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Mode information is of great significance when investigating the Markov jump systems (MJSs). However, it is common in practical scenarios that the mode information is not completely accessible, which probably induces nonsynchronization problems. Taking this into consideration, in this article, we study nonsynchronous $\mathcal H_{\infty }$ model order reduction for 2-D MJSs with model uncertainty. The considered 2-D system and reduced-order model are characterized by the Roesser model. The nonsynchronization phenomenon between the original system and the reduced-order model is dealt with under the framework of the hidden Markov model. By appropriately selecting the Lyapunov function, the asymptotic mean-square stability and the $\mathcal H_{\infty }$ performance of the error system are analyzed, and sufficient conditions are proposed. Based on this, an efficient design method for nonsynchronous model order reduction is further proposed with the help of a projection lemma. Finally, the correctness and effectiveness of the designed reduced-order model are verified through some simulations. [ABSTRACT FROM AUTHOR]

Fluxgate magnetometers have been widely applied in many fields, such as magnetic anomaly detection, e-compass, etc. In this work, a micro-fluxgate magnetometer based on the implementation of the MEMS technology is proposed. The core of the fluxgate was fabricated by using an amorphous Co-based magnetic material, which shows a good degree of linearity below 0.5 Oe. Moreover, a mixed-signal lock-in circuit was designed to drive and capture signals. Magnetic noise density of such a device was limited to the value of 500 pT/ $\surd $ Hz at 1 Hz, while the capability of sensing a DC magnetic field of 6 nT was demonstrated, which presents a good development prospect in the weak signal detection domain. [ABSTRACT FROM AUTHOR]

The semantic SLAM (Simultaneous Localization And Mapping) system is a crucial module for autonomous indoor parking. Visual cameras (monocular/binocular) and IMU (Inertial Measurement Unit) constitute the basic configuration to build such a system. The performance of existing SLAM systems typically deteriorates in the presence of dynamically movable objects or objects with little texture. By contrast, semantic objects on the ground embody the most salient and stable features in the indoor parking environment. Due to their inabilities to perceive such features on the ground, existing SLAM systems are prone to tracking inconsistency during navigation. In this paper, we present MOFISSLAM, a novel tightly-coupled ${M}$ ulti- ${O}$ bject semantic SLAM system integrating ${F}$ ront-view, ${I}$ nertial, and ${S}$ urround-view sensors for autonomous indoor parking. The proposed system moves beyond existing semantic SLAM systems by complementing the sensor configuration with a surround-view system capturing images from a top-down viewpoint. In MOFISSLAM, apart from low-level visual features and inertial motion data, typical semantic objects (parking-slots, parking-slot IDs and speed bumps) detected in surround-views are also incorporated in optimization, forming robust surround-view constraints. Specifically, each surround-view feature imposes a surround-view constraint that can be split into a contact term and a registration term. The former pre-defines the position of each individual surround-view feature subject to whether it has semantic contact with other surround-view features. Three contact modes, defined as complementary, adjacent and coincident, are identified to guarantee a unified form of all contact terms. The latter further constrains by registering each surround-view observation and its position in the world coordinate system. In parallel, to objectively evaluate SLAM studies for autonomous indoor parking, a large-scale dataset with groundtruth trajectories is collected, which is the first of its kind. Its groundtruth trajectories, commonly unavailable, are obtained by tracking artificial features scattered in the indoor parking environment, whose 3D coordinates are measured with an ETS (Electronic Total Station). The collected dataset has been made publicly available at https://shaoxuan92.github.io/MOFIS. [ABSTRACT FROM AUTHOR]

Knowledge graphs (KG) often encounter knowledge incompleteness. The path reasoning that predicts the unknown path relation between pairwise entities based on existing facts is one of the most promising approaches to the knowledge graph completion. However, most conventional path reasoning methods exclusively consider the entity description included in fact triples, ignoring both the type information of entities and the interaction between different semantic representations. In this study, we propose a novel method, Type-aware Attentive Path Reasoning (TAPR), to complete the knowledge graph by simultaneously considering KG structural information, textual information, and type information. More specifically, we first leverage types to enrich the representational learning of entities and relationships. Next, we describe a type-level attention to select the most relevant type of given entity in a specific triple without any predefined rules or patterns to reduce the impact of noisy types. After learning the distributed representation of all paths, path-level attention assigns different weights to paths, from which relations among entity pairs are calculated. We conduct a series of experiments on a real-world dataset to demonstrate the effectiveness of TAPR. Experimental results show that our method significantly outperforms all baselines on link prediction and entity prediction tasks. [ABSTRACT FROM AUTHOR]

