Browsing by Author "Leshem, Guy"

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A study on anomaly detection ensembles
(2017-05-01)
Chiang, Alvin
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David, Esther
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Lee, Yuh-Jye
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Leshem, Guy
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Yeh, Yi-Ren
An anomaly, or outlier, is an object exhibiting differences that suggest it belongs to an as-yet undefined class or category. Early detection of anomalies often proves of great importance because they may correspond to events such as fraud, spam, or device malfunctions. By automating the creation of a ranking or list of deviations, we can save time and decrease the cognitive overload of the individuals or groups responsible for responding to such events. Over the years many anomaly and outlier metrics have been developed. In this paper we propose a clustering-based score ensembling method for outlier detection. Using benchmark datasets we evaluate quantitatively the robustness and accuracy of different ensemble strategies. We find that ensembling strategies offer only limited value for increasing overall performance, but provide robustness by negating the influence of severely underperforming models. (C) 2017 Elsevier B.V. All rights reserved.
Covariant Bethe-Salpeter approximation in models of strongly correlated electron systems
(2020-02-20)
Fan, Zhenhao
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Sun, Zhipeng
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Li, Dingping
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Berenstein, Itzhak
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Leshem, Guy
Strongly correlated electron systems are generally described by tight-binding lattice Hamiltonians with strong local (onsite) interactions, the most popular being the Hubbard model. Although the half-filled Hubbard model can be simulated by Monte Carlo (MC), physically more interesting cases beyond half-filling are plagued by the sign problem. One therefore should resort to other methods. It was demonstrated recently that a systematic truncation of the set of Dyson-Schwinger equations for correlators of the Hubbard, supplemented by a "covariant" calculation of correlators leads to a convergent series of approximants. The covariance preserves all the Ward identities among correlators describing various condensed matter probes. While first-order (classical), second-order (Hartree-Fock or Gaussian), and third-order (Cubic) covariant approximation were worked out, the fourth-order (quartic) seems too complicated to be effectively calculable in fermionic systems. It turns out that the complexity of the quartic calculation in local interaction models,is manageable computationally. The quartic (Bethe-Salpeter-type) approximation is especially important in 1D and 2D models in which the symmetry-broken state does not exists (the Mermin-Wagner theorem), although strong fluctuations dominate the physics at strong coupling. Unlike the lower-order approximations, it respects the Mermin-Wagner theorem. The scheme is tested and exemplified on the single-band 1D and 2D Hubbard model.
Sparse Sampling for Sensing Temporal Data - Building an Optimized Envelope
(2016-01-01)
Domb, Menachem
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Leshem, Guy
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Bonchek-Dokow, Elisheva
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David, Esther
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Lee, Yuh-Jye
IoT systems collect vast amounts of data which can be used in order to track and analyze the structure of future recorded data. However, due to limited computational power, bandwith, and storage capabilities, this data cannot be stored as is, but rather must be reduced in such a way so that the abilities to analyze future data, based on past data, will not be compromised. We propose a parameterized method of sampling the data in an optimal way. Our method has three parameters-an averaging method for constructing an average data cycle from past observations, an envelope method for defining an interval around the average data cycle, and an entropy method for comparing new data cycles to the constructed envelope. These parameters can be adjusted according to the nature of the data, in order to find the optimal representation for classifying new cycles as well as for identifying anomalies and predicting future cycle behavior. In this work we concentrate on finding the optimal envelope, given an averaging method and an entropy method. We demonstrate with a case study of meteorological data regarding El Ninio years.