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Found 34 papers, 7 papers with code
Stock assessment for the western winter-spring cohort of neon flying squid (Ommastrephes bartramii) using environmentally dependent surplus production models
Because this squid has a short lifespan and is an ecological opportunist, the dynamics of its stock is greatly influenced by the environmental conditions, which need to be considered in its assessment and management.
Factorized Q-Learning for Large-Scale Multi-Agent Systems
Deep Q-learning has achieved a significant success in single-agent decision making tasks.
Multiagent Systems
A Self-Organizing Tensor Architecture for Multi-View Clustering
In many real-world applications, data are often unlabeled and comprised of different representations/views which often provide information complementary to each other.
Spike-and-Slab Group Lassos for Grouped Regression and Sparse Generalized Additive Models
We introduce the spike-and-slab group lasso (SSGL) for Bayesian estimation and variable selection in linear regression with grouped variables.
Methodology
Exploring difference in public perceptions on HPV vaccine between gender groups from Twitter using deep learning
In this study, we proposed a convolutional neural network model for gender prediction using English Twitter text as input.
Data Centers Job Scheduling with Deep Reinforcement Learning
Efficient job scheduling on data centers under heterogeneous complexity is crucial but challenging since it involves the allocation of multi-dimensional resources over time and space.
Regularized Non-negative Spectral Embedding for Clustering
Spectral Clustering is a popular technique to split data points into groups, especially for complex datasets.
Deciphering hierarchical organization of topologically associated domains through change-point testing
Background: The nucleus of eukaryotic cells spatially packages chromosomes into a hierarchical and distinct segregation that plays critical roles in maintaining transcription regulation.
Applications Methodology
Hyperspectral Super-Resolution via Coupled Tensor Ring Factorization
In this paper, we propose a new model, named coupled tensor ring factorization (CTRF), for HSR.
Vision-Based Fall Event Detection in Complex Background Using Attention Guided Bi-directional LSTM
Fall event detection, as one of the greatest risks to the elderly, has been a hot research issue in the solitary scene in recent years.
Woodpecker-DL: Accelerating Deep Neural Networks via Hardware-Aware Multifaceted Optimizations
Accelerating deep model training and inference is crucial in practice.
Efficient Robust Watermarking Based on Quaternion Singular Value Decomposition and Coefficient Pair Selection
In this way, compared with conventional QSVD, the proposed watermarking strategy avoids more modifications to a single color image layer and a better visual quality of the watermarked image is observed.
Combining Cox Regressions Across a Heterogeneous Distributed Research Network Facing Small and Zero Counts
Studies of the effects of medical interventions increasingly take place in distributed research settings using data from multiple clinical data sources including electronic health records and administrative claims.
Methodology
Solving localized wave solutions of the derivative nonlinear Schrodinger equation using an improved PINN method
On the bases of the improved method, the effects for different numbers of initial points sampled, residual collocation points sampled, network layers, neurons per hidden layer on the second order genuine rational soliton solution dynamics of the DNLS are considered, and the relevant analysis when the locally adaptive activation function chooses different initial values of scalable parameters are also exhibited in the simulation of the two-order rogue wave solution.
Pattern Formation and Solitons Exactly Solvable and Integrable Systems
Federated Multi-View Learning for Private Medical Data Integration and Analysis
This paper aims to provide a generic Federated Multi-View Learning (FedMV) framework for multi-view data leakage prevention, which is based on different types of local data availability and enables to accommodate two types of problems: Vertical Federated Multi-View Learning (V-FedMV) and Horizontal Federated Multi-View Learning (H-FedMV).
The Federated Tumor Segmentation (FeTS) Challenge
The goals of the FeTS challenge are directly represented by the two included tasks: 1) the identification of the optimal weight aggregation approach towards the training of a consensus model that has gained knowledge via federated learning from multiple geographically distinct institutions, while their data are always retained within each institution, and 2) the federated evaluation of the generalizability of brain tumor segmentation models "in the wild", i. e. on data from institutional distributions that were not part of the training datasets.
