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arXiv research

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.

168,695 papers · 148 categories

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13263952 · May 202619922001200920172026
48 results for wave-like decaying

Unique solutions found for wave-like decaying null infinity equations.

problem Wave-like decaying null infinity equations with spherically symmetric Einstein-scalar-field.
method Local and global unique solutions for small initial data.
result Sharp decaying condition for unique solutions.

Proves global existence and uniqueness of solutions for Einstein-scalar-field equations.

problem Global existence and uniqueness of solutions for specific Einstein-scalar-field equations.
method Proves global existence and uniqueness of classical solutions with small initial data and wake-like decaying null infinity.
result Global existence and uniqueness of solutions for the equations with wake-like decaying null infinity.

We design non-singular cloaks enabling objects to scatter waves like objects with smaller size and very different shapes. We consider the Schrodinger equation which is valid e.g. in the contexts of geometrical and quantum optics. More precisely, we introduce a generalized non-singular transformation for star domains, a…

2010-06-03abs ↗pdf ↗

Study uses reinforcement learning to optimize metachronal paddling at low Reynolds number.

problem Optimizing metachronal paddling strategies for efficient swimming at low Reynolds numbers.
method Applied reinforcement learning to a swimmer model with varying paddle spacings.
result The reinforcement learning algorithm selects a back-to-front metachronal wave-like stroke as the most efficient, regardless of the number of paddles.

The explanation of the photoelectric effect by Einstein and Maxwell's field theory of electromagnetism have motivated De Broglie to make the hypothesis that matter exhibits both waves and particles like-properties. These representations of matter are enlightened by string theory which represents particles with stringli…

2019-04-23abs ↗pdf ↗

Study on scalar curvature decay on non-compact manifolds linked at infinity.

problem Understanding scalar curvature decay on non-compact manifolds with topological linking at infinity.
method Analyzing polynomial decay, developing obstruction theory, using μμ--bubble exhaustions, and index theory.
result Topological linking at infinity forces polynomial decay of scalar curvature on manifolds of weakly bounded geometry.

Study on massless Vlasov equation on Reissner-Nordström spacetimes, showing decay rates and non-decay phenomena.

problem Analyzing decay and non-decay rates of solutions to the massless Vlasov equation on Reissner-Nordström spacetimes.
method Quantitative analysis of geodesic flow and comparison to wave equation instability results.
result Exponential decay rates in subextremal cases and polynomial rates in extremal cases, with non-decay of transversal derivatives in extremal cases.

Study examines wave equation decay and Strichartz estimates on conic manifolds.

problem Analyzing wave equation behavior on conic spaces with critical electromagnetic potentials.
method Established decay and Strichartz estimates through localized spectral measure construction.
result Extended and improved previous results on wave equation behavior with critical potentials.

Weight decay stabilizes training dynamics by slowing progressive sharpening.

problem Understanding how weight decay affects training stability in deep learning models.
method Analyzing weight decay effects at the Edge of Stability, developing a mathematical framework.
result Weight decay dampens oscillations and stabilizes sharpness in CNNs, causing a phase transition in MLPs.

Last SGD iterate bounds for overparameterized linear regression.

problem Analyzing the last iterate risk bounds of SGD with decaying stepsize for overparameterized linear regression.
method Problem-dependent analysis of last iterate risk bounds of SGD with geometrically decaying stepsize.
result Proved nearly matching upper and lower bounds on the excess risk for last iterate SGD with geometrically decaying stepsize.

Optimal learning rates decay to zero in easy tasks and maintain a warmup phase in hard tasks.

problem Optimizing learning rates under functional scaling laws for model training.
method Deriving optimal learning-rate schedules based on exponents ss and ββ.
result Sharp phase transition between easy and hard tasks, with different decay behaviors.

We found that factors decay over time, with momentum fitting best.

problem Understanding how factors decay over time and their impact on performance.
method Derived a hyperbolic decay model for factors, tested against linear and exponential alternatives.
result Momentum exhibits hyperbolic decay, outperforming linear and exponential models.

