Notes

The particular aesthetic arrays activity: Visual storage capacity or even interest control?
For the existing repetitive motion generation (RMG) schemes for kinematic control of redundant manipulators, the position error always exists and fluctuates. This article gives an answer to this phenomenon and presents the theoretical analyses to reveal that the existing RMG schemes exist a theoretical position error related to the joint angle error. To remedy this weakness of existing solutions, an orthogonal projection RMG (OPRMG) scheme is proposed in this article by introducing an orthogonal projection method with the position error eliminated theoretically, which decouples the joint space error and Cartesian space error with joint constraints considered. The corresponding new recurrent neural networks (NRNNs) are structured by exploiting the gradient descent method with the assistance of velocity compensation with theoretical analyses provided to embody the stability and feasibility. In addition, simulation results on a fixed-based redundant manipulator, a mobile manipulator, and a multirobot system synthesized by the existing RMG schemes and the proposed one are presented to verify the superiority and precise performance of the OPRMG scheme for kinematic control of redundant manipulators. Moreover, via adjusting the coefficient, simulations on the position error and joint drift of the redundant manipulator are conducted for comparison to prove the high performance of the OPRMG scheme. PT2977 in vivo To bring out the crucial point, different controllers for the redundancy resolution of redundant manipulators are compared to highlight the superiority and advantage of the proposed NRNN. This work greatly improves the existing RMG solutions in theoretically eliminating the position error and joint drift, which is of significant contributions to increasing the accuracy and efficiency of high-precision instruments in manufacturing production.Finding a desirable sampling estimator has a profound impact on the development of static word embedding models, such as continue-bag-of-words (CBOW) and skip gram (SG), which have been generally accepted as popular low-resource algorithms to generate task-agnostic word representations. Due to the prevalence of large-scale pretrained models, less attention has been paid to these static models in the recent years. However, compared with the dynamic embedding models (e.g., BERT), these static models are straightforward to interpret, cost effective to train, and out-of-box to deploy, thus are still widely used in various downstream models until now. Therefore, it is still of considerable significance to study and improve them, especially the crucial components shared by these static models. In this article, we focus on negative sampling (NS), a key component shared by the sampling-based static models, by investigating and mitigating some critical problems of the sampling core. Concretely, we propose Seeds, a sampling enhanced embedding framework, to learn static word embeddings by a new algorithmic innovation for replacing the NS estimator, in which multifactor global priors are considered dynamically for different training pairs. Then, we implement this framework by four concrete models. For the first two implementations, namely CBOW-GP and SG-GP, both negative words and positive auxiliaries are sampled. And for the other two implementations, CBOW-GN and SG-GN, estimations are simplified by sampling only the negative instances. Extensive experimental results across a variety of standard intrinsic and extrinsic tasks demonstrate that embeddings learned by the proposed models outperform their NS-based counterparts, such as CBOW-NS and SG-NS, as well as other strong baselines.In a virtual reality (VR) environment, where visual stimuli predominate over other stimuli, the user experiences cybersickness because the balance of the body collapses due to self-motion. Accordingly, the VR experience is accompanied by unavoidable sickness referred to as visually induced motion sickness (VIMS). In this article, our primary purpose is to simultaneously estimate the VIMS score by referring to the content and calculate the temporally induced VIMS sensitivity. To seek our goals, we propose a novel architecture composed of two consecutive networks 1) neurological representation and 2) spatiotemporal representation. In the first stage, the network imitates and learns the neurological mechanism of motion sickness. In the second stage, the significant feature of the spatial and temporal domains is expressed over the generated frames. After the training procedure, our model can calculate VIMS sensitivity for each frame of the VR content by using the weakly supervised approach for unannotated temporal VIMS scores. Furthermore, we release a massive VR content database. In the experiments, the proposed framework demonstrates excellent performance for VIMS score prediction compared with existing methods, including feature engineering and deep learning-based approaches. Furthermore, we propose a way to visualize the cognitive response to visual stimuli and demonstrate that the induced sickness tends to be activated in a similar tendency, as done in clinical studies.We propose a potential flow generator with L₂ optimal transport regularity, which can be easily integrated into a wide range of generative models, including different versions of generative adversarial networks (GANs) and normalizing flow models. With only a slight augmentation to the original generator loss functions, our generator not only tries to transport the input distribution to the target one but also aims to find the one with minimum L₂ transport cost. We show the effectiveness of our method in several 2-D problems and illustrate the concept of ``proximity'' due to the L₂ optimal transport regularity. Subsequently, we demonstrate the effectiveness of the potential flow generator in image translation tasks with unpaired training data from the MNIST data set and the CelebA data set with a comparison against vanilla Wasserstein GAN with gradient penalty (WGAN-GP) and CycleGAN.Clustering frequency vectors is a challenging task on large data sets considering its high dimensionality and sparsity nature. Generalized Dirichlet multinomial (GDM) distribution is a competitive generative model for count data in terms of accuracy, yet its parameters estimation process is slow. The exponential-family approximation of the multivariate Polya distribution has shown to be efficient to train and cluster data directly, without dimensionality reduction. In this article, we derive an exponential-family approximation to the GDM distributions, and we call it (EGDM). A mixture model is developed based on the new member of the exponential-family of distributions, and its parameters are learned through the deterministic annealing expectation-maximization (DAEM) approach as a new clustering algorithm for count data. Moreover, we propose to estimate the optimal number of EGDM mixture components based on the minimum message length (MML) criterion. We have conducted a set of empirical experiments, concerning text, image, and video clustering, to evaluate the proposed approach performance.
Homepage: https://www.selleckchem.com/products/pt2977.html