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.
A new algorithm infers causal networks from data using topological thresholds.
Although the threshold network is one of the most used tools to characterize the underlying structure of a stock market, the identification of the optimal threshold to construct a reliable stock network remains challenging. In this paper, the concept of dynamic consistence between the threshold network and the stock ma…
Based on the daily data of American and Chinese stock markets, the dynamic behavior of a financial network with static and dynamic thresholds is investigated. Compared with the static threshold, the dynamic threshold suppresses the large fluctuation induced by the cross-correlation of individual stock prices, and leads…
RCLA reduces noise in topological data analysis, preserving essential structure.
We prove that within a certain threshold, the odd Betti numbers of any compact almost-hermitian manifold satisfying a degenerate Kähler condition are even, and the even Betti numbers are strictly positive.
We consider 2-dimensional random simplicial complexes in the multi-parameter model. We establish the multi-parameter threshold for the property that every 2-dimensional simplicial complex admits a topological embedding into asymptotically almost surely. Namely, if in the procedure of the multi-parameter mod…
Study on critical faces convergence in a Poisson point process.
Study reveals how dengue spread patterns vary across different years in Recife, Brazil.
We consider the effects of the global financial crisis through a local Korean financial market around the 2008 crisis. We analyze 185 individual stock prices belonging to the KOSPI (Korea Composite Stock Price Index), cosidering three time periods: the time before, during, and after the crisis. The complex networks gen…
We consider degenerations of complex projective Calabi--Yau varieties and study the singularities of , Quillen and BCOV metrics on Hodge and determinant bundles. The dominant and subdominant terms in the expansions of the metrics close to non-smooth fibers are shown to be related to well-known topological invarian…
New algorithms detect communities in sparse graphs with labeled data.
Development of stock networks is an important approach to explore the relationship between different stocks in the era of big-data. Although a number of methods have been designed to construct the stock correlation networks, it is still a challenge to balance the selection of prominent correlations and connectivity of …
This study examined how the correlation and network structure of 30 global indices and 145 local Korean indices belonging to the KOSPI 200 have changed during the 13-year period, 2000-2012. The correlations among the indices were calculated. The results showed that although the average correlations of the global indice…
We consider the effects of the 2008 global financial crisis on the global stock market before, during, and after the crisis. We generate complex networks from a cross-correlation matrix such as the threshold network (TN) and the minimal spanning tree (MST). In the threshold network, we assign a threshold value by using…
The topology of a power grid affects its dynamic operation and settlement in the electricity market. Real-time topology identification can enable faster control action following an emergency scenario like failure of a line. This article discusses a graphical model framework for topology estimation in bulk power grids (…
In this study the Voronoi interpolation is used to interpolate a set of points drawn from a topological space with higher homology groups on its filtration. The technique is based on Voronoi tessellation, which induces a natural dual map to the Delaunay triangulation. Advantage is taken from this fact calculating the p…
Iterative thresholding algorithms seek to optimize a differentiable objective function over a sparsity or rank constraint by alternating between gradient steps that reduce the objective, and thresholding steps that enforce the constraint. This work examines the choice of the thresholding operator, and asks whether it i…
Finding optimal correction of errors in generic stabilizer codes is a computationally hard problem, even for simple noise models. While this task can be simplified for codes with some structure, such as topological stabilizer codes, developing good and efficient decoders still remains a challenge. In our work, we syste…
Unified geometric flows improve deep learning efficiency and simplify neural network topologies.
New unknots with geometric constraints exist, proving a long-standing conjecture.
Developed a new thresholding method that connects soft and hard thresholding.
Community detection algorithms are fundamental tools to understand organizational principles in social networks. With the increasing power of social media platforms, when detecting communities there are two possi- ble sources of information one can use: the structure of social network and node attributes. However struc…
Stable topological summary captures evolving dependency structure in dynamic Bayesian networks.
New algorithm for reinforcement learning in uncertain environments with unknown thresholds.
A new SSL method uses instance-dependent thresholds to improve accuracy.
Study stability thresholds of big line bundles, proving bounds and generalizing results.
Adaptive algorithm for outlier detection by balancing arm exploration and threshold estimation.
External or internal shocks may lead to the collapse of a system consisting of many agents. If the shock hits only one agent initially and causes it to fail, this can induce a cascade of failures among neighoring agents. Several critical constellations determine whether this cascade remains finite or reaches the size o…
In this paper we studied about the wavelet identification of the thresholds and time delay for more general case without the constraint that the time delay is smaller than the order of the model. Here we composed an empirical wavelet from the SETAR (Self-Exciting Threshold Autoregressive) model and identified the thres…
The paper proves the existence of singular cscK metrics on smoothable varieties.
An adaptive clustering algorithm learns from evolving data without manual tuning.
The article examines different thresholding methods for improving PAM algorithm in cancer classification.
In the work of Ammann, Dahl and Humbert it has turned out that the Yamabe invariant on closed manifolds is a bordism invariant below a certain threshold constant. A similar result holds for a spinorial analogon. These threshold constants are characterized through Yamabe-type equations on products of spheres with rescal…
Width trees link link invariants and bridge number.
Proposes a conservative LR estimator for infrequent data near a frequency threshold.
Recent papers have formulated the problem of learning graphs from data as an inverse covariance estimation with graph Laplacian constraints. While such problems are convex, existing methods cannot guarantee that solutions will have specific graph topology properties (e.g., being -partite), which are desirable for so…
New method trains neural networks with threshold activation functions efficiently.
Noise makes learning linear thresholds hard, but algorithms can still learn near-optimal thresholds.
Bayesian framework proves thresholds for multi-graph alignment feasibility.
Polynomial neural networks explore thresholds for maximum expressiveness.
Paper introduces threshold invariant fairness to ensure equitable predictions across different groups.
Study evaluates thresholds for removing noise from DNN weights using random matrix theory.
We investigate geometric invariants of the one parameter family of Mukai threefolds that admit action. In particular we find the invariant divisors in the anticanonical system, and thus establish a bound on the log canonical thresholds. Furthermore we find an explicit description of such threefolds in t…
FILTER model uses fusion penalized logistic threshold regression for high-dimensional data with unknown cut points.
The paper considers an investment timing problem appearing in real options theory. Present values from an investment project are modeled by general diffusion process. We prove necessary and sufficient conditions under which an optimal investment time is induced by threshold strategy. We study also the conditions of opt…
Deep learning detects bifurcations in dynamical systems.
Paper proves integrability and entropy compactness for Kähler potentials with uniform log-log threshold.
In this paper, we propose a new threshold-kernel jump-detection method for jump-diffusion processes, which iteratively applies thresholding and kernel methods in an approximately optimal way to achieve improved finite-sample performance. We use the expected number of jump misclassifications as the objective function to…