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Moreover, such a design structure takes the advantage of releasing communication burden.In this article, we study a multiplayer Stackelberg-Nash game (SNG) pertaining to a nonlinear dynamical system, including one leader and multiple followers. HDAC inhibitor At the higher level, the leader makes its decision preferentially with consideration of the reaction functions of all followers, while, at the lower level, each of the followers reacts optimally to the leader's strategy simultaneously by playing a Nash game. First, the optimal strategies for the leader and the followers are derived from down to the top, and these strategies are further shown to constitute the Stackelberg-Nash equilibrium points. Subsequently, to overcome the difficulty in calculating the equilibrium points analytically, we develop a novel two-level value iteration-based integral reinforcement learning (VI-IRL) algorithm that relies only upon partial information of system dynamics. We establish that the proposed method converges asymptotically to the equilibrium strategies under the weak coupling conditions. Moreover, we introduce effective termination criteria to guarantee the admissibility of the policy (strategy) profile obtained from a finite number of iterations of the proposed algorithm. In the implementation of our scheme, we employ neural networks (NNs) to approximate the value functions and invoke the least-squares methods to update the involved weights. Finally, the effectiveness of the developed algorithm is verified by two simulation examples.Data stream applications usually suffer from multiple types of concept drift. However, most existing approaches are only able to handle a subset of types of drift well, hindering predictive performance. We propose to use diversity as a framework to handle multiple types of drift. The motivation is that a diverse ensemble can not only contain models representing different concepts, which may be useful to handle recurring concepts, but also accelerate the adaptation to different types of concept drift. Our framework innovatively uses clustering in the model space to build a diverse ensemble and identify recurring concepts. The resulting diversity also accelerates adaptation to different types of drift where the new concept shares similarities with past concepts. Experiments with 20 synthetic and three real-world data streams containing different types of drift show that our diversity framework usually achieves similar or better prequential accuracy than existing approaches, especially when there are recurring concepts or when new concepts share similarities with past concepts.The emergence of pathogenic bacteria that are multi-resistant to antibiotics lurks in today's society. In the golden age of the discovery of new antibiotic-producing microorganisms, each contribution was a step forward, but currently the progression is no longer so spectacular. The probability of finding new microorganisms and different antibiotics is lower and lower. The use of spatial statistical methods such as the Kriging technique has been shown to be suitable for this purpose, allowing us to determine areas with greater possibilities of locating these microorganisms. For a practical approach of our estimations, a total of 12 isolates capable of inhibiting the growth of several control strains (Escherichia coli, Bacillus cereus and at least one other) were analyzed. The isolates were preliminarily characterized, and subsequently identified at the species level by DNA sequence analyses (16S rDNA PCR) and protein analyses (MALDI-TOF MS). However, between soil pH values and the number of isolates capable of inhibiting the control strains. Geospatial mapping with RStudio software provide a satisfactory predictive tool for isolation of new microbial isolates.In this work, a VR-based basketball training system comprising a standalone VR device and a tablet is proposed. The system is intended to improve the ability of players to understand offensive tactics and to enable them to perform these tactics correctly. We compare the training effectiveness of various degrees of immersion fidelity, including a conventional basketball tactic board, a 2D monitor, and virtual reality. A multi-camera-based human tracking system is developed and built around a real-world basketball court to record and analyze the running trajectory of each player during tactical execution. The accuracy of the running path and hesitation time at each tactical step is evaluated for each participant. Furthermore, we assess several subjective measurements, including simulator sickness, presence, and sport imagery ability, to carry out a more comprehensive exploration of the feasibility of the proposed VR framework for basketball tactics training. The results indicate that the proposed system is useful for learning complex tactics. Also, high VR immersion training benefits athletes in terms of abilities related to strategic imagery.RGB-Infrared person re-identification (RGB-IR Re-ID) is a cross-modality matching problem, where the modality discrepancy is a big challenge. Most existing works use Euclidean metric based constraints to resolve the discrepancy between features of images from different modalities. However, these methods are incapable of learning angularly discriminative feature embedding because Euclidean distance cannot measure the included angle between embedding vectors effectively. As an angularly discriminative feature space is important for classifying the human images based on their embedding vectors, in this paper, we propose a novel ranking loss function, named Bi-directional Exponential Angular Triplet Loss, to help learn an angularly separable common feature space by explicitly constraining the included angles between embedding vectors. Moreover, to help stabilize and learn the magnitudes of embedding vectors, we adopt a common space batch normalization layer. The quantitative and qualitative experiments on the SYSU-MM01 and RegDB dataset support our analysis. On SYSU-MM01 dataset, the performance is improved from 7.40% / 11.46% to 38.57% / 38.61% for rank-1 accuracy / mAP compared with the baseline. The proposed method can be generalized to the task of single-modality Re-ID and improves the rank-1 accuracy / mAP from 92.0% / 81.7% to 94.7% / 86.6% on the Market-1501 dataset, from 82.6% / 70.6% to 87.6% / 77.1% on the DukeMTMC-reID dataset.
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