Time-stamped Events Research Articles

Calibration of a gamma ray Compton camera for radioactivity measurements

A dual-plane Compton imaging detector previously developed for prompt gamma imaging has been further tested and calibrated for quantitative radioactivity determination, specifically to assess radioactive debris from a reactor fuel element. The debris piece contains uranium isotopes and several long-lived fission products, including cesium-137 (Cs-137). The detector consists of two pixelated planes of cadmium-zinc-telluride (CZT) crystals which record gamma ray events interacting within these volumes. In addition to the energy spectrum obtained by pulse height analysis, the time-stamped events from each detector pixel are sorted by location into 3 categories for post-processing: intra-plane, inter-plane, and coincidence. An image reconstruction software enables spatial localization of the select gamma ray peak(s) from the spectrum, thereby separating the gamma ray by specific isotopes. We used the camera to image a small piece of debris for demonstration purposes. A measurement was performed alongside a high purity germanium (HPGe) detector, which could determine the isotopes' absolute activities and be used as a reference for the camera. In addition, a known isotopic point source was used to calibrate the camera’s energy detection efficiency in the same measurement geometry, correlating the image pixel intensity to the isotopic activity.

A recommender system for human operators in industrial automation

This paper presents a recommender system to assist human operators in industrial automation applications, which suggests proper actions to take during process operation and those to avoid. The proposed recommender system is built based on a three-stage computational procedure. In the first stage, historical process data segments containing timestamped events are clustered using a k-medoids-based algorithm, where instead of distance metrics, structural similarity of the segments is used. This is achieved by using a Smith–Waterman-based algorithm to consider all events and their timestamps in the segments. In the second stage, a modified collaborative filtering technique applicable to time sequences is proposed, where not only operator ratings, but also the structural similarity of the segments are taken into account. In the third stage, possible actions are analyzed and accordingly, proper and improper actions are determined and presented to the human operators. The effectiveness of the proposed recommender system is examined through a power system operation example.

A screenshot-based task mining framework for disclosing the drivers behind variable human actions

Robotic Process Automation (RPA) enables subject matter experts to use the graphical user interface as a means to automate and integrate systems. This is a fast method to automate repetitive, mundane tasks. To avoid constructing a software robot from scratch, Task Mining approaches can be used to monitor human behavior through a series of timestamped events, such as mouse clicks and keystrokes. From a so-called User Interface log (UI Log), it is possible to automatically discover the process model behind this behavior. However, when the discovered process model shows different process variants, it is hard to determine what drives a human’s decision to execute one variant over the other. Existing approaches do analyze the UI Log in search for the underlying rules, but neglect what can be seen on the screen. As a result, a major part of the human decision-making remains hidden. To address this gap, this paper describes a Task Mining framework that uses the screenshot of each event in the UI Log as an additional source of information. From such an enriched UI Log, by using image-processing techniques and Machine Learning algorithms, a decision tree is created, which offers a more complete explanation of the human decision-making process. The presented framework can express the decision tree graphically, explicitly identifying which elements in the screenshots are relevant to make the decision. The framework has been evaluated through a case study that involves a process with real-life screenshots. The results indicate a satisfactorily high accuracy of the overall approach, even if a small UI Log is used. The evaluation also identifies challenges for applying the framework in a real-life setting when a high density of interface elements is present.

A Hybrid Continuous-Time Dynamic Graph Representation Learning Model by Exploring Both Temporal and Repetitive Information

Recently, dynamic graph representation learning has attracted more and more attention from both academic and industrial communities due to its capabilities of capturing different real-world phenomena. For a dynamic graph represented as a sequence of timestamped events, there are two kinds of evolutionary essences: temporal and repetitive information. At present, the temporal information of interactions (e.g., timestamps) have been deeply explored. However, as another vital nature of dynamic graphs, the repetitive information of interactions between two nodes is neglected, which may lead to inaccurate node representation. To address this issue, we propose a novel continuous-time dynamic graph representation learning model, which consists of a node-level-memory based module, a historical high-order neighborhood based vertical aggregation module and a repetitive-topological information based horizontal aggregation module. In particular, to characterize the evolving pattern of the repetitive information of interactions between a pair of nodes, we put forward a repetitive-interaction based attention mechanism to integrate the two key attributes (i.e., the content and the number of interactions) of repetitive interactions at different moments, based on the insight that the repetitive behaviors of nodes are widespread and essential. We conduct extensive experiments including future link prediction tasks (for transductive and inductive learning) and dynamic node classification task, and results on three real-life dynamic graph datasets demonstrate that the proposed method significantly outperforms state-of-the-art baselines, for both observed nodes and new ones.

