Academic literature on the topic 'Temporal Event Sequence'

When people think counterfactually about what might have been different for a sequence of events, they are influenced by the order in which the events occurred. They tend to mentally undo the most recent event in a temporal sequence of two events. But they tend to mentally undo the first event in a causal sequence of four events. We report the results of two experiments that show that the temporal and causal order effects are not dependent on the number of events in the sequence. Our first experiment, with 300 participants, shows that the temporal order effect occurs for sequences with four events as well as for sequences with two events. Our second experiment, with 372 participants, shows that the causal order effect occurs for sequences with two events as well as for sequences with four events. We discuss the results in terms of the mental representations that people construct of temporal and causal sequences.

Recent developments in predictive modeling using marked temporal point processes (MTPPs) have enabled an accurate characterization of several real-world applications involving continuous-time event sequences (CTESs). However, the retrieval problem of such sequences remains largely unaddressed in literature. To tackle this, we propose NEUROSEQRET which learns to retrieve and rank a relevant set of continuous-time event sequences for a given query sequence, from a large corpus of sequences. More specifically, NEUROSEQRET first applies a trainable unwarping function on the query sequence, which makes it comparable with corpus sequences, especially when a relevant query-corpus pair has individually different attributes. Next, it feeds the unwarped query sequence and the corpus sequence into MTPP guided neural relevance models. We develop two variants of the relevance model which offer a tradeoff between accuracy and efficiency. We also propose an optimization framework to learn binary sequence embeddings from the relevance scores, suitable for the locality-sensitive hashing leading to a significant speedup in returning top-K results for a given query sequence. Our experiments with several datasets show the significant accuracy boost of NEUROSEQRET beyond several baselines, as well as the efficacy of our hashing mechanism.

Episodic memories are constructed from sequences of events. When recalling such a memory, we not only recall individual events, but we also retrieve information about how the sequence of events unfolded. Here, we focus on the role of the hippocampal–entorhinal region in processing and remembering sequences of events, which are thought to be stored in relational networks. We summarize evidence that temporal relations are a central organizational principle for memories in the hippocampus. Importantly, we incorporate novel insights from recent studies about the role of the adjacent entorhinal cortex in sequence memory. In rodents, the lateral entorhinal subregion carries temporal information during ongoing behavior. The human homologue is recruited during memory recall where its representations reflect the temporal relationships between events encountered in a sequence. We further introduce the idea that the hippocampal–entorhinal region might enable temporal scaling of sequence representations. Flexible changes of sequence progression speed could underlie the traversal of episodic memories and mental simulations at different paces. In conclusion, we describe how the entorhinal cortex and hippocampus contribute to remembering event sequences—a core component of episodic memory.

Measures of similarity or differences between data objects are applied frequently in geography, biology, computer science, linguistics, logic, business analytics, and statistics, among other fields. This work focuses on event sequence similarity among event sequences extracted from time series observed at spatially deployed monitoring locations with the aim of enhancing the understanding of process similarity over time and geospatial locations. We present a framework for a novel matrix-based spatiotemporal event sequence representation that unifies punctual and interval-based representation of events. This unified representation of spatiotemporal event sequences (STES) supports different event data types and provides support for data mining and sequence classification and clustering. The similarity measure is based on the Jaccard index with temporal order constraints and accommodates different event data types. The approach is demonstrated through simulated data examples and the performance of the similarity measures is evaluated with a k-nearest neighbor algorithm (k-NN) classification test on synthetic datasets. As a case study, we demonstrate the use of these similarity measures in a spatiotemporal analysis of event sequences extracted from space time series of a water quality monitoring system.

AbstractAccessing the temporal position of events (early or late in the event sequence) can influence the generation of predictions about upcoming events. However, it is unclear how the temporal position is processed strategically. To investigate this, we presented event pairs to 23 healthy volunteers manipulating temporal order (chronological, inverse) and temporal position (early, late). Pupil dilation, eye movements, and behavioral data, showed that chronological and early event pairs are processed with more ease than inverse and late event pairs. Indexed by the pupillary response late events and inversely presented event pairs elicited greater cognitive processing demands than early events and chronologically presented event pairs. Regarding eye movements, fixation duration was less sensitive to temporal position than to temporal order. Looking at each item of the event sequence only once was behaviorally more effective than looking multiple times at each event regardless of whether temporal position or temporal order was processed. These results emphasize that accessing temporal position and temporal order information results in dissociable behavioral patterns. While more cognitive resources are necessary for processing late and inverse items, change of information acquisition strategies turns out to be most effective when temporal order processing is required. (JINS, 2012,18, 351–360)

