[❓] 𝗗'𝘀 𝗜𝗦 𝗠𝗘 𝗟𝗜𝗠𝗜𝗧𝗘𝗗 𝗘𝗗𝗜𝗧𝗜𝗢𝗡 𝗢𝗡-𝗦𝗜𝗧𝗘 𝗦𝗔𝗟𝗘 𝗢𝗡𝗟𝗬 ⠀ ✔ 2024.11.30. Musashino Forest Sport Plaza Main arena ✔ 2024.12.01. Musashino Forest Sport Plaza Main arena ✔ 2024.12.14 Kobe World Memorial Hall ✔ 2024.12.15 Kobe World Memorial Hall ⠀ #대성# #DAESUNG# #DLITE# #Ds_IS_ME# #Encore# #Live_tour#

𝗗-𝗟𝗜𝗧𝗘 𝗝𝗔𝗣𝗔𝗡 𝗟𝗜𝗩𝗘 𝗧𝗢𝗨𝗥 𝟮𝟬𝟮𝟰 ”𝗗'𝘀 𝗜𝗦 𝗠𝗘” - 𝗘𝗻𝗰𝗼𝗿𝗲 Available only in Japan, exclusively on TELASA (@telasa_jp ) 🔗 [Credit] 🎤 Artist | D-LITE 🎸 Band Bandmaster / Guitar｜Susumu Nishikawa Keyboard｜Hiroshi Uesugi Drums｜Takumi Nishizawa Bass｜FIRE Guitar｜Keisuke Iida Chorus｜Tomohiro ODY Odawara Manipulator｜Takashi Morio 💃 Dancers SARA YAMANAKA / KAHO / MARINA RIO NISHIZUMI / Fumiya / Taabow Kosuke / Shun Suzuki 🎬 Production Production｜Kedama Inc. Director｜Chie Nakamura 🙏 Special Thanks AMUSE Inc. / ON THE LINE Inc. Presented by RND Company Inc. #대성# #DAESUNG# #DLITE# #Ds_IS_ME# #Encore# #Live_tour#

[LIVECLIP] 대성 (DAESUNG) - Umbrella (Japanese ver.) | D-LITE JAPAN LIVE TOUR 2024 “D’s IS ME” - Encore - ⠀ 🔗 ⠀ #DAESUNG# #Umbrella# #LIVECLIP# #DLITE# #テソン# #DsWAVE# #Ds_IS_ME#

대성(DAESUNG) D-LITE JAPAN LIVE TOUR 2024 <D's IS ME> - Encore | DOCUMENTARY 🔗 #대성# #DAESUNG# #DLITE# #DLable# #Ds_IS_ME# #Encore# #Live_tour#

[#TEASER#] D-LITE JAPAN LIVE TOUR 2024 <D's IS ME> - Encore | DOCUMENTARY 🕕 2025.02.06. 6PM (KST) RELEASE 🔗 #대성# #DAESUNG# #DLITE# #DLable# #Ds_IS_ME# #Encore# #Live_tour#

