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 …
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48 results for “time resolution”
Generative adversarial networks generate realistic, time-evolving high-resolution atmospheric fields.
problem Improving spatial resolution of low-resolution atmospheric images.
method Recurrent, stochastic super-resolution GAN for generating ensembles of time-evolving high-resolution atmospheric fields.
result The GAN produces realistic, temporally consistent super-resolution sequences for radar-measured precipitation and cloud optical thickness.
On the Three Demons in Causality in Finance: Time Resolution, Nonstationarity, and Latent Factorsq-fin.ST
The paper tackles three financial issues: time resolution, nonstationarity, and latent factors.
problem Three fundamental issues in financial data: time resolution, nonstationarity, and latent factors.
method A causal perspective to reexamine and solve these issues.
result Provides systematic solutions to financial data issues.
Obtaining magnetic resonance images (MRI) with high resolution and generating quantitative image-based biomarkers for assessing tissue biochemistry is crucial in clinical and research applications. How- ever, acquiring quantitative biomarkers requires high signal-to-noise ratio (SNR), which is at odds with high-resolut…
Time-aware deep learning methods improve spatial downscaling of atmospheric pollutants.
problem Transform coarse satellite data of atmospheric pollutants into high-resolution fields.
method Super-resolution deep residual networks and UNet architectures are extended with a temporal module encoding observation time.
result Temporal modules significantly improve downscaling performance and convergence speed.
Time accelerated image super-resolution using shallow residual feature representative networkeess.IV
SRFRN accelerates image super-resolution using shallow residual units.
problem High computational complexity and time in deep learning image super-resolution.
method SRFRN uses a bicubic interpolated low-resolution image and residual representative units (RFR) for faster and more efficient high-resolution image reconstruction.
result SRFRN achieves superior performance and faster execution time compared to existing methods.
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.
R2D2-GANs generate high-resolution images at real-time speed.
problem Generating high-resolution images at real-time speed.
method Conditional recursive GANs architecture.
result Synthesized images are indistinguishable from real ones.
Deep neural networks with convolutional layers usually process the entire spectrogram of an audio signal with the same time-frequency resolutions, number of filters, and dimensionality reduction scale. According to the constant-Q transform, good features can be extracted from audio signals if the low frequency bands ar…
Background: Three-dimensional, whole heart, balanced steady state free precession (WH-bSSFP) sequences provide delineation of intra-cardiac and vascular anatomy. However, they have long acquisition times. Here, we propose significant speed ups using a deep learning single volume super resolution reconstruction, to reco…
A new pyramidal diffusion model speeds up image generation.
problem Training and evaluating diffusion models are time-consuming.
method Introduces a pyramidal diffusion model with a single score function.
result Generates high-resolution images from low-resolution starting points efficiently.
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.
Efficiently generates high-resolution images with reduced sampling time using LEGO bricks.
problem Efficiently generating high-resolution images with reduced sampling time.
method Introduces LEGO bricks that integrate Local-feature Enrichment and Global-content Orchestration to create a test-time reconfigurable diffusion backbone.
result Significantly reduces sampling time compared to other methods.
Engine predicts real-time air quality with high resolution.
problem Real-time prediction of air pollutants for health monitoring.
method Combines official data, models, land cover, traffic data for high-resolution predictions.
result Engine produces predictions with resolution of a few dozen meters.
Projective resolves symplectic Steinberg module for number rings.
problem Constructing a projective resolution for symplectic Steinberg module.
method Similar to special linear group, but more complex construction.
result Computed top degree cohomology of congruence subgroups.
A novel multi-resolution Gaussian process model for efficient time traversal.
problem Inference for long sequences with fast and slow transitions is difficult.
method A novel Gaussian process state-space architecture composed of multiple components, each trained on a different resolution.
result The combined model allows efficient inference for arbitrarily long sequences with complex dynamics.
Running high-resolution physical models is computationally expensive and essential for many disciplines. Agriculture, transportation, and energy are sectors that depend on high-resolution weather models, which typically consume many hours of large High Performance Computing (HPC) systems to deliver timely results. Many…
New insights into RL efficiency from managing time discretization.
problem The impact of time discretization on RL methods in continuous-time systems.
method Analysis of Monte-Carlo policy evaluation for LQR systems.
result An optimal choice of temporal resolution for a given data budget improves policy evaluation efficiency.
New unsupervised deep learning method improves temporal resolution in tMRA.
problem Limited temporal resolution in tMRA due to fixed view-sharing scheme.
method Optimal transport driven cycle-consistent generative adversarial network (cycleGAN) without fully sampled k-space reference data.
result Can generate high quality reconstructions at various temporal resolutions.
Super-resolution fluorescence microscopy, with a resolution beyond the diffraction limit of light, has become an indispensable tool to directly visualize biological structures in living cells at a nanometer-scale resolution. Despite advances in high-density super-resolution fluorescent techniques, existing methods stil…
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…
Land use classification of low resolution spatial imagery is one of the most extensively researched fields in remote sensing. Despite significant advancements in satellite technology, high resolution imagery lacks global coverage and can be prohibitively expensive to procure for extended time periods. Accurately classi…
Learning Time Series from Scale Informationcond-mat.mes-hall
Proposes a method to predict time series data using scale information.
problem Sequential data exhibits different behavior at different scales.
method Scale-based inference using -nearest neighbors and sequential prediction.
result Asymptotically, the algorithm produces the best prediction error.