Recently a new type of data source came into the focus of knowledge discovery from temporal data: interval sequences. In contrast to event sequences, interval sequences contain labeled events with a temporal extension. However, existing algorithms for mining patterns from interval sequences proved to be far from satisfying our needs. In brief, we missed an approach that, at the same time, defines support as the number of pattern instances, allows input data that consists of more than one sequence, implements time constraints on a pattern instance, and counts multiple instances of a pattern within one interval sequence. In this paper we propose a new support definition which incorporates these properties. We also describe FSMSet, an algorithm that employs the new support definition, and demonstrate its performance on field data from the automotive business.

This thesis deals with how readers understand the sequences of event and time displacement in the literary works especially short story. Temporal deixis, as one of deixis types, have an important role along with tense to help readers understand the story. This thesis identifies the sequences of event, describes time displacement and sequence event in short story by using temporal deixis and tense, and explains the use of temporal deixis in short stories. The result shows that four selected short stories contain temporal deixis and change of tense. In conclusion, the four selected short stories contain temporal deixis and change of tense to signify time displacement from present to past and vice versa.Keywords: temporal deixis; time displacement; sequences of event; tense; short story

Relational event models are becoming increasingly popular in modeling temporal dynamics of social networks. Due to their nature of combining survival analysis with network model terms, standard methods of assessing model fit are not suitable to determine if the models are specified sufficiently to prevent biased estimates. This paper tackles this problem by presenting a simple procedure for model-based simulations of relational events. Predictions are made based on survival probabilities and can be used to simulate new event sequences. Comparing these simulated event sequences to the original event sequence allows for in depth model comparisons (including parameter as well as model specifications) and testing of whether the model can replicate network characteristics sufficiently to allow for unbiased estimates.

This thesis examines two different approaches to parsing event-based vehicular signal data to produce input to a neural network prediction model: event parsing, where the data is kept unevenly spaced over the temporal domain, and slice parsing, where the data is made to be evenly spaced over the temporal domain instead. The dataset used as a basis for these experiments consists of a number of vehicular signal logs taken at Scania AB. Comparisons between the parsing methods have been made by first training long short-term memory (LSTM) recurrent neural networks (RNN) on each of the parsed datasets and then measuring the output error and resource costs of each such model after having validated them on a number of shared validation sets. The results from these tests clearly show that slice parsing compares favourably to event parsing.
Denna avhandling jämför två olika tillvägagångssätt vad gäller parsningen av händelsebaserad signaldata från fordon för att producera indata till en förutsägelsemodell i form av ett neuronnät, nämligen händelseparsning, där datan förblir ojämnt fördelad över tidsdomänen, och skivparsning, där datan är omgjord till att istället vara jämnt fördelad över tidsdomänen. Det dataset som används för dessa experiment är ett antal signalloggar från fordon som kommer från Scania. Jämförelser mellan parsningsmetoderna gjordes genom att först träna ett lång korttidsminne (LSTM) återkommande neuronnät (RNN) på vardera av de skapade dataseten för att sedan mäta utmatningsfelet och resurskostnader för varje modell efter att de validerats på en delad uppsättning av valideringsdata. Resultaten från dessa tester visar tydligt på att skivparsning står sig väl mot händelseparsning.

This diploma thesis deals with automatic soccer event detection . Its goal is to introduce reader to this issue , discuss possible ways of solution of this task and then implement event detection . This work aims at event recognition using spatio - temporal data of gaming objects . Introduced way of dealing with event detection lies in its converting to sequence labeling task . Then such task is solved using LSTM recurrent neural networks . Lastly , result of sequence labeling is interpreted as detected events . Library for event detection has been created as the output of this work . This library allow user to experiment with different variants how to formulate event detection as sequence labeling task .

