Primitive Events Research Articles

"Visual verbs": Dynamic event types are extracted spontaneously during visual perception.

During visual processing, input that is continuous in space and time is segmented, resulting in the representation of discrete tokens-objects or events. And there has been a great deal of research about how object representations are generalized into types-as when we see an object as an instance of a broader category (e.g., an animal or plant). There has been much less attention, however, to the possibility that vision represents dynamic information in terms of a small number of primitive event types (such as twisting or bouncing). (In models that posit a "language of vision," these would be the foundational visual verbs.) Here we ask whether such event types are extracted spontaneously during visual perception, even when entirely task irrelevant during passive viewing. We exploited the phenomenon of categorical perception-wherein differences are more readily noticed when they are represented in terms of different underlying categories. Observers were better at detecting changes to images or short videos when the changes involved switches in the underlying event type-even when the changes that maintained the same event type were objectively larger (in terms of both brute image metrics and higher level feature change). We observed this categorical "cross-event-type" advantage for visual working memory for twisting versus rotating, scooping versus pouring, and rolling versus bouncing. Moreover, additional control experiments confirmed that such effects could not be explained by appeal to lower-level non-categorical stimulus differences. This spontaneous perception of "visual verbs" might promote both generalization and prediction about how events are likely to unfold. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A global model-agnostic rule-based XAI method based on Parameterized Event Primitives for time series classifiers.

Time series classification is a challenging research area where machine learning and deep learning techniques have shown remarkable performance. However, often, these are seen as black boxes due to their minimal interpretability. On the one hand, there is a plethora of eXplainable AI (XAI) methods designed to elucidate the functioning of models trained on image and tabular data. On the other hand, adapting these methods to explain deep learning-based time series classifiers may not be straightforward due to the temporal nature of time series data. This research proposes a novel global post-hoc explainable method for unearthing the key time steps behind the inferences made by deep learning-based time series classifiers. This novel approach generates a decision tree graph, a specific set of rules, that can be seen as explanations, potentially enhancing interpretability. The methodology involves two major phases: (1) training and evaluating deep-learning-based time series classification models, and (2) extracting parameterized primitive events, such as increasing, decreasing, local max and local min, from each instance of the evaluation set and clustering such events to extract prototypical ones. These prototypical primitive events are then used as input to a decision-tree classifier trained to fit the model predictions of the test set rather than the ground truth data. Experiments were conducted on diverse real-world datasets sourced from the UCR archive, employing metrics such as accuracy, fidelity, robustness, number of nodes, and depth of the extracted rules. The findings indicate that this global post-hoc method can improve the global interpretability of complex time series classification models.

Geospatial complex event processing in smart city applications

The extensive deployment of the Internet of things (IoT) devices in urban areas enables smart city systems (e.g., urban traffic detection and air pollution monitoring) to use these devices to discover the affected regions by various events, particularly complex ones. A complex event is inferred from patterns of different individual primitive events according to their spatio-temporal information. Complex event processing (CEP) systems facilitate applications to define their reasoning rules based on event processing languages (EPL), similar to SQL. However, current languages and CEP engines do not efficiently support spatial data characteristics. Geospatial CEP is challenging since applications have diverse definitions for complex events (e.g., air quality index), IoT devices have heterogeneous specifications/capabilities, and complex events may happen concurrently. This paper proposes the Geo-Tesla language that enables smart city applications to define spatial attributes in complex event definitions and reasoning rules. Then, we devise the GeoT-Rex CEP engine that implements geospatial operations that compiles Geo-Tesla language and processes the spatial data. The integration of Geo-Tesla and GeoT-Rex enables applications to process complex events with spatial characteristics in their reasoning rules and identify boundaries of the detected complex event (event footprint). The evaluation results from the proof of concept implementation show that our proposed solution outperforms in identifying the footprint of complex events up to 44% in the largest network size. They also show that the detection percentage in the concurrent appearance of complex events is 40% more on average than a close state-of-the-art baseline.

