Wang Cheng
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View article: Utilising bioinformatics and molecular docking technology to explore the underlying mechanisms of intervertebral disc degeneration with potential therapeutic drugs and formulas
Utilising bioinformatics and molecular docking technology to explore the underlying mechanisms of intervertebral disc degeneration with potential therapeutic drugs and formulas Open
This study identified CYP1B1 and TNFAIP6 as important targets for IDD, developed a predictive nomogram, and explored the application of TCM herbal formulae, providing new insights into the clinical treatment and prescription development of…
View article: Utilising bioinformatics and molecular docking technology to explore the underlying mechanisms of intervertebral disc degeneration with potential therapeutic drugs and formulas
Utilising bioinformatics and molecular docking technology to explore the underlying mechanisms of intervertebral disc degeneration with potential therapeutic drugs and formulas Open
This study identified CYP1B1 and TNFAIP6 as important targets for IDD, developed a predictive nomogram, and explored the application of TCM herbal formulae, providing new insights into the clinical treatment and prescription development of…
View article: An Efficient Unconditionally Energy-Stable Numerical Scheme for Bose--Einstein Condensate
An Efficient Unconditionally Energy-Stable Numerical Scheme for Bose--Einstein Condensate Open
A numerical framework is proposed and analyzed for computing the ground state of Bose--Einstein condensates. A gradient flow approach is developed, incorporating both a Lagrange multiplier to enforce the $L^2$ conservation and a free energ…
View article: An Efficient Unconditionally Energy-Stable Numerical Scheme for Bose--Einstein Condensate
An Efficient Unconditionally Energy-Stable Numerical Scheme for Bose--Einstein Condensate Open
A numerical framework is proposed and analyzed for computing the ground state of Bose--Einstein condensates. A gradient flow approach is developed, incorporating both a Lagrange multiplier to enforce the $L^2$ conservation and a free energ…
View article: An Efficient Unconditionally Energy-Stable Numerical Scheme for Bose--Einstein Condensate
An Efficient Unconditionally Energy-Stable Numerical Scheme for Bose--Einstein Condensate Open
A numerical framework is proposed and analyzed for computing the ground state of Bose--Einstein condensates. A gradient flow approach is developed, incorporating both a Lagrange multiplier to enforce the $L^2$ conservation and a free energ…
View article: Enhancing Micromagnetics Simulations with a Third-Order Semi-Implicit Projection Method
Enhancing Micromagnetics Simulations with a Third-Order Semi-Implicit Projection Method Open
Micromagnetics depends on high-fidelity numerical methods for magnetization dynamics. This work proposes a third-order temporal accuracy scheme for the Landau-Lifshitz-Gilbert equation, addressing accuracy-efficiency trade-offs in existing…
View article: Enhancing Micromagnetics Simulations with a Third-Order Semi-Implicit Projection Method
Enhancing Micromagnetics Simulations with a Third-Order Semi-Implicit Projection Method Open
Micromagnetics depends on high-fidelity numerical methods for magnetization dynamics. This work proposes a third-order temporal accuracy scheme for the Landau-Lifshitz-Gilbert equation, addressing accuracy-efficiency trade-offs in existing…
View article: Enhancing Micromagnetics Simulations with a Third-Order Semi-Implicit Projection Method
Enhancing Micromagnetics Simulations with a Third-Order Semi-Implicit Projection Method Open
Micromagnetics depends on high-fidelity numerical methods for magnetization dynamics. This work proposes a third-order temporal accuracy scheme for the Landau-Lifshitz-Gilbert equation, addressing accuracy-efficiency trade-offs in existing…
View article: Improve Contrastive Clustering Performance by Multiple Fusing-Augmenting ViT Blocks
Improve Contrastive Clustering Performance by Multiple Fusing-Augmenting ViT Blocks Open
In the field of image clustering, the widely used contrastive learning networks improve clustering performance by maximizing the similarity between positive pairs and the dissimilarity of negative pairs of the inputs. Extant contrastive le…
View article: Convergence analysis of a third order semi-implicit projection method for Landau-Lifshitz-Gilbert equation
Convergence analysis of a third order semi-implicit projection method for Landau-Lifshitz-Gilbert equation Open
The convergence analysis of a third-order scheme for the highly nonlinear Landau-Lifshitz-Gilbert equation with a non-convex constraint is considered. In this paper, we first present a fully discrete semi-implicit method for solving the La…
View article: Convergence analysis of a third order semi-implicit projection method for Landau-Lifshitz-Gilbert equation
Convergence analysis of a third order semi-implicit projection method for Landau-Lifshitz-Gilbert equation Open
The convergence analysis of a third-order scheme for the highly nonlinear Landau-Lifshitz-Gilbert equation with a non-convex constraint is considered. In this paper, we first present a fully discrete semi-implicit method for solving the La…
View article: Improve Contrastive Clustering Performance by Multiple Fusing-Augmenting ViT Blocks
Improve Contrastive Clustering Performance by Multiple Fusing-Augmenting ViT Blocks Open
In the field of image clustering, the widely used contrastive learning networks improve clustering performance by maximizing the similarity between positive pairs and the dissimilarity of negative pairs of the inputs. Extant contrastive le…
View article: Quantitative Identification of High-Risk Tricuspid Regurgitation by Cardiac Magnetic Resonance.
