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.
This study prioritizes temporal resolution over spatial in energy systems models due to higher influence.
Pixel-space diffusion models outperform latent models on high-resolution image synthesis.
The study optimizes Gaussian process approximations for finite-rank models.
Improved Mapper algorithm for datasets with varying density.
NKN deep neural network learns governing equations and classifies images.
Paper proposes combining GAM and DNN for accurate peak demand estimation from lower-resolution data.
Efficiently resolves entities via scaled Ewens--Pitman model.
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…
High resolution magnetic resonance (MR) images are desired for accurate diagnostics. In practice, image resolution is restricted by factors like hardware, cost and processing constraints. Recently, deep learning methods have been shown to produce compelling state of the art results for image super-resolution. Paying pa…
The paper reformulates U-Nets as wavelet-based models and applies this to hierarchical VAEs.
Recently, sparsity-based algorithms are proposed for super-resolution spectrum estimation. However, to achieve adequately high resolution in real-world signal analysis, the dictionary atoms have to be close to each other in frequency, thereby resulting in a coherent design. The popular convex compressed sensing methods…
Since manually labeling training data is slow and expensive, recent industrial and scientific research efforts have turned to weaker or noisier forms of supervision sources. However, existing weak supervision approaches fail to model multi-resolution sources for sequential data, like video, that can assign labels to in…
We consider the family of constant curvature fiber metrics for a Lefschetz fibration with regular fibers of genus greater than one. A result of Obitsu and Wolpert is refined by showing that on an appropriate resolution of the total space, constructed by iterated blow-up, this family is log-smooth, i.e. polyhomogeneous …
This paper provides a theoretical justification of the superior classification performance of deep rectifier networks over shallow rectifier networks from the geometrical perspective of piecewise linear (PWL) classifier boundaries. We show that, for a given threshold on the approximation error, the required number of b…
Constructing compact non-Kähler manifolds with and without the Hard Lefschetz Condition
The extension of the classical Bayesian penalized spline method to inference on vector-valued functions is considered, with an emphasis on characterizing the suitability of the method for general application.We show that the standard quadratic penalty is exactly analogous to the energy of a stretched string, with the p…
With super-resolution optical microscopy, it is now possible to observe molecular interactions in living cells. The obtained images have a very high spatial precision but their overall quality can vary a lot depending on the structure of interest and the imaging parameters. Moreover, evaluating this quality is often di…
WavPool improves deep neural networks with wavelet-based pooling.
New method uses hyperspherical geometry to improve community detection.
A new dynamical approach connects resolution cohomology to group representations.
Generative model downgrades coarse satellite images to fine resolution.
A new algorithm reduces distributed learning error to near-optimal levels.
New method calculates degrees of freedom for sparse estimation in continuous models.
SR-NAM maps low-res images to multiple high-res images realistically.
Deep learning speeds up whole heart MRI to 30 seconds.
Deep learning (DL) has shown great potential in medical image enhancement problems, such as super-resolution or image synthesis. However, to date, little consideration has been given to uncertainty quantification over the output image. Here we introduce methods to characterise different components of uncertainty in suc…
SC-Net learns interpretable filters for inverse problems, achieving optimal convergence and super-resolution.
Proposes a method to predict time series data using scale information.
Deep networks generalize well due to hidden mechanisms like renormalization.
We derive a formula expanding the bracket with respect to a natural deformation parameter. The expansion is in terms of a two-variable polynomial algebra of diagram resolutions generated by basic operations involving the Goldman bracket. A functorial characterization of this algebra is given. Differentiability properti…
For many complex diseases, there is a wide variety of ways in which an individual can manifest the disease. The challenge of personalized medicine is to develop tools that can accurately predict the trajectory of an individual's disease, which can in turn enable clinicians to optimize treatments. We represent an indivi…
The paper proposes a method to improve fine-resolution predictions using coarse-resolution data.
Let be a finite group acting linearly on $\C^n$, freely outside the origin, and let be the number of conjugacy classes of minus one. A construction of Kronheimer of moduli spaces of translation-invariant -equivariant instantons on $\C^2$ is generalised to $\C^n$. The moduli spaces depend on a…
Deep learning improves 3D microscopy resolution without matched target images.
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…
Study uses CNNs to upscale wind speed data from 100 km to 3 km, improving subgrid-scale variability.
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…
The paper presents a method to recover high-resolution signals from low-resolution measurements.
Study counts rational curves on hyperKähler ALE 4-manifolds.
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 …
Study contact resolutions for Jacobi structures, providing examples and impossibility results.
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…
Study examines how cluster number affects short-text clustering, introducing a stability metric.
WrapNet optimizes inference for low-resolution neural networks by using 8-bit additions.
Climate projections suffer from uncertain equilibrium climate sensitivity. The reason behind this uncertainty is the resolution of global climate models, which is too coarse to resolve key processes such as clouds and convection. These processes are approximated using heuristics in a process called parameterization. Th…
This paper simplifies diffusion models for high resolution images.
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…