This paper uses bivariate time series to analyze currency similarity in the foreign exchange market.
problem Analyzing similarity among currencies in the foreign exchange market.
method Applies Escoufier's RV coefficient to measure similarity between bivariate time series of currency exchange rates.
result Demonstrates the advantages of using RV coefficient for analyzing currency topological structure.
Modified RV-coefficient reveals how training affects neural network representations.
problem Understanding how training affects intermediate representations in convolutional neural networks.
method Experimented with modified RV-coefficient (RV2) to compare activation patterns in deep networks trained on varying amounts of data and layers.
result RV2 successfully recovered expected similarity patterns and provided interpretable similarity matrices.
Deep learning ν-net automates cardiac MRI segmentation for accurate mass and function parameters.
problem Challenging image segmentation of biventricular cardiac anatomy.
method Deep neural network training on 253 manually segmented cases, evaluated on 1000 cases.
result State-of-the-art performance in LV and RV EF, VM measurements.
Proposes a new dependency function for measuring non-linear relationships.
problem Need for a general-purpose measure of dependency between random variables.
method Revision of ideal properties and proposal of a new dependency function.
result Proposes a new dependency function that meets all desired properties.
This paper proposes a new RV prediction model using neural distributional transformation and co-training.
problem Predicting skewed and fat-tailed realized volatility (RV) is challenging.
method The paper uses a neural distributional transformation and co-training to predict RV. It jointly trains the transformation and prediction model using a maximum-likelihood objective function.
result The proposed method significantly outperforms other methods on a dataset of 100 stocks.
GPRNs accurately model stellar activity affecting RV measurements of exoplanets.
problem Stellar activity limits detection and characterisation of exoplanets.
method Gaussian Process Regression Networks (GPRNs) for joint analysis of RV data and stellar activity indicators.
result GPRNs accurately describe solar RV data, correlating with activity at separations of a few days.
Study of manifolds with nonnegative Ricci curvature and slow relative volume growth.
problem Understanding fundamental groups of manifolds with specific volume growth.
method Defined a function RV(s) to describe volume growth and studied fundamental groups with slow relative volume growth.
result If RV(s) grows sublinearly, fundamental groups are almost abelian or finite.
The study uses machine learning to forecast stock volatility, showing superior performance over traditional methods.
problem Forecasting stock volatility using machine learning.
method Pooling stock data, using a proxy for market volatility, and applying neural networks.
result The proposed methodology yields superior out-of-sample forecasts over traditional methods.
The paper proves a finite diffeomorphism theorem for manifolds with lower Ricci curvature and bounded energy.
problem Proving a finite number of diffeomorphism types for manifolds with specific curvature and energy bounds.
method Analyzing the space of closed manifolds with lower Ricci curvature, volume, diameter, and energy bounds.
result The space of manifolds has at most a finite number of diffeomorphism types.
Graph Signal Processing improves stock market volatility forecasting.
problem Forecasting realized volatility in a global stock market context.
method Integrating Graph Signal Processing into the HAR model.
result The proposed model outperforms HAR-type benchmarks.
The study shows how geometric Weyl bulk-density exponent rigidifies spectral encodings in O-regularly varying classes.
problem Understanding spectral encodings under Weyl growth conditions.
method Analyzing geometric Weyl bulk-density exponent and proving spectral rigidity.
result The geometric Weyl bulk-density exponent (d−2)/2 rigidifies spectral encodings in the O-regularly varying class, leading to unique admissible exponents and scaling laws. Combines k-NN and RVM for improved classification accuracy.
problem Improving k-NN's performance by considering relevancy.
method Integrates k-NN and RVM in kernel space, introduces a new stopping parameter.
result Significantly prunes irrelevant attributes and improves classification accuracy.
Generative Adversarial Model improves RV segmentation accuracy in MRI.
problem Accurate segmentation of the right ventricular blood pool from cine MRI sequences.
method Combines FCNN, Gated Recurrent Units (GRU), Generative Adversarial Networks (GAN), and L1 loss function.
result Improves Dice Index and Hausdorff Distance by 0.05 and 3.49 mm respectively.
The paper optimizes RV estimation by efficient sampling in time-changed diffusion models.
problem Improving realized variance (RV) estimation in time-changed diffusion models.
method Theoretical analysis and simulations of hitting time and realized business time sampling schemes.
result Realized business time sampling is empirically most efficient for high noise levels.
Supervised learning alone can be effective for offline RL, revealing essential elements.
problem Understanding when and how supervised learning alone can be effective for offline RL.
method Extensive experiments to identify essential elements for offline RL via supervised learning.
result Maximizing likelihood with a two-layer feedforward MLP is competitive with more complex methods.
