Anomalous Events Research Articles

Effects of anomalous climatic events on the structure of fish larvae assemblages in the eastern Amazon

The reproduction of neotropical fish is linked to environmental stimuli that act as triggers in this process. Thus, it is possible to affirm that specific environmental changes can delimit most fish’s period and reproductive success. El Niño Southern Oscillation phenomena (ENSO) influence rainfall and, consequently, hydrological dynamics, affecting several fish population aspects, especially reproductive aspects. However, in the Amazon, empirical evidence of the effect of anomalous climatic events on fish reproduction is incipient. In this sense, we investigated the taxonomic and functional structure of parental fish larvae stocks and how species modulate their reproductive activities before (2013 and 2014) and during La Niña (2018) and El Niño (2019) events. The data evidence that the larval assemblages suffered alterations in the taxonomic and functional composition between the three analyzed periods and a pattern of temporal distribution with a high influence of environmental variables. Flow, pH, and electrical conductivity were the most important variables to modulate fish reproductive intensity between periods. The species seem to respond to combinations of these variables that reflect environmental conditions conducive to biological recruitment according to their ecological characteristics, allowing the perpetuation or decline of their abundance in each period. All species of commercial interest associated with the neutral period showed a sharp decline in the subsequent moments. These results suggest that the incidence of anomalous climatic events added to regional fisheries exploitation can negatively affect the phenology and demographic patterns of larvae assemblages on a short temporal scale. During the neutral period, the community was dominated by larvae of periodic large, herbivores, piscivores, epibenthic maneuverable, and nektonic burst swimmers. The decrease in the abundance of certain functional groups at the neutral and La Niña period favored an accelerated ecological succession during the El Niño period, due to the increase of species from basal trophic levels (planktivores), generalists (omnivores), highly resilient (intermediate strategists), sedentary, short-distance migrants and little commercially exploited. Thus, the results contribute to the understanding of the interactions between anomalous climatic events and the reproduction of fish with different ecological characteristics in the Amazon River.

Development of a Water Quality Event Detection and Diagnosis Framework in Drinking Water Distribution Systems with Structured and Unstructured Data Integration

Recently, various detection approaches that identify anomalous events (e.g., discoloration, contamination) by analyzing data collected from smart meters (so-called structured data) have been developed for many water distribution systems (WDSs). However, although some of them have showed promising results, meters often fail to collect/transmit the data (i.e., missing data) thus meaning that these methods may frequently not work for anomaly identification. Thus, the clear next step is to combine structured data with another type of data, unstructured data, that has no structural format (e.g., textual content, images, and colors) and can often be expressed through various social media platforms. However, no previous work has been carried out in this regard. This study proposes a framework that combines structured and unstructured data to identify WDS water quality events by collecting turbidity data (structured data) and text data uploaded to social networking services (SNSs) (unstructured data). In the proposed framework, water quality events are identified by applying data-driven detection tools for the structured data and cosine similarity for the unstructured data. The results indicate that structured data-driven tools successfully detect accidents with large magnitudes but fail to detect small failures. When the proposed framework is used, those undetected accidents are successfully identified. Thus, combining structured and unstructured data is necessary to maximize WDS water quality event detection.

Revisiting the Fluorescence of Benzothiadiazole Derivatives: Anti‐Kasha Emission or Not?

AbstractIn this paper we revisit the fluorescent properties of two fluorophores, namely N‐butyl‐7‐(4‐ methoxyphenyl)‐2,1,3‐benzothiadiazole‐4‐amine (1) and 4‐[[7‐(4‐methoxyphenyl)‐2,1,3‐ benzothiadiazole‐4‐yl]amino]‐benzonitrile (2), whose emissive and fluorescent sensing properties were recently investigated by Peng et al. in Ref. [1]. Experimentally, these two fluorophores were reported to show excitation wavelength‐dependent emission properties, especially 1, which is a dual emitter. The authors of the original manuscript ascribed the excitation‐wavelength‐dependent emission properties to an emission from a higher‐lying excited state (i. e. anti‐Kasha emission). Our investigations, including steady‐state spectroscopy, lifetime measurements and time‐dependent density functional theory and second‐order algebraic diagrammatic construction [ADC(2)] calculations, unambiguously highlight that the fluorophores do not violate Kasha's rule but reveal instead that a protonated species is responsible for the higher‐lying emissive band. In addition, we have elucidated the actual fluorescent pH sensing mechanisms. More and more molecular systems are reported to violate Kasha's rule; sometimes without compelling proof. This study highlights the need to i) exhaustively investigate apparent anomalous cases reported in the literature that seemingly violate Kasha's rule and ii) propose good scientific practices to correctly assign anomalous photochemical events.

