Novel approach detects early warning indicators in complex systems.
problem Detecting abrupt transitions in complex systems.
method Directed anisotropic diffusion map and latent stochastic dynamical systems.
result Early warning indicators can detect tipping points in state transitions.
The paper shows how reducible complexes affect local indicability.
problem The local indicability of subcomplexes in reducible complexes.
method Characterization of diagrammatic reducibility and application to local indicability.
result Injective labeled oriented trees are locally indicable if reducible of degree 2.
New interpretation reconciles country and product complexity.
problem Difficulty in interpreting Economic and Product Complexity Indices.
method Spectral clustering algorithm to separately group similar countries and products.
result Indices identify two co-clusters of similar countries and products.
This research finds three meta-indicators for university rankings.
problem Complexity in university ranking systems.
method Interpretable machine learning approach.
result Identified three meta-indicators: time, space, and relationships.
This research simplifies computation of feature attribution methods under certain conditions.
problem Computational complexity of feature attribution methods, especially power indices.
method Identifying conditions for polynomial computation and introducing new indices.
result Conditions for efficient computation of feature attribution methods are identified.
Study on rational projective planes with small index singularities.
problem Existence and classification of rational homology projective planes with small index quotient singularities.
method Topological and smooth obstructions analysis, classification of singularities.
result Classification of quotient singularities for rational homology projective planes with indices up to three.
Methods of parabolic geometries have been recently used to construct a class of elliptic complexes on quaternionic manifolds, the Salamon's complex being the simplest case. The purpose of this paper is to describe an algorithm how to compute their analytical indices in terms of characteristic classes. Using this, we ar…
M. F. Atiyah proved that the index of a transversally elliptic operator relative to a free action can be computed by using indices of elliptic operators on the orbit manifold. In this paper, we derive an explicit formula for the transversal indices on S^1-bundles over complex projective spaces. Using this explicit form…
Stock market indices are one of the most investigated complex systems in econophysics. Here we extend the existing literature on stock markets in connection with nonextensive statistical mechanics. We explore the nonextensivity of price volatilities for 34 major stock market indices between 2010 and 2019. We discover t…
This study analyzes economic policy uncertainty indices using visibility graphs.
problem Understanding the role of economic policy uncertainty in global economies.
method Visibility graph algorithm applied to economic policy uncertainty indices.
result The economic policy uncertainty indices exhibit persistent behavior and scale-free networks.
Study finds intrinsic multifractality in maize and barley spot markets, but not in wheat and rice.
problem Understanding the complex price behavior of global grain spot markets.
method Utilized multifractal fluctuation analysis (MF-DFA) to investigate intrinsic multifractality.
result Intrinsic multifractality found in maize and barley sub-indices, but not in wheat and rice.
For a G-invariant holomorphic 1-form with an isolated singular point on a germ of a complex-analytic G-variety with an isolated singular point (G is a finite group) one has notions of the equivariant homological index and of the (reduced) equivariant radial index as elements of the ring of complex representations of th…
Real vector bundles are determined by their Dirac indices on specific spin manifolds.
problem Determining real vector bundles using Dirac indices.
method Mapping spin or spinh manifolds into a compact smooth manifold and using Dirac indices. result Real vector bundles are uniquely determined by their Dirac indices on prescribed manifolds.
A method for interpreting SVMs using polynomial kernels, revealing model complexity.
problem Interpreting SVMs built with truncated orthogonal polynomial kernels.
method Orthogonal Representation Contribution Analysis (ORCA) with normalized Orthogonal Kernel Contribution (OKC) indices.
result The method reveals structural aspects of model complexity not captured by predictive accuracy.
We prove a global residual formula in terms of logarithmic indices for one-dimensional holomorphic foliations, with isolated singularities, and logarithmic along normal crossing divisors. We also give a formula for the total sum of the logarithmic indices if the singular set of the foliation is contained in the invaria…
Study fragility in global financial indices using network analysis.
problem Monitor fragility in global financial indices.
method Network-based approach to analyze daily closing prices of global financial indices.
result Network-centric measures reveal fragility in global financial indices.
Optimal trend-following strategy uses simple EMA, avoiding complex cherry-picked signals.
problem Cherry-picking signals for trend-following strategies.
method Simple EMA for trend capture, avoiding complex indicators.
result Simple EMA is optimal for capturing trend, complex indicators are risky.
