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.
Researchers tackle the globalization problem of locally cosymplectic Hamiltonian dynamics.
Model forecasts global stock market volatility using dynamic graphs and all trading days.
Many real-world networks are complex dynamical systems, where both local (e.g., changing node attributes) and global (e.g., changing network topology) processes unfold over time. Local dynamics may provoke global changes in the network, and the ability to detect such effects could have profound implications for a numbe…
Problem of global integration of geometric structures arising in the theory of dynamical systems admitting the normal shift is considered. In the case when such integration is possible the problem of globalization for shift maps is studied.
This paper finds a global surface of section in dynamically convex L(p,p-1) using ECH.
Proposes a differentially private bandit algorithm reducing noise over time.
Optimized DMD for fast atmospheric chemistry forecasting.
Study global geometry of dynamical systems with entire vector fields.
Formula for the force field of Newtonian dynamical systems admitting the normal shift of hypersurfaces in Riemannian manifolds is considered. Problem of globalization for geometric structures associated with this formula is studied.
KSOS improves kernel learning for dynamical systems via global optimization.
Accelerates convergence in global non-convex optimization with reversible diffusion.
Multivariate time series (MTS) forecasting is widely used in various domains, such as meteorology and traffic. Due to limitations on data collection, transmission, and storage, real-world MTS data usually contains missing values, making it infeasible to apply existing MTS forecasting models such as linear regression an…
Softmax policy gradient achieves global optimality in wide neural networks with entropy regularization.
This thesis attempts to contribute to the study of differentiable dynamics both from a semi-local and global point of view. The center of study is differentiable dynamics in manifolds of dimension 3 where we are interested in the understanding of the existence and structure of attractors as well as dynamical and topolo…
This study analyzes dynamic connectedness in global supply chain infrastructure portfolios, identifying key risk factors and extreme events.
New algorithm improves convergence for non-convex problems with boundaries.
This paper explores estimating chaotic dynamics and parameters using local ensemble Kalman filters.
This work addresses decentralized online optimization in non-stationary environments. A network of agents aim to track the minimizer of a global time-varying convex function. The minimizer evolves according to a known dynamics corrupted by an unknown, unstructured noise. At each time, the global function can be cast as…
Solving statistical learning problems often involves nonconvex optimization. Despite the empirical success of nonconvex statistical optimization methods, their global dynamics, especially convergence to the desirable local minima, remain less well understood in theory. In this paper, we propose a new analytic paradigm …
Study shows global oscillatory solutions for Yang-Mills heat flow in 4D space.
We investigated the critical dynamics on the daily Taiwan stock exchange index (TSE) from 1971 to 2005, and the 5-min intraday data from 1996 to 2005. A global persistence exponent was defined for non-equilibrium critical phenomena \cite{Janssen,Majumdar}, and describing dynamic behavior in an economic index \c…
DLNs dynamics change with variance, leading to saddle-to-saddle training phases.
New method for accurately predicting linear dynamical systems.
This work extends locally conformal analysis to multi-Hamiltonian settings, providing new geometric structures and Hamiltonian dynamics.
We study a class of nonlocal, energy-driven dynamical models that govern the motion of closed, embedded curves from both an energetic and dynamical perspective. Our energetic results provide a variety of ways to understand physically motivated energetic models in terms of more classical, combinatorial measures of compl…
We present a unified framework to analyze the global convergence of Langevin dynamics based algorithms for nonconvex finite-sum optimization with component functions. At the core of our analysis is a direct analysis of the ergodicity of the numerical approximations to Langevin dynamics, which leads to faster conver…
Sparse-mode DMD disambiguates local and global modes in spatiotemporal data.
Gradient descent proves global convergence for 4-layer matrix factorization.
We study global aspects of complete, non-singular asymptotically locally AdS spacetimes solving the vacuum Einstein equations whose conformal infinity is an arbitrary globally stationary spacetime. It is proved that any such solution which is asymptotically stationary to the past and future is itself globally stationar…
Using open source data, we observe the fascinating dynamics of nighttime light. Following a global economic regime shift, the planetary center of light can be seen moving eastwards at a pace of about 60 km per year. Introducing spatial light Gini coefficients, we find a universal pattern of human settlements across dif…
This paper deals with global asymptotic stability of prolongations of flows induced by specific vector fields and their prolongations. The method used is based on various estimates of the flows.
The monitoring of large dynamic networks is a major chal- lenge for a wide range of application. The complexity stems from properties of the underlying graphs, in which slight local changes can lead to sizable variations of global prop- erties, e.g., under certain conditions, a single link cut that may be overlooked du…
A method for learning a context latent vector to improve generalization in model-based RL.
The paper analyzes the current state of the world economy and offers a short-term forecast of its development. Our analysis of log-periodic oscillations in the DJIA dynamics suggests that in the second half of 2017 the United States and other more developed countries could experience a new recession, due to the third p…
Elman-type RNNs converge to globally optimal solutions in the mean-field regime.
This work discovers latent field effects governing interacting dynamical systems.
Deep equilibrium models converge globally without explicit computation.
Mathematical model predicts international trade and global economy dynamics.
The paper develops a new algorithm for RBMs using dynamical mean-field theory.
The recent advances in deep transfer learning reveal that adversarial learning can be embedded into deep networks to learn more transferable features to reduce the distribution discrepancy between two domains. Existing adversarial domain adaptation methods either learn a single domain discriminator to align the global …
TMTF improves time series visualization by separating dynamic regimes.
Develop intrinsic consensus-based optimization framework on Riemannian manifolds with bounded curvature.
New algorithm converges to optimal filter for predicting linear dynamical systems.
CREIMBO models diverse brain activity by identifying hidden neural sub-circuits and their non-stationary interactions.
In this paper, we discuss the global aspect of the geometric dynamics of volumetric expansion and its application to the problem of the existence in the space-time of compact and complete spacelike hypersurface.
This paper addresses tracking of a moving target in a multi-agent network. The target follows a linear dynamics corrupted by an adversarial noise, i.e., the noise is not generated from a statistical distribution. The location of the target at each time induces a global time-varying loss function, and the global loss is…
Continuous-time distributed mirror descent with integral feedback converges to global optimum.
Model predicts global financial market risks and asset allocation.