The paper learns pose variations within shape populations using constrained mixtures of factor analyzers.
problem Learning pose variations within a shape population with articulated parts and relative rotations.
method Formulated as mixtures of factor analyzers, segmentation by component posterior probabilities, and constraints on factor loading matrices for rotation matrices.
result Automatic learning of pose variations from shape populations, resulting in smooth and realistic animations.
Efficient algorithm for conditional linear regression.
problem Building models that accurately predict a specific segment of data.
method Efficient algorithm for identifying a significant segment of the population and its linear regression fit.
result Theoretical analysis and efficient algorithm for conditional linear regression.
The paper tackles identifying a small segment of a population with sparse linear regression.
problem Identifying a small segment of a population with a sparse linear regression fit.
method Algorithms for joint identification of a significant segment of a population with a sparse linear regression fit under the sup norm, using k-DNF conditions and s-sparse regression fits.
result Preliminary algorithms and challenges for future work in non-sparse regression and expected error.
Automatically computes reference ranges for UK Biobank cardiac data.
problem Improving healthcare by discovering patterns in large-scale population data.
method Fully automatic pipeline for 3D cardiac MR image analysis.
result Statistically significant agreement between manual and automatic indexes.
The paper proposes a mixture model with segmentation for heterogeneous functional data.
problem Heterogeneity in time and population for functional data.
method Mixture model with segmentation of time, maximum likelihood estimator, EM algorithm with dynamic programming.
result The method is consistent and identifiable, and illustrated on simulated and real datasets.
Novel framework monitors cardiac image segmentation models in real-time.
problem Ensuring continuous high model performance and segmentation results in clinics.
method Formulated as anomaly detection, the framework derives surrogate quality measures for segmentation.
result Demonstrated accurate, fast, and scalable quality control monitoring.
Income and wealth distribution affect stability of a society to a large extent and high inequality affects it negatively. Moreover, in the case of developed countries, recently has been proven that inequality is closely related to all negative phenomena affecting society. So far, Econophysics papers tried to analyse in…
Deep learning method segments and monitors slums from satellite imagery.
problem Slum rehabilitation and improvement in developing countries.
method Regional convolutional neural networks for instance segmentation using transfer learning.
result Maximum AP of 80.0 for slum shape and appearance learning.
Embed-KCPD segments text without labels, outperforming baselines.
problem Unsupervised text segmentation is challenging due to lack of boundary labels.
method Embed-KCPD represents sentences as embeddings and estimates boundaries via penalized KCPD.
result Embed-KCPD outperforms strong unsupervised baselines across benchmarks.
Deep learning predicts AF recurrence from MRI images without pre-processing.
problem Estimating AF recurrence from MRI images efficiently and accurately.
method Deep learning approach to predict shape descriptors directly from MRI images.
result Deep learning method produces similar outcomes to state-of-the-art methods.
Method improves model performance on segments with local distribution shifts.
problem Improving model generalization across multiple data segments with local distribution differences.
method Two-stage multiply robust estimation method for tabular data analysis.
result Significantly improves prediction accuracy and robustness on regression and classification tasks.
A new technique segments customers from diverse data, improving recommendation accuracy.
problem Segmenting customers with diverse, incomplete preference data.
method Model-based projection technique to transform and cluster data.
result Asymptotic recovery of true customer segments with theoretical guarantees.
ObSuRF converts a single image into a 3D model with NeRFs.
problem Creating a 3D model from a single image with object segmentation.
method Unsupervised volume segmentation using Neural Radiance Fields (NeRFs).
result ObSuRF can segment a 3D scene into objects from a single image.
Study uses data to analyze COPD patients' impact on hospital systems.
problem Understanding and quantifying resource requirements for COPD patients.
method Combines segmentation, queuing theory, and data recovery techniques.
result Finding useful operational results from incomplete administrative data.
A novel method for estimating group-representative functional networks from multi-subject fMRI data.
problem Estimating common neuronal characteristics in a population from multi-subject fMRI data.
method Two-phase approach: clustering-based ICA for component maps, MAP-MRF labeling for group-representative map estimation.
result Demonstrated the viability of the proposed method in extracting group-representative functional networks from simulated fMRI data.
