Latest AI and machine learning research in adhd/add for healthcare professionals.
In the application of brain-computer interface (BCI), while pursuing accurate decoding of brain signals, we also need consider the computational efficiency of BCI devices. ECoG signals are multi-channel temporal signals which is collected using a high-density electrode array at a high sampling frequency. The data between channels has a high similarity or redundancy in the temporal domain. The re...
Visual place recognition (VPR) aims to determine the general geographical location of a query image by retrieving visually similar images from a large geo-tagged database. To obtain a global representation for each place image, most approaches typically focus on the aggregation of deep features extracted from a backbone through using current prominent architectures (e.g., CNNs, MLPs, pooling lay...
In the last decade, artificial intelligence (AI) has influenced the field of cardiac computed tomography (CT), with its scope further enhanced by adva...
Generating high-fidelity 3D content from text prompts remains a significant challenge in computer vision due to the limited size, diversity, and ann...
As one of the most prevalent diseases worldwide, plaque formation in human arteries, known as atherosclerosis, is the focus of many research efforts...
In computational metabolic design, it is often necessary to modify the original constraint-based metabolic networks to lead to growth-coupled produc...
The personalization techniques of diffusion models succeed in generating specific concepts but also pose threats to copyright protection and illegal...
From an architectural perspective with the main goal of reducing the effective traffic load in the network and thus gaining more operational efficie...
Adding Object into images based on text instructions is a challenging task in semantic image editing, requiring a balance between preserving the ori...
This study investigated the integration of cutting-edge technologies and methodologies for creating dynamic, user-centered library environments. In ...
Traditional mental health diagnoses rely on symptom-based classifications. Yet this approach can oversimplify clinical presentations as diagnoses ofte...
Within-disorder heterogeneity complicates mapping the neurobiological features of psychopathology to Diagnostic and Statistical Manual of Mental Disor...
In-memory computing (IMC) architecture emerges as a promising paradigm, improving the energy efficiency of multiply-and-accumulate (MAC) operations ...
A central question in human immunology is how a patient's repertoire of T cells impacts disease. Here, we introduce a method to infer the causal eff...
With the growing volume of data in society, the need for privacy protection in data analysis also rises. In particular, private selection tasks, whe...
Electroencephalography (EEG)-based attention disorder research seeks to understand brain activity patterns associated with attention. Previous studi...
Engagement estimation plays a crucial role in understanding human social behaviors, attracting increasing research interests in fields such as affec...
This paper introduces a new learning paradigm termed Neural Metamorphosis (NeuMeta), which aims to build self-morphable neural networks. Contrary to...
Partial Differential Equations (PDEs) model various physical phenomena, such as electromagnetic fields and fluid mechanics. Methods like Sparse Iden...
The insufficient supervision limit the performance of the deep supervised models for brain disease diagnosis. It is important to develop a learning ...