Two important things happened on January 20, 2025. In Washington, D.C., Donald Trump was inaugurated as President of the United States. In Hangzhou, China, a little-known Chinese firm called DeepSeek ...

ABSTRACT: Graph burning is a model to describe the spread of social influence. In 2023, Song et al. proposed the Independent Cascade Graph Burning model, where a vertex v can be burned by its burning ...

Human motion prediction aims to forecast future motions based on historical motion sequences. Graph Convolutional Networks (GCNs) are widely used in this field. However, the high computational cost of ...

In a new study published in Nature Physics, researchers achieved the first experimental observation of a time rondeau crystal—a novel phase of matter where long-range temporal order coexists with ...

Abstract: Graph Convolution Networks (GCNs) have achieved remarkable success in representation of structured graph data. As we know that traditional GCNs are generally defined on the fixed first-order ...

ABSTRACT: Let G=( V,E ) be a graph. The first Zagreb index of a graph G is defined as ∑ u∈V d G 2 ( u ) , where d G ( u ) is the degree of vertex u in G . In this paper, we obtain two lower bounds ...

CEO Sam Altman called a strange graph in its GPT-5 presentation a ‘mega chart screwup.’ CEO Sam Altman called a strange graph in its GPT-5 presentation a ‘mega chart screwup.’ is a senior reporter ...

STG-DMD (Sparse-Coded Time-Delay Graph Dynamic Mode Decomposition) is a data-driven framework for modeling nonlinear dynamics on graph structures. It integrates: StgDmd/ ├── code/ │ ├── artificial/ │ ...

Zeroing neural network (ZNN) is viewed as an effective solution to time-varying nonlinear equation (TVNE). In this paper, a further study is shown by proposing a novel combined discrete-time ZNN ...

Physics and Python stuff. Most of the videos here are either adapted from class lectures or solving physics problems. I really like to use numerical calculations without all the fancy programming ...