上肢控制和功能的丧失是中风后患者的不懈症状。这将使他们的日常生活活动施加艰辛。引入了超级机器人四肢（SRL）作为解决方案，以通过引入独立的新肢体来恢复损失的自由度（DOF）。 SRL中的致动系统可以分为刚性和软致动器。通过固有的安全性，成本和能源效率，软执行器已证明对刚性的刚性有利。但是，它们的刚度低，这危害了其准确性。可变的刚度执行器（VSA）是新开发的技术，已被证明可确保准确性和安全性。在本文中，我们介绍了基于可变刚度执行器的新型超级机器人肢。根据我们的知识，提议的概念验证SRL是第一个利用可变刚度执行器的人。开发的SRL将帮助中风后患者完成双重任务，例如用叉子和刀进食。说明了系统的建模，设计和实现。评估并通过预定义轨迹对其准确性进行了评估和验证。通过利用动量观察者进行碰撞检测来验证安全性，并通过软组织损伤测试评估了几种冲突后反应策略。通过标准的用户满意度问卷对援助过程进行定性验证。

在本文中，提出了一种基于进发神经网络的方法来减少单眼视觉探针算法漂移的方法。视觉轨道图算法计算连续摄像机框架之间车辆的增量运动，然后集成这些增量以确定车辆的姿势。提出的神经网络减少了车辆的姿势估计中的误差，这是由于特征检测和匹配，摄像机固有参数等不准确而导致的。这些不准确性传播到对车辆的运动估计，从而导致大量估计误差。降低神经网络的漂移基于连续的摄像头框架中特征的运动来识别此类错误，从而导致更准确的增量运动估计值。使用KITTI数据集对拟议的漂移减少神经网络进行了训练和验证，结果表明，所提出的方法在减少增量方向估计中的误差方面的疗效，从而减少了姿势估计中的总体错误。

基于注意力的神经网络在许多AI任务中都普遍存在。尽管其出色的算法性能，但注意力机制和前馈网络（FFN）的使用仍需要过多的计算和内存资源，这通常会损害其硬件性能。尽管已经引入了各种稀疏变体，但大多数方法仅着重于缓解算法级别上的二次注意力缩放，而无需明确考虑将其方法映射到真实硬件设计上的效率。此外，大多数努力仅专注于注意机制或FFN，但没有共同优化这两个部分，导致当前的大多数设计在处理不同的输入长度时缺乏可扩展性。本文从硬件角度系统地考虑了不同变体中的稀疏模式。在算法级别上，我们提出了Fabnet，这是一种适合硬件的变体，它采用统一的蝴蝶稀疏模式来近似关注机制和FFN。在硬件级别上，提出了一种新颖的适应性蝴蝶加速器，可以在运行时通过专用硬件控件配置，以使用单个统一的硬件引擎加速不同的蝴蝶层。在远程 - ARENA数据集上，FabNet达到了与香草变压器相同的精度，同时将计算量减少10到66次，参数数量为2至22次。通过共同优化算法和硬件，我们的基于FPGA的蝴蝶加速器在归一化到同一计算预算的最新加速器上达到了14.2至23.2倍的速度。与Raspberry Pi 4和Jetson Nano上优化的CPU和GPU设计相比，我们的系统在相同的功率预算下的最大273.8和15.1倍。

人工智能（AI）见证了各种物联网（IoT）应用和服务的重大突破，从推荐系统到机器人控制和军事监视。这是由更容易访问感官数据的驱动以及生成实时数据流的Zettabytes（ZB）的普遍/普遍存在的设备的巨大范围。使用此类数据流来设计准确的模型，以预测未来的见解并彻底改变决策过程，将普遍的系统启动为有价值的范式，以实现更好的生活质量。普遍的计算和人工智能的汇合普遍AI的汇合将无处不在的物联网系统的作用从主要是数据收集到执行分布式计算，并具有集中学习的有希望的替代方案，带来了各种挑战。在这种情况下，应设想在物联网设备（例如智能手机，智能车辆）和基础架构（例如边缘节点和基站）之间进行明智的合作和资源调度，以避免跨越开销和计算计算并确保最大的性能。在本文中，我们对在普遍AI系统中克服这些资源挑战开发的最新技术进行了全面的调查。具体而言，我们首先介绍了普遍的计算，其架构以及与人工智能的相交。然后，我们回顾AI的背景，应用和性能指标，尤其是深度学习（DL）和在线学习，在无处不在的系统中运行。接下来，我们从算法和系统观点，分布式推理，培训和在线学习任务中，对物联网设备，边缘设备和云服务器的组合进行了分布式推理，培训和在线学习任务的深入文献综述。最后，我们讨论我们的未来愿景和研究挑战。

While the brain connectivity network can inform the understanding and diagnosis of developmental dyslexia, its cause-effect relationships have not yet enough been examined. Employing electroencephalography signals and band-limited white noise stimulus at 4.8 Hz (prosodic-syllabic frequency), we measure the phase Granger causalities among channels to identify differences between dyslexic learners and controls, thereby proposing a method to calculate directional connectivity. As causal relationships run in both directions, we explore three scenarios, namely channels' activity as sources, as sinks, and in total. Our proposed method can be used for both classification and exploratory analysis. In all scenarios, we find confirmation of the established right-lateralized Theta sampling network anomaly, in line with the temporal sampling framework's assumption of oscillatory differences in the Theta and Gamma bands. Further, we show that this anomaly primarily occurs in the causal relationships of channels acting as sinks, where it is significantly more pronounced than when only total activity is observed. In the sink scenario, our classifier obtains 0.84 and 0.88 accuracy and 0.87 and 0.93 AUC for the Theta and Gamma bands, respectively.