Graphs with given k vertices generate an (acyclic) simplicial complex. We describe the homology of its quotient complex, formed by all connected graphs, and demonstrate its applications to the topology of braid groups, knot theory, combinatorics, and singularity theory. The multidimensional analogues of this complex ar…
Using the tools developed for statistical physics, we simultaneously analyze statistical properties of the Jakarta and Kuala Lumpur Stock Exchange indices. In spite of the small number of data used in the analysis, the result shows the universal behavior of complex systems previously found in the leading stock indices.…
Libgober and Wood proved that the Chern number c1cn−1 of a n-dimensional compact complex manifold can be determined by its Hirzebruch χy-genus. Inspired by the idea of their proof, we show that, for compact, spin, almost-complex manifolds, more Chern numbers can be determined by the indices of some twist…
ODBAE detects complex phenotypes in biological data.
problem Challenges in identifying complex phenotypes from high-dimensional biological data.
method ODBAE (Outlier Detection using Balanced Autoencoders) identifies influential and high leverage points in latent relationships among multiple physiological parameters.
result ODBAE reveals novel metabolism-related genes and uncovers coordinated abnormalities across metabolic indicators.
Develops virtual Morse-Bott indices for four-manifolds, proving inequalities.
problem Proving inequalities for four-manifolds of Seiberg-Witten simple type.
method Uses virtual Morse-Bott indices and Hirzebruch-Riemann-Roch Theorem.
result Proves positivity of virtual Morse-Bott indices, leading to inequalities.
The paper evaluates integrals for fBm with various Hurst indices.
problem Evaluating integrals for stochastic processes with fractional Brownian motion for different Hurst indices.
method Analytic continuation from complex analysis to extend integral domain.
result Integral formulas for fBm with Hurst indices H∈(0,1) are derived. The paper describes the structure of injective LOT-complexes and proves they are aspherical.
problem The unresolved asphericity question for labeled oriented trees encoding spines of ribbon discs.
method Complete description of the link of a reduced injective LOT complex, proving asphericity.
result Reduced injective LOT complexes are aspherical, with specific conditions for non-boundary sub-LOTs.
We relax indicator matrices to form a manifold for faster optimization.
problem Optimizing indicator matrices is NP-hard.
method Developed a Riemannian manifold (RIM) and Riemannian optimization methods.
result RIM manifold optimization is significantly faster and yields better results.
We consider the problem of learning models for forecasting multiple time-series systems together with discovering the leading indicators that serve as good predictors for the system. We model the systems by linear vector autoregressive models (VAR) and link the discovery of leading indicators to inferring sparse graphs…
Symplectic solitons rigid if bounded, study shows.
problem Understanding symplectic translating solitons with bounded second fundamental form.
method Using complex phase map to prove rigidity.
result Symplectic translating solitons rigid without bounded second fundamental form assumption.
Enhances RL for better stock market trading decisions.
problem Lack of practical RL evidence in finance.
method Advanced RL framework using financial indicators.
result Improved differentiation between buy/sell actions.
The investigations of financial markets from a complex network perspective have unveiled many phenomenological properties, in which the majority of these studies map the financial markets into one complex network. In this work, we investigate 30 world stock market indices through their visibility graphs by adopting the…
This article demonstrates the possibility of constructing indicators of critical and crisis phenomena in the volatile market of cryptocurrency. For this purpose, the methods of the theory of complex systems such as recurrent analysis of dynamic systems and the calculation of permutation entropy are used. It is shown th…
Explains complex analytic invariants of vector fields and foliations.
problem Integrating theories of singular varieties and foliations.
method Expository discussion of invariants.
result Introduces connections between complex analytic singular varieties and foliations.
Paper introduces efficient methods for estimating cross-partial derivatives and sensitivity indices.
problem Efficiently estimating cross-partial derivatives and sensitivity indices in complex models.
method Using randomized points and constraints, the paper develops estimators with optimal convergence rates and low bias.
result The estimators achieve optimal rates of convergence and do not suffer from the curse of dimensionality.
Complex Chern-Simons theory reveals peacock patterns in perturbative series.
problem Understanding the structure of partition functions in complex Chern-Simons theory.
method Analyzing the partition function as a holomorphic function and using resurgence theory.
result Perturbative series are resurgent, with trans-series involving non-perturbative variables.
