377 research outputs found
Deformations of Lie 2-algebras
In this paper, we consider deformations of Lie 2-algebras via the cohomology
theory. We prove that a 1-parameter infinitesimal deformation of a Lie
2-algebra \g corresponds to a 2-cocycle of \g with the coefficients in the
adjoint representation. The Nijenhuis operator for Lie 2-algebras is introduced
to describe trivial deformations. We also study abelian extensions of Lie
2-algebras from the viewpoint of deformations of semidirect product Lie
2-algebras.Comment: 20 page
AutoEncoder Inspired Unsupervised Feature Selection
High-dimensional data in many areas such as computer vision and machine
learning tasks brings in computational and analytical difficulty. Feature
selection which selects a subset from observed features is a widely used
approach for improving performance and effectiveness of machine learning models
with high-dimensional data. In this paper, we propose a novel AutoEncoder
Feature Selector (AEFS) for unsupervised feature selection which combines
autoencoder regression and group lasso tasks. Compared to traditional feature
selection methods, AEFS can select the most important features by excavating
both linear and nonlinear information among features, which is more flexible
than the conventional self-representation method for unsupervised feature
selection with only linear assumptions. Experimental results on benchmark
dataset show that the proposed method is superior to the state-of-the-art
method.Comment: accepted by ICASSP 201
Self-Discriminative Modeling for Anomalous Graph Detection
This paper studies the problem of detecting anomalous graphs using a machine
learning model trained on only normal graphs, which has many applications in
molecule, biology, and social network data analysis. We present a
self-discriminative modeling framework for anomalous graph detection. The key
idea, mathematically and numerically illustrated, is to learn a discriminator
(classifier) from the given normal graphs together with pseudo-anomalous graphs
generated by a model jointly trained, where we never use any true anomalous
graphs and we hope that the generated pseudo-anomalous graphs interpolate
between normal ones and (real) anomalous ones. Under the framework, we provide
three algorithms with different computational efficiencies and stabilities for
anomalous graph detection. The three algorithms are compared with several
state-of-the-art graph-level anomaly detection baselines on nine popular graph
datasets (four with small size and five with moderate size) and show
significant improvement in terms of AUC. The success of our algorithms stems
from the integration of the discriminative classifier and the well-posed
pseudo-anomalous graphs, which provide new insights for anomaly detection.
Moreover, we investigate our algorithms for large-scale imbalanced graph
datasets. Surprisingly, our algorithms, though fully unsupervised, are able to
significantly outperform supervised learning algorithms of anomalous graph
detection. The corresponding reason is also analyzed.Comment: This work was submitted to NeurIPS 2023 but was unfortunately
rejecte
Degradation of Cry1Ac Protein Within Transgenic Bacillus thuringiensis Rice Tissues Under Field and Laboratory Conditions
To clarify the environmental fate of the Cry1Ac protein from Bacillus thuringiensis subsp. kurstaki (Bt) contained in transgenic rice plant stubble after harvest, degradation was monitored under field conditions using an enzyme-linked immunosorbent assay. In stalks, Cry1Ac protein concentration decreased rapidly to 50% of the initial amount during the first month after harvest; subsequently, the degradation decreased gradually reaching 21.3% when the experiment was terminated after 7 mo. A similar degradation pattern of the Cry1Ac protein was observed in rice roots. However, when the temperature increased in April of the following spring, protein degradation resumed, and no protein could be detected by the end of the experiment. In addition, a laboratory experiment was conducted to study the persistence of Cry1Ac protein released from rice tissue in water and paddy soil. The protein released from leaves degraded rapidly in paddy soil under flooded conditions during the first 20 d and plateaued until the termination of this trial at 135 d, when 15.3% of the initial amount was still detectable. In water, the Cry1Ac protein degraded more slowly than in soil but never entered a relatively stable phase as in soil. The degradation rate of Cry1Ac protein was significantly faster in nonsterile water than in sterile water. These results indicate that the soil environment can increase the degradation of Bt protein contained in plant residues. Therefore, plowing a field immediately after harvest could be an effective method for decreasing the persistence of Bt protein in transgenic rice field
Sparse Representation of Deformable 3D Organs with Spherical Harmonics and Structured Dictionary
This paper proposed a novel algorithm to sparsely represent a deformable surface (SRDS) with low dimensionality based on spherical harmonic decomposition (SHD) and orthogonal subspace pursuit (OSP). The key idea in SRDS method is to identify the subspaces from a training data set in the transformed spherical harmonic domain and then cluster each deformation into the best-fit subspace for fast and accurate representation. This algorithm is also generalized into applications of organs with both interior and exterior surfaces. To test the feasibility, we first use the computer models to demonstrate that the proposed approach matches the accuracy of complex mathematical modeling techniques and then both ex vivo and in vivo experiments are conducted using 3D magnetic resonance imaging (MRI) scans for verification in practical settings. All results demonstrated that the proposed algorithm features sparse representation of deformable surfaces with low dimensionality and high accuracy. Specifically, the precision evaluated as maximum error distance between the reconstructed surface and the MRI ground truth is better than 3 mm in real MRI experiments
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