To address three significant challenges of massive wireless communications including propagation loss, long-distance transmission, and channel fading, we aim at establishing the hybrid antenna array with bowtie elements in a compact size for beamforming applications. In this work we rigorously demonstrate that bowtie elements allow for a significant improvement in the beamforming performance of the hybrid antenna array compared to not only other available antenna arrays, but also its geometrical counterpart with dipole elements. We have achieved a greater than 15 dB increase in SINR values, a greater than 20% improvement in the antenna efficiency, a significant enhancement in the DoA estimation, and 20 increments in the directivity for the hybrid antenna array with bowtie elements, compared to its geometrical counterpart, by performing a three-class classification LS-SVM (Least-Squares Support Vector Machine) optimization method. The proposed hybrid antenna array has shown a 3D uniform directivity, which is accompanied by its superior performance in the 3D uniform beam-scanning capability. The directivities remain almost constant at 40.83 dBi with the variation of angle θ, and 41.21 dBi with the variation of angle φ. The unrivaled functionality and performance of the hybrid antenna array with bowtie elements makes it a potential candidate for beamforming applications in massive wireless communications. ECENT advances and innovative solutions have been extensively developed for deploying smart array antennas with a highly-directive radiation pattern in order to overcome the long-distance challenges and minimize interference signals, as well as enhance the spherical coverage and capacity in massive wireless communication channels. In this sense, in addition to performing optimization methods, the geometrical configuration, inter-element spacing, excitation phase, and amplitude of the individual array elements of each antenna array have been designed and synthesized for controlling its radiation patterns.

Clustering based on the random walk operator has been proven effective for undirected graphs, but its generalization to directed graphs (digraphs) is much more challenging. Although the random walk operator is well-defined for digraphs, in most cases such graphs are not strongly connected, and hence the associated random walks are not irreducible, which is a crucial property for clustering that exists naturally in the undirected setting. To remedy this, the usual workaround is to either naively symmetrize the adjacency matrix or to replace the natural random walk operator by the teleporting random walk operator, but this can lead to the loss of valuable information carried by edge directionality. In this paper, we introduce a new clustering framework, the Parametrized Random Walk Diffusion Kernel Clustering (P-RWDKC), which is suitable for handling both directed and undirected graphs. Our framework is based on the diffusion geometry (Coifman & Lafon, 2006) and the generalized spectral clustering framework (Sevi et al., 2022). Accordingly, we propose an algorithm that automatically reveals the cluster structure at a given scale, by considering the random walk dynamics associated with a parametrized kernel operator, and by estimating its critical diffusion time. Experiments on K-NN graphs constructed from real-world datasets and real-world graphs show that our clustering approach performs well in all tested cases, and outperforms existing approaches in most of them.

Machine learning systems are not robust against adversarial examples, meticulously crafted perturbation of the inputs that fool the classifier [4, 14, 21, 27, 28]. As Deep Neural Networks and other machine learning models become more and more important in everyday life and safety-critical applications such as autonomous navigation, surveillance systems, medical diagnosis, and malware analysis, it is fundamental to understand their robustness in the worst-case scenario. Evaluating the robustness of models is extremely challenging, so it is critical to develop stronger adversarial attacks to better understand the limits and capabilities of machine learning models. Many adversarial attacks have been proposed, however, many of them focus on the white-box setting, where the attacker has full knowledge of the victim model. However, in the real world scenario, generally, the attacker knows neither the kind of models used nor the weights and parameters of the models. To overcome the limitations of the white-box setting various black-box settings were defined. In the black-box settings, generally, the attacker has limited information about the model and the defender architecture, for example, it might not know the weights of the model or it has no access to the gradient of the model. In addition, adversarial attacks can be targeted (in this case the goal is to cause the model to classify the samples to a chosen class) or untargeted (in this case the goal is to cause the model to classify the samples to a different class from the initial ones).

Grammatical inference is a classical problem in computational learning theory and a topic of wider influence in natural language processing. We treat grammars as a model of computation and propose a novel neural approach to induction of regular grammars from positive and negative examples. Our model is fully explainable, its intermediate results are directly interpretable as partial parses, and it can be used to learn arbitrary regular grammars when provided with sufficient data. We find that our method consistently attains high recall and precision scores across a range of tests of varying complexity.

