在过去的几年中，自动睡眠评分的研究主要集中在开发日益复杂的深度学习体系结构上。但是，最近，这些方法仅实现了边际改进，通常以需要更多数据和更昂贵的培训程序为代价。尽管所有这些努力及其令人满意的表现，但在临床背景下，自动睡眠期临时解决方案并未被广泛采用。我们认为，由于很难训练，部署和繁殖，大多数对睡眠评分的深度学习解决方案在现实世界中的适用性受到限制。此外，这些解决方案缺乏可解释性和透明度，这通常是提高采用率的关键。在这项工作中，我们使用经典的机器学习来重新审视睡眠阶段分类的问题。结果表明，通过传统的机器学习管道可以实现最新的性能，该管道包括预处理，功能提取和简单的机器学习模型。特别是，我们分析了线性模型和非线性（梯度提升）模型的性能。我们的方法超过了两个公共数据集上的最新方法（使用相同的数据）：Sleep--EDF SC-20（MF1 0.810）和Sleep-eDF ST（MF1 0.795），同时在Sleep-eDF上取得了竞争成果SC-78（MF1 0.775）和质量SS3（MF1 0.817）。我们表明，对于睡眠阶段评分任务，工程特征向量的表现力与深度学习模型的内部学表现相当。该观察结果为临床采用打开了大门，因为代表性功能向量允许利用传统机器学习模型的可解释性和成功记录。
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视觉分析可以说是熟悉数据的最重要步骤。时间序列尤其如此，因为此数据类型很难描述，并且在使用例如摘要统计信息时无法完全理解。要实现有效的时间序列可视化，必须满足四个要求；工具应为（1）交互式，（2）可扩展到数百万个数据点，（3）在常规数据科学环境中可集成，以及（4）高度可配置。我们观察到，开源Python可视化工具包在大多数视觉分析任务中赋予了数据科学家的能力，但是缺乏可扩展性和交互性的组合来实现有效的时间序列可视化。为了促进这些要求，我们创建了Plotly-Resampler，这是一个开源Python库。 Plotly-resampler是Plotly的Python绑定的附加组件，通过汇总基础数据，根据当前的图形视图来增强线图可伸缩性。绘制构建的绘制是活跃的，因为工具的反应性在定性上影响分析师在视觉探索和分析数据的方式。基准任务强调了我们的工具包在样本数和时间序列方面如何比替代方案更好。此外，Plotly-Resmpler的灵活数据聚合功能为研究新型聚合技术铺平了道路。 Plotly-Resampler的集成性以及其可配置性，便利性和高可扩展性，可以有效地分析您日常的Python环境中的高频数据。
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时间序列加工和特征提取是传统机器学习管道中的关键和时间密集步骤。现有软件包的实际适用性受到限制，因为它们无法应对不规则采样和异步数据。因此，我们呈现$ \ texttt {tsflex} $，用于处理和特征提取的域无关，灵活和序列的第一个Python工具包，其能够处理具有未对准测量的不规则采样的序列。此工具包是首先序列，因为（1）基于序列的参数，用于STRIVELD-WONETS功能提取，并且（2）通过所有支持的操作维护序列索引。 $ \ texttt {tsflex} $ fasel fasel fasel，因为它本地支持（1）多变量时间序列，（2）多个窗口级别配置，（3）与其他包的处理和功能功能集成，而（4）没有假设关于数据采样率规律性和同步。来自此包的其他功能是多处理，深入执行时间记录，支持基于分类和时间的数据，块序列和嵌入式序列化。 $ \ TextTT {TSFlex} $是开发的，以实现快速和内存高效的时间序列处理和特征提取。结果表明，$ \ texttt {tsflex} $比类似的包更灵活，同时在运行时和内存使用情况下表现出这些工具包。
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Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.