Unsupervised video anomaly detection based on multi-timescale trajectory prediction

Video anomaly detection refers to the identification of events that differ from normal behavior. Most of the commonly used methods currently use the reconstruction method based on appearance features. However, appearance features are unstructured signals that are sensitive to noise, and the redundant information contained will also increase the burden of distinguishing signals from noise during training. Reconstruction methods try to minimize the reconstruction error of the training data but cannot guarantee that the reconstruction error of anomalous events is large. From this, we propose an unsupervised video anomaly detection algorithm based on multi-timescale trajectory prediction. We use the object tracking network to detect and track pedestrians in the scene and send them to the multi-timescale trajectory prediction and velocity calculation modules for training. Due to the different motion durations, we add a multi-timescale mechanism to predict pedestrian trajectories and introduce step signals in digital signal processing for trajectory subsequence segmentation. During the testing of abnormal videos, the irregular motion behavior of pedestrians cannot be predicted by the normal model and will result in higher trajectory outliers. Similarly, the velocity calculation module constrains and calculates pedestrian velocities at different camera views, and events that differ from normal velocity can be detected under the dual constraints of space and motion (time). Compared with state-of-the-art methods and other anomalous event detection methods, the proposed model has certain advantages in frame-level AUC.

Smart Chatbot for User Authentication

Despite being the most widely used authentication mechanism, password-based authentication is not very secure, being easily guessed or brute-forced. To address this, many systems which especially value security adopt Multi-Factor Authentication (MFA), in which multiple different authentication mechanisms are used concurrently. JitHDA (Just-in-time human dynamics based authentication engine) is a new authentication mechanism which can add another option to MFA capabilities. JitHDA observes human behaviour and human dynamics to gather up to date information on the user from which authentication questions can be dynamically generated. This paper proposes a system that implements JitHDA, which we call Autonomous Inquiry-based Authentication Chatbot (AIAC). AIAC uses anomalous events gathered from a user’s recent activity to create personalized questions for the user to answer, and is designed to improve its own capabilities over time using neural networks trained on data gathered during authentication sessions. Due to using the user’s recent activity, they will be easy for the authentic user to answer and hard for a fraudulent user to guess, and as the user’s recent history updates between authentication sessions new questions will be dynamically generated to replace old ones. We intend to show in this paper that AIAC is a viable implementation of JitHDA.

Ecological Response to Climate Change Across China From Combined Soil Temperature and Moisture Changes

AbstractA coupled soil temperature (ST) and moisture (SM) balance reflects a synthetic climate regime, having huge ecological impacts. This paper used ST and SM data from the European Center for Medium‐Range Weather Forecasts climate reanalysis‐Land and the Coupled Model Intercomparison Project Phase 6 and leaf area index (LAI) data from the Global Land Surface Satellite Product Suite. The focus was on understanding joint ST‐SM changes and the resulting ecological response across China. The results show that during 2000–2020, 24.5% of the land area in China experienced a warming‐drying trend resulting in a 9.7% LAI decrease, while 6.4% of the area experienced a warming‐wetting trend leading to an 8.6% LAI increase. During 2015–2100, 30.6% of the land area in China will be warmer and drier, while 55.2% of the area will be warmer but wetter across three shared socioeconomic pathways (SSP126, 245, and 585). Superimposed on the long‐term trends, there are also significant spatiotemporal variabilities in ST and SM on annual to decadal timescales. The LAI also showed substantial short‐term fluctuations in both typical regions and ecosystems despite consistent long‐term increases. Our findings suggest that ecosystems could be impaired on annual to decadal scales by adverse soil conditions in the twenty‐first century, but in terms of long‐term trends, ecosystems may be resilient partly because of the compensating effects of global warming and regional hydrological changes. Impact studies should thus focus more on annual to decadal soil‐ecosystem anomalous events.