Synchronous speculative simulation of tightly coupled agents in continuous time on CPUs and GPUs

Traditionally, parallel discrete-event simulations of agent-based models in continuous time are organized around logical processes exchanging time-stamped events, which clashes with the properties of models in which tightly coupled agents frequently and instantaneously access each other’s states. To illustrate the challenges of such models and to derive a solution, we consider the domain-specific modeling language ML3, which allows modelers to succinctly express transitions and interactions of linked agents based on a continuous-time Markov chain (CTMC) semantics. We propose synchronous optimistic synchronization algorithms tailored toward simulations of fine-grained interactions among tightly coupled agents in highly dynamic topologies and present implementations targeting multicore central processing units (CPUs) as well as many-core graphics processing units (GPUs). By dynamically restricting the temporal progress per round to ensure that at most one transition or state access per agent, the synchronization algorithms enable efficient direct agent interaction and limit the required agent state history to only a single current and projected state. To maintain concurrency given actions that depend on dynamically updated macro-level properties, we introduce a simple relaxation scheme with guaranteed error bounds. Using an extended variant of the classical susceptible-infected-recovered network model, we benchmark and profile the performance of the different algorithms running on CPUs and on a data center GPU.

Event-case correlation for process mining using probabilistic optimization

Process mining supports the analysis of the actual behavior and performance of business processes using event logs. An essential requirement is that every event in the log must be associated with a unique case identifier (e.g., the order ID of an order-to-cash process). In reality, however, this case identifier may not always be present, especially when logs are acquired from different systems or extracted from non-process-aware information systems. In such settings, the event log needs to be pre-processed by grouping events into cases — an operation known as event correlation. Existing techniques for correlating events have worked with assumptions to make the problem tractable: some assume the generative processes to be acyclic, while others require heuristic information or user input. Moreover, they abstract the log to activities and timestamps, and miss the opportunity to use data attributes. In this paper, we lift these assumptions and propose a new technique called EC-SA-Data based on probabilistic optimization. The technique takes as inputs a sequence of timestamped events (the log without case IDs), a process model describing the underlying business process, and constraints over the event attributes. Our approach returns an event log in which every event is associated with a case identifier. The technique allows users to flexibly incorporate rules on process knowledge and data constraints. The approach minimizes the misalignment between the generated log and the input process model, maximizes the support of the given data constraints over the correlated log, and the variance between activity durations across cases. Our experiments with various real-life datasets show the advantages of our approach over the state of the art.

Embedding and Trajectories of Temporal Networks

Temporal network data are increasingly available in various domains, and often represent highly complex systems with intricate structural and temporal evolutions. Due to the difficulty of processing such complex data, it may be useful to coarse grain temporal network data into a numeric trajectory embedded in a low-dimensional space. We refer to such a procedure as temporal network embedding, which is distinct from procedures that aim at embedding individual nodes. Temporal network embedding is a challenging task because we often have access only to discrete time-stamped events between node pairs, and, in general, the events occur with irregular intervals, making the construction of the network at a given time a nontrivial question already. We propose a method to generate trajectories of temporal networks embedded in a low-dimensional space given a sequence of time-stamped events as input. We realize this goal by combining the landmark multidimensional scaling, which is an out-of-sample extension of the well-known multidimensional scaling method, and the framework of tie-decay temporal networks. This combination enables us to obtain a continuous-time trajectory describing the evolution of temporal networks. We then study mathematical properties of the proposed temporal network embedding framework. Finally, we showcase the method with empirical data of social contacts to find temporal organization of contact events and loss of them over a single day and across different days.

Event-based imaging velocimetry: an assessment of event-based cameras for the measurement of fluid flows

Contrary to conventional frame-based imaging, event-based vision or dynamic vision sensing asynchronously records binary signals of intensity changes for given pixels with microsecond resolution. The present work explores the possibilities of harnessing the potential of event-based vision for fluid flow measurement. The described implementations of event-based imaging velocimetry rely on imaging small particles that are illuminated by a laser light sheet which is similar to classical two-dimensional, two-component (2d–2c) PIV with the difference that a continuously operating laser light sheet is used without modulation of the laser or camera. The moving particles generate continuous time-stamped events on the detector that are later used to infer their velocity using patch-wise processing schemes. Two flow estimation algorithms are proposed; one uses a “motion compensation” that maximizes the local contrast and the other is based on a sum-of-correlations approach. The underlying motion detection schemes in combination with the complete absence of background signal allow straightforward retrieval of the events associated with individual particles thereby allowing the reconstruction of individual particle tracks. Alternatively, the event data can be processed with conventional PIV algorithms using images reconstructed from the event data stream. The concepts are demonstrated on simple flows in water and air.Graphic abstract

