Prescriptions

Interpretable Hierarchical Attention Network for Medical Condition Identification

Accurate prediction of medical conditions with straight past clinical evidence is a long-sought topic in the medical management and health insurance field. Although great progress has been made with machine learning algorithms, the medical community is still skeptical about the model accuracy and interpretability. This paper presents an innovative hierarchical attention deep learning model to ac...

Inst-IT: Boosting Multimodal Instance Understanding via Explicit Visual Prompt Instruction Tuning

Large Multimodal Models (LMMs) have made significant breakthroughs with the advancement of instruction tuning. However, while existing models can understand images and videos at a holistic level, they still struggle with instance-level understanding that requires a more nuanced comprehension and alignment. Instance-level understanding is crucial, as it focuses on the specific elements that we ar...

GSOT3D: Towards Generic 3D Single Object Tracking in the Wild

In this paper, we present a novel benchmark, GSOT3D, that aims at facilitating development of generic 3D single object tracking (SOT) in the wild. S...

Research on Cervical Cancer p16/Ki-67 Immunohistochemical Dual-Staining Image Recognition Algorithm Based on YOLO

The p16/Ki-67 dual staining method is a new approach for cervical cancer screening with high sensitivity and specificity. However, there are issues ...

FIction: 4D Future Interaction Prediction from Video

Anticipating how a person will interact with objects in an environment is essential for activity understanding, but existing methods are limited to ...

Examining Identity Drift in Conversations of LLM Agents

Large Language Models (LLMs) show impressive conversational abilities but sometimes show identity drift problems, where their interaction patterns o...

GENNDTI: Drug-Target Interaction Prediction Using Graph Neural Network Enhanced by Router Nodes.

Identifying drug-target interactions (DTI) is crucial in drug discovery and repurposing, and in silico techniques for DTI predictions are becoming inc...

DeepDR: a deep learning library for drug response prediction.

SUMMARY: Accurate drug response prediction is critical to advancing precision medicine and drug discovery. Recent advances in deep learning (DL) have ...

MIN: Multi-channel Interaction Network for Drug-Target Interaction with Protein Distillation

Traditional drug discovery processes are both time-consuming and require extensive professional expertise. With the accumulation of drug-target inte...

Detecting Visual Triggers in Cannabis Imagery: A CLIP-Based Multi-Labeling Framework with Local-Global Aggregation

This study investigates the interplay of visual and textual features in online discussions about cannabis edibles and their impact on user engagemen...

Geminio: Language-Guided Gradient Inversion Attacks in Federated Learning

Foundation models that bridge vision and language have made significant progress, inspiring numerous life-enriching applications. However, their pot...

Utilizing RAG and GPT-4 for Extraction of Substance Use Information from Clinical Notes.

This research investigates the application of a hybrid Retrieval-Augmented Generation (RAG) and Generative Pre-trained Transformer (GPT) pipeline for ...

A review of deep learning models for the prediction of chromatin interactions with DNA and epigenomic profiles.

Advances in three-dimensional (3D) genomics have revealed the spatial characteristics of chromatin interactions in gene expression regulation, which i...

DD-PRiSM: a deep learning framework for decomposition and prediction of synergistic drug combinations.

Combination therapies have emerged as a promising approach for treating complex diseases, particularly cancer. However, predicting the efficacy and sa...

Introducing TEC-LncMir for prediction of lncRNA-miRNA interactions through deep learning of RNA sequences.

The interactions between long noncoding RNA (lncRNA) and microRNA (miRNA) play critical roles in life processes, highlighting the necessity to enhance...

S$^2$ALM: Sequence-Structure Pre-trained Large Language Model for Comprehensive Antibody Representation Learning

Antibodies safeguard our health through their precise and potent binding to specific antigens, demonstrating promising therapeutic efficacy in the t...

DrugGen: Advancing Drug Discovery with Large Language Models and Reinforcement Learning Feedback

Traditional drug design faces significant challenges due to inherent chemical and biological complexities, often resulting in high failure rates in ...

FabuLight-ASD: Unveiling Speech Activity via Body Language

Active speaker detection (ASD) in multimodal environments is crucial for various applications, from video conferencing to human-robot interaction. T...

Towards Scalable Insect Monitoring: Ultra-Lightweight CNNs as On-Device Triggers for Insect Camera Traps

Camera traps, combined with AI, have emerged as a way to achieve automated, scalable biodiversity monitoring. However, the passive infrared (PIR) se...

No-regret Exploration in Shuffle Private Reinforcement Learning

Differential privacy (DP) has recently been introduced into episodic reinforcement learning (RL) to formally address user privacy concerns in person...