The malleability of our subjective perception of time has recently received a great amount of empirical attention with respect to the Temporal Binding of intentional actions to their subsequent effects (e.g. Haggard, Clark & Kalogeras, 2002). Given the number of studies utilizing the venerable Libet clock method, this thesis presents several novel methods for the investigation of the Temporal Binding phenomenon. Firstly, five experiments requiring the numerical estimation of intervals between operant action and subsequent effect, as well as intervals between superficially identical (based on stimulus properties) observed sequences showed that participants judged operant intervals to be shorter than relative observed intervals. This effect existed at intervals far longer than those previously reported (Haggard et al., 2002), whilst demonstrating a pattern of results similar to previous studies involving hypothesized changes of internal clock speeds (e.g. Penton-Voak, Edwards, Percival, & Wearden, 1996). This shortening of the reported interval between cause and effect also occurred when numerical estimation was replaced with the reproduction of the inter-event interval. Having demonstrated a Temporal Binding effect with these novel methods, I then investigate the Causality based explanation of Eagleman & Holcombe (2002), employing keypress timings as an indicator of the perceived timing of the awareness of events. In three experiments, when both conditions involve intentional action, a Binding effect only occurs when this intentional action results in a caused effect. Having thus demonstrated that Causality is an essential pre-requisite of Temporal Binding, two experiments involving the judgment of the length of an object between two classic Michottean launching stimuli show shorter reported lengths for causally related (instantaneous launch) relative to unrelated (delayed launch) trials. I therefore argue that Binding is an online process that influences our perception of the relationship between causal action and effect in both temporal and spatial domains.

De nos jours, afin de répondre aux exigences économiques et sociales, les systèmes de transport ferroviaire ont la nécessité d'être exploités avec un haut niveau de sécurité et de fiabilité. On constate notamment un besoin croissant en termes d'outils de surveillance et d'aide à la maintenance de manière à anticiper les défaillances des composants du matériel roulant ferroviaire. Pour mettre au point de tels outils, les trains commerciaux sont équipés de capteurs intelligents envoyant des informations en temps réel sur l'état de divers sous-systèmes. Ces informations se présentent sous la forme de longues séquences temporelles constituées d'une succession d'événements. Le développement d'outils d'analyse automatique de ces séquences permettra d'identifier des associations significatives entre événements dans un but de prédiction d'événement signant l'apparition de défaillance grave. Cette thèse aborde la problématique de la fouille de séquences temporelles pour la prédiction d'événements rares et s'inscrit dans un contexte global de développement d'outils d'aide à la décision. Nous visons à étudier et développer diverses méthodes pour découvrir les règles d'association entre événements d'une part et à construire des modèles de classification d'autre part. Ces règles et/ou ces classifieurs peuvent ensuite être exploités pour analyser en ligne un flux d'événements entrants dans le but de prédire l'apparition d'événements cibles correspondant à des défaillances. Deux méthodologies sont considérées dans ce travail de thèse: La première est basée sur la recherche des règles d'association, qui est une approche temporelle et une approche à base de reconnaissance de formes. Les principaux défis auxquels est confronté ce travail sont principalement liés à la rareté des événements cibles à prédire, la redondance importante de certains événements et à la présence très fréquente de "bursts". Les résultats obtenus sur des données réelles recueillies par des capteurs embarqués sur une flotte de trains commerciaux permettent de mettre en évidence l'efficacité des approches proposées
In order to meet the mounting social and economic demands, railway operators and manufacturers are striving for a longer availability and a better reliability of railway transportation systems. Commercial trains are being equipped with state-of-the-art onboard intelligent sensors monitoring various subsystems all over the train. These sensors provide real-time flow of data, called floating train data, consisting of georeferenced events, along with their spatial and temporal coordinates. Once ordered with respect to time, these events can be considered as long temporal sequences which can be mined for possible relationships. This has created a neccessity for sequential data mining techniques in order to derive meaningful associations rules or classification models from these data. Once discovered, these rules and models can then be used to perform an on-line analysis of the incoming event stream in order to predict the occurrence of target events, i.e, severe failures that require immediate corrective maintenance actions. The work in this thesis tackles the above mentioned data mining task. We aim to investigate and develop various methodologies to discover association rules and classification models which can help predict rare tilt and traction failures in sequences using past events that are less critical. The investigated techniques constitute two major axes: Association analysis, which is temporal and Classification techniques, which is not temporal. The main challenges confronting the data mining task and increasing its complexity are mainly the rarity of the target events to be predicted in addition to the heavy redundancy of some events and the frequent occurrence of data bursts. The results obtained on real datasets collected from a fleet of trains allows to highlight the effectiveness of the approaches and methodologies used

