An Approximate Bayesian Long Short-Term Memory Algorithm for Outlier Detection

Long Short-Term Memory networks trained with gradient descent and back-propagation have received great success in various applications. However, point estimation of the weights of the networks is prone to over-fitting problems and lacks important uncertainty information associated with the estimation. However, exact Bayesian neural network methods are intractable and non-applicable for real-world applications. In this study, we propose an approximate estimation of the weights uncertainty using Ensemble Kalman Filter, which is easily scalable to a large number of weights. Furthermore, we optimize the covariance of the noise distribution in the ensemble update step using maximum likelihood estimation. To assess the proposed algorithm, we apply it to outlier detection in five real-world events retrieved from the Twitter platform

A novel feature selection-based sequential ensemble learning method for class noise detection in high-dimensional data

© 2018, Springer Nature Switzerland AG. Most of the irrelevant or noise features in high-dimensional data present significant challenges to high-dimensional mislabeled instances detection methods based on feature selection. Traditional methods often perform the two dependent step: The first step, searching for the relevant subspace, and the second step, using the feature subspace which obtained in the previous step training model. However, Feature subspace that are not related to noise scores and influence detection performance. In this paper, we propose a novel sequential ensemble method SENF that aggregate the above two phases, our method learns the sequential ensembles to obtain refine feature subspace and improve detection accuracy by iterative sparse modeling with noise scores as the regression target attribute. Through extensive experiments on 8 real-world high-dimensional datasets from the UCI machine learning repository [3], we show that SENF performs significantly better or at least similar to the individual baselines as well as the existing state-of-the-art label noise detection method

Learning Graph Patterns of Reflection Coefficient for Non-destructive Diagnosis of Cu Interconnects

With the increasing operating frequencies and clock speeds in processors, interconnects affect both the reliability and performance of entire electronic systems. Fault detection and diagnosis of the interconnects are crucial for prognostics and health management (PHM) of electronics. However, traditional approaches using electrical signals as prognostic factors often face challenges in distinguishing defect root causes, necessitating additional destructive evaluations, and are prone to noise interference, leading to potential false alarms. To address these limitations, this paper introduces a novel approach for non-destructive detection and diagnosis of defects in Cu interconnects, offering early detection, enhanced diagnostic accuracy, and noise resilience. Our approach uniquely analyzes both the root cause and severity of interconnect defects by leveraging graph patterns of reflection coefficient, a technique distinct from traditional time series signal analysis. We experimentally demonstrate that the graph patterns possess the capability for fault diagnosis and serve as effective input data for learning algorithms. Additionally, we introduce a novel severity rating ensemble learning (SREL) approach, which significantly enhances diagnostic accuracy and noise robustness. Experimental results demonstrate that the proposed method outperforms conventional machine learning methods and multi-class convolutional neural networks (CNN), achieving a maximum accuracy of 99.3%, especially under elevated noise levels

Minimum Requirements for Detecting a Stochastic Gravitational Wave Background Using Pulsars

We assess the detectability of a nanohertz gravitational wave (GW) background with respect to additive red and white noise in the timing of millisecond pulsars. We develop detection criteria based on the cross-correlation function summed over pulsar pairs in a pulsar timing array. The distribution of correlation amplitudes is found to be non-Gaussian and highly skewed, which significantly influences detection and false-alarm probabilities. When only white noise and GWs contribute, our detection results are consistent with those found by others. Red noise, however, drastically alters the results. We discuss methods to meet the challenge of GW detection ("climbing mount significance") by distinguishing between GW-dominated and red or white-noise limited regimes. We characterize detection regimes by evaluating the number of millisecond pulsars that must be monitored in a high-cadence, 5-year timing program for a GW background spectrum $h_c(f) = A f^{-2/3}$ with $A = 10^{-15}$ yr$^{-2/3}$. Unless a sample of 20 super-stable millisecond pulsars can be found --- those with timing residuals from red-noise contributions $\sigma_r \lesssim 20$ ns --- a much larger timing program on $\gtrsim 50 - 100$ MSPs will be needed. For other values of $A$, the constraint is $\sigma_r \lesssim 20 {\rm ns} (A/10^{-15} {\rm yr}^{-2/3})$. Identification of suitable MSPs itself requires an aggressive survey campaign followed by characterization of the level of spin noise in the timing residuals of each object. The search and timing programs will likely require substantial fractions of time on new array telescopes in the southern hemisphere as well as on existing ones.Comment: Submitted to the Astrophysical Journa

Bolt Detection Signal Analysis Method Based on ICEEMD

The construction quality of the bolt is directly related to the safety of the project, and as such, it must be tested. In this paper, the improved complete ensemble empirical mode decomposition (ICEEMD) method is introduced to the bolt detection signal analysis. The ICEEMD is used in order to decompose the anchor detection signal according to the approximate entropy of each intrinsic mode function (IMF). The noise of the IMFs is eliminated by the wavelet soft threshold de-noising technique. Based on the approximate entropy, and the wavelet de-noising principle, the ICEEMD-De anchor signal analysis method is proposed. From the analysis of the vibration analog signal, as well as the bolt detection signal, the result shows that the ICEEMD-De method is capable of correctly separating the different IMFs under noisy conditions, and also that the IMF can effectively identify the reflection signal of the end of the bolt

Harnessing nuclear spin polarization fluctuations in a semiconductor nanowire

Soon after the first measurements of nuclear magnetic resonance (NMR) in a condensed matter system, Bloch predicted the presence of statistical fluctuations proportional to $1/\sqrt{N}$ in the polarization of an ensemble of $N$ spins. First observed by Sleator et al., so-called "spin noise" has recently emerged as a critical ingredient in nanometer-scale magnetic resonance imaging (nanoMRI). This prominence is a direct result of MRI resolution improving to better than 100 nm^3, a size-scale in which statistical spin fluctuations begin to dominate the polarization dynamics. We demonstrate a technique that creates spin order in nanometer-scale ensembles of nuclear spins by harnessing these fluctuations to produce polarizations both larger and narrower than the natural thermal distribution. We focus on ensembles containing ~10^6 phosphorus and hydrogen spins associated with single InP and GaP nanowires (NWs) and their hydrogen-containing adsorbate layers. We monitor, control, and capture fluctuations in the ensemble's spin polarization in real-time and store them for extended periods. This selective capture of large polarization fluctuations may provide a route for enhancing the weak magnetic signals produced by nanometer-scale volumes of nuclear spins. The scheme may also prove useful for initializing the nuclear hyperfine field of electron spin qubits in the solid-state.Comment: 18 pages, 5 figure