Cancer incidence during follow-up in patients with new-onset atrial fibrillation treated with DOACs and its impact on bleeding risk

Abstract Background Cancer is increasingly recognized as strictly related to atrial fibrillation (AF). In patients with AF, the relationship between cancer and cardioembolic or bleeding risk during oral anticoagulant therapy is unknown. Purpose To assess the bleeding and ischaemic burden of a baseline or newly diagnosed cancer in patients treated with direct oral anticoagulants (DOACs) for non-valvular atrial fibrillation (NVAF). Methods All consecutive patients treated with DOACs were enrolled among those with new-onset atrial fibrillation and indication for oral anticoagulant between January 2017 and March 2019. During follow-up, bleeding events, newly diagnosed primitive or metastatic malignancy and major cardiovascular events (MACE) were evaluated. At baseline, CHA2DS2-VASc, HAS-BLED, ATRIA, and ORBIT scores were used to assess the hemorrhagic and ischaemic risk. Major bleedings (MB) were defined according to the ISTH definition. Anemia was defined as haemoglobin levels below 11 g/dL in women and 12 mg/dL in men. Results 1258 patients constituted the study population and followed for a mean time of 21.6±9.5 months. Overall, 66 patients (5.2%) were affected by malignant neoplasia at baseline, whereas 59 (4.7%) were diagnosed with a malignancy during follow-up. Among baseline characteristics, anemia was associated with cancer at enrolment (43.9% vs 22.5%, p<0.001) but not at follow up (29.3% vs 23.4%, p=0.341). MACEs were not associated with cancer at baseline (5.3% vs 5.2%, p=1.0) and at follow up (5% vs 4.9%, p=1.0). No association was observed between major ischaemic events and cancer at enrolment or follow up (5.3% vs 4.4%, p=0.83 and 4.4% vs 5%, p=0.82). Despite no statistically significant differences in haemorrhagic risk at baseline, the overall bleeding events and MB were associated with newly diagnosed cancer (9.2% vs 3.9%, p=0.001 and 13.8% vs 4.5%, p=0.001, respectively) but not at baseline (5.2% vs 5.5%, p=0.82 and 9.2% vs 5.2%, p=0.162). At multivariate analysis adjusted for age, hypertension and renal function, anemia and a newly diagnosed cancer during follow up remained independent predictors of MB (respectively, HR 1.27, 95% CI 1.52–1.06, p=0.009 and HR 3.53, 95% CI 7.71–1.62, p=0.001). Conclusion Bleeding risk assessment is an ongoing challenge in patients with NVAF on DOACs. During follow-up, newly diagnosed primitive or metastatic cancer is a strong predictor of bleeding regardless of baseline haemorrhagic risk assessment. In contrast, such association is not observed with malignancy at baseline. A proper diagnosis and treatment could therefore decrease cancer-related bleeding risk. On the contrary, our study shows that cancer is not an ischaemic risk modifier, either diagnosed at baseline or follow-up. Funding Acknowledgement Type of funding sources: None.

Fatal Mesenteric Ischemia in a Severely Ill COVID-19 Patient: a Case Report

Background: Emerging data suggest a complex role for immuno-thrombosis in the thrombotic complications in COVID-19. We report the case of a fatal mesenteric ischemia to illustrate the mechanisms of endothelial dysfunction in the gut, and the resulting hypercoagulability. Case Report: A 58-year-old woman was admitted in Intensive Care Unit, with initial severe acute respiratory distress syndrome due to SARS-CoV-2 pneumonia. Her respiratory status worsened, with a septic shock thirteen days after symptoms onset. The blood culture was positive with Pseudomonas aeruginosa and Candida. A rapid consecutive deterioration leading to multiorgan failure revealed colic perforation complicated with secondary peritonitis. Histopathological findings revealed small vein occlusions. Surprisingly, no SARS-CoV-2 viral particles could be detected in the bowel. We found intense endothelial dysfunction, contrasting with only mild neutrophil activation. A major increase in von Willebrand factor and resistance to fibrinolysis were the main hemostatic abnormalities. Discussion: This case highlights that COVID-19-associated hypercoagulability can lead to early primitive thrombotic events in small vessels, and adds new evidence concerning a potential role for immuno-thrombosis. Close monitoring of endothelial dysfunction, and further consideration of new targets to prevent thrombosis by adjunctive therapies may thus appear fundamental.

