In this paper, we applied the multifractal detrended fluctuation analysis to the daily means of wind speed measured by 119 weather stations distributed over the territory of Switzerland. The analysis was focused on the inner time fluctuations of wind speed, which could be more linked with the local conditions of the hi…
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.
168,742 papers · 148 categories
Trend · papers per month
48 results for “speed measure”
Quantum walks blend patterns into splines when averaged.
problem Understanding the asymptotic patterns of quantum random walks.
method Averaging over quantum coins using the Haar measure.
result Patterns blend into splines, showing a unified behavior.
Researchers use Gaussian Process Regression to improve accuracy of a low-cost hot-wire anemometer.
problem Improving accuracy of low-cost hot-wire anemometers in varying temperatures.
method Probabilistic calibration using Gaussian Process Regression.
result The method provides good performance in estimating actual wind speeds, including uncertainty.
A neural network speeds up computation of Wasserstein barycenters by 60x.
problem Computing Wasserstein barycenters is computationally demanding.
method Trained a deep convolutional neural network to compute Wasserstein barycenters.
result Computational times reduced from milliseconds to seconds.
This paper establishes for the first time the predictive performance of speed priors and their computational complexity. A speed prior is essentially a probability distribution that puts low probability on strings that are not efficiently computable. We propose a variant to the original speed prior (Schmidhuber, 2002),…
Wind energy resource quantification, air pollution monitoring, and weather forecasting all rely on rapid, accurate measurement of local wind conditions. Visual observations of the effects of wind---the swaying of trees and flapping of flags, for example---encode information regarding local wind conditions that can pote…
A meticulous assessment of the risk of impacts associated with extreme wind events is of great necessity for populations, civil authorities as well as the insurance industry. Using the concept of spatial risk measure and related set of axioms introduced by Koch (2017, 2019), we quantify the risk of losses due to extrem…
Symbolic dynamics for flows in high dimensions, extending previous work.
problem Coding flows with positive speed in high dimensions.
method Construct symbolic dynamics for flows with positive speed in any dimension.
result Extended symbolic dynamics to flows in high dimensions, including homoclinic classes.
We introduce the SaaS Algorithm for semi-supervised learning, which uses learning speed during stochastic gradient descent in a deep neural network to measure the quality of an iterative estimate of the posterior probability of unknown labels. Training speed in supervised learning correlates strongly with the percentag…
We present a new similarity measure based on information theoretic measures which is superior than Normalized Compression Distance for clustering problems and inherits the useful properties of conditional Kolmogorov complexity. We show that Normalized Compression Dictionary Size and Normalized Compression Dictionary En…
Paper investigates separating times for general diffusions, providing new insights.
problem Understanding phase transitions between equivalence and singularity in diffusions.
method Representation of separating time as hitting time of a deterministic set, characterized by speed and scale.
result Explicit and easy-to-check conditions for absolute continuity and singularity of diffusions.
Random walk speed on Teichmüller space is a proper function.
problem Understanding the speed of random walks on Teichmüller space.
method Adaptation of Gouëzel's pivoting techniques to Teichmüller space.
result Speed of random walk is a proper function on Teichmüller space.
Both the scientific community and the popular press have paid much attention to the speed of the Securities Information Processor, the data feed consolidating all trades and quotes across the US stock market. Rather than the speed of the Securities Information Processor, or SIP, we focus here on its accuracy. Relying o…
Computing the Wasserstein barycenter of a set of probability measures under the optimal transport metric can quickly become prohibitive for traditional second-order algorithms, such as interior-point methods, as the support size of the measures increases. In this paper, we overcome the difficulty by developing a new ad…
New method combines model forecasts and real-time observations for hourly wind speed predictions.
problem Filling the six-hour gap between weather model runs for accurate hourly wind speed forecasts.
method Combines quasi-real-time observed wind speed and weather model predictions using a novel Ensemble Model Output Statistics (EMOS) strategy.
result Successfully improved wind speed predictions compared to observed data from SYNOP stations.
