Agent-based model for spot and balancing electricity markets.
problem Accurately modeling the complex dynamics of electricity markets.
method Agent-based model with one-minute resolution.
result Simulation results match Nord Pool data.
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.
169,341 papers · 148 categories
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48 results for “one-minute resolution”
A new deep learning method for tissue-cleared image registration.
problem Efficient registration of high-resolution tissue-cleared images.
method Densely connected convolutional architecture for deformable image registration, unsupervised training.
result Comparable and superior registration performance to state-of-the-art methods, especially at higher resolutions.
Improved video emotion estimation in one minute.
problem Estimating continuous emotional states from short videos.
method Used four base regression models and a spectral approach to fuse them.
result Improved performance in estimating arousal and valence values.
audEERING improves emotion recognition in 1 minute.
problem Improving emotion recognition in short timeframes.
method Submissions and evaluations for the OMG emotion recognition challenge.
result Achieved high CCC scores for arousal and valence.
Herd Behaviors in Financial Marketscond-mat.stat-mech
We investigate the herd behavior of returns for the yen-dollar exchange rate in the Japanese financial market. It is obtained that the probability distribution of returns satisfies the power-law behavior with the exponents (the time interval one minute) and 3.36( one da…
Real-time Ionospheric Imaging of S4 Scintillation from Limited Data with Parallel Kalman Filters and Smoothnessphysics.ao-ph
Real-time ionospheric images created from limited data using parallel Kalman filters.
problem Monitoring ionospheric irregularities using limited spatio-temporal observations.
method Bayesian framework with parallel Kalman filters and connectivity information.
result Real-time ionospheric images with high spatio-temporal resolution can be produced.
The paper optimizes predicting future window states in buildings using past climate data.
problem Predicting future window opening actions based on past climate data.
method Trained a deep neural network to predict window states using indoor climate data sequences of 30-240 time-steps.
result Optimal predictive performance achieved with 60 time-steps of indoor climate data, and long sequences could be addressed efficiently.
Dynamical Volatilities for Yen-Dollar Exchange Ratescond-mat.other
We study the continuous time random walk theory from financial tick data of the yen-dollar exchange rate transacted at the Japanese financial market. The dynamical behavior of returns and volatilities in this case is particularly treated at the long-time limit. We find that the volatility for prices shows a power-law w…
The inversion formula for conservative multifractal measures was unveiled mathematically a decade ago, which is however not well tested in real complex systems. In this Letter, we propose to verify the inversion formula using high-frequency turbulent financial data. We construct conservative volatility measure based on…
Many studies have shown that there are good reasons to claim very low predictability of currency nevertheless, the deviations from true randomness exist which have potential predictive and prognostic power [J.James, Quantitative finance 3 (2003) C75-C77]. We analyze the local trends which are of the main focus of the t…
NYSE stock prices show persistent correlations over years, exploitable through arbitrage strategies.
problem Predicting and exploiting long-term price correlations in NYSE stocks.
method Analyzed 1000 NYSE stocks over 5 years, measured discrepancies from Brownian motion, and tested arbitrage strategies.
result 45% of a stock's 1-hour returns variance is explained by cross-correlations with other stocks, especially during high volatility periods.
The statistical properties of the multipliers of the absolute returns are investigated using one-minute high-frequency data of financial time series. The multiplier distribution is found to be independent of the box size when is larger than some crossover scale, providing direct evidence of the existence of sca…
Study shows priority access in ELA auctions is less valuable due to volatility risks.
problem Impact of volatility on time-based transaction ordering policies.
method Single-round model with risk-averse bidders, tested using ETH price data.
result Priority access is discounted due to volatility and risk aversion.
We present a nonlinear stochastic differential equation (SDE) which mimics the probability density function (PDF) of the return and the power spectrum of the absolute return in financial markets. Absolute return as a measure of market volatility is considered in the proposed model as a long-range memory stochastic vari…
Study shows Bitcoin futures increased initial volatility but stabilized over time.
problem Impact of Bitcoin futures on Bitcoin market volatility.
method Examined intraday volatility using one-minute price data from four exchanges, analyzed with Fourier transform.
result Bitcoin market became more stable over time after the introduction of Bitcoin futures.
Mapping Low-Resolution Images To Multiple High-Resolution Images Using Non-Adversarial Mappingeess.IV
SR-NAM maps low-res images to multiple high-res images realistically.
problem Mapping low-resolution images to multiple high-resolution images realistically.
method SR-NAM using Non-Adversarial Mapping (NAM) technique and a degradation model.
result Realistic degradation and down-sampling of high-resolution images.
