Pathogen stress heightens sensorimotor dimensions in the human collective semantic space.

Infectious diseases have been major causes of death throughout human history and are assumed to broadly affect human psychology. However, whether and how conceptual processing, an internal world model central to various cognitive processes, adapts to such salient stress variables remains largely unknown. To address this, we conducted three studies examining the relationship between pathogen severity and semantic space, probed through the main neurocognitive semantic dimensions revealed by large-scale text analyses: one cross-cultural study (across 43 countries) and two historical studies (over the past 100 years).

ChatMol: interactive molecular discovery with natural language.

Motivation: Natural language is poised to become a key medium for human-machine interactions in the era of large language models. In the field of biochemistry, tasks such as property prediction and molecule mining are critically important yet technically challenging. Bridging molecular expressions in natural language and chemical language can significantly enhance the interpretability and ease of these tasks.

Epitaxy of (11-22) AlN Films on a Sputtered Buffer Layer with Different Annealing Temperatures via Hydride Vapour Phase Epitaxy.

AlN epilayers were grown on magnetron-sputtered (MS) (11-22) AlN buffers on -plane sapphire substrates at 1450 °C via hydride vapour phase epitaxy (HVPE). The MS buffers were annealed at high temperatures of 1400-1600 °C. All the samples were characterised using X-ray diffraction, atomic force microscopy, scanning electron microscope and Raman spectrometry.

Transcription between human-readable synthetic descriptions and machine-executable instructions: an application of the latest pre-training technology.

AI has been widely applied in scientific scenarios, such as robots performing chemical synthetic actions to free researchers from monotonous experimental procedures. However, there exists a gap between human-readable natural language descriptions and machine-executable instructions, of which the former are typically in numerous chemical articles, and the latter are currently compiled manually by experts. We apply the latest technology of pre-trained models and achieve automatic transcription between descriptions and instructions.

A Unified Understanding of Deep NLP Models for Text Classification.

The rapid development of deep natural language processing (NLP) models for text classification has led to an urgent need for a unified understanding of these models proposed individually. Existing methods cannot meet the need for understanding different models in one framework due to the lack of a unified measure for explaining both low-level (e.g.

Interfacial engineering of an FeOOH@CoO heterojunction for efficient overall water splitting and electrocatalytic urea oxidation.

Constructing heterostructure is an efficient method to provide more active sites and optimize electronic structure for improving the oxygen evolution reaction (OER) and urea oxidation reaction (UOR) performance. Herein, the 3D FeOOH@CoO heterostructure was constructed using FeOOH layer (10-20 nm) coated on the surface of CoO nanoneedles through the strong hydrolysis of Fe. The FeOOH@CoO heterostructure not only retains the nanoneedle structure with open frameworks, but also improves the specific surface area and expedites the charge transfer.

A deep-learning system bridging molecule structure and biomedical text with comprehension comparable to human professionals.

To accelerate biomedical research process, deep-learning systems are developed to automatically acquire knowledge about molecule entities by reading large-scale biomedical data. Inspired by humans that learn deep molecule knowledge from versatile reading on both molecule structure and biomedical text information, we propose a knowledgeable machine reading system that bridges both types of information in a unified deep-learning framework for comprehensive biomedical research assistance. We solve the problem that existing machine reading models can only process different types of data separately, and thus achieve a comprehensive and thorough understanding of molecule entities.

Improvement of Crystal Quality of AlN Films with Different Polarities by Annealing at High Temperature.

High-quality AlN film is a key factor affecting the performance of deep-ultraviolet optoelectronic devices. In this work, high-temperature annealing technology in a nitrogen atmosphere was used to improve the quality of AlN films with different polarities grown by magnetron sputtering. After annealing at 1400-1650 °C, the crystal quality of the AlN films was improved.

Full-Scale Information Diffusion Prediction With Reinforced Recurrent Networks.

Information diffusion prediction is an important task, which studies how information items spread among users. With the success of deep learning techniques, recurrent neural networks (RNNs) have shown their powerful capability in modeling information diffusion as sequential data. However, previous works focused on either microscopic diffusion prediction, which aims at guessing who will be the next influenced user at what time, or macroscopic diffusion prediction, which estimates the total numbers of influenced users during the diffusion process.

Knowledge Transfer via Pre-training for Recommendation: A Review and Prospect.

Recommender systems aim to provide item recommendations for users and are usually faced with data sparsity problems (e.g., cold start) in real-world scenarios.

Country Image in COVID-19 Pandemic: A Case Study of China.

Country image has a profound influence on international relations and economic development. In the worldwide outbreak of COVID-19, countries and their people display different reactions, resulting in diverse perceived images among foreign public. Therefore, in this article, we take China as a specific and typical case and investigate its image with aspect-based sentiment analysis on a large-scale Twitter dataset.

Bootstrapping Knowledge Graphs From Images and Text.

The problem of generating structured Knowledge Graphs (KGs) is difficult and open but relevant to a range of tasks related to decision making and information augmentation. A promising approach is to study generating KGs as a relational representation of inputs (e.g.

Incorporating linguistic knowledge for learning distributed word representations.

Combined with neural language models, distributed word representations achieve significant advantages in computational linguistics and text mining. Most existing models estimate distributed word vectors from large-scale data in an unsupervised fashion, which, however, do not take rich linguistic knowledge into consideration. Linguistic knowledge can be represented as either link-based knowledge or preference-based knowledge, and we propose knowledge regularized word representation models (KRWR) to incorporate these prior knowledge for learning distributed word representations.