We describe a Physics-Informed Neural Network (PINN) that simulates the flow induced by the astronomical tide in a synthetic port channel, with dimensions based on the Santos - S\~ao Vicente - Bertioga Estuarine System. PINN models aim to combine the knowledge of physical systems and data-driven machine learning models. This is done by training a neural network to minimize the residuals of the governing equations in sample points. In this work, our flow is governed by the Navier-Stokes equations with some approximations. There are two main novelties in this paper. First, we design our model to assume that the flow is periodic in time, which is not feasible in conventional simulation methods. Second, we evaluate the benefit of resampling the function evaluation points during training, which has a near zero computational cost and has been verified to improve the final model, especially for small batch sizes. Finally, we discuss some limitations of the approximations used in the Navier-Stokes equations regarding the modeling of turbulence and how it interacts with PINNs.
translated by 谷歌翻译
研究人员通常会采用数值方法来理解和预测海洋动力学,这是掌握环境现象的关键任务。在地形图很复杂,有关基础过程的知识不完整或应用程序至关重要的情况下,此类方法可能不适合。另一方面,如果观察到海洋动力学,则可以通过最近的机器学习方法来利用它们。在本文中,我们描述了一种数据驱动的方法,可以预测环境变量,例如巴西东南海岸的Santos-Sao Vicente-Bertioga estuarine系统的当前速度和海面高度。我们的模型通过连接最新的序列模型(LSTM和Transformers)以及关系模型(图神经网络)来利用时间和空间归纳偏见,以学习时间特征和空间特征,观察站点之间共享的关系。我们将结果与桑托斯运营预测系统(SOFS)进行比较。实验表明,我们的模型可以实现更好的结果,同时保持灵活性和很少的领域知识依赖性。
translated by 谷歌翻译
The promise of Mobile Health (mHealth) is the ability to use wearable sensors to monitor participant physiology at high frequencies during daily life to enable temporally-precise health interventions. However, a major challenge is frequent missing data. Despite a rich imputation literature, existing techniques are ineffective for the pulsative signals which comprise many mHealth applications, and a lack of available datasets has stymied progress. We address this gap with PulseImpute, the first large-scale pulsative signal imputation challenge which includes realistic mHealth missingness models, an extensive set of baselines, and clinically-relevant downstream tasks. Our baseline models include a novel transformer-based architecture designed to exploit the structure of pulsative signals. We hope that PulseImpute will enable the ML community to tackle this significant and challenging task.
translated by 谷歌翻译
主动感知和凹觉视觉是人类视觉系统的基础。虽然动脉凹视觉减少了在注视期间要处理的信息的量,但主动感知会将凝视方向转变为视野中最有前途的部分。我们提出了一种方法,以模仿人类和机器人使用中央摄像机探索场景,并以最少的凝视转移来识别周围环境中存在的物体。我们的方法基于三种关键方法。首先,我们采用现成的深度对象检测器,并在大量的常规图像数据集上进行了预训练,并将分类输出校准为foveateat图像的情况。其次,考虑了几种数据融合技术,对对象分类和相应的不确定性编码对象分类和相应的不确定性进行了依次更新。第三,下一个最好的目光固定点是基于信息理论指标确定的,旨在最大程度地减少语义图的总预期不确定性。与随机选择的下一个凝视转移相比,提出的方法可以使检测的F1分数增加2-3个百分点,以相同数量的凝视偏移,并减少三分之一,而三分之一则是所需的凝视转移数量以达到相似的性能。
translated by 谷歌翻译
有必要开发负担得起且可靠的诊断工具,该工具允许包含COVID-19的扩散。已经提出了机器学习(ML)算法来设计支持决策系统以评估胸部X射线图像,事实证明,这些图像可用于检测和评估疾病进展。许多研究文章围绕此主题发表,这使得很难确定未来工作的最佳方法。本文介绍了使用胸部X射线图像应用于COVID-19检测的ML的系统综述,旨在就方法,体系结构,数据库和当前局限性为研究人员提供基线。
translated by 谷歌翻译
语言模型既展示了定量的改进,又展示了新的定性功能,随着规模的增加。尽管它们具有潜在的变革性影响,但这些新能力的特征却很差。为了为未来的研究提供信息,为破坏性的新模型能力做准备,并改善社会有害的效果,至关重要的是,我们必须了解目前和近乎未来的能力和语言模型的局限性。为了应对这一挑战,我们介绍了超越模仿游戏基准(Big Bench)。 Big Bench目前由204个任务组成,由132家机构的442位作者贡献。任务主题是多样的,从语言学,儿童发展,数学,常识性推理,生物学,物理学,社会偏见,软件开发等等。 Big-Bench专注于被认为超出当前语言模型的功能的任务。我们评估了OpenAI的GPT型号,Google内部密集变压器体系结构和大型基础上的开关稀疏变压器的行为,跨越了数百万到数十亿个参数。此外,一个人类专家评估者团队执行了所有任务,以提供强大的基准。研究结果包括:模型性能和校准都随规模改善,但绝对的术语(以及与评估者的性能相比);在模型类中的性能非常相似,尽管带有稀疏性。逐渐和预测的任务通常涉及大量知识或记忆成分,而在临界规模上表现出“突破性”行为的任务通常涉及多个步骤或组成部分或脆性指标;社交偏见通常会随着含糊不清的环境而随着规模而增加,但这可以通过提示来改善。
