Anomalous Clustering Research Articles

Energy big data abnormal cluster detection method based on redundant convolution codec

Due to the scattered distribution and poor clustering of abnormal clusters in energy big data, the ability to detect anomalies is poor. Therefore, a high-energy data anomaly clustering detection method based on redundant convolutional encoding is proposed. Quantitative analysis of the coupling characteristics of electrical thermal gas optical time series for multi energy users based on Copula function, and incorporating quantitative values into multi energy feature indicators to extract the energy consumption behavior characteristics of multi energy users. Utilize redundant convolutional codecs to recombine and structurally encode abnormal features of energy big data, and capture multi energy coupling time features using coupling time capsule layers. Then, coupling time features are synthesized through fully connected linear regression layers to generate anomalous clustering feature components, and the energy time series data is then transformed into feature values of the time series in three-dimensional space. Based on this, a comprehensive energy system and massive multi energy user energy big data anomaly clustering analysis are carried out to determine the optimal number of multi energy users. Then, based on linear layers, the electricity heat gas light load characteristic map of multi energy users is transformed into one-dimensional form, and an energy big data anomaly clustering detection model is constructed to complete anomaly detection. The simulation results show that the proposed method has excellent feature clustering performance, detection accuracy above 98.7%, fast convergence speed, and an error rate below 0.1, which has reliable application value.

PrOuD: Probabilistic Outlier Detection Solution for Time-Series Analysis of Real-World Photovoltaic Inverters

Anomaly detection methods applied to time series are mostly viewed as black boxes that solely provide a deterministic answer for the detected target. Without a convincing explanation, domain experts can hardly trust the detection results and must conduct further time-series diagnoses in real-world applications. To overcome this challenge, we mathematically analyzed the sources of anomalies and novelties in multivariate time series as well as their relationships from the perspective of Gaussian-distributed non-stationary noise. Furthermore, we proposed mathematical methods to generate artificial time series and synthetic anomalies, with the goal of solving the problem of it being difficult to train and evaluate models for real-world applications due to the lack of sufficient data. In addition, we designed Probabilistic Outlier Detection (PrOuD), which is a general solution to provide interpretable detection results to assist domain experts with time-series analysis. PrOuD can convert the predictive uncertainty of a time-series value from a trained model into the estimated uncertainty of the detected outlier through Monte Carlo estimation. The experimental results obtained on both artificial time series and real-world photovoltaic inverter data demonstrated that the proposed solution can detect emerging anomalies accurately and quickly. The implemented PrOuD demo case shows its potential to make the detection results of existing detection methods more convincing so that domain experts can more efficiently complete their tasks, such as time-series diagnosis and anomalous pattern clustering.

Evolution of spatiotemporal patterns in vegetation net primary productivity and the driving forces on the Loess Plateau

In this study, we determined whether changes in vegetation net primary productivity (NPP) can be used to characterize the quality of terrestrial ecosystems, which is critical for global change and carbon balance. We first explored the spatial correlation of NPP and its impact on vegetation restoration. MOD17A3 remote sensing products were used to analyze the temporal and spatial changes in NPP on the Loess Plateau (LP) over the last two decades (2000–2020). The resulting spatial autocorrelation indices identified cold and hot spots in the spatial clustering patterns. The effects of climate change and human activities on the anomalous clustering of NPP were assessed using Pearson correlation analysis and multi-temporal land use land cover data. The results indicate that i) Temporally, from 2000 to 2020, the NPP of the LP increased significantly by 6.88 gCm−2yr−1 and so did the proportion of revegetated land area >400 gCm−2yr−1 from 4% to 37%. Spatially, NPP showed an increasing trend from northwest to southeast. ii) The vegetation NPP on the LP showed a strong positive global spatial autocorrelation (p< 0.01). The hot and cold regions were polarized; the cold spots were clustered in the northwest, while the hot spots in the south and east. The spatial clustering patterns were dominated by high-high (HH) and low-low (LL) clusters. Abnormal patterns existed mainly in the transition areas between HH and LL clusters and insignificant regions, which were jointly affected by human activities and climate change. iii) Precipitation was the dominant climatic factor (86%) affecting the NPP variation in the LP, with the annual minimum precipitation showing a significantly positive relationship with the interannual variability in NPP, while the maximum precipitations greatly influenced the variation in local spatial anomaly patterns. This suggests that climatic extremes affect vegetation. Our study helps to facilitate green ecological management and high-quality development in the LP.

