Effective Probabilistic Neural Networks Model for Model-Based Reinforcement Learning in USV

Engineering, Information Science, Computer Science, Automation,
Probabilistic Neural Networks   Model Predictive Control   Unmanned Surface Vehicles   Reinforcement Learning   Uncertainty Propagation  

A New Approach to Model Predictive Control for Unmanned Surface Vehicles (USV): A Probabilistic Neural Network-Based MBRL Framework Academic Background Unmanned Surface Vehicles (USVs) have seen rapid development in recent years within the field of marine science, finding extensive applications in scenarios such as marine transportation, environmen...

Oral-Anatomical Knowledge-Informed Semi-Supervised Learning for 3D Dental CBCT Segmentation and Lesion Detection

Information Science, Computer Science, Automation, Medicine, Oral Sciences, Medical Imaging,
dental CBCT   semi-supervised learning   lesion detection   oral-anatomical knowledge   3D image segmentation  

Academic Background and Research Motivation In the field of dental healthcare, Cone Beam Computed Tomography (CBCT) is a widely used three-dimensional imaging technique. CBCT provides three-dimensional images of the oral cavity and is particularly effective in diagnosing odontogenic lesions. However, the segmentation of CBCT images—labeling each vo...

Multi-Task Aquatic Toxicity Prediction Model Based on Multi-Level Features Fusion

Chemistry, Computational Chemistry, Environmental Chemistry, Engineering, Environmental Engineering, Information Science, Computer Science, Artificial Intelligence, Medicine, Environmental and Public Health,
aquatic toxicity   deep learning   multi-task model   molecular fingerprints   molecular graph features  

Academic Background With the growing threat of organic compounds to environmental pollution, studying the toxic responses of different aquatic organisms to these compounds has become crucial. Such research not only helps assess the potential ecological impacts of pollutants on the overall aquatic ecosystem but also provides significant scientific f...

Resistive Memory-Based Zero-Shot Liquid State Machine for Multimodal Event Data Learning

Information Science, Electronic Information, Computer Science, Artificial Intelligence,
resistive memory   zero-shot learning   liquid state machine   multimodal data   neuromorphic hardware   event-driven learning   brain-inspired computing  

Novel Resistive Memory-Driven Zero-Shot Multimodal Event Learning System: A Report on Hardware-Software Co-Design Academic Background The human brain is a complex spiking neural network (SNN) capable of zero-shot learning in multimodal signals with minimal power consumption, allowing generalization of existing knowledge to address new tasks. Howeve...

Efficient Scaling of Large Language Models with Mixture of Experts and 3D Analog In-Memory Computing

Information Science, Electronic Information, Computer Science, Artificial Intelligence,
large language models   mixture of experts   3D in-memory computing   energy efficiency   hardware acceleration  

Efficient Scaling of Large Language Models with Mixture of Experts and 3D Analog In-Memory Computing Academic Background In recent years, large language models (LLMs) have demonstrated remarkable capabilities in natural language processing, text generation, and other fields. However, as the scale of these models continues to grow, the costs of trai...

Multimodal Learning for Mapping Genotype–Phenotype Dynamics

Information Science, Computer Science, Artificial Intelligence, Medicine, Life Sciences, Cell Biology, Biochemistry, Bioengineering, Genetics,
multimodal learning   genotype-phenotype relationship   single-cell RNA sequencing   machine learning   cellular heterogeneity  

Multimodal Learning Reveals Genotype–Phenotype Dynamics Background The complex relationship between genotype and phenotype has long been a central question in biology. Genotype refers to the genetic information of an organism, while phenotype is the manifestation of this genetic information in a specific environment. Although Wilhelm Johannsen intr...

Leveraging Pharmacovigilance Data to Predict Population-Scale Toxicity Profiles of Checkpoint Inhibitor Immunotherapy

Information Science, Computer Science, Medicine, Oncology, Life Sciences, Immunology,
Pharmacovigilance   Checkpoint Inhibitors   Toxicity Prediction   Deep Learning   Cancer Immunotherapy   Population Analysis   Dynamic Graph Convolutional Network  

Predicting and Monitoring the Toxicity of Immune Checkpoint Inhibitors: Breakthrough Application of the DysPred Deep Learning Framework Academic Background Immune checkpoint inhibitors (ICIs) represent a major breakthrough in cancer immunotherapy in recent years, enhancing the body’s antitumor immune response by inhibiting immune checkpoint signali...

Deep Bayesian Active Learning Using In-Memory Computing Hardware

Chemistry, Nanoscience, Engineering, Electrical Engineering, Information Science, Computer Science, Semiconductors and Information Devices, Artificial Intelligence,
in-memory computing   deep Bayesian learning   uncertainty capture   robot skill learning   efficient learning  

With the rapid development of artificial intelligence (AI) technologies, deep learning has made significant progress in complex tasks. However, the success of deep learning largely relies on massive amounts of labeled data, and the data labeling process is not only time-consuming and labor-intensive but also requires specialized domain knowledge, m...

A Programmable Environment for Shape Optimization and Shapeshifting Problems

Physics, Mechanics, Chemistry, Polymer Chemistry, Materials Science, Information Science, Computer Science,
Shape optimization   Soft matter   Programmable environment   Morpho   Complex fluids  

Research on Programmable Shape Optimization and Deformation Problems: Development and Application of the Morpho Environment Academic Background Soft materials play a crucial role in the fields of science and engineering, particularly in areas such as soft robotics, structured fluids, biological materials, and particulate media. These materials unde...

A Spatiotemporal Style Transfer Algorithm for Dynamic Visual Stimulus Generation

Information Science, Computer Science, Artificial Intelligence, Medicine, Neurology,
dynamic visual stimulus   style transfer   deep learning   vision science   spatiotemporal features   artificial vision   neuroscience  

Research Report on the Spatiotemporal Style Transfer Algorithm for Dynamic Visual Stimulus Generation Academic Background The encoding and processing of visual information has been a significant focus in the fields of neuroscience and vision science. With the rapid development of deep learning techniques, investigating the similarities between arti...