Les données portant sur des événements abondent dans notre société technologique. Une façon intéressante de présenter des données temporelles réelles pour faciliter leur interprétation est un récit généré automatiquement. La compréhension de récits implique la construction d'un réseau causal par le lecteur. Les systèmes de data-to-text narratifs semblent reconnaître l'importance des relations causales. Cependant, celles-ci jouent un rôle secondaire dans leurs planificateurs de document et leur identification repose principalement sur des connaissances du domaine. Cette thèse propose un modèle d'interprétation assistée de données temporelles par génération de récits structurés à l'aide d'un mélange de règles d'association automatiquement extraites et définies manuellement. Les associations suggèrent des hypothèses au lecteur qui peut ainsi construire plus facilement une représentation causale des événements. Ce modèle devrait être applicable à toutes les données temporelles répétitives, comprenant de préférence des actions ou activités, telles que les données d'activités de la vie quotidienne. Les règles d'association séquentielles sont choisies en fonction des critères de confiance et de signification statistique tels que mesurés dans les données d'entraînement. Les règles d'association basées sur les connaissances du monde et du domaine exploitent la similitude d'un certain aspect d'une paire d'événements ou des patrons causaux difficiles à détecter statistiquement. Pour interpréter une période à résumer déterminée, les paires d'événements pour lesquels une règle d'association s'applique sont associées et certaines associations supplémentaires sont dérivées pour former un réseau associatif. L'étape la plus importante du pipeline de génération automatique de texte (GAT) est la planification du document, comprenant la sélection des événements et la structuration du document. Pour la sélection des événements, le modèle repose sur la confiance des associations séquentielles pour sélectionner les faits les plus inhabituels. L'hypothèse est qu'un événement qui est impliqué par un autre avec une probabilité relativement élevée peut être laissé implicite dans le texte. La structure du récit est appelée le fil associatif ramifié, car il permet au lecteur de suivre les associations du début à la fin du texte. Il prend la forme d'un arbre couvrant sur le sous-réseau associatif précédemment sélectionné. Les associations qu'il contient sont sélectionnées en fonction de préférences de type d'association et de la distance temporelle relative. Le fil associatif ramifié est ensuite segmenté en paragraphes, phrases et syntagmes et les associations sont converties en relations rhétoriques. L'étape de microplanification définit des patrons lexico-syntaxiques décrivant chaque type d'événement. Lorsque deux descriptions d'événement doivent être assemblées dans la même phrase, un marqueur discursif exprimant la relation rhétorique spécifiée est employé. Un événement principal et un événement principal précédent sont déterminés pour chaque phrase. Lorsque le parent de l'événement principal dans le fil associatif n'est pas l'événement principal précédent, un anaphorique est ajouté au marqueur discursif frontal de la phrase. La réalisation de surface peut être effectuée en anglais ou en français grâce à des spécifications lexico-syntaxiques bilingues et à la bibliothèque Java SimpleNLG-EnFr. Les résultats d'une évaluation de la qualité textuelle montrent que les textes sont compréhensibles et les choix lexicaux adéquats.
Data about events abounds in our technological society. An attractive way of presenting real-life temporal data to facilitate its interpretation is an automatically generated narrative. Narrative comprehension involves the construction of a causal network by the reader. Narrative data-to-text systems seem to acknowledge causal relations as important. However, they play a secondary role in their document planners and their identification relies mostly on domain knowledge. This thesis proposes an assisted temporal data interpretation model by narrative generation in which narratives are structured with the help of a mix of automatically mined and manually defined association rules. The associations suggest causal hypotheses to the reader who can thus construct more easily a causal representation of the events. This model should be applicable to any repetitive temporal data, preferably including actions or activities, such as Activity of Daily Living (ADL) data. Sequential association rules are selected based on the criteria of confidence and statistical significance as measured in training data. World and domain knowledge association rules are based on the similarity of some aspect of a pair of events or on causal patterns difficult to detect statistically. To interpret a specific period to summarize, pairs of events for which an association rule applies are associated. Some extra associations are then derived. Together the events and associations form an associative network. The most important step of the Natural Language Generation (NLG) pipeline is document planning, comprising event selection and document structuring. For event selection, the model relies on the confidence of sequential associations to select the most unusual facts. The assumption is that an event that is implied by another one with a relatively high probability may be left implicit in the text. The structure of the narrative is called the connecting associative thread because it allows the reader to follow associations from the beginning to the end of the text. It takes the form of a spanning tree over the previously selected associative sub-network. The associations it contains are selected based on association type preferences and relative temporal distance. The connecting associative thread is then segmented into paragraphs, sentences, and phrases and the associations are translated to rhetorical relations. The microplanning step defines lexico-syntactic templates describing each event type. When two event descriptions need to be assembled in the same sentence, a discourse marker expressing the specified rhetorical relation is employed. A main event and a preceding main event are determined for each sentence. When the associative thread parent of the main event is not the preceding main event, an anaphor is added to the sentence front discourse marker. Surface realization can be performed in English or French thanks to bilingual lexico-syntactic specifications and the SimpleNLG-EnFr Java library. The results of a textual quality evaluation show that the texts are understandable and the lexical choices adequate.