A multiple-pattern complex event matching model based on merge sharing for massive event streams

Quickly matching the related primitive events for multiple complex events from the massive event streams usually faces with a great challenge due to the single-pattern characteristics of the existing complex event matching models. Aiming to solve the problem, a multiple-pattern complex event matching model based on merge sharing is proposed in this paper. The achievement of the paper lies in the fact that a multiple-pattern complex event matching model based on merge sharing is presented to successfully realize the quick matching of related primitive events for multiple complex events from the massive event streams. Specifically, in our scheme, we successfully use merge sharing technology to merge all the same prefixes, suffixes or subpatterns existing in single-pattern matching models into shared ones and to construct a multiple-pattern complex event matching model. As a result, our proposed matching model in this paper can effectively solve the above problem. The experimental results show that our proposed matching model in this paper outperforms the existing single-pattern matching models in model construction and related events matching for massive event streams.

Dempster-Shafer Theory for Modeling and Treating Uncertainty in IoT Applications Based on Complex Event Processing.

The Internet of Things (IoT) has emerged from the proliferation of mobile devices and objects connected, resulting in the acquisition of periodic event flows from different devices and sensors. However, such sensors and devices can be faulty or affected by failures, have poor calibration, and produce inaccurate data and uncertain event flows in IoT applications. A prominent technique for analyzing event flows is Complex Event Processing (CEP). Uncertainty in CEP is usually observed in primitive events (i.e., sensor readings) and rules that derive complex events (i.e., high-level situations). In this paper, we investigate the identification and treatment of uncertainty in CEP-based IoT applications. We propose the DST-CEP, an approach that uses the Dempster–Shafer Theory to treat uncertainties. By using this theory, our solution can combine unreliable sensor data in conflicting situations and detect correct results. DST-CEP has an architectural model for treating uncertainty in events and its propagation to processing rules. We describe a case study using the proposed approach in a multi-sensor fire outbreak detection system. We submit our solution to experiments with a real sensor dataset, and evaluate it using well-known performance metrics. The solution achieves promising results regarding Accuracy, Precision, Recall, F-measure, and ROC Curve, even when combining conflicting sensor readings. DST-CEP demonstrated to be suitable and flexible to deal with uncertainty.

A Machine Learning Approach to EEG-based Prediction of Human Affective States Using Recursive Feature Elimination Method

Emotion recognition, as a branch of affective computing, has attracted great attention in the last decades as it can enable more natural brain-computer interface systems. Electroencephalography (EEG) has proven to be an effective modality for emotion recognition, with which user affective states can be tracked and recorded, especially for primitive emotional events such as arousal and valence. Although brain signals have been shown to correlate with emotional states, the effectiveness of proposed models is somewhat limited. The challenge is improving accuracy, while appropriate extraction of valuable features might be a key to success. This study proposes a framework based on incorporating fractal dimension features and recursive feature elimination approach to enhance the accuracy of EEG-based emotion recognition. The fractal dimension and spectrum-based features to be extracted and used for more accurate emotional state recognition. Recursive Feature Elimination will be used as a feature selection method, whereas the classification of emotions will be performed by the Support Vector Machine (SVM) algorithm. The proposed framework will be tested with a widely used public database, and results are expected to demonstrate higher accuracy and robustness compared to other studies. The contributions of this study are primarily about the improvement of the EEG-based emotion classification accuracy. There is a potential restriction of how generic the results can be as different EEG dataset might yield different results for the same framework. Therefore, experimenting with different EEG dataset and testing alternative feature selection schemes can be very interesting for future work.

A multiple pattern complex event detection scheme based on decomposition and merge sharing for massive event streams

Quickly detecting related primitive events for multiple complex events from massive event stream usually faces with a great challenge due to their single pattern characteristic of the existing complex event detection methods. Aiming to solve the problem, a multiple pattern complex event detection scheme based on decomposition and merge sharing is proposed in this article. The achievement of this article lies that we successfully use decomposition and merge sharing technology to realize the high-efficient detection for multiple complex events from massive event streams. Specially, in our scheme, we first use decomposition sharing technology to decompose pattern expressions into multiple subexpressions, which can provide many sharing opportunities for subexpressions. We then use merge sharing technology to construct a multiple pattern complex events by merging sharing all the same prefix, suffix, or subpattern into one based on the above decomposition results. As a result, our proposed detection method in this article can effectively solve the above problem. The experimental results show that the proposed detection method in this article outperforms some general detection methods in detection model and detection algorithm in multiple pattern complex event detection as a whole.

