Deep Eutectic Solvent-Assisted Corrosion Boosting Bulk FeCoNiCrMo High-Entropy Alloys as Highly Efficient Oxygen Evolution Reaction Catalyst.

The key to enhancing water electrolysis efficiency lies in selecting highly efficient catalysts. Currently, high-entropy alloys (HEAs) are utilized in electrocatalysis applications owing to their diverse elemental composition, disordered elemental distribution, and the high solubility of each element, endowing them with excellent catalytic performance. The experiments were conducted using isoatomic FeNiCrMo HEA as a precursor, with a high-activity three-dimensional nanoporous structure rapidly synthesized via electrochemical one-step dealloying in a choline chloride-thiourea (ChCl-TU) deep eutectic solvent (DES).

Bubble Management for Electrolytic Water Splitting by Surface Engineering: A Review.

During electrocatalytic water splitting, the management of bubbles possesses great importance to reduce the overpotential and improve the stability of the electrode. Bubble evolution is accomplished by nucleation, growth, and detachment. The expanding nucleation sites, decreasing bubble size, and timely detachment of bubbles from the electrode surface are key factors in bubble management.

MMNet: A Model-Based Multimodal Network for Human Action Recognition in RGB-D Videos.

Human action recognition (HAR) in RGB-D videos has been widely investigated since the release of affordable depth sensors. Currently, unimodal approaches (e.g.

Steganographer detection via a similarity accumulation graph convolutional network.

Steganographer detection aims to identify guilty users who conceal secret information in a number of images for the purpose of covert communication in social networks. Existing steganographer detection methods focus on designing discriminative features but do not explore relationship between image features or effectively represent users based on features. In these methods, each image is recognized as an equivalent, and each user is regarded as the distribution of all images shared by the corresponding user.

EEG-based image classification via a region-level stacked bi-directional deep learning framework.

Background: As a physiological signal, EEG data cannot be subjectively changed or hidden. Compared with other physiological signals, EEG signals are directly related to human cortical activities with excellent temporal resolution. After the rapid development of machine learning and artificial intelligence, the analysis and calculation of EEGs has made great progress, leading to a significant boost in performances for content understanding and pattern recognition of brain activities across the areas of both neural science and computer vision.

Human Eye Movements Reveal Video Frame Importance.

Human eye movements indicate important spatial information in static images as well as videos. Yet videos contain additional temporal information and convey a storyline. Video summarization is a technique that reduces video size, but maintains the essence of the storyline.

Grouping by proximity and the visual impression of approximate number in random dot arrays.

We address the challenges of how to model human perceptual grouping in random dot arrays and how perceptual grouping affects human number estimation in these arrays. We introduce a modeling approach relying on a modified k-means clustering algorithm to formally describe human observers' grouping behavior. We found that a default grouping window size of approximately 4° of visual angle describes human grouping judgments across a range of random dot arrays (i.

Why do people appear not to extrapolate trajectories during multiple object tracking? A computational investigation.

Intuitively, extrapolating object trajectories should make visual tracking more accurate. This has proven to be true in many contexts that involve tracking a single item. But surprisingly, when tracking multiple identical items in what is known as "multiple object tracking," observers often appear to ignore direction of motion, relying instead on basic spatial memory.

Water reflection recognition based on motion blur invariant moments in curvelet space.

Water reflection, a typical imperfect reflection symmetry problem, plays an important role in image content analysis. Existing techniques of symmetry recognition, however, cannot recognize water reflection images correctly because of the complex and various distortions caused by the water wave. Hence, we propose a novel water reflection recognition technique to solve the problem.