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Found 28 papers, 8 papers with code
ViPOcc: Leveraging Visual Priors from Vision Foundation Models for Single-View 3D Occupancy Prediction
The recovered metric depth is then utilized in temporal photometric alignment and spatial geometric alignment to ensure accurate and consistent 3D occupancy prediction.
Regulator-Manufacturer AI Agents Modeling: Mathematical Feedback-Driven Multi-Agent LLM Framework
The increasing complexity of regulatory updates from global authorities presents significant challenges for medical device manufacturers, necessitating agile strategies to sustain compliance and maintain market access.
The Use of Readability Metrics in Legal Text: A Systematic Literature Review
From the analysis, it is clear that not all legal domains are well represented in terms of readability metrics and that there is a further need to develop more consensus on which metrics should be applied for legal documents.
Data-Driven Analysis of AI in Medical Device Software in China: Deep Learning and General AI Trends Based on Regulatory Data
Artificial intelligence (AI) in medical device software (MDSW) represents a transformative clinical technology, attracting increasing attention within both the medical community and the regulators.
Foundation Models in Electrocardiogram: A Review
The electrocardiogram (ECG) is ubiquitous across various healthcare domains, such as cardiac arrhythmia detection and sleep monitoring, making ECG analysis critically essential.
aiXcoder-7B: A Lightweight and Effective Large Language Model for Code Completion
In this paper, we propose a lightweight and effective LLM for code completion named aiXcoder-7B.
Q-value Regularized Decision ConvFormer for Offline Reinforcement Learning
However, due to the inconsistency between the sampled returns within a single trajectory and the optimal returns across multiple trajectories, it is challenging to set an expected return to output the optimal action and stitch together suboptimal trajectories.
Decision Transformer for IRS-Assisted Systems with Diffusion-Driven Generative Channels
In this paper, we propose a novel diffusion-decision transformer (D2T) architecture to optimize the beamforming strategies for intelligent reflecting surface (IRS)-assisted multiple-input single-output (MISO) communication systems.
Generalized Correspondence Matching via Flexible Hierarchical Refinement and Patch Descriptor Distillation
Taking into account the practical applicability of our method in real-world robotics applications, we also propose a novel patch descriptor distillation strategy to further reduce the computational complexity of correspondence matching.
Mixture-of-LoRAs: An Efficient Multitask Tuning for Large Language Models
These LoRA modules can be aligned with the expert design principles observed in Mixture-of-Experts (MoE).
CycLight: learning traffic signal cooperation with a cycle-level strategy
This study introduces CycLight, a novel cycle-level deep reinforcement learning (RL) approach for network-level adaptive traffic signal control (NATSC) systems.
Revolutionizing Pharma: Unveiling the AI and LLM Trends in the Pharmaceutical Industry
This document offers a critical overview of the emerging trends and significant advancements in artificial intelligence (AI) within the pharmaceutical industry.
Uncovering Regulatory Affairs Complexity in Medical Products: A Qualitative Assessment Utilizing Open Coding and Natural Language Processing (NLP)
This study investigates the complexity of regulatory affairs in the medical device industry, a critical factor influencing market access and patient care.
Adaptive Training Meets Progressive Scaling: Elevating Efficiency in Diffusion Models
Diffusion models have demonstrated remarkable efficacy in various generative tasks with the predictive prowess of denoising model.
Contrast Everything: A Hierarchical Contrastive Framework for Medical Time-Series
The results demonstrate that COMET consistently outperforms all baselines, particularly in setup with 10% and 1% labeled data fractions across all datasets.
Qwen Technical Report
Large language models (LLMs) have revolutionized the field of artificial intelligence, enabling natural language processing tasks that were previously thought to be exclusive to humans.
Ranked #3 on
Multi-Label Text Classification
on CC3M-TagMask
Perimeter Control with Heterogeneous Metering Rates for Cordon Signals: A Physics-Regularized Multi-Agent Reinforcement Learning Approach
PC strategies with heterogeneous metering rates for cordon signals allow precise control for the perimeter but the complexity of the problem increases exponentially with the scale of the PN.
Multi-modal Learning based Prediction for Disease
From the dataset, we quantitatively analyze and select clinical metadata that most contribute to NAFLD prediction.
OFASys: A Multi-Modal Multi-Task Learning System for Building Generalist Models
As a starting point, we provide presets of 7 different modalities and 23 highly-diverse example tasks in OFASys, with which we also develop a first-in-kind, single model, OFA+, that can handle text, image, speech, video, and motion data.
The Application of Zig-Zag Sampler in Sequential Markov Chain Monte Carlo
Particle filtering methods are widely applied in sequential state estimation within nonlinear non-Gaussian state space model.
On Channel Reciprocity in Reconfigurable Intelligent Surface Assisted Wireless Network
Channel reciprocity greatly facilitates downlink precoding in time-division duplexing (TDD) multiple-input multiple-output (MIMO) communications without the need for channel state information (CSI) feedback.
Information Theory Information Theory
A self-adaptive and robust fission clustering algorithm via heat diffusion and maximal turning angle
The RFC and the self-adaptive noise identification method are combine to propose a self-adaptive robust fission clustering (SARFC) algorithm.
Path Loss Modeling and Measurements for Reconfigurable Intelligent Surfaces in the Millimeter-Wave Frequency Band
The refined model gives more accurate estimates of the path loss of RISs comprised of unit cells with a deep sub-wavelength size.
Graph Random Neural Networks for Semi-Supervised Learning on Graphs
We study the problem of semi-supervised learning on graphs, for which graph neural networks (GNNs) have been extensively explored.
From Design Draft to Real Attire: Unaligned Fashion Image Translation
Moreover, built upon the sampling network, we present design draft to real fashion item translation network (D2RNet), where two separate translation streams that focus on texture and shape, respectively, are combined tactfully to get both benefits.
Graph Random Neural Network for Semi-Supervised Learning on Graphs
We study the problem of semi-supervised learning on graphs, for which graph neural networks (GNNs) have been extensively explored.
Wireless Communications with Reconfigurable Intelligent Surface: Path Loss Modeling and Experimental Measurement
The measurement results match well with the modeling results, thus validating the proposed free-space path loss models for RIS, which may pave the way for further theoretical studies and practical applications in this field.
Physical Layer Security Enhancement Exploiting Intelligent Reflecting Surface
In this letter, the use of intelligent reflecting surface (IRS) to enhance the physical layer security of downlink wireless communication is investigated.