Quantitative Identification of High-Risk Tricuspid Regurgitation by Cardiac Magnetic Resonance. Open
Background The role of cardiac magnetic resonance (CMR) quantification of tricuspid regurgitation (TR) to identify high-risk patients with TR remains poorly defined. The aim of this study was to assess the prognostic relevance of TR quanti…
View article: Convergence analysis of a third order semi-implicit projection method for Landau-Lifshitz-Gilbert equation
Convergence analysis of a third order semi-implicit projection method for Landau-Lifshitz-Gilbert equation Open
The convergence analysis of a third-order scheme for the highly nonlinear Landau-Lifshitz-Gilbert equation with a non-convex constraint is considered. In this paper, we first present a fully discrete semi-implicit method for solving the La…
View article: Improve Contrastive Clustering Performance by Multiple Fusing-Augmenting ViT Blocks
Improve Contrastive Clustering Performance by Multiple Fusing-Augmenting ViT Blocks Open
In the field of image clustering, the widely used contrastive learning networks improve clustering performance by maximizing the similarity between positive pairs and the dissimilarity of negative pairs of the inputs. Extant contrastive le…
View article: FP8-Flow-MoE: A Casting-Free FP8 Recipe without Double Quantization Error
FP8-Flow-MoE: A Casting-Free FP8 Recipe without Double Quantization Error Open
Training large Mixture-of-Experts (MoE) models remains computationally prohibitive due to their extreme compute and memory demands. Although low-precision training promises to accelerate computation and reduce memory footprint, existing im…
View article: FP8-Flow-MoE: A Casting-Free FP8 Recipe without Double Quantization Error
FP8-Flow-MoE: A Casting-Free FP8 Recipe without Double Quantization Error Open
Training large Mixture-of-Experts (MoE) models remains computationally prohibitive due to their extreme compute and memory demands. Although low-precision training promises to accelerate computation and reduce memory footprint, existing im…
View article: FP8-Flow-MoE: A Casting-Free FP8 Recipe without Double Quantization Error
FP8-Flow-MoE: A Casting-Free FP8 Recipe without Double Quantization Error Open
Training large Mixture-of-Experts (MoE) models remains computationally prohibitive due to their extreme compute and memory demands. Although low-precision training promises to accelerate computation and reduce memory footprint, existing im…
View article: Convergence analysis of positivity-preserving finite difference scheme for the Flory-Huggins-Cahn-Hilliard equation with dynamical boundary condition
Convergence analysis of positivity-preserving finite difference scheme for the Flory-Huggins-Cahn-Hilliard equation with dynamical boundary condition Open
The Cahn-Hilliard equation has a wide range of applications in many areas of physics and chemistry. To describe the short-range interaction between the solution and the boundary, scientists have constructed dynamical boundary conditions by…
View article: Convergence analysis of positivity-preserving finite difference scheme for the Flory-Huggins-Cahn-Hilliard equation with dynamical boundary condition
Convergence analysis of positivity-preserving finite difference scheme for the Flory-Huggins-Cahn-Hilliard equation with dynamical boundary condition Open
The Cahn-Hilliard equation has a wide range of applications in many areas of physics and chemistry. To describe the short-range interaction between the solution and the boundary, scientists have constructed dynamical boundary conditions by…
View article: OmniBrainBench: A Comprehensive Multimodal Benchmark for Brain Imaging Analysis Across Multi-stage Clinical Tasks
OmniBrainBench: A Comprehensive Multimodal Benchmark for Brain Imaging Analysis Across Multi-stage Clinical Tasks Open
Brain imaging analysis is vital for diagnosing and treating brain disorders, and multimodal large language models (MLLMs) are increasingly assisting in that analysis. However, current brain-oriented visual question-answering (VQA) benchmar…
