Kernelized SUSAN fuzzy clustering improves noisy image segmentation.
problem Inability of existing algorithms to preserve edge information and structural details.
method Kernelized Weighted SUSAN fuzzy C-Means clustering with spatial constraints and edge quality metric.
result Improved segmentation of noisy images with preserved edge information and structural details.
Statistical inference on graphs is a burgeoning field in the applied and theoretical statistics communities, as well as throughout the wider world of science, engineering, business, etc. In many applications, we are faced with the reality of errorfully observed graphs. That is, the existence of an edge between two vert…
Survey examines data quality challenges in edge ML.
problem Data quality issues in edge ML due to limited resources and decentralized data.
method Provides a comprehensive survey of existing literature on data quality in edge ML.
result No comprehensive survey of data quality in edge ML exists.
Edge computing tackles dynamic data in IIoT with incremental learning.
problem Latency and bandwidth limitations in IoT devices.
method Incremental learning applied to edge-computing systems for continual learning.
result Reduces catastrophic forgetting and provides efficient real-time quality control.
SPX optimizes multiple graph drawing metrics for better readability.
problem Graph drawing algorithms often optimize one metric at a time, leading to suboptimal layouts.
method Introduces Stress-Plus-X (SPX) framework that optimizes stress, crossings, angles, and upwardness simultaneously.
result SPX achieves results close to state-of-the-art algorithms that optimize metrics individually.
Proposes a new method for evaluating and constructing hierarchical topic models.
problem Evaluation and construction of hierarchical topic models.
method Represent HTM as layers and edges, introduce quality measures, and develop a heterogeneous algorithm.
result The proposed heterogeneous algorithm significantly outperforms baseline approaches.
The paper uses interpretable ML to secure data quality in IoT edge computing.
problem Ensuring data quality in IoT edge computing environments.
method Interpretable machine learning methods for feature selection and ensemble learning.
result The proposed method efficiently selects significant features for data processing.
The paper studies Kähler-Einstein metrics with singularities and their limits.
problem Analyzing Kähler-Einstein metrics with crossing edge singularities and their limits.
method Extending Guenancia's techniques, the paper shows convergence of metrics under specific angle conditions.
result Negatively curved Kähler-Einstein crossing edge metrics converge to mixed cusp and edge metrics smoothly away from the divisor.
Enhances GNNs by improving input data quality from topology and labels.
problem Poor quality of graph data limits GNN performance.
method Improves graph data quality using model outputs for better semi-supervised node classification.
result SEG consistently improves GNN performance across various datasets.
Paper determines Assouad-Nagata dimension for all minor-closed metrics.
problem Understanding the Assouad-Nagata dimension of minor-closed metrics.
method Using edge-weighted graphs and edge-deletion/contraction to model minor-closed metrics, determining their Assouad-Nagata dimension.
result Determined the Assouad-Nagata dimension for every minor-closed metric.
Learning the right graph representation from noisy, multisource data has garnered significant interest in recent years. A central tenet of this problem is relational learning. Here the objective is to incorporate the partial information each data source gives us in a way that captures the true underlying relationships.…
Study Kähler-Einstein edge metrics on Hirzebruch surfaces, verifying a conjecture and finding a rigid singularity.
problem Verifying a conjecture about Kähler-Einstein edge metrics on Hirzebruch surfaces.
method Using the Calabi ansatz, constructing a family of metrics and studying their angle deformation.
result Verification of a conjecture and finding a rigid singularity.
We propose an approach for approximating the partition function which is based on two steps: (1) computing the partition function of a simplified model which is obtained by deleting model edges, and (2) rectifying the result by applying an edge-by-edge correction. The approach leads to an intuitive framework in which o…
Best-choice edge grafting speeds up MRF structure learning.
problem Efficiently learning the structure of Markov random fields (MRFs) in a scalable manner.
method Incremental, structured approach that activates edges in groups of features.
result Significant speedup in structure learning with a controllable trade-off between speed and quality.
This paper presents a new approach for filter design based on stochastic distances and tests between distributions. A window is defined around each pixel, overlapping samples are compared and only those which pass a goodness-of-fit test are used to compute the filtered value. The technique is applied to intensity SAR d…
Characterizes metrics on triangulated surfaces using glued Euclidean triangles.
problem Describing metrics on triangulated surfaces constructed from glued Euclidean triangles.
method Carefully constructing polyhedral metrics and proving their uniqueness.
result Polyhedral metrics are the only intrinsic metrics preserving Euclidean triangle lengths.
