A novel time series clustering method that considers segment typologies.
problem Lack of consideration for the similarity of different subsequences in time series clustering.
method Two-stage clustering: polynomial segmentation followed by hierarchical clustering of segments, then final clustering of time series.
result The method outperforms state-of-the-art techniques on UCR Time Series Classification Archive datasets.
In this paper we present a review of the existing typologies of Internet service users. We zoom in on social networking services including blogs and crowdsourcing websites. Based on the results of the analysis of the considered typologies obtained by means of FCA we developed a new user typology of a certain class of I…
Computational model uncovers linguistic universals.
problem Manual processing of linguistic typology by linguists is time-consuming and leaves key universals unexplored.
method Presented a computational model to identify known and new linguistic universals.
result The model successfully identifies known universals and uncovers new ones.
Performance metrics (error measures) are vital components of the evaluation frameworks in various fields. The intention of this study was to overview of a variety of performance metrics and approaches to their classification. The main goal of the study was to develop a typology that will help to improve our knowledge a…
Model shows how confidence feedback can lead to different crisis outcomes.
problem Characterizing the impact of economic recessions on different social strata.
method A self-reflexive DSGE model with heterogeneous households, varying parameters to analyze crisis typologies.
result Crisis propagation can be confined to high or low income households, depending on social network structure and income inequality.
The paper investigates skin tone estimation and its impact on dermatology model performance.
problem The impact of skin tone on dermatology model performance is not well understood.
method The authors use individual typology angle (ITA) to estimate skin tone in dermatology datasets and analyze model performance.
result There is no measurable correlation between model performance and ITA values.
Complex network theory models China's credit system to control systemic risk.
problem Insufficient understanding of China's credit network structure during financial crises.
method Constructed bipartite financial institution-firm network and analyzed its typological properties.
result Credit network structure can amplify local risks to the whole economy.
This is the first of two articles in which we provide detailed and self-contained account of the construction of a system of Kuranishi structures on the moduli spaces of pseudo holomorphic disks, using the exponential decay estimate given in [FOOO7]. This article completes the construction of a Kuranishi structure of a…
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.
Segmental structure is a common pattern in many types of sequences such as phrases in human languages. In this paper, we present a probabilistic model for sequences via their segmentations. The probability of a segmented sequence is calculated as the product of the probabilities of all its segments, where each segment …
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.
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.
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.
Learning-based methods for visual segmentation have made progress on particular types of segmentation tasks, but are limited by the necessary supervision, the narrow definitions of fixed tasks, and the lack of control during inference for correcting errors. To remedy the rigidity and annotation burden of standard appro…
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.
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.
DNA rearrangement processes recombine gene segments that are organized on the chromosome in a variety of ways. The segments can overlap, interleave or one may be a subsegment of another. We use directed graphs to represent segment organizations on a given locus where contigs containing rearranged segments represent ver…
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.
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 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.
Proposes a new method for medical image segmentation.
problem Medical image segmentation challenges.
method Decompose-and-Integrate Learning.
result Improves segmentation performance on multiple datasets.
Wavelet features improve image clustering and segmentation accuracy.
problem Noise and lack of spatial context in pixel intensity-based methods.
method Modified K-means, Fuzzy c-means, and ACWE algorithms incorporating Wavelet features.
result Wavelet-based algorithms converge to different segmentation results based on frequency information.
This paper presents an agent-based artificial cryptocurrency market in which heterogeneous agents buy or sell cryptocurrencies, in particular Bitcoins. In this market, there are two typologies of agents, Random Traders and Chartists, which interact with each other by trading Bitcoins. Each agent is initially endowed wi…
UOLO detects and segments objects in medical images, achieving state-of-the-art performance.
problem Automatic detection and segmentation of objects in medical images.
method UOLO combines object segmentation and detection using a novel loss function.
result UOLO achieves state-of-the-art performance on optic disc and fovea detection and segmentation from retinal images.
