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

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

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

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

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...

A Simulated Annealing Algorithm for Randomizing Weighted Networks

Research on Weighted Network Randomization Based on Simulated Annealing Algorithm Background Introduction In the field of neuroscience, connectomics is an important branch for studying the structure and function of brain neural networks. With the development of modern imaging technologies, researchers are able to acquire a wealth of biologically me...

A Scalable Framework for Learning the Geometry-Dependent Solution Operators of Partial Differential Equations

Introduction In recent years, solving partial differential equations (PDEs) using numerical methods has played a significant role in various fields such as engineering and medicine. These methods have shown remarkable effectiveness in applications like topology and design optimization as well as clinical prognostication. However, the high computati...