我们提出了Memprop，即采用基于梯度的学习来培训完全的申请尖峰神经网络（MSNNS）。我们的方法利用固有的设备动力学来触发自然产生的电压尖峰。这些由回忆动力学发出的尖峰本质上是类似物，因此完全可区分，这消除了尖峰神经网络（SNN）文献中普遍存在的替代梯度方法的需求。回忆性神经网络通常将备忘录集成为映射离线培训网络的突触，或者以其他方式依靠关联学习机制来训练候选神经元的网络。相反，我们直接在循环神经元和突触的模拟香料模型上应用了通过时间（BPTT）训练算法的反向传播。我们的实现是完全的综合性，因为突触重量和尖峰神经元都集成在电阻RAM（RRAM）阵列上，而无需其他电路来实现尖峰动态，例如模数转换器（ADCS）或阈值比较器。结果，高阶电物理效应被充分利用，以在运行时使用磁性神经元的状态驱动动力学。通过朝着非同一梯度的学习迈进，我们在以前报道的几个基准上的轻巧密集的完全MSNN中获得了高度竞争的准确性。

由于Covid-19-19疫苗可用，因此没有研究量化不同的灾难疏散策略如何减轻避难所中的大流行风险。因此，我们应用了一个年龄结构化的流行病学模型，称为易感性暴露感染（SEIR）模型，以研究台湾不同的疫苗摄取水平以及在台湾实施的转移方案在多大程度上降低了感染和延迟流行峰值的情况。台湾的转移协议涉及转移因曝光而自我占用的人，从而阻止了他们与集体庇护所的普通公众融合。转移方案，结合足够的疫苗摄取，可以减少相对于没有这种策略的情况，相对于场景，感染的最大数量和延迟爆发。当所有暴露的人的转移是不可能的，或者疫苗的摄取不足时，转移方案仍然很有价值。此外，一组主要由年轻人人口组成的撤离者往往会早日出现大流行峰值，并且在实施转移方案时，多数老年人组的感染比多数老年人多。但是，当不执行转移方案时，多数老年人群体比大多数年轻成人群体高达20％。

尖峰神经网络已显示出具有人工神经网络的节能替代品。但是，对于常见的神经形态视觉基准（如分类），了解传感器噪声和输入编码对网络活动和性能的影响仍然很困难。因此，我们提出了一种使用替代梯度下降训练的单个对象定位的尖峰神经网络方法，用于基于框架和事件的传感器。我们将我们的方法与类似的人工神经网络进行比较，并表明我们的模型在准确性，对各种腐败的鲁棒性方面具有竞争力/更好的性能，并且能耗较低。此外，我们研究了神经编码方案对准确性，鲁棒性和能源效率的静态图像的影响。我们的观察结果与以前关于生物成分学习规则的研究重要差​​异，该规则有助于设计替代梯度训练的体系结构，并就噪声特征和数据编码方法方面的未来神经形态技术设计优先级。

超越地球轨道的人类空间勘探将涉及大量距离和持续时间的任务。为了有效减轻无数空间健康危害，数据和空间健康系统的范式转移是实现地球独立性的，而不是Earth-Reliance所必需的。有希望在生物学和健康的人工智能和机器学习领域的发展可以解决这些需求。我们提出了一个适当的自主和智能精密空间健康系统，可以监控，汇总和评估生物医学状态;分析和预测个性化不良健康结果;适应并响应新累积的数据;并提供对其船员医务人员的个人深度空间机组人员和迭代决策支持的预防性，可操作和及时的见解。在这里，我们介绍了美国国家航空航天局组织的研讨会的建议摘要，以便在太空生物学和健康中未来的人工智能应用。在未来十年，生物监测技术，生物标志科学，航天器硬件，智能软件和简化的数据管理必须成熟，并编织成精确的空间健康系统，以使人类在深空中茁壮成长。