A Candidate Location for Planet Nine from an Interstellar Meteoroid: The Messenger Hypothesis

The existence of a hypothetical Planet Nine lurking in the outer solar system has been invoked as a plausible explanation for the anomalous clustering in the orbits of trans-Neptunian objects. Here we propose that some meteoroids arriving at Earth could serve as messengers with the potential of revealing the presence of a hitherto undiscovered massive object. The peculiar meteor CNEOS 2014-01-08 recently put forward as the first interstellar meteor, might be one such messenger. The meteor radiant is in the maximum probability region calculated for the Planet Nine location in previous works. The odds of this coincidence being due to chance are ∼1%. Furthermore, some statistical anomalies about CNEOS 2014-01-08 are resolved under the hypothesis that it was flung at Earth by a gravitational encounter. Integrating its trajectory backwards in time would then lead to the region of the sky where Planet Nine is more likely to reside. Based on the available data, we propose the region at coordinates R.A. 53.°0 ± 4.°3, decl. 9.°2 ± 1.°3 as a plausible candidate location for Planet Nine.

Motility-induced clustering and meso-scale turbulence in active polar fluids

Meso-scale turbulence was originally observed experimentally in various suspensions of swimming bacteria, as well as in the collective motion of active colloids. The corresponding large scale dynamical patterns were reproduced in a simple model of a polar fluid, assuming a constant density of active particles. Recent, more detailed studies in a variety of experimental realizations of active polar fluids revealed additional interesting aspects, such as anomalous velocity statistics and clustering phenomena. Those phenomena cannot be explained by currently available models for active polar fluids. Herein, we extend the continuum model suggested by Dunkel et al to include density variations and a local feedback between the local density and self-propulsion speed of the active polar particles. If the velocity decreases strong enough with the density, a linear stability analysis of the resulting model shows that, in addition to the short-wavelength instability of the original model, a long-wavelength instability occurs. This is typically observed for high densities of polar active particles and is analogous to the well-known phenomenon of motility-induced phase separation (MIPS) in scalar active matter. We determine a simple phase diagram indicating the linear instabilities and perform systematic numerical simulations for the various regions in the corresponding parameter space. The interplay between the well understood short-range instability (leading to meso-scale turbulence) and the long-range instability (associated with MIPS) leads to interesting dynamics and novel phenomena concerning nucleation and coarsening processes. Our simulation results display a rich variety of novel patterns, including phase separation into domains with dynamically changing irregularly shaped boundaries. Anomalous velocity statistics are observed in all phases where the system segregates into regions of high and low densities. This offers a simple explanation for their occurrence in recent experiments with bacterial suspensions.

More Than 40 yr of Potentially Induced Seismicity Close to the San Andreas Fault in San Ardo, Central California

