ADHD/ADD

Leveraging large language models and traditional machine learning ensembles for ADHD detection from narrative transcripts

Despite rapid advances in large language models (LLMs), their integration with traditional supervised machine learning (ML) techniques that have proven applicability to medical data remains underexplored. This is particularly true for psychiatric applications, where narrative data often exhibit nuanced linguistic and contextual complexity, and can benefit from the combination of multiple models ...

Embed Progressive Implicit Preference in Unified Space for Deep Collaborative Filtering

Embedding-based collaborative filtering, often coupled with nearest neighbor search, is widely deployed in large-scale recommender systems for personalized content selection. Modern systems leverage multiple implicit feedback signals (e.g., clicks, add to cart, purchases) to model user preferences comprehensively. However, prevailing approaches adopt a feedback-wise modeling paradigm, which (1) ...

Embed Progressive Implicit Preference in Unified Space for Deep Collaborative Filtering

Embedding-based collaborative filtering, often coupled with nearest neighbor search, is widely deployed in large-scale recommender systems for perso...

Incorporating Flexible Image Conditioning into Text-to-Video Diffusion Models without Training

Text-image-to-video (TI2V) generation is a critical problem for controllable video generation using both semantic and visual conditions. Most existi...

ADD-SLAM: Adaptive Dynamic Dense SLAM with Gaussian Splatting

Recent advancements in Neural Radiance Fields (NeRF) and 3D Gaussian-based Simultaneous Localization and Mapping (SLAM) methods have demonstrated ex...

Knoll: Creating a Knowledge Ecosystem for Large Language Models

Large language models are designed to encode general purpose knowledge about the world from Internet data. Yet, a wealth of information falls outsid...

Holistic White-light Polyp Classification via Alignment-free Dense Distillation of Auxiliary Optical Chromoendoscopy

White Light Imaging (WLI) and Narrow Band Imaging (NBI) are the two main colonoscopic modalities for polyp classification. While NBI, as optical chr...

Statistical Inference under Performativity

Performativity of predictions refers to the phenomena that prediction-informed decisions may influence the target they aim to predict, which is wide...

Privacy-Preserving Conformal Prediction Under Local Differential Privacy

Conformal prediction (CP) provides sets of candidate classes with a guaranteed probability of containing the true class. However, it typically relie...

Visual Question Answering on Multiple Remote Sensing Image Modalities

The extraction of visual features is an essential step in Visual Question Answering (VQA). Building a good visual representation of the analyzed sce...

Technical Report on classification of literature related to children speech disorder

This technical report presents a natural language processing (NLP)-based approach for systematically classifying scientific literature on childhood ...

A computational system to handle the orthographic layer of tajwid in contemporary Quranic Orthography

Contemporary Quranic Orthography (CQO) relies on a precise system of phonetic notation that can be traced back to the early stages of Islam, when th...

Low-Resource Language Processing: An OCR-Driven Summarization and Translation Pipeline

This paper presents an end-to-end suite for multilingual information extraction and processing from image-based documents. The system uses Optical C...

Transforming 3D MRI to 2D Feature Maps Using Pre-Trained Models for Diagnosis of Attention Deficit Hyperactivity Disorder.

According to the World Health Organization (WHO), approximately 5% of children and 2.5% of adults suffer from attention deficit hyperactivity disorde...

Advanced smart assistance with enhancing social interaction and daily activities for visually impaired individuals using deep learning with modified seagull optimization.

Visually impaired individuals face daily challenges in social engagement and routine activities due to limited access to real-time environmental infor...

An MRI-based deep transfer learning radiomics nomogram for predicting meningioma grade.

The aim of this study was to establish a nomogram based on clinical, radiomics, and deep transfer learning (DTL) features to predict meningioma grade....

Artificial Intelligence-Guided Cancer Engineering for Tumor Normalization Executed by Tumor Lysosomal-Triggered Supramolecular Chiral Peptide.

Cancer engineering for tumor normalization offers a promising therapeutic strategy to reverse malignant cells and their supportive tumor microenvironm...

Physics-driven self-supervised learning for fast high-resolution robust 3D reconstruction of light-field microscopy.

Light-field microscopy (LFM) and its variants have significantly advanced intravital high-speed 3D imaging. However, their practical applications rema...

Unsupervised Learning for Class Distribution Mismatch

Class distribution mismatch (CDM) refers to the discrepancy between class distributions in training data and target tasks. Previous methods address ...

DiffLocks: Generating 3D Hair from a Single Image using Diffusion Models

We address the task of generating 3D hair geometry from a single image, which is challenging due to the diversity of hairstyles and the lack of pair...