Potential Anomalies Research Articles

Uncertainty-aware body composition analysis with deep regression ensembles on UK Biobank MRI

Along with rich health-related metadata, medical images have been acquired for over 40,000 male and female UK Biobank participants, aged 44–82, since 2014. Phenotypes derived from these images, such as measurements of body composition from MRI, can reveal new links between genetics, cardiovascular disease, and metabolic conditions. In this work, six measurements of body composition and adipose tissues were automatically estimated by image-based, deep regression with ResNet50 neural networks from neck-to-knee body MRI. Despite the potential for high speed and accuracy, these networks produce no output segmentations that could indicate the reliability of individual measurements. The presented experiments therefore examine uncertainty quantification with mean-variance regression and ensembling to estimate individual measurement errors and thereby identify potential outliers, anomalies, and other failure cases automatically. In 10-fold cross-validation on data of about 8500 subjects, mean-variance regression and ensembling showed complementary benefits, reducing the mean absolute error across all predictions by 12%. Both improved the calibration of uncertainties and their ability to identify high prediction errors. With intra-class correlation coefficients (ICC) above 0.97, all targets except the liver fat content yielded relative measurement errors below 5%. Testing on another 1000 subjects showed consistent performance, and the method was finally deployed for inference to 30,000 subjects with missing reference values. The results indicate that deep regression ensembles could ultimately provide automated, uncertainty-aware measurements of body composition for more than 120,000 UK Biobank neck-to-knee body MRI that are to be acquired within the coming years.

The potential benefit of the use of seasonal forecast during the agricultural economic year 2019-2020 in Bulgaria

Seasonal forecasting gained ground in the last decades by building up knowledge on the processes staying behind the climate variability at the seasonal time scale, constructing ever more sophisticated general circulation models and ensemble prediction systems and thus enhancing forecast skill. The seasonal forecast is a climate forecast and is therefore probabilistic in nature. The predictability of the atmospheric circulation at the seasonal scale is limited in the middle latitudes, where Europe and Bulgaria are situated, by its chaotic nature. The current standard is to give forecast of the potential anomalies of the mean seasonal temperature and the seasonal amount of precipitation. The National Institute of Meteorology and Hydrology of Bulgaria has been issuing operationally seasonal forecast for the country since 2005. The goal of this work is to discuss the seasonal forecast for the last agricultural year 2019-2020. The year was characterized by its drought conditions especially in Eastern Bulgaria. This work would show the extent to which it was successfully predicted and how the seasonal forecast could have been used for decision making. The use of agrometeorological indices for the analysis of the skill of the seasonal forecast has been shown.

Towards cloud‐based unobtrusive monitoring in remote multi‐vendor environments

AbstractNowadays, many complex multi‐vendor production environments, such as telecom infrastructures in smart cities or on‐board passenger information systems in trains, are based on micro‐services and deployed in the cloud. From a service integrator viewpoint, building new solutions for these environments, which can host a large number of externally designed and developed micro‐services, is often complex and error‐prone. This is in part due to undocumented behaviour or undocumented architectural specifications of such systems. Advanced service monitoring can offer a solution to quickly detect anomalies or unexpected service interaction behaviour during on‐site integration. However, the monitoring service should not have an impact on the production environment itself. Therefore, this article proposes an agent‐based unobtrusive monitoring platform, capable of monitoring both internally developed and externally developed services through the use of sidecar containers. It monitors state, metrics and network traffic at micro‐service level and the research was conducted as part of the DynAMo research project, a collaboration with various industry partners. Prototype evaluation proves that our solution has a negligible impact (below 0.02% CPU usage on average) on an existing micro‐service environment just as other monitoring systems like Prometheus while offering additional functionality focused on multi‐vendor service integration. This makes it suitable to be deployed in complex production domains to further aid on‐site integration and quickly find potential new anomalies.

