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 study derives generalization bounds for neural oscillators, improving their performance with regularization.
KuramotoGNN uses Kuramoto model to prevent over-smoothing in graph neural networks.
A new RNN model based on coupled oscillators mitigates gradient issues.
Graph-Coupled Oscillator Networks (GraphCON) tackles graph-based learning problems.
This paper extends the earlier work on an oscillating error correction technique. Specifically, it extends the design to include further corrections, by adding new layers to the classifier through a branching method. This technique is still consistent with earlier work and also neural networks in general. With this ext…
Large learning rates cause oscillations in NN weights that improve generalization.
Gaussian process models improve MJO predictions with better uncertainty quantification.
CNN improves neutrino event reconstruction in IceCube DeepCore.
GD monotonically decreases GFS sharpness in neural networks and scalar models.
Graph neural networks improve El Niño forecasts.
A new oscillator measures trending behavior of financial instruments.
Minimal vector fields on oscillator groups studied, with specific conditions for minimality.
Study examines boundedness of oscillating singular integrals on specific Lie groups.
Log-periodic oscillations have been used to predict price trends and crashes on financial markets. So far two types of log-periodic oscillations have been associated with the real markets. The first type are oscillations which accompany a rising market and which ends in a crash. The second type oscillations, called "an…
Estimates box dimension of fractal interpolation surfaces using oscillation vectors.
Soft-constrained PINN solves ODEs with minimal data, improving efficiency and robustness.
Researchers classify lattices in a specific four-dimensional group.
The Duffing oscillator's parameters are identified online using variational message passing.
Deep linear networks oscillate beyond the edge of stability in a predictable manner.
New method linearizes nonlinear coupled oscillators on graphs.
Research explores how interconnected systems synchronize and how to control their behavior.
RaNNDy uses randomized neural networks to learn transfer operators efficiently.
The present paper introduces a majority orienting model in which the dealers' behavior changes based on the influence of the price to show the oscillation of stock price in the stock market. We show the oscillation of the price for the model by applying the van der Pol equation which is a deterministic approximation of…
SGDm with fixed step-size diverges under covariate shift, similar to a parametric oscillator.
Study finds the spectrum of a cubic Dirac operator on specific oscillator group manifolds.
Study on black hole interiors with matter fields, showing oscillation condition impacts blow-up.
We study geometric quantization of the harmonic oscillator in terms of a singular real polarization given by fibres of the energy momentum map.
Using geometric quantization procedure, the quantization of algebra of observables for physical system with Ricci-flat phase space is obtained. In the classical case the appointed physical system is reduced to harmonic oscillator when the one real parameter is vanished.
Oscillations lie at the core of many biological processes, from the cell cycle, to circadian oscillations and developmental processes. Time-keeping mechanisms are essential to enable organisms to adapt to varying conditions in environmental cycles, from day/night to seasonal. Transcriptional regulatory networks are one…
A new RNN model tackles long-time dependencies with fast, invertible, and memory-efficient hidden states.
New non-separable covariance kernels for spatiotemporal data derived from harmonic oscillator physics.
In a complex system, the interactions between individual agents often lead to emergent collective behavior like spontaneous synchronization, swarming, and pattern formation. The topology of the network of interactions can have a dramatic influence over those dynamics. In many studies, researchers start with a specific …
Quantifies fractional isoperimetric inequality with strong control over boundary oscillation.
Gradient descent near stability threshold shows sharpness oscillations.
Networks of coupled dynamical systems provide a powerful way to model systems with enormously complex dynamics, such as the human brain. Control of synchronization in such networked systems has far reaching applications in many domains, including engineering and medicine. In this paper, we formulate the synchronization…
A Lie group is called orthogonal if it carries a bi-invariant pseudo Riemannian metric. Oscillator Lie groups constitutes a subclass of the class of orthogonal Lie groups. In this paper, we determine the Lie bialgebra structures and the solutions of the classical Yang-Baxter equation on a generic class of oscillator Li…
We explore the efficacy of using a novel activation function in Artificial Neural Networks (ANN) in characterizing exoplanets into different classes. We call this Saha-Bora Activation Function (SBAF) as the motivation is derived from long standing understanding of using advanced calculus in modeling habitability score …
We show that there are minimal graphs in R^{n+1} whose intersection with the portion of the horizontal hyperplane contained in the unit ball has any prescribed geometry, up to a small deformation. The proof hinges on the construction of minimal graphs that are almost flat but have small oscillations whose geometry we c…
To overcome the oscillation problem in the classical momentum-based optimizer, recent work associates it with the proportional-integral (PI) controller, and artificially adds D term producing a PID controller. It suppresses oscillation with the sacrifice of introducing extra hyper-parameter. In this paper, we start by …
Alternative wavelet analysis method for financial signals.
The Black-Scholes model anticipates rather well the observed prices for options in the case of a strike price that is not too far from the current price of the underlying asset. Some useful extensions can be obtained by an adequate modification of the coefficients in the Black-Scholes equation. We investigate from a ma…
The goal of this paper is to provide a method, based on the theory of extensions of left-symmetric algebras, for classifying left-invariant affine structures on a given solvable Lie group of low dimension. To better illustrate our method, we shall apply it to classify complete left-invariant affine structures on the os…
The main result is a version of Morse inequalities for the minimum and maximum ideal boundary conditions of the de Rham complex on strata of compact Thom-Mather stratifications, endowed with adapted metrics. An adaptation of the analytic method of Witten is used in the proof, as well as certain perturbation of the harm…
Graph Neural Networks improve El Niño forecasts.
The sectoral synchronization observed for the Japanese business cycle in the Indices of Industrial Production data is an example of synchronization. The stability of this synchronization under a shock, e.g., fluctuation of supply or demand, is a matter of interest in physics and economics. We consider an economic syste…
The analysis of classical consensus algorithms relies on contraction properties of adjoints of Markov operators, with respect to Hilbert's projective metric or to a related family of seminorms (Hopf's oscillation or Hilbert's seminorm). We generalize these properties to abstract consensus operators over normal cones, w…
In this paper, we consider the motion of a particle on a surface of revolution under the influence of a central force field. We prove that there are at most two analytic central potentials for which all the bounded, nonsingular orbits are closed and that there are exactly two on some surfaces with constant Gaussian cur…
Proposes a new method combining Reservoir Computing and Normalizing Flow for predicting stochastic dynamical systems.