The paper proves local and long-term existence of Ricci de Turck flow on incomplete edge manifolds.
problem Proving existence of Ricci de Turck flow on incomplete edge manifolds.
method Careful analysis of the Lichnerowicz Laplacian and the Ricci de Turck flow equation.
result Local and long-term existence of Ricci de Turck flow on incomplete edge manifolds.
Spectral embedding improves with edge weight transformations.
problem Improving spectral embedding for weighted networks.
method Analyzed edge weight transformations for spectral embedding quality.
result Transformations like tempering or thresholding can significantly enhance spectral embedding.
Edge device deep learning improved with noise handling model.
problem Noise and low quality data degrade deep learning performance on edge devices.
method Mixture of Pre-processing Experts (MoPE) model with adversarially trained autoencoder.
result The MoPE model achieves better accuracy on noisy images without sacrificing clean image accuracy.
New ML approach for edge devices tackles deployment challenges.
problem Challenges in deploying ML models on edge devices.
method Specialized ML development and deployment approach for edge devices.
result Prototype demonstrates efficient and high-quality solutions.
Newly discovered Eguchi-Hanson metric arises from edge metrics.
problem Understanding limits of compact singular Einstein spaces.
method Constructing Kahler-Einstein edge metrics on Calabi-Hirzebruch manifolds.
result Eguchi-Hanson metric emerges as a Gromov-Hausdorff limit.
New approach solves Calabi problem on manifolds with edge-cone singularities.
problem Solving the Calabi problem on manifolds with edge-cone singularities.
method Proposes a new approach using a good reference metric and equivalent equations with different reference metrics.
result Extends methods from smooth settings to edge settings, generalizing to multiple hypersurfaces.
This note demonstrates how both the concept of distance and the concept of holonomy can be constructed from a suitable network with directed edges (and no lengths). The number of different edge types depends on the signature of the metric and the dimension of the holonomy group. If the holonomy group is of dimension on…
Edge filters reduce video data transmission to datacenters.
problem Strain on wide area network infrastructure due to video camera deployments.
method FilterForward system with lightweight edge filters and microclassifiers.
result Reduces bandwidth use by an order of magnitude.
Paper explores how text generation quality and diversity metrics relate to distribution fitting.
problem Unclear relation between text generation quality and diversity metrics and distribution fitting.
method Theoretical approach to prove a linear combination of quality and diversity metrics can be a divergence metric.
result CR/NRR proposed as a better substitute for BLEU/Self-BLEU metrics.
NetScore evaluates deep networks for practical edge use, balancing accuracy, complexity.
problem Designing deep networks for practical edge devices, especially mobile.
method NetScore metric balances accuracy, complexity, and architecture complexity.
result NetScore outperforms top-1 accuracy and information density metrics in diverse networks.
A normal form for edge metrics is derived under the necessary conditions that the metric be normalized and exact. The normal forms for such an edge metric are shown to be in 1-1 correspondence with representative metrics for a reduced conformal infinity on the boundary. The normal form is constructed via solution of a …
No minimizer exists for certain spherical metrics with edge-cones.
problem Existence of Yamabe minimizers on singular spheres.
method Analyzing standard edge-cone spherical metrics of cone angles greater than or equal to 4π. result No minimizer exists for the specified spherical metrics.
This paper presents a new approach for filter design based on stochastic distances and tests between distributions. A window is defined around each pixel, samples are compared and only those which pass a goodness-of-fit test are used to compute the filtered value. The technique is applied to intensity Synthetic Apertur…
Study of Ricci flow on trees, focusing on edge weights and curvatures.
problem Understanding the evolution of metrics on trees under Ricci flow.
method Continuous-time Ricci flow based on Lin-Lu-Yau Ollivier Ricci curvature.
result Ricci flow converges to zero curvature on edge weights of positive normalized values in caterpillar trees.
Recently, Atiyah and LeBrun proved versions of the Gauss-Bonnet and Hirzebruch signature Theorems for metrics with edge-cone singularities in dimension four, which they applied to obtain an inequality of Hitchin-Thorpe type for Einstein edge-cone metrics. Interestingly, many natural examples of edge-cone metrics in dim…
Study positive scalar curvature on manifolds with skeleton singularities.
problem Positive scalar curvature on manifolds with skeleton singularities.
method Polyhedral comparison theory and edge metrics.
result Edge singularities do not affect the Yamabe type in all dimensions.
