arXiv research

The article confirms two quasi-alternating surgeries for 9 asymmetric L-space knots.

New infinite family of knots found with unique properties.

We construct the first examples of asymmetric L-space knots in $S^3$. More specifically, we exhibit a construction of hyperbolic knots in $S^3$ with both (i) a surgery that may be realized as a surgery on a strongly invertible link such that the result of the surgery is the double branched cover of an alternating link …

The study examines quasi-alternating surgeries on knots and their properties.

In Dunfield's catalog of the hyperbolic manifolds in the SnapPy census which are complements of L-space knots in $S^3$, we determine that $22$ have tunnel number $2$ while the remaining all have tunnel number $1$. Notably, these $22$ manifolds contain $9$ asymmetric L-space knot complements. Furthermore, using SnapPy a…

The study calculates and analyzes alternating surgeries for various knots.

We study random knotting by considering knot and link diagrams as decorated, (rooted) topological maps on spheres and pulling them uniformly from among sets of a given number of vertices $n$, as first established in recent work with Cantarella and Mastin. The knot diagram model is an exciting new model which captures b…

This paper describes a Dehn surgery approach to generating asymmetric hyperbolic manifolds with two distinct lens space fillings. Such manifolds were first identified in work of Dunfield-Hoffman-Licata as the result of a computer search of the SnapPy census, but the current work establishes a topological framework for …

Study of geometric analysis on asymmetric metric spaces, including heat flow and Sobolev spaces.

A new asymmetric correntropy method improves robust adaptive filtering for asymmetric error distributions.

New metrics for Anosov representations defined from Thurston's asymmetric metrics.

Generalizes Thurston's asymmetric metric to flat metrics.

This study examines asymmetric cross-correlations in cryptocurrency markets using fractal analysis.

Theoretical justification for asymmetric actor-critic algorithms in reinforcement learning.

This work presents deep asymmetric networks with a set of node-wise variant activation functions. The nodes' sensitivities are affected by activation function selections such that the nodes with smaller indices become increasingly more sensitive. As a result, features learned by the nodes are sorted by the node indices…

We consider the problem of designing locality sensitive hashes (LSH) for inner product similarity, and of the power of asymmetric hashes in this context. Shrivastava and Li argue that there is no symmetric LSH for the problem and propose an asymmetric LSH based on different mappings for query and database points. Howev…

New asymmetric kernel methods improve feature learning.

The paper improves asymmetric causality tests by addressing inefficiencies and statistical significance issues.

Asymmetric expansion preserves convexity in hyperbolic geometry.

We propose Deep Asymmetric Multitask Feature Learning (Deep-AMTFL) which can learn deep representations shared across multiple tasks while effectively preventing negative transfer that may happen in the feature sharing process. Specifically, we introduce an asymmetric autoencoder term that allows reliable predictors fo…

In this paper we show how the study of asymmetric R&D alliances, that are those between young and small firms and large and MNEs firms for knowledge exploration and/or exploitation, requires the adoption of a coopetitive framework which consider both collaboration and competition. We draw upon the literature on asymmet…

Bayesian VI copula models capture asymmetric intraday equity dependence.

This work describes compactifications of metric spaces and vector spaces using asymmetric norms.

Extends multidimensional scaling to analyze three-way asymmetric proximities.

Extends metric to Margulis spacetimes for convex properties.

This paper introduces constrained mixtures for continuous distributions, characterized by a mixture of distributions where each distribution has a shape similar to the base distribution and disjoint domains. This new concept is used to create generalized asymmetric versions of the Laplace and normal distributions, whic…

Enhances reinforcement learning with partial state information.

This paper studies gradient flows in asymmetric metric spaces and proves existence results.

Paper defines saddle points in asymmetric Dynkin games using martingale theory.

Proposes a new OAL algorithm for imbalanced data with limited labels.

Study uncovers new phase transitions in asymmetric causal inference scenarios.

Modified asymmetric hidden Markov models for time series with autoregressive components.

Mixtures of multivariate contaminated shifted asymmetric Laplace distributions are developed for handling asymmetric clusters in the presence of outliers (also referred to as bad points herein). In addition to the parameters of the related non-contaminated mixture, for each (asymmetric) cluster, our model has one param…

In this paper, we propose a novel asymmetric $ε$-insensitive pinball loss function for quantile estimation. There exists some pinball loss functions which attempt to incorporate the $ε$-insensitive zone approach in it but, they fail to extend the $ε$-insensitive approach for quantile estimation in true sense. The propo…

Study proves value of non-Markovian games with partial, asymmetric info.

We highlight several analogies between the Finsler (infinitesimal) properties of Teichmüller's metric and Thurston's asymmetric metric on Teichmüller space. Thurston defined his asymmetric metric in analogy with Teichmüllers' metric, as a solution to an extremal problem, which consists, in the case of the asymmetric me…

Innovative extensions to option pricing models using asymmetric Brownian motion and random walk approaches.

This paper applies Thompson Sampling to asymmetric $\alpha$-stable bandits for financial and wireless data.

Machine learning improves beta forecasts, enhancing equity valuation and portfolio performance.

Proposes a method to generate prediction intervals using weighted asymmetric loss functions.

Study binary choice with asymmetric loss, offering simple solutions.

Despite the non-convex nature of their loss functions, deep neural networks are known to generalize well when optimized with stochastic gradient descent (SGD). Recent work conjectures that SGD with proper configuration is able to find wide and flat local minima, which have been proposed to be associated with good gener…

The generalized correlation approach, which has been successfully used in statistical radio physics to describe non-Gaussian random processes, is proposed to describe stochastic financial processes. The generalized correlation approach has been used to describe a non-Gaussian random walk with independent, identically d…

We quantitatively relate the Patterson-Sullivant currents and generic stretching factors for free group automorphisms to the asymmetric Lipschitz metric on Outer space and to Guirardel's intersection number.

In this paper, we test a partially segmented ICAPM for two developed markets, two emerging markets and World market, using an asymmetric extension of the multivariate GARCH process of De Santis and Gerard (1997,1998). We find that this asymmetric process provides a significantly better fit of the data than a standard s…

R2T hybrid model improves robust regression for asymmetric noise.

A family of parsimonious shifted asymmetric Laplace mixture models is introduced. We extend the mixture of factor analyzers model to the shifted asymmetric Laplace distribution. Imposing constraints on the constitute parts of the resulting decomposed component scale matrices leads to a family of parsimonious models. An…

Gradient descent solves asymmetric low-rank matrix factorization efficiently.