Relation Structure-Aware Heterogeneous Information Network Embedding

Heterogeneous information network (HIN) embedding aims to embed multiple types of nodes into a low-dimensional space. Although most existing HIN embedding methods consider heterogeneous relations in HINs, they usually employ one single model for all relations without distinction, which inevitably restricts the capability of network embedding. In this paper, we take the structural characteristics of heterogeneous relations into consideration and propose a novel Relation structure-aware Heterogeneous Information Network Embedding model (RHINE). By exploring the real-world networks with thorough mathematical analysis, we present two structure-related measures which can consistently distinguish heterogeneous relations into two categories: Affiliation Relations (ARs) and Interaction Relations (IRs). To respect the distinctive characteristics of relations, in our RHINE, we propose different models specifically tailored to handle ARs and IRs, which can better capture the structures and semantics of the networks. At last, we combine and optimize these models in a unified and elegant manner. Extensive experiments on three real-world datasets demonstrate that our model significantly outperforms the state-of-the-art methods in various tasks, including node clustering, link prediction, and node classification

Isolation Housing Exacerbates Alzheimer\u27s Disease-Like Pathophysiology in Aged APP/PS1 Mice

BACKGROUND: Alzheimer\u27s disease is a neurodegenerative disease characterized by gradual declines in social, cognitive, and emotional functions, leading to a loss of expected social behavior. Social isolation has been shown to have adverse effects on individual development and growth as well as health and aging. Previous experiments have shown that social isolation causes an early onset of Alzheimer\u27s disease-like phenotypes in young APP695/PS1-dE9 transgenic mice. However, the interactions between social isolation and Alzheimer\u27s disease still remain unknown. METHODS: Seventeen-month-old male APP695/PS1-dE9 transgenic mice were either singly housed or continued group housing for 3 months. Then, Alzheimer\u27s disease-like pathophysiological changes were evaluated by using behavioral, biochemical, and pathological analyses. RESULTS: Isolation housing further promoted cognitive dysfunction and Aβ plaque accumulation in the hippocampus of aged APP695/PS1-dE9 transgenic mice, associated with increased γ-secretase and decreased neprilysin expression. Furthermore, exacerbated hippocampal atrophy, synapse and myelin associated protein loss, and glial neuroinflammatory reactions were observed in the hippocampus of isolated aged APP695/PS1-dE9 transgenic mice. CONCLUSIONS: The results demonstrate that social isolation exacerbates Alzheimer\u27s disease-like pathophysiology in aged APP695/PS1-dE9 transgenic mice, highlighting the potential role of group life for delaying or counteracting the Alzheimer\u27s disease process

Valley: Video Assistant with Large Language model Enhanced abilitY

Large language models (LLMs), with their remarkable conversational capabilities, have demonstrated impressive performance across various applications and have emerged as formidable AI assistants. In view of this, it raises an intuitive question: Can we harness the power of LLMs to build multimodal AI assistants for visual applications? Recently, several multi-modal models have been developed for this purpose. They typically pre-train an adaptation module to align the semantics of the vision encoder and language model, followed by fine-tuning on instruction-following data. However, despite the success of this pipeline in image and language understanding, its effectiveness in joint video and language understanding has not been widely explored. In this paper, we aim to develop a novel multi-modal foundation model capable of comprehending video, image, and language within a general framework. To achieve this goal, we introduce Valley, a Video Assistant with Large Language model Enhanced abilitY. The Valley consists of a LLM, a temporal modeling module, a visual encoder, and a simple projection module designed to bridge visual and textual modes. To empower Valley with video comprehension and instruction-following capabilities, we construct a video instruction dataset and adopt a two-stage tuning procedure to train it. Specifically, we employ ChatGPT to facilitate the construction of task-oriented conversation data encompassing various tasks, including multi-shot captions, long video descriptions, action recognition, causal relationship inference, etc. Subsequently, we adopt a pre-training-then-instructions-tuned pipeline to align visual and textual modalities and improve the instruction-following capability of Valley. Qualitative experiments demonstrate that Valley has the potential to function as a highly effective video assistant that can make complex video understanding scenarios easy

Characteristics of resistome and bacterial community structure in constructed wetland during dormant period: A fullscale study from Annan wetland