The article proves long-time existence and convergence of the edge Yamabe flow.
problem Analyzing the normalized Yamabe flow on incomplete edge singularities.
method Novel maximum principle results and uniform bounds established without barrier functions or Krylov-Safonov estimates.
result Long-time existence and convergence of the edge Yamabe flow.
Proposes a new loss function for better super-resolution images.
problem Improving the quality of super-resolution images.
method Introduces a robust loss function based on edge preservation using the Canny operator.
result Enhanced performance in PSNR and SSIM metrics compared to MSE loss function.
Defines odd Pfaffian form for odd-dimensional manifolds, proving Chern-Gauss-Bonnet formula.
problem Proving Chern-Gauss-Bonnet formula for incomplete edge singularities.
method Defining odd Pfaffian form through curvature tensor, proving formula for various metrics.
result Proves intrinsic Chern-Gauss-Bonnet formula for edge singularities and complete manifolds.
New algorithm reduces graph learning cost from quadratic to nearly linear.
problem High computational cost in graph learning models.
method Uses approximate nearest neighbor techniques to reduce variables and automatically selects model parameters.
result Achieves approximation with O(nlog(n)) cost, approaching exact graph learning model quality. The paper predicts edge weights in weighted directed networks using metric geometry.
problem Predicting edge weights in weighted directed networks.
method Introducing new types of weighted directed networks (AWDNs), constructing metrics, and proposing modified kNN and SVM methods.
result The proposed methods outperform traditional approaches in predicting edge weights.
In this paper we characterize logarithmic surfaces which admit Kähler-Einstein metrics with negative scalar curvature and small edge singularities along a normal crossing divisor.
Existence and uniqueness of discrete Einstein metrics on trees proven.
problem Existence and uniqueness of discrete Einstein metrics on trees.
method Using Perron-Frobenius theory and Lin-Lu-Yau Ricci curvature.
result Existence and uniqueness of discrete Einstein metrics on trees established.
The paper develops formulas for hyperbolic simplices based on edge lengths.
problem Understanding the geometry of hyperbolic simplices using only edge lengths.
method Develops geometric formulas for hyperbolic simplices based on edge lengths.
result Distance and projection formulas in hyperbolic simplices.
Paper proposes metrics to evaluate both quality and diversity in text generation models.
problem Existing metrics only evaluate quality or diversity, not both.
method Approximate the distance between generative model and real data distribution using n-gram and BERT features.
result Proposed metrics better evaluate both quality and diversity of text generation models.
The paper evaluates and compares dimensionality reduction quality metrics without tuning.
problem Evaluating the quality of nonlinear dimensionality reduction visualizations is challenging.
method Comparison of dimensionality reduction quality metrics on datasets with known ground truth manifolds.
result A few methods consistently perform well, with one proposed as a benchmark.
This paper compares automatic metrics for re-speaking quality assessment.
problem Estimating the quality of re-speaking results is challenging.
method Comparing and adapting automatic evaluation metrics (BLEU, EBLEU, NIST, METEOR, etc.) to re-speaking quality.
result Automatic metrics are compared to human-derived NER metric for re-speaking quality.
New metric correlates local topic quality with human judgments.
problem Evaluation of topic models focuses on global metrics, ignoring token-level assignments.
method Proposed a human evaluation task and automated metrics to assess local topic quality.
result Consistency metric correlates best with human judgments of local topic quality.
Proposes BSF to evaluate structure learning algorithms without bias.
problem Evaluation bias in structure learning algorithms.
method Balanced Scoring Function (BSF) to adjust reward based on edge difficulty.
result Eliminates bias in favour of underfitted graphs.
Coded Federated Learning speeds up training in edge computing networks.
problem Slow convergence in Federated Learning due to heterogeneity and stochastic fluctuations.
method Exploiting statistical properties of compute and communication delays, distributed kernel embedding, and random Fourier features.
result Significant performance gains for CodedFedL in distributed non-linear regression and classification problems.
This paper presents two approaches for filter design based on stochastic distances for intensity speckle reduction. A window is defined around each pixel, overlapping samples are compared and only those which pass a goodness-of-fit test are used to compute the filtered value. The tests stem from stochastic divergences …
Recovering edge activities from node activity data in temporal networks.
problem Recovering lost edge activity data from aggregated node activity data in temporal networks.
method Analyzing the relationship between edge activity and node activity data, using both theoretical and empirical methods to show recovery is possible and under what conditions.
result Recovery of edge activities from node activities is possible with surprising accuracy, even when network density increases.