对于过去的几年来，冠状病毒通常被称为Covid-19，通过施加几年，通过施加几年，在美国居住在美国居住的所有公民的日常生活受到忽视。为了应对日益增长的恐惧和危险的Covid-19对美国的社会造成造成的，已经成为个人利用的常设补救措施。在本文中，我们研究了Covid-19疫苗和助推器之间的关系，以及美国多个州的冠状病毒的总案例计数。此外，本文讨论了几个，底层健康状况与Covid-19之间的关系。为了有效地讨论这些关系，本文将利用统计测试和机器学习方法进行分析和讨论。此外，本文反映了关于教育程度，种族和Covid-19之间关系的结论，以及可以以潜在的健康状况，疫苗接种率和Covid-19的总案例和死亡计数建立的可能连接。

Automotive radar sensors provide valuable information for advanced driving assistance systems (ADAS). Radars can reliably estimate the distance to an object and the relative velocity, regardless of weather and light conditions. However, radar sensors suffer from low resolution and huge intra-class variations in the shape of objects. Exploiting the time information (e.g., multiple frames) has been shown to help to capture better the dynamics of objects and, therefore, the variation in the shape of objects. Most temporal radar object detectors use 3D convolutions to learn spatial and temporal information. However, these methods are often non-causal and unsuitable for real-time applications. This work presents RECORD, a new recurrent CNN architecture for online radar object detection. We propose an end-to-end trainable architecture mixing convolutions and ConvLSTMs to learn spatio-temporal dependencies between successive frames. Our model is causal and requires only the past information encoded in the memory of the ConvLSTMs to detect objects. Our experiments show such a method's relevance for detecting objects in different radar representations (range-Doppler, range-angle) and outperform state-of-the-art models on the ROD2021 and CARRADA datasets while being less computationally expensive. The code will be available soon.

Deep learning models are being increasingly applied to imbalanced data in high stakes fields such as medicine, autonomous driving, and intelligence analysis. Imbalanced data compounds the black-box nature of deep networks because the relationships between classes may be highly skewed and unclear. This can reduce trust by model users and hamper the progress of developers of imbalanced learning algorithms. Existing methods that investigate imbalanced data complexity are geared toward binary classification, shallow learning models and low dimensional data. In addition, current eXplainable Artificial Intelligence (XAI) techniques mainly focus on converting opaque deep learning models into simpler models (e.g., decision trees) or mapping predictions for specific instances to inputs, instead of examining global data properties and complexities. Therefore, there is a need for a framework that is tailored to modern deep networks, that incorporates large, high dimensional, multi-class datasets, and uncovers data complexities commonly found in imbalanced data (e.g., class overlap, sub-concepts, and outlier instances). We propose a set of techniques that can be used by both deep learning model users to identify, visualize and understand class prototypes, sub-concepts and outlier instances; and by imbalanced learning algorithm developers to detect features and class exemplars that are key to model performance. Our framework also identifies instances that reside on the border of class decision boundaries, which can carry highly discriminative information. Unlike many existing XAI techniques which map model decisions to gray-scale pixel locations, we use saliency through back-propagation to identify and aggregate image color bands across entire classes. Our framework is publicly available at \url{https://github.com/dd1github/XAI_for_Imbalanced_Learning}

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.

Proteins play a central role in biology from immune recognition to brain activity. While major advances in machine learning have improved our ability to predict protein structure from sequence, determining protein function from structure remains a major challenge. Here, we introduce Holographic Convolutional Neural Network (H-CNN) for proteins, which is a physically motivated machine learning approach to model amino acid preferences in protein structures. H-CNN reflects physical interactions in a protein structure and recapitulates the functional information stored in evolutionary data. H-CNN accurately predicts the impact of mutations on protein function, including stability and binding of protein complexes. Our interpretable computational model for protein structure-function maps could guide design of novel proteins with desired function.