AbstractEvidence for fluid-injection-induced seismicity is rare in California hydrocarbon basins, despite widespread injection close to seismically active faults. We investigate a potential case of injection-induced earthquakes associated with San Ardo oilfield operations that began in the early 1950s. The largest potentially induced events occurred in 1955 (ML 5.2) and 1985 (Mw 4.5) within ∼6 km from the oilfield. We analyze Synthetic Aperture Radar interferometric images acquired by Sentinel-1A/B satellites between 2016 and 2020 and find surface deformation of up to 1.5 cm/yr, indicating pressure-imbalance in parts of the oilfield. Fluid injection in San Ardo is concentrated within highly permeable rocks directly above the granitic basement at a depth of ∼800 m. Seismicity predominantly occurs along basement faults at 6–13 km depths. Seismicity and wastewater disposal wells are spatially correlated to the north of the oilfield. Temporal correlations are observed over more than 40 yr with correlation coefficients of up to 0.71 for seismicity within a 24 km distance from the oilfield. Such large distances have not previously been observed in California but are similar to the large spatial footprint of injection in Oklahoma. The San Ardo seismicity shows anomalous clustering with earthquakes consistently occurring at close spatial proximity but long interevent times. Similar clustering has previously been reported in California geothermal fields and may be indicative of seismicity driven by long-term, spatially persistent external forcing.The complexity of seismic behavior at San Ardo suggests that multiple processes, such as elastic stress transfer and aseismic slip transients, contribute to the potentially induced earthquakes. The present observations show that fluid-injection operations occur close to seismically active faults in California. Yet, seismicity is predominantly observed on smaller unmapped faults with little observational evidence that large faults are sensitive to induced stress changes.

Collective phenomena in Dy-doped silver halides in the near- and mid-IR

The kinetics of the electronic transitions within the f-shell of Dy3+ ions were studied with monitoring near- and mid-IR luminescence decay under pulsed laser excitation at 1.3 µm. The luminescence decay curves were found to be profoundly non-exponential in all bands in the range between 1.3-5.5 µm. Such behavior is attributed to cross-relaxation and up-conversion processes dominating in relaxation of Dy3+ ions from the laser-excited multiplet 6H9/2+6F11/2. We suggest that strong collective phenomena occurring under relatively low concentrations are due to anomalous clustering of Dy3+ ions. The cross-relaxation enables an efficient population of 6H13/2 and 6H11/2 multiplets, offering this material as an active medium for a 3-µm and 4.3-µm lasers.

Anomalous Telecom Customer Behavior Detection and Clustering Analysis Based on ISP’s Operating Data

Mobile networks and smart phones have become ubiquitous in our daily life. Large amount of customer related telecom data from various sources are generated every day, from which diversified behavior patterns can be revealed, including some anomalous behaviors that are vicious. It becomes increasingly important to achieve both efficient and effective customer behavior analysis based on the telecom big data. In this paper, the Multi-faceted Telecom Customer Behavior Analysis (MTCBA) framework for anomalous telecom customer behavior detection and clustering analysis is proposed. In this framework, we further design the hierarchical Locality Sensitive Hashing-Local Outlier Factor (hierarchical LSH-LOF) scheme for suspicious customer detection, and the Autoencoders with Factorization Machines (FM-AE) structure for dimension reduction to achieve more efficient clustering. Hierarchical LSH-LOF is an improved algorithm of LOF, in which we design a hierarchical LSH process that selects the approximate k nearest neighbors from coarse to fine by gradually narrowing down the scope. Experiments show its superiority over KD-tree w.r.t searching speed. FM-AE exploits Factorization Machines for learning second order feature interactions, which we prove to be useful by designing comparative experiments with five dimension reduction algorithms. With the proposed MTCBA framework, efficient and effective telecom customer behavior analysis including anomalous customer behavior detection and clustering analysis is performed on the real world telecom operating data provided by one of the major Internet service providers (ISPs) in China. Meanwhile, interpretable clustering results of six clusters are obtained to provide valuable information for the precision marketing of telecom operators, criminal combating, and social credit system construction.