Fast Retinomorphic Event-Driven Representations for Video Gameplay and Action Recognition

Good temporal representations are crucial for video understanding, and the state-of-the-art video recognition framework is based on two-stream networks. In such framework, besides the regular ConvNets responsible for RGB frame inputs, a second network is introduced to handle the temporal representation, usually the optical flow (OF). However, OF or other task-oriented flow is computationally costly, and is thus typically pre-computed. Critically, this prevents the two-stream approach from being applied to reinforcement learning (RL) applications such as video game playing, where the next state depends on current state and action choices. Inspired by the early vision systems of mammals and insects, we propose a fast event-driven representation (EDR) that models several major properties of early retinal circuits: (1) logarithmic input response, (2) multi-timescale temporal smoothing to filter noise, and (3) bipolar (ON/OFF) pathways for primitive event detection. Trading off the directional information for fast speed ( $>$ 9000 fps), EDR enables fast real-time inference/learning in video applications that require interaction between an agent and the world such as game-playing, virtual robotics, and domain adaptation. In this vein, we use EDR to demonstrate performance improvements over state-of-the-art reinforcement learning algorithms for Atari games, something that has not been possible with pre-computed OF. Moreover, with UCF-101 video action recognition experiments, we show that EDR performs near state-of-the-art in accuracy while achieving a 1,500x speedup in input representation processing, as compared to optical flow.

Complex Event Processing for Sensor Stream Data

As a large amount of stream data are generated through sensors over the Internet of Things environment, studies on complex event processing have been conducted to detect information required by users or specific applications in real time. A complex event is made by combining primitive events through a number of operators. However, the existing complex event-processing methods take a long time because they do not consider similarity and redundancy of operators. In this paper, we propose a new complex event-processing method considering similar and redundant operations for stream data from sensors in real time. In the proposed method, a similar operation in common events is converted into a virtual operator, and redundant operations on the same events are converted into a single operator. The event query tree for complex event detection is reconstructed using the converted operators. Through this method, the cost of comparison and inspection of similar and redundant operations is reduced, thereby decreasing the overall processing cost. To prove the superior performance of the proposed method, its performance is evaluated in comparison with existing methods.

Join query optimization techniques for complex event processing applications

Complex event processing (CEP) is a prominent technology used in many modern applications for monitoring and tracking events of interest in massive data streams. CEP engines inspect real-time information flows and attempt to detect combinations of occurrences matching predefined patterns. This is done by combining basic data items, also called "primitive events", according to a pattern detection plan, in a manner similar to the execution of multi-join queries in traditional data management systems. Despite this similarity, little work has been done on utilizing existing join optimization methods to improve the performance of CEP-based systems. In this paper, we provide the first theoretical and experimental study of the relationship between these two research areas. We formally prove that the CEP Plan Generation problem is equivalent to the Join Query Plan Generation problem for a restricted class of patterns and can be reduced to it for a considerably wider range of classes. This result implies the NP-completeness of the CEP Plan Generation problem. We further show how join query optimization techniques developed over the last decades can be adapted and utilized to provide practically efficient solutions for complex event detection. Our experiments demonstrate the superiority of these techniques over existing strategies for CEP optimization in terms of throughput, latency, and memory consumption.

An Efficient Complex Event Processing Algorithm Based on NFA-HTBTS for Massive RFID Event Stream

This article describes how quickly picking up some valuable information from massive RFID event stream often faces with the problem of long detection time, high memory consumption and low detection efficiency due to its stream characteristics of volume, velocity, variety, value and veracity. Aim to solving the problems above, an efficient complex event processing method based on NFA-HTBTS (Nondeterministic Finite Automaton-Hash Table B+ Tree Structure) is presented in this article. The achievement of this article lies in that we successfully use the union of NFA and HTBTS to realize the detection of complex event in massive RFID event stream. Specially, in our scheme, after using NFA to match related primitive events from massive RFID event stream, we use hash table and B+ tree structure to successfully realize the detection of complex event from large matched results above, as a result, these problems existed in current methods above can be effectively solved by our scheme. The simulation results show that our proposed scheme outperforms some general methods for massive RFID event stream.

