Neural networks have revolutionized the area of artificial intelligence and introduced transformative applications to almost every scientific field and industry. However, this success comes at a great price; the energy requirements for training advanced models are unsustainable. One promising way to address this pressing issue is by developing low-energy neuromorphic hardware that directly supports the algorithm's requirements. The intrinsic non-volatility, non-linearity, and memory of spintronic devices make them appealing candidates for neuromorphic devices. Here we focus on the reservoir computing paradigm, a recurrent network with a simple training algorithm suitable for computation with spintronic devices since they can provide the properties of non-linearity and memory. We review technologies and methods for developing neuromorphic spintronic devices and conclude with critical open issues to address before such devices become widely used.

现有的球形卷积神经网络（CNN）框架在计算方面既可以扩展又是旋转等值的。连续的方法捕获旋转模棱两可，但通常在计算上是过时的。离散的方法提供了更有利的计算性能，但付出了损失。我们开发了一个混合离散（迪斯科）组卷积，该卷积同时均具有等效性，并且在计算上可扩展到高分辨率。虽然我们的框架可以应用于任何紧凑的组，但我们专注于球体。我们的迪斯科球形卷积不仅表现出$ \ text {so}（3）$ rotational equivariance，而且还表现出一种渐近$ \ text {so}（3）/\ text {so}（so}（so}（2）$ rotationation eporational ecorivarianciancience，对于许多应用程序（其中$ \ text {so}（n）$是特殊的正交组，代表$ n $ dimensions中的旋转）。通过稀疏的张量实现，我们可以在球体上的像素数量进行线性缩放，以供计算成本和内存使用情况。对于4K球形图像，与最有效的替代替代品量球卷积相比，我们意识到节省了$ 10^9 $的计算成本和$ 10^4 $的内存使用情况。我们将迪斯科球形CNN框架应用于球体上的许多基准密集预测问题，例如语义分割和深度估计，在所有这些问题上，我们都达到了最先进的性能。

我们考虑单个强化学习与基于事件驱动的代理商金融市场模型相互作用时学习最佳执行代理的学习动力。交易在事件时间内通过匹配引擎进行异步进行。最佳执行代理在不同级别的初始订单尺寸和不同尺寸的状态空间上进行考虑。使用校准方法考虑了对基于代理的模型和市场的影响，该方法探讨了经验性风格化事实和价格影响曲线的变化。收敛，音量轨迹和动作痕迹图用于可视化学习动力学。这表明了最佳执行代理如何在模拟的反应性市场框架内学习最佳交易决策，以及如何通过引入战略订单分类来改变模拟市场的反反应。

深度是自治车辆以感知障碍的重要信息。由于价格相对较低，单目一体相机的小尺寸，从单个RGB图像的深度估计引起了对研究界的兴趣。近年来，深神经网络（DNN）的应用已经显着提高了单眼深度估计（MDE）的准确性。最先进的方法通常设计在复杂和极其深的网络架构之上，需要更多的计算资源，而不使用高端GPU实时运行。虽然一些研究人员试图加速运行速度，但深度估计的准确性降低，因为压缩模型不代表图像。另外，现有方法使用的特征提取器的固有特性导致产生的特征图中的严重空间信息丢失，这也损害了小型图像的深度估计的精度。在本研究中，我们有动力设计一种新颖且有效的卷积神经网络（CNN），其连续地组装两个浅编码器解码器样式子网，以解决这些问题。特别是，我们强调MDE准确性和速度之间的权衡。已经在NYU深度V2，Kitti，Make3D和虚幻数据集上进行了广泛的实验。与拥有极其深层和复杂的架构的最先进的方法相比，所提出的网络不仅可以实现可比性的性能，而且在单个不那么强大的GPU上以更快的速度运行。

There are multiple scales of abstraction from which we can describe the same image, depending on whether we are focusing on fine-grained details or a more global attribute of the image. In brain mapping, learning to automatically parse images to build representations of both small-scale features (e.g., the presence of cells or blood vessels) and global properties of an image (e.g., which brain region the image comes from) is a crucial and open challenge. However, most existing datasets and benchmarks for neuroanatomy consider only a single downstream task at a time. To bridge this gap, we introduce a new dataset, annotations, and multiple downstream tasks that provide diverse ways to readout information about brain structure and architecture from the same image. Our multi-task neuroimaging benchmark (MTNeuro) is built on volumetric, micrometer-resolution X-ray microtomography images spanning a large thalamocortical section of mouse brain, encompassing multiple cortical and subcortical regions. We generated a number of different prediction challenges and evaluated several supervised and self-supervised models for brain-region prediction and pixel-level semantic segmentation of microstructures. Our experiments not only highlight the rich heterogeneity of this dataset, but also provide insights into how self-supervised approaches can be used to learn representations that capture multiple attributes of a single image and perform well on a variety of downstream tasks. Datasets, code, and pre-trained baseline models are provided at: https://mtneuro.github.io/ .