[📢] 𝗗-𝗟𝗜𝗧𝗘 𝗝𝗔𝗣𝗔𝗡 𝗟𝗜𝗩𝗘 𝗧𝗢𝗨𝗥 𝟮𝟬𝟮𝟰 - 𝗘𝗻𝗰𝗼𝗿𝗲- 𝗨𝗺𝗯𝗿𝗲𝗹𝗹𝗮 (傘) (𝗗’𝘀 𝗜𝗦 𝗠𝗘 𝗟𝗶𝗺𝗶𝘁𝗲𝗱 𝗘𝗱𝗶𝘁𝗶𝗼𝗻) 𝗢𝗡-𝗦𝗜𝗧𝗘 𝗦𝗔𝗟𝗘 𝗢𝗡𝗟𝗬 ⠀ ■詳細 1.OUT BOX (68x99mm) 2.IMAGE CARD (55x85mm) CARD (55x85mm / Random 1ea out of 2ea) PHOTO CARD (55x85mm) 5.SCRATCH CARD (85x55mm / Random 1ea out of 2ea) 6.LYRICS (255x165mm) ⠀ ■当日直接販売開始時間 ✔ 2024年11月30日(土) 東京・武蔵野の森総合スポーツプラザ メインアリーナ 会場前物販ブースにて　12:00～終演後まで ✔ 2024年12月1日(日)　 東京・武蔵野の森総合スポーツプラザ メインアリーナ 会場前物販ブースにて　11:00～終演後まで ✔ 2024年12月14日(土)　 兵庫・神戸ワールド記念ホール 会場前物販ブースにて　12:00～終演後まで ✔ 2024年12月15日(日)　 兵庫・神戸ワールド記念ホール 会場前物販ブースにて　11:00～終演後まで ⠀ ■PLVEアルバムの使い方 2.SNSアカウントで認証後、ログイン。 3.画面下部の「イメージボタン」をタップして、イメージカードをスキャン。 4.シリアルナンバーを登録。 5.アルバムをダウンロード。 ※一度登録したアルバムは、再登録不可になりますので、予めご了承ください。 ※2024年11月30日(土)19時以降にシリアルナンバーが記載されているイメージカードのスキャンが可能となります。 ※商品に不備がない限り、返品はご遠慮いただきますようお願いいたします。ご購入時に商品に不備がないか必ずご確認ください。 ⠀ ■PLVE販売 ※本商品は、各会場のグッズ販売所にて販売いたします。 ※音楽の視聴開始は、11月30日19時からとなります。 ※本商品は会場限定・数量限定での販売となります。 予めご了承ください。 ※当日の公演チケットをお持ちのお客様のみ、お一人様2点までご購入いただけます。 ※当日の状況により、販売時間が変更となる場合がございます。予めご了承ください。 ⠀ ■PLVEお問い合わせ ✔ アプリ内：設定ボタン（⚙️）>> 「1対1お問い合わせ」 ✔ メールアドレス：umkent@gadi.co.kr ⠀ #대성# #DAESUNG# #DLITE# #Ds_IS_ME# #Encore# #Live_tour#

🚨 Claude Code made me 6 trading bots in 15 mins In the US alone, emotional retail traders lost more than $1.8 billion on liquidations While billions of amateur traders were staring at charts, overtrading, and getting wrecked on fees, a quiet group of algorithmic traders treated prediction markets like a hyper-liquid data engine They didn't guess outcomes -> they knew the structural price gaps in advance Here is how they did it, and why manual trading is completely dead: It's all about removing emotion and deploying cross-market statistical arbitrage Linear Spread Cointegration Formula: S_t = P_P,t - β * P_K,t - μ Ornstein-Uhlenbeck Continuous Dynamics Formula: dS_t = θ(μ - S_t)dt + σ dW_t Euler-Maruyama Discretization (MLE Calibration) Formula: S_t_i = S_t_i-1 * e^(-θΔt) + μ(1 - e^(-θΔt)) + ε_t Level 1 Order Book Imbalance (OBI) Formula: I_t = (V_b(t) - V_a(t)) / (V_b(t) + V_a(t)) Volume-Weighted Micro-Price Prediction Formula: P_micro(t) = P_mid(t) + I_t * (Δspread / 2) Cross-Venue Predictive Signal Optimization Formula: ΔP_Kalshi(t + δ) = f(I_Polymarket(t), P_micro,Polymarket(t) - P_micro,Kalshi(t)) In the era of advanced AI, the winner is not the one who guesses the score, but the one who lets automated systems execute with absolute patience and discipline. AI does the hard parts now -> you don't even need a CS degree to build this The full behind-the-scenes live system build is now available to the public 📝

Kye-hyun Kyung, Samsung Electronics Senior Advisor: "Memory prices to fall in H2 next year… Korea must cultivate deep-tech manufacturing" Kye-hyun Kyung, Senior Advisor and former head of Samsung Electronics' Device Solutions (DS) Division, forecast that memory semiconductor prices will decline starting in the second half of next year, and urged Korean industry to prepare in advance. Delivering the keynote at the 285th NAEK Forum, hosted by the National Academy of Engineering of Korea (NAEK) at L-Tower in Seocho-gu, Seoul on the 18th, Advisor Kyung said, "Chinese players are aggressively expanding production capacity (CAPA)," adding that "as memory supply surges, the market could shift starting in the second half of next year or the first half of 2028." Citing global market research firms, Kyung projected that memory prices will fall from H2 next year, when global memory CAPA is expected to surge to 6 million wafers per month. "If Big Tech's return on capex deteriorates, there is a possibility that investment could be scaled back," he said, also warning that memory demand itself could contract from 2028 onwards. While Korean industry, led by Samsung Electronics and SK Hynix, is currently enjoying unprecedented growth by capturing Big Tech's memory demand, the former head of Samsung's semiconductor business argued that Korea must prepare in advance for the post-boom period. Advisor Kyung pointed out, "Korea holds nearly 70% share of the DRAM market, but only 1.5% of the fabless market, and unlike Taiwan, Korea lacks a full-stack semiconductor ecosystem that includes fabless." He went on to advise that "Korea must leap forward as a deep-tech-based manufacturing nation." The point is that Korea should independently build advanced technology capabilities—not only in memory but also in fabless-based system semiconductors and sovereign AI—and actively apply them to its existing strength in manufacturing. He added, "It is difficult for Korea to compete simultaneously with the U.S. and China in both hardware and software," and that "it is important for Korea to do what it does well, and to that end, we must seriously consider how to deploy AI."

