尖峰神经网络（SNN）提供了一个新的计算范式，能够高度平行，实时处理。光子设备是设计与SNN计算范式相匹配的高带宽，平行体系结构的理想选择。 CMO和光子元件的协整允许将低损耗的光子设备与模拟电子设备结合使用，以更大的非线性计算元件的灵活性。因此，我们在整体硅光子学（SIPH）过程上设计和模拟了光电尖峰神经元电路，该过程复制了超出泄漏的集成和火（LIF）之外有用的尖峰行为。此外，我们探索了两种学习算法，具有使用Mach-Zehnder干涉法（MZI）网格作为突触互连的片上学习的潜力。实验证明了随机反向传播（RPB）的变体，并在简单分类任务上与标准线性回归的性能相匹配。同时，将对比性HEBBIAN学习（CHL）规则应用于由MZI网格组成的模拟神经网络，以进行随机输入输出映射任务。受CHL训练的MZI网络的性能比随机猜测更好，但不符合理想神经网络的性能（没有MZI网格施加的约束）。通过这些努力，我们证明了协调的CMO和SIPH技术非常适合可扩展的SNN计算体系结构的设计。

We present the development of a semi-supervised regression method using variational autoencoders (VAE), which is customized for use in soft sensing applications. We motivate the use of semi-supervised learning considering the fact that process quality variables are not collected at the same frequency as other process variables leading to many unlabelled records in operational datasets. These unlabelled records are not possible to use for training quality variable predictions based on supervised learning methods. Use of VAEs for unsupervised learning is well established and recently they were used for regression applications based on variational inference procedures. We extend this approach of supervised VAEs for regression (SVAER) to make it learn from unlabelled data leading to semi-supervised VAEs for regression (SSVAER), then we make further modifications to their architecture using additional regularization components to make SSVAER well suited for learning from both labelled and unlabelled process data. The probabilistic regressor resulting from the variational approach makes it possible to estimate the variance of the predictions simultaneously, which provides an uncertainty quantification along with the generated predictions. We provide an extensive comparative study of SSVAER with other publicly available semi-supervised and supervised learning methods on two benchmark problems using fixed-size datasets, where we vary the percentage of labelled data available for training. In these experiments, SSVAER achieves the lowest test errors in 11 of the 20 studied cases, compared to other methods where the second best gets 4 lowest test errors out of the 20.

未来的机场变得越来越复杂，并且随着旅行者数量的增加而拥挤。尽管机场更有可能成为潜在冲突的热点，这可能会导致航班和几个安全问题的严重延误。一种使安全监视更有效地检测冲突的智能算法将在其安全，财务和旅行效率方面为乘客带来许多好处。本文详细介绍了机器学习模型的开发，以对人群中的冲突行为进行分类。 HRNET用于分割图像，然后采用两种方法通过多个分类器对框架中的人的姿势进行分类。其中，发现支持向量机（SVM）达到了最出色的精度为94.37％。该模型不足的地方是反对模棱两可的行为，例如拥抱或失去框架中主题的轨道。如果进行改进以应对大量潜在的乘客，以及针对在机场环境中会出现的进一步歧义行为的培训，则最终的模型具有在机场内部署的潜力。反过来，将提供提高安全监视并提高机场安全的能力。

满意度测量，在今天的每个部门都出现，是许多公司的一个非常重要的因素。在本研究中，旨在通过使用yemek Sepeti的数据和该数据的变化来达到各种机器学习算法的最高精度率。每种算法的精度值都与所使用的各种自然语言处理方法一起计算。在计算这些精度值时，尝试优化使用的算法的参数。在本研究中培训的模型可以在未标记的数据上使用，并且可以在衡量客户满意度时给公司一个想法。观察到施加的3种不同的自然语言处理方法导致大部分开发模型中的大约5％的精度增加。

在这项研究中，我们提出了一种基于聚类的方法在时间序列数据上，以在大流行早期捕获Covid-19传播模式。我们根据不同地理位置的不同国家的Covid-19早期和柱阶段分析了传播动力学。此外，我们调查监禁政策及其对蔓延的影响。我们发现相同监禁政策的实施表现出不同国家的不同结果。具体而言，由于遵守社会疏远措施，锁定在密集的地区变得不那么有效。在一些国家缺乏测试，联系跟踪和社会意识，迫使人们从自隔离和维持社会距离。具有不健康的生活条件的大型劳营营也有助于根据异国劳动的国家高社区传输。在政府政策和假新闻中的不信任煽动于发达国家和发达国家的传播。大型社交聚会在几乎到处造成快速爆发时发挥着至关重要的作用。虽然一些国家能够通过实施严格和广泛采用的监禁政策来遏制传播，但其他一些其他国家在社会疏远措施和严格的测试能力的帮助下载有传播。在大流行开始的早期和快速的反应是包含传播所必需的，但它并不总是足够的。