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.
The paper proposes a method to improve fine-resolution predictions using coarse-resolution data.
problem Limited supervision for fine-resolution predictions with scarce data.
method Attention-based regularization on multi-view coarse-resolution data.
result The method improves fine-resolution predictions using coarse-resolution data.
Super-resolution improves MRI resolution and accuracy for biomarker assessment.
problem Inadequate SNR for accurate quantification in high-resolution MRI.
method Utilized deep learning super-resolution to maintain SNR for T2 relaxation time biomarkers while generating high-resolution images.
result Super-resolution successfully maintains high-resolution and accurate biomarkers for MRI.
This study prioritizes temporal resolution over spatial in energy systems models due to higher influence.
problem The impact of spatial and temporal resolution on energy system models.
method Global sensitivity analysis to compare structural aspects, spatial, and temporal resolution.
result Temporal resolution has a higher influence on all results parameters compared to spatial resolution.
Deep learning improves 3D microscopy resolution without matched target images.
problem Anisotropic resolution in volumetric fluorescence microscopy.
method Cycle-consistent generative adversarial network trained on unpaired 2D images.
result Enhanced axial resolution and restored details between imaging planes.
There are 2^n possible resolutions of a smooth pseudodiagram with n precrossings. If we consider piecewise-linear (PL) pseudodiagrams and resolutions that themselves are PL, certain resolutions of the pseudodiagram may not exist in three-space. We investigate this situation and its impact on the weighted resolution set…
Recently M. Kreck introduced a class of stratified spaces called p-stratifolds [M. Kreck, Stratifolds, Preprint]. He defined and investigated resolutions of p-stratifolds analogously to resolutions of algebraic varieties. In this note we study a very special case of resolutions, so called optimal resolutions, for p-str…
High-resolution signal recovery via generalized sampling and functional principal component analysismath.ST
The paper presents a method to recover high-resolution signals from low-resolution measurements.
problem Recovering high-resolution signals from low-resolution indirect measurements.
method Combining generalized sampling and functional principal component analysis.
result High-resolution recovery is possible under certain conditions and with a sufficiently large training set.
A resolution of the St. Petersburg paradox is presented. In contrast to the standard resolution, utility is not required. Instead, the time-average performance of the lottery is computed. The final result can be phrased mathematically identically to Daniel Bernoulli's resolution, which uses logarithmic utility, but is …
The `Folk Theorem' that a smooth action by a compact Lie group can be (canonically) resolved, by iterated blow up, to have unique isotropy type is proved in the context of manifolds with corners. This procedure is shown to capture the simultaneous resolution of all isotropy types in a `resolution tower' which projects …
Market Mill Dependence Pattern in the Stock Market: Asymmetry Structure, Nonlinear Correlations and Predictabilityphysics.soc-ph
An empirical study of joint bivariate probability distribution of two consecutive price increments for a set of stocks at time scales ranging from one minute to thirty minutes reveals asymmetric structures with respect to the axes y=0, y=x, x=0 and y=-x. All four asymmetry patterns remarkably resemble a four-blade mill…
DeepDownscale uses deep learning to upscale weather forecasts.
problem Computational expense of high-resolution weather models limits their use.
method Supervised deep learning to learn high-resolution from low-resolution forecasts.
result Significant improvement in weather forecast quality.
Develops a multi-resolution multi-task framework for integrating noisy, varying data.
problem Integrating evidence from multiple observation processes with varying resolutions and noise levels.
method Multi-resolution Multi-task Gaussian Processes (MRGP) framework, shallow and deep Gaussian Process mixtures.
result Generalizes and outperforms state-of-the-art GP compositions, offering efficient corrections and approximations.
Study contact resolutions for Jacobi structures, providing examples and impossibility results.
problem Understanding contact resolutions of Jacobi structures.
method Examining various classes of Jacobi structures and their contact properties.
result Identified conditions under which contact resolutions exist and those where they do not.
Study proposes CNN for reconstructing high-res urban DEMs.
problem Lack of high-res urban DEM datasets for flood modeling.
method Multi-scale CNN model trained on urban DEMs of varying resolutions.
result CNN-based method produces superior high-res urban DEMs.
WrapNet optimizes inference for low-resolution neural networks by using 8-bit additions.
problem Reducing multiplication complexity in low-resolution neural networks.
method Adapting neural networks to use low-resolution (8-bit) additions in accumulators, with a cyclic activation layer and overflow penalty regularizer.
result Achieves comparable classification accuracy to 32-bit counterparts using low-resolution additions.