Real-Time Mapping of Tissue Properties for Magnetic Resonance Fingerprinting
Magnetic resonance Fingerprinting (MRF) is a relatively new multi-parametric quantitative imaging method that involves a two-step process: (i) reconstructing a series of time frames from highly-undersampled non-Cartesian spiral k-space data and (ii) pattern matching using the time frames to infer tissue properties (e. g., T1 and T2 relaxation times).
Multiplex Graph Networks for Multimodal Brain Network Analysis
In this paper, we propose MGNet, a simple and effective multiplex graph convolutional network (GCN) model for multimodal brain network analysis.
Federated Learning Algorithms for Generalized Mixed-effects Model (GLMM) on Horizontally Partitioned Data from Distributed Sources
Objectives: This paper develops two algorithms to achieve federated generalized linear mixed effect models (GLMM), and compares the developed model's outcomes with each other, as well as that from the standard R package (`lme4').
Mining On Alzheimer's Diseases Related Knowledge Graph to Identity Potential AD-related Semantic Triples for Drug Repurposing
The 1, 672, 110 filtered triples were used to train with knowledge graph completion algorithms (i. e., TransE, DistMult, and ComplEx) to predict candidates that might be helpful for AD treatment or prevention.
Two-stage Hypothesis Tests for Variable Interactions with FDR Control
In many scenarios such as genome-wide association studies where dependences between variables commonly exist, it is often of interest to infer the interaction effects in the model.
A Survey on Knowledge-Enhanced Pre-trained Language Models
Natural Language Processing (NLP) has been revolutionized by the use of Pre-trained Language Models (PLMs) such as BERT.
TegFormer: Topic-to-Essay Generation with Good Topic Coverage and High Text Coherence
Creating an essay based on a few given topics is a challenging NLP task.
Beyond Graph Convolutional Network: An Interpretable Regularizer-centered Optimization Framework
Graph convolutional networks (GCNs) have been attracting widespread attentions due to their encouraging performance and powerful generalizations.
FedScore: A privacy-preserving framework for federated scoring system development
We also calculated the average AUC values and SDs for each local model, and the FedScore model showed promising accuracy and stability with a high average AUC value which was closest to the one of the pooled model and SD which was lower than that of most local models.
Scene Graph Based Fusion Network For Image-Text Retrieval
A critical challenge to image-text retrieval is how to learn accurate correspondences between images and texts.
PerCoNet: News Recommendation with Explicit Persona and Contrastive Learning
Personalized news recommender systems help users quickly find content of their interests from the sea of information.
Patchwork Learning: A Paradigm Towards Integrative Analysis across Diverse Biomedical Data Sources
By offering a more comprehensive approach to healthcare data integration, patchwork learning has the potential to revolutionize the clinical applicability of ML models.
BiomedGPT: A Unified and Generalist Biomedical Generative Pre-trained Transformer for Vision, Language, and Multimodal Tasks
Conventional task- and modality-specific artificial intelligence (AI) models are inflexible in real-world deployment and maintenance for biomedicine.
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Leveraging Generative AI for Clinical Evidence Summarization Needs to Ensure Trustworthiness
Evidence-based medicine promises to improve the quality of healthcare by empowering medical decisions and practices with the best available evidence.
Towards Architecture-Agnostic Untrained Network Priors for Image Reconstruction with Frequency Regularization
We show that even with just one extra line of code, the overfitting issues in underperforming architectures can be alleviated such that their performance gaps with the high-performing counterparts can be largely closed despite their distinct configurations, mitigating the need for architecture tuning.
Large Model based Sequential Keyframe Extraction for Video Summarization
Keyframe extraction aims to sum up a video's semantics with the minimum number of its frames.
TrustLLM: Trustworthiness in Large Language Models
This paper introduces TrustLLM, a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions.
GraphAD: Interaction Scene Graph for End-to-end Autonomous Driving
With the representation of the ISG, the driving agents aggregate essential information from the most influential elements, including the road agents with potential collisions and the map elements to follow.