We provide a framework for probabilistic reasoning in Vadalog-based Knowledge Graphs (KGs), satisfying the requirements of ontological reasoning: full recursion, powerful existential quantification, expression of inductive definitions. Vadalog is a Knowledge Representation and Reasoning (KRR) language based on Warded Datalog+/-, a logical core language of existential rules, with a good balance between computational complexity and expressive power. Handling uncertainty is essential for reasoning with KGs. Yet Vadalog and Warded Datalog+/- are not covered by the existing probabilistic logic programming and statistical relational learning approaches for several reasons, including insufficient support for recursion with existential quantification, and the impossibility to express inductive definitions. In this work, we introduce Soft Vadalog, a probabilistic extension to Vadalog, satisfying these desiderata. A Soft Vadalog program induces what we call a Probabilistic Knowledge Graph (PKG), which consists of a probability distribution on a network of chase instances, structures obtained by grounding the rules over a database using the chase procedure. We exploit PKGs for probabilistic marginal inference. We discuss the theory and present MCMC-chase, a Monte Carlo method to use Soft Vadalog in practice. We apply our framework to solve data management and industrial problems, and experimentally evaluate it in the Vadalog system.

The post Boosting in Machine Learning:-A Brief Overview appeared first on Data Science Tutorials What do you have to lose?. Check out Data Science tutorials here Data Science Tutorials. Boosting in Machine Learning, A single predictive model, such as linear regression, logistic regression, ridge regression, etc., is the foundation of the majority of supervised machine learning methods. However, techniques such as bagging and random forests provide a wide range of models from repeated bootstrapped samples of the original dataset. The average of the predictions... Read More “Boosting in Machine Learning:-A Brief Overview” » The post Boosting in Machine Learning:-A Brief Overview appeared first on Data Science Tutorials Learn how to expert in the Data Science field with Data Science Tutorials.

This Free Udemy course has 3 sections. In the first section, you will learn R basics and how to download R and Rstudio. In the next section, you will learn how to code in R programming and understand functions, loops, R datasets, and R dataframes. The last section teaches how to load CSV files in R, how to apply a family of functions, how to test for normality, KNN classification, LDA(Linear Discriminant Analysis), etc. Overall, this is a good course for beginners to learn R programming basics.

This article is part of a series where we walk step by step in solving fintech problems with Machine Learning using "All lending club loan data". In previous articles, we prepared a dataset and built a Logistic Regression model, and we discussed the most common "ML model evaluation metrics" for a classification problem in the fintech space. This article will try to "understand" how our model decision works and what packages can help us to answer this question. Machine learning models are frequently named "black boxes". They produce highly accurate predictions.

As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented attention because of its promise to further optimize process design, quality control, health monitoring, decision and policy making, and more, by comprehensively modeling the physical world as a group of interconnected digital models. In a two-part series of papers, we examine the fundamental role of different modeling techniques, twinning enabling technologies, and uncertainty quantification and optimization methods commonly used in digital twins. This first paper presents a thorough literature review of digital twin trends across many disciplines currently pursuing this area of research. Then, digital twin modeling and twinning enabling technologies are further analyzed by classifying them into two main categories: physical-to-virtual, and virtual-to-physical, based on the direction in which data flows. Finally, this paper provides perspectives on the trajectory of digital twin technology over the next decade, and introduces a few emerging areas of research which will likely be of great use in future digital twin research. In part two of this review, the role of uncertainty quantification and optimization are discussed, a battery digital twin is demonstrated, and more perspectives on the future of digital twin are shared.

Auto-encoder models that preserve similarities in the data are a popular tool in representation learning. In this paper we introduce several auto-encoder models that preserve local distances when mapping from the data space to the latent space. We use a local distance-preserving loss that is based on the continuous k-nearest neighbours graph which is known to capture topological features at all scales simultaneously. To improve training performance, we formulate learning as a constraint optimisation problem with local distance preservation as the main objective and reconstruction accuracy as a constraint. Our method provides state-ofthe-art or comparable performance across several standard datasets and evaluation metrics. Auto-encoders and variational auto-encoders (Kingma & Welling, 2014; Rezende et al., 2014) are often used in machine learning to find meaningful latent representations of the data. What constitutes meaningful usually depends on the application and on the downstream tasks, for example, finding representations that have important factors of variations in the data (disentanglement) (Higgins et al., 2017; Chen et al., 2018), have high mutual information with the data (Chen et al., 2016), or show clustering behaviour w.r.t. These representations are usually incentivised by regularisers or architectural/structural choices. One criterion for finding a meaningful latent representation is geometric faithfulness to the data. This is important for data visualisation or further downstream tasks that involve geometric algorithms such as clustering or kNN classification. The data often lies in a small, sparse, low-dimensional manifold in the space it inhabits and finding a lower dimensional projection that is geometrically faithful to it can help not only in visualisation and interpretability but also in predictive performance and robustness (e.g.