Cookmorales1517

A simulator is built and trained with the Manhattan road network and New York City yellow taxi data to simulate the real-time vehicle dispatching environment. Both centralized and decentralized taxi dispatching policies are examined with the simulator. This case study shows that the proposed approach can further improve taxi drivers' profits while reducing customers' waiting times compared to several existing vehicle dispatching algorithms.Multiview clustering as an important unsupervised method has been gathering a great deal of attention. However, most multiview clustering methods exploit the self-representation property to capture the relationship among data, resulting in high computation cost in calculating the self-representation coefficients. In addition, they usually employ different regularizers to learn the representation tensor or matrix from which a transition probability matrix is constructed in a separate step, such as the one proposed by Wu et al.. Thus, an optimal transition probability matrix cannot be guaranteed. To solve these issues, we propose a unified model for multiview spectral clustering by directly learning an adaptive transition probability matrix (MCA^2M), rather than an individual representation matrix of each view. Different from the one proposed by Wu et al., MCA^2M utilizes the one-step strategy to directly learn the transition probability matrix under the robust principal component analysis framework. Unlike existing methods using the absolute symmetrization operation to guarantee the nonnegativity and symmetry of the affinity matrix, the transition probability matrix learned from MCA^2M is nonnegative and symmetric without any postprocessing. An alternating optimization algorithm is designed based on the efficient alternating direction method of multipliers. Extensive experiments on several real-world databases demonstrate that the proposed method outperforms the state-of-the-art methods.This article studies the state estimation for probabilistic Boolean networks via observing output sequences. Detectability describes the ability of an observer to uniquely estimate system states. By defining the probability of an observed output sequence, a new concept called detectability measure is proposed. The detectability measure is defined as the limit of the sum of probabilities of all detectable output sequences when the length of output sequences goes to infinity, and it can be regarded as a quantitative assessment of state estimation. A stochastic state estimator is designed by defining a corresponding nondeterministic stochastic finite automaton, which combines the information of state estimation and probability of output sequences. The proposed concept of detectability measure further performs the quantitative analysis on detectability. Furthermore, by defining a Markov chain, the calculation of detectability measure is converted to the calculation of the sum of probabilities of certain specific states in Markov chain. Finally, numerical examples are given to illustrate the obtained theoretical results.Person re-identification (Re-ID) aims to retrieve images of the same person across disjoint camera views. Most Re-ID studies focus on pedestrian images captured by visible cameras, without considering the infrared images obtained in the dark scenarios. Person retrieval between visible and infrared modalities is of great significance to public security. Current methods usually train a model to extract global feature descriptors and obtain discriminative representations for visible infrared person Re-ID (VI-REID). Nevertheless, they ignore the detailed information of heterogeneous pedestrian images, which affects the performance of Re-ID. In this article, we propose a flexible body partition (FBP) model-based adversarial learning method (FBP-AL) for VI-REID. To learn more fine-grained information, FBP model is exploited to automatically distinguish part representations according to the feature maps of pedestrian images. Specially, we design a modality classifier and introduce adversarial learning which attempts to discriminate features between visible and infrared modality. Adaptive weighting-based representation learning and threefold triplet loss-based metric learning compete with modality classification to obtain more effective modality-sharable features, thus shrinking the cross-modality gap and enhancing the feature discriminability. Extensive experimental results on two cross-modality person Re-ID data sets, i.e., SYSU-MM01 and RegDB, exhibit the superiority of the proposed method compared with the state-of-the-art solutions.We focus on the occlusion problem in person re-identification (re-id), which is one of the main challenges in real-world person retrieval scenarios. Previous methods on the occluded re-id problem usually assume that only the probes are occluded, thereby removing occlusions by manually cropping. However, this may not always hold in practice. Voxtalisib This article relaxes this assumption and investigates a more general occlusion problem, where both the probe and gallery images could be occluded. The key to this challenging problem is depressing the noise information by identifying bodies and occlusions. We propose to incorporate the pose information into the re-id framework, which benefits the model in three aspects. First, it provides the location of the body. We then design a Pose-Masked Feature Branch to make our model focus on the body region only and filter those noise features brought by occlusions. Second, the estimated pose reveals which body parts are visible, giving us a hint to construct more informative person features. We propose a Pose-Embedded Feature Branch to adaptively re-calibrate channel-wise feature responses based on the visible body parts. Third, in testing, the