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Found 24 papers, 6 papers with code
Tracking Any Point with Frame-Event Fusion Network at High Frame Rate
Tracking any point based on image frames is constrained by frame rates, leading to instability in high-speed scenarios and limited generalization in real-world applications.
Famba-V: Fast Vision Mamba with Cross-Layer Token Fusion
This work introduces Fast Mamba for Vision (Famba-V), a cross-layer token fusion technique to enhance the training efficiency of Vim models.
Benchmarking and Boosting Radiology Report Generation for 3D High-Resolution Medical Images
Automatic radiology report generation can significantly benefit the labor-intensive process of report writing by radiologists, especially for 3D radiographs like CT scans, which are crucial for broad clinical diagnostics yet underexplored compared to 2D radiographs.
A Staged Approach using Machine Learning and Uncertainty Quantification to Predict the Risk of Hip Fracture
Furthermore, the staged model suggested that 54. 49% of patients did not require DXA scanning.
MEIT: Multi-Modal Electrocardiogram Instruction Tuning on Large Language Models for Report Generation
Electrocardiogram (ECG) is the primary non-invasive diagnostic tool for monitoring cardiac conditions and is crucial in assisting clinicians.
Multi-View Variational Autoencoder for Missing Value Imputation in Untargeted Metabolomics
By learning the latent representations of both omics data, our method can effectively impute missing metabolomics values based on genomic information.
FedAIoT: A Federated Learning Benchmark for Artificial Intelligence of Things
However, most existing FL works do not use datasets collected from authentic IoT devices and thus do not capture unique modalities and inherent challenges of IoT data.
Consistency of Lloyd's Algorithm Under Perturbations
In canonical settings with ground truth clusters, we derive bounds for algorithms such as $k$-means$++$ to find good initializations and thus leading to the correctness of clustering via the main result.
Evaluating the Robustness to Instructions of Large Language Models
The focus is on the robustness of instruction-tuned LLMs to seen and unseen tasks.
Somatomotor-Visual Resting State Functional Connectivity Increases After Two Years in the UK Biobank Longitudinal Cohort
Using the full FC and a training set of 2, 000 subjects, one is able to predict which scan is older 82. 5\% of the time using either the full Power264 FC or the UKB-provided ICA-based FC.
Identifiability in Functional Connectivity May Unintentionally Inflate Prediction Results
Using the UK Biobank dataset, we find one can achieve the same level of variance explained with 50 training subjects by exploiting identifiability as with 10, 000 training subjects without double-dipping.
CLCLSA: Cross-omics Linked embedding with Contrastive Learning and Self Attention for multi-omics integration with incomplete multi-omics data
Integration of heterogeneous and high-dimensional multi-omics data is becoming increasingly important in understanding genetic data.
ImageNomer: description of a functional connectivity and omics analysis tool and case study identifying a race confound
The reason is that it is too slow and cumbersome to use a programming interface to create all the necessary visualizations required to identify all correlations, confounding effects, or quality control problems in a dataset.
Multi-view information fusion using multi-view variational autoencoders to predict proximal femoral strength
The aim of this paper is to design a deep learning-based model to predict proximal femoral strength using multi-view information fusion.
A robust kernel machine regression towards biomarker selection in multi-omics datasets of osteoporosis for drug discovery
Many statistical machine approaches could ultimately highlight novel features of the etiology of complex diseases by analyzing multi-omics data.
Frequency-specific segregation and integration of human cerebral cortex: an intrinsic functional atlas
The frequency-specific coupling mechanism of the functional human brain networks underpins its complex cognitive and behavioral functions.
PP-YOLO: An Effective and Efficient Implementation of Object Detector
We mainly try to combine various existing tricks that almost not increase the number of model parameters and FLOPs, to achieve the goal of improving the accuracy of detector as much as possible while ensuring that the speed is almost unchanged.
A Deep Learning-Based Method for Automatic Segmentation of Proximal Femur from Quantitative Computed Tomography Images
During the experiments for the entire cohort then for male and female subjects separately, 90% of the subjects were used in 10-fold cross-validation for training and internal validation, and to select the optimal parameters of the proposed models; the rest of the subjects were used to evaluate the performance of models.
A generalized kernel machine approach to identify higher-order composite effects in multi-view datasets
In this paper, we propose a novel generalized kernel machine approach to identify higher-order composite effects in multi-view biomedical datasets.
Solution for Large-Scale Hierarchical Object Detection Datasets with Incomplete Annotation and Data Imbalance
This report demonstrates our solution for the Open Images 2018 Challenge.
Training Auto-encoders Effectively via Eliminating Task-irrelevant Input Variables
Auto-encoders are often used as building blocks of deep network classifier to learn feature extractors, but task-irrelevant information in the input data may lead to bad extractors and result in poor generalization performance of the network.
An Adaptive Successive Cancellation List Decoder for Polar Codes with Cyclic Redundancy Check
In this letter, we propose an adaptive SC (Successive Cancellation)-List decoder for polar codes with CRC.
Information Theory Information Theory