Deep Learning-Based Cyber–Physical Feature Fusion for Anomaly Detection in Industrial Control Systems

In this paper, we propose an unsupervised anomaly detection method based on the Autoencoder with Long Short-Term Memory (LSTM-Autoencoder) network and Generative Adversarial Network (GAN) to detect anomalies in industrial control system (ICS) using cyber–physical fusion features. This method improves the recall of anomaly detection and overcomes the challenges of unbalanced datasets and insufficient labeled samples in ICS. As a first step, additional network features are extracted and fused with physical features to create a cyber–physical dataset. Following this, the model is trained using normal data to ensure that it can properly reconstruct the normal data. In the testing phase, samples with unknown labels are used as inputs to the model. The model will output an anomaly score for each sample, and whether a sample is anomalous depends on whether the anomaly score exceeds the threshold. Whether using supervised or unsupervised algorithms, experimentation has shown that (1) cyber–physical fusion features can significantly improve the performance of anomaly detection algorithms; (2) the proposed method outperforms several other unsupervised anomaly detection methods in terms of accuracy, recall, and F1 score; (3) the proposed method can detect the majority of anomalous events with a low false negative rate.

The tropical Atlantic as a negative feedback on ENSO

The influence of the tropical Atlantic on El Niño-Southern Oscillation (ENSO) is examined using dedicated climate model experiments with sea-surface temperature (SST) restoring. Partial SST restoring to climatology in the tropical Atlantic leads to slower decay of ENSO events and to a shift of the power spectrum to longer periods. Perfect model hindcast experiments with and without restoring tropical Atlantic SST to climatology indicate that both the northern tropical and equatorial Atlantic have a very small influence on ENSO development. During decaying ENSO events, on the other hand, northern tropical Atlantic SST anomalies strongly accelerate the decay. Key to the Atlantic influence on ENSO decay are Atlantic SST anomalies just north of the equator (~ 5°N). These lead to local convection anomalies that change the Walker circulation so as to accelerate ENSO decay. Importantly, anomalous events in either the northern tropical or equatorial Atlantic fail to develop in the hindcast ensemble mean, when tropical Pacific SSTs are restored to climatology. This indicates that anomalous tropical Atlantic events in boreal spring and summer are strongly dependent on preceding ENSO events in boreal winter. Thus, the role of the tropical Atlantic is to mediate a negative feedback of ENSO on itself. Despite this passive role of the tropical Atlantic in the Pacific-Atlantic interaction, accurate simulation of the Atlantic feedback should play some role in ENSO prediction. Further model experiments will be required to evaluate model dependence of these findings and to quantify the impact of the Atlantic on ENSO prediction skill.

An IoT-based and cloud-assisted AI-driven monitoring platform for smart manufacturing: design architecture and experimental validation

PurposeThis work aims at proposing a novel Internet of Things (IoT)-based and cloud-assisted monitoring architecture for smart manufacturing systems able to evaluate their overall status and detect eventual anomalies occurring into the production. A novel artificial intelligence (AI) based technique, able to identify the specific anomalous event and the related risk classification for possible intervention, is hence proposed.Design/methodology/approachThe proposed solution is a five-layer scalable and modular platform in Industry 5.0 perspective, where the crucial layer is the Cloud Cyber one. This embeds a novel anomaly detection solution, designed by leveraging control charts, autoencoders (AE) long short-term memory (LSTM) and Fuzzy Inference System (FIS). The proper combination of these methods allows, not only detecting the products defects, but also recognizing their causalities.FindingsThe proposed architecture, experimentally validated on a manufacturing system involved into the production of a solar thermal high-vacuum flat panel, provides to human operators information about anomalous events, where they occur, and crucial information about their risk levels.Practical implicationsThanks to the abnormal risk panel; human operators and business managers are able, not only of remotely visualizing the real-time status of each production parameter, but also to properly face with the eventual anomalous events, only when necessary. This is especially relevant in an emergency situation, such as the COVID-19 pandemic.Originality/valueThe monitoring platform is one of the first attempts in leading modern manufacturing systems toward the Industry 5.0 concept. Indeed, it combines human strengths, IoT technology on machines, cloud-based solutions with AI and zero detect manufacturing strategies in a unified framework so to detect causalities in complex dynamic systems by enabling the possibility of products’ waste avoidance.

