ADHD/ADD

Differentially Private Selection using Smooth Sensitivity

With the growing volume of data in society, the need for privacy protection in data analysis also rises. In particular, private selection tasks, wherein the most important information is retrieved under differential privacy are emphasized in a wide range of contexts, including machine learning and medical statistical analysis. However, existing mechanisms use global sensitivity, which may add la...

SimBrainNet: Evaluating Brain Network Similarity for Attention Disorders

Electroencephalography (EEG)-based attention disorder research seeks to understand brain activity patterns associated with attention. Previous studies have mainly focused on identifying brain regions involved in cognitive processes or classifying Attention-Deficit Hyperactivity Disorder (ADHD) and control subjects. However, analyzing effective brain connectivity networks for specific attentional...

DAT: Dialogue-Aware Transformer with Modality-Group Fusion for Human Engagement Estimation

Engagement estimation plays a crucial role in understanding human social behaviors, attracting increasing research interests in fields such as affec...

Neural Metamorphosis

This paper introduces a new learning paradigm termed Neural Metamorphosis (NeuMeta), which aims to build self-morphable neural networks. Contrary to...

Learning Image Derived PDE-Phenotypes from fMRI Data

Partial Differential Equations (PDEs) model various physical phenomena, such as electromagnetic fields and fluid mechanics. Methods like Sparse Iden...

Multi-Stage Graph Learning for fMRI Analysis to Diagnose Neuro-Developmental Disorders

The insufficient supervision limit the performance of the deep supervised models for brain disease diagnosis. It is important to develop a learning ...

HyperBrain: Anomaly Detection for Temporal Hypergraph Brain Networks

Identifying unusual brain activity is a crucial task in neuroscience research, as it aids in the early detection of brain disorders. It is common to...

KIPPS: Knowledge infusion in Privacy Preserving Synthetic Data Generation

The integration of privacy measures, including differential privacy techniques, ensures a provable privacy guarantee for the synthetic data. However...

Active Learning to Guide Labeling Efforts for Question Difficulty Estimation

In recent years, there has been a surge in research on Question Difficulty Estimation (QDE) using natural language processing techniques. Transforme...

Biomedical knowledge graph-optimized prompt generation for large language models.

MOTIVATION: Large language models (LLMs) are being adopted at an unprecedented rate, yet still face challenges in knowledge-intensive domains such as ...

Floating-Point Multiply-Add with Approximate Normalization for Low-Cost Matrix Engines

The widespread adoption of machine learning algorithms necessitates hardware acceleration to ensure efficient performance. This acceleration relies ...

Detecting Adversarial Attacks in Semantic Segmentation via Uncertainty Estimation: A Deep Analysis

Deep neural networks have demonstrated remarkable effectiveness across a wide range of tasks such as semantic segmentation. Nevertheless, these netw...

AltCanvas: A Tile-Based Image Editor with Generative AI for Blind or Visually Impaired People

People with visual impairments often struggle to create content that relies heavily on visual elements, particularly when conveying spatial and stru...

Are gene-by-environment interactions leveraged in multi-modality neural networks for breast cancer prediction?

Polygenic risk scores (PRSs) can significantly enhance breast cancer risk prediction when combined with clinical risk factor data. While many studie...

Enhancing context models for point cloud geometry compression with context feature residuals and multi-loss

In point cloud geometry compression, context models usually use the one-hot encoding of node occupancy as the label, and the cross-entropy between t...

PICO-RAM: A PVT-Insensitive Analog Compute-In-Memory SRAM Macro with In-Situ Multi-Bit Charge Computing and 6T Thin-Cell-Compatible Layout

Analog compute-in-memory (CIM) in static random-access memory (SRAM) is promising for accelerating deep learning inference by circumventing the memo...

SwiftDiffusion: Efficient Diffusion Model Serving with Add-on Modules

Text-to-image (T2I) generation using diffusion models has become a blockbuster service in today's AI cloud. A production T2I service typically invol...

Deep Generative Replay-based Class-incremental Continual Learning in sEMG-based Pattern Recognition.

Developments in neural networks and sensing technologies have increased focus on modules for surface electromyogram (sEMG)-based pattern recognition. ...

Complexity Analysis based on Parietal Fuzzy Entropy to Facilitate ADHD Diagnosis in Young Children.

Attention deficit hyperactivity disorder (ADHD) is the most common condition affecting the development of neurons in children. Therefore, early and ac...

Neurodevelopmental disorders modeling using isogeometric analysis, dynamic domain expansion and local refinement

Neurodevelopmental disorders (NDDs) have arisen as one of the most prevailing chronic diseases within the US. Often associated with severe adverse i...