VADAF: Visualization for Abnormal Client Detection and Analysis in Federated Learning

Federated Learning (FL) provides a powerful solution to distributed machine learning on a large corpus of decentralized data. It ensures privacy and security by performing computation on devices (which we refer to as clients) based on local data to improve the shared global model. However, the inaccessibility of the data and the invisibility of the computation make it challenging to interpret and analyze the training process, especially to distinguish potential client anomalies. Identifying these anomalies can help experts diagnose and improve FL models. For this reason, we propose a visual analytics system, VADAF, to depict the training dynamics and facilitate analyzing potential client anomalies. Specifically, we design a visualization scheme that supports massive training dynamics in the FL environment. Moreover, we introduce an anomaly detection method to detect potential client anomalies, which are further analyzed based on both the client model’s visual and objective estimation. Three case studies have demonstrated the effectiveness of our system in understanding the FL training process and supporting abnormal client detection and analysis.

An efficient anomaly detection method for uncertain data based on minimal rare patterns with the consideration of anti-monotonic constraints

The pattern-based anomaly detection method has obtained more attention since it was proposed. This is due to its ability to fully identify anomalies by considering two key features, including appearing rarely and deviating from most data elements. Although most pattern-based anomaly detection methods identify the anomalies from full data space, the process involved in performing this functionality is very time-consuming. Therefore, to solve this problem, this paper introduces a novel method, namely minimal rare pattern-based anomaly detection method through considering anti-monotonic constraints (MRPAC), for identifying anomalies in uncertain data. MRPAC detects the anomalies from a small scale of uncertain data that satisfy preset anti-monotonic constraints by mining constrained minimal rare patterns, thus, the time efficiency is increased. In addition, MRPAC also defines multiple deviation factors to compute the anomaly score for all transactions, to accurately discover potential anomalies through sorting their anomaly scores. Extensive experimental outcomes indicate that the MRPAC significantly outperforms five state-of-the-art pattern-based anomaly methods in terms of detection accuracy and time efficiency, and obtains good scalability.

Anomaly Detection in Dam Behaviour with Machine Learning Classification Models

Dam safety assessment is typically made by comparison between the outcome of some predictive model and measured monitoring data. This is done separately for each response variable, and the results are later interpreted before decision making. In this work, three approaches based on machine learning classifiers are evaluated for the joint analysis of a set of monitoring variables: multi-class, two-class and one-class classification. Support vector machines are applied to all prediction tasks, and random forest is also used for multi-class and two-class. The results show high accuracy for multi-class classification, although the approach has limitations for practical use. The performance in two-class classification is strongly dependent on the features of the anomalies to detect and their similarity to those used for model fitting. The one-class classification model based on support vector machines showed high prediction accuracy, while avoiding the need for correctly selecting and modelling the potential anomalies. A criterion for anomaly detection based on model predictions is defined, which results in a decrease in the misclassification rate. The possibilities and limitations of all three approaches for practical use are discussed.

Effects of Long-Term Increases in Water Temperature and Stratification on Large Artificial Water-Source Lakes in South Korea

Long-term changes in air and water temperatures and the resulted stratification phenomena were observed for Soyang Lake (SY), Paldang Lake (PD), Chungju Lake (CJ), and Daecheong Lake (DC) in South Korea. Non-parametric seasonal Kendall and Mann-Kendall tests, Sen slope estimator, and potential energy anomaly (PEA) were applied. The lake surface water temperatures (LSWTs) of SY and DC increased at the same rate (0.125 °C/y), followed by those of CJ (0.071 °C/y) and PD (0.06 °C/y). Seasonally, the LSWT increase rates for all lakes, except PD, were 2–3 times higher than the air temperature increase rates. The lake stratification intensity order was similar to those of the LSWT increases and correlations. SY and DC displayed significant correlations between LSWT (0.99) and PEA (0.91). Thus, the LSWT significantly affected stratification when the water temperature increased. PD demonstrated the lowest correlation between LSWT and PEA. Inflow, outflow, rainfall, wind speed, and retention time were significantly correlated, which varied within and between lakes depending on lake topographical, hydraulic, and hydrological factors. Thus, hydraulic problems and nutrients should be managed to minimize their effects on lake water quality and aquatic ecosystems because lake cyanobacteria can increase as localized water temperatures increase.

