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.
The paper develops a cross-validation method for improving signal denoising techniques.
A blindfolded LLM trading framework validates market signals without ticker memorization.
Proposes a new signal model for high-dimensional, small-sample-size data.
Develops a validated trading framework for market microstructure signals.
Optimizes signal detection in particle physics by decorrelating classifiers.
Study finds no statistically significant trading edge in MNQ futures signals from OHLCV data.
The paper shows that causal identification is not essential for efficient portfolios, focusing on geometric sufficiency conditions.
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…
Study on signal-plus-noise decomposition in nonlinear spiked random matrices.
New framework models graph signals as distribution-valued signals in Wasserstein space.
This work improves safety validation of autonomous vehicles by finding interpretable failures.
This work tried to detect the existence of a relationship between the graphic signals - or patterns - observed day by day in the Brazilian stock market and the trends which happen after these signals, within a period of 8 years, for a number of securities. The results obtained from this study show evidence of the exist…
In this study, we propose a novel deep neural network and its supervised learning method that uses a feedforward supervisory signal. The method is inspired by the human visual system and performs human-like association-based learning without any backward error propagation. The feedforward supervisory signal that produc…
Satellite-based positioning system such as GPS often suffers from large amount of noise that degrades the positioning accuracy dramatically especially in real-time applications. In this work, we consider a data-mining approach to enhance the GPS signal. We build a large-scale high precision GPS receiver grid system to …
Conformal prediction improves signal detection accuracy in railway images.
The paper validates a classifier for identifying intraday regime shifts in MNQ futures.
This paper proposes a stochastic model using the concept of Markov chains for the inter-state transitions of the millisecond order quasi-stable phase synchronized patterns or synchrostates, found in multi-channel Electroencephalogram (EEG) signals. First and second order transition probability matrices are estimated fo…
Framework for causal signals in non-stationary financial markets.
The aim of this paper is to propose distributed strategies for adaptive learning of signals defined over graphs. Assuming the graph signal to be bandlimited, the method enables distributed reconstruction, with guaranteed performance in terms of mean-square error, and tracking from a limited number of sampled observatio…
Coherent Multiplex analyzes real-time wavelet coherence among multiple signals.
There is a need for affordable, widely deployable maternal-fetal ECG monitors to improve maternal and fetal health during pregnancy and delivery. Based on the diffusion-based channel selection, here we present the mathematical formalism and clinical validation of an algorithm capable of accurate separation of maternal …
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…
We build a rigorous bridge between deep networks (DNs) and approximation theory via spline functions and operators. Our key result is that a large class of DNs can be written as a composition of max-affine spline operators (MASOs), which provide a powerful portal through which to view and analyze their inner workings. …
Paper improves signal proportion estimation by accounting for variable dependence.
Blind single-channel source separation is a long standing signal processing challenge. Many methods were proposed to solve this task utilizing multiple signal priors such as low rank, sparsity, temporal continuity etc. The recent advance of generative adversarial models presented new opportunities in signal regression …
CNN model for efficient wireless spectrum sensing and signal identification.
Javanmard and Montanari propose a debiased estimator for high-dimensional regression.
Paper develops an AI-driven framework for systematic investing.
This research improves asset life prediction by integrating deep learning with mixture distributions.
Proposes a deep multi-scale neural network for EEG signal representation learning.
Bayesian priors improve neural network performance on weak signals.
Proposes LSGP for better graph signal representation.
Matched filters reveal optimal normalization methods for different market participants.
Dictionary Learning has proven to be a powerful tool for many image processing tasks, where atoms are typically defined on small image patches. As a drawback, the dictionary only encodes basic structures. In addition, this approach treats patches of different locations in one single set, which means a loss of informati…
New method detects change points in quasi-periodic signals without supervision.
Models with many signals, high-dimensional models, often impose structures on the signal strengths. The common assumption is that only a few signals are strong and most of the signals are zero or close (collectively) to zero. However, such a requirement might not be valid in many real-life applications. In this article…
This paper introduces a novel graph signal processing framework for building graph-based models from classes of filtered signals. In our framework, graph-based modeling is formulated as a graph system identification problem, where the goal is to learn a weighted graph (a graph Laplacian matrix) and a graph-based filter…
Self-training in linear models shows a U-shaped test-risk curve due to signal forgetting and denoising.
We consider the problem of reconstructing a signal from under-determined modulo observations (or measurements). This observation model is inspired by a (relatively) less well-known imaging mechanism called modulo imaging, which can be used to extend the dynamic range of imaging systems; variations of this model have al…
PHASE predicts surgical complications from physiological signals.
Paper proposes a deep learning model for real-time ECG signal segmentation.
AI-driven investment strategies self-defeat at scale due to signal crowding and erosion.
We propose Gaussian processes for signals over graphs (GPG) using the apriori knowledge that the target vectors lie over a graph. We incorporate this information using a graph- Laplacian based regularization which enforces the target vectors to have a specific profile in terms of graph Fourier transform coeffcients, fo…
This paper tackles non-convex phase retrieval with structured assumptions.
Paper introduces SCI to distinguish market signals from coordination.
In many signal processing problems, it may be fruitful to represent the signal under study in a frame. If a probabilistic approach is adopted, it becomes then necessary to estimate the hyper-parameters characterizing the probability distribution of the frame coefficients. This problem is difficult since in general the …
Researchers create benchmarks to compare graph inference methods.
Deep generative models have been successfully used to learn representations for high-dimensional discrete spaces by representing discrete objects as sequences and employing powerful sequence-based deep models. Unfortunately, these sequence-based models often produce invalid sequences: sequences which do not represent a…