Anomalous Observations Research Articles

Theoretical study of the influence of hydrides on the performance of Mg and Y photocathodes

Our understanding of material properties in the broadest sense is based on our ability to observe and disentangle underlying mechanisms. This has been aided enormously by the discovery and exploitation of synchrotron radiation. The next generation of light sources will be based on free electron lasers with potentially much greater light intensity and time resolution. This requires the development of new photocathode materials with high quantum efficiency (QE) and low emittance that are chemically and mechanically robust. One prospect is the use of yttrium (Y) and/or magnesium (Mg) thin films, but here, a fundamental understanding of the photoemission process from realistic materials is lacking. Observations of photoemissive performance would appear to contradict simple models. It is well known that materials with a lower work function are expected to facilitate photoemission, but the measured QE of Mg is higher than that of Y despite its nominal work function (3.7 eV) being significantly higher than that of Y (3.1 eV). In this work, these apparently anomalous observations are explained and rationalized by combining a simple three-step model of photoemission with large scale density functional theory calculations to predict the QE for realistic models of both materials in a special chemical environment. This approach allows us to identify the material parameters that govern the efficiency of the photoemission process. A detailed comparison with the experimental data suggests that, in this case, hydride formation on the Y surface, invisible to most experimental probes, nevertheless has a surprisingly large influence and reduces the photoemission significantly.

Observing EeV neutrinos through Earth: GZK and the anomalous ANITA events

Tau neutrinos are unique cosmic messengers, especially at extreme energies. When they undergo a charged-current interaction, the short lifetime of the produced tau gives rise to secondary tau neutrinos that carry a significant fraction of the primary neutrino energy. Here we apply this effect, known as tau neutrino regeneration, to extremely high energy neutrinos passing through Earth. We find that for most column depths, with the exception of propagation through the core, Earth-traversing tau neutrinos emerge at \U0001d4aa(PeV) energies. We use these secondaries to estimate the expected signal from cosmogenic fluxes at IceCube and find a non-negligible contribution to the astrophysical component above 1 PeV . We also constrain the anomalous ANITA observations via the accompanying secondaries expected at IceCube. We calculate that ANITA should see fewer than 10−7 events in the reported direction, regardless of assumed source energy spectrum, ruling out the possibility that these events are astrophysical in origin under Standard Model assumptions.

Maternal cannibalism in two populations of wild chimpanzees

Maternal cannibalism has been reported in several animal taxa, prompting speculations that the behavior may be part of an evolved strategy. In chimpanzees, however, maternal cannibalism has been conspicuously absent, despite high levels of infant mortality and reports of non-maternal cannibalism. The typical response of chimpanzee mothers is to abandon their deceased infant, sometimes after prolonged periods of carrying and grooming the corpse. Here, we report two anomalous observations of maternal cannibalism in communities of wild chimpanzees in Uganda and Ivory Coast and discuss the evolutionary implications. Both infants likely died under different circumstances; one apparently as a result of premature birth, the other possibly as a result of infanticide. In both cases, the mothers consumed parts of the corpse and participated in meat sharing with other group members. Neither female presented any apparent signs of ill health before or after the events. We concluded that, in both cases, cannibalizing the infant was unlikely due to health-related issues by the mothers. We discuss these observations against a background of chimpanzee mothers consistently refraining from maternal cannibalism, despite ample opportunities and nutritional advantages. We conclude that maternal cannibalism is extremely rare in this primate, likely due to early and strong mother–offspring bond formation, which may have been profoundly disrupted in the current cases.

The optimal estimation of parameters of models of controlled stochastic systems based on the experiment design

The procedure of active identification, which is resistant to the appearance of anomalous observations, includes robust estimation of unknown parameters and optimal design of input signals for models of non-stationary linear discrete systems is proposed. The general case of the entry of unknown parameters into the equations of state and observation, the initial condition and the covariance noise matrix of the system is considered. Using the developed software, the efficiency of this procedure is demonstrated by the example of a direct current motor control system.