Universality, criticality and complexity of information propagation in social media

Statistical laws of information avalanches in social media appear, at least according to existing empirical studies, not robust across systems. As a consequence, radically different processes may represent plausible driving mechanisms for information propagation. Here, we analyze almost one billion time-stamped events collected from several online platforms – including Telegram, Twitter and Weibo – over observation windows longer than ten years, and show that the propagation of information in social media is a universal and critical process. Universality arises from the observation of identical macroscopic patterns across platforms, irrespective of the details of the specific system at hand. Critical behavior is deduced from the power-law distributions, and corresponding hyperscaling relations, characterizing size and duration of avalanches of information. Statistical testing on our data indicates that a mixture of simple and complex contagion characterizes the propagation of information in social media. Data suggest that the complexity of the process is correlated with the semantic content of the information that is propagated.

Video Analysis and Verification of Direct Head Impacts Recorded by Wearable Sensors in Junior Rugby League Players

BackgroundRugby league is a high-intensity collision sport that carries a risk of concussion. Youth athletes are considered to be more vulnerable and take longer to recover from concussion than adult athletes.PurposeTo review head impact events in elite-level junior representative rugby league and to verify and describe characteristics of X-patchTM-recorded impacts via video analysis.Study DesignObservational case series.MethodsThe X-patchTM was used on twenty-one adolescent players (thirteen forwards and eight backs) during a 2017 junior representative rugby league competition. Game-day footage, recorded by a trained videographer from a single camera, was synchronised with X-patchTM-recorded timestamped events. Impacts were double verified by video review. Impact rates, playing characteristics, and gameplay situations were described.ResultsThe X-patchTM-recorded 624 impacts ≥ 20g between game start and finish, of which 564 (90.4%) were verified on video. Upon video review, 413 (73.2%) of all verified impacts ≥ 20g where determined to be direct head impacts. Direct head impacts ≥ 20g occurred at a rate of 5.2 impacts per game hour; 7.6 for forwards and 3.0 for backs (range = 0–18.2). A defender’s arm directly impacting the head of the ball carrier was the most common event, accounting for 21.3% (n = 120) of all impacts, and 46.7% of all “hit-up” impacts. There were no medically diagnosed concussions during the competition.ConclusionThe majority (90.4%) of head impacts ≥ 20g recorded by the X-patchTM sensor were verified by video. Double verification of direct head impacts in addition to cross-verification of sensor-recorded impacts using a secondary source such as synchronised video review can be used to ensure accuracy and validation of data.

Trajectories through temporal networks

What do football passes and financial transactions have in common? Both are networked walk processes that we can observe, where records take the form of timestamped events that move something tangible from one node to another. Here we propose an approach to analyze this type of data that extracts the actual trajectories taken by the tangible items involved. The main advantage of analyzing the resulting trajectories compared to using, e.g., existing temporal network analysis techniques, is that sequential, temporal, and domain-specific aspects of the process are respected and retained. As a result, the approach lets us produce contextually-relevant insights. Demonstrating the usefulness of this technique, we consider passing play within association football matches (an unweighted process) and e-money transacted within a mobile money system (a weighted process). Proponents and providers of mobile money care to know how these systems are used—using trajectory extraction we find that 73% of e-money was used for stand-alone tasks and only 21.7% of account holders built up substantial savings at some point during a 6-month period. Coaches of football teams and sports analysts are interested in strategies of play that are advantageous. Trajectory extraction allows us to replicate classic results from sports science on data from the 2018 FIFA World Cup. Moreover, we are able to distinguish teams that consistently exhibited complex, multi-player dynamics of play during the 2017–2018 club season using ball passing trajectories, coincidentally identifying the winners of the five most competitive first-tier domestic leagues in Europe.

