Multi-feature fusion based face forgery detection with local and global characteristics.

The malicious use of deepfake videos seriously affects information security and brings great harm to society. Currently, deepfake videos are mainly generated based on deep learning methods, which are difficult to be recognized by the naked eye, therefore, it is of great significance to study accurate and efficient deepfake video detection techniques. Most of the existing detection methods focus on analyzing the discriminative information in a specific feature domain for classification from a local or global perspective.

Genetic Evidence of in Leaf Morphology Variation of 'Mojo'.

Leaves play a crucial role as ornamental organs in , exhibiting distinct differences across various varieties. Leaf development is intricately linked to processes of cell proliferation and expansion, with cell morphology often regulated by plant cell walls, primarily composed of cellulose. Alterations in cellulose content can impact cell morphology, subsequently influencing the overall shape of plant organs.

DyCARS: A dynamic context-aware recommendation system.

Dynamic recommendation systems aim to achieve real-time updates and dynamic migration of user interests, primarily utilizing user-item interaction sequences with timestamps to capture the dynamic changes in user interests and item attributes. Recent research has mainly centered on two aspects. First, it involves modeling the dynamic interaction relationships between users and items using dynamic graphs.

Co-embedding of edges and nodes with deep graph convolutional neural networks.

Graph neural networks (GNNs) have significant advantages in dealing with non-Euclidean data and have been widely used in various fields. However, most of the existing GNN models face two main challenges: (1) Most GNN models built upon the message-passing framework exhibit a shallow structure, which hampers their ability to efficiently transmit information between distant nodes. To address this, we aim to propose a novel message-passing framework, enabling the construction of GNN models with deep architectures akin to convolutional neural networks (CNNs), potentially comprising dozens or even hundreds of layers.

MLSFF: Multi-level structural features fusion for multi-modal knowledge graph completion.

With the rise of multi-modal methods, multi-modal knowledge graphs have become a better choice for storing human knowledge. However, knowledge graphs often suffer from the problem of incompleteness due to the infinite and constantly updating nature of knowledge, and thus the task of knowledge graph completion has been proposed. Existing multi-modal knowledge graph completion methods mostly rely on either embedding-based representations or graph neural networks, and there is still room for improvement in terms of interpretability and the ability to handle multi-hop tasks.

MLNet: a multi-level multimodal named entity recognition architecture.

In the field of human-computer interaction, accurate identification of talking objects can help robots to accomplish subsequent tasks such as decision-making or recommendation; therefore, object determination is of great interest as a pre-requisite task. Whether it is named entity recognition (NER) in natural language processing (NLP) work or object detection (OD) task in the computer vision (CV) field, the essence is to achieve object recognition. Currently, multimodal approaches are widely used in basic image recognition and natural language processing tasks.

A dynamic graph Hawkes process based on linear complexity self-attention for dynamic recommender systems.

The dynamic recommender system realizes the real-time recommendation for users by learning the dynamic interest characteristics, which is especially suitable for the scenarios of rapid transfer of user interests, such as e-commerce and social media. The dynamic recommendation model mainly depends on the user-item history interaction sequence with timestamp, which contains historical records that reflect changes in the true interests of users and the popularity of items. Previous methods usually model interaction sequences to learn the dynamic embedding of users and items.

Attention-based message passing and dynamic graph convolution for spatiotemporal data imputation.

Although numerous spatiotemporal approaches have been presented to address the problem of missing spatiotemporal data, there are still limitations in concurrently capturing the underlying spatiotemporal dependence of spatiotemporal graph data. Furthermore, most imputation methods miss the hidden dynamic connection associations that exist between graph nodes over time. To address the aforementioned spatiotemporal data imputation challenge, we present an attention-based message passing and dynamic graph convolution network (ADGCN).

A deep graph convolutional neural network architecture for graph classification.

Graph Convolutional Networks (GCNs) are powerful deep learning methods for non-Euclidean structure data and achieve impressive performance in many fields. But most of the state-of-the-art GCN models are shallow structures with depths of no more than 3 to 4 layers, which greatly limits the ability of GCN models to extract high-level features of nodes. There are two main reasons for this: 1) Overlaying too many graph convolution layers will lead to the problem of over-smoothing.

[Dihydroartemisinin Induces AML cell Apoptosis by Inhibition of PTEN/AKT pathway].

Objective: To study the effects of dihydroartemisinin (DHA) on the proliferation and apoptosis of acute myeloid leukemia (AML) cells.

Methods: The effects of DHA on the proliferation of acute myeloid leukemia cells and the inhibitory effect of Z-VAD-FMK on the DHA-induced cell apoptosis were detected by CCK-8 assay. The expression level of cleaved-caspased 3 was detected by indirect immunofluorescence.

PINK1 alleviates myocardial hypoxia-reoxygenation injury by ameliorating mitochondrial dysfunction.

PTEN inducible kinase-1 (PINK1) mutant induces mitochondrial dysfunction of cells, resulting in an inherited form of Parkinson's disease. However its exact role in the cardiomyocytes is unclear. The present study examined the function of PINK1 in hypoxia-reoxygenation (H/R) induced H9c2 cell damage and its potential mechanism.

KLF5 overexpression attenuates cardiomyocyte inflammation induced by oxygen-glucose deprivation/reperfusion through the PPARγ/PGC-1α/TNF-α signaling pathway.

The primary physiological function of Krüppel-like zinc-finger transcription factor (KLF5) is the regulation of cardiovascular remodeling. Vascular remodeling is closely related to the amelioration of various ischemic diseases. However, the underlying correlation of KLF5 and ischemia is not clear.

The protective effect of picroside II against hypoxia/reoxygenation injury in neonatal rat cardiomyocytes.

Context: Picroside II, an iridoid glucoside found in the root of Picrorhiza scrophulariiflora Pennell (Scrophulariaceae), has been demonstrated to possess potent antioxidant activity. However, whether picroside II has a protective effect against hypoxia/reoxygenation (H/R)-induced cardiomyocyte injury is poorly understood.

Objective: To explore the cardioprotective role of picroside II against oxidative stress induced by H/R injury in neonatal rat cardiacmyocytes.