Nonparametric semi-supervised classification with application to signal detection in high energy physics

Model-independent searches in particle physics aim at completing our knowledge of the universe by looking for new possible particles not predicted by the current theories. Such particles, referred to as signal, are expected to behave as a deviation from the background, representing the known physics. Information available on the background can be incorporated in the search, in order to identify potential anomalies. From a statistical perspective, the problem is recasted to a peculiar classification one where only partial information is accessible. Therefore a semi-supervised approach shall be adopted, either by strengthening or by relaxing assumptions underlying clustering or classification methods respectively. In this work, following the first route, we semi-supervise nonparametric clustering in order to identify a possible signal. The main contribution consists in tuning a nonparametric estimate of the density underlying the experimental data to identify a partition which guarantees a signal warning while allowing for an accurate classification of the background. As a side contribution, a variable selection procedure is presented. The whole procedure is tested on a dataset mimicking proton–proton collisions performed within a particle accelerator. While finding motivation in the field of particle physics, the approach is applicable to various science domains, where similar problems of anomaly detection arise.

Retracted] Function of miR‑212 as a tumor suppressor in thyroid cancer by targeting SIRT1.

Following the publication of this paper, it was drawn to the Editors' attention by a concerned reader that the tumor images shown in Fig.6B bore unexpected similarities to data appearing in different form in other articles by different authors. In addition, there were potential anomalies associated with the cell migration assay data shown in Fig.2E. Owing to the fact that some of the contentious data in the above article had already been published elsewhere, or were already under consideration for publication, prior to its submission to Oncology Reports, the Editor has decided that this paper should be retracted from the Journal. The authors agree with the decision to retract the paper. The Editor apologizes to the readership for any inconvenience caused.[the original article was published in Oncology Reports39: 695-702, 2018; DOI: 10.3892/or.2017.6119].

Fabrication of an All-Solid-State Carbonate Ion-Selective Electrode with Carbon Film as Transducer and Its Preliminary Application in Deep-Sea Hydrothermal Field Exploration

Real-time measurements of carbonate ion concentrations in the ocean are critical to advancing marine environmental monitoring and research into deep-sea hydrothermal activity. Herein, we report the first example of deep-sea hydrothermal field exploration using a carbonate ion-selective electrode (ISE). The novel carbonate ISE was composed of a Ni wire as substrate, carbon film as transducers and carbonate-selective membrane layers. This paper describes the preparation process of the electrode and characterises its performance via scanning electron microscopy (SEM) and electrochemical analysis. The detection limit of the electrode for CO32− is 2.821 × 10−6 mol/L, the linear response range is 1.0 × 10−5–1.0 × 10−1 mol/L and the Nernst slope was −30.4 mV/decade. In April 2021, the carbonate ISE was mounted on multi-parameter sensors with pH and Eh (redox) electrodes for the search of hydrothermal activity at the Southwest Indian Ridge. The simultaneous potential anomalies appeared at this carbonate electrode with the pH and Eh electrodes when passing through the hydrothermal field. The study of the hydrothermal field was supported by the in situ camera video and the sulphide samples. Additionally, the carbonate electrode provides enhanced information of water chemistry for the study of the hydrothermal field.

Discriminating the Multi-Frequency Microwave Brightness Temperature Anomalies Relating to 2017 Mw 7.3 Sarpol Zahab (Iran-Iraq Border) Earthquake

