Modality-agnostic decoders leverage modality-invariant representations in human subjects' brain activity to predict stimuli irrespective of their modality (image, text, mental imagery).

Final random-forest-based models outperformed all publicly available risk scores on internal and external test sets.

Head and neck cancers represent a biologically heterogeneous group of malignancies requiring accurate diagnosis, staging, and risk stratification for ...

MANILA, Philippines — The Legal Education Board (LEB) has authorized legal education institutions (LEIs) to adopt online ...

A senator on Friday urged the Commission on Higher Education (CHED) to establish clear benchmarks and a defined review ...

Schools are enhancing classroom technology to protect off-premises class attendees from audiovisual gaps and other issues.

A global simulation study suggests that pandemic school shutdowns did more than interrupt learning, they may have widened inequality and reduced children’s chances of surpassing their parents’ ...

Denton ISD will revive its virtual learning program for the 2026-2027 school year following new state legislation, according to a presentation at the Feb. 10 board of trustees meeting. The online ...

For decades, robots have excelled in structured settings like assembly lines, where tasks are predictable and tightly scripted. “The emergence of vision-language-action (VLA) models for physical ...

To be useful in more dynamic and less structured environments, robots need artificial intelligence trained on a variety of sensory inputs. Microsoft Corp. today announced Rho-alpha, or ρα, the first ...

Abstract: Multimodal MR image synthesis aims to generate missing modality images by effectively fusing and mapping from a subset of available MRI modalities. Most existing methods adopt an ...