As a green technology, constructed wetlands (CWs) can provide a low-cost solution for wastewater treatment. Either as a standalone treatment or integrated with conventional treatment, nutrients, antibiotic resistant bacteria (ARB)/antibiotic resistance genes (ARGs) can be removed by CW efficiently. While, few studies have focused on characteristics of resistome and bacterial community (BC) structure in CW during dormant period. Therefore, in this study, Annan CW (a full-scale hybrid CW) was selected to characterize resistome and BC during dormant period. The profiles of bacteria / ARGs were monitored in combination of shotgun sequencing and metagenomic assembly analysis. And multidrug ARGs are the most abundant in Annan CW, and surface flow wetland had the relatively high ARG diversity and abundance compared with subsurface flow wetland and the front pond. The most dominant phylum in CW is Proteobacteria, while the other dominant phylum in three parts have different order. COD, TP, TN, ARGs, and mobile genetic genes (MGEs) were removed by subsurface flow CW with better performance, but virulent factors (VFs) were removed by surface flow CW with better performance. Based on the spatiotemporal distribution of ARGs, the internal mechanism of ARGs dynamic variation was explored by the redundancy analysis (RDA) and variation partitioning analysis (VPA). BCs, MGEs and environmental factors (EFs) were responsible for 45.6 %, 28.3 % and 15.4 % of the ARGs variations. Among these factors, BCs and MGEs were the major co-drivers impacting the ARG profile, and EFs indirectly influence the ARG profile. This study illustrates the specific functions of ARG risk elimination in different CW components, promotes a better understanding of the efficiency of CWs for the reduction of ARG and ARB, contributing to improve the removal performance of constructed wetlands. And provide management advice to further optimize the operation of CWs during dormant period

What Happens Next? Future Subevent Prediction Using Contextual Hierarchical LSTM

Events are typically composed of a sequence of subevents. Predicting a future subevent of an event is of great importance for many real-world applications. Most previous work on event prediction relied on hand-crafted features and can only predict events that already exist in the training data. In this paper, we develop an end-to-end model which directly takes the texts describing previous subevents as input and automatically generates a short text describing a possible future subevent. Our model captures the two-level sequential structure of a subevent sequence, namely, the word sequence for each subevent and the temporal order of subevents. In addition, our model incorporates the topics of the past subevents to make context-aware prediction of future subevents. Extensive experiments on a real-world dataset demonstrate the superiority of our model over several state-of-the-art methods

A Survey on Multimodal Knowledge Graphs: Construction, Completion and Applications

As an essential part of artificial intelligence, a knowledge graph describes the real-world entities, concepts and their various semantic relationships in a structured way and has been gradually popularized in a variety practical scenarios. The majority of existing knowledge graphs mainly concentrate on organizing and managing textual knowledge in a structured representation, while paying little attention to the multimodal resources (e.g., pictures and videos), which can serve as the foundation for the machine perception of a real-world data scenario. To this end, in this survey, we comprehensively review the related advances of multimodal knowledge graphs, covering multimodal knowledge graph construction, completion and typical applications. For construction, we outline the methods of named entity recognition, relation extraction and event extraction. For completion, we discuss the multimodal knowledge graph representation learning and entity linking. Finally, the mainstream applications of multimodal knowledge graphs in miscellaneous domains are summarized

Research on Mechanical Properties of High-Pressure Anhydrite Based on First Principles

This article focuses on the elucidation of a three-dimensional model of the structure of anhydrite crystal (CaSO4). The structure parameters of anhydrite crystal were obtained by means of first principles after structure optimization at 0~120 MPa. In comparison with previous experimental and theoretical calculation values, the results we obtained are strikingly similar to the previous data. The elastic constants and physical parameters of anhydrite crystal were also studied by the first-principles method. Based on this, we further studied the Young’s modulus and Poisson’s ratio of anhydrite crystal, the anisotropy factor, the speed of sound, the minimum thermal conductivity and the hardness of the material. It was shown that the bulk modulus and Poisson’s ratio of anhydrite crystal rose slowly with increasing pressure. The anisotropy characteristics of the Young’s modulus and shear modulus of anhydrite crystal were consistent under various pressure levels, while the difference in the anisotropy characteristics of the bulk modulus appeared. The acoustic velocities of anhydrite crystal tended to be stable with increasing pressure. The minimum thermal conductivity remained relatively unchanged with increasing pressure. However, the material hardness declined gradually with increasing pressure