This paper simplifies diffusion models for high resolution images.
problem Applying diffusion models to high resolution images is challenging.
method Adjust noise schedule, scale specific parts, add dropout, and use downsampling.
result Achieved state-of-the-art image generation performance.
A refined form of the `Folk Theorem' that a smooth action by a compact Lie group can be (canonically) resolved, by iterated blow up, to have unique isotropy type was established by the authors in the context of manifolds with corners; the canonical construction induces fibrations on the boundary faces of the resolution…
New deep learning method improves 4D Flow MRI super-resolution under domain shift.
problem Domain shift in low-resolution 4D Flow MRI data.
method Distributional deep learning framework for domain generalization.
result Framework significantly outperforms traditional methods in real data applications.
Bayesian framework for solar magnetogram super-resolution with uncertainty quantification.
problem Uncertainty in super-resolving solar magnetic field images.
method Bayesian decomposition of uncertainties into epistemic and aleatoric.
result Generation of maps measuring the range of possible high-resolution explanations.
Unified proofs for various resolution theorems using simpler techniques.
problem Proving the cell-like, -, and -resolution theorems.
method Unified proofs employing simpler extensions and topological methods.
result Simpler proofs for the resolution theorems.
Generates high-resolution images from low-resolution inputs.
problem Generating realistic images from low-resolution inputs.
method Latent Adversarial Generator (LAG) using perceptual loss.
result Samples of high-resolution images from low-resolution inputs.
3D CNNs improve brain tumor segmentation using multi-resolution features.
problem Brain tumor segmentation in MR images.
method Three 3D CNN architectures combining fine and coarse features.
result Multi-resolution architectures outperform single-resolution networks.
A refined form of the `Folk Theorem' that a smooth action by a compact Lie group can be (canonically) resolved, by iterated blow up, to have unique isotropy type is proved in the context of manifolds with corners. This procedure is shown to capture the simultaneous resolution of all isotropy types in a `resolution stru…
Unique shrinking solitons found on cone resolutions.
problem Uniqueness of shrinking Kähler-Ricci solitons on cone resolutions.
method Analyzing asymptotic conical properties and using Esparza's result.
result At most one complete shrinking Kähler-Ricci soliton exists on resolutions of Kähler cones.
MeshfreeFlowNet generates high-resolution spatio-temporal solutions from low-resolution inputs.
problem Generating high-resolution spatio-temporal solutions from low-resolution inputs.
method Physics-constrained deep learning framework using fully convolutional encoders.
result Significantly outperforms existing baselines in super-resolution of turbulent flows.
In the paper titled "Bockstein basis and resolution theorems in extension theory" (arXiv:0907.0491v2), we stated a theorem that we claimed to be a generalization of the Edwards-Walsh resolution theorem. The goal of this note is to show that the main theorem from (arXiv:0907.0491v2) is in fact equivalent to the Edwards-…
Developed AI models for multi-gas detection in near IR spectrums.
problem Detecting multiple gases in near IR spectrums.
method Used Monte Carlo KNN and multi-resolution CNN, synthesized near IR spectrums, optimized kernel sizes and channels.
result Multi-resolution CNN outperforms other models.
Deep learning boosts micro-CT image resolution and texture recovery.
problem Compensating for image resolution trade-offs in micro-CT imaging.
method EDSRGAN trained on a diverse dataset of uCT images.
result EDSRGAN outperforms other methods in texture recovery and resolution.
Researchers create a partial resolution of Coulomb branches for gauge theories.
problem Understanding partial resolutions of Coulomb branches in gauge theories.
method Constructing partial resolutions as variants of generalized slices in geometric contexts.
result Identified partial resolutions with specific geometric objects.
Method fuses low and high-resolution data for better health estimates.
problem Improving high-resolution health estimates from mixed data sources.
method Fusion of unbiased low-resolution and potentially biased high-resolution data, learning a distribution consistent with sampling bias.
result Significant reduction in bias in high-resolution estimates.
Constant Scalar Curvature Kähler Metrics on Resolutions of an Orbifold Singularity of Depth 1math.DG
New metrics found on orbifold resolutions with specific curvature.
problem Finding metrics with constant scalar curvature on orbifolds.
method Constructing metrics on resolutions of orbifolds with type I singularities.
result Constant scalar curvature Kähler metrics constructed on resolutions.