This is paper for the smooth function approximation by neural networks (NN). Mathematical or physical functions can be replaced by NN models through regression. In this study, we get NNs that generate highly accurate and highly smooth function, which only comprised of a few weight parameters, through discussing a few topics about regression. First, we reinterpret inside of NNs for regression; consequently, we propose a new activation function--integrated sigmoid linear unit (ISLU). Then special charateristics of metadata for regression, which is different from other data like image or sound, is discussed for improving the performance of neural networks. Finally, the one of a simple hierarchical NN that generate models substituting mathematical function is presented, and the new batch concept ``meta-batch" which improves the performance of NN several times more is introduced. The new activation function, meta-batch method, features of numerical data, meta-augmentation with metaparameters, and a structure of NN generating a compact multi-layer perceptron(MLP) are essential in this study.

We present a novel dataset named as HPointLoc, specially designed for exploring capabilities of visual place recognition in indoor environment and loop detection in simultaneous localization and mapping. The loop detection sub-task is especially relevant when a robot with an on-board RGB-D camera can drive past the same place (``Point") at different angles. The dataset is based on the popular Habitat simulator, in which it is possible to generate photorealistic indoor scenes using both own sensor data and open datasets, such as Matterport3D. To study the main stages of solving the place recognition problem on the HPointLoc dataset, we proposed a new modular approach named as PNTR. It first performs an image retrieval with the Patch-NetVLAD method, then extracts keypoints and matches them using R2D2, LoFTR or SuperPoint with SuperGlue, and finally performs a camera pose optimization step with TEASER++. Such a solution to the place recognition problem has not been previously studied in existing publications. The PNTR approach has shown the best quality metrics on the HPointLoc dataset and has a high potential for real use in localization systems for unmanned vehicles. The proposed dataset and framework are publicly available: https://github.com/metra4ok/HPointLoc.

In the Earth's magnetosphere, there are fewer than a dozen dedicated probes beyond low-Earth orbit making in-situ observations at any given time. As a result, we poorly understand its global structure and evolution, the mechanisms of its main activity processes, magnetic storms, and substorms. New Artificial Intelligence (AI) methods, including machine learning, data mining, and data assimilation, as well as new AI-enabled missions will need to be developed to meet this Sparse Data challenge.

The field of autonomous mobile robots has undergone dramatic advancements over the past decades. Despite achieving important milestones, several challenges are yet to be addressed. Aggregating the achievements of the robotic community as survey papers is vital to keep the track of current state-of-the-art and the challenges that must be tackled in the future. This paper tries to provide a comprehensive review of autonomous mobile robots covering topics such as sensor types, mobile robot platforms, simulation tools, path planning and following, sensor fusion methods, obstacle avoidance, and SLAM. The urge to present a survey paper is twofold. First, autonomous navigation field evolves fast so writing survey papers regularly is crucial to keep the research community well-aware of the current status of this field. Second, deep learning methods have revolutionized many fields including autonomous navigation. Therefore, it is necessary to give an appropriate treatment of the role of deep learning in autonomous navigation as well which is covered in this paper. Future works and research gaps will also be discussed.

We introduce a machine-learning (ML)-based weather simulator--called "GraphCast"--which outperforms the most accurate deterministic operational medium-range weather forecasting system in the world, as well as all previous ML baselines. GraphCast is an autoregressive model, based on graph neural networks and a novel high-resolution multi-scale mesh representation, which we trained on historical weather data from the European Centre for Medium-Range Weather Forecasts (ECMWF)'s ERA5 reanalysis archive. It can make 10-day forecasts, at 6-hour time intervals, of five surface variables and six atmospheric variables, each at 37 vertical pressure levels, on a 0.25-degree latitude-longitude grid, which corresponds to roughly 25 x 25 kilometer resolution at the equator. Our results show GraphCast is more accurate than ECMWF's deterministic operational forecasting system, HRES, on 90.0% of the 2760 variable and lead time combinations we evaluated. GraphCast also outperforms the most accurate previous ML-based weather forecasting model on 99.2% of the 252 targets it reported. GraphCast can generate a 10-day forecast (35 gigabytes of data) in under 60 seconds on Cloud TPU v4 hardware. Unlike traditional forecasting methods, ML-based forecasting scales well with data: by training on bigger, higher quality, and more recent data, the skill of the forecasts can improve. Together these results represent a key step forward in complementing and improving weather modeling with ML, open new opportunities for fast, accurate forecasting, and help realize the promise of ML-based simulation in the physical sciences.