Outbreak detection for temporal contact data

Epidemic spreading is a widely studied process due to its importance and possibly grave consequences for society. While the classical context of epidemic spreading refers to pathogens transmitted among humans or animals, it is straightforward to apply similar ideas to the spread of information (e.g., a rumor) or the spread of computer viruses. This paper addresses the question of how to optimally select nodes for monitoring in a network of timestamped contact events between individuals. We consider three optimization objectives: the detection likelihood, the time until detection, and the population that is affected by an outbreak. The optimization approach we use is based on a simple greedy approach and has been proposed in a seminal paper focusing on information spreading and water contamination. We extend this work to the setting of disease spreading and present its application with two example networks: a timestamped network of sexual contacts and a network of animal transports between farms. We apply the optimization procedure to a large set of outbreak scenarios that we generate with a susceptible-infectious-recovered model. We find that simple heuristic methods that select nodes with high degree or many contacts compare well in terms of outbreak detection performance with the (greedily) optimal set of nodes. Furthermore, we observe that nodes optimized on past periods may not be optimal for outbreak detection in future periods. However, seasonal effects may help in determining which past period generalizes well to some future period. Finally, we demonstrate that the detection performance depends on the simulation settings. In general, if we force the simulator to generate larger outbreaks, the detection performance will improve, as larger outbreaks tend to occur in the more connected part of the network where the top monitoring nodes are typically located. A natural progression of this work is to analyze how a representative set of outbreak scenarios can be generated, possibly taking into account more realistic propagation models.

Globally-Optimal Contrast Maximisation for Event Cameras.

Event cameras are bio-inspired sensors that perform well in challenging illumination conditions and have high temporal resolution. However, their concept is fundamentally different from traditional frame-based cameras. The pixels of an event camera operate independently and asynchronously. They measure changes of the logarithmic brightness and return them in the highly discretised form of time-stamped events indicating a relative change of a certain quantity since the last event. New models and algorithms are needed to process this kind of measurements. The present work looks at several motion estimation problems with event cameras. The flow of the events is modelled by a general homographic warping in a space-time volume, and the objective is formulated as a maximisation of contrast within the image of warped events. Our core contribution consists of deriving globally optimal solutions to these generally non-convex problems, which removes the dependency on a good initial guess plaguing existing methods. Our methods rely on branch-and-bound optimisation and employ novel and efficient, recursive upper and lower bounds derived for six different contrast estimation functions. The practical validity of our approach is demonstrated by a successful application to three different event camera motion estimation problems.

Pd-parser: A tool for Matching Photodiode Deflection Events to Time-Stamped Events.

SummaryPrecise synchronization of events displayed on a monitor to recordings of time series data is critical for applications such as vision or psychophysics research. To achieve this, researchers often use a photodiode to convert the luminance on a monitor over time into a voltage time course, which is what is recorded. pd-parser matches photodiode deflection events to time-stamped events; it is particularly useful when the photodiode signal is corrupted or there is drift between the clock of the computer controlling the monitor and the data acquisition computer clock.

Latent Theme Dictionary Model for Finding Co-occurrent Patterns in Process Data.

Process data, which are temporally ordered sequences of categorical observations, are of recent interest due to its increasing abundance and the desire to extract useful information. A process is a collection of time-stamped events of different types, recording how an individual behaves in a given time period. The process data are too complex in terms of size and irregularity for the classical psychometric models to be directly applicable and, consequently, new ways for modeling and analysis are desired. We introduce herein a latent theme dictionary model for processes that identifies co-occurrent event patterns and individuals with similar behavioral patterns. Theoretical properties are established under certain regularity conditions for the likelihood-based estimation and inference. A nonparametric Bayes algorithm using the Markov Chain Monte Carlo method is proposed for computation. Simulation studies show that the proposed approach performs well in a range of situations. The proposed method is applied to an item in the 2012 Programme for International Student Assessment with interpretable findings.

A White Rabbit-Synchronized Accurate Time-Stamping Solution for the Small-Sized Cameras of the Cherenkov Telescope Array

This article presents the Zynq-embedded node for the Cherenkov telescope array (ZEN-CTA) node, a programmable system-on-chip (SoC) with White Rabbit (WR)-synchronization capability. It targets a solution for the uniform clock and trigger time-stamping module of the small-sized telescopes in the CTA. This module is tasked as a distributed acquisition device with a focus on obtaining time stamps for candidate Cherenkov events, which could be generated at potentially high rates from very-high-energy gamma rays and their subsequent distribution over Ethernet. In this context, the customized design of the ZEN-CTA node is examined thoroughly, including its generic implementation aspects and its main functional blocks. The design of the WR-assisted time-to-digital converters (TDCs) for time-stamping analog triggers is presented in detail alongside the implementation of an upgraded high-speed data path (1 Gb/s) for the WR-compatible Ethernet interfaces of the node. The new data path will feature a direct memory access engine for direct software transmissions and a hardware description language (HDL) coprocessor for high-speed forwarding. Next, the time-stamping accuracy of the WR-enhanced TDCs will be characterized alongside the forwarding efficiency of the new data path. Finally, conclusions are drawn, and the main contributions of this research are enumerated, a potential deployment within the CTA infrastructure to support the acquisition of Cherenkov light is considered, and additional use cases are mentioned.