碩士
國立中山大學
資訊管理學系研究所
93
Recent advances in information and networking technologies have contributed significantly to global connectivity and greatly facilitated and fostered information creation, distribution, and access. The resultant ever-increasing volume of online textual documents creates an urgent need for new text mining techniques that can intelligently and automatically extract implicit and potentially useful knowledge from these documents for decision support. This research focuses on identifying and discovering event episodes together with their temporal relationships that occur frequently (referred to as evolution patterns in this study) in sequences of documents. The discovery of such evolution patterns can be applied in such domains as knowledge management and used to facilitate existing document management and retrieval techniques (e.g., event tracking). Specifically, we propose and design an evolution pattern (EP) discovery technique for mining evolution patterns from sequences of documents. We experimentally evaluate our proposed EP technique in the context of facilitating event tracking. Measured by miss and false alarm rates, the evolution-pattern supported event-tracking (EPET) technique exhibits better tracking effectiveness than a traditional event-tracking technique. The encouraging performance of the EPET technique demonstrates the potential usefulness of evolution patterns in supporting event tracking and suggests that the proposed EP technique could effectively discover event episodes and evolution patterns in sequences of documents.

Temporal data mining is concerned with the exploration of large sequential (or temporally ordered) data sets to discover some nontrivial information that was previously unknown to the data owner. Sequential data sets come up naturally in a wide range of application domains, ranging from bioinformatics to manufacturing processes. Pattern discovery refers to a broad class of data mining techniques in which the objective is to unearth hidden patterns or unexpected trends in the data. In general, pattern discovery is about finding all patterns of 'interest' in the data and one popular measure of interestingness for a pattern is its frequency in the data. The problem of frequent pattern discovery is to find all patterns in the data whose frequency exceeds some user-defined threshold. Discovery of temporal patterns that occur frequently in sequential data has received a lot of attention in recent times. Different approaches consider different classes of temporal patterns and propose different algorithms for their efficient discovery from the data. This thesis is concerned with a specific class of temporal patterns called episodes and their discovery in large sequential data sets. In the framework of frequent episode discovery, data (referred to as an event sequence or an event stream) is available as a single long sequence of events. The ith event in the sequence is an ordered pair, (Et,tt), where Et takes values from a finite alphabet (of event types), and U is the time of occurrence of the event. The events in the sequence are ordered according to these times of occurrence. An episode (which is the temporal pattern considered in this framework) is a (typically) short partially ordered sequence of event types. Formally, an episode is a triple, (V,<,9), where V is a collection of nodes, < is a partial order on V and 9 is a map that assigns an event type to each node of the episode. When < is total, the episode is referred to as a serial episode, and when < is trivial (or empty), the episode is referred to as a parallel episode. An episode is said to occur in an event sequence if there are events in the sequence, with event types same as those constituting the episode, and with times of occurrence respecting the partial order in the episode. The frequency of an episode is some measure of how often it occurs in the event sequence. Given a frequency definition for episodes, the task is to discover all episodes whose frequencies exceed some threshold. This is done using a level-wise procedure. In each level, a candidate generation step is used to combine frequent episodes from the previous level to build candidates of the next larger size, and then a frequency counting step makes one pass over the event stream to determine frequencies of all the candidates and thus identify the frequent episodes. Frequency counting is the main computationally intensive step in frequent episode discovery. Choice of frequency definition for episodes has a direct bearing on the efficiency of the counting procedure. In the original framework of frequent episode discovery, episode frequency is defined as the number of fixed-width sliding windows over the data in which the episode occurs at least once. Under this frequency definition, frequency counting of a set of |C| candidate serial episodes of size N has space complexity O(N|C|) and time complexity O(ΔTN|C|) (where ΔT is the difference between the times of occurrence of the last and the first event in the data stream). The other main frequency definition available in the literature, defines episode frequency as the number of minimal occurrences of the episode (where, a minimal occurrence is a window on the time axis containing an occurrence of the episode, such that, no proper sub-window of it contains another occurrence of the episode). The algorithm for obtaining frequencies for a set of |C| episodes needs O(n|C|) time (where n denotes the number of events in the data stream). While this is time-wise better than the the windows-based algorithm, the space needed to locate minimal occurrences of an episode can be very high (and is in fact of the order of length, n, of the event stream). This thesis proposes a new definition for episode frequency, based on the notion of, what is called, non-overlapped occurrences of episodes in the event stream. Two occurrences are said to be non-overlapped if no event corresponding to one occurrence appears in between events corresponding to the other. Frequency of an episode is defined as the maximum possible number of non-overlapped occurrences of the episode in the data. The thesis also presents algorithms for efficient frequent episode discovery under this frequency definition. The space and time complexities for frequency counting of serial episodes are O(|C|) and O(n|C|) respectively (where n denotes the total number of events in the given event sequence and |C| denotes the num-ber of candidate episodes). These are arguably the best possible space and time complexities for the frequency counting step that can be achieved. Also, the fact that the time needed by the non-overlapped occurrences-based algorithm is linear in the number of events, n, in the event sequence (rather than the difference, ΔT, between occurrence times of the first and last events in the data stream, as is the case with the windows-based algorithm), can result in considerable time advantage when the number of time ticks far exceeds the number of events in the event stream. The thesis also presents efficient algorithms for frequent episode discovery under expiry time constraints (according to which, an occurrence of an episode can be counted for its