Reliable Indoor Pseudolite Positioning Based on a Robust Estimation and Partial Ambiguity Resolution Method

The unscented Kalman filter (UKF) can effectively reduce the linearized model error and the dependence on initial coordinate values for indoor pseudolite (PL) positioning unlike the extended Kalman filter (EKF). However, PL observations are prone to various abnormalities because the indoor environment is usually complex. Standard UKF (SUKF) lacks resistance to frequent abnormal observations. This inadequacy brings difficulty in guaranteeing the accuracy and reliability of indoor PL positioning, especially for phase-based high-precision positioning. In this type of positioning, the ambiguity resolution (AR) will be difficult to achieve in the presence of abnormal observations. In this study, a robust UKF (RUKF) and partial AR (PAR) algorithm are introduced and applied in indoor PL positioning. First, the UKF is used for parameter estimation. Then, the anomaly recognition statistics and optimal ambiguity subset of PAR are constructed on the basis of the posterior residuals. The IGGIII scheme is adopted to weaken the influence of abnormal observation, and the PAR strategy is conducted in case of failure of the conventional PL-AR. The superiority of our proposed algorithm is validated using the measured indoor PL data for code-based differential PL (DPL) and phase-based real-time kinematic (RTK) positioning modes. Numerical results indicate that the positioning accuracy of RUKF-based indoor DPL is higher with a decimeter-level improvement compared that of the SUKF, especially in the presence of large gross errors. In terms of high-precision RTK positioning, RUKF can correctly identify centimeter-level anomalous observations and obtain a corresponding positioning accuracy improvement compared with the SUKF. When relatively large gross errors exist, the conventional method cannot easily realize PL-AR. By contrast, the combination of RUKF and the PAR algorithm can achieve PL-AR for the selected ambiguity subset successfully and can improve the positioning accuracy and reliability significantly. In summary, our proposed algorithm has certain resistance ability for abnormal observations. The indoor PL positioning of this algorithm outperforms that of the conventional method. Thus, the algorithm has some practical application value, especially for kinematic positioning.

Control chart based on residues: Is a good methodology to detect outliers?

The purpose of this article is to evaluate the application of forecasting models along with the use of residual control charts to assess production processes whose samples have autocorrelation characteristics. The main objective is to determine the efficiency of control charts for individual observations (CCIO) and exponentially weighted moving average (EWMA) charts when they are applied to residuals of models of AR(1) or MA(1) to detect outlier in autocorrelated processes. Considering autocorrelation strength and sign in the data series and the outlier range, the series were simulated accomplishing 640,000 sets. The series were contaminated by anomalous observations at 100th position, an AR(1) or MA(1) model were fitted, and the residuals were evaluated by CCIO and EWMA control charts; the points correctly detected as an autocorrelation were recorded. For the parameters investigated (autocorrelation and outlier range), a detection rate was generated in each chart, and nonparametric comparison tests were applied. The result of the tests showed the superiority (p < 0.05) of the CCIO chart for both models. The study of the influence of the sign and magnitude of the autocorrelation parameter showed no significant (p > 0.05) for either AR(1) or MA(1) charts and models. In this context, control charts for individual observations (CCIO) were confirmed to effectively detect outliers through residuals in industrial autocorrelated processes originated in first-order AR and MA models.

Security Against False Data-Injection Attack in Cyber-Physical Systems

In this paper, secure, remote estimation of a linear Gaussian process via observations at multiple sensors is considered. Such a framework is relevant to many cyber-physical systems and Internet-of-things applications. Sensors make sequential measurements that are shared with a fusion center; the fusion center applies a filtering algorithm to make its estimates. The challenge is the presence of a few unknown malicious sensors which can inject anomalous observations to skew the estimates at the fusion center. The set of malicious sensors may be time varying. The problems of malicious sensor detection and secure estimation are considered. First, an algorithm for secure estimation is proposed. The proposed estimation scheme uses a novel filtering and learning algorithm, where an optimal filter is learned over time by using the sensor observations in order to filter out malicious sensor observations while retaining other sensor measurements. Next, a novel detector to detect injection attacks on an unknown sensor subset is developed. Numerical results demonstrate up to 3-dB gain in the mean-squared error and up to $\text{75} \%$ higher attack detection probability under a small false alarm rate constraint, against a competing algorithm that requires additional side information.

Influential diagnostics with Pena’s statistic for the modified ridge regression

In a linear regression model, the estimation of regression parameters is affected by some anomalous observations in the data set. Thus, identification of these observations is one of the essential steps in regression analysis. Detection of these influential observations becomes more complex when serious multicollinearity among regressors is also observed. In the present article, we formulated Pena’s statistic for each point while considering the modified ridge regression (MRR) estimator. Using this statistic, we showed that when MRR was used to mitigate the effects of multicollinearity, the influence of some observations could be significantly changed. The normality of this statistic was also discussed and it was proved that it could detect a subset of high modified ridge leverage outliers. The Monte Carlo simulations were used for empirical results and an example of real data was presented for illustration.

