603 research outputs found
Simulation of exciton states and optical properties in atomically thin semiconducting materials
令和4年度 京都大学化学研究所 スーパーコンピュータシステム 利用報告
DHC: Dual-debiased Heterogeneous Co-training Framework for Class-imbalanced Semi-supervised Medical Image Segmentation
The volume-wise labeling of 3D medical images is expertise-demanded and
time-consuming; hence semi-supervised learning (SSL) is highly desirable for
training with limited labeled data. Imbalanced class distribution is a severe
problem that bottlenecks the real-world application of these methods but was
not addressed much. Aiming to solve this issue, we present a novel
Dual-debiased Heterogeneous Co-training (DHC) framework for semi-supervised 3D
medical image segmentation. Specifically, we propose two loss weighting
strategies, namely Distribution-aware Debiased Weighting (DistDW) and
Difficulty-aware Debiased Weighting (DiffDW), which leverage the pseudo labels
dynamically to guide the model to solve data and learning biases. The framework
improves significantly by co-training these two diverse and accurate
sub-models. We also introduce more representative benchmarks for
class-imbalanced semi-supervised medical image segmentation, which can fully
demonstrate the efficacy of the class-imbalance designs. Experiments show that
our proposed framework brings significant improvements by using pseudo labels
for debiasing and alleviating the class imbalance problem. More importantly,
our method outperforms the state-of-the-art SSL methods, demonstrating the
potential of our framework for the more challenging SSL setting. Code and
models are available at: https://github.com/xmed-lab/DHC.Comment: Accepted at MICCAI202
Topological phase transition from periodic edge states in moir\'e superlattices
Topological mosaic pattern (TMP) can be formed in two-dimensional (2D)
moir\'e superlattices, a set of periodic and spatially separated domains with
distinct topologies give rise to periodic edge states on the domain walls. In
this study, we demonstrate that these periodic edge states play a crucial role
in determining global topological properties. By developing a continuum model
for periodic edge states with C6z and C3z rotational symmetry, we predict that
a global topological phase transition at the charge neutrality point (CNP) can
be driven by the size of domain walls and moir\'e periodicity. The Wannier
representation analysis reveals that these periodic edge states are
fundamentally chiral px +- ipy orbitals. The interplay between on-site chiral
orbital rotation and neighboring hopping among chiral orbitals leads to band
inversion and a topological phase transition. Our work establishes a general
model for tuning local and global topological phases, paving the way for future
research on strongly correlated topological flat minibands within topological
mosaic pattern
Object oriented data analysis: Sets of trees
Object oriented data analysis is the statistical analysis of populations of
complex objects. In the special case of functional data analysis, these data
objects are curves, where standard Euclidean approaches, such as principal
component analysis, have been very successful. Recent developments in medical
image analysis motivate the statistical analysis of populations of more complex
data objects which are elements of mildly non-Euclidean spaces, such as Lie
groups and symmetric spaces, or of strongly non-Euclidean spaces, such as
spaces of tree-structured data objects. These new contexts for object oriented
data analysis create several potentially large new interfaces between
mathematics and statistics. This point is illustrated through the careful
development of a novel mathematical framework for statistical analysis of
populations of tree-structured objects.Comment: Published in at http://dx.doi.org/10.1214/009053607000000217 the
Annals of Statistics (http://www.imstat.org/aos/) by the Institute of
Mathematical Statistics (http://www.imstat.org
An Unsupervised Approach to DDoS Attack Detection and Mitigation in Near-Real Time
We present an approach for Distributed Denial of Service (DDoS) attack detection and mitigation in near-real time. The adaptive unsupervised machine learning methodology is based on volumetric thresholding, Functional Principal Component Analysis, and K-means clustering (with tuning parameters for flexibility), which dissects the dataset into categories of outlier source IP addresses. A probabilistic risk assessment technique is used to assign “threat levels” to potential malicious actors. We use our approach to analyze a synthetic DDoS attack with ground truth, as well as the Network Time Protocol (NTP) amplification attack that occurred during January of 2014 at a large mountain-range university. We demonstrate the speed and capabilities of our technique through replay of the NTP attack. We show that we can detect and attenuate the DDoS within two minutes with significantly reduced volume throughout the six waves of the attack
Comparison of Supervised and Unsupervised Learning for Detecting Anomalies in Network Traffic
Adversaries are always probing for vulnerable spots on the Internet so they can attack their target. By examining traffic at the firewall, we can look for anomalies that may represent these probes. To help select the right techniques we conduct comparisons of supervised and unsupervised machine learning on network flows to find sets of outliers flagged as potential threats. We apply Functional PCA and K-Means together versus Multilayer Perceptron on a real-world dataset of traffic prior to an NTP DDoS attack in January 2014; scanning activity was heightened during this pre-attack period. We partition data to evaluate detection powers of each technique and show that FPCA+Kmeans outperforms MLP. We also present a new variation of the circle plot for visualization of resulting outliers which we suggest excels at displaying multidimensional attributes of an individual IP\u27s behavior over time. In small multiples, circle plots show a gestalt overview of traffic
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