New analysis shows transfer learning can significantly reduce sample size for complex models.
problem Reducing sample size needed for complex models like large language models.
method Optimal transport viewpoint applied to analyze transfer learning efficiency.
result Transfer learning can achieve better sample efficiency for complex models.
Human mobility has a significant impact on several layers of society, from infrastructural planning and economics to the spread of diseases and crime. Representing the system as a complex network, in which nodes are assigned to regions (e.g., a city) and links indicate the flow of people between two of them, physics-in…
We indicate a C-Fuchsian counter-example to the result with the above title announced at http://www.maths.dur.ac.uk/events/Meetings/LMS/2011/GAL11/program.pdf and prove a stronger statement.
Study uses topological signatures to quantify financial market complexity.
problem Capturing temporal organization beyond volatility measures.
method Null validated topological approach using L1 norm of persistence landscapes. result Persistence landscape norms reveal dynamical structure during market stress.
Graph Neural Networks improve volatility prediction in financial markets.
problem Traditional models struggle with complex, non-linear interdependencies in financial markets.
method Temporal Graph Attention Network (Temporal GAT) combines GCNs and GATs to capture dynamic graph structures.
result Temporal GAT outperforms traditional GARCH models in volatility forecasting, especially for short- to mid-term predictions.
Unified framework for measuring concentration in weighted networks considering both weight distributions and network structure.
problem Traditional indices neglect the topology of relationships among network elements.
method Develops a family of topology-aware concentration indices that jointly account for weight distributions and network structure.
result The proposed indices preserve key properties and allow concentration to be evaluated across different dimensions of dependence.
CNN model predicts financial market movement with better performance.
problem Difficult to predict financial markets due to complex dynamics.
method Proposes a novel one-dimensional CNN model for financial market prediction.
result CNN model achieves more robust and profitable performance than previous approaches.
New model predicts financial market abnormalities using stock index uncertainties.
problem Forecasting abnormal financial fluctuations in the market.
method Quantitative analysis of mean and volatility uncertainties, constructing early warning indicators.
result Established a new abnormal fluctuations warning model.
Cubic predicts stock market indices by fusing stock latent embeddings and converting to binary classification.
problem Challenges in predicting stock market indices due to isolated time series treatment and simple regression.
method Fusion of stock latent embeddings, binary encoding classification, and confidence-guided prediction.
result Cubic outperforms state-of-the-art baselines in stock index prediction tasks.
NeuMiss networks tackle supervised learning with missing values, offering efficient and robust predictions.
problem Challenges in supervised learning with missing values, especially when the response is a linear function of the complete data.
method Derive analytical form of optimal predictor under linearity assumption and various missing data mechanisms. Propose NeuMiss networks using multiplication by missingness indicator.
result Upper bound on Bayes risk and good predictive accuracy with independent complexity of missing data patterns.
We classify invariant almost complex structures on homogeneous manifolds of dimension 6 with semi-simple isotropy. Those with non-degenerate Nijenhuis tensor have the automorphism group of dimension either 14 or 9. An invariant almost complex structure with semi-simple isotropy is necessarily either of specified 6 homo…
Data imbalance remains one of the most widespread problems affecting contemporary machine learning. The negative effect data imbalance can have on the traditional learning algorithms is most severe in combination with other dataset difficulty factors, such as small disjuncts, presence of outliers and insufficient numbe…
Stable planes are locally isomorphic to classical projective planes.
problem Characterizing stable planes that are locally isomorphic to classical projective planes.
method Analyzing properties of stable planes and comparing them to classical projective planes over specific fields.
result Simply connected stable planes with connected lines are isomorphic to open subplanes of classical projective planes.
Paper forecasts recession indicators using yield spread models.
problem Forecasting the leading indicator of a recession using yield spread.
method Applied econometric time series and machine learning models to forecast yield spread.
result Parsimonious univariate ARIMA model outperforms richly parameterized VAR method.
Generically an almost complex structure has no symmetries at all, but there exist symmetric structures. In this paper we describe how to guarantee that the pseudogroup of local symmetries is small (finite-dimensional). It will be indicated that a large symmetry pseudogroup (infinite-dimensional) is a signature of some …