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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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2815628431,124 · Jun 202019922001200920182026
48 results for data centering

Refines geometric center of mass analysis for Einstein field equations.

problem Analyzing the geometric center of mass of Willmore surfaces in initial data for Einstein field equations.
method Refined Lyapunov-Schmidt analysis to study geometric center of mass of area-constrained Willmore surfaces.
result The geometric center of mass agrees with the Hamiltonian center of mass under specific conditions.

A new method for clustering using graph connectivity centers.

problem Inappropriate scale leads to unreasonable cluster centers.
method Convert data similarity to graph connectivity, define connection center as cluster center, and use powers of similarity matrix for dynamic evolution.
result Cluster centers evolve naturally from local to global, suggesting appropriate scales and skipping unreasonable clusters.

Many pattern recognition methods rely on statistical information from centered data, with the eigenanalysis of an empirical central moment, such as the covariance matrix in principal component analysis (PCA), as well as partial least squares regression, canonical-correlation analysis and Fisher discriminant analysis. R…

2014-07-10abs ↗pdf ↗

The paper proposes a method to train deep neural networks across multiple data centers without centralizing data.

problem Training deep neural networks across multiple data centers with privacy and bandwidth constraints.
method Model averaging with cyclical learning rate and increased number of epochs for local model training.
result Model averaging can provide competitive performance in decentralized mode compared to centralized training.

This work analyzes centered binary Restricted Boltzmann Machines (RBMs) and binary Deep Boltzmann Machines (DBMs), where centering is done by subtracting offset values from visible and hidden variables. We show analytically that (i) centering results in a different but equivalent parameterization for artificial neural …

2013-11-06abs ↗pdf ↗

This paper characterizes deep learning models in Facebook's data centers and suggests optimizations.

problem Improving performance of deep learning models in data centers.
method Detailed characterizations, high performance optimizations, co-design suggestions.
result Need for better co-design of algorithms, numerics, and computing platforms.

A new method for brain tissue segmentation across medical centers using a smoothness prior.

problem Tissue segmentation challenges due to center-specific acquisition protocols.
method Developed a smoothness prior that is fit to segmentations from another medical center, integrated into an unsupervised Bayesian model.
result Segmentations are similarly smooth across centers, improving generalization.

Geo-distributed machine learning tackles global data challenges.

problem Latency and regulatory requirements push for global data centers and centralized training.
method Proposes a geo-distributed training system to reduce costs and privacy risks.
result Geo-distributed training is more cost-effective and privacy-friendly than centralized.

The paper analyzes kk-means clustering for missing data, proving statistical guarantees under MCAR.

problem Statistical guarantees for kk-means clustering with missing data, especially under Missing Completely at Random (MCAR).
method Established n\sqrt{n}-excess risk bound and consistency of cluster centers under general missing mechanisms; derived n\sqrt{n}-convergence rate and asymptotic normality for MCAR.
result Achieving n\sqrt{n}-rate and converging to true cluster centers requires distinct true cluster centers in every dimension under MCAR.

The paper introduces a new Gaussian Process model that learns target variance in multi-modal data.

problem Learning target variance in multi-modal data distributions.
method The approach involves metric learning over data centers, each with its own kernel metric and precision matrix.
result The model demonstrates improved reliability in learning target variance in multi-modal data.

The classical notion of center of mass for an isolated system in general relativity is derived from the Hamiltonian formulation and represented by a flux integral at infinity. In contrast to mass and linear momentum which are well-defined for asymptotically flat manifolds, center of mass and angular momentum seem less …

2011-01-03abs ↗pdf ↗

The paper explores uniform perfectness and centers in Morse boundaries.

problem Detecting κκ-center exhaustivity in uniformly perfect Morse boundaries.
method Analyzes CAT(0) and geodesic spaces, using visual boundary data and metric transforms.
result Fixed-basepoint uniform perfectness is insufficient for κκ-center exhaustivity.

Unified theory linking atom-centered and message-passing models for molecular properties.

problem Combining atom-centered and message-passing models for accurate molecular property prediction.
method Generalizing ACDC framework to include multi-centered information, providing a complete linear basis for regression.
result Unified understanding of atom-centered and message-passing models, providing a coherent foundation.

A new federated learning method clusters users into multiple models for better data distribution handling.

problem Non-IID data from heterogeneous sources in federated learning.
method Proposes a multi-center aggregation mechanism to learn multiple global models and optimally match users to centers.
result Our method outperforms existing federated learning methods on benchmark datasets.

MDCN improves treatment effect estimation in multicenter observational studies.

problem Incongruities in multicenter observational studies due to center-specific protocols and treatment reactions.
method MDCN learns a new feature embedding to address selection bias and strengthen information sharing between similar centers.
result MDCN provides more accurate treatment insights for new, unobserved centers compared to existing methods.

We define the (total) center of mass for suitably asymptotically hyperbolic time-slices of asymptotically anti-de Sitter spacetimes in general relativity. We do so in analogy to the picture that has been consolidated for the (total) center of mass of suitably asymptotically Euclidean time-slices of asymptotically Minko…

2015-01-22abs ↗pdf ↗

Proposes a framework for energy-efficient AIGC workload scheduling in cloud data centers.

problem Challenges of scheduling AIGC workloads for energy efficiency and quality control.
method Joint energy management and coordinated AIGC workload scheduling framework with diffusion model-aided reward shaping.
result Effective learning of scheduling policies under sparse environmental feedback.

