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…
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48 results for “global development”
Develops methods to calculate global index of real polynomials.
problem Calculating the global index of real polynomials.
method Two methods: via atypical fibres and Milnor arcs clusters.
result Derives upper bounds for the global index, refining Durfee's degree-based bound.
A global consumer-led strategy to tackle climate changephysics.soc-ph
A successful response to climate change needs vast investments in low-carbon research, energy, and sustainable development. Governments can drive research, provide environmental regulation, and accelerate global development, but the necessary low-carbon investments of 2-3% GDP have yet to materialise. A new strategy to…
Maximality of the futures of points in globally hyperbolic maximal conformally flat spacetimesmath.DG
Maximal spacetimes have unique past/future sets.
problem Characterizing maximal spacetimes.
method Developing map analysis and diamond properties.
result Maximal spacetimes have unique past/future sets.
We introduce a notion of probabilistic convexity and generalize some classical globalization theorems in Alexandrov geometry. A weighted Alexandrov's lemma is developed as a basic tool.
Global moduli theory for symplectic varieties proven.
problem Proving global moduli theory for symplectic varieties.
method Developed global moduli theory following Beauville's approach.
result Proved a global Torelli theorem for symplectic varieties.
On the Existence of a Maximal Cauchy Development for the Einstein Equations - a Dezornificationgr-qc
In 1969, Choquet-Bruhat and Geroch established the existence of a unique maximal globally hyperbolic Cauchy development of given initial data for the Einstein equations. Their proof, however, has the unsatisfactory feature that it relies crucially on the axiom of choice in the form of Zorn's lemma. In this paper we pre…
We consider the problem of finding sufficient conditions for a locally Lipschitz mapping between Finsler manifolds to be a global homeomorphism. For this purpose, we develop the notion of Clarke generalized differential in this context and, using this, we obtain a version of the Hadamard integral condition for invertib…
Develop intrinsic consensus-based optimization framework on Riemannian manifolds with bounded curvature.
problem Nonconvex optimization on manifolds
method Intrinsic consensus-based optimization on Riemannian manifolds with bounded curvature
result Global convergence of the mean-field equation toward a global minimizer of the objective function.
Survey on CR Paneitz operator and manifold embeddability.
problem Global embeddability of CR manifolds.
method Survey of recent developments.
result Relations between CR Paneitz operator and embeddability.
Nanotechnology is the first major worldwide research initiative of the 21st century and probably is the solution vector in the economic environment. Also, innovation is widely recognized as a key factor in the economic development of nations, and is essential for the competitiveness of the industrial firms as well. Pol…
Mixed membership factorization is a popular approach for analyzing data sets that have within-sample heterogeneity. In recent years, several algorithms have been developed for mixed membership matrix factorization, but they only guarantee estimates from a local optimum. Here, we derive a global optimization (GOP) algor…
Develops a novel global pooling framework using optimal transport.
problem Sub-optimal performance in global pooling operations.
method Regularized Optimal Transport (ROT) for generalized global pooling.
result ROTP layers can improve performance in various machine learning scenarios.
Establishes existence of maximal globally hyperbolic development for Einstein equations.
problem Initial value problem for generalised Einstein equations.
method Generalised Lorentz gauge, adapted from Ringström's approach.
result Existence and geometric uniqueness of maximal globally hyperbolic development.
Develops an SSBO algorithm for global optimization of expensive models.
problem Global optimization of expensive black-box models.
method Asynchronous hybrid-criterion with interval reduction.
result Improves global search ability and local search efficiency.
The paper generates a comprehensive training dataset for land cover classification.
problem Insufficient training datasets for land cover classification.
method Public Sentinel-2 data at 10m resolution matched with accurate labels, filtered and classified by Random Forests.
result Over 80% model accuracy for various locations.
A global view of equivariant vector bundles and Dirac operators on some compact homogeneous spacesmath.DG
In order to facilitate the comparison of Riemannian homogeneous spaces of compact Lie groups with noncommutative geometries ("quantizations") that approximate them, we develop here the basic facts concerning equivariant vector bundles and Dirac operators over them in a way that uses only global constructions and argume…
New tensor approach models global fixed income risks across maturities and economies.
problem Lack of models capturing multi-dimensional data in global fixed income markets.
method Introduces tensor-valued approach to model shared risks among multiple interest rate curves.
result Estimates risk factors decomposable into maturity and country domains, enabling tailored portfolio management.