在新颖的类发现（NCD）中，目标是在一个未标记的集合中找到新的类，并给定一组已知但不同的类别。尽管NCD最近引起了社区的关注，但尽管非常普遍的数据表示，但尚未提出异质表格数据的框架。在本文中，我们提出了TabularNCD，这是一种在表格数据中发现新类别的新方法。我们展示了一种从已知类别中提取知识的方法，以指导包含异质变量的表格数据中新型类的发现过程。该过程的一部分是通过定义伪标签的新方法来完成的，我们遵循多任务学习中的最新发现以优化关节目标函数。我们的方法表明，NCD不仅适用于图像，而且适用于异质表格数据。进行了广泛的实验，以评估我们的方法并证明其对7种不同公共分类数据集的3个竞争对手的有效性。

从有限的资源中获得最大收益可以进步自然语言处理（NLP）研究和实践，同时保守资源。这些资源可能是数据，时间，存储或能源。NLP的最新工作从缩放率产生了有趣的结果。但是，仅使用比例来改善结果意味着资源消耗也会扩展。这种关系激发了对有效方法的研究，这些方法需要更少的资源才能获得相似的结果。这项调查涉及NLP效率的方法和发现，旨在指导该领域的新研究人员并激发新方法的发展。

将包装从存储设施运送到消费者前门的物流通常采用高度专业的机器人，通常会将子任务分配到不同的系统，例如，操纵器臂进行分类和轮式车辆进行交付。最近的努力试图通过腿部和人形机器人进行统一的方法。但是，这些解决方案占据了大量空间，从而减少了可以适合运送车辆的包装数量。结果，这些庞大的机器人系统通常会降低可伸缩性和并行任务的潜力。在本文中，我们介绍了Limms（锁存智能模块化移动系统），以解决典型的最后一英里交付的操纵和交付部分，同时保持最小的空间足迹。 Limms是一种对称设计的，6型自由度（DOF）的类似于附件的机器人，两端都带有轮子和闩锁机构。通过将锁在表面上并锚定在一端，Limms可以充当传统的6多型操纵器臂。另一方面，多个lims可以锁在一个盒子上，并且像腿部机器人系统一样行为，包装是身体。在运输过程中，与传统的机器人系统相比，LIMM紧紧地折叠起来，占用的空间要少得多。一大批limms单元可以安装在单个送货工具内部，为新的交付优化和混合计划方法开放，从未做过。在本文中，使用硬件原型研究和呈现了LIMM的可行性，以及在典型的最后一英里交付中的一系列子任务的仿真结果。

计算模型是系统的定量表示。通过分析和比较此类模型的输出，可以更好地了解系统本身。但是，随着模型输出的复杂性的增加，将模拟彼此比较变得越来越困难。虽然只能比较多个模拟的一些特定模型输出是很简单的，但是能够比较整个模型仿真是更有用的。但是，很难以公正的方式整体比较模型模拟。为了解决这些局限性，我们使用暹罗神经网络将模型模拟与单个值进行比较，而神经网络捕获了模型输出之间的关系。我们为模型模拟培训暹罗网络提供了一种方法，并显示如何使用训练有素的网络来提供模型输出的整体比较。该方法可以应用于广泛的模型类型，提供了分析计算模型复杂输出的定量方法。

这项研究的重点是用于监视和控制用户头部和智能头盔外壳之间的相互作用的软机器人膀胱。这些膀胱的压缩决定了影响耗散；因此，本文的重点是膀胱压缩的感应和估计。使用回归技术和神经网络评估基于IR测距仪的解决方案，以估计膀胱压缩。还检查了霍尔效应（HE）磁传感系统，其中传感器嵌入膀胱底部，感觉磁体在膀胱顶部的位置。本文介绍了HE传感器阵列，HE电压数据的信号处理，然后介绍了用于预测膀胱压缩的神经网络（NN）。研究了不同培训数据集关于NN性能的功效。检查了不同的NN配置，以确定与尽可能少的节点提供准确估计值的配置。评估不同的膀胱压缩曲线以表征IR范围查找，并在应用程序方案中基于HE的技术。