With the continuous advancement of data acquisition technology, obtaining large amounts of high-dimensional data containing multiple features has become very easy, such as images and vision data. However, traditional machine learning methods, especially those based on vectors and matrices, face challenges such as the curse of dimensionality, increa...
Autonomous Navigation and Walking Wheel-Leg Robot Background Introduction The acceleration of urbanization has posed significant challenges for supply chain logistics, especially for last-mile delivery. As traffic pressure increases and the demand for faster delivery services rises, particularly with complex routes indoors and on city streets, trad...
Research Report on the Analysis of Immunological Characteristics of Peripheral Blood in Cancer Patients Cancer is a major and pervasive health problem globally. Despite significant advances in cancer treatment in recent years, many challenges remain, including how to accurately predict patients’ responses to various treatments. Immunotherapy, parti...
Background and Research Motivation Parkinson’s Disease (PD) is a neurodegenerative disorder primarily affecting patients’ motor abilities, leading to tremors, bradykinesia, limb rigidity, and problems with gait and balance. These motor deficits significantly impact patients’ ability to live independently and their quality of life. Statistics predic...
StrokeClassifier: An AI Tool for Etiological Classification of Ischemic Stroke Based on Electronic Health Records Project Background and Motivation Identifying the etiology of strokes, particularly acute ischemic stroke (AIS), is crucial for secondary prevention, but it is often very challenging. In the United States, there are nearly 676,000 new c...
Generation of Synthetic Whole-Slide Images of Tumors from RNA Sequencing Data via Cascaded Diffusion Models A recent study published in Nature Biomedical Engineering, titled “Generation of Synthetic Whole-Slide Image Tiles of Tumours from RNA-Sequencing Data via Cascaded Diffusion Models,” has garnered significant attention. This research, conducte...
Background Introduction Over the long term, normative and descriptive models have been trying to explain and predict human decision-making behavior in the face of risk choices such as products or gambling. A recent study discovered a more accurate human decision model by training Neural Networks (NNs) on a new large-scale online dataset called choi...
Geometric Enhanced Pretraining for Interatomic Potentials Introduction Molecular dynamics (MD) simulations play an important role in fields such as physics, chemistry, biology, and materials science, providing insights into atomic-level processes. The accuracy and efficiency of MD simulations depend on the choice of interatomic potential functions ...
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...