The United States coastline spans 95,471 miles; a distance that cannot be effectively patrolled or secured by manual human effort alone. Unmanned Aerial Vehicles (UAVs) equipped with infrared cameras and deep-learning based algorithms represent a more efficient alternative for identifying and segmenting objects of interest - namely, ships. However, standard approaches to training these algorithms require large-scale datasets of densely labeled infrared maritime images. Such datasets are not publicly available and manually annotating every pixel in a large-scale dataset would have an extreme labor cost. In this work we demonstrate that, in the context of segmenting ships in infrared imagery, weakly-supervising an algorithm with sparsely labeled data can drastically reduce data labeling costs with minimal impact on system performance. We apply weakly-supervised learning to an unlabeled dataset of 7055 infrared images sourced from the Naval Air Warfare Center Aircraft Division (NAWCAD). We find that by sparsely labeling only 32 points per image, weakly-supervised segmentation models can still effectively detect and segment ships, with a Jaccard score of up to 0.756.

Microswimmers can acquire information on the surrounding fluid by sensing mechanical queues. They can then navigate in response to these signals. We analyse this navigation by combining deep reinforcement learning with direct numerical simulations to resolve the hydrodynamics. We study how local and non-local information can be used to train a swimmer to achieve particular swimming tasks in a non-uniform flow field, in particular a zig-zag shear flow. The swimming tasks are (1) learning how to swim in the vorticity direction, (2) the shear-gradient direction, and (3) the shear flow direction. We find that access to lab frame information on the swimmer's instantaneous orientation is all that is required in order to reach the optimal policy for (1,2). However, information on both the translational and rotational velocities seem to be required to achieve (3). Inspired by biological microorganisms we also consider the case where the swimmers sense local information, i.e. surface hydrodynamic forces, together with a signal direction. This might correspond to gravity or, for micro-organisms with light sensors, a light source. In this case, we show that the swimmer can reach a comparable level of performance as a swimmer with access to lab frame variables. We also analyse the role of different swimming modes, i.e. pusher, puller, and neutral swimmers.

Voice assistants are deployed widely and provide useful functionality. However, recent work has shown that commercial systems like Amazon Alexa and Google Home are vulnerable to voice-based confusion attacks that exploit design issues. We propose a systems-oriented defense against this class of attacks and demonstrate its functionality for Amazon Alexa. We ensure that only the skills a user intends execute in response to voice commands. Our key insight is that we can interpret a user's intentions by analyzing their activity on counterpart systems of the web and smartphones. For example, the Lyft ride-sharing Alexa skill has an Android app and a website. Our work shows how information from counterpart apps can help reduce dis-ambiguities in the skill invocation process. We build SkilIFence, a browser extension that existing voice assistant users can install to ensure that only legitimate skills run in response to their commands. Using real user data from MTurk (N = 116) and experimental trials involving synthetic and organic speech, we show that SkillFence provides a balance between usability and security by securing 90.83% of skills that a user will need with a False acceptance rate of 19.83%.

Dynamical systems are found in innumerable forms across the physical and biological sciences, yet all these systems fall naturally into universal equivalence classes: conservative or dissipative, stable or unstable, compressible or incompressible. Predicting these classes from data remains an essential open challenge in computational physics at which existing time-series classification methods struggle. Here, we propose, \texttt{phase2vec}, an embedding method that learns high-quality, physically-meaningful representations of 2D dynamical systems without supervision. Our embeddings are produced by a convolutional backbone that extracts geometric features from flow data and minimizes a physically-informed vector field reconstruction loss. In an auxiliary training period, embeddings are optimized so that they robustly encode the equations of unseen data over and above the performance of a per-equation fitting method. The trained architecture can not only predict the equations of unseen data, but also, crucially, learns embeddings that respect the underlying semantics of the embedded physical systems. We validate the quality of learned embeddings investigating the extent to which physical categories of input data can be decoded from embeddings compared to standard blackbox classifiers and state-of-the-art time series classification techniques. We find that our embeddings encode important physical properties of the underlying data, including the stability of fixed points, conservation of energy, and the incompressibility of flows, with greater fidelity than competing methods. We finally apply our embeddings to the analysis of meteorological data, showing we can detect climatically meaningful features. Collectively, our results demonstrate the viability of embedding approaches for the discovery of dynamical features in physical systems.

Question Answering (QA) is a growing area of research, often used to facilitate the extraction of information from within documents. State-of-the-art QA models are usually pre-trained on domain-general corpora like Wikipedia and thus tend to struggle on out-of-domain documents without fine-tuning. We demonstrate that synthetic domain-specific datasets can be generated easily using domain-general models, while still providing significant improvements to QA performance. We present two new tools for this task: A flexible pipeline for validating the synthetic QA data and training downstream models on it, and an online interface to facilitate human annotation of this generated data. Using this interface, crowdworkers labelled 1117 synthetic QA pairs, which we then used to fine-tune downstream models and improve domain-specific QA performance by 8.75 F1.