New cycles found in moduli space from quadratic differentials.
problem Finding new cycles in moduli space from quadratic differentials.
method Exhibiting rigid and extremal effective codimension j cycles in Mg,n from strata of quadratic differentials. result Infinitely many new rigid and extremal effective codimension j cycles in Mg,n are exhibited. To a tropical p-cycle VT in Rn, we naturally associate a normal closed and (p,p)-dimensional current on (C∗)n denoted by Tnp(VT). Such a "tropical current" Tnp(VT) will not be an integration current along any analytic set, si…
Cryptocurrency markets show higher spreads during extreme fear and greed phases.
problem Understanding and predicting liquidity withdrawal in cryptocurrency markets.
method Analysis of Crypto Fear & Greed Index and Bitcoin daily data.
result Extreme fear and greed regimes exhibit significantly higher spreads than neutral periods.
This paper focuses on the interplay between the intersection theory and the Teichmueller dynamics on the moduli space of curves. As applications, we study the cycle class of strata of the Hodge bundle, present an algebraic method to calculate the class of the divisor parameterizing abelian differentials with a non-simp…
We consider smoothings of a complex surface with singularities of class T and no nontrivial holomorphic vector field. Under an hypothesis of non degeneracy of the smoothing at each singular point, we prove that if the singular surface admits an extremal metric, then the smoothings also admit extremal metrics in nearby …
I use local differential geometric techniques to prove that the algebraic cycles in certain extremal homology classes in Hermitian symmetric spaces are either rigid (i.e., deformable only by ambient motions) or quasi-rigid (roughly speaking, foliated by rigid subvarieties in a nontrivial way). These rigidity results ha…
We introduce the problem of hidden Hamiltonian cycle recovery, where there is an unknown Hamiltonian cycle in an n-vertex complete graph that needs to be inferred from noisy edge measurements. The measurements are independent and distributed according to $\calP_n$ for edges in the cycle and $\calQ_n$ otherwise. This …
Cycles in causal learning cause feedback loops under intervention.
problem Cyclic causal structures lead to feedback loops in causal inference.
method Theoretical observations about self-referential distributions and their factorizations.
result Cyclic causal dependence can exist even when observational data suggest independence.
mcanalysis quantifies menstrual cycle effects in health data.
problem Lack of standardised statistical methods for menstrual cycle research.
method Fourier-basis generalised additive model (GAM) pipeline.
result Nine out of 15 health outcomes showed significant association with menstrual cycle.
We investigate the supports of extremal martingale measures with pre-specified marginals in a two-period setting. First, we establish in full generality the equivalence between the extremality of a given measure Q and the denseness in L1(Q) of a suitable linear subspace, which can be seen in a financial context as…
CDLF predicts product life-cycles in cold-start phases with high accuracy.
problem Forecasting new products in early phases when data is scarce.
method Conditional Diffusion Life-cycle Forecaster (CDLF) combining static descriptors, reference trajectories, and new observations.
result CDLF outperforms classical models in accuracy and probabilistic forecasting.
We extend causal calculus to models with cycles, latent confounders, and selection bias.
problem Causal reasoning in the presence of cycles, latent confounders, and selection bias.
method Prove rules of causal calculus for i/o structural causal models, generalize adjustment criteria, and extend ID algorithm.
result Enable causal reasoning in complex models with cycles, latent confounders, and selection bias.
We consider an open string version of the topological twist previously proposed for sigma-models with G2 target spaces. We determine the cohomology of open strings states and relate these to geometric deformations of calibrated submanifolds and to flat or anti-self-dual connections on such submanifolds. On associative …
New framework estimates treatment effects in extreme data.
problem Hindered by unavailability of counterfactual outcomes and rarity of extreme data.
method Proposes a new framework based on extreme value theory.
result Quantifies treatment effects using tail decay rates of potential outcomes.
CycleMorph improves image registration by preserving topology with cycle consistency.
problem Preserving original topology during deformation in image registration.
method Cycle-consistent deformable image registration approach.
result Effective and accurate registration on diverse image pairs within seconds.
Estimates treatment effects in rare extreme events using EVT.
problem Estimating treatment effects in rare, impactful events like extreme climate events.
method Introduces a novel framework using EVT and multivariate regular variation for consistent treatment effect estimation.
result Developed a consistent estimator for extreme treatment effects with rigorous non-asymptotic analysis.