‘Flux+Mutability’: a conditional generative approach to one-class classification and anomaly detection

Anomaly Detection is becoming increasingly popular within the experimental physics community. At experiments such as the Large Hadron Collider, anomaly detection is growing in interest for finding new physics beyond the Standard Model. This paper details the implementation of a novel Machine Learning architecture, called Flux+Mutability, which combines cutting-edge conditional generative models with clustering algorithms. In the ‘flux’ stage we learn the distribution of a reference class. The ‘mutability’ stage at inference addresses if data significantly deviates from the reference class. We demonstrate the validity of our approach and its connection to multiple problems spanning from one-class classification to anomaly detection. In particular, we apply our method to the isolation of neutral showers in an electromagnetic calorimeter and show its performance in detecting anomalous dijets events from standard QCD background. This approach limits assumptions on the reference sample and remains agnostic to the complementary class of objects of a given problem. We describe the possibility of dynamically generating a reference population and defining selection criteria via quantile cuts. Remarkably this flexible architecture can be deployed for a wide range of problems, and applications like multi-class classification or data quality control are left for further exploration.

Dynamic nucleation in sub-critically undercooled melts during electromagnetic levitation

By classical nucleation theory, sub-critically undercooled melts in electromagnetic levitation are expected to be very stable for extended periods of time. While electromagnetic levitation experiments are typically consistent with classical nucleation theory, there have been several historical instances including experiments on zirconium during the MSL-1 campaign and during the IML-2 experiments where the molten sample solidified at sub-critical undercoolings. While different electromagnetic levitation conditions were present in these anomalous nucleation events, both sets of anomalous nucleation events were attributed to dynamic nucleation. The work presented here investigates more recent experiments in the International Space Station Electromagnetic Levitation facility in which both sets of anomalous nucleation events were replicated and further investigated. The conditions of the MSL-1 solidification events were replicated in the ISS-EML using a pure zirconium sample held in isothermal conditions between 45 °C and 290 °C below the melting temperature. The sample successfully solidified in 18 of these experiments in less than 600 s where classical nucleation theory predicts the melt to remain liquid for very long periods of time. The IML-2 result, was replicated in the ISS-EML in which a Zr64Ni36 sample was used to demonstrate pulse-triggered nucleation events. During these experiments, the excitation pulse triggered solidification in the sample which was held between 59.5 °C and 64.5 °C below the melting temperature. Again, classical nucleation predicts that under these conditions the sample will remain molten. The results of both sets of experiments are consistent with dynamic nucleation theory in which nucleation events are affected by the flow conditions within the drop.

PMUVis : A Large-Scale Platform to Assist Power System Operators in a Smart Grid.

Electric transmission power grids are being revamped with the widespread deployment of GPS-enabled phasor measurement units (PMUs) for real-time wide-area monitoring and control via precise, time-synchronized measurements of voltage and current. Large, concurrently produced volumes of noisy data hinder PMU usability, particularly for the analysis of power oscillation and load fluctuation events in the grid. We examine visualization challenges for events in the electric power grid and develop PMUVis, a visualization platform that supports scalable analysis of grid network topology and anomalous events in near time. PMUVis incorporates a novel FFT-based approach over raw and temporally aggregated data to examine oscillation event propagation through the grid network. We validate PMUVis with expert reviews and a case study and discuss how visualization can be leveraged to enhance real-time spatiotemporal grid analysis by advancing operator capabilities.

TAROGE-M: radio antenna array on antarctic high mountain for detecting near-horizontal ultra-high energy air showers

