arXiv research

An iterative SE(3)-Transformer model is developed for graph data.

Study uses multidimensional SE-NBD process to analyze default portfolios and identify shock amplification.

Speech enhancement (SE) aims to reduce noise in speech signals. Most SE techniques focus only on addressing audio information. In this work, inspired by multimodal learning, which utilizes data from different modalities, and the recent success of convolutional neural networks (CNNs) in SE, we propose an audio-visual de…

Speech enhancement (SE) aims to reduce noise in speech signals. Most SE techniques focus only on addressing audio information. In this work, inspired by multimodal learning, which utilizes data from different modalities, and the recent success of convolutional neural networks (CNNs) in SE, we propose an audio-visual de…

The statistically equivalent signature (SES) algorithm is a method for feature selection inspired by the principles of constrained-based learning of Bayesian Networks. Most of the currently available feature-selection methods return only a single subset of features, supposedly the one with the highest predictive power.…

Smooth SE structures on Sasaki-joins and Bott orbifolds constructed.

Sep-SpectralNet improves SE for broader applicability and scalability.

A five dimensional Sasaki-Einstein (SE) manifold provides a AdS/CFT pair for four dimensional $\mathcal{N}=1$ SCFT, and those pairs are very useful in studying field theory and AdS/CFT correspondence. The space of known SE manifolds is increased significantly in the last decade, and we initiated the study of various fi…

Study cohomological equation for robotic screw motions on SE(3).

Improving speech system performance in noisy environments remains a challenging task, and speech enhancement (SE) is one of the effective techniques to solve the problem. Motivated by the promising results of generative adversarial networks (GANs) in a variety of image processing tasks, we explore the potential of cond…

One of the ways to train deep neural networks effectively is to use residual connections. Residual connections can be classified as being either identity connections or bridge-connections with a reshaping convolution. Empirical observations on CIFAR-10 and CIFAR-100 datasets using a baseline Resnet model, with bridge-c…

SE(3)-Transformers maintain equivariance for 3D data under rotations and translations.

Tiled Squeeze-and-Excite improves channel attention with local spatial context.

Closed-form relations and approximations for SE(3) derivatives for robust numerical simulations.

SE-RRMs solve structured problems like Sudoku and ARC-AGI by enforcing symbol equivariance.

Study on Langevin dynamics on planar motion group, highlighting geometric mechanism.

Most recent studies on deep learning based speech enhancement (SE) focused on improving denoising performance. However, successful SE applications require striking a desirable balance between denoising performance and computational cost in real scenarios. In this study, we propose a novel parameter pruning (PP) techniq…

A new diffusion model generates novel protein backbones without relying on pretrained networks.

P-SE explains model decisions with minimal feature subsets and fast estimators.

Artificial neural network (ANN) provides superior accuracy for nonlinear alternating current (AC) state estimation (SE) in smart grid over traditional methods. However, research has discovered that ANN could be easily fooled by adversarial examples. In this paper, we initiate a new study of adversarial false data injec…

We use self-report and electrodermal activity (EDA) wearable sensor data from 77 nights of sleep on six participants to test the efficacy of EDA data for sleep monitoring. We used factor analysis to find latent factors in the EDA data, and causal model search to find the most probable graphical model accounting for sel…

Replication study shows Deep-SE still not as effective as previously thought for agile effort estimation.

Researchers find metric lines in SE(2) using Hamilton-Jacobi theory.

We propose a notion of distance between two parametrized planar curves, called their discrepancy, and defined intuitively as the minimal amount of deformation needed to deform the source curve into the target curve. A precise definition of discrepancy is given as follows. A curve of transformations in the special Eucli…

Using the basic Lie symmetry method, we find the most general Lie point symmetries group of the $\nabla u=f(u)$ Poisson's equation, which has a subalgebra isomorphic to the $3-$dimensional special Euclidean group ${\rm SE}(3)$ or group of rigid motions of ${\Bbb R}^3$. Looking the adjoint representation of ${\rm SE}(3)…

MAPS and MAPS-SE learn from multiple suboptimal experts to improve policies efficiently.

This paper summarizes closed-form relations for SE(3) maps and their derivatives.

Improved scaffold generation for protein motifs using SE(3) flow matching.

Proposes SE(3) equivariant graph neural networks with local frames for efficient geometric approximation.

A large consensus now seems to take for granted that the distributions of empirical returns of financial time series are regularly varying, with a tail exponent close to 3. We revisit this results and use standard tests as well as develop a battery of new non-parametric and parametric tests (in particular with stretche…

The so-called risk diversification principle is analyzed, showing that its convenience depends on individual characteristics of the risks involved and the dependence relationship among them. ----- Se analiza el principio de diversificación de riesgos y se demuestra que no siempre resulta mejor que no diversificar, pues…

Attention mechanism is a hot spot in deep learning field. Using channel attention model is an effective method for improving the performance of the convolutional neural network. Squeeze-and-Excitation block takes advantage of the channel dependence, selectively emphasizing the important channels and compressing the rel…

The paper optimizes stock portfolios with constraints based on performance attribution.

SE-KGE embeds spatial data into KGs for better spatial reasoning.

CeCNN predicts SE and AL from UWF images, improving myopia screening.

New method improves sparse signal reconstruction using 1RSB-AMP.

Improves RLHF sample efficiency by scaling reward complexity polynomially.

Proposes HypCSE for enhanced hierarchical clustering.

PenduMAV is a 6-input omnidirectional MAV without internal forces.

The paper explores metrics on Lie groups and their connections to dual quaternions.

Vector approximate message passing (VAMP) is a computationally simple approach to the recovery of a signal $\mathbf{x}$ from noisy linear measurements $\mathbf{y}=\mathbf{Ax}+\mathbf{w}$. Like the AMP proposed by Donoho, Maleki, and Montanari in 2009, VAMP is characterized by a rigorous state evolution (SE) that holds …

GS-B$^3$SE improves label shift estimation by smoothing priors on a graph.

Characterizes stably elliptic elements in Lie groups and their properties.

Paper derives and applies a parallel transport equation on Lie groups.

DBNs improve ES and SES estimation for market risk, but tail behavior remains challenging.

Noether's First Theorem yields conservation laws for Lagrangians with a variational symmetry group. The explicit formulae for the laws are well known and the symmetry group is known to act on the linear space generated by the conservation laws. In recent work the authors showed the mathematical structure behind both th…

The present paper proposes a unified geometric framework for coordinated motion on Lie groups. It first gives a general problem formulation and analyzes ensuing conditions for coordinated motion. Then, it introduces a precise method to design control laws in fully actuated and underactuated settings with simple integra…

We present a convolutional network that is equivariant to rigid body motions. The model uses scalar-, vector-, and tensor fields over 3D Euclidean space to represent data, and equivariant convolutions to map between such representations. These SE(3)-equivariant convolutions utilize kernels which are parameterized as a …