The 20/60/20 rule improves risk management and portfolio optimization in finance.
problem Understanding and managing financial data with heavy tails.
method Application of the 20/60/20 rule to stock market data, development of new measures for tail heaviness, and integration into portfolio optimization.
result The 20/60/20 rule enhances robustness and performance in portfolio optimization.
This paper distills a complex travel mode choice model into simpler, interpretable models.
problem Lack of interpretability in complex machine learning models for travel behavior.
method Model distillation combined with market segmentation.
result Generated interpretable models that closely match the predictions of the original complex model.
This work analyzes how users and services adapt to reduce risk, leading to specialization.
problem Adaptation of users and services to reduce risk affects learning and performance.
method Analyzed a class of dynamics where users allocate participation and services update parameters.
result Repeated myopic updates with multiple learners lead to better outcomes than repeated risk minimization.
Automated process links oral health to systemic conditions using machine learning.
problem Correlating oral health with systemic health conditions.
method Intraoral fluorescent biomarker imaging, machine learning segmentation, and clinical examination.
result Machine learning classifier achieved AUC of 0.677, indicating a learned association between disease signatures in images and periodontal disease.
Loyalty emerges in markets through repeated interaction and adaptation.
problem Can loyalty arise spontaneously in markets?
method Stylized model of double auction markets with adaptive traders.
result Segregated market states are stable and provide higher rewards.
Machine learning detects subhalos in lensed images with high accuracy and low false positives.
problem Detecting substructure in strongly lensed images.
method Developed a neural network for image segmentation to locate and mass estimate subhalos.
result The network can detect subhalos with masses m≳108.5M⊙ and measure the subhalo mass function. Model sequences through their segmentations using dynamic programming.
problem Modeling sequences with unknown segmentations.
method Probabilistic model based on segmentations, using existing tools for segments, summing over all valid segmentations, dynamic programming for efficient computation.
result Demonstrated on text and speech recognition, discovering meaningful segments.
DeepMRSeg uses deep learning for brain tissue segmentation.
problem Automated segmentation of brain tissues and abnormalities.
method Modified UNet architecture with multi-scale feature extraction.
result DeepMRSeg achieves high accuracy on various brain segmentation tasks.
Bayesian nonparametric method segments multi-sequence time series data.
problem Temporal segmentation of multi-sequence time series data into stationary segments.
method Gaussian process priors and nonparametric distribution for segment partitioning.
result Model effectively segments synthetic and real-time series data.
Proposes guided networks for fast, accurate few-shot segmentation.
problem Few supervision limits learning-based segmentation methods.
method Extracts latent task representation from supervision, optimizes end-to-end.
result First results for segmentation from one pixel per concept, real-time interactive video segmentation.
The paper develops a method for forecasting power consumption at various levels of aggregation.
problem Forecasting power consumption at different levels of household aggregation.
method Three-step process: feature generation, aggregation, and projection.
result The method provides theoretical guarantees on prediction error and performs well on real data.
BiPE blends intra-segment and inter-segment encodings for better length extrapolation.
problem Improving length extrapolation in language models.
method Bilevel Positional Encoding (BiPE) that separates intra-segment and inter-segment encodings.
result BiPE enhances length extrapolation across various text modalities.
CNNs can be trained with clinically available segmentations for OARs in radiotherapy.
problem Lack of dedicated training volumes for CNNs in radiotherapy.
method Used clinically available segmentations from PACS, applied multi-label segmentation, empirically assessed training set size.
result Clinically available segmentations can be used to train an accurate OAR segmentation model.
Novel framework provides statistical significance for image segmentation results.
problem Evaluating the reliability of individual image segmentation results.
method Selective inference to account for segmentation bias in p-value computation.
result Valid p-values for segmentation results, accounting for bias.
A hierarchical segmentation method for images with weak supervision.
problem Weakly supervised image segmentation.
method Flexible hierarchical segmentation considering prior spatial information.
result Enhanced segmentation of regions of interest while preserving important structures.
QANet estimates image segmentation quality without human inspection.
problem Quantitative evaluation of image segmentation quality.
method QANet is a Quality Assurance Network that solves a regression problem to estimate quality measures.
result QANet accurately estimates segmentation quality with respect to ground truth.
Paper reduces Hausdorff Distance in medical image segmentation.
problem Reduction of Hausdorff Distance in medical image segmentation.
method Three novel loss functions for training CNNs to estimate and reduce HD.
result Approximately 18-45% reduction in HD without degrading other performance metrics.
