Estimation of Gaussian graphical models is important in natural science when modeling the statistical relationships between variables in the form of a graph. The sparsity and clustering structure of the concentration matrix is enforced to reduce model complexity and describe inherent regularities. We propose a model to estimate the sparse Gaussian graphical models with hidden clustering structure, which also allows additional linear constraints to be imposed on the concentration matrix. We design an efficient two-phase algorithm for solving the proposed model. We develop a symmetric Gauss-Seidel based alternating direction method of the multipliers (sGS-ADMM) to generate an initial point to warm-start the second phase algorithm, which is a proximal augmented Lagrangian method (pALM), to get a solution with high accuracy. Numerical experiments on both synthetic data and real data demonstrate the good performance of our model, as well as the efficiency and robustness of our proposed algorithm.

Multi-label learning deals with the problem that each instance is associated with multiple labels simultaneously. Due to its ability to cope with the real-world objects with multiple semantic meanings, multi-label learning has been successfully applied in various application domains [1], such as tag recommendation [2, 3], bioinformatics [4, 5, 6], information retrieval [7, 8], rule mining [9, 10], web mining [11, 12], and so on. Formally speaking, suppose the given multi-label data set is denoted by D {x i, y i } n i 1 where x i R d is a feature vector with d dimensions (features) and y i { 1, 1} q is the corresponding label vector with the size of label space being q. Here, y ij 1 indicates that the i-th instance x i has the j-th label (or equivalently, the j-th label is a relevant label of x i), otherwise the j-th label is an irrelevant label of x i . Let X R d be the d-dimensional feature space, and Y { 1, 1} q be the q-dimensional label space, multi-label learning aims to induce a mapping function f: X Y, which is able to correctly predict the label vector of unseen instances. To solve the multi-label learning problem, the most straightforward solution is Binary Relevance (BR) [13, 14], which aims to decompose the original learning problem into a set of independent binary classification problems. However, this solution generally achieves mediocre performance, as label correlations are regrettably ignored. To ease this problem, a large number of multi-label learning approaches take into account label correlations explicitly or implicitly to improve the learning performance.

Ensemble-based methods are highly popular approaches that increase the accuracy of a decision by aggregating the opinions of individual voters. The common point is to maximize accuracy; however, a natural limitation occurs if incremental costs are also assigned to the individual voters. Consequently, we investigate creating ensembles under an additional constraint on the total cost of the members. This task can be formulated as a knapsack problem, where the energy is the ensemble accuracy formed by some aggregation rules. However, the generally applied aggregation rules lead to a nonseparable energy function, which takes the common solution tools -- such as dynamic programming -- out of action. We introduce a novel stochastic approach that considers the energy as the joint probability function of the member accuracies. This type of knowledge can be efficiently incorporated in a stochastic search process as a stopping rule, since we have the information on the expected accuracy or, alternatively, the probability of finding more accurate ensembles. Experimental analyses of the created ensembles of pattern classifiers and object detectors confirm the efficiency of our approach. Moreover, we propose a novel stochastic search strategy that better fits the energy, compared with general approaches such as simulated annealing.

When a user connects to the Internet to fulfill his needs, he often encounters a huge amount of related information. Recommender systems are the techniques for massively filtering information and offering the items that users find them satisfying and interesting. The advances in machine learning methods, especially deep learning, have led to great achievements in recommender systems, although these systems still suffer from challenges such as cold-start and sparsity problems. To solve these problems, context information such as user communication network is usually used. In this paper, we have proposed a novel recommendation method based on Matrix Factorization and graph analysis methods, namely Louvain for community detection and HITS for finding the most important node within the trust network. In addition, we leverage deep Autoencoders to initialize users and items latent factors, and the Node2vec deep embedding method gathers users' latent factors from the user trust graph. The proposed method is implemented on Ciao and Epinions standard datasets. The experimental results and comparisons demonstrate that the proposed approach is superior to the existing state-of-the-art recommendation methods. Our approach outperforms other comparative methods and achieves great improvements, i.e., 15.56% RMSE improvement for Epinions and 18.41% RMSE improvement for Ciao.