UMRL network tackles single image de-raining by learning rain content at different scales.
problem De-raining single images with varying rain streaks of different sizes, directions, and densities.
method UMRL network learns rain content at different scales and uses confidence measures to guide learning.
result UMRL achieves significant improvements over state-of-the-art methods.
The accurate characterization of the business cycles in the nonlinear dynamic financial and economic systems in the time of globalization represents a formidable research problem. The central banks and other financial institutions make their decisions on the minimum capital requirements, countercyclical capital buffer …
This study optimizes cycle representatives in persistent homology using linear programming.
problem Non-uniqueness of cycle representatives in persistent homology creates ambiguity.
method Optimization of cycle representatives using linear programming methods.
result Optimization reduces the size of cycle representatives and is effective in most data sets.
New method solves group synchronization with cycle-edge message passing.
problem Solving group synchronization with adversarial or uniform corruption and small noise.
method Cycle-edge message passing procedure using cycle consistency information.
result Exact recovery and linear convergence guarantees under adversarial corruption.
We identify a large class R of three-dimensional N=2 superconformal field theories. This class includes the effective theories T_M of M5-branes wrapped on 3-manifolds M, discussed in previous work by the authors, and more generally comprises theories that admit a UV description as abelian Chern-Simons-matter theories w…
Improves training GANs by escaping limit cycles.
problem Limit cycling behavior in training GANs.
method Predictive Centripetal Acceleration Algorithm (PCAA) combined with Adam.
result PCAA improves convergence rates and effectively trains GANs.
Business cycles affect startup valuations, both directly and indirectly.
problem How do business cycles impact startup valuations?
method Structural Equation Model approach using a dataset of 1,089 venture capital investments.
result Business cycles impact startup valuations both directly and indirectly.
Extends effect variable concept to finite states for web search evaluation.
problem Finding effect of variant variables in changes of observable variables.
method Theoretical analysis and simultaneous distribution decomposition.
result States of extreme effect variable are minimally affected by variant and highly different in observable variable.
New CycleGAN uses invertible generator for faster, less resource-intensive CT denoising.
problem Efficient unsupervised CT denoising without paired data.
method Single generator with wavelet residual domain, no discriminators, cycle consistency via invertible generator.
result Significantly improved denoising performance with faster training and less parameters.
The simplest patterns of qualitative changes on the configurations of lines of principal curvature} around umbilic points on surfaces whose immersions into R3 depend smoothly on a real parameter (codimension one umbilic bifurcations) are described in this paper. Global effects, due to umbilic bifurcations, o…
This paper uses spectrum analysis to understand price behavior in the Indian stock market.
problem Understanding price formation and discovery in the Indian stock market.
method Adapting mathematical physics theories and spectrum analysis to decompose price cycles.
result Decomposing price cycles helps in understanding the effect of information on price formation and discovery.
Study proposes a new resilience metric for stock market performance analysis.
problem Quantifying resilience cycles in stock market performance.
method Systems-oriented approach with Robustness Range and Elasticity Threshold.
result New metric quantifies non-linear resilience cycles in stock markets.
Paper develops a neural model to assess cascading extreme events.
problem Risk assessment of domino effects like earthquakes and tsunamis.
method Develops a Kolmogorov-Arnold neural network (KANE) framework.
result Estimates the probability of one extreme event triggering another.
New method identifies causal parameters in tree-shaped linear models using cycles.
problem Identifying causal parameters from correlations in tree-shaped linear models.
method Investigates tree-shaped linear models, uses missing cycles to identify causal parameters, solves quadratic equations.
result Shows how missing cycles can be combined to obtain a unique solution for causal parameters.
ML models predict stock prices poorly during recessions.
problem Predicting stock prices during economic downturns.
method Examined S&P 500 index, analyzed performance during recessions and expansions.
result ML models perform better during expansions, not due to ML methods but effective monetary policies.
Kernel PCA helps analyze multivariate extremes and clusters them effectively.
problem Analyzing the dependence structure of multivariate extremes.
method Kernel PCA as a method for clustering and dimension reduction.
result Kernel PCA preimages effectively identify clusters in multivariate extremes.
