State-of-the-art performance in electroencephalography (EEG) decoding tasks is currently often achieved with either Deep-Learning or Riemannian-Geometry-based decoders. Recently, there is growing interest in Deep Riemannian Networks (DRNs) possibly combining the advantages of both previous classes of methods. However, there are still a range of topics where additional insight is needed to pave the way for a more widespread application of DRNs in EEG. These include architecture design questions such as network size and end-to-end ability as well as model training questions. How these factors affect model performance has not been explored. Additionally, it is not clear how the data within these networks is transformed, and whether this would correlate with traditional EEG decoding. Our study aims to lay the groundwork in the area of these topics through the analysis of DRNs for EEG with a wide range of hyperparameters. Networks were tested on two public EEG datasets and compared with state-of-the-art ConvNets. Here we propose end-to-end EEG SPDNet (EE(G)-SPDNet), and we show that this wide, end-to-end DRN can outperform the ConvNets, and in doing so use physiologically plausible frequency regions. We also show that the end-to-end approach learns more complex filters than traditional band-pass filters targeting the classical alpha, beta, and gamma frequency bands of the EEG, and that performance can benefit from channel specific filtering approaches. Additionally, architectural analysis revealed areas for further improvement due to the possible loss of Riemannian specific information throughout the network. Our study thus shows how to design and train DRNs to infer task-related information from the raw EEG without the need of handcrafted filterbanks and highlights the potential of end-to-end DRNs such as EE(G)-SPDNet for high-performance EEG decoding.

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.

地理定位的概念是指确定地球上的某些“实体”的位置的过程，通常使用全球定位系统（GPS）坐标。感兴趣的实体可以是图像，图像序列，视频，卫星图像，甚至图像中可见的物体。由于GPS标记媒体的大规模数据集由于智能手机和互联网而迅速变得可用，而深入学习已经上升以提高机器学习模型的性能能力，因此由于其显着影响而出现了视觉和对象地理定位的领域广泛的应用，如增强现实，机器人，自驾驶车辆，道路维护和3D重建。本文提供了对涉及图像的地理定位的全面调查，其涉及从捕获图像（图像地理定位）或图像内的地理定位对象（对象地理定位）的地理定位的综合调查。我们将提供深入的研究，包括流行算法的摘要，对所提出的数据集的描述以及性能结果的分析来说明每个字段的当前状态。

我们展示了MapReader，一个在Python中编写的免费开源软件库，用于分析大地图集合（扫描或出生）。此库转换历史人员可以通过转动广泛的均匀地图设置到可搜索的主要源来使用映射的方式。 MapReader允许使用很少或没有计算机视觉专业知识的用户来通过Web服务器检索地图; ii）预处理并将它们分成补丁; iii）涂布补丁; iv）火车，微调和评估深度神经网络模型; v）创建有关地图内容的结构化数据。我们展示了MAPREADER如何使历史学家解释$ \ \左右16千世纪的军械调查地图表（$ \大约30.5M补丁），将视觉标记转化为机器可读数据的挑战。我们展示了一个案例研究，重点是英国铁路基础设施和建筑物，如这些地图所示。我们还展示了MapReader管道的输出如何链接到我们用于评估的其他外部数据集以及丰富和解释结果。我们释放$ \大约62万美元手动注释的补丁，用于培训和评估模型。

无缝人体机器人互动（HRI）和合作人员（HR）批判性地依靠准确和及时的人类心理工作量（MW）模型。认知负载理论（CLT）表明代表性的物理环境产生代表性的心理过程;物理环境保护程度对应于改进的建模精度。虚拟现实（VR）系统提供能够复制复杂情景的沉重环境，特别是那些与高风险高应力场景相关的复杂情景。被动生物数据建模显示了承诺作为MW建模的非侵入性方法。然而，VR系统很少包括多模式心理生理反馈或大写在线MW建模的生物功能数据。在这里，我们开发了一种新的VR仿真管线，受到NASA多属性任务电池II（MATB-II）任务架构的启发，能够在模拟危险勘探环境中同步地收集客观性能，主观性能和被动人体生物的。我们的系统设计提取并通过机器人操作系统（ROS）提取生物斑点，促进基于心理生理学的MW模型集成到完整的端到端系统中。能够在线评估MWS的VR模拟管道可以通过使这些系统能够以响应于操作者MW自适应地改变其行为来推进人力资源系统和VR经验。