translated by 谷歌翻译
在人类生活的最初阶段,沟通被视为社会互动的过程,始终是达成当事方之间达成共识的最佳方法。在此过程中的理解和可信度对于相互协议的验证至关重要。但是,如何做到这一沟通才能达到巨大的群众?当寻求的是信息及其批准时,这是主要的挑战。在这种情况下,本研究介绍了ALT软件,该软件是由适应葡萄牙语的原始可读性指标开发的,以减少通信困难。该软件的开发是由哈贝马斯(Habermas)的沟通行动理论激励的,哈贝马斯(Habermas)使用多学科风格来衡量与公众建立和维持与公众建立和保持安全健康关系的沟通渠道中话语的可信度。 - 没有est \'agio da vida humana a comunica \ c {c} \ 〜ao,vista como um como um como um como de intera \ c {c} \ 〜ao社交,foi semper o melhor caminho para para para o consenso Entre作为partes。 o entendimento e credibilidade nesse processo s \ 〜Ao Fundamentais para para que o acordo m \'utuo seja seja valyado。 Mas,Como faz \^e-lo de forma que essa comunica \ c {c} \ 〜ao alcance a grande massa? eSse \'o principtal desafio que se busca \'e difus \ 〜ao da informa \ c {c} \ 〜ao a sua aprova \ c {c {c} \ 〜ao。 Nesse Contectiono,Este estudo apresenta o Software Alt,desenvolvido a partir de m \'eTricas de legibilidade originais aDaptadas para a l \'ingua polduguesa,dispon \'ivel'ivel na web,para reduzir,dificuldades na comunica na comunica \ comunica \ c \ c} AO。 O desenvolvimento do software foi motivado pela teoria do agir comunicativo de Habermas, que faz uso de um estilo multidisciplinar para medir a credibilidade do discurso nos canais de comunica\c{c}\~ao utilizados para construir e manter uma rela\c{c } \ 〜Ao Segura E Saud \'avel com o p \'ublico。
translated by 谷歌翻译
注意机制期望关于概率权重的数据表示。这会创建摘要统计,重点关注重要功能。最近,(Martins等,2020,2021)提出了不断的注意机制,重点关注指数和变形指数家庭的单峰关注密度:后者稀疏支持。(Farinhas等人2021)扩展了这一点,以利用高斯混合的注意力密度,这是一种具有密集支持的灵活级别。在本文中,我们将此扩展到两个一般灵活类:内核指数系列和我们的新稀疏对方内核变形指数家庭。从理论上讲,我们对内核指数和变形的指数系列表示新的存在结果,并且变形的情况对内核指数系列具有类似的近似能力。实验表明,内核变形指数系列可以参加数据域的多个紧凑区域。
translated by 谷歌翻译
生态瞬间评估(EMAS)是用于测量移动卫生(MHECHEATH)研究和治疗方案的当前认知状态,影响,行为和环境因素的重要心理数据源。非反应,其中参与者未能响应EMA提示,是一个地方问题。准确预测非响应的能力可用于改善EMA交付和发展顺应性干预。事先工作已经探索了古典机器学习模型,以预测非反应。然而,正如越来越大的EMA数据集可用,有可能利用在其他领域有效的深度学习模型。最近,变压器模型在NLP和其他域中显示了最先进的性能。这项工作是第一个探索用于EMA数据分析的变压器的使用。我们在将变压器应用于EMA数据时解决了三个关键问题:1。输入表示,2.编码时间信息,3.预先培训提高下游预测任务性能的效用。变压器模型实现了0.77的非响应预测AUC,并且明显优于古典ML和基于LSTM的深度学习模型。我们将使我们的一个预测模型在研究界可自由地提供40k EMA样品的核查,以便于开发未来的基于变压器的EMA分析工作。
translated by 谷歌翻译
The recent increase in public and academic interest in preserving biodiversity has led to the growth of the field of conservation technology. This field involves designing and constructing tools that utilize technology to aid in the conservation of wildlife. In this article, we will use case studies to demonstrate the importance of designing conservation tools with human-wildlife interaction in mind and provide a framework for creating successful tools. These case studies include a range of complexities, from simple cat collars to machine learning and game theory methodologies. Our goal is to introduce and inform current and future researchers in the field of conservation technology and provide references for educating the next generation of conservation technologists. Conservation technology not only has the potential to benefit biodiversity but also has broader impacts on fields such as sustainability and environmental protection. By using innovative technologies to address conservation challenges, we can find more effective and efficient solutions to protect and preserve our planet's resources.
translated by 谷歌翻译