There are different problems for resolution of complex LC-MS or GC-MS data, such as the existence of embedded chromatographic peaks, continuum background and overlapping in mass channels for different components. These problems cause rotational ambiguity in recovered profiles calculated using multivariate curve resolut…
On-device research index
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.
168,657 papers · 148 categories
Trend · papers per month
48 results for “Multivariate Curve Resolution”
Untargeted Region of Interest Selection for GC-MS Data using a Pseudo F-Ratio Moving Window (FRMV)stat.ML
A new method selects regions of interest in GC-MS data without prior target selection.
problem Challenges in GC-MS data analysis due to fragmentation and shared fragment ions.
method Uses a pseudo F-ratio moving window (FRMV) to automatically select regions of interest.
result Algorithm can accurately identify signal regions in GC-MS data.
MPPN network improves long-term time series forecasting accuracy.
problem Inaccurate long-term time series forecasting due to noise and lack of interpretability.
method MPPN network constructs context-aware multi-resolution semantic units and employs multi-periodic pattern mining and channel adaptive module.
result MPPN significantly outperforms state-of-the-art methods on nine real-world benchmarks.
The resolution and calibration of pure spectra of minority components in measurements of chemical mixtures without prior knowledge of the mixture is a challenging problem. In this work, a combination of band target entropy minimization (BTEM) and target partial least squares (T-PLS) was used to obtain estimates for sin…
Generative model improves wind field downscaling from coarse climate models.
problem Limited spatial resolution and biases in GCMs for wind energy studies.
method SerpentFlow for domain alignment and conditional fine-scale learning.
result Improved spatial coherence, inter-variable consistency, robustness under climate change.
A commuting -tuple of bounded linear operators on a Hilbert space $\clh$ associate a Hilbert module over in the following sense: \[\mathbb{C}[z_1, \ldots, z_n] \times \mathcal{H} \rightarrow \mathcal{H}, \quad \quad (p, h) \mapsto p(T_1, \ldots, T_n)h…
We study the analytic and topological invariants associated with complex normal surface singularities. Our goal is to provide topological formulae for several discrete analytic invariants whenever the analytic structure is generic (with respect to a fixed topological type), under the condition that the link is a ration…
Missing values, irregularly collected samples, and multi-resolution signals commonly occur in multivariate time series data, making predictive tasks difficult. These challenges are especially prevalent in the healthcare domain, where patients' vital signs and electronic records are collected at different frequencies an…
EnScale: Temporally-consistent multivariate generative downscaling via proper scoring rulesphysics.ao-ph
EnScale learns to downscale climate models efficiently, capturing both spatial and temporal consistency.
problem Downscaling climate models from coarse to high-resolution data is computationally expensive and challenging.
method EnScale uses generative models and proper scoring rules to map GCM data to RCM data, reducing computational cost.
result EnScale achieves competitive performance and computational efficiency in downscaling multiple climate variables.
Proposes FunNoL for better curve classification and reconstruction in multivariate functional data.
problem Linear methods fail to capture nonlinear structures in multivariate functional data.
method Functional nonlinear learning (FunNoL) method using nonlinear mapping.
result FunNoL outperforms FPCA in curve classification and reconstruction, especially in multivariate settings.
STanHop predicts multivariate time series with memory-enhanced capabilities.
problem Predicting multivariate time series with memory-enhanced capabilities.
method Sparse Tandem Hopfield Network (STanHop) with two external memory modules.
result STanHop outperforms dense Hopfield models in memory retrieval error.
Study analyzes stock market correlations using multivariate distributions.
problem Capturing the correlation structure of complex, non-stationary systems.
method Applied Random Matrix Model to empirical data of 479 US stocks.
result Described and quantified changes in empirical distributions due to non-stationarity.
New method uses contours of segmented images for X-ray classification.
problem Classifying X-ray images of segmented radiography.
method Develops a new approach for image analysis of multivariate planar curves, addressing alignment issues.
result Demonstrates the robustness and appeal of the proposed method through detection of cardiomegaly and numerical experiments.
Proposes a model to handle mobile health data with irregular measurements.
problem Handling heterogeneous, multi-resolution data in mobile health.
method Individualized dynamic latent factor model for irregular multi-resolution time series data.
result Superior performance compared to existing methods in simulation and smartwatch data applications.
New sampling strategy preserves relationships in multivariate scientific data.
problem Reducing storage and enabling efficient multivariate analyses on large scientific data.
method Uses principal component analysis for multivariate data and combines with existing univariate sampling algorithms.
result Efficacy demonstrated on real-world data sets, showing data reduction and multivariate analysis ease.