儿童健康研究支持孕产妇环境暴露与儿童的出生结果之间的联系。一个共同的目标是确定敏感性的关键窗口 - 妊娠期间与孕产妇暴露与未来结果之间的关联增加的妊娠期。关键窗户的时间和关联的大小可能在不同级别的个体，家庭和邻里特征之间是异质的。使用行政科罗拉多州出生队列，我们​​估计妊娠和出生体重期间每周暴露于细颗粒物（PM2.5）之间的个性化关系。为了实现这一目标，我们提出了一种统计学习方法，将分布式滞后模型和贝叶斯添加剂回归树结合在一起，以估算单个级别的关键窗口，并确定从一组高维的潜在修改因素集中诱导异质性的特征。我们发现PM2.5出生体重关系中异质性的证据，一些母子二元组显示出3倍的出生体重下降3倍，IQR的暴露量增加（5.9至8.5 $ \ MU G/m^3 $ PM2 .5）与人口平均水平相比。具体而言，我们发现对年轻的非西班牙裔母亲的敏感性增加，体重指数更高或受教育程度较低。我们的案例研究是关键窗口的首次精确健康研究。

Bayesian optimization provides sample-efficient global optimization for a broad range of applications, including automatic machine learning, engineering, physics, and experimental design. We introduce BOTORCH, a modern programming framework for Bayesian optimization that combines Monte-Carlo (MC) acquisition functions, a novel sample average approximation optimization approach, autodifferentiation, and variance reduction techniques. BOTORCH's modular design facilitates flexible specification and optimization of probabilistic models written in PyTorch, simplifying implementation of new acquisition functions. Our approach is backed by novel theoretical convergence results and made practical by a distinctive algorithmic foundation that leverages fast predictive distributions, hardware acceleration, and deterministic optimization. We also propose a novel "one-shot" formulation of the Knowledge Gradient, enabled by a combination of our theoretical and software contributions. In experiments, we demonstrate the improved sample efficiency of BOTORCH relative to other popular libraries.34th Conference on Neural Information Processing Systems (NeurIPS 2020),

Convolutional Neural Networks (CNNs) have proven very effective in image classification and show promise for audio. We use various CNN architectures to classify the soundtracks of a dataset of 70M training videos (5.24 million hours) with 30,871 video-level labels. We examine fully connected Deep Neural Networks (DNNs), AlexNet [1], VGG [2], Inception [3], and ResNet [4]. We investigate varying the size of both training set and label vocabulary, finding that analogs of the CNNs used in image classification do well on our audio classification task, and larger training and label sets help up to a point. A model using embeddings from these classifiers does much better than raw features on the Audio Set [5] Acoustic Event Detection (AED) classification task.

The performance of inertial navigation systems is largely dependent on the stable flow of external measurements and information to guarantee continuous filter updates and bind the inertial solution drift. Platforms in different operational environments may be prevented at some point from receiving external measurements, thus exposing their navigation solution to drift. Over the years, a wide variety of works have been proposed to overcome this shortcoming, by exploiting knowledge of the system current conditions and turning it into an applicable source of information to update the navigation filter. This paper aims to provide an extensive survey of information aided navigation, broadly classified into direct, indirect, and model aiding. Each approach is described by the notable works that implemented its concept, use cases, relevant state updates, and their corresponding measurement models. By matching the appropriate constraint to a given scenario, one will be able to improve the navigation solution accuracy, compensate for the lost information, and uncover certain internal states, that would otherwise remain unobservable.

We consider infinite horizon Markov decision processes (MDPs) with fast-slow structure, meaning that certain parts of the state space move "fast" (and in a sense, are more influential) while other parts transition more "slowly." Such structure is common in real-world problems where sequential decisions need to be made at high frequencies, yet information that varies at a slower timescale also influences the optimal policy. Examples include: (1) service allocation for a multi-class queue with (slowly varying) stochastic costs, (2) a restless multi-armed bandit with an environmental state, and (3) energy demand response, where both day-ahead and real-time prices play a role in the firm's revenue. Models that fully capture these problems often result in MDPs with large state spaces and large effective time horizons (due to frequent decisions), rendering them computationally intractable. We propose an approximate dynamic programming algorithmic framework based on the idea of "freezing" the slow states, solving a set of simpler finite-horizon MDPs (the lower-level MDPs), and applying value iteration (VI) to an auxiliary MDP that transitions on a slower timescale (the upper-level MDP). We also extend the technique to a function approximation setting, where a feature-based linear architecture is used. On the theoretical side, we analyze the regret incurred by each variant of our frozen-state approach. Finally, we give empirical evidence that the frozen-state approach generates effective policies using just a fraction of the computational cost, while illustrating that simply omitting slow states from the decision modeling is often not a viable heuristic.