bluesky/Mastodon. I nonetheless discover myself picking up my phone absent-mindedly to take a look at things, realising I don't desire to have a look at social media, and putting it down again. I'd hoped that being on social media much less would result in extra weblog posts, but it did not; most of the time, the thought of writing a publish felt like work, so I chose to simply keep offline as an alternative.

Abstract:Domain reweighting can enhance pattern efficiency and downstream generalization, however information-mixture optimization for multimodal midtraining stays largely unexplored. Current multimodal training recipes tune mixtures alongside a single dimension, usually information format or task kind. We introduce MixAtlas, a method that produces benchmark-focused data recipes that can be inspected, tailored, and transferred to new corpora. MixAtlas decomposes the coaching corpus alongside two axes: image concepts (10 visual-domain clusters found via CLIP embeddings) and activity supervision (5 goal types including captioning, OCR, grounding, detection, and VQA). Using small proxy fashions (Qwen2-0.5B) paired with a Gaussian-process surrogate and GP-UCB acquisition, MixAtlas searches the ensuing mixture house with the identical proxy finances as regression-based mostly baselines however finds better-performing mixtures. We evaluate on 10 benchmarks spanning visible understanding, document reasoning, and multimodal reasoning. On Qwen2-7B, optimized mixtures improve average performance by 8.5%-17.6% over the strongest baseline; on Qwen2.5-7B, features are 1.0%-3.3%. Both settings reach baseline-equal coaching loss in up to 2 instances fewer steps. Recipes discovered on 0.5B proxies switch to 7B-scale coaching across Qwen mannequin households.

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