The paper predicts and explains the decay of stock anomaly performance over time.

problem Predicting and explaining the drop in risk-adjusted performance of stock anomalies.
method The authors propose ex-ante characteristics based on hypotheses of out-of-sample decay and in-sample overfitting.
result The year of publication explains 30% of the variance in Sharpe decay across factors.

We give conditions which imply that a complete noncompact manifold with quadratic curvature decay has finite topological type. In particular, we find links between the topology of a manifold with quadractic curvature decay and some properties of the asymptotic cones of such a manifold.

2005-10-27abs ↗pdf ↗

The paper studies harmonic map heat flow stability and decay rates.

problem Analyzing stability and decay rates of harmonic map heat flow solutions.
method Use of homogeneous Besov space B˙p,dp(Rd)\dot{B}^{\frac{d}{p}}_{p,\infty}(\mathbb{R}^d) for small initial data and self-similar decay assumption.
result Decay rates for solutions of the harmonic map flow of the form ablau(t)L(Rd)Ct12\| abla u(t) \|_{L^\infty(\mathbb{R}^d)}\leq Ct^{-\frac12} and self-similar decay under stronger initial conditions.

SHIFT framework identifies subgroups with large ML model performance decay.

problem Large model performance decay in subgroups when deployed.
method Subgroup-scanning Hierarchical Inference Framework (SHIFT) for performance drift.
result SHIFT identifies interpretable subgroups with large performance decay and suggests targeted actions to mitigate it.

Study on optimal ReLU networks with weight decay for interpolation.

problem Interpolating data with radially symmetric distributions using shallow ReLU networks.
method Weight decay regularization in infinite neuron, infinite data limit; analysis of growth rates.
result Existence and growth rates of unique radially symmetric minimizers with weight decay.

Novel Adam-family method with decoupled weight decay for training neural networks.

problem Training nonsmooth neural networks with weight decay.
method Proposes a novel Adam-family method with decoupled weight decay, establishing convergence properties and demonstrating superior performance.
result Asymptotically approximates SGD and enhances generalization performance.

Regularization in the optimization of deep neural networks is often critical to avoid undesirable over-fitting leading to better generalization of model. One of the most popular regularization algorithms is to impose L-2 penalty on the model parameters resulting in the decay of parameters, called weight-decay, and the …

2019-07-21abs ↗pdf ↗

Paper shows existence of vortex solutions with specific decay properties.

problem Existence of solutions to Seiberg-Witten equations with specific decay properties.
method Dimensional reduction of Seiberg-Witten equations on the plane.
result Contains both exponentially decayed and polynomial growth solutions.

SGD and weight decay encourage neural networks to learn low-rank weight matrices.

problem The bias of SGD towards low-rank weight matrices in neural networks.
method The study investigates the effect of SGD and weight decay on the rank of weight matrices in neural networks, both theoretically and empirically.
result Training with SGD and weight decay induces a bias towards rank minimization in weight matrices, which becomes more pronounced with smaller batch sizes and stronger weight decay.

Weight decay is one of the standard tricks in the neural network toolbox, but the reasons for its regularization effect are poorly understood, and recent results have cast doubt on the traditional interpretation in terms of L2L_2 regularization. Literal weight decay has been shown to outperform L2L_2 regularization for…

2018-10-29abs ↗pdf ↗

In this paper, we prove the linear stability to gravitational and electromagnetic perturbations of the Reissner-Nordström family of charged black holes with small charge. Solutions to the linearized Einstein-Maxwell equations around a Reissner-Nordström solution arising from regular initial data remain globally bounded…

2019-04-09abs ↗pdf ↗

The paper improves energy decay estimates for Dir-stationary Q-valued functions and applies them to Liouville-type theorems and continuity.

problem Improving energy decay estimates for Dir-stationary Q-valued functions.
method Establishing improved decay estimates and applying them to derive Liouville-type theorems and continuity.
result Dir-stationary Q-valued functions exhibit the Lebesgue property and reside in a generalized Campanato-Morrey space.