frequency only if the total time span of the occurrence is less than a user-defined threshold). It is shown through simulation experiments that, in terms of actual run-times, frequent episode discovery under the non-overlapped occurrences-based frequency (using the algorithms developed here) is much faster than existing methods. There is also a second frequency measure that is proposed in this thesis, which is based on, what is termed as, non-interleaved occurrences of episodes in the data. This definition counts certain kinds of overlapping occurrences of the episode. The time needed is linear in the number of events, n, in the data sequence, the size, N, of episodes and the number of candidates, |C|. Simulation experiments show that run-time performance under this frequency definition is slightly inferior compared to the non-overlapped occurrences-based frequency, but is still better than the run-times under the windows-based frequency. This thesis also establishes the following interesting property that connects the non-overlapped, the non-interleaved and the minimal occurrences-based frequencies of an episode in the data: the number of minimal occurrences of an episode is bounded below by the maximum number of non-overlapped occurrences of the episode, and is bounded above by the maximum number of non-interleaved occurrences of the episode in the data. Hence, non-interleaved occurrences-based frequency is an efficient alternative to that based on minimal occurrences. In addition to being superior in terms of both time and space complexities compared to all other existing algorithms for frequent episode discovery, the non-overlapped occurrences-based frequency has another very important property. It facilitates a formal connection between discovering frequent serial episodes in data streams and learning or estimating a model for the data generation process in terms of certain kinds of Hidden Markov Models (HMMs). In order to establish this connection, a special class of HMMs, called Episode Generating HMMs (EGHs) are defined. The symbol set for the HMM is chosen to be the alphabet of event types, so that, the output of EGHs can be regarded as event streams in the frequent episode discovery framework. Given a serial episode, α, that occurs in the event stream, a method is proposed to uniquely associate it with an EGH, Λα. Consider two N-node serial episodes, α and β, whose (non-overlapped occurrences-based) frequencies in the given event stream, o, are fα and fβ respectively. Let Λα and Λβ be the EGHs associated with α and β. The main result connecting episodes and EGHs states that, the joint probability of o and the most likely state sequence for Λα is more than the corresponding probability for Λβ, if and only if, fα is greater than fβ. This theoretical connection has some interesting consequences. First of all, since the most frequent serial episode is associated with the EGH having the highest data likelihood, frequent episode discovery can now be interpreted as a generative model learning exercise. More importantly, it is now possible to derive a formal test of significance for serial episodes in the data, that prescribes, for a given size of the test, a minimum frequency for the episode needed in order to declare it as statistically significant. Note that this significance test for serial episodes does not require any separate model estimation (or training). The only quantity required to assess significance of an episode is its non-overlapped occurrences-based frequency (and this is obtained through the usual counting procedure). The significance test also helps to automatically fix the frequency threshold for the frequent episode discovery process, so that it can lead to what may be termed parameterless data mining. In the framework considered so far, the input to frequent episode discovery process is a sequence of instantaneous events. However, in many applications events tend to persist for different periods of time and the durations may carry important information from a data mining perspective. This thesis extends the framework of frequent episodes to incorporate such duration information directly into the definition of episodes, so that, the patterns discovered will now carry this duration information as well. Each event in this generalized framework looks like a triple, (Ei, ti, τi), where Ei, as earlier, is the event type (from some finite alphabet) corresponding to the ith event, and ti and τi denote the start and end times of this event. The new temporal pattern, called the generalized episode, is a quadruple, (V, <, g, d), where V, < and g, as earlier, respectively denote a collection of nodes, a partial order over this collection and a map assigning event types to nodes. The new feature in the generalized episode is d, which is a map from V to 2I, where, I denotes a collection of time interval possibilities for event durations, which is defined by the user. An occurrence of a generalized episode in the event sequence consists of events with both 'correct' event types and 'correct' time durations, appearing in the event sequence in 'correct' time order. All frequency definitions for episodes over instantaneous event streams are applicable for generalized episodes as well. The algorithms for frequent episode discovery also easily extend to the case of generalized episodes. The extra design choice that the user has in this generalized framework, is the set, I, of time interval possibilities. This can be used to orient and focus the frequent episode discovery process to come up with temporal correlations involving only time durations that are of interest. Through extensive simulations the utility and effectiveness of the generalized framework are demonstrated. The new algorithms for frequent episode discovery presented in this thesis are used to develop an application for temporal data mining of some data from car engine manufacturing plants. Engine manufacturing is a heavily automated and complex distributed controlled process with large amounts of faults data logged each day. The goal of temporal data mining here is to unearth some strong time-ordered correlations in the data which can facilitate quick diagnosis of root causes for persistent problems and predict major breakdowns well in advance. This thesis presents an application of the algorithms developed here for such analysis of the faults data. The data consists of time-stamped faults logged in car engine manufacturing plants of General Motors. Each fault is logged using an extensive list of codes (which constitutes the alphabet of event types for frequent episode discovery). Frequent episodes in fault logs represent temporal correlations among faults and these can be used for fault diagnosis in the plant. This thesis describes how the outputs from the frequent episode discovery framework, can be used to help plant engineers interpret the large volumes of faults logged, in an efficient and convenient manner. Such a system, based on the algorithms developed in this thesis, is currently being used in one of the engine manufacturing plants of General Motors. Some examples of the results obtained that were regarded as useful by the plant engineers are also presented.