Multi-View Anomaly Detection: Neighborhood in Locality Matters

Identifying anomalies in multi-view data is a difficult task due to the complicated data characteristics of anomalies. Specifically, there are two types of anomalies in multi-view data–anomalies that have inconsistent features across multiple views and anomalies that are consistently anomalous in each view. Existing multi-view anomaly detection approaches have some issues, e.g., they assume multiple views of a normal instance share consistent and normal clustering structures while anomaly exhibits anomalous clustering characteristics across multiple views. When there are no clusters in data, it is difficult for existing approaches to detect anomalies. Besides, existing approaches construct a profile of normal instances, then identify instances that do not conform to the normal profile as anomalies. The objective is formulated to profile normal instances, but not to estimate the set of normal instances, which results in sub-optimal detectors. In addition, the model trained to profile normal instances uses the entire dataset including anomalies. However, anomalies could undermine the model, i.e., the model is not robust to anomalies. To address these issues, we propose the nearest neighborbased MUlti-View Anomaly Detection (MUVAD) approach. Specifically, we first propose an anomaly measurement criterion and utilize this criterion to formulate the objective of MUVAD to estimate the set of normal instances explicitly. We further develop two concrete relaxations for implementing the MUVAD as MUVAD-QPR and MUVAD-FSR. Experimental results validate the superiority of the proposed MUVAD approaches.

Non-intuitive clustering of 9,10-phenanthrenequinone on Au(111).

The direct injection of a 9,10-phenanthrenequinone in tetrahydrofuran solution on a Au(111) substrate in high vacuum results in the formation of metastable clusters with a non-intuitive structure. Metastable, rectangular tetramers of this molecule form in which the net molecular dipoles all orient toward the center of the cluster. This structure does not allow for additional hydrogen bonding and thus the origin of its metastability is not clear. We compare this feature to other structures observed on this surface, as well as those formed during the deposition of 9-fluorenone, which does not exhibit this anomalous clustering behavior.

Anomalous Diffusion and Compartmentalization on the Surface of Mammalian Cells

Tracking individual proteins on the surface of live mammalian cells reveals complex dynamics involving anomalous diffusion and clustering into nanoscale domains. Theoretical models show that anomalous diffusion can be caused by various processes. By performing time series and ensemble analysis of extensive single-molecule tracks, we show that different subdiffusive processes simultaneously coexist in the plasma membrane. Interestingly, ergodicity breaking is found to be maintained by immobilization events that take place when proteins are transiently captured within endocytic pits.

Data Analytics for Smart Parking Applications.

We consider real-life smart parking systems where parking lot occupancy data are collected from field sensor devices and sent to backend servers for further processing and usage for applications. Our objective is to make these data useful to end users, such as parking managers, and, ultimately, to citizens. To this end, we concoct and validate an automated classification algorithm having two objectives: (1) outlier detection: to detect sensors with anomalous behavioral patterns, i.e., outliers; and (2) clustering: to group the parking sensors exhibiting similar patterns into distinct clusters. We first analyze the statistics of real parking data, obtaining suitable simulation models for parking traces. We then consider a simple classification algorithm based on the empirical complementary distribution function of occupancy times and show its limitations. Hence, we design a more sophisticated algorithm exploiting unsupervised learning techniques (self-organizing maps). These are tuned following a supervised approach using our trace generator and are compared against other clustering schemes, namely expectation maximization, k-means clustering and DBSCAN, considering six months of data from a real sensor deployment. Our approach is found to be superior in terms of classification accuracy, while also being capable of identifying all of the outliers in the dataset.

Chronology of rock falls and slides in a desert mountain range: Case study from the Sonoran Desert in south-central Arizona

In order to respond to the general paucity of information on the chronology of ubiquitous small rock falls and slides that litter the slopes of desert mountain ranges, a case study in the Sonoran Desert reveals new insight into the desert geomorphology of mountain slopes. Rock falls and rock slides in the McDowell Mountains that abut metropolitan Phoenix, USA, fall in three chronometric groupings dated by conventional radiocarbon and rock varnish microlamination methods. First, the oldest events are >74ka and take the form of stable colluvial boulder fields — positive relief features that are tens of meters long and a few meters wide. Second, randomly sampled slides and falls of various sizes and positions wasted during wetter periods of the terminal Pleistocene and Holocene. Third, an anomalous clustering of slides and falls occurred during the late Medieval Warm Period (Medieval Climatic Anomaly) when an extreme storm was a possible but unlikely trigger. One speculative hypothesis for the cluster of Medieval Warm Period events is that a small to moderate sized earthquake shook heavily shattered bedrock — close to failure — just enough to cause a spate of rock falls and slides. A second speculative hypothesis is that this dry period enhanced physical weathering processes such as dirt cracking. However, the reasons for the recent clustering of rock falls remain enigmatic. While the temporal distribution of slides and falls suggests a minimal hazard potential for homes and roads on the margins of the McDowell Mountains, this finding may not necessary match other desert ranges in metropolitan Phoenix or mountains with different rock types and structures that abut other arid urban centers.

