The Experts below are selected from a list of 21963 Experts worldwide ranked by ideXlab platform
Mathias S Scheurer - One of the best experts on this subject based on the ideXlab platform.
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Unsupervised Machine Learning and Band Topology.
Physical review letters, 2020Co-Authors: Mathias S Scheurer, Robert-jan SlagerAbstract:The study of topological band structures is an active area of research in condensed matter physics and beyond. Here, we combine recent progress in this field with developments in Machine Learning, another rising topic of interest. Specifically, we introduce an Unsupervised Machine Learning approach that searches for and retrieves paths of adiabatic deformations between Hamiltonians, thereby clustering them according to their topological properties. The algorithm is general, as it does not rely on a specific parametrization of the Hamiltonian and is readily applicable to any symmetry class. We demonstrate the approach using several different models in both one and two spatial dimensions and for different symmetry classes with and without crystalline symmetries. Accordingly, it is also shown how trivial and topological phases can be diagnosed upon comparing with a generally designated set of trivial atomic insulators.
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identifying topological order through Unsupervised Machine Learning
Nature Physics, 2019Co-Authors: Joaquin F Rodrigueznieva, Mathias S ScheurerAbstract:The Landau description of phase transitions relies on the identification of a local order parameter that indicates the onset of a symmetry-breaking phase. In contrast, topological phase transitions evade this paradigm and, as a result, are harder to identify. Recently, Machine Learning techniques have been shown to be capable of characterizing topological order in the presence of human supervision. Here, we propose an Unsupervised approach based on diffusion maps that learns topological phase transitions from raw data without the need for manual feature engineering. Using bare spin configurations as input, the approach is shown to be capable of classifying samples of the two-dimensional XY model by winding number and capture the Berezinskii–Kosterlitz–Thouless transition. We also demonstrate the success of the approach on the Ising gauge theory, another paradigmatic model with topological order. In addition, a connection between the output of diffusion maps and the eigenstates of a quantum-well Hamiltonian is derived. Topological classification via diffusion maps can therefore enable fully Unsupervised studies of exotic phases of matter. Machine Learning techniques have latterly gained currency in condensed-matter physics, for example by identifying phase transitions. An Unsupervised Machine Learning algorithm that identifies topological order is now demonstrated.
Wutung Cheng - One of the best experts on this subject based on the ideXlab platform.
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scan chain diagnosis based on Unsupervised Machine Learning
Asian Test Symposium, 2017Co-Authors: Yu Huang, B Benware, Randy Klingenberg, Huaxing Tang, Jayant Dsouza, Wutung ChengAbstract:Scan-based testing has proven to be a cost-effective method for achieving good test coverage in digital circuits. It was reported in prior papers that about 30% to 50% of all failing die were due to defects that cause scan chains to fail [1][2]. Therefore, scan chain failure diagnosis is very important to improve yield. The previously proposed methods of chain diagnosis were primarily based on either deterministic fault models and simulation algorithms or probabilistic analysis. To handle hard-to-model defect behaviors more robustly, in this paper, we propose a new scan chain diagnosis algorithm based on Unsupervised Machine Learning. Its application on "scannable memory designs" (SMD) is demonstrated to illustrate the effectiveness of the proposed algorithm.
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ATS - Scan Chain Diagnosis Based on Unsupervised Machine Learning
2017 IEEE 26th Asian Test Symposium (ATS), 2017Co-Authors: Yu Huang, B Benware, Randy Klingenberg, Huaxing Tang, Jayant D'souza, Wutung ChengAbstract:Scan-based testing has proven to be a cost-effective method for achieving good test coverage in digital circuits. It was reported in prior papers that about 30% to 50% of all failing die were due to defects that cause scan chains to fail [1][2]. Therefore, scan chain failure diagnosis is very important to improve yield. The previously proposed methods of chain diagnosis were primarily based on either deterministic fault models and simulation algorithms or probabilistic analysis. To handle hard-to-model defect behaviors more robustly, in this paper, we propose a new scan chain diagnosis algorithm based on Unsupervised Machine Learning. Its application on "scannable memory designs" (SMD) is demonstrated to illustrate the effectiveness of the proposed algorithm.
R B Metcalf - One of the best experts on this subject based on the ideXlab platform.
