Efficient Halftoning via Deep Reinforcement Learning

Halftoning aims to reproduce a continuous-tone image with pixels whose intensities are constrained to two discrete levels. This technique has been deployed on every printer, and the majority of them adopt fast methods (e.g., ordered dithering, error diffusion) that fail to render structural details, which determine halftone's quality. Other prior methods of pursuing visual pleasure by searching for the optimal halftone solution, on the contrary, suffer from their high computational cost. In this paper, we propose a fast and structure-aware halftoning method via a data-driven approach. Specifically, we formulate halftoning as a reinforcement learning problem, in which each binary pixel's value is regarded as an action chosen by a virtual agent with a shared fully convolutional neural network (CNN) policy. In the offline phase, an effective gradient estimator is utilized to train the agents in producing high-quality halftones in one action step. Then, halftones can be generated online by one fast CNN inference. Besides, we propose a novel anisotropy suppressing loss function, which brings the desirable blue-noise property. Finally, we find that optimizing SSIM could result in holes in flat areas, which can be avoided by weighting the metric with the contone's contrast map. Experiments show that our framework can effectively train a light-weight CNN, which is 15x faster than previous structure-aware methods, to generate blue-noise halftones with satisfactory visual quality. We also present a prototype of deep multitoning to demonstrate the extensibility of our method

Electrical Detection of Ferroelectric-like Metals through Nonlinear Hall Effect

Ferroelectric-like metals are a relatively rare class of materials that have ferroelectric-like distortion and metallic conductivity. LiOsO$_3$ is the first demonstrated and the most investigated ferroelectric-like metal. The presence of free carriers makes them difficult to be studied by traditional ferroelectric techniques. In this paper, using the symmetry analysis and first-principles calculations, we demonstrate that the ferroelectric-like transition of LiOsO$_3$ can be probed by a kind of electrical transport method based on nonlinear Hall effect. The Berry curvature dipole exists in the ferroelectric-like phase, and it can lead to a measurable nonlinear Hall conductance with a conventional experimental setup. However, the symmetry of the paraelectric-like phase LiOsO$_3$ vanishes the Berry curvature dipole. The Berry curvature dipole shows a strong dependence on the polar displacement, which might be helpful for the detection of polar order. The nonlinear Hall effect provides an effective method for the detection of phase transition in the study of the ferroelectric-like metals and promotes them to be applied in the ferroelectric-like electronic devices

Electrical detection of ferroelectriclike metals through the nonlinear Hall effect

Ferroelectriclike metals are a relatively rare class of materials that have ferroelectriclike distortion and metallic conductivity. LiOsO3 is the first demonstrated and the most investigated ferroelectriclike metal. The presence of free carriers makes them difficult to be studied by traditional ferroelectric techniques. In this paper, using symmetry analysis and first-principles calculations, we demonstrate that the ferroelectriclike transition of LiOsO3 can be probed by a kind of electrical transport method based on nonlinear Hall effect. The Berry curvature dipole exists in the ferroelectriclike phase and it can lead to a measurable nonlinear Hall conductance with a conventional experimental setup. However, the symmetry of the paraelectriclike phase LiOsO3 vanishes the Berry curvature dipole. The Berry curvature dipole shows a strong dependence on the polar displacement, which might be helpful for the detection of polar order. The nonlinear Hall effect provides an effective method for the detection of phase transition in the study of the ferroelectriclike metals and promotes them to be applied in ferroelectriclike electronic devices

Wi-Fi-Based Location-Independent Human Activity Recognition via Meta Learning

Wi-Fi-based device-free human activity recognition has recently become a vital underpinning for various emerging applications, ranging from the Internet of Things (IoT) to Human-Computer Interaction (HCI). Although this technology has been successfully demonstrated for location-dependent sensing, it relies on sufficient data samples for large-scale sensing, which is enormously labor-intensive and time-consuming. However, in real-world applications, location-independent sensing is crucial and indispensable. Therefore, how to alleviate adverse effects on recognition accuracy caused by location variations with the limited dataset is still an open question. To address this concern, we present a location-independent human activity recognition system based on Wi-Fi named WiLiMetaSensing. Specifically, we first leverage a Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) feature representation method to focus on location-independent characteristics. Then, in order to well transfer the model across different positions with limited data samples, a metric learning-based activity recognition method is proposed. Consequently, not only the generalization ability but also the transferable capability of the model would be significantly promoted. To fully validate the feasibility of the presented approach, extensive experiments have been conducted in an office with 24 testing locations. The evaluation results demonstrate that our method can achieve more than 90% in location-independent human activity recognition accuracy. More importantly, it can adapt well to the data samples with a small number of subcarriers and a low sampling rate

Group properties and invariant solutions of a sixth-order thin film equation in viscous fluid

Using group theoretical methods, we analyze the generalization of a one-dimensional sixth-order thin film equation which arises in considering the motion of a thin film of viscous fluid driven by an overlying elastic plate. The most general Lie group classification of point symmetries, its Lie algebra, and the equivalence group are obtained. Similar reductions are performed and invariant solutions are constructed. It is found that some similarity solutions are of great physical interest such as sink and source solutions, travelling-wave solutions, waiting-time solutions, and blow-up solutions.Comment: 8 page