This paper reviews deep learning for multi-modality medical image segmentation.
problem Improving segmentation accuracy in medical images using multiple modalities.
method Overview of deep learning and multi-modal medical image segmentation, analysis of different network architectures and fusion strategies.
result Later fusion of modalities can lead to more accurate segmentation results.
A novel 3D U-Net approach improves kidney tumor segmentation in medical imaging.
problem Challenging manual annotation and great medical impact of kidney tumor segmentation.
method End-to-end cascaded U-Nets with a localization network.
result Achieves Sørensen-Dice coefficients of 0.902 for kidney and 0.408 for tumor segmentation.
A new deep neural network improves short-speech recognition.
problem Improving speaker recognition for short-duration speech segments.
method Proposes UtterIdNet, a novel deep neural network architecture.
result Consistent and stable performance for short segments, especially sub-second durations.
We derive a convex optimization problem for the task of segmenting sequential data, which explicitly treats presence of outliers. We describe two algorithms for solving this problem, one exact and one a top-down novel approach, and we derive a consistency results for the case of two segments and no outliers. Robustness…
A new heuristic LM algorithm improves k-segmentation accuracy with less computation.
problem Efficiently segmenting large video streams into meaningful piecewise-linear segments.
method Inspired by Lloyd's and Lloyd-Max algorithms, LM algorithm iteratively minimizes a cost function.
result LM algorithm achieves competitive accuracy with exact methods at a fraction of the computational cost.
Proposes a new loss function for deep learning image segmentation.
problem Efficiency and reliance on labeled data in deep learning image segmentation.
method Mumford-Shah functional adapted for deep learning.
result Improves efficiency and effectiveness of deep learning for image segmentation.
V-Net speeds up brain tumor segmentation in MRI scans.
problem Manual tumor segmentation is time-consuming and inaccurate.
method Applied a volumetric, fully convolutional neural network (V-Net) to MRI scans.
result Achieved a whole tumor dice score of 0.89.
Combines spatial context priors with data term for better dendritic spine segmentation.
problem Poor segmentation results due to overlapping pixel intensity distributions.
method Combines nonparametric context priors with learned-intensity data term and nonparametric shape priors.
result Significant improvements in dendritic spine segmentation.
Proposes adversarial normalization for multi-domain image segmentation.
problem Current image normalization is per-dataset, limiting multi-domain segmentation.
method Adversarial training to learn common normalizing functions across multiple datasets.
result Optimal normalizer improves segmentation accuracy and realism.
This paper improves level generation using VAEs for coherent, logically following segments.
problem Generating coherent levels of non-fixed length and blending levels from different games.
method Sequential segment-based level generation using VAEs with a classifier for logical placement.
result Generated levels are more coherent and capable of blending levels from different games.
Music generation research has grown in popularity over the past decade, thanks to the deep learning revolution that has redefined the landscape of artificial intelligence. In this paper, we propose a novel approach to music generation inspired by musical segment concatenation methods and hash learning algorithms. Given…
Deep learning model uses mixed supervision for brain tumor segmentation.
problem Costly manual tumor segmentation data.
method Extends segmentation networks with an image-level classification branch.
result Significant improvement in segmentation performance.
Novel framework for medical image segmentation using deep learning.
problem Class imbalance and domain adaptation in medical image segmentation.
method Biophysics-based domain adaptation and automatic segmentation of white, gray, and cerebrospinal fluid.
result Improved segmentation performance, especially with the biophysics-based domain adaptation.
Study reduces redundant information in multi-modal datasets.
problem Redundant information increases data size and costs.
method Multimodal framework using entropy, mutual info, Lasso, SHAP, PCA, topic modeling.
result Significant feature space can be pruned with minimal loss in performance.
Simplified interactive image segmentation using kNN graphs.
problem Interactive image segmentation with user-provided labels.
method Undirected kNN graphs for label propagation.
result Effective interactive segmentation with significant accuracy.