We prove the "End Curve Theorem," which states that a normal surface singularity with rational homology sphere link is a splice-quotient singularity if and only if it has an end curve function for each leaf of a good resolution tree. An "end-curve function" is an analytic function $(X,o)\to (\C,0)$ whose ze…
US Yield curve has recently collapsed to its most flattened level since subprime crisis and is close to the inversion. This fact has gathered attention of investors around the world and revived the discussion of proper modeling and forecasting yield curve, since changes in interest rate structure are believed to repres…
This paper is devoted to the study of geometric structures modeled on homogeneous spaces G/P, where G is a real or complex semisimple Lie group and is a parabolic subgroup. We use methods from differential geometry and very elementary finite-dimensional representation theory to construct sequences of invar…
COVID-19 mortality analysis from soft-data multivariate curve regression and machine learningstat.ML
Study uses regression and ML for COVID-19 mortality forecasting.
problem Forecasting COVID-19 mortality during the first wave in Spain.
method Cyclical curve log-regression, multivariate time series spatial residual correlation analysis, Bayesian approach, machine learning.
result Empirical analysis shows ML regression models perform better than traditional methods.
We consider the problem of universal joint clustering and registration of images and define algorithms using multivariate information functionals. We first study registering two images using maximum mutual information and prove its asymptotic optimality. We then show the shortcomings of pairwise registration in multi-i…
Compactness theorem for quasiregular curves proves normality and resolves nodal points.
problem Proving compactness and normality for quasiregular curves.
method Using Gromov's compactness theorem and bubble trees to associate a limit curve and measure.
result A nodal resolution of quasiregular curves via bubble trees.
New algorithms select and rank features from MTS without feature extraction.
problem Feature extraction step for MTS classification.
method Directly computes similarity between time series and assesses cluster structure matching labels.
result Techniques match labels well without feature extraction.
The multivariate probit model (MVP) is a popular classic model for studying binary responses of multiple entities. Nevertheless, the computational challenge of learning the MVP model, given that its likelihood involves integrating over a multidimensional constrained space of latent variables, significantly limits its a…
MFSSA improves reconstruction accuracy of multivariate functional time series.
problem Improving reconstruction accuracy of multivariate functional time series.
method Developed MFSSA, a functional extension of MSSA, for different dimensional domains.
result Better reconstruction accuracy of MFTS signals using MFSSA compared to other methods.
We give a slope equality for fibered surfaces whose general fiber is a smooth plane curve. As a corollary, we prove a "strong" Durfee-type inequality for isolated hypersurface surface singularities, which implies Durfee's strong conjecture for such singularities with non-negative topological Euler number of the excepti…
Modeling massive highly-multivariate nonstationary spatial data with the basis graphical lassostat.ME
New framework models complex spatial data with basis functions and graphical vectors.
problem Modeling highly-multivariate spatial processes with varying resolutions.
method Extends graphical lasso to multivariate Gaussian processes with independent graphical vectors at different resolutions, using an orthogonal basis and fusion penalty.
result Linear complexity and parsimonious conditional independence structure in multilevel graphical model.
Constructs bivariate quantiles using vine copulas for multivariate analysis.
problem Need for research in multivariate quantiles, especially for bivariate responses.
method Constructs bivariate (conditional) quantiles using vine copula based bivariate regression model with a novel tree sequence graph structure.
result Avoids typical shortfalls of regression like transformations, interactions, collinearity, and quantile crossings.
Study counts rational curves on hyperKähler ALE 4-manifolds.
problem Counting rational curves on hyperKähler ALE 4-manifolds.
method Using the period map of the twistor space, count rational curves and specify complex structures.
result The integer function on the parameter space is lower semi-continuous.
Variable Selection Methods for Multivariate, Functional, and Complex Biomedical Data in the AI Agestat.ML
New methods for selecting variables in complex biomedical data.
problem Selecting important variables in multivariate, functional, and complex biomedical data.
method Optimization-based variable selection methods for various regression models.
result Outperforms state-of-the-art methods in accuracy and speed.
We give the explicit algorithm computing the motivic generalization of the Poincare series of the plane curve singularity introduced by A. Campillo, F. Delgado and S. Gusein-Zade. It is done in terms of the embedded resolution of the curve. The result is a rational function depending of the parameter q, at q=1 it coinc…
The paper resolves kinks on curves on surfaces with punctures.
problem Resolving kinks of curves on surfaces with punctures.
method Application of the Diamond Lemma.
result The resolution of kinks of a curve on a surface with punctures is uniquely determined up to homotopy.
