Prescriptions

V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning

A major challenge for modern AI is to learn to understand the world and learn to act largely by observation. This paper explores a self-supervised approach that combines internet-scale video data with a small amount of interaction data (robot trajectories), to develop models capable of understanding, predicting, and planning in the physical world. We first pre-train an action-free joint-embeddin...

ELBO-T2IAlign: A Generic ELBO-Based Method for Calibrating Pixel-level Text-Image Alignment in Diffusion Models

Diffusion models excel at image generation. Recent studies have shown that these models not only generate high-quality images but also encode text-image alignment information through attention maps or loss functions. This information is valuable for various downstream tasks, including segmentation, text-guided image editing, and compositional image generation. However, current methods heavily re...

LPO: Towards Accurate GUI Agent Interaction via Location Preference Optimization

The advent of autonomous agents is transforming interactions with Graphical User Interfaces (GUIs) by employing natural language as a powerful inter...

HunyuanVideo-HOMA: Generic Human-Object Interaction in Multimodal Driven Human Animation

To address key limitations in human-object interaction (HOI) video generation -- specifically the reliance on curated motion data, limited generaliz...

PolyVivid: Vivid Multi-Subject Video Generation with Cross-Modal Interaction and Enhancement

Despite recent advances in video generation, existing models still lack fine-grained controllability, especially for multi-subject customization wit...

PhysiInter: Integrating Physical Mapping for High-Fidelity Human Interaction Generation

Driven by advancements in motion capture and generative artificial intelligence, leveraging large-scale MoCap datasets to train generative models fo...

Heterogeneous Graph Contrastive Learning with Graph Diffusion for Drug Repositioning.

Drug repositioning, which identifies novel therapeutic applications for existing drugs, offers a cost-effective alternative to traditional drug develo...

Deep-Learning-Based Integration of Sequence and Structure Information for Efficiently Predicting miRNA-Drug Associations.

Extensive research has shown that microRNAs (miRNAs) play a crucial role in cancer progression, treatment, and drug resistance. They have been recogni...

Comprehensive Drug-Likeness Prediction Using a Pretrained Transformer Model and Multitask Learning.

Drug-likeness is essential in drug discovery, indicating the potential of a compound to become a successful therapeutic. However, existing rule-based ...

SMVSNN: An Intelligent Framework for Anticancer Drug-Drug Interaction Prediction Utilizing Spiking Multi-view Siamese Neural Networks.

The study of synergistic drug combinations is vital in cancer treatment, enhancing efficacy, reducing resistance, and minimizing side effects through ...

GICL: A Cross-Modal Drug Property Prediction Framework Based on Knowledge Enhancement of Large Language Models.

Deep learning models have demonstrated their potential in learning effective molecular representations critical for drug property prediction and drug ...

RADAR: Recall Augmentation through Deferred Asynchronous Retrieval

Modern large-scale recommender systems employ multi-stage ranking funnel (Retrieval, Pre-ranking, Ranking) to balance engagement and computational c...

Stepwise Decomposition and Dual-stream Focus: A Novel Approach for Training-free Camouflaged Object Segmentation

While promptable segmentation (\textit{e.g.}, SAM) has shown promise for various segmentation tasks, it still requires manual visual prompts for eac...

Zero Shot Composed Image Retrieval

Composed image retrieval (CIR) allows a user to locate a target image by applying a fine-grained textual edit (e.g., ``turn the dress blue'' or ``re...

Predicting ICU In-Hospital Mortality Using Adaptive Transformer Layer Fusion

Early identification of high-risk ICU patients is crucial for directing limited medical resources. We introduce ALFIA (Adaptive Layer Fusion with In...

AUTOCT: Automating Interpretable Clinical Trial Prediction with LLM Agents

Clinical trials are critical for advancing medical treatments but remain prohibitively expensive and time-consuming. Accurate prediction of clinical...

Plant Bioelectric Early Warning Systems: A Five-Year Investigation into Human-Plant Electromagnetic Communication

We present a comprehensive investigation into plant bioelectric responses to human presence and emotional states, building on five years of systemat...

Vocabulary-free few-shot learning for Vision-Language Models

Recent advances in few-shot adaptation for Vision-Language Models (VLMs) have greatly expanded their ability to generalize across tasks using only a...

Adverse Outcome Pathway and Machine Learning to Predict Drug Induced Seizure Liability.

Central nervous system (CNS) drugs have the highest clinical attrition, often due to CNS-related toxicities such as drug-induced seizures (DIS). Early...

Geometric Visual Fusion Graph Neural Networks for Multi-Person Human-Object Interaction Recognition in Videos

Human-Object Interaction (HOI) recognition in videos requires understanding both visual patterns and geometric relationships as they evolve over tim...