Develops deep learning for logical code segmentation.
problem Lack of logically segmented source code.
method Novel deep learning approach to generate logical code segments.
result Improves software analysis tasks like commenting, bug detection, and code synthesis.
A framework uses complex networks for image segmentation.
problem Over-segmentation in image segmentation.
method Initial segmentation, adaptive network construction, community detection.
result The proposed framework improves segmentation performance.
Hybrid CNN improves segmentation and registration of white matter tracts.
problem Accurate analysis of longitudinal brain imaging data.
method A hybrid CNN integrating segmentation and registration into a single procedure.
result Hybrid CNN outperforms multistage pipelines in segmentation accuracy, consistency, and speed.
UNet++ improves medical image segmentation with deep supervision.
problem Improving accuracy in medical image segmentation.
method Nested U-Net architecture with deep supervision.
result UNet++ achieves significant improvements in IoU scores.
New CRF model segments music into chords with rich features.
problem Harmonic analysis of music segments.
method Semi-Markov Conditional Random Field (semi-CRF) with segment-level features.
result Semi-CRF model performs better than previous approaches.
Graphs represent gene segment organization, revealing complex interrelationships in a scrambled genome.
problem Understanding gene segment organization and interrelationships in a scrambled genome.
method Directed graphs representing gene segments and their relationships, with graph properties mapped to higher-dimensional space for analysis.
result Emerging star-like structures indicate complex interrelationships, including segments from multiple genes interleaving or overlapping.
Preformer improves Transformer for long-term time series forecasting.
problem Transformer's quadratic complexity and lack of context-awareness for long-term forecasting.
method Introduces Multi-Scale Segment-Correlation mechanism for efficient time series segmentation and context-aware attention.
result Preformer outperforms other Transformer-based methods in long-term time series forecasting.
A new method for instance segmentation using a single network.
problem Precisely detecting and delineating objects in images.
method Proposes a solution based on DCME technique, using a single segmentation network.
result Reduces computational cost by repurposing the network encoder for classification.
DSHL generates music using segment concatenation and hash learning.
problem Music generation using deep learning.
method Deep recurrent neural network and ranking-based hash learning.
result DSHL generates original and enjoyable music.
Bayesian approach labels segment parameters for improved change detection.
problem Improving Bayesian change point detection by leveraging segment parameter patterns.
method Proposes a Bayesian mean-shift change point detection algorithm with a Dirichlet process prior for segment class labels.
result Enhanced performance in synthetic and real-world data.
Study uses LLM to extract and compare segment disclosures from financial filings.
problem Challenges in completeness and comparability of segment disclosures in financial reports.
method Developed a large language model framework to extract and preserve segment information from Form 10-K filings.
result The LLM accurately extracts segment-level information and addresses cross-period knowledge questions.
A new probabilistic model for image segmentation using region trees.
problem Image segmentation, especially multiscale and distributional.
method Probabilistic generative model based on region trees, exact inference via dynamic programming.
result Ability to sample segmentations at various scales and produce a distribution over all possible segmentations.
A new method models uncertainty in medical image segmentation.
problem Inherent ambiguity in anatomical structure and pathology segmentation.
method Hierarchical probabilistic model with variational autoencoder.
result Generates more realistic and diverse segmentation samples.
Proposes a method to learn speaker embeddings for variable duration utterances.
problem Mismatch between training and testing utterance durations degrades speaker verification performance.
method Sliding window segmentation, LSTM, attentive pooling, segment-level and utterance-level embeddings, similarity loss.
result Significant improvement in robustness for duration variant utterances.
Spatially-aware metrics improve uncertainty evaluation in segmentation.
problem Uncertainty evaluation metrics treat voxels independently, ignoring spatial context.
method Proposed three spatially aware metrics incorporating structural and boundary information.
result Improved alignment with clinically important factors and better discrimination between uncertainty patterns.
Deep learning improves automatic image segmentation.
problem Automatic object localization and boundary delineation in images and medical scans.
method Proposed and evaluated novel dilated dense encoder-decoder architectures for salient object segmentation and lesion localization in medical images.
result Proposed architectures outperform state-of-the-art models in accuracy and efficiency.