Study finds 'Dragon Kings' in stock market volatility during major economic crises.
problem Identifying significant deviations from normal market volatility.
method Analyzed S&P500 index volatility, categorized as Black Swans, Dragon Kings, or Negative Dragon Kings, using modified Generalized Beta and Generalized Beta Prime distributions.
result Observed 'potential' Dragon Kings that eventually turn into Negative Dragon Kings, with more pronounced phenomenon as time averaging increases.
Proves L2 curvature bound for manifolds with bounded Ricci curvature.
problem Bounding L2 curvature on manifolds with controlled Ricci curvature. method Structural analysis of limits of non-collapsed manifolds with bounded Ricci curvature.
result Proves L2 curvature bound $\fint_{B_1(p)}|\Rm|^2 < C(n,
v)$. Study on implied volatility for multi-factor rough volatility models.
problem Understanding implied volatility in multi-factor rough volatility models.
method Large deviations principle and numerical methods to compute rate function.
result Identification of models generating non-linear smiles.
Study improves S&P 500 volatility forecasting through regime-switching methods.
problem Accurate prediction of S&P 500 volatility for risk management and investment.
method Regime-switching methods including soft Markov switching, spectral clustering, and coefficient-based clustering.
result Coefficient-based clustering algorithm outperformed other models during all time periods.
This work proposes a new method to estimate joint probability from pairwise marginals, reducing sample complexity.
problem Direct nonparametric estimation of high-dimensional joint probability is infeasible due to the curse of dimensionality.
method Developed a coupled nonnegative matrix factorization (CNMF) framework using only pairwise marginals.
result The method provably recovers the joint probability mass function up to bounded error in finite iterations under reasonable conditions.
The study analyzes macroeconomic factors affecting copper futures volatility and long-term correlation with S&P 500.
problem Understanding the impact of macroeconomic variables on copper futures volatility and long-term correlation.
method Employed GARCH-MIDAS and DCC-MIDAS modeling frameworks to examine the influence of low-frequency macroeconomic variables on copper futures returns and long-term correlation with S&P 500.
result PPI is the most efficient macroeconomic variable impacting copper futures returns, and MIDAS filter improves model fitness and long-run relationship.
A new unbiased Hessian estimator for expectation-based objectives.
problem Estimating Hessian for objectives with non-reparameterizable nodes.
method GO Hessian estimator for expectation-based objectives.
result GO Hessian provides unbiased and low-variance estimation of Hessian.
Method bounds tail probabilities of continuous RVs.
problem Bounding tail probabilities of continuous random variables.
method Setting continuous, positive, and strictly decreasing/increasing functions to derive upper and lower bounds.
result Provides tighter bounds than existing methods, including a novel asymptotic capacity bound for AWGN channel.
A new model decomposes market variability into interpretable components.
problem Understanding the factors driving market variability and predicting future movements.
method H-SGDLM framework with HAR-RV model for GPU-scalable multivariate volatility estimation.
result Superior performance in predicting large moves and longer-term market variability.
Enhanced volatility forecasting using options data and rough volatility model.
problem Improving realized volatility forecasting accuracy.
method Infer spot volatility from options data using rough stochastic volatility model, accelerate estimation with deep learning, benchmark against traditional models.
result Augmented HAR-RV-RHeston model outperforms traditional models in daily and long-term forecasting.
We prove an extension of the Index Theorem for Morse-Sturm systems of the form −V′′+RV=0, where R is symmetric with respect to a (non positive) symmetric bilinear form, and thus the corresponding differential operator is not self-adjoint. The result is then applied to the case of a Jacobi equation along a geodesic in…
DynamicPPL speeds up probabilistic modeling in Julia.
problem Developing and executing complex dynamic probabilistic models efficiently.
method Modular Julia library with a DSL, tracing data structure, and contextual dispatch.
result Achieves computational performance close to or better than Stan.
Study of quasimorphisms and bounded cohomology in braided Thompson groups.
problem Investigate quasimorphisms and bounded cohomology in braided versions of Thompson groups.
method Analyze quasimorphisms and bounded cohomology of various braided Thompson groups.
result Found infinite-dimensional spaces of quasimorphisms in some braided Thompson groups and trivial second bounded cohomology in others.
In this short report, we investigate the ability of the DCCA coefficient to measure correlation level between non-stationary series. Based on a wide Monte Carlo simulation study, we show that the DCCA coefficient can estimate the correlation coefficient accurately regardless the strength of non-stationarity (measured b…
Formula connects linking coefficients to Kontsevich integral coefficients.
problem Linking coefficients from Kontsevich integral.
method Purely combinatorial approach.
result Expresses linking coefficients as combinations of Kontsevich integral coefficients.
We study the statistics of record-breaking events in daily stock prices of 366 stocks from the Standard and Poors 500 stock index. Both the record events in the daily stock prices themselves and the records in the daily returns are discussed. In both cases we try to describe the record statistics of the stock data with…
Computed distortion coefficients for the α-Grushin plane.
problem Analyzing the distortion coefficients of the α-Grushin plane.
method Using generalised trigonometric functions and synthetic curvature conditions.
result Estimates for distortion coefficients and a curvature condition conjecture.