View article: OmniBrainBench: A Comprehensive Multimodal Benchmark for Brain Imaging Analysis Across Multi-stage Clinical Tasks
OmniBrainBench: A Comprehensive Multimodal Benchmark for Brain Imaging Analysis Across Multi-stage Clinical Tasks Open
Brain imaging analysis is vital for diagnosing and treating brain disorders, and multimodal large language models (MLLMs) are increasingly assisting in that analysis. However, current brain-oriented visual question-answering (VQA) benchmar…
View article: OmniBrainBench: A Comprehensive Multimodal Benchmark for Brain Imaging Analysis Across Multi-stage Clinical Tasks
OmniBrainBench: A Comprehensive Multimodal Benchmark for Brain Imaging Analysis Across Multi-stage Clinical Tasks Open
Brain imaging analysis is vital for diagnosing and treating brain disorders, and multimodal large language models (MLLMs) are increasingly assisting in that analysis. However, current brain-oriented visual question-answering (VQA) benchmar…
View article: When Audio and Text Disagree: Revealing Text Bias in Large Audio-Language Models
When Audio and Text Disagree: Revealing Text Bias in Large Audio-Language Models Open
Large Audio-Language Models (LALMs) are augmented with the ability to perceive audio, demonstrating impressive capabilities in processing combined audio and text signals. However, their reliability when faced with conflicting inputs across…
View article: Fostering cultural change in research through innovative knowledge sharing, evaluation, and community engagement strategies
Fostering cultural change in research through innovative knowledge sharing, evaluation, and community engagement strategies Open
Scientific research needs a new system that appropriately values science and scientists. Key innovations, within institutions and funding agencies, are driving better assessment of research, with open knowledge and FAIR (findable, accessib…
View article: False Sense of Security: Why Probing-based Malicious Input Detection Fails to Generalize
False Sense of Security: Why Probing-based Malicious Input Detection Fails to Generalize Open
Large Language Models (LLMs) can comply with harmful instructions, raising serious safety concerns despite their impressive capabilities. Recent work has leveraged probing-based approaches to study the separability of malicious and benign …
View article: Mirage or Method? How Model-Task Alignment Induces Divergent RL Conclusions
Mirage or Method? How Model-Task Alignment Induces Divergent RL Conclusions Open
Recent advances in applying reinforcement learning (RL) to large language models (LLMs) have led to substantial progress. In particular, a series of remarkable yet often counterintuitive phenomena have been reported in LLMs, exhibiting pat…
View article: SWE-Dev: Evaluating and Training Autonomous Feature-Driven Software Development
SWE-Dev: Evaluating and Training Autonomous Feature-Driven Software Development Open
Large Language Models (LLMs) have shown strong capability in diverse software engineering tasks, e.g. code completion, bug fixing, and document generation. However, feature-driven development (FDD), a highly prevalent real-world task that …
View article: Threshold-less and Flexibly Tunable Frequency Comb via Floquet Engineering
Threshold-less and Flexibly Tunable Frequency Comb via Floquet Engineering Open
Frequency combs have revolutionized communication, metrology and spectroscopy. Numerous efforts have been dedicated to developing integrated combs, predominantly relying on Pockels or Kerr mechanisms. In this work, we propose and demonstra…
View article: Experimental study of velocity statistics in wall-bounded turbulent emulsions
Experimental study of velocity statistics in wall-bounded turbulent emulsions Open
Turbulent emulsions are ubiquitous in chemical engineering, food processing, pharmaceuticals, and other fields. However, our experimental understanding of this area remains limited due to the multi-scale nature of turbulent flow and the pr…