Historical institutionalism offers original ways of thinking about the origins, evolution, and consequences of political institutions—including, international order. This chapter argues that a “rise and decline” theory of international order based solely on the distribution of power is inadequate. The idea that leading states periodically have found themselves in a position to build or at least shape international order is not in dispute. But the explanation for the variations in the character of orders depends on more than simply the presence of a powerful lead state. Moments of opportunity for order building open up and close. The character of the state that finds itself with the opportunity to build order also matters. Employing insights from historical institutionalism, this chapter directs attention to the temporal dynamics that shape international orders, including the timing and sequence of past events that set the stage for subsequent struggles over political institutions.

Multi-dimensional event sequence clustering applies to many scenarios e.g. e-Commerce and electronic health. Traditional clustering models fail to characterize complex real-world processes due to the strong parametric assumption. While Neural Temporal Point Processes (NTPPs) mainly focus on modeling similar sequences instead of clustering. To fill the gap, we propose Mixture of Neural Temporal Point Processes (NTPP-MIX), a general framework that can utilize many existing NTPPs for multi-dimensional event sequence clustering. In NTPP-MIX, the prior distribution of coefficients for cluster assignment is modeled by a Dirichlet distribution. When the assignment is given, the conditional probability of a sequence is modeled by the mixture of a series of NTPPs. We combine variational EM algorithm and Stochastic Gradient Descent (SGD) to efficiently train the framework. In E-step, we fix parameters for NTPPs and approximate the true posterior with variational distributions. In M-step, we fix variational distributions and use SGD to update parameters of NTPPs. Extensive experimental results on four synthetic datasets and three real-world datasets show the effectiveness of NTPP-MIX against state-of-the-arts.

Video deblurring with event information has attracted considerable attention. To help deblur each frame, existing methods usually compress a specific event sequence into a feature tensor with the same size as the corresponding video. However, this strategy neither considers the pixel-level spatial brightness changes nor the temporal correlation between events at each time step, resulting in insufficient use of spatio-temporal information. To address this issue, we propose a new Spatio-Temporal Relation-Attention network (STRA), for the specific event-based video deblurring. Concretely, to utilize spatial consistency between the frame and event, we model the brightness changes as an extra prior to aware blurring contexts in each frame; to record temporal relationship among different events, we develop a temporal memory block to restore long-range dependencies of event sequences continuously. In this way, the complementary information contained in the events and frames, as well as the correlation of neighboring events, can be fully utilized to recover spatial texture from events constantly. Experiments show that our STRA significantly outperforms several competing methods, e.g., on the HQF dataset, our network achieves up to 1.3 dB in terms of PSNR over the most advanced method. The code is available at https://github.com/Chengzhi-Cao/STRA.