Machine learning predicts Kronecker coefficients with high accuracy.
problem Predicting Kronecker coefficients from tensor products of symmetric group representations.
method Training machine learning models (NN, CNN, GBDT) to classify Kronecker coefficients as zero or non-zero.
result Trained models achieve high accuracy (≈0.98) in classifying Kronecker coefficients. Abstract: Determines thermoelastic coefficients from boundary data.
problem Determining coefficients of thermoelastic system from boundary information.
method Explicit expression for thermoelastic Dirichlet-to-Neumann map with variable coefficients.
result Thermoelastic Dirichlet-to-Neumann map uniquely determines coefficients on the manifold.
New filling functions for groups with coefficients show different asymptotic behavior.
problem Difficulty in filling loops with surfaces in Cayley graphs.
method Defining homological filling functions with coefficients and proving their differences.
result Filling functions for n-cycles with coefficients in different groups have distinct asymptotic behavior. Bayesian model merges multi-view latent models and kernel methods.
problem Handling high-dimensionality and non-linear issues in multi-view data.
method Combines probabilistic factor analysis with kernelized observations.
result Compact solutions for kernelized observations and feature selection.
Abstract: Determines Lamé coefficients from boundary measurements.
problem Determining Lamé coefficients from elastic boundary measurements.
method Explicit symbol of elastic Dirichlet-to-Neumann map, partial derivatives determination.
result Elastic Dirichlet-to-Neumann map uniquely determines Lamé coefficients.
Defines and proves properties of weighted renormalized volume coefficients.
problem None explicitly stated; focuses on mathematical definitions and proofs.
method Defines weighted renormalized volume coefficients and proves their variational nature and polynomial representation.
result Weighted renormalized volume coefficients are variational and can be expressed as polynomials of specific tensors.
Improved 3D LiDAR data classification using product coefficients.
problem Enhancing accuracy in 3D LiDAR data classification.
method Introducing product coefficients derived from measure theory as additional features in the classification process, alongside PCA.
result Significant improvement in classification accuracy with product coefficients.
High-dimensional, large-sample astrophysical databases of galaxy clusters, such as the Chandra Deep Field South COMBO-17 database, provide measurements on many variables for thousands of galaxies and a range of redshifts. Current understanding of galaxy formation and evolution rests sensitively on relationships between…
We study the structure of the stable coefficients of the Jones polynomial of an alternating link. We start by identifying the first four stable coefficients with polynomial invariants of a (reduced) Tait graph of the link projection. This leads us to introduce a free polynomial algebra of invariants of graphs whose ele…
Develops local elliptic regularity for geometrically-natural operators with low regularity coefficients.
problem Local elliptic regularity for operators with low regularity coefficients in Sobolev-type spaces.
method Rescaling estimates and multiplication results for function spaces.
result Unified set of interior estimates and regularity inference for operators with Sobolev-type coefficients.
Introduces higher-order clustering coefficients to better understand network structures.
problem Understanding the clustering behavior of higher-order network cliques in complex networks.
method Develops higher-order clustering coefficients as a generalization of traditional clustering coefficients.
result Provides new insights into the structure of real-world networks.
Paper calculates third coefficient in Kaehler-Einstein metric expansion.
problem Understanding Kaehler-Einstein metrics and their epsilon functions.
method Computes the third coefficient in the TYCZ-expansion of the epsilon function.
result Discovers the vanishing of the third coefficient's significance.
Zeroth HOMFLY polynomial coefficients can't tell mutant knots apart.
problem Mutants in knots cannot be distinguished by the zeroth coefficient of colored HOMFLY polynomials.
method Examined the cables of the HOMFLY polynomial to show invariance of the zeroth coefficient.
result The zeroth coefficient of the colored HOMFLY polynomial is invariant to mutation.
The paper uses Floer homology to study twist coefficients and their behavior after capping off.
problem Behavior of twist coefficients after capping off a boundary component.
method Heegaard Floer homology to constrain twist coefficients.
result Results about fractional Dehn twists and Floer homology of cyclic branched covers.
Extends A-type coefficient polynomials to B-type setting, introducing new invariants.
problem Tackles the B-type skein relation and introduces new coefficient polynomials.
method Introduces coefficient polynomials associated with the B-type skein relation and proves their invariance under Reidemeister moves.
result Shows that the generating series of these coefficient polynomials recovers the Kauffman polynomial.
We simplify complex regression coefficients using linearization and feature comparison.
problem Interpreting high-dimensional regression coefficients from nonlinear responses.
method Developed a linearization method to derive feature coefficients and compare them with regression coefficients.
result Shows how regression coefficients relate to linearized feature coefficients and how they change under regularization.