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.
Optimal crypto order execution using cross-exchange signals.
Let be a fixed link. Given a link diagram , is there a sequence of crossing exchanges and smoothings on that yields a diagram of ? We approach this problem from the computational complexity point of view. It follows from work by Endo, Itoh, and Taniyama that if is a prime link with crossing number at …
A blockchain replaces central counterparties with time-consuming consensus protocols to record the transfer of ownership. This settlement latency slows cross-exchange trading, exposing arbitrageurs to price risk. Off-chain settlement, instead, exposes arbitrageurs to costly default risk. We show with Bitcoin network an…
We study petal diagrams of knots, which provide a method of describing knots in terms of permutations in a symmetric group . We define two classes of moves on such permutations, called trivial petal additions and crossing exchanges, which do not change the isotopy class of the underlying knot. We prove that a…
A plane graph is a {\em plane minor} of a plane graph if there is a sequence of vertex and edge deletions, and edge contractions performed on the plane, that takes to . Motivated by knot theory problems, it has been asked if the plane minor relation is a well-quasi-order. We settle this in the affirmativ…
The paper investigates cyclic arbitrage opportunities in decentralized exchanges.
Study compares market microstructure between two South African exchanges.
A new geometry for comparing signals, overcoming traditional limitations.
Paper presents a unique method to recover signals from their bispectrum.
Paper proposes efficient methods for clustering and signal recovery in high-dimensional data with block structures.
Proposes a novel graph signal model using narrowband kernels.
New framework models graph signals as distribution-valued signals in Wasserstein space.
New algorithms improve signal processing in federated learning.
We consider the problem of signal recovery on graphs as graphs model data with complex structure as signals on a graph. Graph signal recovery implies recovery of one or multiple smooth graph signals from noisy, corrupted, or incomplete measurements. We propose a graph signal model and formulate signal recovery as a cor…
Proposes a new signal model for high-dimensional, small-sample-size data.
This paper reconstructs complex graph signals using kernel methods on manifolds.
Study on signal-plus-noise decomposition in nonlinear spiked random matrices.
We find ways to make physical signals misclassified by computer vision models.
The presence of noise is common in signal processing regardless the signal type. Deep neural networks have shown good performance in noise removal, especially on the image domain. In this work, we consider deep neural networks as a denoising tool where our focus is on one dimensional signals. We introduce an encoder-de…
In this paper, we address the problem of reconstructing a time-domain signal (or a phase spectrogram) solely from a magnitude spectrogram. Since magnitude spectrograms do not contain phase information, we must restore or infer phase information to reconstruct a time-domain signal. One widely used approach for dealing w…
Optimizes signal detection in particle physics by decorrelating classifiers.
Signal recovery is one of the key techniques of Compressive sensing (CS). It reconstructs the original signal from the linear sub-Nyquist measurements. Classical methods exploit the sparsity in one domain to formulate the L0 norm optimization. Recent investigation shows that some signals are sparse in multiple domains.…
Paper improves signal proportion estimation by accounting for variable dependence.
Signal retrieval from a series of indirect measurements is a common task in many imaging, metrology and characterization platforms in science and engineering. Because most of the indirect measurement processes are well-described by physical models, signal retrieval can be solved with an iterative optimization that enfo…
Goal: This paper deals with the problems that some EEG signals have no good sparse representation and single channel processing is not computationally efficient in compressed sensing of multi-channel EEG signals. Methods: An optimization model with L0 norm and Schatten-0 norm is proposed to enforce cosparsity and low r…
A new model classifies lightning signals more accurately across different scales.
Generalizes PCA and ICA for continuous-time signals using neural networks.
Research compares ML and Time Series methods for generating trading signals.
In this manuscript, we investigate deep invertible networks for EEG-based brain signal decoding and find them to generate realistic EEG signals as well as classify novel signals above chance. Further ideas for their regularization towards better decoding accuracies are discussed.
We study the problem of corrupted sensing, a generalization of compressed sensing in which one aims to recover a signal from a collection of corrupted or unreliable measurements. While an arbitrary signal cannot be recovered in the face of arbitrary corruption, tractable recovery is possible when both signal and corrup…
The paper presents a novel approach of spoofing wireless signals by using a general adversarial network (GAN) to generate and transmit synthetic signals that cannot be reliably distinguished from intended signals. It is of paramount importance to authenticate wireless signals at the PHY layer before they proceed throug…
There are three equivalent ways of representing two jointly observed real-valued signals: as a bivariate vector signal, as a single complex-valued signal, or as two analytic signals known as the rotary components. Each representation has unique advantages depending on the system of interest and the application goals. I…
Machine learning detects foreign stock market signals for U.S. companies.
Signals are submanifolds; bounds on energy calculated.
CNN model for efficient wireless spectrum sensing and signal identification.
We consider the problem of detecting whether a tensor signal having many missing entities lies within a given low dimensional Kronecker-Structured (KS) subspace. This is a matched subspace detection problem. Tensor matched subspace detection problem is more challenging because of the intertwined signal dimensions. We s…
High throughput biomedical measurements normally capture multiple overlaid biologically relevant signals and often also signals representing different types of technical artefacts like e.g. batch effects. Signal identification and decomposition are accordingly main objectives in statistical biomedical modeling and data…
Many problems in image processing and computer vision (e.g. colorization, style transfer) can be posed as 'manipulating' an input image into a corresponding output image given a user-specified guiding signal. A holy-grail solution towards generic image manipulation should be able to efficiently alter an input image wit…
Paper learns hypergraph structures from signals with smoothness priors.
Study detects signal in financial stock correlations using phase-ordering kinetics.
This paper presents a unified framework to tackle estimation problems in Digital Signal Processing (DSP) using Support Vector Machines (SVMs). The use of SVMs in estimation problems has been traditionally limited to its mere use as a black-box model. Noting such limitations in the literature, we take advantage of sever…
Response calibration is the process of inferring how much the measured data depend on the signal one is interested in. It is essential for any quantitative signal estimation on the basis of the data. Here, we investigate self-calibration methods for linear signal measurements and linear dependence of the response on th…
Paper reviews multi-way graph signal processing for tensor data.
Tutorials on signal processing on higher-order networks like simplicial complexes and hypergraphs.
Deep learning models detect nanopore translocation events with high accuracy.
Paper introduces signal processing on cell complexes.
This work optimizes signal estimation for sparse MRA with collision-free signals.
In sensing applications, sensors cannot always measure the latent quantity of interest at the required resolution, sometimes they can only acquire a blurred version of it due the sensor's transfer function. To recover latent signals when only noisy mixed measurements of the signal are available, we propose the Gaussian…