The TAROGE-M radio observatory is a self-triggered antenna array on top of the ∼2700 m high Mt. Melbourne in Antarctica, designed to detect impulsive geomagnetic emission from extensive air showers induced by ultra-high energy (UHE) particles beyond 1017 eV, including cosmic rays, Earth-skimming tau neutrinos, and particularly, the “ANITA anomalous events” (AAE) from near and below the horizon. The six AAE discovered by the ANITA experiment have signal features similar to tau neutrinos but that hypothesis is in tension either with the interaction length predicted by Standard Model or with the flux limits set by other experiments. Their origin remains uncertain, requiring more experimental inputs for clarification.The detection concept of TAROGE-M takes advantage of a high altitude with synoptic view toward the horizon as an efficient signal collector, and the radio quietness as well as strong and near vertical geomagnetic field in Antarctica, enhancing the relative radio signal strength. This approach has a low energy threshold, high duty cycle, and is easy to extend for quickly enlarging statistics. Here we report experimental results from the first TAROGE-M station deployed in January 2020, corresponding to approximately one month of livetime. The station consists of six receiving antennas operating at 180–450 MHz, and can reconstruct source directions of impulsive events with an angular resolution of ∼0.3°, calibrated in situ with a drone-borne pulser system. To demonstrate TAROGE-M's ability to detect UHE air showers, a search for cosmic ray signals in 25.3-days of data together with the detection simulation were conducted, resulting in seven identified candidates. The detected events have a mean reconstructed energy of 0.95-0.31 +0.46 EeV and zenith angles ranging from 25° to 82°, with both distributions agreeing with the simulations, indicating an energy threshold at about 0.3 EeV. The estimated cosmic ray flux at that energy is 1.2-0.9 +0.7 × 10-16 eV-1 km-2 yr-1 sr-1, also consistent with results of other experiments. The TAROGE-M sensitivity to AAEs is approximated by the tau neutrino exposure with simulations, which suggests comparable sensitivity as ANITA's at around 1 EeV energy with a few station-years of operation. These first results verified the station design and performance in a polar and high-altitude environment, and are promising for further discovery of tau neutrinos and AAEs after an extension in the near future.

Anomalous event detection and localization in dense crowd scenes

Anomalous event detection and localization in dense crowd scenes

Winners and losers of the productivity gains of the American agricultural sector

ABSTRACT The aim of this study is twofold. First, to identify who benefited from the productivity growth of the American agricultural sector from 1960 to 2004. Second, to measure the relationship between changes in productivity, its distribution, and the evolution of variables linked with climate change. This study shifts the attention from the drivers of productivity change to how it is distributed. Our results show that the stakeholders of the US agricultural sector do not benefit equally from productivity growth. Moreover, it provides empirical evidence that supports the treadmill theory about how technological innovation pushes some farmers out of the market. Concerning the relationship between extreme weather variables (precipitation, temperature, and droughts) and the distribution of productivity change, this depends on the geographical situation of the state. Some stakeholders might be the winners of anomalous climate events in some regions of the US. These findings suggest that reaching a consensus on initiatives to stop climate change will be extremely difficult.

Four-Loop Rapidity Anomalous Dimension and Event Shapes to Fourth Logarithmic Order.

We obtain the quark and gluon rapidity anomalous dimension to fourth order in QCD. We calculate the N^{3}LO rapidity anomalous dimensions to higher order in the dimensional regulator and make use of the soft and rapidity anomalous dimension correspondence in conjunction with the recent determination of the N^{4}LO threshold anomalous dimensions to achieve our result. We show that the results for the quark and gluon rapidity anomalous dimensions at four loops are related by generalized Casimir scaling. Using the N^{4}LO rapidity anomalous dimension, we perform the resummation of the energy-energy correlation in the back-to-back limit at N^{4}LL, achieving for the first time the resummation of an event shape at this logarithmic order. We present numerical results and observe a reduction of perturbative uncertainties on the resummed cross section to below 1%.

V2AnomalyVec: Deep Discriminative Embeddings for Detecting Anomalous Activities in Surveillance Videos

Understanding patterns in surveillance videos is always challenging due to the rapid movement of the crowd, occlusions, cluttered backgrounds, intraclass variations, and interclass similarities existing among normal and abnormal event classes. In the case of autoencoder-based normality model trained using segments of normal events only, there may be chances of anomalous events to be reconstructed well, leading to anomalous events detected as normal events. In this work, we propose deep discriminative embedding (DDE)-based framework using residual spatiotemporal auotencoders (R-STAEs) that learn V2AnomalyVec embeddings. In place of R-STAEs, any promising deep architectures to extract deep features from video segments of normal and abnormal events can also be used to form V2AnomalyVec. Results on four benchmark datasets demonstrate that the V2AnomalyVec-based approach performs significantly better than the normality model-based approaches and other state-of-the-art approaches. Studies with a reduced number of abnormal video segments to train the model prove that the proposed approach is also appropriate for detecting anomalous events even with less amount of abnormal training data.

