Time-series forecasting is crucial for numerous real-world applications including weather prediction and financial market modeling. While temporal-domain methods remain prevalent, frequency-domain approaches can effectively capture multi-scale periodic patterns, reduce sequence dependencies, and naturally denoise signals. However, existing approaches typically train model components for all frequencies under a unified training objective, often leading to mismatched learning speeds: high-frequency components converge faster and risk overfitting, while low-frequency components underfit due to insufficient training time. To deal with this challenge, we propose BEAT (Balanced frEquency Adaptive Tuning), a novel framework that dynamically monitors the training status for each frequency and adaptively adjusts their gradient updates. By recognizing convergence, overfitting, or underfitting for each frequency, BEAT dynamically reallocates learning priorities, moderating gradients for rapid learners and increasing those for slower ones, alleviating the tension between competing objectives across frequencies and synchronizing the overall learning process. Extensive experiments on seven real-world datasets demonstrate that BEAT consistently outperforms state-of-the-art approaches.

The blocks keep coming and the game itself gets reinterpreted, twisted and remade for new generations. Now, a 13-year-old boy has become the first person to'beat' the NES version of Tetris, 34 years after it was first released. Yes, 'beat' goes in quotes because there's no way to complete the game. Instead, he played such a flawless game that he forced a kill screen, from an overflow error. While he's the first person to do this, but not the first time it's been achieved: An AI program called StackRabbit forced a kill screen with the NES Tetris back in 2021.

An Amazon listing may have spilled the beans early, but today Beats is officially debuting its latest true wireless earbuds. That premature appearance was mostly accurate: the Studio Buds have a familiar design with loads of improvements on the inside. Those upgrades include better battery life, retooled call performance and updated noise cancellation. There's also a new transparent design option that offers a look at all of those internal components. However, they come with a slightly higher price tag at $170, which means the new version isn't quite as good of a deal as the original.

Achieving realistic, vivid, and human-like synthesized conversational gestures conditioned on multi-modal data is still an unsolved problem due to the lack of available datasets, models and standard evaluation metrics. To address this, we build Body-Expression-Audio-Text dataset, BEAT, which has i) 76 hours, high-quality, multi-modal data captured from 30 speakers talking with eight different emotions and in four different languages, ii) 32 millions frame-level emotion and semantic relevance annotations. Our statistical analysis on BEAT demonstrates the correlation of conversational gestures with facial expressions, emotions, and semantics, in addition to the known correlation with audio, text, and speaker identity. Based on this observation, we propose a baseline model, Cascaded Motion Network (CaMN), which consists of above six modalities modeled in a cascaded architecture for gesture synthesis. To evaluate the semantic relevancy, we introduce a metric, Semantic Relevance Gesture Recall (SRGR). Qualitative and quantitative experiments demonstrate metrics' validness, ground truth data quality, and baseline's state-of-the-art performance. To the best of our knowledge, BEAT is the largest motion capture dataset for investigating human gestures, which may contribute to a number of different research fields, including controllable gesture synthesis, cross-modality analysis, and emotional gesture recognition.

Beat is one of the most exciting companies to ever come out of the ride-hailing space. One city at a time, all across the globe we make transportation affordable, convenient, and safe for everyone. We also help hundreds of thousands of people earn extra income as drivers. Today we are the fastest-growing ride-hailing service in Latin America. But serving millions of rides every day pales in comparison to what lies ahead.

Beat is the fastest growing ride hailing app in Latin America and a part of the international FreeNow Group, the multi-service mobility joint venture backed by BMW Group and Daimler AG. One city at a time, we are on a mission to develop seamless mobility for a safe and sustainable urban life. We are proud to say we have launched Beat Tesla / Loonshot, the first and largest private all-electric vehicle service in Latin America. As an organization, we are committed to our drivers with ethical practices and a safe working environment. To our customers, we differentiate ourselves from other ride-hailing apps with our super user-friendly app and excellent customer service.

Would you buy a 43-inch TV that works in vertical mode? Why didn't you buy Anki's cute toy robots? When are you going to try that meatless Burger King Whopper? Cozmo and Vector couldn't save it.Anki is closing the doors on its toy-robot business Anki, the startup responsible for adorable robotics, is closing its doors and will terminate nearly 200 employees Wednesday. Recode reported CEO Boris Sofman broke the news to staff Monday.

The article introduces the reader to a large interdisciplinary research project whose goal is to use AI to gain new insight into a complex artistic phenomenon. We study fundamental principles of expressive music performance by measuring performance aspects in large numbers of recordings by highly skilled musicians (concert pianists) and analyzing the data with state-of-the-art methods from areas such as machine learning, data mining, and data visualization. The article first introduces the general research questions that guide the project and then summarizes some of the most important results achieved to date, with an emphasis on the most recent and still rather speculative work. A broad view of the discovery process is given, from data acquisition through data visualization to inductive model building and pattern discovery, and it turns out that AI plays an important role in all stages of such an ambitious enterprise. Our current results show that it is possible for machines to make novel and interesting discoveries even in a domain such as music and that even if we might never find the "Horowitz Factor," AI can give us completely new insights into complex artistic behavior.