The paper studies a rebalanced dataset for imbalanced classification using Centered Random Forests.

problem Imbalanced classification where one class is underrepresented.
method Theoretical analysis of Centered Random Forests (CRF) with rebalanced datasets and debiasing techniques.
result Theoretical Central Limit Theorem (CLT) for the infinite CRF and debiased estimator IS-ICRF.

New foliation method for isolated systems in General Relativity.

problem Defining a robust center of mass for isolated systems in GR.
method Foliation by constant spacetime mean curvature (STCMC) 2-spheres.
result Unique STCMC-foliation exists near infinity of any asymptotically Euclidean initial data set.

Distributed sensors compress and send features to a fusion center for linear regression.

problem Efficiently compress and transmit features from distributed sensors to a fusion center under varying communication constraints.
method Designs a distributed and adaptive feature compression scheme using optimal quantizers and simple adaptive strategies.
result Demonstrates improved inference performance through simulated experiments.

The k-means++ algorithm is generalized by choosing the most distant point from the nearest center.

problem Improving the initialization of k-means clustering.
method Generalizing the center initialization process by selecting the most distant point from the nearest center.
result Choosing the most distant point from the nearest center achieves similar clustering quality to k-means++.

The center of mass in General Relativity is hard to define due to coordinate freedom.

problem Defining the center of mass in General Relativity rigorously and consistently.
method Analyzing the challenges in Newtonian Gravity and using Bartnik's asymptotic harmonic coordinates.
result Examples of initial data sets in General Relativity that do not satisfy center of mass definitions.

Center identified in stated skein algebra for quantum traces.

problem Understanding the center of the stated skein algebra.
method Analyzing the algebra as a generalization of Kauffman bracket skein algebra, focusing on the case when the quantum parameter is a root of unity.
result Simple description and dimension calculation of the center over the center module.

Study centers of convex polyhedrons that are independent of parameters.

problem Characterize centers of convex polyhedrons that are independent of parameters.
method Investigate centers defined by Riesz potential and Poisson's integral, providing necessary and sufficient conditions for independence.
result Necessary and sufficient condition for existence of centers independent of parameters.

Uniform bounds on center leaves volume for codimension one center foliations.

problem Bounding the volume of center leaves in codimension one center foliations.
method Analyzing dynamically coherent partially hyperbolic diffeomorphisms with one-dimensional unstable bundle.
result Volume of center leaves is uniformly bounded.

Bayesian Federated Inference combines local data analyses to estimate regression models.

problem Estimating accurate parameters with limited data from different centers.
method Bayesian Federated Inference (BFI) for pooling local data analyses.
result Excellent performance of BFI methodology shown in real-life examples.

Novel method uses image descriptors to harmonize MRI brain volumes across centers.

problem Inconsistencies in MRI brain volume measurements across different centers and scanners.
method Trained a Relevance Vector Machine (RVM) model using image descriptors to harmonize brain volumes.
result Decreases scanner and center variability while preserving measurements for longitudinal studies.

New method for better initial centers in clustering with improved accuracy and privacy.

problem Improving the quality of clustering centers in metric spaces.
method HST initialization based on metric embedding tree structure, combined with efficient search algorithm and DP extension.
result HST initialization produces better initial centers than kk-median++ with comparable efficiency and improved privacy.

Paper analyzes CKRR for large data, showing risks converge to deterministic values.

problem Analyzing risks of kernel ridge regression with large data.
method Large dimensional analysis using centered kernels and random matrix theory.
result Empirical and prediction risks converge to deterministic values under specific conditions.

Let f:M->M be a partially hyperbolic diffeomorphism such that all of its center leaves are compact. We prove that Sullivan's example of a circle foliation that has arbitrary long leaves cannot be the center foliation of f. This is proved by thorough study of the accessible boundaries of the center-stable and the center…

2011-04-28abs ↗pdf ↗

This paper presents eight PAC-Bayes bounds to analyze the generalization performance of multi-view classifiers. These bounds adopt data dependent Gaussian priors which emphasize classifiers with high view agreements. The center of the prior for the first two bounds is the origin, while the center of the prior for the t…

2014-06-21abs ↗pdf ↗

A flexible machine learning model infers the morphology of the Galactic Center Excess.

problem Inferring the unknown morphology of the Galactic Center Excess using Fermi gamma-ray data.
method Used a Gaussian process (GP) to model the Galactic Center Excess (GCE) as a flexible, non-parametric machine learning model.
result The best-fit GP contains morphological features not typically associated with traditional GCE studies, such as a localized bright source and a diagonal arm.

New distributed clustering algorithms show resilience to initialization issues.

problem Resilience of distributed gradient-based clustering algorithms to center initialization.
method Distributed gradient-based clustering algorithms with novel center initialization.
result The algorithms are more resilient to initialization compared to baseline methods.

Predicts and classifies computational jobs for efficient resource allocation in cloud centers.

problem Efficiently scheduling and assigning resources to computational jobs in cloud centers.
method Applied LSTM neural network for job arrival prediction and BIRCH clustering for job classification.
result Improved accuracy in predicting and classifying computational jobs compared to existing methods.

The paper quantizes concatenated noisy vectors to a common cluster center, improving performance over naive methods.

problem Clustering concatenated noisy vectors from multiple sources.
method Asymptotic analysis of weighted sum of distances to a common cluster center.
result The clustering approach outperforms naive methods in terms of average distortion.