Paper develops exact convex optimization for neural networks with polynomial activations.
problem Training two-layer neural networks with nonlinear polynomial activations.
method Exact convex optimization using semidefinite programming.
result Global optimization of neural networks is polynomial-time computable.
We are concerned with the global weak rigidity of the Gauss-Codazzi-Ricci (GCR) equations on Riemannian manifolds and the corresponding isometric immersions of Riemannian manifolds into the Euclidean spaces. We develop a unified intrinsic approach to establish the global weak rigidity of both the GCR equations and isom…
This paper proposes a new global optimization algorithm using deep learning.
problem Developing efficient algorithms for global optimization of non-convex functions.
method Two-phase approach: minimization phase with model-driven deep learning, escaping phase with reinforcement learning.
result The proposed algorithm significantly outperforms classical optimization methods and handles ill-posed functions.
China and EU race to develop hydrogen for energy transition.
problem Developing hydrogen for sustainable energy systems.
method Comparative analysis framework using key factors.
result Customized solutions for local hydrogen industries.
Develops a new framework to measure network connectedness across and within markets.
problem Lack of flexible methods to measure network connectedness and its evolution.
method Allows network nodes to be connected in clusters, with shocks orthogonal across clusters and correlated within clusters.
result Demonstrates the effectiveness of the new framework in a detailed empirical analysis of equity markets.
Multi-GCN: Graph Convolutional Networks for Multi-View Networks, with Applications to Global Povertycs.LG
Develops a graph-based convolutional network for multi-view networks to improve poverty research.
problem Binary treatment of social network relations in graph learning models.
method Multi-GCN: Graph Convolutional Networks for Multi-View Networks.
result Multi-GCN outperforms state-of-the-art algorithms on poverty prediction tasks and broader multi-view network tasks.
We survey some recent developments in the quest for global surfaces of section for Reeb flows in dimension three using methods from Symplectic Topology. We focus on applications to geometry, including existence of closed geodesics and sharp systolic inequalities. Applications to topology and celestial mechanics are als…
Study analyzes global public sentiment on DeFi from 2012-2022.
problem Global public sentiment on DeFi is understudied.
method Sentiment analysis, spatial econometrics, clustering, topic modeling.
result Economic development significantly influences DeFi engagement, especially after 2015.
The paper proposes a new model for predicting and analyzing economic variables.
problem Predicting and analyzing economic variables in developed regions.
method Time-varying parameter global vector autoregressive (TVP-GVAR) framework combined with machine learning models.
result The proposed model provides high precision out-of-sample predictions and novel insights into economic variable connectedness.
Novel framework finds globally optimal energy-efficient power control in wireless networks.
problem Energy-efficient power control in wireless networks.
method Branch-and-bound procedure with problem-specific bounds for faster convergence.
result Global solution for common energy-efficient power control problems with reduced complexity.
Pseudo-differential operators on Homogeneous vector bundles over compact homogeneous manifoldsmath.AP
Develops global pseudo-differential calculus on homogeneous vector bundles.
problem Global theory of subelliptic pseudo-differential operators on homogeneous vector bundles.
method Global symbolic calculus, complex functional calculus, Hörmander system of vector-fields.
result Global pseudo-differential calculus on homogeneous vector bundles.
ProGO optimizes non-convex functions without gradients, outperforming existing methods.
problem Challenges in global optimization, especially with non-convex functions and limited gradient information.
method Probabilistic approach using multidimensional integration and latent slice sampler.
result ProGO converges to global optima efficiently and outperforms existing methods.
Global and uniqueness properties of stationary and static spacetimes with outer trapped surfacesgr-qc
Global properties of maximal future Cauchy developments of stationary, m-dimensional asymptotically flat initial data with an outer trapped boundary are analyzed. We prove that, whenever the matter model is well posed and satisfies the null energy condition, the future Cauchy development of the data is a black hole spa…
Under weak regularity assumptions, only, we develop a fully geometric theory of vacuum Einstein spacetimes with T2 symmetry, establish the global well-posedness of the initial value problem for Einstein's field equations, and investigate the global causal structure of the constructed spacetimes. Our weak regularity ass…
Global gradient estimates for Fisher-KPP equation on Finsler metric measure spaces.
problem Establishing gradient estimates for the Finslerian Fisher-KPP equation.
method Global gradient estimates on compact and noncompact Finsler metric measure spaces using the traditional condition and new comparison theorems.
result Global gradient estimates for positive solutions of the Finslerian Fisher-KPP equation.
