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Found 7 papers, 1 papers with code
Towards Medical Machine Reading Comprehension with Structural Knowledge and Plain Text
Machine reading comprehension (MRC) has achieved significant progress on the open domain in recent years, mainly due to large-scale pre-trained language models.
Exploring the Potential of Large Language Models in Artistic Creation: Collaboration and Reflection on Creative Programming
Recently, the potential of large language models (LLMs) has been widely used in assisting programming.
VR PreM+ : An Immersive Pre-learning Branching Visualization System for Museum Tours
We present VR PreM+, an innovative VR system designed to enhance web exploration beyond traditional computer screens.
Towards AI-Architecture Liberty: A Comprehensive Survey on Designing and Collaborating Virtual Architecture by Deep Learning in the Metaverse
This survey investigates the latest approaches to 3D object generation with deep generative models (DGMs) and summarizes four characteristics of deep-learning generation approaches for virtual architecture.
Infrared and visible image fusion based on Multi-State Contextual Hidden Markov Model
The traditional two-state hidden Markov model divides the high frequency coefficients only into two states (large and small states).
EndHiC: assemble large contigs into chromosomal-level scaffolds using the Hi-C links from contig ends
As the Hi-C links of two adjacent contigs concentrate only at the neighbor ends of the contigs, larger contig size will reduce the power to differentiate adjacent (signal) and non-adjacent (noise) contig linkages, leading to a higher rate of mis-assembly.
A Two-Sample Robust Bayesian Mendelian Randomization Method Accounting for Linkage Disequilibrium and Idiosyncratic Pleiotropy with Applications to the COVID-19 Outcome
Mendelian randomization (MR) is a statistical method exploiting genetic variants as instrumental variables to estimate the causal effect of modifiable risk factors on an outcome of interest.
Methodology
