arXiv research

Unified framework for robust causal directionality in quantum systems under MNAR observation.

New Gaussian DPP model reveals directionality in data.

A new algorithm learns graph embeddings considering directionality, improving multiple tasks.

Hollow-tree Super resolves feature importance in large datasets.

New copula models capture volatility and directionality in financial time series.

We investigate deep generative models that can exchange multiple modalities bi-directionally, e.g., generating images from corresponding texts and vice versa. Recently, some studies handle multiple modalities on deep generative models, such as variational autoencoders (VAEs). However, these models typically assume that…

CDFD analyzes circularity and directionality in weighted directed networks.

Convolution has been playing a prominent role in various applications in science and engineering for many years. It is the most important operation in convolutional neural networks. There has been a recent growth of interests of research in generalizing convolutions on curved domains such as manifolds and graphs. Howev…

We estimate the second order linking invariants of Lipschitz maps from an n-dimensional ellipse. The estimate uses a new directionally-dependent version of the isoperimetric inequality for cycles inside the ellipse. Using this work, we prove new lower bounds for the k-dilation of maps from one ellipse to another.

Inferring causal interactions from observed data is a challenging problem, especially in the presence of measurement noise. To alleviate the problem of spurious causality, Haufe et al. (2013) proposed to contrast measures of information flow obtained on the original data against the same measures obtained on time-rever…

We investigate the strength and the direction of information transfer in the U.S. stock market between the composite stock price index of stock market and prices of individual stocks using the transfer entropy. Through the directionality of the information transfer, we find that individual stocks are influenced by the …

New methods improve neural directed link prediction across all sub-tasks.

We propose a novel approach for learning node representations in directed graphs, which maintains separate views or embedding spaces for the two distinct node roles induced by the directionality of the edges. We argue that the previous approaches either fail to encode the edge directionality or their encodings cannot b…

In terms of transfer entropy, we investigated the strength and the direction of information transfer in the US stock market. Through the directionality of the information transfer, the more influential company between the correlated ones can be found and also the market leading companies are selected. Our entropy analy…

We first describe the numerical invariants attached to the second fundamental form of a spacelike surface in four-dimensional Minkowski space. We then study the configuration of the nu-principal curvature lines on a spacelike surface, when the normal field nu is lightlike (the lightcone configuration). Some observation…

We augment the nonnegative matrix factorization method for audio source separation with cues about directionality of sound propagation. This improves separation quality greatly and removes the need for training data, with only a twofold increase in run time. This is the first method which can exploit directional inform…

We investigate deep generative models that can exchange multiple modalities bi-directionally, e.g., generating images from corresponding texts and vice versa. A major approach to achieve this objective is to train a model that integrates all the information of different modalities into a joint representation and then t…

DIGRAC clusters directed graphs using flow imbalance, outperforming existing methods.

The study categorizes Korean Exchange member firms into three types and analyzes their trading behavior.

New TDA approach using Finsler metrics.

Characterizes minimizing curves in Riemannian manifolds.

We consider the problem of finding a consistent upper price bound for exotic options whose payoff depends on the stock price at two different predetermined time points (e.g. Asian option), given a finite number of observed call prices for these maturities. A model-free approach is used, only taking into account that th…

We study the second order invariants of a Lorentzian surface in $\mathbb{R}^{2,2},$ and the curvature hyperbolas associated to its second fundamental form. Besides the four natural invariants, new invariants appear in some degenerate situations. We then introduce the Gauss map of a Lorentzian surface and give an extrin…

The relationship between statistical dependency and causality lies at the heart of all statistical approaches to causal inference. Recent results in the ChaLearn cause-effect pair challenge have shown that causal directionality can be inferred with good accuracy also in Markov indistinguishable configurations thanks to…

Early training of deep neural networks leads to small, directionally converging weights.

ParPIC clusters directed graphs using random walks and diffusion operators.

Novel Haar-Laplacian for directed graphs enhances spectral graph applications.

Efficient inference for adaptive data with directional stability condition.

The study finds that factor momentum is significant only at short lags compared to stock momentum.

New method learns dynamic brain communication patterns across regions.

A financial system contains many elements networked by their relationships. Extensive works show that topological structure of the network stores rich information on evolutionary behaviors of the system such as early warning signals of collapses and/or crises. Existing works focus mainly on the network structure within…

Graph Signal Processing improves stock market volatility forecasting.

Meta-analysis improves personalized treatment rules across multiple sites.

CSHT predicts financial returns from news using a novel transformer model on a sphere.

LLMs outperform human analysts in predicting earnings direction.

In this paper we consider the problem of graph-based transductive classification, and we are particularly interested in the directed graph scenario which is a natural form for many real world applications. Different from existing research efforts that either only deal with undirected graphs or circumvent directionality…

The paper develops CI tests for causal discovery in SDEs.

Triangulation filters spurious circuits in multilingual models.

New approach improves linear-time attention for language models.

Models assess how USDA orange production forecasts impact FCOJ market decisions.

Bayesian optimization offers a flexible framework to optimize an objective function that is expensive to be evaluated. A Bayesian optimizer iteratively queries the function values on its carefully selected points. Subsequently, it makes a sensible recommendation about where the optimum locates based on its accumulated …

We develop an entropic framework to model the dynamics of stocks and European Options. Entropic inference is an inductive inference framework equipped with proper tools to handle situations where incomplete information is available. The objective of the paper is to lay down an alternative framework for modeling dynamic…

GCAO improves clustering of high-dimensional data by grouping low-density boundary points.

This study examines fees in AMMs to reduce losses from informed orderflow.

Constraint-based structure learning algorithms infer the causal structure of multivariate systems from observational data by determining an equivalent class of causal structures compatible with the conditional independencies in the data. Methods based on additive-noise (AN) models have been proposed to further discrimi…

Model optimizes trading strategy with unobservable toxicity.

A new algorithm infers causal networks from data using topological thresholds.

Order-flow entropy predicts price magnitude without directionality.