METHODS OF IMPROVING NEURAL NETWORK SYSTEMS EFFICIENCY IN CONDITIONS OF LIMITED DATA SETS WITH COMPLEX CORRELATIONS

Methods are described that make it possible to increase the efficiency of using neural network systems used in areas for which it is difficult to collect a large amount of data to form a training set: a method for detecting anomalous observations in a variety of empirical data, a method for calculating the informativeness of the input parameters of a neural network model, a method for tuning the sensitivity of learning algorithms to errors first and second kind and two methods to improve the accuracy of forecasting the development of complex processes in time using neural networks models.

Nonparametric geometric outlier detection

AbstractOutlier detection is a major topic in robust statistics due to the high practical significance of anomalous observations. Many existing methods, however, either are parametric or cease to perform well when the data are far from linearly structured. In this paper, we propose a quantity, Delaunay outlyingness, that is a nonparametric outlyingness score applicable to data with complicated structure. The approach is based on a well‐known triangulation of the sample, which seems to reflect the sparsity of the pointset to different directions in a useful way. We derive results on the asymptotic behavior of Delaunay outlyingness in case of a sufficiently simple set of observations. Simulations and an application to empirical data are also discussed.

Ground-based ozone profiles over central Europe: incorporating anomalous observations into the analysis of stratospheric ozone trends

Abstract. Observing stratospheric ozone is essential to assess whether the Montreal Protocol has succeeded in saving the ozone layer by banning ozone depleting substances. Recent studies have reported positive trends, indicating that ozone is recovering in the upper stratosphere at mid-latitudes, but the trend magnitudes differ, and uncertainties are still high. Trends and their uncertainties are influenced by factors such as instrumental drifts, sampling patterns, discontinuities, biases, or short-term anomalies that may all mask a potential ozone recovery. The present study investigates how anomalies, temporal measurement sampling rates, and trend period lengths influence resulting trends. We present an approach for handling suspicious anomalies in trend estimations. For this, we analysed multiple ground-based stratospheric ozone records in central Europe to identify anomalous periods in data from the GROund-based Millimetre-wave Ozone Spectrometer (GROMOS) located in Bern, Switzerland. The detected anomalies were then used to estimate ozone trends from the GROMOS time series by considering the anomalous observations in the regression. We compare our improved GROMOS trend estimate with results derived from the other ground-based ozone records (lidars, ozonesondes, and microwave radiometers), that are all part of the Network for the Detection of Atmospheric Composition Change (NDACC). The data indicate positive trends of 1 % decade−1 to 3 % decade−1 at an altitude of about 39 km (3 hPa), providing a confirmation of ozone recovery in the upper stratosphere in agreement with satellite observations. At lower altitudes, the ground station data show inconsistent trend results, which emphasize the importance of ongoing research on ozone trends in the lower stratosphere. Our presented method of a combined analysis of ground station data provides a useful approach to recognize and to reduce uncertainties in stratospheric ozone trends by considering anomalies in the trend estimation. We conclude that stratospheric trend estimations still need improvement and that our approach provides a tool that can also be useful for other data sets.

Unsupervised Anomaly Detection Based on Minimum Spanning Tree Approximated Distance Measures and its Application to Hydropower Turbines

Anomalies are data points or a cluster of data points that lie away from the neighboring points or clusters and are inconsistent with the overall pattern of the data. Anomaly detection techniques help distinguish the anomalous observations from the regular ones, and thus provide the basis for developing a standard performance guideline for process control. The process of identifying anomalies becomes complicated in the absence of labeled training data as in supervised learning. Moreover, Euclidean distance between two points is less likely able to reflect the intrinsic structural distance imposed by the underlying manifold structure. In this paper, the authors propose a minimum spanning tree (MST)-based anomaly detection method. The merit of the method is that an MST provides a new distance measure, capable of capturing the relative connectedness of data points/clusters in a complicated manifold, and could be a better (dis)similarity metric, than the simple Euclidean distance, to identify anomalies in unsupervised learning settings. The proposed method is compared with 13 popular anomaly detection methods on 20 benchmark data sets, demonstrating a considerable improvement in its ability of identifying anomalies. Furthermore, the MST-based anomaly detection is applied to the data set from a hydropower turbine and demonstrates remarkable detection competence. Note to Practitioners —This paper is motivated by the problem of unsupervised anomaly detection in a hydropower generation plant, which operates with turbine systems that are instrumented with dozens of sensors. Each turbine has subcomponents or functional areas such as several bearing systems, a generator, and so on. Sensors collect various types of data in real time such as temperature of oil inside the bearing systems, temperature of the bearings, ambient temperature, vibrations in each functional areas, a variety of harmonics in functional areas, temperature of the coil in the generator, and many more. In total, each turbine collects more than 200 attributes from its sensors. The sensor data are then stored in a control system and kept as time stamped historical data points. When a service/maintenance engineer suspects that there is a malfunction in a turbine, she/he extracts a data set from the control system that contains the collected sensor data for that turbine for the selected period of time (few weeks to few months), and then stores this data in a relational databases or simply in a comma separate value (csv) file for further analysis. The objective is to efficiently identify and isolate anomalies in the turbines. Toward this goal, we propose a new solution for tackling this challenging problem, which is an unsupervised method based on the concept of MST. The proposed method can be used as a competitive tool to aid the practitioners in their search of anomalies for making their systems better.