Variational inference uses optimization, rather than integration, to approximate the marginal likelihood, and thereby the posterior, in a Bayesian model. Thanks to advances in computational scalability made in the last decade, variational inference is now the preferred choice for many high-dimensional models and large datasets. This tutorial introduces variational inference from the parametric perspective that dominates these recent developments, in contrast to the mean-field perspective commonly found in other introductory texts.

Knowledge graphs (KG) have served as the key component of various natural language processing applications. Commonsense knowledge graphs (CKG) are a special type of KG, where entities and relations are composed of free-form text. However, previous works in KG completion and CKG completion suffer from long-tail relations and newly-added relations which do not have many know triples for training. In light of this, few-shot KG completion (FKGC), which requires the strengths of graph representation learning and few-shot learning, has been proposed to challenge the problem of limited annotated data. In this paper, we comprehensively survey previous attempts on such tasks in the form of a series of methods and applications. Specifically, we first introduce FKGC challenges, commonly used KGs, and CKGs. Then we systematically categorize and summarize existing works in terms of the type of KGs and the methods. Finally, we present applications of FKGC models on prediction tasks in different areas and share our thoughts on future research directions of FKGC.

Modeling lies at the core of both the financial and the insurance industry for a wide variety of tasks. The rise and development of machine learning and deep learning models have created many opportunities to improve our modeling toolbox. Breakthroughs in these fields often come with the requirement of large amounts of data. Such large datasets are often not publicly available in finance and insurance, mainly due to privacy and ethics concerns. This lack of data is currently one of the main hurdles in developing better models. One possible option to alleviating this issue is generative modeling. Generative models are capable of simulating fake but realistic-looking data, also referred to as synthetic data, that can be shared more freely. Generative Adversarial Networks (GANs) is such a model that increases our capacity to fit very high-dimensional distributions of data. While research on GANs is an active topic in fields like computer vision, they have found limited adoption within the human sciences, like economics and insurance. Reason for this is that in these fields, most questions are inherently about identification of causal effects, while to this day neural networks, which are at the center of the GAN framework, focus mostly on high-dimensional correlations. In this paper we study the causal preservation capabilities of GANs and whether the produced synthetic data can reliably be used to answer causal questions. This is done by performing causal analyses on the synthetic data, produced by a GAN, with increasingly more lenient assumptions. We consider the cross-sectional case, the time series case and the case with a complete structural model. It is shown that in the simple cross-sectional scenario where correlation equals causation the GAN preserves causality, but that challenges arise for more advanced analyses.

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.

Adversarial robustness assessment for video recognition models has raised concerns owing to their wide applications on safety-critical tasks. Compared with images, videos have much high dimension, which brings huge computational costs when generating adversarial videos. This is especially serious for the query-based black-box attacks where gradient estimation for the threat models is usually utilized, and high dimensions will lead to a large number of queries. To mitigate this issue, we propose to simultaneously eliminate the temporal and spatial redundancy within the video to achieve an effective and efficient gradient estimation on the reduced searching space, and thus query number could decrease. To implement this idea, we design the novel Adversarial spatial-temporal Focus (AstFocus) attack on videos, which performs attacks on the simultaneously focused key frames and key regions from the inter-frames and intra-frames in the video. AstFocus attack is based on the cooperative Multi-Agent Reinforcement Learning (MARL) framework. One agent is responsible for selecting key frames, and another agent is responsible for selecting key regions. These two agents are jointly trained by the common rewards received from the black-box threat models to perform a cooperative prediction. By continuously querying, the reduced searching space composed of key frames and key regions is becoming precise, and the whole query number becomes less than that on the original video. Extensive experiments on four mainstream video recognition models and three widely used action recognition datasets demonstrate that the proposed AstFocus attack outperforms the SOTA methods, which is prevenient in fooling rate, query number, time, and perturbation magnitude at the same.