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特征选择是开发强大而强大的机器学习模型的关键步骤。特征选择技术可以分为两类：过滤器和包装器方法。尽管包装器方法通常会产生强大的预测性能，但它们具有很大的计算复杂性，因此需要大量时间完成，尤其是在处理高维度集合时。或者，滤波器方法的速度要快得多，但是遭受了其他几个缺点，例如（i）需要阈值值，（ii）不考虑特征之间的相互关系，并且（iii）忽略与模型的特征相互作用。为此，我们提出了一种新颖的包装器特征选择方法PowerShap，该方法将统计假设测试和功率计算与Shapley值结合使用，以进行快速和直观的特征选择。 PowerShap建立在核心假设的基础上：与已知的随机功能相比，信息功能将对预测产生更大的影响。基准和仿真表明，PowerShap的表现优于其他过滤器方法，具有与包装器方法相同的预测性能，同时显着更快，甚至达到执行时间的一半或三分之一。因此，PowerShap提供了一种竞争和快速算法，可以在不同域中的各种模型使用。此外，PowerShap是作为插件和开源的Sklearn组件实现的，可以轻松地集成在传统的数据科学管道中。通过提供自动模式，可以自动调整PowerShap算法的超参数，从而进一步增强用户体验，从而可以使用该算法而无需任何配置。
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This chapter sheds light on the synaptic organization of the brain from the perspective of computational neuroscience. It provides an introductory overview on how to account for empirical data in mathematical models, implement them in software, and perform simulations reflecting experiments. This path is demonstrated with respect to four key aspects of synaptic signaling: the connectivity of brain networks, synaptic transmission, synaptic plasticity, and the heterogeneity across synapses. Each step and aspect of the modeling and simulation workflow comes with its own challenges and pitfalls, which are highlighted and addressed in detail.
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实用的图像分割任务涉及必须从嘈杂，扭曲和/或不完整的观察值重建的图像。解决此类任务的最新方法是使用分段共同执行此次重建，使用每个分段来指导彼此。但是，迄今为止，这项工作采用了相对简单的分割方法，例如Chan - VESE算法。在本文中，我们提出了一种使用基于图的分割方法进行联合重建分割的方法，该方法一直在看到最近的兴趣增加。由于涉及的矩阵尺寸较大而引起并发症，我们展示了如何管理这些并发症。然后，我们分析我们方案的收敛属性。最后，我们将此方案应用于``两个母牛''图像的扭曲版本，该版本是先前基于图的分割文献中熟悉的``两个奶牛''图像，首先是高度噪声的版本，其次是模糊的版本，在两种情况下都可以实现高度准确的细分。我们将这些结果与通过顺序重建分割方法获得的结果进行比较，发现我们的方法与重建和分割精度相比，甚至均超过了这些方法。
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We present the interpretable meta neural ordinary differential equation (iMODE) method to rapidly learn generalizable (i.e., not parameter-specific) dynamics from trajectories of multiple dynamical systems that vary in their physical parameters. The iMODE method learns meta-knowledge, the functional variations of the force field of dynamical system instances without knowing the physical parameters, by adopting a bi-level optimization framework: an outer level capturing the common force field form among studied dynamical system instances and an inner level adapting to individual system instances. A priori physical knowledge can be conveniently embedded in the neural network architecture as inductive bias, such as conservative force field and Euclidean symmetry. With the learned meta-knowledge, iMODE can model an unseen system within seconds, and inversely reveal knowledge on the physical parameters of a system, or as a Neural Gauge to "measure" the physical parameters of an unseen system with observed trajectories. We test the validity of the iMODE method on bistable, double pendulum, Van der Pol, Slinky, and reaction-diffusion systems.
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Recent advancements in sensing and communication facilitate obtaining high-frequency real-time data from various physical systems like power networks, climate systems, biological networks, etc. However, since the data are recorded by physical sensors, it is natural that the obtained data is corrupted by measurement noise. In this paper, we present a novel algorithm for online real-time learning of dynamical systems from noisy time-series data, which employs the Robust Koopman operator framework to mitigate the effect of measurement noise. The proposed algorithm has three main advantages: a) it allows for online real-time monitoring of a dynamical system; b) it obtains a linear representation of the underlying dynamical system, thus enabling the user to use linear systems theory for analysis and control of the system; c) it is computationally fast and less intensive than the popular Extended Dynamic Mode Decomposition (EDMD) algorithm. We illustrate the efficiency of the proposed algorithm by applying it to identify the Van der Pol oscillator, the IEEE 68 bus system, and a ring network of Van der Pol oscillators.
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2022-12-06
We introduce PRISM, a method for real-time filtering in a probabilistic generative model of agent motion and visual perception. Previous approaches either lack uncertainty estimates for the map and agent state, do not run in real-time, do not have a dense scene representation or do not model agent dynamics. Our solution reconciles all of these aspects. We start from a predefined state-space model which combines differentiable rendering and 6-DoF dynamics. Probabilistic inference in this model amounts to simultaneous localisation and mapping (SLAM) and is intractable. We use a series of approximations to Bayesian inference to arrive at probabilistic map and state estimates. We take advantage of well-established methods and closed-form updates, preserving accuracy and enabling real-time capability. The proposed solution runs at 10Hz real-time and is similarly accurate to state-of-the-art SLAM in small to medium-sized indoor environments, with high-speed UAV and handheld camera agents (Blackbird, EuRoC and TUM-RGBD).
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