ADHD/ADD

StarWhisper Telescope: Agent-Based Observation Assistant System to Approach AI Astrophysicist

With the rapid advancements in Large Language Models (LLMs), LLM-based agents have introduced convenient and user-friendly methods for leveraging tools across various domains. In the field of astronomical observation, the construction of new telescopes has significantly increased astronomers' workload. Deploying LLM-powered agents can effectively alleviate this burden and reduce the costs associ...

Taming Sensitive Weights : Noise Perturbation Fine-tuning for Robust LLM Quantization

Quantization is a critical step to enable efficient LLM serving under limited resource. However, previous research observes that certain weights in the LLM, known as outliers, are significantly sensitive to quantization noises. Existing quantization methods leave these outliers as floating points or higher precisions to retain performance, posting challenges on the efficient hardware deployment ...

HumanEdit: A High-Quality Human-Rewarded Dataset for Instruction-based Image Editing

We present HumanEdit, a high-quality, human-rewarded dataset specifically designed for instruction-guided image editing, enabling precise and divers...

Gesture Classification in Artworks Using Contextual Image Features

Recognizing gestures in artworks can add a valuable dimension to art understanding and help to acknowledge the role of the sense of smell in cultura...

CleanDIFT: Diffusion Features without Noise

Internal features from large-scale pre-trained diffusion models have recently been established as powerful semantic descriptors for a wide range of ...

An ADHD Diagnostic Interface Based on EEG Spectrograms and Deep Learning Techniques

This paper introduces an innovative approach to Attention-deficit/hyperactivity disorder (ADHD) diagnosis by employing deep learning (DL) techniques...

A Simple Channel Compression Method for Brain Signal Decoding on Classification Task

In the application of brain-computer interface (BCI), while pursuing accurate decoding of brain signals, we also need consider the computational eff...

EDTformer: An Efficient Decoder Transformer for Visual Place Recognition

Visual place recognition (VPR) aims to determine the general geographical location of a query image by retrieving visually similar images from a lar...

Advancements in Cardiac CT Imaging: The Era of Artificial Intelligence.

In the last decade, artificial intelligence (AI) has influenced the field of cardiac computed tomography (CT), with its scope further enhanced by adva...

MARVEL-40M+: Multi-Level Visual Elaboration for High-Fidelity Text-to-3D Content Creation

Generating high-fidelity 3D content from text prompts remains a significant challenge in computer vision due to the limited size, diversity, and ann...

Advanced Plaque Modeling for Atherosclerosis Detection Using Molecular Communication

As one of the most prevalent diseases worldwide, plaque formation in human arteries, known as atherosclerosis, is the focus of many research efforts...

RatGene: Gene deletion-addition algorithms using growth to production ratio for growth-coupled production in constraint-based metabolic networks

In computational metabolic design, it is often necessary to modify the original constraint-based metabolic networks to lead to growth-coupled produc...

Conceptwm: A Diffusion Model Watermark for Concept Protection

The personalization techniques of diffusion models succeed in generating specific concepts but also pose threats to copyright protection and illegal...

Next-generation optical networks to sustain connectivity of the future: All roads lead to optical-computing-enabled network?

From an architectural perspective with the main goal of reducing the effective traffic load in the network and thus gaining more operational efficie...

Add-it: Training-Free Object Insertion in Images With Pretrained Diffusion Models

Adding Object into images based on text instructions is a challenging task in semantic image editing, requiring a balance between preserving the ori...

Empowering Library Users: Creative Strategies for Engagement and Innovation

This study investigated the integration of cutting-edge technologies and methodologies for creating dynamic, user-centered library environments. In ...

Using machine learning to derive neurobiological subtypes of general psychopathology in late childhood.

Traditional mental health diagnoses rely on symptom-based classifications. Yet this approach can oversimplify clinical presentations as diagnoses ofte...

Making the most of errors: Utilizing erroneous classifications generated by machine-learning models of neuroimaging data to capture disorder heterogeneity.

Within-disorder heterogeneity complicates mapping the neurobiological features of psychopathology to Diagnostic and Statistical Manual of Mental Disor...

Energy Efficient Dual Designs of FeFET-Based Analog In-Memory Computing with Inherent Shift-Add Capability

In-memory computing (IMC) architecture emerges as a promising paradigm, improving the energy efficiency of multiply-and-accumulate (MAC) operations ...

Estimating the Causal Effects of T Cell Receptors

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...