标签归档:Machine Learning

Cetacean Translation Initiative: a roadmap to deciphering the communication of sperm whales

The past decade has witnessed a groundbreaking rise of machine learning for human language analysis, with current methods capable of automatically accurately recovering various aspects of syntax and semantics – including sentence structure and grounded word meaning – from large data collections. Recent research showed the promise of such tools for analyzing acoustic communication in nonhuman species. We posit that machine learning will be the cornerstone of future collection, processing, and analysis of multimodal streams of data in animal communication studies, including bioacoustic, behavioral, biological, and environmental data. Cetaceans are unique non-human model species as they possess sophisticated acoustic communications, but utilize a very different encoding system that evolved in an aquatic rather than terrestrial medium. Sperm whales, in particular, with their highly-developed neuroanatomical features, cognitive abilities, social structures, and discrete click-based encoding make for an excellent starting point for advanced machine learning tools that can be applied to other animals in the future. This paper details a roadmap toward this goal based on currently existing technology and multidisciplinary scientific community effort. We outline the key elements required for the collection and processing of massive bioacoustic data of sperm whales, detecting their basic communication units and language-like higher-level structures, and validating these models through interactive playback experiments. The technological capabilities developed by such an undertaking are likely to yield cross-applications and advancements in broader communities investigating non-human communication and animal behavioral research.

https://arxiv.org/abs/2104.08614

最近的机器学习算法可以精确地重构句法和语义,这包括从大规模数据集中提取的句子结构和词汇含义。最近的研究也表明这样的技术可以用于分析动物之间的语言交流。我们使用机器学习的算法分析抹香鲸的交流,抹香鲸拥有高度发达的神经系统,感知能力以及社交结构。这将为未来在其他生物上的研究带来参考。本文详细地展示了一个路线图,这个路线图描绘了如何收集和处理抹香鲸的生物声学信号,侦测基本的沟通单元以及语言相关的高级结构,并且在新的数据上进行验证。

Identifying Exoplanets with Deep Learning. IV. Removing Stellar Activity Signals from Radial Velocity Measurements Using Neural Networks

Identifying Exoplanets with Deep Learning. IV. Removing Stellar Activity  Signals from Radial Velocity Measurements Using Neural Networks | DeepAI

Exoplanet detection with precise radial velocity (RV) observations is currently limited by spurious RV signals introduced by stellar activity. We show that machine learning techniques such as linear regression and neural networks can effectively remove the activity signals (due to starspots/faculae) from RV observations. Previous efforts focused on carefully filtering out activity signals in time using modeling techniques like Gaussian Process regression (e.g. Haywood et al. 2014). Instead, we systematically remove activity signals using only changes to the average shape of spectral lines, and no information about when the observations were collected. We trained our machine learning models on both simulated data (generated with the SOAP 2.0 software; Dumusque et al. 2014) and observations of the Sun from the HARPS-N Solar Telescope (Dumusque et al. 2015; Phillips et al. 2016; Collier Cameron et al. 2019). We find that these techniques can predict and remove stellar activity from both simulated data (improving RV scatter from 82 cm/s to 3 cm/s) and from more than 600 real observations taken nearly daily over three years with the HARPS-N Solar Telescope (improving the RV scatter from 1.47 m/s to 0.78 m/s, a factor of ~ 1.9 improvement). In the future, these or similar techniques could remove activity signals from observations of stars outside our solar system and eventually help detect habitable-zone Earth-mass exoplanets around Sun-like stars.

https://arxiv.org/pdf/2011.00003v1.pdf

使用精确径向速度(RV)法观察系外行星会受到虚假的由恒星活动引起的RV信号的干扰。我们观察到例如线性回归和神经网络之类的机器学习算法可以有效的从RV信号中去除这些干扰(这些干扰来自恒星黑子或者光斑)。