空间生物学研究旨在了解太空飞行对生物的根本影响，制定支持深度空间探索的基础知识，最终生物工程航天器和栖息地稳定植物，农作物，微生物，动物和人类的生态系统，为持续的多行星寿命稳定。要提高这些目标，该领域利用了来自星空和地下模拟研究的实验，平台，数据和模型生物。由于研究扩展到低地球轨道之外，实验和平台必须是最大自主，光，敏捷和智能化，以加快知识发现。在这里，我们介绍了由美国国家航空航天局的人工智能，机器学习和建模应用程序组织的研讨会的建议摘要，这些应用程序为这些空间生物学挑战提供了关键解决方案。在未来十年中，将人工智能融入太空生物学领域将深化天空效应的生物学理解，促进预测性建模和分析，支持最大自主和可重复的实验，并有效地管理星载数据和元数据，所有目标使生活能够在深空中茁壮成长。

现代深度学习框架提供嵌入在Python中的必要的急切执行编程接口，以提供生产的开发体验。但是，深度学习从业者有时需要捕获和转换程序结构以进行性能优化，可视化，分析和硬件集成。我们研究了深度学习中使用的程序捕获和转型的不同设计。通过设计典型的深度学习用例而不是长尾部，可以为程序捕获和转换创建更简单的框架。我们在Torch.fx中应用了这一原理，是一个完全在Python写入的Pytorch的程序捕获和转换库，并通过ML从业者进行高开发人员生产力优化。我们存在案例研究，展示了Torch.fx如何实现先前在Pytorch生态系统中无法访问的工作流程。

Designing experiments often requires balancing between learning about the true treatment effects and earning from allocating more samples to the superior treatment. While optimal algorithms for the Multi-Armed Bandit Problem (MABP) provide allocation policies that optimally balance learning and earning, they tend to be computationally expensive. The Gittins Index (GI) is a solution to the MABP that can simultaneously attain optimality and computationally efficiency goals, and it has been recently used in experiments with Bernoulli and Gaussian rewards. For the first time, we present a modification of the GI rule that can be used in experiments with exponentially-distributed rewards. We report its performance in simulated 2- armed and 3-armed experiments. Compared to traditional non-adaptive designs, our novel GI modified design shows operating characteristics comparable in learning (e.g. statistical power) but substantially better in earning (e.g. direct benefits). This illustrates the potential that designs using a GI approach to allocate participants have to improve participant benefits, increase efficiencies, and reduce experimental costs in adaptive multi-armed experiments with exponential rewards.

Modelling and forecasting real-life human behaviour using online social media is an active endeavour of interest in politics, government, academia, and industry. Since its creation in 2006, Twitter has been proposed as a potential laboratory that could be used to gauge and predict social behaviour. During the last decade, the user base of Twitter has been growing and becoming more representative of the general population. Here we analyse this user base in the context of the 2021 Mexican Legislative Election. To do so, we use a dataset of 15 million election-related tweets in the six months preceding election day. We explore different election models that assign political preference to either the ruling parties or the opposition. We find that models using data with geographical attributes determine the results of the election with better precision and accuracy than conventional polling methods. These results demonstrate that analysis of public online data can outperform conventional polling methods, and that political analysis and general forecasting would likely benefit from incorporating such data in the immediate future. Moreover, the same Twitter dataset with geographical attributes is positively correlated with results from official census data on population and internet usage in Mexico. These findings suggest that we have reached a period in time when online activity, appropriately curated, can provide an accurate representation of offline behaviour.

Existing federated classification algorithms typically assume the local annotations at every client cover the same set of classes. In this paper, we aim to lift such an assumption and focus on a more general yet practical non-IID setting where every client can work on non-identical and even disjoint sets of classes (i.e., client-exclusive classes), and the clients have a common goal which is to build a global classification model to identify the union of these classes. Such heterogeneity in client class sets poses a new challenge: how to ensure different clients are operating in the same latent space so as to avoid the drift after aggregation? We observe that the classes can be described in natural languages (i.e., class names) and these names are typically safe to share with all parties. Thus, we formulate the classification problem as a matching process between data representations and class representations and break the classification model into a data encoder and a label encoder. We leverage the natural-language class names as the common ground to anchor the class representations in the label encoder. In each iteration, the label encoder updates the class representations and regulates the data representations through matching. We further use the updated class representations at each round to annotate data samples for locally-unaware classes according to similarity and distill knowledge to local models. Extensive experiments on four real-world datasets show that the proposed method can outperform various classical and state-of-the-art federated learning methods designed for learning with non-IID data.

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.