Berberine induced modulation of PHLPP2-Akt-MST1 kinase signaling is coupled with mitochondrial impairment and hepatoma cell death

Pleckstrin homology domain leucine-rich repeat protein phosphatase 2 (PHLPP2) has been known to exert tumor suppressive activity for long without much knowledge about its regulation and implications. Protein kinase B (Akt), Protein kinase C (PKC) and Ribosomal protein S6 Kinase (S6K) are known downtargets of PHLPP2, regulating a plethora of life processes viz. cell growth, survival and evasion from apoptosis. Present study decoded the crucial role of PHLPP2 in inducing apoptosis by its interaction with the newly found binding partner Mammalian sterile 20-like kinase 1 (Mst1) in berberine (BBR)-treated human hepatoma cells. HepG2 cells were exposed to (50 μM, 100 μM) berberine for different time intervals (18 h, 24 h). The results showed enhanced expression of PHLPP2 at transcriptional (2.13 fold, P < 0.01) and translational level (4 fold, P < 0.001), but not of PHLPP1, in berberine-treated HepG2 cells. Elevated expression of PHLPP2 was reported to inactivate Akt by dephosphorylating it on Ser473 (P < 0.001). As Akt is known to inhibit apoptotic effect of Mst1, we found that PHLPP2 mediated inactivation of Akt releases its repression from Mst1 leading to heightened phosphorylation of Mst1 on its activating site Thr183 (1.5 fold, P < 0.001). Consequently, coordination between PHLPP2, Akt and Mst1 stimulated downstream targets c-jun N-terminal kinase (JNK), Bim and Bak which are direct activators of pro-apoptotic proteins leading to cell death. Further, PHLPP2/Mst1 knock-down efficiently curtailed anti-proliferative effect of berberine by restoring the basal level of downstream anti-apoptotic proteins. In addition, pre-treatment of NAC (5 mM) showed that ROS generation was a primitive event to initiate activation of stress kinases. Thus, our findings suggest that PHLPP2, Akt and Mst1 constitute an autoinhibitory triangle which may be partly responsible for antiproliferative effect of berberine.

An algebraic complex event processing method for cyber-physical system

Cyber-physical system (CPS) is an orchestration of cyber and physical system, in which enormous events are generated. The emerging technology of complex event processing (CEP) could be utilized to generate composite events which are combined with atomic events through composition operation. In this paper, an algebra of interaction events (AI(E)) is proposed to describe various kinds of interaction events. AI(E) has two binary operators of union “+” and synchronization “·”. Further, the algebra structure (AI(E), + , · , 0, 1) is strictly proved to be a commutative semiring in theory. Additionally, a graphic description method of partial-order tree is introduced to specify the construction of interaction events. Meanwhile, this paper discusses how to detect the significant events from numerous primitive events on the basis of partial-order tree. Finally, a simulation of smart home is executed to verify out method. Simulation result shows that the numbers of events will be decreased in each cycle of CEP by using AI(E).

LongLine: Visual Analytics System for Large-scale Audit Logs

Audit logs are different from other software logs in that they record the most primitive events (i.e., system calls) in modern operating systems. Audit logs contain a detailed trace of an operating system, and thus have received great attention from security experts and system administrators. However, the complexity and size of audit logs, which increase in real time, have hindered analysts from understanding and analyzing them. In this paper, we present a novel visual analytics system, LongLine, which enables interactive visual analyses of large-scale audit logs. LongLine lowers the interpretation barrier of audit logs by employing human-understandable representations (e.g., file paths and commands) instead of abstract indicators of operating systems (e.g., file descriptors) as well as revealing the temporal patterns of the logs in a multi-scale fashion with meaningful granularity of time in mind (e.g., hourly, daily, and weekly). LongLine also streamlines comparative analysis between interesting subsets of logs, which is essential in detecting anomalous behaviors of systems. In addition, LongLine allows analysts to monitor the system state in a streaming fashion, keeping the latency between log creation and visualization less than one minute. Finally, we evaluate our system through a case study and a scenario analysis with security experts.

