Axonis Decision Intelligence Establishes Trust and Ownership in AI-Assisted Decisions

Operationalizes human-in-the-loop AI-assisted decisions through a secure system-of-record that captures real-time decision traces and context ...

OpenAI's new GPT-5.4 clobbers humans on pro-level work in tests - by 83%

OpenAI's new GPT-5.4 clobbers humans on pro-level work in tests - by 83% ...
IEEE

Explicit and Implicit Fusion Model-Based Digital-Twin Virtual Scene Construction Under Dynamic and Irregular Engineering Scene

In recent years, digital-twin technology has gained significant attention in the field of engineering construction due to its potential in project planning, execution, and management. However, ...
IEEE

A Stable Discontinuous Galerkin Time-Domain Method With Implicit–Explicit Time-Marching for Lossy Media

Abstract: When discontinuous Galerkin time-domain (DGTD) methods with explicit Runge-Kutta (RK) schemes are applied to the analysis of electromagnetic (EM) problems involving lossy media, the solution ...
Investopedia

Supply: Definition, Calculation, and Factors Impacting It

Will Kenton is an expert on the economy and investing laws and regulations. He previously held senior editorial roles at Investopedia and Kapitall Wire and holds a MA in Economics from The New School ...
Frontiers

From Implicit to Explicit: Overcoming Common Barriers to Teaching Critical Thinking Through an Innovative Online Module

1 Cornell University College of Agriculture and Life Sciences, Ithaca, United States 2 Cornell University College of Arts and Sciences, Ithaca, United States The final, formatted version of the ...
GitHub

[AAAI 2025] FAOR: Fast Omni-Directional Image Super-Resolution: Adapting the Implicit Image Function with Pixel and Semantic-Wise Spherical Geometric Priors

We proposes a new ODI-SR model characterized by its capacity to perform Fast and Arbitrary-scale ODI-SR processes, denoted as FAOR. The key innovation lies in adapting the implicit image function from ...
GitHub

Graph Explicit Pooling for Graph-Level Representation Learning

Graph pooling has been increasingly recognized as crucial for Graph Neural Networks (GNNs) to facilitate hierarchical graph representation learning. Existing graph pooling methods commonly consist of ...