QSSC model’s next step is the STOE

The Quasi-Steady State Cosmology (QSSC) posits a continuous creation at the center of galaxies. The Scalar Theory of Everything (STOE) posits the components of the universe have a creation and an end. A Sink at the center of elliptical galaxies is added to the Source of the QSSC at the center of Spiral galaxies. This allows the explanation of many more anomalous cosmological observations and allows correspondence with quantum mechanics.

Robust estimation in single-index models when the errors have a unimodal density with unknown nuisance parameter

This paper develops a robust profile estimation method for the parametric and nonparametric components of a single-index model when the errors have a strongly unimodal density with unknown nuisance parameter. We derive consistency results for the link function estimators as well as consistency and asymptotic distribution results for the single-index parameter estimators. Under a log-Gamma model, the sensitivity to anomalous observations is studied using the empirical influence curve. We also discuss a robust K-fold cross-validation procedure to select the smoothing parameters. A numerical study carried on with errors following a log-Gamma model and for contaminated schemes shows the good robustness properties of the proposed estimators and the advantages of considering a robust approach instead of the classical one. A real data set illustrates the use of our proposal.

Are segmented fracture zones weak? Analytical and numerical models constrain anomalous bathymetry at the Clarion and Murray fracture zones

Between 150°W and 135°W, the Clarion Fracture Zone (FZ) coalesces from six discrete FZ traces to a single FZ over a period of ∼30 million years, coinciding with a change in plate motion that placed the associated transform fault (TF) in compression. Between 160° and 157°, the Murray FZ is also comprised of several closely-spaced (<50 km) discrete FZs and the associated TF experienced similar transpressional motion. Analysis of newly collected bathymetry data along the Clarion and Murray FZs reveals FZ structures that are inconsistent with predictions from a simple locked fault thermal subsidence model. These structures include FZ-bounded lithosphere that dips towards the old side of the FZ, whereas locked fault models predict it should dip towards the young side, and reversed fault scarp relationships where younger lithosphere lies deeper than older lithosphere. We investigate these anomalous observations using a combination of analytical and numerical models of two closely-spaced fracture zones to test how FZ strength and tectonic motions (compression or extension) affect the evolution of the intra-transform lithosphere. Our models predict that interior blocks within a segmented FZ, initially in isostatic equilibrium, can develop anomalous tilts and scarp depth relationships if the intervening FZs are relatively weak. Model results suggest that unusually low apparent coefficients of friction (<0.01) and tectonic compression are required to reproduce the magnitude of reverse tilt and scarp reversals observed in the Clarion and Murray FZs. We suggest that these low apparent friction coefficients might be the result of near lithostatic pore-fluid pressures associated with compression and alteration processes. Our results provide a tool to constrain histories of tectonic compression across FZs in places where rotation poles or other measures of plate motion may not be well constrained. Our results also imply that FZs are tectonically weak regions of hydrothermal alteration that may carry large volumes of volatiles into the mantle when subducted.

Robust estimators in a generalized partly linear regression model under monotony constraints

In this paper, we consider the situation in which the observations follow an isotonic generalized partly linear model. Under this model, the mean of the responses is modelled, through a link function, linearly on some covariates and nonparametrically on an univariate regressor in such a way that the nonparametric component is assumed to be a monotone function. A class of robust estimates for the monotone nonparametric component and for the regression parameter, related to the linear one, is defined. The robust estimators are based on a spline approach combined with a score function which bounds large values of the deviance. As an application, we consider the isotonic partly linear log-Gamma regression model. Under regularity conditions, we derive consistency results for the nonparametric function estimators as well as consistency and asymptotic distribution results for the regression parameter estimators. Besides, the empirical influence function allows us to study the sensitivity of the estimators to anomalous observations. Through a Monte Carlo study, we investigate the performance of the proposed estimators under a partly linear log-Gamma regression model with increasing nonparametric component. The proposal is illustrated on a real data set.