A Mw 7.3 earthquake occurred near the Iran-Iraq border on November 12, 2017, as the result of oblique-thrust squeezing of the Eurasian plate and the Arabian plate. By employing the spatio-temporally weighted two-step method (STW-TSM) and microwave brightness temperature (MBT) data from AMSR-2 instrument on board Aqua satellite, this paper investigates carefully the spatiotemporal features of multi-frequency MBT anomalies relating to the earthquake. Soil moisture (SM), satellite cloud image, regional geological map and surface landcover data are utilized to discriminate the potential MBT anomalies revealed from STW-TSM. The low-frequency MBT residual images shows that positive anomalies mainly occurred in the mountainous Urmia lake and the plain region, which were 300 km north and 200 km southwest about to the epicenter, respectively. The north MBT anomaly firstly appeared 51 days before the mainshock and its magnitude increased over time with a maximum of about +40K. Then the anomaly disappeared 3 days before, reappeared 1d after and diminished completely 10 days after the mainshock. Meanwhile, the southwest MBT anomaly firstly occurred 18 days before and peaked 3 days before the mainshock with a maximum of about +20K, and then diminished gradually with aftershocks. It is speculated that the positive MBT anomaly in the Urmia lake was caused by microwave dielectric property change of water body due to gas bubbles leaking from the bottom of the lake disturbed by local crust stress alteration, while the southwest MBT positive anomaly was caused by microwave dielectric constant change of shallow surface due to accumulation of seismically-activated positive charges originated at deep crust. Besides, some accidental abnormal residual stripes existed in line with satellite orbit, which turned out to be periodic data errors of the satellite sensor. High-frequency MBT residual images exhibit some significant negative anomalies, including a narrow stripe pointing to the forthcoming epicenter, which were confirmed to be caused by synchronous altostratus clouds. This study is of guidance meaning for distinguishing non-seismic disturbances and identifying seismic MBT anomaly before, during and after some large earthquakes.

Local separation of potential field anomalies using equivalent sources: application for the 3-D structure of mantle uplift beneath Von Kármán crater, the Moon

SUMMARY The separation of regional-residual anomalies plays an important role in the processing of potential field anomalies for obtaining better understandings of the nature of the underground sources. Many methods have been developed to achieve the separation of anomalies that are of distinct wavelengths. On the other hand, fewer studies have addressed the separation of local anomalies from the observed potential field anomalies. In this paper, we introduce a new process for separating localized anomalies from the observations under the Cartesian and spherical coordinates. The separation is achieved using the equivalent source technique and an iterative inversion process which is to refine and finalize the separated local anomalies. Additionally, we introduce an inversion method for determining the equivalent sources that are of varying dimensions, as well as a quantitative measurement to assess the accuracy of the separation process. Verified with synthetic examples, the proposed method could extract arbitrary shaped local anomalies from the rest with low error levels. Subsequently, we apply the method to the construction of a 3-D model of the mantle uplift beneath the Von Kármán crater (VKC) on the Moon. The VKC is the landing site of the Chinese lunar exploration mission Chang'e 4, which lies in the northwestern portion of the South-Pole Aitken (SPA) basin on the far side of the Moon. Multiple generations of mare basalts are identified within the VKC, which indicates a complex geological history of the basin. Insights into the evolutionary history of this region can be obtained by investigating the deep crustal structure of the VKC using topographic and gravity data. Processed with the proposed method, the 3-D structure we obtain provides evidence for separated mantle uplifting events triggered by the two impact events that created the VKC and the Von Kármán M crater, respectively.

Superdiffusion in spin chains

This review summarizes recent advances in our understanding of anomalous transport in spin chains, viewed through the lens of integrability. Numerical advances, based on tensor-network methods, have shown that transport in many canonical integrable spin chains—most famously the Heisenberg model—is anomalous. Concurrently, the framework of generalized hydrodynamics has been extended to explain some of the mechanisms underlying anomalous transport. We present what is currently understood about these mechanisms, and discuss how they resemble (and differ from) the mechanisms for anomalous transport in other contexts. We also briefly review potential transport anomalies in systems where integrability is an emergent or approximate property. We survey instances of anomalous transport and dynamics that remain to be understood.

Seafloor Hydrothermal Activity around a Large Non-Transform Discontinuity along Ultraslow-Spreading Southwest Indian Ridge (48.1–48.7° E)