The delta invariant of curves on rational surfaces II: Poincaré series and topological aspectsmath.GT
Study delta invariant of curves on rational surfaces using topological methods.
problem Calculate delta invariant for curves embedded in rational singularities.
method Use topological techniques and Poincaré series.
result Develop formulae for delta invariant in terms of embedded data.
This paper aims at formulating the issue of ranking multivariate unlabeled observations depending on their degree of abnormality as an unsupervised statistical learning task. In the 1-d situation, this problem is usually tackled by means of tail estimation techniques: univariate observations are viewed as all the more …
Feature extraction and dimensionality reduction are important tasks in many fields of science dealing with signal processing and analysis. The relevance of these techniques is increasing as current sensory devices are developed with ever higher resolution, and problems involving multimodal data sources become more comm…
ALE spaces and nodal curvesmath.DG
Study ALE spaces via nodal curves and compactifications.
problem Understanding ALE spaces through nodal curves and compactifications.
method Circle action on 4-manifold, C^* action on compactification, identification of nodal curves.
result Identified nodal rational curve in a projective rational surface.
Study singular fibers in genus 2 algebraic fibrations and their monodromy factorizations.
problem Classify singular fibers in genus 2 algebraic fibrations and relate them to Lefschetz fibrations.
method Analyze four families of hypersurface singularities in C^3, determine resolutions, and find flat deformations into simpler pieces.
result Establish a dictionary between configurations of curves and monodromy factorizations for some genus 2 fibrations.
We consider the topic of multivariate regression on manifold-valued output, that is, for a multivariate observation, its output response lies on a manifold. Moreover, we propose a new regression model to deal with the presence of grossly corrupted manifold-valued responses, a bottleneck issue commonly encountered in pr…
The orientable cover of the moduli space of real genus zero algebraic curves with marked points is a compact aspherical manifold tiled by associahedra, which resolves the singularities of the space of phylogenetic trees. The resolution maps planar metric trees to their underlying abstract representatives, collapsing an…
Learning how to rank multivariate unlabeled observations depending on their degree of abnormality/novelty is a crucial problem in a wide range of applications. In practice, it generally consists in building a real valued "scoring" function on the feature space so as to quantify to which extent observations should be co…
In the paper, we use and investigate copulas models to represent multivariate dependence in financial time series. We propose the algorithm of risk measure computation using copula models. Using the optimal mean- portfolio we compute portfolio's Profit and Loss series and corresponded risk measures curves. Value-…
Paper benchmarks machine learning for detecting process curve drifts.
problem Detecting drifts in multivariate manufacturing process data.
method Synthetic data generation and evaluation score introduction.
result Existing algorithms often fail with complex drift scenarios.
Solve for the Hyperparameter, Skip the Search: Kolmogorov-Optimal Scaling Laws for Spline Regressioncs.LG
KOLMOGOROV-OPTIMAL RESOLUTION ESTIMATION (KORE) solves spline regression without exhaustive search
problem Hyperparameter tuning in spline regression
method Solving for optimal resolution analytically
result KORE matches exhaustive cross-validation and outperforms tuned models
The classification of multivariate functional data is an important task in scientific research. Unlike point-wise data, functional data are usually classified by their shapes rather than by their scales. We define an outlyingness matrix by extending directional outlyingness, an effective measure of the shape variation …
Paper combines geometry and time-series analysis for spatiotemporal data.
problem Multivariate time-series data from multiple sensors.
method Combines manifold learning, Riemannian geometry, and spectral analysis.
result Proposes Riemannian multi-resolution analysis (RMRA) for dynamic mode extraction.
Modelling and simulation of dependence structures in nonlife insurance with Bernstein copulasstat.ME
The study models insurance dependence using Bernstein copulas.
problem Modeling dependence structures in nonlife insurance data.
method Review and suggest fitting Bernstein copulas to empirical data.
result Monte Carlo simulation and PML estimation for aggregate losses.
New GoF test improves change point detection in multivariate time series.
problem Detecting changes in multivariate time series data efficiently and robustly.
method Developed a novel multivariate rank-energy GoF test (sRE) for change point detection.
result sRE-based CPD outperforms existing methods in AUC and F1-score.
Study flat connections with logarithmic singularities on complex plane curves.
problem Modeling flat connections with logarithmic singularities.
method Explicit finite-dimensional model construction and detailed investigation of specific cases.
result Construction of shifted Poisson structure on moduli spaces.
Esnault-Viehweg developed the theory of cyclic branched coverings of smooth surfaces providing a very explicit formula for the decomposition of in terms of a resolution of the ramification locus. Later, the first author applies this to the particular case of coverings of $\mat…