Towards complex activity recognition using a Bayesian network-based probabilistic generative framework

Complex activity recognition is challenging since a complex activity can be performed in different ways, with each having its own configuration of primitive events and their temporal dependencies. To address such temporal relational variabilities in complex activity recognition, we propose a Bayesian network-based probabilistic generative framework that employs Allen’s interval relation network to represent local temporal dependencies in a generative way. By employing the Chinese restaurant process and introducing relation generation constraints, our framework can characterize these unique internal configurations of a particular complex activity as a joint distribution. Three concrete models are implemented based on our framework. Specifically, in this paper we improve two of our previous models and provide an enhanced model to handle temporal relational variabilities in complex activities more efficiently. Empirical evaluations on three benchmark datasets demonstrate the competitiveness of our framework. In particular, it is shown that our models are rather robust against errors caused by the low-level predictions from raw signals.

An Efficient Complex Event Processing Algorithm based on INFA-HTS for Out-of-order RFID Event Streams

With the aim of solving the problems of long processing times, high memory consumption and low event throughput in the current processing approaches in out-of-order RFID event streams, an efficient complex event processing method based on INFA-HTS (Improved Nondeterministic Finite Automaton-Hash Table Structure) is presented in this paper. The contribution of this paper lies in the fact that we use INFA and HTS to successfully realize the detection of complex events for out-of-order RFID event streams. Specifically, in our scheme, to detect the disorder of out-of-order event streams, we expand the traditional NFA model into a new INFA model to capture the related RFID primitive events from the out-of-order event stream. To high-efficiently manage the large intermediate capturing results, we use the HTS to store and process them. As a result, these problems in the existing methods can be effectively solved by our scheme. The simulation results of our experiments show that our proposed method in this paper outperforms some of the current general processing approaches used to process out-of-order RFID event streams.

A process algebra model of QED

The process algebra approach to quantum mechanics posits a finite, discrete, determinate ontology of primitive events which are generated by processes (in the sense of Whitehead). In this ontology, primitive events serve as elements of an emergent space-time and of emergent fundamental particles and fields. Each process generates a set of primitive elements, using only local information, causally propagated as a discrete wave, forming a causal space termed a causal tapestry. Each causal tapestry forms a discrete and finite sampling of an emergent causal manifold (space-time) M and emergent wave function. Interactions between processes are described by a process algebra which possesses 8 commutative operations (sums and products) together with a non-commutative concatenation operator (transitions). The process algebra possesses a representation via nondeterministic combinatorial games. The process algebra connects to quantum mechanics through the set valued process and configuration space covering maps, which associate each causal tapestry with sets of wave functions over M. Probabilities emerge from interactions between processes. The process algebra model has been shown to reproduce many features of the theory of non-relativistic scalar particles to a high degree of accuracy, without paradox or divergences. This paper extends the approach to a semi-classical form of quantum electrodynamics.

Unsupervised discovery of human activities from long‐time videos

In this study, the authors propose a complete framework based on a hierarchical activity model to understand and recognise activities of daily living in unstructured scenes. At each particular time of a long‐time video, the framework extracts a set of space‐time trajectory features describing the global position of an observed person and the motion of his/her body parts. Human motion information is gathered in a new feature that the authors call perceptual feature chunks (PFCs). The set of PFCs is used to learn, in an unsupervised way, particular regions of the scene (topology) where the important activities occur. Using topologies and PFCs, the video is broken into a set of small events (‘primitive events’) that have a semantic meaning. The sequences of ‘primitive events’ and topologies are used to construct hierarchical models for activities. The proposed approach has been tested with the medical field application to monitor patients suffering from Alzheimer's and dementia. The authors have compared their approach to their previous study and a rule‐based approach. Experimental results show that the framework achieves better performance than existing works and has the potential to be used as a monitoring tool in medical field applications.