You're in an ML Engineer interview at Apple. The interviewer asks: "Two models are 88% accurate. - Model A is 89% confident. - Model B is 99% confident. Which one would you pick?" You: "Any would work since both have same accuracy." Interview over. Here's what you missed: Modern neural networks can be misleading. They are overconfident in their predictions. For instance, I saw an experiment that used the CIFAR-100 dataset to compare LeNet with ResNet. LeNet produced: - Accuracy = ~0.55 - Average confidence = ~0.54 ResNet produced: - Accuracy = ~0.7 - Average confidence = ~0.9 Despite being more accurate, the ResNet model is overconfident in its predictions. While the model thinks it's 90% confident in its predictions, in reality, it only turns out to be 70% accurate. Calibration solves this. A model is calibrated if the predicted probabilities align with the actual outcomes. For instance, say a model predicts an event with a 70% probability. Then, ideally, out of 100 such predictions, ~70 should result in the event. Handling this is important because the model will be used in decision-making. In fact, an overly confident that is not equally accurate model can be highly misleading. To exemplify, say a government hospital wants to conduct an expensive medical test on patients. To ensure that the govt. funding is used optimally, a reliable probability estimate can help the doctors make this decision. If the model isn't calibrated, it will produce overly confident predictions. Reliability Diagrams are a visual way to inspect how well the model is currently calibrated. More specifically, this diagram plots the expected sample accuracy as a function of the corresponding confidence value (softmax) output by the model. If the model is perfectly calibrated, then the diagram should look like the identity function. That said, it is often also useful to compute a scalar value that measures the amount of miscalibration, called expected calibration error (ECE). One way to approximate the expected calibration error shown above is by partitioning predictions into equally spaced bins and taking a weighted average of the bins’ accuracy/confidence difference. These are some common techniques to calibrate ML models: > For binary classification models: - Histogram binning - Isotonic regression - Platt scaling > For multiclass classification models: - Binning methods - Matrix and vector scaling 👉 If you care about probabilities and both models are operationally similar, which model would you prefer? ____ Find me → @_avichawla Every day, I share tutorials and insights on DS, ML, LLMs, and RAGs.

𝗗𝗔𝗘𝗦𝗨𝗡𝗚 𝟮𝟬𝟮𝟱 𝗔𝗦𝗜𝗔 𝗧𝗢𝗨𝗥: 𝗗’𝘀 𝗪𝗔𝗩𝗘 𝗘𝗡𝗖𝗢𝗥𝗘 𝗠𝗘𝗥𝗖𝗛 𝗣𝗿𝗲-𝗼𝗿𝗱𝗲𝗿 & 𝗗𝗔𝗘𝗦𝗨𝗡𝗚 𝗢𝗙𝗙𝗜𝗖𝗜𝗔𝗟 𝗟𝗜𝗚𝗛𝗧 𝗦𝗧𝗜𝗖𝗞 𝗡𝗢𝗪 𝗢𝗣𝗘𝗡❗️ ⠀ D's WAVE ENCORE MERCH Pre-order & DAESUNG OFFICIAL LIGHT STICEK is now open! ⠀ ✔ 판매 오픈 - Pre-order: 2026.01.05. MON 6PM (KST) ~ 2026.01.11. SUN 11:59PM (KST) - Light Stick: 2026.01.05. MON 6PM (KST) ~ 재고 소진 시까지 ⠀ ✔ 배송 예정일 Pre-order: 3월 중 순차 배송 예정 * D'spirit Paw Keychain은 4월 하순부터 배송될 예정입니다. * 응원봉 단일 구매 시에는 결제 완료 순으로 순차 배송되며, Pre-order 상품과 함께 주문한 경우에는 해당 Pre-order 상품 배송 일정에 맞춰 합배송됩니다. ⠀ ✔ 링크 #대성# #DAESUNG# #DLITE# #DsWAVE# #Asia_tour# #Pre_order#