We prove some criteria for uniform K-stability of log Fano pairs. In particular, we show that uniform K-stability is equivalent to -invariant having a positive lower bound. Then we study the relation between optimal destabilization conjecture and the conjectural equivalence between uniform K-stability and K-stabilit…
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Proposes a new stability measure for model fitting on similar feature data sets.
Let be a Kähler manifold and let be a compact group that acts on in a Hamiltonian fashion. We study the action of on probability measures on . First of all we identify an abstract setting for the momentum mapping and give numerical criteria for stability, semi-stability and polystabili…
We study the stability of partitions involving two or more phases in convex domains under the assumption of at most two-phase contact, thus excluding in particular triple junctions. We present a detailed derivation of the second variation formula with particular attention to the boundary terms, and then study the sign …
We find computable criteria for stability of symplectic leaves of Poisson manifolds. Using Poisson geometry as an inspiration, we also give a general criterion for stability of leaves of Lie algebroids, including singular ones. This not only extends but also provides a new approach (and proofs) to the classical stabili…
The paper develops stability criteria for real reductive Lie groups acting on manifolds.
New stability and isolation results for Einstein manifolds.
From the work of Dervan-Keller, there exists a quantization of the critical equation for the J-flow. This leads to the notion of J-balanced metrics. We prove that the existence of J-balanced metrics has a purely algebro-geometric characterization in terms of Chow stability, complementing the result of Dervan-Keller. We…
Uniform criteria for stability of fixed points in various geometric structures.
S3VDC improves DC methods for scalability, stability, and simplicity.
We apply a recent theorem of Li and the first author to give some criteria for the K-stability of Fano varieties in terms of anticanonical Q-divisors. First, we propose a condition in terms of certain anticanonical Q-divisors of given Fano variety, which we conjecture to be equivalent to the K-stability. We prove that …
Solves complex Hessian equations in unstable cases, proving unique canonical solutions with singularities.
We show a concise extension of the monotone stability approach to backward stochastic differential equations (BSDEs) that are jointly driven by a Brownian motion and a random measure for jumps, which could be of infinite activity with a non-deterministic and time inhomogeneous compensator. The BSDE generator function c…
Paper presents a novel method to assess boundedness and stability of nonlinear systems with variable delays.
AI threatens financial stability through misuse and stealth adoption.
We consider a modified Ricci flow equation whose stationary solutions include Einstein and Ricci soliton metrics, and we study the linear stability of those solutions relative to the flow. After deriving various criteria that imply linear stability, we turn our attention to left-invariant soliton metrics on (non-compac…
Mathematical framework investigates fire sales amplification and stability.
MOSS optimizes decision rules for accuracy and stability.
We give a systematic treatment of the stability theory for action of a real reductive Lie group G on a topological space. More precisely, we introduce an abstract setting for actions of non-compact real reductive Lie groups on topological spaces that admit functions similar to the Kempf-Ness function. The point of this…
New methods improve stability of Sinkhorn algorithm in machine learning.
New cyclicity measures defined in weighted Besov spaces, with stability and geometric analysis.
New stability criteria for Fano varieties using generalized b-divisors.
We present a new implementation of anisotropic mean curvature flow for contour recognition. Our procedure couples the mean curvature flow of planar closed smooth curves, with an external field from a potential of point-wise charges. This coupling constrains the motion when the curve matches a picture placed as backgrou…
Sequence discriminative training criteria have long been a standard tool in automatic speech recognition for improving the performance of acoustic models over their maximum likelihood / cross entropy trained counterparts. While previously a lattice approximation of the search space has been necessary to reduce computat…
The paper studies conjugate points on Lie groups with specific metrics.
New stability criteria for vector bundles linked to Hermite-Einstein geometry.
Characterizes geometric actions on graphs with flexible stabilizers.
Random forests are among the most popular classification and regression methods used in industrial applications. To be effective, the parameters of random forests must be carefully tuned. This is usually done by choosing values that minimize the prediction error on a held out dataset. We argue that error reduction is o…
In this paper we consider the problem of finding stable maxima of expensive (to evaluate) functions. We are motivated by the optimisation of physical and industrial processes where, for some input ranges, small and unavoidable variations in inputs lead to unacceptably large variation in outputs. Our approach uses multi…
Functionals involving surface curvature are important across a range of scientific disciplines, and their extrema are representative of physically meaningful objects such as atomic lattices and biomembranes. Inspired in particular by the relationship of the Willmore energy to lipid bilayers, we consider a general funct…
This work examines the stability of GD and SGD near minima, revealing nonlinear dynamics that differ from linear analysis.
The paper classifies Toda equations for noncompact symmetric spaces and their solutions.
The paper provides guarantees for clustering validity without distributional assumptions.
Minimal surfaces in 4D space are stable if their Gauss map spherical area is less than 2π.
Smooth actions on manifolds can be globally defined under certain conditions.
We propose a combination of cluster analysis and stochastic process analysis to characterize high-dimensional complex dynamical systems by few dominating variables. As an example, stock market data are analyzed for which the dynamical stability as well as transitions between different stable states are found. This comb…
The paper studies deformations of Kundt metrics using nil-Killing vector fields.
Stochastic stability is a popular solution concept for stochastic learning dynamics in games. However, a critical limitation of this solution concept is its inability to distinguish between different learning rules that lead to the same steady-state behavior. We address this limitation for the first time and develop a …
Study finds no statistically significant trading edge in MNQ futures signals from OHLCV data.
RENT selects stable features for robust model interpretation.
The new business paradigms originate a strong necessity to re-think the theory of the firm with the aim to get a better understanding on the organizational and functional principles of the firm, operating in the investment economies in the prosperous societies. In this connection, we make the innovative research to adv…
Robo-advisors use MPC to create dynamic investment strategies.
Penalized regression models are popularly used in high-dimensional data analysis to conduct variable selection and model fitting simultaneously. Whereas success has been widely reported in literature, their performances largely depend on the tuning parameters that balance the trade-off between model fitting and model s…
High-dimensional models become unstable when sample size falls below a critical level, leading to a phase transition.
We describe a new technique which minimizes the amount of neurons in the hidden layer of a random recurrent neural network (rRNN) for time series prediction. Merging Takens-based attractor reconstruction methods with machine learning, we identify a mechanism for feature extraction that can be leveraged to lower the net…
We propose the Margin Adaptation for Generative Adversarial Networks (MAGANs) algorithm, a novel training procedure for GANs to improve stability and performance by using an adaptive hinge loss function. We estimate the appropriate hinge loss margin with the expected energy of the target distribution, and derive princi…
Constructs stable bundles on K3 surfaces using monad construction.
A surface of constant mean curvature (CMC) equal to in a sub-Riemannian -manifold is strongly stable if it minimizes the functional up to second order. In this paper we obtain some criteria ensuring strong stability of surfaces in Sasakian -manifolds. We also produce new exampl…