Objective: Ultrasound elastography is gaining traction as an accessible and useful diagnostic tool for such things as cancer detection and differentiation and thyroid disease diagnostics. Unfortunately, state of the art shear wave imaging techniques, essential to promote this goal, are limited to high-end ultrasound ha…
Heteroscedastic Gaussian processes for uncertainty modeling in large-scale crowdsourced traffic datastat.ML
Accurately modeling traffic speeds is a fundamental part of efficient intelligent transportation systems. Nowadays, with the widespread deployment of GPS-enabled devices, it has become possible to crowdsource the collection of speed information to road users (e.g. through mobile applications or dedicated in-vehicle dev…
Let be a smooth flow with positive speed and positive topological entropy on a compact smooth three dimensional manifold, and let be an ergodic measure of maximal entropy. We show that either is Bernoulli, or is isomorphic to the product of a Bernoulli flow and a rotational flow. Appli…
Measures difficulty of predictions to improve deep learning models.
problem Understanding and improving deep learning models.
method Introduces a measure called prediction depth to assess example difficulty.
result Surprising relationships between prediction depth and model performance.
Paper proves convergence of Kalman filter on Stiefel manifolds with measurement errors.
problem Filtering constant particle with measurement errors on Stiefel manifolds.
method Extended Kalman filter applied to Stiefel manifold-valued observations.
result Convergence of the extended Kalman filter proved for constant system process.
Paper introduces S3W distance for spherical probability distributions.
problem Comparing spherical probability distributions efficiently and accurately.
method S3W distance using stereographic projection and generalized Radon transform.
result Extensive theoretical analysis and evaluation of S3W performance.
The paper studies how convex hypersurfaces in hyperbolic space evolve under a specific curvature flow.
problem Volume preserving Gauss curvature flow in hyperbolic space.
method Analyzes a flow of smooth, closed, and convex hypersurfaces in hyperbolic space with a nonhomogeneous speed function.
result The flow remains convex, exists for all time, and converges to a geodesic sphere exponentially.
Genie clusters faster and resists outliers.
problem Hierarchical clustering's sensitivity to outliers and slow computation.
method Genie uses an economic inequity measure to link clusters, balancing speed and quality.
result Genie outperforms other linkage methods in clustering quality and speed.
In this paper, the agent-based modeling is employed to model the effect of intellectual property policy at the speed of technological advancement. Every agent has inborn preferences towards investing their capital into independent technological development, innovation appropriation, and production. The relative cost of…
Machine learning speeds up FLIM analysis in biomedical research.
problem Complex, slow, and computationally expensive FLIM analysis.
method Machine learning techniques for faster and smarter FLIM data extraction and interpretation.
result Higher accuracy in classifying and segmenting FLIM images compared to conventional methods.
Study large deviations and speed of random walks in hyperbolic spaces.
problem Understanding the speed of random walks in hyperbolic spaces.
method Large deviations analysis for random walks with a non-elementary semi-group.
result Established large deviations results for random walk distances.
We recover the higher order terms for the acoustic wave equation from measurements of the modulus of the solution. The recovery of these coefficients is reduced to a question of stability for inverting a Hamiltonian flow transform, not the geodesic X-ray transform encountered in other inverse boundary problems like the…
Study investor attention using search volume data before and after mobile device popularity.
problem Accurately measure investor attention in a fast-paced market.
method Compare investor attention using search volume data before and after mobile device popularization.
result Investor attention measured using search volume data is more accurate and faster after mobile device popularization.
Estimates neural architecture performance speedily.
problem Accurately evaluating neural architectures' generalization performance.
method Estimates final test performance based on training speed.
result Consistently outperforms other alternatives in correlation with true test performance.
Four new methods for computing generalized chi-square distribution.
problem Computing the generalized chi-square distribution accurately and efficiently.
method Two exact and two approximate methods, with software for cdf, pdf, and inverse cdf.
result Comparison of methods' accuracy and speed, identifying best for different cases.
Adaptive sampling improves graph diffusion models by maintaining uniform information speed.
problem Standard diffusion models overlook non-homogeneous dynamics on complex manifolds.
method Information-geometric framework using Fisher-Rao metric and Drift Variation Score (DVS).
result DVS solver ensures uniform rate of distributional change, improving structural fidelity and efficiency.
The paper proposes using function approximations to reduce the computational burden in measuring counterparty credit exposure.
problem The need for regular exposure calculations in finance, balancing between computational cost and risk simplification.
method Replacing derivative pricers with function approximations, proving error bounds, and using Chebyshev interpolation for convergence.
result Derives probabilistic and finite sample error bounds, showing significant run-time reductions and asymptotic efficiency gains.
