A Neural Speech Decoding Framework Leveraging Deep Learning and Speech Synthesis

A Neural Speech Decoding Framework Leveraging Deep Learning and Speech Synthesis

Major Breakthrough in Neuroscience Research: Deep Learning Technique Achieves Decoding of Natural Speech from Brain Signals A cross-disciplinary research team at New York University recently achieved a major breakthrough in the fields of neuroscience and artificial intelligence. They developed a novel deep learning-based framework that can directly...

Equivariant 3D Conditional Diffusion Model for Molecular Linker Design

Equivariant 3D Conditional Diffusion Model for Molecular Linker Design

From early drug discovery researchers face a daunting challenge – to find drug-like candidate molecules among approximately 10^60 possible molecular structures. One successful solution is to start from smaller “fragment” molecules, a strategy known as fragment-based drug design (FBDD). In the FBDD process, the first step is to computationally scree...

Tandem mass spectrum prediction for small molecules using graph transformers

This is a paper about MassFormer, a graph transformer model for small molecule mass spectrometry prediction. This research addresses the problem of molecular identification in mass spectrometry data and proposes a novel deep learning approach to predict mass spectra of small molecules. Background: Mass spectrometry (MS) is an analytical technique w...

Synthetic Lagrangian Turbulence by Generative Diffusion Models

Currently, there are significant challenges in studying the statistical and geometrical properties of particles carried by the fluid in turbulence. Despite outstanding efforts in theory, numerical simulations, and experiments over the past 30 years, there is still a lack of models that can realistically reproduce the statistical and topological fea...

Efficient Learning of Accurate Surrogates for Simulations of Complex Systems

This research proposes an online learning method for efficiently constructing surrogate models that can accurately emulate complex systems. The method consists of three key components: Sampling strategy for generating new training and testing data; Learning strategy for generating candidate surrogate models based on the training data; Validation me...

Exploring the Psychology of LLMs' Moral and Legal Reasoning

Current Situation Nowadays, large language models (LLMs) have demonstrated expert-level performance in multiple fields, which has sparked great interest in understanding their internal reasoning processes. Comprehending how LLMs generate these remarkable results is crucial for the future development of artificial intelligence agents and ensuring th...