In this brief, the mean stabilization of positive Markov jump systems (PMJSs) with piecewise-homogeneous Markov chain with two switching signals simultaneously is considered. Different from the existing results, asynchronous state feedback controllers are considered to achieve the control objective. Based on the hidden Markov model, the closed-loop systems are modeled as hidden Markov jump systems (HMJSs) with piecewise-homogeneous Markov chain. The definitions of positivity and mean stabilization are introduced for the considered system. Some sufficient conditions are derived to ensure that the HMJSs with piecewise-homogeneous Markov chain are positive and mean stable when the Markov chain follows a conditional probability distribution and Bernoulli distribution, respectively. Simulation results are described to demonstrate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

In Markov jump systems (MJSs), it is really difficult for the controller to perfectly follow the plant’s mode transitions due to a number of practical factors. Therefore, this article considers to develop an asynchronous controller for a class of two-dimensional continuous-time MJSs. The relation of the plant’s mode and the controller’s mode is modeled by the hidden Markov model, i.e., the plant’s mode influences the controller’s transitions in a conditional probabilistic style. By defining a vector Lyapunov function, a sufficient condition for mean-square exponential stability and (U, R, S)-&#949 dissipativity is derived, whose feasible solutions give birth to a desired asynchronous controller. Finally, the influence of conditional probabilities on dissipativity performance is further studied through simulations. [ABSTRACT FROM AUTHOR]

Haze-free images are the prerequisites of many vision systems and algorithms, and thus single image dehazing is of paramount importance in computer vision. In this field, prior-based methods have achieved initial success. However, they often introduce annoying artifacts to outputs because their priors can hardly fit all situations. By contrast, learning-based methods can generate more natural results. Nonetheless, due to the lack of paired foggy and clear outdoor images of the same scenes as training samples, their haze removal abilities are limited. In this work, we attempt to merge the merits of prior-based and learning-based approaches by dividing the dehazing task into two sub-tasks, i.e., visibility restoration and realness improvement. Specifically, we propose a two-stage weakly supervised dehazing framework, RefineDNet. In the first stage, RefineDNet adopts the dark channel prior to restore visibility. Then, in the second stage, it refines preliminary dehazing results of the first stage to improve realness via adversarial learning with unpaired foggy and clear images. To get more qualified results, we also propose an effective perceptual fusion strategy to blend different dehazing outputs. Extensive experiments corroborate that RefineDNet with the perceptual fusion has an outstanding haze removal capability and can also produce visually pleasing results. Even implemented with basic backbone networks, RefineDNet can outperform supervised dehazing approaches as well as other state-of-the-art methods on indoor and outdoor datasets. To make our results reproducible, relevant code and data are available at https://github.com/xiaofeng94/RefineDNet-for-dehazing. [ABSTRACT FROM AUTHOR]

This work takes the lead to study the aspect-level sentiment classification in the domain adaptation scenario. Given a document of any domains, the model needs to figure out the sentiments with respect to fine-grained aspects in the documents. Two main challenges exist in this problem. One is to build a robust document modeling across domains; the other is to mine the domain-specific aspects and make use of the sentiment lexicon. In this paper, we propose a novel approach Neural Attentive model for cross-domain Aspect-level sentiment CLassification (NAACL), which leverages the benefits of the supervised deep neural network as well as the unsupervised probabilistic generative model to strengthen the representation learning. NAACL jointly learns two tasks: (i) a domain classifier, working on documents in both the source and target domains to recognize the domain information of input texts and transfer knowledge from the source domain to the target domain. In particular, a weakly supervised Latent Dirichlet Allocation model (wsLDA) is proposed to learn the domain-specific aspect and sentiment lexicon representations that are then used to calculate the aspect/lexicon-aware document representations via a multi-view attention mechanism; (ii) an aspect-level sentiment classifier, sharing the document modeling with the domain classifier. It makes use of the domain classification results and the aspect/sentiment-aware document representations to classify the aspect-level sentiment of the document in domain adaptation scenario. NAACL is evaluated on both English and Chinese datasets with the out-of-domain as well as in-domain setups. Quantitatively, the experiments demonstrate that NAACL has robust superiority over the compared methods in terms of classification accuracy and F1 score. The qualitative evaluation also shows that the proposed model is capable of reasonably paying attention to those words that are important to judge the sentiment polarity of the input text given an aspect. [ABSTRACT FROM AUTHOR]