Structure of a tractable stochastic mimic of soft particles.

The structure and assembly of soft particles is difficult to characterize because their interpenetrability allows them to be packed at ever higher density albeit with an increasing penalty in energy and/or pressure. Alternatively, the use of impenetrable particles (such as hard spheres) as a reference model for soft particles can fail because the packing densities are limited by the impossibility of complete space filling. We recently introduced the stochastic penetration algorithm (SPA) so as to allow for the computationally efficient integration of hard sphere models while including overlaps seen in soft interactions [Craven et al., J. Chem. Phys., 2013, 138, 244901]. Moving beyond the initial one-dimensional case studied earlier, we now consider the spatial properties of systems of stochastically penetrable spheres in dimensions d≤ 3 through the use of molecular dynamics simulations and analytic methods. The stochastic potential allows spheres to either interpenetrate with a probability δ or collide elastically otherwise. For δ > 0 the particles interpenetrate (overlap), reducing the effective volume occupied by the particles in the system. We find that the occupied volume can be accurately predicted using analytic expressions derived from mean field arguments for the particle overlap probabilities with the exception of an observed clustering regime. This anomalous clustering behavior occurs at high densities and small δ. We find that this regime is coincident with that observed in deterministic penetrable models. The behavior of the stochastic penetrable particles also indicates that soft particles would be characterizable through a single reduced parameter that captures their overlap probability.

Clustering of galaxies around gamma-ray burst sight-lines

There is evidence of an overdensity of strong intervening MgII absorption line systems distributed along the lines of sight towards GRB afterglows relative to quasar sight-lines. If this excess is real, one should also expect an overdensity of field galaxies around GRB sight-lines, as strong MgII tends to trace these sources. In this work, we test this expectation by calculating the two point angular correlation function of galaxies within 120$^{\prime\prime}$ ($\sim470~h_{71}^{-1}~\mathrm{Kpc}$ at $\langle z\rangle \sim0.4$) of GRB afterglows. We compare the Gamma-ray burst Optical and Near-infrared Detector (GROND) GRB afterglow sample -- one of the largest and most homogeneous samples of GRB fields -- with galaxies and AGN found in the COSMOS-30 photometric catalog. We find no significant signal of anomalous clustering of galaxies at an estimated median redshift of $z\sim0.3$ around GRB sight-lines, down to $K_{\mathrm{AB}}<19.3$. This result is contrary to the expectations from the MgII excess derived from GRB afterglow spectroscopy, although many confirmed galaxy counterparts to MgII absorbers may be too faint to detect in our sample -- especially those at $z>1$. We note that the addition of higher sensitivity Spitzer IRAC or HST WFC3 data for even a subset of our sample would increase this survey's depth by several orders of magnitude, simultaneously increasing statistics and enabling the investigation of a much larger redshift space.}

Anomalous activation of shallow B + implants in Ge

The electrical activation of B + implantation at 2 keV to doses of 5.0 × 10 13–5.0 × 10 15 cm −2 in crystalline and pre-amorphized Ge following annealing at 400 °C for 1.0 h was studied using micro Hall effect measurements. Preamorphization improved activation for all samples with the samples implanted to a dose of 5.0 × 10 15 cm −2 displaying an estimated maximum active B concentration of 4.0 × 10 20 cm −3 as compared to 2.0 × 10 20 cm −3 for the crystalline sample. However, incomplete activation was observed for all samples across the investigated dose range. For the sample implanted to a dose of 5.0 × 10 13 cm −2, activation values were 7% and 30%, for c-Ge and PA-Ge, respectively. The results suggest the presence of an anomalous clustering phenomenon of shallow B + implants in Ge.