Study reveals a link between Ruelle-Pollicott resonances and cohomology eigenvalues for Anosov diffeomorphisms.
problem Understanding the speed of mixing in Anosov diffeomorphisms.
method Investigates Ruelle-Pollicott resonances on manifolds of any dimension, connecting them to cohomology eigenvalues of a quasi-compact transfer operator.
result Established a cohomological bound for the speed of mixing of Anosov diffeomorphisms.
Deep learning speeds up whole heart MRI to 30 seconds.
problem Long acquisition times in whole heart MRI.
method Deep learning, specifically a 3D residual U-Net, to reconstruct high-resolution images from low-resolution data.
result Super-resolution images show better edge sharpness and fewer artefacts than low-resolution images.
New algorithm speeds Bayesian nonparametric model inference.
problem Slow inference in Bayesian nonparametric models.
method Decompose random measures into finite and infinite sub-measures; use different algorithms for each.
result Hybrid algorithm improves scalability and mixing.
The study proves the uniqueness of entropy-maximizing measures for geodesic flows on specific manifolds.
problem Uniqueness of entropy-maximizing measures for geodesic flows on rank 1 manifolds.
method Symbolic dynamics applied to countable topological Markov flows.
result Proof of the uniqueness of the measure of maximal entropy.
Preconditioned NFs speed up sampling from complex posterior distributions in inverse problems.
problem Sampling from posterior distributions of inverse problems with expensive forward operators.
method Preconditioning a conditional normalizing flow (NF) to speed up training.
result Significant speed-ups achieved compared to training NFs from scratch.
Quantum algorithm reduces qubit usage for Monte Carlo simulations.
problem High qubit requirements for Monte Carlo simulations on quantum computers.
method Use of pseudo-random number generator (PRNG) on a quantum circuit.
result Significant reduction in qubit usage without sacrificing quantum speed.
The paper sets criteria for no arbitrage in complex financial models.
problem Determining conditions for the absence of arbitrage in financial markets.
method Established deterministic conditions for no arbitrage, NUPBR, and NFLVR in diffusion market models.
result Provided criteria in terms of scale function and speed measure.
New measures detect HFT activity, revealing its impact on stock prices.
problem Lack of public data on HFT activity.
method Developed machine learning models to predict HFT activity using proprietary and public data.
result Measures outperform conventional proxies and reveal HFT's impact on price discovery.
LEEP measures transferability of learned representations efficiently.
problem Evaluating the transferability of learned representations in machine learning.
method LEEP: Log Expected Empirical Prediction, a simple measure requiring one pass through the target data set.
result LEEP predicts transfer and meta-transfer learning performance and convergence speed, outperforming existing measures.
MTFL improves UA and speeds convergence in personalised DNNs on edge devices.
problem Non-IID user data harms FL convergence and global UA is not always the goal.
method Introduces non-federated BN layers into federated DNNs for personalised training.
result MTFL reduces UA rounds by up to 5x and convergence time by up to 3x.
LOT Wassmap speeds up Wasserstein space manifold learning.
problem Finding low-dimensional structures in Wasserstein space datasets.
method Linearized optimal transport and approximation schemes.
result LOT Wassmap provides accurate embeddings with computational efficiency.
New method speeds up lead-lag detection between asynchronous time series.
problem Slow inference of lead-lag networks between long time series.
method Derive asymptotic distribution of Transfer Entropy and introduce time-shifted time series.
result Statistically validated lead-lag networks between time series.
SIVF k-means algorithm speeds up sparse data clustering.
problem Efficiently clustering large-scale high-dimensional sparse data.
method Inverted-file structure for centroids, filter-based similarity reduction.
result SIVF achieves higher speed and lower memory consumption.
Importance sampling has become an important tool for the computation of tail-based risk measures. Since such quantities are often determined mainly by rare events standard Monte Carlo can be inefficient and importance sampling provides a way to speed up computations. This paper considers moderate deviations for the wei…
We report on experimental measurement of the Hilbert-Schmidt distance between two two-qubit states by many-particle interference. We demonstrate that our three-step method for measuring distances in Hilbert space is far less complex than reconstructing density matrices and that it can be applied in quantum-enhanced mac…
Study increasing profits in a flexible financial market model.
problem Characterize increasing profits in a 1D diffusion market with interest rates.
method Characterize increasing profits using an auxiliary deterministic signed measure and a canonical trading strategy.
result Existence and characterization of increasing profits in terms of and .