Baldwinmccaffrey6702

Besides, a low-rank bilinear fusion module is proposed to enhance the model's ability to recognize similar features. This module is based on the low-rank bilinear model to capture the inter-layer feature relations. It integrates the location details from low-level features and semantic information from high-level features. Various semantics can be represented more accurately, which effectively improves feature representation. The proposed network achieves state-of-the-art performance on cataract image segmentation dataset CataSeg and robotic image segmentation dataset EndoVis 2018.Methamphetamine use disorder (MUD) is a brain disease that leads to altered regional neuronal activity. Virtual reality (VR) is used to induce the drug cue reactivity. Previous studies reported significant frequency-specific neuronal abnormalities in patients with MUD during VR induction of drug craving. However, whether those patients exhibit neuronal abnormalities after VR induction that could serve as the treatment target remains unclear. Here, we used an integrated VR system for inducing drug related changes and investigated the neuronal abnormalities after VR exposure in patients. Fifteen patients with MUD and ten healthy subjects were recruited and exposed to drug-related VR environments. Resting-state EEG were recorded for 5 minutes twice-before and after VR and transformed to obtain the frequency-specific data. Three self-reported scales for measurement of the anxiety levels and impulsivity of participants were obtained after VR task. Statistical tests and machine learning methods were employed to reveal the differences between patients and healthy subjects. The result showed that patients with MUD and healthy subjects significantly differed in Θ, α, and γ power changes after VR. These neuronal abnormalities in patients were associated with the self-reported behavioral scales, indicating impaired impulse control. Our findings of resting-state EEG abnormalities in patients with MUD after VR exposure have the translational value and can be used to develop the treatment strategies for methamphetamine use disorder.Mesh is a type of data structure commonly used for 3-D shapes. Representation learning for 3-D meshes is essential in many computer vision and graphics applications. The recent success of convolutional neural networks (CNNs) for structured data (e.g., images) suggests the value of adapting insights from CNN for 3-D shapes. However, 3-D shape data are irregular since each node's neighbors are unordered. Various graph neural networks for 3-D shapes have been developed with isotropic filters or predefined local coordinate systems to overcome the node inconsistency on graphs. However, isotropic filters or predefined local coordinate systems limit the representation power. In this article, we propose a local structure-aware anisotropic convolutional operation (LSA-Conv) that learns adaptive weighting matrices for each template's node according to its neighboring structure and performs shared anisotropic filters. In fact, the learnable weighting matrix is similar to the attention matrix in the random synthesizer--a new Transformer model for natural language processing (NLP). Since the learnable weighting matrices require large amounts of parameters for high-resolution 3-D shapes, we introduce a matrix factorization technique to notably reduce the parameter size, denoted as LSA-small. Furthermore, a residual connection with a linear transformation is introduced to improve the performance of our LSA-Conv. Comprehensive experiments demonstrate that our model produces significant improvement in 3-D shape reconstruction compared to state-of-the-art methods.Large amounts of labeled data are urgently required for the training of robust text recognizers. However, collecting handwriting data of diverse styles, along with an immense lexicon, is considerably expensive. Although data synthesis is a promising way to relieve data hunger, two key issues of handwriting synthesis, namely, style representation and content embedding, remain unsolved. To this end, we propose a novel method that can synthesize parameterized and controllable handwriting Styles for arbitrary-Length and Out-of-vocabulary text based on a Generative Adversarial Network (GAN), termed SLOGAN. Specifically, we propose a style bank to parameterize specific handwriting styles as latent vectors, which are input to a generator as style priors to achieve the corresponding handwritten styles. The training of the style bank requires only writer identification of the source images, rather than attribute annotations. Moreover, we embed the text content by providing an easily obtainable printed style image, so that the diversity of the content can be flexibly achieved by changing the input printed image. Finally, the generator is guided by dual discriminators to handle both the handwriting characteristics that appear as separated characters and in a series of cursive joins. Our method can synthesize words that are not included in the training vocabulary and with various new styles. Extensive experiments have shown that high-quality text images with great style diversity and rich vocabulary can be synthesized using our method, thereby enhancing the robustness of the recognizer.In this article, we propose a computationally and communicationally efficient approach for decision-making in nonequilibrium stochastic games. In particular, due to the inherent complexity of computing Nash equilibria, as well as the innate tendency of agents to choose nonequilibrium strategies, we construct two models of bounded rationality based on recursive reasoning. In the first