Anomaly detection using Long Short Term Memory Networks and its applications in Supply Chain Management

Anomaly detection has been becoming an important problem in several domains. In this paper, we propose a new method to detect anomalies in time series based on Long Short Term Memory (LSTM) networks. After being trained on normal data, the networks are used to predict interested steps in time series. The difference between the predicted values and observed values is calculated as prediction errors. Then we use a kernel estimator of the quantile function to compute a threshold, which is used to determine anomalous observations. The performance of proposed method is illustrated through an example of anomaly detection of consumer demand in supply chain management. The numerical experiment shows that our approach achieve a higher level of detection accuracy and a lower percentage of false alarm rate compared to the previous One-Class Support Vector Machine method.

Non-Native Cooperative Interactions Modulate Protein Folding Rates.

The energy landscape theory and the funnel description have had remarkable success in describing protein folding mechanisms and function. However, there are experimental results that are not understood using this approach. Among the puzzling examples are the α-spectrin results, in which the R15 domain folds 3 orders of magnitude more rapidly than the homologous R16 and R17, even though they are structurally very similar to each other. Such anomalous observations are usually attributed to the influence of internal friction on protein folding rates, but this is not a satisfactory explanation. In this study, this phenomenon is addressed by focusing on non-native interactions that could account for this effect. We carried out molecular dynamics simulations with structure-based C α models, in which the folding process of α-spectrin domains was investigated. The simulations take into account the hydrophobic and electrostatic contributions separately. The folding time results have shown qualitative agreement with the experimental data. We have also investigated mutations in R16 and R17, and the simulation folding time results correlate with the observed experimental ones. We suggest that the origin of the internal friction, at least in this case, might emerge from a cooperativity effect of these non-native interactions.

Capability of the Bayesian Forecasting Method to Predict Field Time Series

This paper addresses the challenge of forecasting the future values of gas turbine measureable quantities. The final aim is the simulation of “virtual sensors” capable of producing statistically coherent measurements aimed at replacing anomalous observations discarded from the time series. Among the different available approaches, the Bayesian forecasting method (BFM) adopted in this paper uses the information held by a pool of observations as knowledge base to forecast the values at a future state. The BFM algorithm is applied in this paper to Siemens field data to assess its prediction capability, by considering two different approaches, i.e., single-step prediction (SSP) and multistep prediction (MSP). While SSP predicts the next observation by using true data as base of knowledge, MSP uses previously predicted data as base of knowledge to perform the prediction of future time steps. The results show that BFM single-step average prediction error can be very low, when filtered field data are analyzed. On the contrary, the average prediction error achieved in case of BFM multistep prediction is remarkably higher. To overcome this issue, the BFM single-step prediction scheme is also applied to clusters of time-wise averaged data. In this manner, an acceptable average prediction error can be achieved by considering clusters composed of 60 observations.

Hydrogen and deuterium isotope effects beyond the electromagnetic force

A mechanism is presented concerning electrolysis of deuteriated water with a palladium cathode that is infused with deuterium (palladium deuteride) resulting in the formation of small amounts of radioactive tritium, excess energy (more than allowed by EMF chemistry alone) and the concomitant liberation of 4He. These net electron catalyzed nuclear chemical reactions (2H + 2H + e− → 4He + e− + heat) and (2H + 1H + e− → 3H + e−) appear to be a result of respectively four and three isotope effects acting in combination with each other in a non-linear (chaotic) fashion to produce a metastable nuclear isomer of hydrogen-4 or hydrogen-3. The four isotope effects begin with the influx of electrons into the –PdDPdD Bravais lattice conduction band and consequent preferred rupture of individual weak Pd-D bonds (over those of PdH) in the cathode liberating D2. This is followed by the newly freed deuterium capturing an electron yielding a di-neutron (onon). The onon then reacts with a deuterium or hydrogen (from protic impurity in the lattice) via phonon enforced quantum tunneling resulting in 4mH or 3H respectively. The 4mH quickly undergoes nuclear internal conversion to form 4He. These reactions involve the weak force (Feynman Diagrams are shown), but they take place in simple electrochemical systems that are normally thought of in terms of the electromagnetic forces only. The combined influence of the four isotope effects explains thousands of, what were considered, anomalous observations by top electrochemical researchers. The newly described mechanistic effects involve a very important and almost forgotten intermediate (the di-neutron) and may even involve unique safety concerns.