Interactive recommendation aims to learn from dynamic interactions between items and users to achieve responsiveness and accuracy. Reinforcement learning is inherently advantageous for coping with dynamic environments and thus has attracted increasing attention in interactive recommendation research. Inspired by knowledge-aware recommendation, we proposed Knowledge-Guided deep Reinforcement learning (KGRL) to harness the advantages of both reinforcement learning and knowledge graphs for interactive recommendation. This model is implemented upon the actor-critic network framework. It maintains a local knowledge network to guide decision-making and employs the attention mechanism to capture long-term semantics between items. We have conducted comprehensive experiments in a simulated online environment with six public real-world datasets and demonstrated the superiority of our model over several state-of-the-art methods.

A yuru-chara is a mascot character created by local governments and companies for publicizing information on areas and products. Because it takes various costs to create a yuruchara, the utilization of machine learning techniques such as generative adversarial networks (GANs) can be expected. In recent years, it has been reported that the use of class conditions in a dataset for GANs training stabilizes learning and improves the quality of the generated images. However, it is difficult to apply class conditional GANs when the amount of original data is small and when a clear class is not given, such as a yuruchara image. In this paper, we propose a class conditional GAN based on clustering and data augmentation. Specifically, first, we performed clustering based on K-means++ on the yuru-chara image dataset and converted it into a class conditional dataset. Next, data augmentation was performed on the class conditional dataset so that the amount of data was increased five times. In addition, we built a model that incorporates ResBlock and self-attention into a network based on class conditional GAN and trained the class conditional yuru-chara dataset. As a result of evaluating the generated images, the effect on the generated images by the difference of the clustering method was confirmed.

This paper documents the consequences of the identification failures for a class of linear ill-posed inverse models. The Tikhonov-regularized estimator converges to a well-defined limit equal to the best approximation of the structural parameter in the orthogonal complement to the null space of the operator. We illustrate that in many cases the best approximation may coincide with the structural parameter or at least may reasonably approximate it. We characterize the nonasymptotic Hilbert space norm and the uniform norm convergence rates for the best approximation. Nonidentification has important implications for the large sample distribution of the Tikhonov-regularized estimator, and we document the transition between the Gaussian and the weighted chi-squared limits. The theoretical results are illustrated for the nonparametric IV and the functional linear IV regressions and are further supported by the Monte Carlo experiments.

Tesla vehicles are going to be able to drop you off and park themselves later this year, according to a new comment from Tesla CEO Elon Musk. With Smart Summon, Tesla introduced a significant upgrade to its capacity to remotely and autonomously move its car, which the automaker refers to as "summoning." Tesla owners can "summon" their car when parked in a parking lot -- triggering the vehicle to drive to you. The feature has been working relatively well, but many owners don't find it that useful. However, many have suggested that a "reverse smart summon," which would enable owners to be dropped off in a convenient location within a parking lot, and then the car could go find its own parking spot, would be a much more useful feature.

Beat is one of the most exciting companies to ever come out of the ride-hailing space. One city at a time, all across the globe we make transportation affordable, convenient, and safe for everyone. We also help hundreds of thousands of people earn extra income as drivers. Today we are the fastest-growing ride-hailing service in Latin America. But serving millions of rides every day pales in comparison to what lies ahead.

In today's environment, seasoned companies who continue to weather the tests of time suddenly demand greater attention. How do they retain their grit and withstand adversity, whether from the COVID-19 pandemic or digital disruption? How much do values play out in their approach to customers, their ability to embrace new technology, or adopt a new method? U.S. Southwest steel building and metal roof manufacturer Mueller Inc. has long impressed both technology and industry observers for its unobstructed perspective and ability to succeed. At its essence, it's a small-town company that – despite having spread its wings through several U.S. states – remains defiantly headquartered in Ballinger, the West Texas town where it was founded in 1931.