model, named level-k thinking, each agent assumes that everyone else has a cognitive level immediately lower than theirs and--given such an assumption--chooses their policy to be a best response to them. In the second model, named cognitive hierarchy, each agent conjectures that the rest of the agents have a cognitive level that is lower than theirs, but follows a distribution instead of being deterministic. To explicitly compute the boundedly rational policies, a level-recursive algorithm and a level-paralleled algorithm are constructed, where the latter one can have an overall reduced computational complexity. To further reduce the complexity in the communication layer, modifications of the proposed nonequilibrium strategies are presented, which do not require the action of a boundedly rational agent to be updated at each step of the stochastic game. Simulations are performed that demonstrate our results.Variational quantum algorithms (VQAs) use classical computers as the quantum outer loop optimizer and update the circuit parameters to obtain an approximate ground state. In this article, we present a meta-learning variational quantum algorithm (meta-VQA) by recurrent unit, which uses a technique called ``meta-learner. Motivated by the hybrid quantum-classical algorithms, we train classical recurrent units to assist quantum computing, learning to find approximate optima in the parameter landscape. Here, aiming to reduce the sampling number more efficiently, we use the quantum stochastic gradient descent method and introduce the adaptive learning rate. Finally, we deploy on the TensorFlow Quantum processor within approximate quantum optimization for the Ising model and variational quantum eigensolver for molecular hydrogen (H₂), lithium hydride (LiH), and helium hydride cation (HeH⁺). Our algorithm can be expanded to larger system sizes and problem instances, which have higher performance on near-term processors.Many real-world applications are inherently modeled as signed heterogeneous networks or graphs with positive and negative links. Signed graph embedding embeds rich structural and semantic information of a signed graph into low-dimensional node representations. Existing methods usually exploit social structural balance theory to capture the semantics of the complex structure in a signed graph. These methods either omit the node features or may discard the direction information of the links. To address these issues, we propose a new framework, called a status-aware graph neural network (S-GNN), to boost the representation learning performance. S-GNN is equipped with a loss function designed based on status theory, a social-psychological theory specifically developed for directed signed graphs. Extensive experimental results on benchmarking datasets verified that S-GNN can distill comprehensive information ingrained in a signed graph in the embedding space. Specifically, S-GNN achieves state-of-the-art accuracy, robustness, and scalability it speeds up the processing time of link sign prediction by up to 6.5x and increases accuracy by up to 18.8% as compared with the alternatives. We also show that S-GNN can obtain effective status scores of nodes for link sign prediction and node ranking tasks, both of which yield state-of-the-art performance.This study aimed to identify the physical factors contributing to the perception of the hardness of objects tapped using white canes, which are commonly used by the visually impaired for autonomous walking. First, physical factors such as vibration tapping sounds and reaction forces were measured during the indirect tapping of rubber sheets with different hardness using a white cane. Second, we determined the relationship between the subjective hardness perceived by the visually impaired individuals and the physical factors through multivariate analysis. In addition, we estimated the contribution of each factor. The result indicates that the white cane vibrated at similar frequencies during tapping even when the hardness of objects changed. In contrast, the tapping sound varied widely with the variation of the hardness of the rubber sheet. In addition, the peak values of the reaction forces significantly changed depending on the hardness. Based on the results of the multivariate analysis, the contributions of tapping sounds and reaction forces to hardness perception were remarkable. In contrast, the contributions of vibrations could not be found using our analytic method. These findings will help in the design and evaluation of support equipment for the visually impaired.Electrocardiography is the gold standard technique for detecting abnormal heart conditions. Automatic analysis of electrocardiogram (ECG) can help physicians in the interoperation of the large amount of data produced daily by cardiac monitors. As the successful application of supervised machine learning algorithms relies on unprecedented amounts of labeled training data, there is a growing need for unsupervised algorithms for ECG analysis. Unsupervised learning aims to partition ECG into distinct abnormality classes without cardiologist-supplied labelsa process referred to as ECG clustering. In addition to abnormality detection, ECG clustering can discover inter and intra-individual patterns that carry valuable information about the whole body and mind, such as emotions and mental disorders. ECG clustering can also resolve specific challenges facing supervised learning systems, such as the imbalanced data problem, and can enhance biometric systems. check details While several reviews exist on supervised ECG analysis, a comprehensive review of unsupervised ECG analysis techniques is still lacking.