Phylogenetic and evolutionary relationships and developmental expression patterns of the zebrafish twist gene family

Four members of the twist gene family (twist1a, 1b, 2, and 3) are found in the zebrafish, and they are thought to have arisen through three rounds of gene duplication, two of which occurred prior to the tetrapod-fish split. Phylogenetic analysis groups most of the vertebrate Twist1 peptides into clade I, except for the Twist1b proteins of the acanthopterygian fish (medaka, pufferfish, stickleback), which clustered within clade III. Paralogies and orthologies among the zebrafish, medaka, and human twist genes were determined using comparative synteny analysis of the chromosomal regions flanking these genes. Comparative nucleotide substitution analyses also revealed a faster rate of nucleotide mutation/substitution in the acanthopterygian twist1b compared to the zebrafish twist1b, thus accounting for their anomalous phylogenetic clustering. We also observed minimal expression overlap among the four twist genes, suggesting that despite their significant peptide similarity, their regulatory controls have diverged considerably, with minimal functional redundancy between them.

Anomalous diffusion, clustering, and pinch of impurities in plasma edge turbulence

The turbulent transport of impurity particles in plasma edge turbulence is investigated. The impurities are modeled as a passive fluid advected by the electric and polarization drifts, while the ambient plasma turbulence is modeled using the two-dimensional Hasegawa–Wakatani paradigm for resistive drift-wave turbulence. The features of the turbulent transport of impurities are investigated by numerical simulations using a novel code that applies semi-Lagrangian pseudospectral schemes. The diffusive character of the turbulent transport of ideal impurities is demonstrated by relative-diffusion analysis of the evolution of impurity puffs. Additional effects appear for inertial impurities as a consequence of compressibility. First, the density of inertial impurities is found to correlate with the vorticity of the electric drift velocity, that is, impurities cluster in vortices of a precise orientation determined by the charge of the impurity particles. Second, a radial pinch scaling linearly with the mass–charge ratio of the impurities is discovered. Theoretical explanation for these observations is obtained by analysis of the model equations.

Fractal properties of seismicity in regions affected by large, shallow earthquakes in western Japan: Implications for fault formation processes based on a binary fractal fracture network model

The temporal variation in the b value (which is defined as the slope of the Gutenberg‐Richter frequency N–magnitude M relationship), log N = a − bM, and the multifractal dimension of the spatial distribution Dq of seismic activity are analyzed using earthquake catalog data for regions affected by two recent large earthquakes in Japan. An anomalous decrease in Dq and an increase in b value appear as a common seismic rupture response during the period of decreasing seismic energy release in the lead up to these large, shallow earthquakes. The change in fractal dimensions of faults inferred from sequential seismic events reflects the transition of fracture from a dispersive rupture process to an anomalous rupture process and shows that the growth of the fault network through fault linkage is abruptly restrained in the fault formation process leading up to the main shock. The anomalies in the fractal and multifractal dimensions prior to large earthquakes indicate that the anomalous activation and clustering of smaller earthquakes represents criticality of the state around asperities along a fault.

Two X-ray bright cataclysmic variables with unusual activities: GZ Cnc and NSV 10934

We report on a discovery of unexpected activities in two X-ray bright dwarf novae. GZ Cnc showed an anomalous clustering of outbursts in 2002, in contrast to a low outburst frequency in the past record. The activity resembles an increased activity seen in some intermediate polars or candidates. We identified NSV 10934, X-ray selected high-amplitude variable star, as a dwarf nova with unusually rapid decline. The outburst characteristics make NSV 10934 a twin of recently discovered intermediate polar (HT Cam) with dwarf nova-type outbursts. We propose that these activities in X-ray strong dwarf novae may be a previously overlooked manifestation of outburst activities in magnetic cataclysmic variables.