We review the decomposition method of stock return cross-correlations, presented previously for studying the dependence of the correlation coefficient on the resolution of data (Epps effect). Through a toy model of random walk/Brownian motion and memoryless renewal process (i.e. Poisson point process) of observation ti…
CNN improves medium-range temperature forecasts with limited resources.
problem Limited computational resources for high-resolution temperature forecasts.
method CNN post-processing with ensemble NWP models for bias correction and spatial downscaling.
result High-resolution (5-km) surface temperature forecasts with lead times up to 5.5 days.
Implicit Transfer Operator Learning: Multiple Time-Resolution Surrogates for Molecular Dynamicsphysics.chem-ph
Framework learns surrogates for molecular dynamics across multiple time-scales.
problem Stable molecular dynamics simulations require small time-steps, but long-time-scale moments need repeated simulations.
method Implicit Transfer Operator Learning with denoising diffusion probabilistic models and SE(3) equivariant architecture.
result Models can generate self-consistent stochastic dynamics across multiple time-scales.
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.
In this paper, we consider the problem of predicting demographics of geographic units given geotagged Tweets that are composed within these units. Traditional survey methods that offer demographics estimates are usually limited in terms of geographic resolution, geographic boundaries, and time intervals. Thus, it would…
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.
Construct -instantons on orbifold resolutions using specific geometric ingredients.
problem Constructing -instantons on orbifold resolutions.
method Combining -instantons on orbifolds with Fueter sections over singular sets.
result Construction of numerous -instantons on resolutions of .
This study proposes a trainable adaptive window switching (AWS) method and apply it to a deep-neural-network (DNN) for speech enhancement in the modified discrete cosine transform domain. Time-frequency (T-F) mask processing in the short-time Fourier transform (STFT)-domain is a typical speech enhancement method. To re…
We propose Progressive Structure-conditional Generative Adversarial Networks (PSGAN), a new framework that can generate full-body and high-resolution character images based on structural information. Recent progress in generative adversarial networks with progressive training has made it possible to generate high-resol…
The main goal of this study is to extract a set of brain networks in multiple time-resolutions to analyze the connectivity patterns among the anatomic regions for a given cognitive task. We suggest a deep architecture which learns the natural groupings of the connectivity patterns of human brain in multiple time-resolu…
Super-resolution methods form high-resolution images from low-resolution images. In this paper, we develop a new Bayesian nonparametric model for super-resolution. Our method uses a beta-Bernoulli process to learn a set of recurring visual patterns, called dictionary elements, from the data. Because it is nonparametric…
This research accelerates sampling methods using Nesterov's Acceleration.
problem Improving sampling efficiency in MCMC methods.
method Developed a Hessian-Free High-Resolution ODE reformulation of NAG-SC, injected noise, and discretized the diffusion process.
result Quantified acceleration beyond underdamped Langevin in distance for log-strongly-concave targets.
We analyze high-resolution foreign exchange data consisting of 20 million data points of USD-JPY for 13 years to report firm statistical laws in distributions and correlations of exchange rate fluctuations. A conditional probability density analysis clearly shows the existence of trend-following movements at time scale…
CESAR improves wind speed and power forecasting for high-resolution simulations.
problem Accurate high-resolution wind forecasting for efficient power grid management.
method A spatio-temporal neural network model using deep convolutional autoencoder and echo state network.
result CESAR provides up to 17% improvement in wind speed and power forecasting compared to best alternatives.
Study symplectic cohomology of certain singularities using homological mirror symmetry.
problem Compute symplectic cohomology for specific singularities.
method Use homological mirror symmetry to compute symplectic cohomology.
result Suggests a new conjecture about the relationship between small resolutions and symplectic cohomology.
not-so-BigGAN: Generating High-Fidelity Images on Small Compute with Wavelet-based Super-Resolutioneess.IV
A cost-effective method to generate high-resolution images using wavelet-based super-resolution.
problem High compute resources required for training state-of-the-art image generation models.
method Wavelet-based two-step training framework: low-frequency sampling followed by super-resolution.
result Achieves FID of 10.59 on ImageNet 512x512 with half the compute of BigGAN.
Factorial moments are convenient tools in nuclear physics to characterize the multiplicity distributions when phase-space resolution () becomes small. For uncorrelated particle production within , Gaussian statistics holds and factorial moments are equal to unity for all orders . Correlations between par…
Recently, several models based on deep neural networks have achieved great success in terms of both reconstruction accuracy and computational performance for single image super-resolution. In these methods, the low resolution (LR) input image is upscaled to the high resolution (HR) space using a single filter, commonly…
Let be an affine variety with only normal isolated singularity and a smooth resolution of the singularity with trivial canonical line bundle . If the complement of the affine variety is the cone of an Einstein-Sasakian manifold , we shall p…
Generative model downgrades coarse satellite images to fine resolution.
problem Reconstructing fine resolution satellite images from coarse scale inputs.
method Combines U-Net transfer encoder with diffusion-based generative model.
result Excellent performance (R2 = 0.65 to 0.94) across seasonal regional splits.
MDEQ models learn multi-resolution features efficiently.
problem Large-scale, hierarchical pattern recognition.
method Implicit differentiation, multiscale deep equilibrium model.
result MDEQs achieve performance on par with recent models.
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 recent application of deep learning in various areas of medical image analysis has brought excellent performance gains. In particular, technologies based on deep learning in medical image registration can outperform traditional optimisation-based registration algorithms both in registration time and accuracy. Howev…
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.
Recent advances in video super-resolution have shown that convolutional neural networks combined with motion compensation are able to merge information from multiple low-resolution (LR) frames to generate high-quality images. Current state-of-the-art methods process a batch of LR frames to generate a single high-resolu…