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.
Global minima found for multidimensional scaling with penalties.
Using Hilbert's criterion, we consider the stress-energy tensor associated to the bienergy functional. We show that it derives from a variational problem on metrics and exhibit the peculiarity of dimension four. First, we use this tensor to construct new examples of biharmonic maps, then classify maps with vanishing or…
Proposes a reverse stress testing framework for dynamic models.
Derives stress-energy identities in Liouville theory on compact surfaces.
Develops a method for stress testing correlations of financial portfolios.
Various psychological factors affect how individuals express emotions. Yet, when we collect data intended for use in building emotion recognition systems, we often try to do so by creating paradigms that are designed just with a focus on eliciting emotional behavior. Algorithms trained with these types of data are unli…
Bayesian active learning improves stress and affect detection on wearable devices.
We consider the energy and bienergy functionals as variational problems on the set of Riemannian metrics and present a study of the biharmonic stress-energy tensor. This approach is then applied to characterise weak conformality of the Gauss map of a submanifold. Finally, working at the level of functionals, we recover…
An integrated and extendable approach for stress-testing loan portfolios
In this work, we develop Gaussian process regression (GPR) models of hyperelastic material behavior. First, we consider the direct approach of modeling the components of the Cauchy stress tensor as a function of the components of the Finger stretch tensor in a Gaussian process. We then consider an improvement on this a…
Study reviews machine learning techniques for stress monitoring.
Method generates plausible financial stress scenarios using large deviations.
Shear stress distribution prediction in open channels is of utmost importance in hydraulic structural engineering as it directly affects the design of stable channels. In this study, at first, a series of experimental tests were conducted to assess the shear stress distribution in prismatic compound channels. The shear…
In this paper, we introduce the stress-energy tensors of the partial energies E'(f) and E"(f) of maps between Kaehler manifolds. Assuming the domain manifolds poss some special exhaustion functions, we use these stress-energy tensors to establish some monotonicity formulae of the partial energies of pluriharmonic maps …
This note improves correlation stress tests using geodesic distance.
Improved algorithm for multidimensional scaling reduces stress.
A method using optimal transport removes arbitrage in option prices for stress-testing.
The MSPI predicts market stress with machine learning.
Develops a method for reverse stress testing in multivariate scenarios.
A new model explains relative spreads between economies using dynamic Nelson-Siegel and functional regression.
A hybrid model combines diffusion and neural operator methods for stress prediction in hyperelastic materials.
Deep learning predicts stress levels from mouse hippocampus activity.
Stress research is a rapidly emerging area in thefield of electroencephalography (EEG) based signal processing.The use of EEG as an objective measure for cost effective andpersonalized stress management becomes important in particularsituations such as the non-availability of mental health facilities.In this study, lon…
Environmental stresses such as drought and heat can cause substantial yield loss in agriculture. As such, hybrid crops that are tolerant to drought and heat stress would produce more consistent yields compared to the hybrids that are not tolerant to these stresses. In the 2019 Syngenta Crop Challenge, Syngenta released…
The dominant automatic lexical stress detection method is to split the utterance into syllable segments using phoneme sequence and their time-aligned boundaries. Then we extract features from syllable to use classification method to classify the lexical stress. However, we can't get very accurate time boundaries of eac…
Credit risk stress tests can misrepresent default probabilities due to inconsistent parameterization.
The demand for fast and accurate structural analysis is becoming increasingly more prevalent with the advance of generative design and topology optimization technologies. As one step toward accelerating structural analysis, this work explores a deep learning based approach for predicting the stress fields in 2D linear …
We construct a continuous time model for price-mediated contagion precipitated by a common exogenous stress to the banking book of all firms in the financial system. In this setting, firms are constrained so as to satisfy a risk-weight based capital ratio requirement. We use this model to find analytical bounds on the …
We derive the stress-energy tensor for polyharmonic maps between Riemannian manifolds. Moreover, we employ the stress-energy tensor to characterize polyharmonic maps where we pay special attention to triharmonic maps.
Machine learning improves financial stress testing in Indian markets.
Paper proposes MAST to identify stress conditions in forecasting models.
Paper defines p-biharmonic submanifolds and stress tensors in space forms.
Reverse sensitivity analysis for risk models under various stresses.
Two new methods score stress test scenarios for risk managers.
The relation between performance and stress is described by the Yerkes-Dodson Law but varies significantly between individuals. This paper describes a method for determining the individual optimal performance as a function of physiological signals. The method is based on attention and reasoning tests of increasing comp…
This paper uses multivariate probability models to assess financial system risks.
Research shows ESG signals lower exposure to market fragility during stress periods.
Develops a robust hedging valuation adjustment measure for dynamic hedging under liquidity-demand stress.
With the growing popularity of wearable devices, the ability to utilize physiological data collected from these devices to predict the wearer's mental state such as mood and stress suggests great clinical applications, yet such a task is extremely challenging. In this paper, we present a general platform for personaliz…
Neural networks predict flow and elastic stresses in viscoelastic turbulence.
Paper develops a robust HVA measure for dynamic hedging under liquidity stress.
This work extends elasticity theory to curved spaces, solving stress potentials.
Paper improves SVaR estimation for stress testing under macro scenarios using a hybrid GPR-HS framework.
The paper studies critical points of horizontal energy functional in Riemannian foliations.
In this work we perform a study of various unsupervised methods to identify mental stress in firefighter trainees based on unlabeled heart rate variability data. We collect RR interval time series data from nearly 100 firefighter trainees that participated in a drill. We explore and compare three methods in order to pe…
In this paper, we present an experimental study for the classification of perceived human stress using non-invasive physiological signals. These include electroencephalography (EEG), galvanic skin response (GSR), and photoplethysmography (PPG). We conducted experiments consisting of steps including data acquisition, fe…
One has not any conventional energy-momentum conservation law in Lagrangian field theory, but relations involving different stress-energy-momentum tensors associated with different connections. It is not obvious how to choose the true energy-momentum tensor. This problem is solved in the framework of the multimomentum …
Study improves prediction of commodity futures using multi-factor model.