A latent topic model with Markov transition for process data.

We propose a latent topic model with a Markov transition for process data, which consists of time-stamped events recorded in a log file. Such data are becoming more widely available in computer-based educational assessment with complex problem-solving items. The proposed model can be viewed as an extension of the hierarchical Bayesian topic model with a hidden Markov structure to accommodate the underlying evolution of an examinee's latent state. Using topic transition probabilities along with response times enables us to capture examinees' learning trajectories, making clustering/classification more efficient. A forward-backward variational expectation-maximization (FB-VEM) algorithm is developed to tackle the challenging computational problem. Useful theoretical properties are established under certain asymptotic regimes. The proposed method is applied to a complex problem-solving item in the 2012 version of the Programme for International Student Assessment (PISA).

Using cluster analysis to explore students' interactions with automated feedback in an online Earth science task

Digitally delivered tasks can provide students opportunities to interact with disciplinary content while their interactions with the tasks can be recorded as time-stamped log events in the system server. Through post-hoc analysis of log data, we can re-enact and discover patterns in students' activities. This study addresses the online Earth science module where students engaged in writing and revising scientific arguments in a structured format. We adopted natural language processing (NLP) techniques to analyse students' responses, which enabled us to provide immediate feedback to students on their responses and revisions. Cluster analyses were conducted on the action sequences in four argumentation tasks embedded in the module. For each task, the cluster analyses identified two clusters of students who showed different revision patterns with allocation of time on different items. In addition, students in those two clusters also differed in their initial item scores and item score changes after revision.

Математичні методи розпізнавання надзвичайних ситуацій в умовах невизначеності

Показано, що системи розпізнавання надзвичайних подій виявляють різні типи невизначеності: неповні потоки даних, помилки в потоках даних і невідповідні шаблони складних подій. Показано, що потоки подій, що потрапляють на вхід системи розпізнавання складних подій, характеризуються певним ступенем невизначеності. Джерела даних є неоднорідними і характеризуються різною структуризацією даних і відповідними процедурами реагування на пошкоджені блоки даних. Навіть для даних, визначених достатньо точно, система може некоректно моделювати складні події, що призводить до подальшого типу невизначеності. Отже, зазначено, що важливо розглянути методи розпізнавання складних подій, які можна віднести до невизначених. З цією метою було запропоновано відповідні модельні об'єкти. Проведений аналіз ключових моментів побудови систем розпізнавання складних подій, які здатні ефективно працювати в умовах невизначеності, охоплював методи стохастичного моделювання, моделі часового представлення та реляційні моделі. Розглянуто методики, що базуються на абстрактних автоматах, імовірнісних моделях графів, системах логіки першого порядку, мережах Петрі та прихованих мережах Петрі. Зазначено, що проміжним етапом роботи відповідних алгоритмів має бути створення ієрархії складних об'єктів, що не завжди піддаються чіткому визначенню. Виявлено низку обмежень щодо використовуваного синтаксису, моделей і ефективності, які були зіставлені з конкретними варіантами їх реалізації. Запропоновано підхід щодо переходу від детерміністичного математичного апарату до системи розпізнавання складних подій в умовах невизначеності, через введення функції вірогідності події. Розроблена методологія дала змогу виділити напрями досліджень і оцінити продуктивність використовуваних математичних методів.

Time Windowed Data Structures for Graphs

We present data structures that can answer time windowed queries for a set of timestamped events in a relational event graph. We study the relational event graph as input to solve (a) time windowed decision problems for monotone graph properties, such as disconnectedness and bipartiteness, and (b) time windowed reporting problems such as reporting the minimum spanning tree, the minimum time interval, and the graph edit distance for obtaining spanning forests. We also present results of window queries for counting subgraphs of a given pattern, such as paths of length 2 (in general graphs) and paths of length 3 (in bipartite graphs), quadrangles and complete subgraphs of a fixed order or of all orders $\ell \geq 3$ (i.e., cliques of size $\ell$). These query results can be used to compute graph parameters that are important for social network analysis, e.g., clustering coefficients, embeddedness and neighborhood overlapping.