As one of Bayesian analysis tools, Hidden Markov Model (HMM) has been used to in extensive applications. Most HMMs are solved by Baum-Welch algorithm (BWHMM) to predict the model parameters, which is difficult to find global optimal solutions. This paper proposes an optimized Hidden Markov Model with Particle Swarm Optimization (PSO) algorithm and so is called PSOHMM. In order to overcome the statistical constraints in HMM, the paper develops re-normalization and re-mapping mechanisms to ensure the constraints in HMM. The experiments have shown that PSOHMM can search better solution than BWHMM, and has faster convergence speed.

This paper addresses the mapping problem. Using a conjugate prior form, we derive the exact theoretical batch multi-object posterior density of the map given a set of measurements. The landmarks in the map are modeled as extended objects, and the measurements are described as a Poisson process, conditioned on the map. We use a Poisson process prior on the map and prove that the posterior distribution is a hybrid Poisson, multi-Bernoulli mixture distribution. We devise a Gibbs sampling algorithm to sample from the batch multi-object posterior. The proposed method can handle uncertainties in the data associations and the cardinality of the set of landmarks, and is parallelizable, making it suitable for large-scale problems. The performance of the proposed method is evaluated on synthetic data and is shown to outperform a state-of-the-art method.

Multi-view subspace learning (MSL) aims to find a low-dimensional subspace of the data obtained from multiple views. Different from single view case, MSL should take both common and specific knowledge among different views into consideration. To enhance the robustness of model, the complexity, non-consistency and similarity of noise in multi-view data should be fully taken into consideration. Most current MSL methods only assume a simple Gaussian or Laplacian distribution for the noise while neglect the complex noise configurations in each view and noise correlations among different views of practical data. To this issue, this work initiates a MSL method by encoding the multi-view-shared and single-view-specific noise knowledge in data. Specifically, we model data noise in each view as a separated Mixture of Gaussians (MoG), which can fit a wider range of complex noise types than conventional Gaussian/Laplacian. Furthermore, we link all single-view-noise as a whole by regularizing them by a common MoG component, encoding the shared noise knowledge among them. Such regularization component can be formulated as a concise KL-divergence regularization term under a MAP framework, leading to good interpretation of our model and simple EM-based solving strategy to the problem. Experimental results substantiate the superiority of our method.

In our work, we propose to represent HTM as a set of flat models, or layers, and a set of topical hierarchies, or edges. We suggest several quality measures for edges of hierarchical models, resembling those proposed for flat models. We conduct an assessment experimentation and show strong correlation between the proposed measures and human judgement on topical edge quality. We also introduce heterogeneous algorithm to build hierarchical topic models for heterogeneous data sources. We show how making certain adjustments to learning process helps to retain original structure of customized models while allowing for slight coherent modifications for new documents. We evaluate this approach using the proposed measures and show that the proposed heterogeneous algorithm significantly outperforms the baseline concat approach. Finally, we implement our own ESE called Rysearch, which demonstrates the potential of ARTM approach for visualizing large heterogeneous document collections.

In recent years, pattern analysis plays an important role in data mining and recognition, and many variants have been proposed to handle complicated scenarios. In the literature, it has been quite familiar with high dimensionality of data samples, but either such characteristics or large data have become usual sense in real-world applications. In this work, an improved maximum margin criterion (MMC) method is introduced firstly. With the new definition of MMC, several variants of MMC, including random MMC, layered MMC, 2D^2 MMC, are designed to make adaptive learning applicable. Particularly, the MMC network is developed to learn deep features of images in light of simple deep networks. Experimental results on a diversity of data sets demonstrate the discriminant ability of proposed MMC methods are compenent to be adopted in complicated application scenarios.

Conditional kernel mean embeddings are nonparametric models that encode conditional expectations in a reproducing kernel Hilbert space. While they provide a flexible and powerful framework for probabilistic inference, their performance is highly dependent on the choice of kernel and regularization hyperparameters. Nevertheless, current hyperparameter tuning methods predominantly rely on expensive cross validation or heuristics that is not optimized for the inference task. For conditional kernel mean embeddings with categorical targets and arbitrary inputs, we propose a hyperparameter learning framework based on Rademacher complexity bounds to prevent overfitting by balancing data fit against model complexity. Our approach only requires batch updates, allowing scalable kernel hyperparameter tuning without invoking kernel approximations. Experiments demonstrate that our learning framework outperforms competing methods, and can be further extended to incorporate and learn deep neural network weights to improve generalization.

The agency emphasized that the computer problem wouldn't affect the station's crew -- NASA's Serena Aunon-Chancellor, Russian Sergei Prokopyev and German Alexander Gerst. It said two other computers can maintain the station's operation.

In a first-of-its-kind partnership, Fortnite developer Epic Games has partnered with the National Football League (NFL) in order to bring team outfits to the hugely popular video game. From 9 November, Fortnite players will be able to get hold of outfits from all 32 teams in the NFL, as well as other American Football-themed items from the game's Battle Royale Item Shop. "We see the popularity of Fortnite every day at the NFL as many of our players are passionate about this game," said Brian Rolapp, chief media and business officer at the NFL. "This partnership represents a great opportunity for millions of NFL fans who are Fortnite players to express their fandom inside the game while at the same time exposing our brand to countless others." The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph.

Samsung will reveal details of its much-anticipated foldable smartphone at its developer conference this week, an official at the South Korean electronics giant has revealed. The disruptive device, potentially named the Galaxy X or Galaxy F, was first teased in 2014 but details of the project have remained secret in the years since. In a break with its usual policy of keeping new products under wraps until they're ready to launch, Samsung plans to share some of the phone's features at the Samsung Developer Conference 2018, taking place in San Francisco from 7-8 November. Speaking anonymously to Reuters, a Samsung official said the company would seek critical feedback from developers so they can make sure apps are compatible with a folding device. "Unlike our flagship products, the foldable phone is a completely new concept in terms of design and user experience, which requires a different approach," the official said.

Chinese authorities have begun deploying a new surveillance tool: 'gait recognition' software that uses people's body shapes and how they walk to identify them, even when their faces are hidden from cameras. Already used by police on the streets of Beijing and Shanghai, 'gait recognition' is part of a push across China to develop artificial-intelligence and data-driven surveillance. However, it's raised concerns among critics about how far the technology will go. China is deploying a new surveillance tool: 'gait recognition' software that uses people's body shapes and how they walk to identify them, even when their faces are hidden. Chinese startup Watrix's software extracts a person's silhouette from video and analyzes the silhouette's movement to create a model of the way the person walks.