Timing and Structure of 10.9 and 10.3 ka BP Events Revealed by Annually Laminated Stalagmite Records From Shihua Cave, Northern China

AbstractStudies have shown that East Asia experienced several abrupt centennial‐scale weak monsoonal events in the early Holocene. However, the timing and structure of these climate events remain unclear due to the lack of precisely dated and highly resolved records. In this study, we present a well‐dated high‐resolution East Asia summer monsoon record from 11.32 to 10 ka BP based on two coeval stalagmites, one of which developed clear annual lamina, from Shihua Cave, northern China. Stalagmite δ18O records show 2 weak summer monsoon events at 10.9 and 10.3 ka. The 10.9‐ka event occurred from 10.93 to 10.79 ka BP for ∼140 years and features a “W”‐shaped structure. The 10.3‐ka event, also known as the ice‐rafted debris event 7 in the North Atlantic, lasted ∼260 years with an asymmetric double‐plunging structure. The two events are concurrent with the cold conditions in Greenland ice core record, indicating a close correlation between East Asia summer monsoon and North Atlantic climate. Spectral analyses on Shihua Cave stalagmite δ18O time series and annually lamina thickness sequence both show a statistically significant periodicity of 200 years, indicating that anomalous climate events may be triggered by solar activity. The 10.3‐ka event also coincides with three small freshwater outbursts in the high‐latitude North Atlantic. This agreement suggests that freshwater outbursts may have played a role in reinforcing this weak 10.3‐ka event in monsoonal China.

ANITA anomalous events and axion quark nuggets

The Antarctic Impulse Transient Antenna (ANITA) Collaboration [1--3] has reported two anomalous events with noninverted polarity. These events are hard to explain in terms of conventional cosmic rays (CRs). We explore a new possible explanation for these anomalous events by suggesting that these events can be related to the dark matter (DM) annihilations within the so-called axion quark nugget (AQN) DM model. This model was initially invented for a completely different purpose: to explain the observed similarity between the dark and the visible components in the Universe---i.e., ${\mathrm{\ensuremath{\Omega}}}_{\mathrm{DM}}\ensuremath{\sim}{\mathrm{\ensuremath{\Omega}}}_{\text{visible}}$---without any fitting parameters. We investigate the signal properties of the upward-going AQN events, including the event rate, the pulse duration, and the electric field strength, and find them consistent with the observations. We list several features of the upward-going AQN events distinct from conventional CR air showers. The observations (or nonobservation) of these features may substantiate (or refute) our proposal.

Investigating the relative contribution of anthropogenic increase in greenhouse gas and land use and land cover change to Asian climate: A dynamical downscaling study

AbstractHuman activity have caused significant increase in greenhouse gas (GHG) concentration and land use and land cover change (LULCC), which can in turn affect regional and global climate. We investigate the relative contributions of increased GHG concentration and LULCC to the precipitation and surface air temperature (SAT) in Asia through three dynamical downscaling simulations with different land cover maps and GHG concentrations. The results suggest that the increased GHG concentration leads to an increase (decrease) in precipitation over East Asia and Southeast Asia (South Asia) in spring and winter. Over Southeast Asia and the Bay of Bengal, LULCC induced a significant decrease in precipitation in autumn and winter, and the change is greater than that induced by GHG. Moreover, the enhanced GHG concentration generally leads to a decrease in dry days or light rain days and an increase in heavy rain days over the land area. However, LULCC‐induced changes in extreme precipitation are generally weak. The increased GHG concentration induces a significant increase of 1–3°C in the SAT throughout the year. LULCC plays a more important role than the increased GHG concentration in modulating the SAT over South Asia, Southeast Asia, and East Asia throughout the year. The increased GHG concentration and LULCC cause a broader range of changes in the daily temperature frequency. The anomalous warm event of 8–10°C increases by 1–1.5 days per season in the middle latitude regions in boreal spring due to the increased GHG concentration. LULCC also leads to an increase in high‐temperature events except central Asia.