New theory tackles AGI by breaking data constraints and minimizing global risk.
problem Current AI's limitations in handling complex real-world data and making reasonable judgments.
method Developed subjectivity learning theory to break data constraints and minimize global risk.
result Subjectivity learning holds a lower risk bound than traditional machine learning.
Understanding the global optimality in deep learning (DL) has been attracting more and more attention recently. Conventional DL solvers, however, have not been developed intentionally to seek for such global optimality. In this paper we propose a novel approximation algorithm, BPGrad, towards optimizing deep models glo…
Study applies Gai-Kapadia framework to global equity markets to assess systemic risk and default cascades.
problem Assessing systemic risk and default cascades in global equity markets.
method Used Gai-Kapadia framework, 20-asset network, Monte Carlo simulations, and deterministic propagation analysis.
result High clustering among Brazilian assets leads to localized contagion, while developed markets show resilience.
GWHD dataset offers 4,700 high-res images of wheat heads.
problem Challenges in wheat head detection from high-resolution imagery.
method Large, diverse dataset with detailed metadata.
result Benchmark for wheat head detection methods.
This study analyzes global oil trade networks to assess their efficiency and robustness.
problem Dynamic monitoring and warning of international trade risks in global oil trade.
method Constructing unweighted and weighted global oil trade networks (OTNs) using UN Comtrade data from 1988 to 2017, and applying complex network theories.
result Efficiency of oil flows increases with complexity of OTNs, and weighted efficiency indicators highlight major events.
The paper develops residue currents for cohesive modules and proves a generalized Poincaré-Lelong formula.
problem Analyzing coherent sheaves on complex manifolds using global analytic methods.
method Developing residue currents for cohesive modules and proving their properties.
result Proves a generalized Poincaré-Lelong formula for cohesive modules.
Stochastic Subgradient Methods with Guaranteed Global Stability in Nonsmooth Nonconvex Optimizationmath.OC
The paper guarantees global stability for stochastic subgradient methods in nonsmooth nonconvex optimization.
problem Minimizing nonsmooth nonconvex functions with convergence guarantees.
method Developed a framework for stochastic subgradient methods with global stability guarantees.
result Iterates are uniformly bounded and asymptotically stabilize around the stable set of the differential inclusion.
In the current era of worldwide stock market interdependencies, the global financial village has become increasingly vulnerable to systemic collapse. The recent global financial crisis has highlighted the necessity of understanding and quantifying interdependencies among the world's economies, developing new effective …
Contemporary global optimization algorithms are based on local measures of utility, rather than a probability measure over location and value of the optimum. They thus attempt to collect low function values, not to learn about the optimum. The reason for the absence of probabilistic global optimizers is that the corres…
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…
Develops a curvature-corrected tangent space method for manifold-valued data.
problem Generalizing real-valued data approximation to manifold-valued data.
method Systematic approach to developing global-geometry aware, computationally feasible approximation schemes.
result Proposes CC-tHOSVD for low-rank approximation of manifold-valued data.
Global homotopies upgrade classical map in differential geometry.
problem Upgrade classical Hochschild-Kostant-Rosenberg map to a deformation retract.
method Combining symbol calculus and coalgebraic van Est theorem.
result Develop deformation retracts in various settings.
For a singular Riemannian foliation whose leaves are properly embedded, we show in the first part of this article the existence of global tubular neighbourhoods, and we develop a global description of the foliation as stratification by types of leaves. The second part deals with the further restriction to a foliation w…
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…
New AI algorithm improves multi-layer optical film design efficiency.
problem Traditional algorithms converge to local optima, limiting global optimal solutions.
method Deep Q-learning for global optimal multi-layer optical film design.
result Deep Q-learning model converges global optimum of optical thin film structure.