This paper suggests that business cycles may be a manifestation of coupled real economy and stock market dynamics and describes a mechanism that can generate economic fluctuations consistent with observed business cycles. To this end, we seek to incorporate into the macroeconomic framework a dynamic stock market model …
Improved GPLVM model for single-cell RNA-seq data.
problem Lack of effective scalable models for clustering cell types in large-scale single-cell RNA-seq data.
method Introduces amortized stochastic variational Bayesian GPLVM (BGPLVM) tailored for single-cell RNA-seq.
result Matches the performance of scVI on synthetic and real-world datasets and reveals more interpretable latent structures.
Dynamic fairness tackles long-term inequalities in decision making processes.
problem Machine learning models can reproduce and exacerbate human bias, leading to discriminatory outcomes.
method Theoretical model considering time dynamics of decision making processes and fairness definitions.
result Demographic parity is the only fairness notion that avoids long-term inequalities and leads to accurate and fair classification.
Study examines dependence of extreme electricity prices in Australian markets.
problem Understanding and managing risks of extreme price outcomes in Australian electricity markets.
method Examined extremal dependence using extremograms for 5-minute and 30-minute price data.
result Persistence and dependence of extreme prices are influenced by market structure and renewable energy share.
Develop a variational framework for statistical inference on cyclic interactions.
problem Estimating and comparing large-scale recurrent organization in directed interactions.
method Represent directed interactions as edge flows on a simplicial complex and evolve under an energy-minimizing dynamical system.
result Separate transient interaction components from persistent harmonic flows, yielding a low-dimensional cycle space.
We introduce and study a non-equilibrium continuous-time dynamical model of the price of a single asset traded by a population of heterogeneous interacting agents in the presence of uncertainty and regulatory constraints. The model takes into account (i) the price formation delay between decision and investment by the …
New approach to extremal hyperbolic surfaces using NEC groups.
problem Structural description of extremal hyperbolic surfaces.
method Uniformization by NEC groups for surfaces with cusps and/or geodesic boundary.
result Full description of automorphism groups of extremal surfaces.
We develop a method to summarize causal models with cycles in cubic time.
problem Cycles in high-dimensional causal models limit applicability of existing methods.
method We relax the acyclicity assumption in LiNG models and develop a low-dimensional DAG summary.
result Our method allows recovery of a low-dimensional DAG from high-dimensional data with cycles.
Odd crossing numbers and even rotation numbers for cycles in plane immersions.
problem Analyzing crossing and rotation numbers of cycles in plane immersions of graphs.
method Generic immersions and Legendrian embeddings of graphs, focusing on cycles of specific lengths.
result Sum of rotation numbers of all 5-cycles is even, and sum of crossing numbers is odd.
We present a simple agent-based model of a financial system composed of leveraged investors such as banks that invest in stocks and manage their risk using a Value-at-Risk constraint, based on historical observations of asset prices. The Value-at-Risk constraint implies that when perceived risk is low, leverage is high…
We study the distribution of the common zero sets of m-tuples of holomorphic sections of powers of m singular Hermitian pseudo-effective line bundles on a compact Kähler manifold. As an application, we obtain sufficient conditions which ensure that the wedge product of the curvature currents of these line bundles c…
Schubert varieties are irreducible subvarieties of homogeneous manifold, which are important to understand the geometry of homogeneous manifold G/P and the action of the semisimple Lie group G. Consider the space of effective cycles in G/P with homology class equal to an integral multiple of the homology class of a Sch…
Proves inequality for 1-dimensional cycles.
problem Proving the Parametric Coarea Inequality for 1-cycles.
method Analytical proof based on conjecture by Guth and Liokumovich.
result Proved the Parametric Coarea Inequality for 1-cycles.
This work introduces novel methods to identify and compare cycles across topological objects.
problem Identifying and comparing topological features, particularly cycles, across different topological objects.
method Two complementary approaches: dendrogram-based merge-tree algorithms and Stratified Gradient Sampling.
result Transformed cycle matching into hierarchical clustering and topological optimization framework.
This article introduces proximal planar vortex 1-cycles, resembling the structure of vortex atoms introduced by William Thomson (Lord Kelvin) in 1867 and recent work on the proximity of sets that overlap either spatially or descriptively. Vortex cycles resemble Thomson's model of a vortex atom, inspired by P.G. Tait's …
This paper identifies the unique efficient cycle for most hyperbolic manifolds but not for the figure-8 knot complement.
problem Identifying the unique efficient cycle for hyperbolic manifolds.
method Analyzing the limit of fundamental cycles and their ℓ1-norm convergence. result The uniqueness of the efficient cycle is proven for most hyperbolic manifolds but not for the figure-8 knot complement.