Non-transform discontinuity (NTD) is one category of tectonic units along slow- and ultraslow-spreading ridges. Some NTD-related hydrothermal fields that may reflect different driving mechanisms have been documented along slow-spreading ridges, but the discrete survey strategy makes it hard to evaluate the incidence of hydrothermal activity. On ultraslow-spreading ridges, fewer NTD-related hydrothermal activities were reported. Factors contributing to the occurrence of hydrothermal activities at NTDs and whether they could be potential targets for hydrothermal exploration are poorly known. Combining turbidity and oxidation reduction potential (ORP) sensors with a near-bottom camera, Chinese Dayang cruises from 2014 to 2018 have conducted systematic towed surveys for hydrothermal activity around a large NTD along the ultraslow-spreading Southwest Indian Ridge (SWIR, 48.1–48.7° E). Five new potential hydrothermal anomaly sites (2 inferred and 3 suspected) of high or low temperature and the previously inferred Sudi hydrothermal field occurred in diverse morphotectonic settings along a 78 km long ridge axis. The calculated vent frequency (Fs, sites/100 km) was ~7.7 over the entire study area, higher than the modified value (Fs ≈ 6.5) between 48 and 52° E of SWIR. Even only for the 54 km long large NTD, three hydrothermal anomaly sites yielded an Fs of ~5.6, which is higher than that of most ridge sections and is comparable to some fast-spreading ridges with high-resolution surveys. This indicates that NTDs along ultraslow-spreading ridges could be promising areas in fertilizing hydrothermal activities. Moreover, the deeply penetrating faults on the rift valley walls and strain-focused areas may contribute to the formation of NTD-related hydrothermal circulations. We suggest that NTDs along ultraslow-spreading ridges may be potential targets for further exploration of hydrothermal activities and seafloor sulfide deposits.

Bayesian network modelling provides spatial and temporal understanding of ecosystem dynamics within shallow shelf seas

Understanding ecosystem dynamics within shallow shelf seas is of great importance to support marine spatial management of natural populations and activities such as fishing and offshore renewable energy production to combat climate change. Given the possibility of future changes, a baseline is needed to predict ecosystems responses to such changes. This study uses Bayesian techniques to find the data-driven estimates of interactions among a set of physical and biological variables and a human pressure within the last 30 years in a well-studied shallow sea (North Sea, UK) with four contrasting regions and their associated ecosystems. A hidden variable is incorporated to model functional ecosystem change, where the underlying interactions dramatically change, following natural or anthropogenic disturbance. Data-driven estimates of interactions were identified, highlighting physical (e.g. bottom temperature, potential energy anomaly) and biological variables (e.g. sandeel larvae, net primary production) to be strong indicators of ecosystem change. There was consistency in the physical and biological variables, identified as good indicators in three of the regions, however the shallower region (with depths < 50 m, that is targeted for static offshore wind developments) was the most dissimilar. The use of contrasting regions provided useful insights on responses linked to ecosystem disturbances and identified the top predators as better indicators for each region, with the harbour porpoise being a particularly valuable indicator of ecosystem change across most regions. Another important finding was the dramatic changes in the strength of many interactions over time. This suggests that physical and biological indicators should only be used with additional temporal information, as changes in strength led to the identification of two potentially significant periods of ecosystem change (after 2005 and after 2010), linked to physical pressures (e.g. cold-water anomalies, seen in bottom temperatures; salinity changes, seen in the potential energy anomaly) and primary production changes. The hidden variable also modelled a change in the early 2000s for all the regions and identified maximum chlorophyll-a and sea surface temperature as some of the better indicators of these ecosystem changes.

RAMP-TAO

Facebook's graph store TAO, like many other distributed data stores, traditionally prioritizes availability, efficiency, and scalability over strong consistency or isolation guarantees to serve its large, read-dominant workloads. As product developers build diverse applications on top of this system, they increasingly seek transactional semantics. However, providing advanced features for select applications while preserving the system's overall reliability and performance is a continual challenge. In this paper, we first characterize developer desires for transactions that have emerged over the years and describe the current failure-atomic (i.e., write) transactions offered by TAO. We then explore how to introduce an intuitive read transaction API. We highlight the need for atomic visibility guarantees in this API with a measurement study on potential anomalies that occur without stronger isolation for reads. Our analysis shows that 1 in 1,500 batched reads reflects partial transactional updates, which complicate the developer experience and lead to unexpected results. In response to our findings, we present the RAMP-TAO protocol, a variation based on the Read Atomic Multi-Partition (RAMP) protocols that can be feasibly deployed in production with minimal overhead while ensuring atomic visibility for a read-optimized workload at scale.

