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Found 26 papers, 7 papers with code
CIM-NET: A Video Denoising Deep Neural Network Model Optimized for Computing-in-Memory Architectures
While deep neural network (DNN)-based video denoising has demonstrated significant performance, deploying state-of-the-art models on edge devices remains challenging due to stringent real-time and energy efficiency requirements.
Spatially-Heterogeneous Causal Bayesian Networks for Seismic Multi-Hazard Estimation: A Variational Approach with Gaussian Processes and Normalizing Flows
These results highlight the critical importance of modeling spatial heterogeneity in causal mechanisms for accurate disaster assessment, with direct implications for improving emergency response resource allocation.
Multi-resolution Score-Based Variational Graphical Diffusion for Causal Disaster System Modeling and Inference
These systems manifest in both static scenarios with instantaneous causal chains and temporal scenarios with evolving dynamics, complicating modeling efforts.
TextCrafter: Accurately Rendering Multiple Texts in Complex Visual Scenes
This paper explores the task of Complex Visual Text Generation (CVTG), which centers on generating intricate textual content distributed across diverse regions within visual images.
Segment Any-Quality Images with Generative Latent Space Enhancement
To address this, we propose GleSAM, which utilizes Generative Latent space Enhancement to boost robustness on low-quality images, thus enabling generalization across various image qualities.
Less is More: On the Importance of Data Quality for Unit Test Generation
To evaluate its effectiveness, we apply CleanTest on two widely-used test generation datasets, i. e., Methods2Test and Atlas.
Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide
Dynamic predictive modeling using electronic health record (EHR) data has gained significant attention in recent years.
Stability and List-Replicability for Agnostic Learners
As part of the proof of this theorem, we strengthen the celebrated result of Bun et al. by showing that classes with infinite Littlestone dimension are not stably PAC learnable, even if we allow the stability parameter to depend on the excess error.
The Prompt Alchemist: Automated LLM-Tailored Prompt Optimization for Test Case Generation
Test cases are essential for validating the reliability and quality of software applications.
APDDv2: Aesthetics of Paintings and Drawings Dataset with Artist Labeled Scores and Comments
Datasets play a pivotal role in training visual models, facilitating the development of abstract understandings of visual features through diverse image samples and multidimensional attributes.
Why mamba is effective? Exploit Linear Transformer-Mamba Network for Multi-Modality Image Fusion
To solve this problem, we propose a dual-branch image fusion network called Tmamba.
SAM-COD: SAM-guided Unified Framework for Weakly-Supervised Camouflaged Object Detection
Most Camouflaged Object Detection (COD) methods heavily rely on mask annotations, which are time-consuming and labor-intensive to acquire.
Just a Hint: Point-Supervised Camouflaged Object Detection
Camouflaged Object Detection (COD) demands models to expeditiously and accurately distinguish objects which conceal themselves seamlessly in the environment.
missForestPredict -- Missing data imputation for prediction settings
Prediction models are used to predict an outcome based on input variables.
Paintings and Drawings Aesthetics Assessment with Rich Attributes for Various Artistic Categories
This dataset solely comprises overall scores for high-quality artistic images.
Comparison of static and dynamic random forests models for EHR data in the presence of competing risks: predicting central line-associated bloodstream infection
We included data from 27478 admissions to the University Hospitals Leuven, covering 30862 catheter episodes (970 CLABSI, 1466 deaths and 28426 discharges) to build static and dynamic RF models for binary (CLABSI vs no CLABSI), multinomial (CLABSI, discharge, death or no event), survival (time to CLABSI) and competing risks (time to CLABSI, discharge or death) outcomes to predict the 7-day CLABSI risk.
Early detection of disease outbreaks and non-outbreaks using incidence data
In summary, we showed that there are statistical features that distinguish outbreak and non-outbreak sequences long before outbreaks occur.
An early warning indicator trained on stochastic disease-spreading models with different noises
The timely detection of disease outbreaks through reliable early warning signals (EWSs) is indispensable for effective public health mitigation strategies.
Tracking without Label: Unsupervised Multiple Object Tracking via Contrastive Similarity Learning
Unsupervised learning is a challenging task due to the lack of labels.
Left Ventricle Contouring of Apical Three-Chamber Views on 2D Echocardiography
We propose a new method to automatically contour the left ventricle on 2D echocardiographic images.
Direct source and early reflections localization using deep deconvolution network under reverberant environment
This paper proposes a deconvolution-based network (DCNN) model for DOA estimation of direct source and early reflections under reverberant scenarios.
Contrastive Learning for View Classification of Echocardiograms
Analysis of cardiac ultrasound images is commonly performed in routine clinical practice for quantification of cardiac function.
Multi-wavelength and neutrino emission from blazar PKS 1502+106
We find two hadronic models that can describe the multi-wavelength emission during all three states: a leptohadronic model with a contribution from photo-hadronic processes to X-rays and gamma rays, and a proton synchrotron model, where the emission from keV to 10 GeV comes from proton synchrotron radiation.
High Energy Astrophysical Phenomena
A Graphical Social Topology Model for Multi-Object Tracking
Inspired with the social affinity property of moving objects, we propose a Graphical Social Topology (GST) model, which estimates the group dynamics by jointly modeling the group structure and the states of objects using a topological representation.
