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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,341 papers · 148 categories

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326495127 · Jul 202619922001200920182026
48 results for segment boundary

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.

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.

The paper explores reflection principles for lightlike line segments on maximal surfaces.

problem Reflection property does not hold for lightlike line segments on maximal surfaces.
method Analyzes reflection properties for lightlike line segments connecting shrinking singularities.
result Shows a kind of reflection principle for lightlike line segments on maximal surfaces.

NuClick uses clicks inside nuclei to improve nuclear segmentation.

problem Lack of efficient tools for nuclear segmentation due to labor-intensive annotation.
method Convolutional neural network framework that uses single point clicks for nuclei segmentation.
result NuClick generates superior segmentation results and facilitates more annotations.

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.

Consider a compact Riemannian manifold with boundary. Assume all maximally extended geodesics intersect the boundary at both ends. Then to each maximal geodesic segment one can form a triple consisting of the initial and final vectors of the segment and the length of the segment. The collection of all such triples comp…

2008-12-03abs ↗pdf ↗

Unsupervised speech recognition without labeled data using novel cost function and MAP refinement.

problem Training speech recognition systems without labeled data.
method Alternates between phoneme classifier learning and boundary refinement using Segmental Empirical Output Distribution Matching and MAP approach.
result Achieves phone error rate (PER) of 41.6% on TIMIT dataset.

Paper connects minimal and maximal surfaces' boundary value problems.

problem Existence and uniqueness of minimal surfaces' boundary value problems.
method One-to-one correspondence between minimal and maximal surfaces' solutions.
result One-to-one correspondence between minimal and maximal surfaces' boundary value problems.

The paper simplifies quickshift hyperparameter tuning for larger images.

problem Understanding and tuning hyperparameters for quickshift image segmentation.
method Theoretical analysis of a modified quickshift algorithm for homogeneous patches with i.i.d. noise.
result A heuristic to scale quickshift hyperparameters based on image size.

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.

PFE embeds images into sparse regions for better segmentation.

problem Image segmentation challenges with slowly varying signals and sparse region boundaries.
method Piecewise Flat Embedding (PFE) using sparse signal recovery theory, L1,p regularization, and Bregman iterations.
result PFE enhances image segmentation performance on multiple datasets.

Given a compact closed subset MM of a line segment in R3\mathbb{R}^3, we construct a sequence of minimal surfaces ΣkΣ_k embedded in a neighborhood CC of the line segment that converge smoothly to a limit lamination of CC away from MM. Moreover, the curvature of this sequence blows up precisely on MM, and the limit…

2009-10-01abs ↗pdf ↗

The aim of this paper is to develop a new axiomatization of planar geometry by reinterpreting the original axioms of Euclid. The basic concept is still that of a line segment but its equivalent notion of betweenness is viewed as a topological, not a metric concept. That leads quickly to the notion of connectedness with…

2015-01-01abs ↗pdf ↗

We investigate the Tits boundary of locally compact CAT(0) 2-complexes. In particular we show that away from the endpoints, a geodesic segment in the Tits boundary is the ideal boundary of an isometrically embedded Euclidean sector. As applications, we provide sufficient conditions for two points in the Tits boundary t…

2003-03-11abs ↗pdf ↗

BshapeNet improves object detection and instance segmentation using bounding shape masks.

problem Improving object detection and instance segmentation performance.
method Proposes bbox and bshape masks, and combines them with Faster R-CNN and Mask R-CNN frameworks.
result BshapeNet+ achieves highly competitive results with state of the art models, improving detection and instance segmentation.

Enhances uncertainty estimation in medical image segmentation.

problem Frequency-related noise in medical imaging leads to biased uncertainty estimates.
method Extends MC-Dropout to the frequency domain for better uncertainty estimation.
result MC-Frequency Dropout improves calibration and uncertainty in semantic segmentation.

Automated LV segmentation across the cardiac cycle using deep learning.

problem Segmentation of left ventricle from CINE MRI images using only two phases.
method A deep learning workflow that learns from images throughout the cardiac cycle, including localization, cropping, and contour identification using a Temporal FCNN with CRFs and Semantic Flow.
result Significant improvement in performance by explicitly learning cardiac motion patterns.

Enhanced deep learning model improves tumor segmentation in ultrasound images.

problem Challenges in integrating patient-specific medical priors into deep learning models.
method Integrates visual saliency into a U-Net architecture with attention blocks.
result Achieved a Dice similarity coefficient of 90.5 percent on a dataset of 510 images.

This research improves spoken language translation by refining ASR outputs.

problem Improving automatic sentence segmentation for ASR systems in Polish.
method Reverse normalization of ASR results to enhance MT performance.
result Enhanced MT results through better ASR sentence segmentation.

Improves TTS accuracy by correcting context-dependent units.

problem Improves text-to-speech accuracy through speaker adaptation.
method Statistical model predicting context-dependent phonetic unit classes and their mean error values.
result Corrected boundaries of units improve TTS accuracy compared to HMM segmentation.

Paper proposes neural approach for Chinese named entity recognition.

problem Challenges in Chinese named entity recognition due to context-dependency and lack of word delimiters.
method Introduces a CNN-LSTM-CRF neural architecture and a unified framework for joint training with word segmentation.
result Improves Chinese named entity recognition performance, especially with limited training data.

The paper analyzes American options with time-varying caps, finding complex exercise regions and deriving option pricing formulas.

problem Valuation of American capped call options with time-varying caps, especially when the cap grows or decreases over time.
method Probabilistic arguments and local time, characterizing exercise boundaries through recursive integral equations and piecewise constant segments.
result General representation formulas for option prices, derived from exercise boundaries and local time of the underlying process.

Characterizes convexity of distance functions on Riemannian manifolds.

problem Understanding convexity of distance functions on Riemannian manifolds.
method Characterization of proximal normal cones, separation theorems, and analysis of convex subsets' boundaries.
result Convexity of distance functions for various boundary conditions on Riemannian manifolds.

CompILE learns reusable segments from demonstrations for hierarchical task execution.

problem Learning reusable, variable-length segments of hierarchical behavior from demonstrations.
method Unsupervised, fully-differentiable sequence segmentation module for latent encoding and re-composition.
result Model generalizes to longer sequences and unseen environments, learns task boundaries and event encodings.

Study analyzes overfitting in neural nets under class imbalance, proposing new loss functions.

problem Overfitting and class imbalance in neural nets, particularly in image segmentation.
method Analyzes logits distribution, derives asymmetric loss functions and regularizers.
result Proposed loss functions improve segmentation performance in brain tumor core.

End-to-end transformer model improves lexical stress detection accuracy.

problem Inaccurate phoneme boundaries and limited features for stress classification.
method End-to-end sequence to sequence model using transformer trained on feature sequences and phoneme sequences with stress marks.
result End-to-end model achieves better performance and lower phoneme error rate (6.36%) compared to syllable segmentation methods.