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identifying strong lenses with Unsupervised Machine Learning using convolutional autoencoder
Monthly Notices of the Royal Astronomical Society, 2020Co-Authors: Tingyun Cheng, Christopher J Conselice, Alfonso Aragonsalamanca, S Dye, R B MetcalfAbstract:In this paper, we develop a new Unsupervised Machine Learning technique comprised of a feature extractor, a convolutional autoencoder, and a clustering algorithm consisting of a Bayesian Gaussian mixture model. We apply this technique to visual band space-based simulated imaging data from the Euclid Space Telescope using data from the strong gravitational lenses finding challenge. Our technique promisingly captures a variety of lensing features such as Einstein rings with different radii, distorted arc structures, etc., without using predefined labels. After the clustering process, we obtain several classification clusters separated by different visual features which are seen in the images. Our method successfully picks up ∼63 per cent of lensing images from all lenses in the training set. With the assumed probability proposed in this study, this technique reaches an accuracy of 77.25 ± 0.48 per cent in binary classification using the training set. Additionally, our Unsupervised clustering process can be used as the preliminary classification for future surveys of lenses to efficiently select targets and to speed up the labelling process. As the starting point of the astronomical application using this technique, we not only explore the application to gravitationally lensed systems, but also discuss the limitations and potential future uses of this technique.
Pak-lok Poon - One of the best experts on this subject based on the ideXlab platform.
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METTLE: a METamorphic testing approach to assessing and validating Unsupervised Machine Learning systems
IEEE Transactions on Reliability, 2020Co-Authors: Xiaoyuan Xie, Zhiyi Zhang, Tsong Yueh Chen, Yang Liu, Pak-lok PoonAbstract:Unsupervised Machine Learning is the training of an artificial intelligence system using information that is neither classified nor labeled, with a view to modeling the underlying structure or distribution in a dataset. Since Unsupervised Machine Learning systems are widely used in many real-world applications, assessing the appropriateness of these systems and validating their implementations with respect to individual users' requirements and specific application scenarios$\,/\,$contexts are indisputably two important tasks. Such assessment and validation tasks, however, are fairly challenging due to the absence of a priori knowledge of the data. In view of this challenge, we develop a $\textbf{MET}$amorphic $\textbf{T}$esting approach to assessing and validating Unsupervised Machine $\textbf{LE}$arning systems, abbreviated as METTLE. Our approach provides a new way to unveil the (possibly latent) characteristics of various Machine Learning systems, by explicitly considering the specific expectations and requirements of these systems from individual users' perspectives. To support METTLE, we have further formulated 11 generic metamorphic relations (MRs), covering users' generally expected characteristics that should be possessed by Machine Learning systems. To demonstrate the viability and effectiveness of METTLE we have performed an experiment involving six commonly used clustering systems. Our experiment has shown that, guided by user-defined MR-based adequacy criteria, end users are able to assess, validate, and select appropriate clustering systems in accordance with their own specific needs. Our investigation has also yielded insightful understanding and interpretation of the behavior of the Machine Learning systems from an end-user software engineering's perspective, rather than a designer's or implementor's perspective, who normally adopts a theoretical approach.
Weilin Huang - One of the best experts on this subject based on the ideXlab platform.
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Seismic signal recognition by Unsupervised Machine Learning
Geophysical Journal International, 2019Co-Authors: Weilin HuangAbstract:SUMMARYSeismic signal recognition can serve as a powerful auxiliary tool for analysing and processing ever-larger volumes of seismic data. It can facilitate many subsequent procedures such as first-break picking, statics correction, denoising, signal detection, events tracking, structural interpretation, inversion and imaging. In this study, I propose an automatic technique of seismic signal recognition taking advantage of Unsupervised Machine Learning. In the proposed technique, seismic signal recognition is considered as a problem of clustering data points. All the seismic sampling points in time domain are clustered into two clusters, that is, signal or non-signal. The hierarchical clustering algorithm is used to group these sampling points. Four attributes, that is, two short-term-average-to-long-term-average ratios, variance and envelope are investigated in the clustering process. In addition, to quantitatively evaluate the performance of seismic signal recognition properly, I propose two new statistical indicators, namely, the rate between the total energies of original and recognized signals (RTE), and the rate between the average energies of original and recognized signals (RAE). A large number of numerical experiments show that when the signal is slightly corrupted by noise, the proposed technique performs very well, with recognizing accuracy, precision and RTE of nearly 1 (i.e. 100 per cent), recall greater than 0.8 and RAE about 1–1.3. When the signal is moderately corrupted by noise, the proposed technique can hold recognizing accuracy about 0.9, recognizing precision nearly to 1, RTE about 0.9, recall around 0.6 and RAE about 1.5. Applications of the proposed technique to real microseismic data induced from hydraulic fracturing and reflection seismic data demonstrate its feasibility and encouraging prospect.