Model-based diagnosis is typically set-oriented. In static systems, such as combinational circuits, a candidate (or diagnosis) is a set of faulty components that explains a set of observations. In discrete-event systems (DESs), a candidate is a set of faulty events occurring in a sequence of state changes that conforms with a sequence of observations. Invariably, a candidate is a set. This set-oriented perspective makes diagnosis of DESs narrow in explainability, owing to the lack of any temporal knowledge relevant to the faults within a candidate, along with the inability to discriminate between single and multiple occurrences of the same fault. Embedding temporal knowledge in a candidate, such as the relative temporal ordering of faults and the multiplicity of the same fault, may be essential for critical decision making. To favor explainability, the notions of temporal fault, explanation, and explainer are introduced in diagnosis of DESs. The explanation engine reacts to a given sequence of observations by generating and refining in real-time a sequence of regular expressions, where the language of each expression is a set of temporal faults. Moreover, to avoid total knowledge compilation, the explainer can be generated incrementally either offline, based on meaningful behavioral scenarios, or online, when being operated in solving specific diagnosis problems.

The coordination and regulation of growth and development in multicellular organisms is dependent, in part, on the controlled short and long-distance transport of signaling molecule: In plants, symplastic communication is provided by trans-wall co-axial membranous tunnels termed plasmodesmata (Pd). Plant viruses spread cell-to-cell by altering Pd. This movement scenario necessitates a targeting mechanism that delivers the virus to a Pd and a transport mechanism to move the virion or viral nucleic acid through the Pd channel. The identity of host proteins with which MP interacts, the mechanism of the targeting of the MP to the Pd and biochemical information on how Pd are alter are questions which have been dealt with during this BARD project. The research objectives of the two labs were to continue their biochemical, cellular and molecular studies of Pd composition and function by employing infectious modified clones of TMV in which MP is fused with GFP. We examined Pd composition, and studied the intra- and intercellular targeting mechanism of MP during the infection cycle. Most of the goals we set for ourselves were met. The Israeli PI and collaborators (Oparka et al., 1999) demonstrated that Pd permeability is under developmental control, that Pd in sink tissues indiscriminately traffic proteins of sizes of up to 50 kDa and that during the sink to source transition there is a substantial decrease in Pd permeability. It was shown that companion cells in source phloem tissue export proteins which traffic in phloem and which unload in sink tissue and move cell to cell. The TAU group employing MP:GFP as a fluorescence probe for optimized the procedure for Pd isolation. At least two proteins kinases found to be associated with Pd isolated from source leaves of N. benthamiana, one being a calcium dependent protein kinase. A number of proteins were microsequenced and identified. Polyclonal antibodies were generated against proteins in a purified Pd fraction. A T-7 phage display library was created and used to "biopan" for Pd genes using these antibodies. Selected isolates are being sequenced. The TAU group also examined whether the subcellular targeting of MP:GFP was dependent on processes that occurred only in the presence of the virus or whether targeting was a property indigenous to MP. Mutant non-functional movement proteins were also employed to study partial reactions. Subcellular targeting and movement were shown to be properties indigenous to MP and that these processes do not require other viral elements. The data also suggest post-translational modification of MP is required before the MP can move cell to cell. The USA group monitored the development of the infection and local movement of TMV in N. benthamiana, using viral constructs expressing GFP either fused to the MP of TMV or expressing GFP as a free protein. The fusion protein and/or the free GFP were expressed from either the movement protein subgenomic promoter or from the subgenomic promoter of the coat protein. Observations supported the hypothesis that expression from the cp sgp is regulated differently than expression from the mp sgp (Szecsi et al., 1999). Using immunocytochemistry and electron microscopy, it was determined that paired wall-appressed bodies behind the leading edge of the fluorescent ring induced by TMV-(mp)-MP:GFP contain MP:GFP and the viral replicase. These data suggest that viral spread may be a consequence of the replication process. Observation point out that expression of proteins from the mp sgp is temporary regulated, and degradation of the proteins occurs rapidly or more slowly, depending on protein stability. It is suggested that the MP contains an external degradation signal that contributes to rapid degradation of the protein even if expressed from the constitutive cp sgp. Experiments conducted to determine whether the degradation of GFP and MP:GFP was regulated at the protein or RNA level, indicated that regulation was at the protein level. RNA accumulation in infected protoplast was not always in correlation with protein accumulation, indicating that other mechanisms together with RNA production determine the final intensity and stability of the fluorescent proteins.