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

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

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