Group 1 - The core achievement of the research team is the first identification of a third compact object near a binary black hole merger event, which provides new insights into the formation of binary black holes [1][3] - The research focuses on the gravitational wave event GW190814, where the mass difference between the two black holes is nearly tenfold, suggesting two potential formation mechanisms involving a supermassive black hole or an accretion disk in an active galactic nucleus [2] - The team developed a gravitational wave waveform template that includes line-of-sight acceleration, which significantly outperformed traditional models, indicating strong evidence for the presence of a third compact object [2] Group 2 - This discovery marks the first clear indication of a third compact object in a binary black hole merger, suggesting a more complex gravitational system than previously understood [3] - The advancement in gravitational wave detection technology, including next-generation ground-based and space-based detectors, is expected to enhance the ability to capture subtle changes in gravitational wave signals, leading to further discoveries [3]

点击下方 卡片 ，关注" 具身智能 之心 "公众号 作者丨 Bin Liu等 编辑丨具身智能之心 本文只做学术分享，如有侵权，联系删文 >> 点击进入→ 具身智能之心 技术交流群 更多干货，欢迎加入国内首个具身智能全栈学习社区 ： 具身智能之心知识星球 (戳我) ， 这里包含所有你想要 的。 核心观点与背景 本篇综述探讨了贝叶斯统计与具身智能之间深层次的概念联系。具身智能理论认为，认知能力从根本上源 于并受制于智能体与环境的实时传感器交互。这种适应性行为本质上需要在不确定性下进行持续推理。贝 叶斯统计为此提供了一个原则性的概率框架，通过将知识表示为概率分布，并根据新证据更新信念。 研究指出，尽管存在这种深层概念联系，贝叶斯原则在当今的具身智能系统中并未得到广泛应用。本文通 过两个关键视角分析这一现象：搜索和学习——这两个主题被Rich Sutton在著名文章"The Bitter Lesson"中 强调为现代AI的核心。 搜索与学习：现代AI的两个基础主题 Rich Sutton的"The Bitter Lesson"强调，搜索和学习代表了能够随着计算能力增加而驱动AI重大突破的通用 方法。搜索指系统地探索大 ...

Group 1 - The market cannot be precisely predicted, and the goal is to build a cognitive framework to understand its current state and infer short-term evolution [1] - Traditional technical analysis attempts to reduce the complexity of market processes but often overlooks the high-dimensional latent space that drives price movements [1] Group 2 - Early deep learning models like CNNs capture local spatial patterns but fail to understand the path dependency of time series data [2] - LSTM and its variants address the limitations of CNNs by capturing sequential dependencies, but they assume a linear flow of information, which does not reflect the complex interactions in real markets [3] Group 3 - A paradigm shift is needed from sequential dependency modeling to spatio-temporal structural dependency modeling to better capture market dynamics [5] - The core of the proposed approach is a dynamic temporal knowledge graph that models relationships among entities, which is essential for understanding market interactions [6] Group 4 - The use of heterogeneous Hawkes processes allows for modeling event flows within the knowledge graph, capturing the ripple effects of market events [6] - By maximizing the log-likelihood function, the system can derive embedding vectors for entities and relationships, projecting the knowledge graph into a lower-dimensional latent space [7] Group 5 - The model's output is a posterior probability that combines likelihood from data and prior probability based on human insights, emphasizing the importance of human judgment in the decision-making process [9][10] - The company aims to create a decision framework that optimizes long-term expected value rather than focusing on short-term gains, leveraging the cognitive spread between its insights and market averages [11]