estimated pose indicates which regions are informative and reliable for both probe and gallery images. Then we explicitly split the extracted spatial feature into parts. Only part features from those commonly visible parts are utilized in the retrieval. To better evaluate the performances of the occluded re-id, we also propose a large-scale data set for the occluded re-id with more than 35 000 images, namely Occluded-DukeMTMC. Extensive experiments show our approach surpasses previous methods on the occluded, partial, and non-occluded re-id data sets.Reservoir computing is a popular approach to design recurrent neural networks, due to its training simplicity and approximation performance. The recurrent part of these networks is not trained (e.g., via gradient descent), making them appealing for analytical studies by a large community of researchers with backgrounds spanning from dynamical systems to neuroscience. However, even in the simple linear case, the working principle of these networks is not fully understood and their design is usually driven by heuristics. A novel analysis of the dynamics of such networks is proposed, which allows the investigator to express the state evolution using the controllability matrix. Such a matrix encodes salient characteristics of the network dynamics; in particular, its rank represents an input-independent measure of the memory capacity of the network. Using the proposed approach, it is possible to compare different reservoir architectures and explain why a cyclic topology achieves favorable results as verified by practitioners.In this paper, an adaptive admittance control scheme is developed for robots to interact with time-varying environments. Admittance control is adopted to achieve a compliant physical robot-environment interaction, and the uncertain environment with time-varying dynamics is defined as a linear system. A critic learning method is used to obtain the desired admittance parameters based on the cost function composed of interaction force and trajectory tracking without the knowledge of the environmental dynamics. To deal with dynamic uncertainties in the control system, a neural-network (NN)-based adaptive controller with a dynamic learning framework is developed to guarantee the trajectory tracking performance. Experiments are conducted and the results have verified the effectiveness of the proposed method.Visualizing objects as they are perceived in the real world is often critical in our daily experiences. We previously focused on objects' surface glossiness visualized with a 3D display and found that a multi-view 3D display reproduces perceived glossiness more accurately than a 2D display. This improvement of glossiness reproduction can be explained by the fact that a glossy surface visualized by a multi-view 3D display appropriately provides luminance differences between the two eyes and luminance changes accompanying the viewer's lateral head motion. In the present study, to determine the requirements of a multi-view 3D display for the accurate reproduction of perceived glossiness, we developed a simulator of a multi-view 3D display to independently and simultaneously manipulate the viewpoint interval and the magnitude of the optical inter-view crosstalk. Using the simulator, we conducted a psychophysical experiment and found that glossiness reproduction is most accurate when the viewpoint interval is small and there is just a small (but not too small) amount of crosstalk. We proposed a simple yet perceptually valid model that quantitatively predicts the reproduction accuracy of perceived glossiness.Face illumination perception and processing is a significantly difficult issue especially due to asymmetric shadings, local highlights, and local shadows. This study focuses on the face illumination transfer problem, which is to transfer the illumination style from a reference face image to a target face image while preserving other attributes. Such an instance-level transfer task is more challenging than the domain-level one that only considers the pre-defined lighting categories. To tackle this problem, we develop an instance-level conditional Generative Adversarial Networks (GAN). Specifically, face identifier is integrated into GAN learning, which enables an individual-specific low-level visual generation. Moreover, the illumination-inspired attention mechanism is conducted to allow GAN to well handle the local lighting effect. Our method requires neither lighting categorization, 3D information, nor strict face alignment, which are often employed by traditional methods. Experiments demonstrate that our method achieves significantly better results than previous methods.Matrix and tensor completion aim to recover the incomplete two- and higher-dimensional observations using the low-rank property. Conventional techniques usually minimize the convex surrogate of rank (such as the nuclear norm), which, however, leads to the suboptimal solution for the low-rank recovery. In this paper, we propose a new definition of matrix/tensor logarithmic norm to induce a sparsity-driven surrogate for rank. More importantly, the factor matrix/tensor norm surrogate theorems are derived, which are capable of factoring the norm of large-scale matrix/tensor into those of small-scale matrices/tensors equivalently. Based upon surrogate theorems, we propose two new algorithms called Logarithmic norm Regularized Matrix Factorization (LRMF) and Logarithmic norm Regularized Tensor Factorization (LRTF). These two algorithms incorporate the logarithmic norm regularization with the matrix/tensor factorization and hence achieve more accurate low-rank approximation and high computational efficiency. The resulting optimization problems are solved using the framework of alternating minimization with the proof of convergence.