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.
Develops a multi-resolution multi-task framework for integrating noisy, varying data.
Improved Mapper algorithm for datasets with varying density.
Modeling complex systems with multi-resolution data and causal dependencies.
Physics-informed neural operator learns from coarse to fine discretized data.
The paper tackles three financial issues: time resolution, nonstationarity, and latent factors.
Many graph clustering quality functions suffer from a resolution limit, the inability to find small clusters in large graphs. So called resolution-limit-free quality functions do not have this limit. This property was previously introduced for hard clustering, that is, graph partitioning. We investigate the resolution-…
PIE-PINN estimates elastic properties from noisy, low-res displacement data.
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…
Methods for learning feature representations for Offline Handwritten Signature Verification have been successfully proposed in recent literature, using Deep Convolutional Neural Networks to learn representations from signature pixels. Such methods reported large performance improvements compared to handcrafted feature …
New method learns time-varying home field advantage in football.
SIGL learns scalable graphons from graphs.
Study examines how cluster number affects short-text clustering, introducing a stability metric.
Develops a new volatility model for prediction markets.
Applying deep learning methods to mammography assessment has remained a challenging topic. Dense noise with sparse expressions, mega-pixel raw data resolution, lack of diverse examples have all been factors affecting performance. The lack of pixel-level ground truths have especially limited segmentation methods in push…
The point of the paper is to show some limitations of geometrical optics in the analysis of subwavelength focusing. We analyze the resolution of the image of a line source radiating in the Maxwell fisheye and the Veselago-Pendry slab lens. The former optical medium is deduced from the stereographic projection of a virt…
Using a recently developed piecewise flat method, numerical evolutions of the Ricci flow are computed for a number of manifolds, using a number of different mesh types, and shown to converge to the expected smooth behaviour as the mesh resolution is increased. The manifolds were chosen to have varying degrees of homoge…
The resolution and calibration of pure spectra of minority components in measurements of chemical mixtures without prior knowledge of the mixture is a challenging problem. In this work, a combination of band target entropy minimization (BTEM) and target partial least squares (T-PLS) was used to obtain estimates for sin…
Develops a new volatility model for prediction markets.
SR-NAM maps low-res images to multiple high-res images realistically.
Deep learning speeds up whole heart MRI to 30 seconds.
We seek to better understand the difference in quality of the several publicly released embeddings. We propose several tasks that help to distinguish the characteristics of different embeddings. Our evaluation of sentiment polarity and synonym/antonym relations shows that embeddings are able to capture surprisingly nua…
FedSpace optimizes ML training on satellites and ground stations.
The paper proposes a method to improve fine-resolution predictions using coarse-resolution data.
This study prioritizes temporal resolution over spatial in energy systems models due to higher influence.
Black-Scholes (BS) is the standard mathematical model for option pricing in financial markets. Option prices are calculated using an analytical formula whose main inputs are strike (at which price to exercise) and volatility. The BS framework assumes that volatility remains constant across all strikes, however, in prac…
ICR speeds up GP modeling on unevenly spaced data.
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…
New CTRL algorithm adapts to varying problem difficulty.
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.
Machine learning and deep learning infer surface/groundwater exchange from temperature data.
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 …
Deep learning improves MRI image quality from down-sampled data.
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…
Bayesian method for imputing actigraph data from mobile devices.
WrapNet optimizes inference for low-resolution neural networks by using 8-bit additions.
Study proposes CNN for reconstructing high-res urban DEMs.
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…
We consider the problem of efficiently approximating and encoding high-dimensional data sampled from a probability distribution in , that is nearly supported on a -dimensional set - for example supported on a -dimensional Riemannian manifold. Geometric Multi-Resolution Analysis (GM…
Some Poisson structures do admit resolutions by symplectic manifolds of the same dimension. We give examples and simple conditions under which such resolutions can not exist.
New deep learning method improves 4D Flow MRI super-resolution under domain shift.
Bayesian framework for solar magnetogram super-resolution with uncertainty quantification.
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…
Generates high-resolution images from low-resolution inputs.