The relationship between atmospheric potential gradient descent along with negative potential gradient anomalies and earthquake precursors

The relationship between atmospheric potential gradient descent along with negative potential gradient anomalies and earthquake precursors

Influence of Features on Accuracy of Anomaly Detection for an Energy Trading System.

The biggest problem with conventional anomaly signal detection using features was that it was difficult to use it in real time and it requires processing of network signals. Furthermore, analyzing network signals in real-time required vast amounts of processing for each signal, as each protocol contained various pieces of information. This paper suggests anomaly detection by analyzing the relationship among each feature to the anomaly detection model. The model analyzes the anomaly of network signals based on anomaly feature detection. The selected feature for anomaly detection does not require constant network signal updates and real-time processing of these signals. When the selected features are found in the received signal, the signal is registered as a potential anomaly signal and is then steadily monitored until it is determined as either an anomaly or normal signal. In terms of the results, it determined the anomaly with 99.7% (0.997) accuracy in f(4)(S0) and in case f(4)(REJ) received 11,233 signals with a normal or 171anomaly judgment accuracy of 98.7% (0.987).

Natural language-based detection of semantic execution anomalies in event logs

Anomaly detection in process mining aims to recognize outlying or unexpected behavior in event logs for purposes such as the removal of noise and identification of conformance violations. Existing techniques for this task are primarily frequency-based, arguing that behavior is anomalous because it is uncommon. However, such techniques ignore the semantics of recorded events and, therefore, do not take the meaning of potential anomalies into consideration. In this work, we overcome this caveat and focus on the detection of anomalies from a semantic perspective, arguing that anomalies can be recognized when process behavior does not make sense. To achieve this, we propose an approach that exploits the natural language associated with events. Our key idea is to detect anomalous process behavior by identifying semantically inconsistent execution patterns. To detect such patterns, we first automatically extract business objects and actions from the textual labels of events. We then compare these against a process-independent knowledge base. By populating this knowledge base with patterns from various kinds of resources, our approach can be used in a range of contexts and domains. We demonstrate the capability of our approach to successfully detect semantic execution anomalies through an evaluation based on a set of real-world and synthetic event logs and show the complementary nature of semantics-based anomaly detection to existing frequency-based techniques.

Evaluation of sex steroid hormones and reproductive irregularities in diethyl phthalate-exposed premature mice: modulatory effect of raw honey against potential anomalies.

Phthalates, plasticizing chemicals, are top-rated environmental contaminants. Diethyl phthalate (DEP), a chief member of this family, was declared a potent endocrine disruptor and carcinogen in animals and humans. The current study was designed to explore the probable reproductive damage induced by DEP and the therapeutic efficacy of raw honey in male albino mice. Four-week-old 50 male mice were randomized equally in five groups, as control (C) received 0.1 ml distilled water; vehicle control (VC) received corn oil (0.1 ml/mouse); DEP (3mg/g/BW) dissolved in corn oil; honey control (HC) administered with honey (0.2 mg/g/day); and phthalate plus honey (P+H) administered with DEP and honey (3mg and 0.2 mg/g/BW/day respectively). Mice were treated through oral gavage for 54 days routinely, acclimatized for 6 days, and dissected. In the first instance, the antioxidant potential and total phenolic contents (TPC) of honey were analyzed through ferric reducing antioxidant power (FRAP) assay and Folin-Ciocalteu assay to confirm the antioxidant capacity of honey. The morphological, morphometric, histological, micrometric, sperm count, and hormonal analyses, and antioxidant capacity test in tissue homogenates were conducted by using tissues (testis, epididymis) and blood samples of mice. Mice exposed to DEP have a significant increase in body weight, LH level, and seminiferous tubule lumen diameter and decrease in the gonado-somatic index, testosterone level, sperm count, and seminiferous tubule diameter. Additionally, histopathology of testes showed interstitial space dilations, exfoliations, Leydig cell atrophy, germ cell degenerations, and spermatid retention in DEP-exposed testes sections. However, concomitant use of honey and DEP had shown a significant improvement in histopathological lesions, steroid hormone levels, and healthy sperm count. By these results, it is concluded that honey possessed antioxidant potential that can efficiently protect DEP-induced anomalies in male mice.