State Transition Events Research Articles

Source Identification of Asymptomatic Spread on Networks

Identifying the sources of spreading dynamics on networks has drawn extensive attention in recent years, where a variety of epidemic models have been adopted to simulate the propagation dynamics among network nodes, such as the Susceptible-Infectious (SI) model. The objective is to identify the most possible source of infection based on the observed state transition events (e.g., from susceptible to infectious) on a small set of monitored nodes. Most existing studies assumed that once a monitored node is infected, it will be immediately observed. However, in reality, it is likely that for many infectious diseases, a newly infected person becomes infectious without showing any disease symptoms. In this case, the state transition cannot be observed until symptoms arise after a time period (i.e., the incubation period). Accordingly, in this paper, we focus on investigating the source identification problem of asymptomatic spread on complex networks by monitoring only a small number of network nodes. Specifically, we adopt a continuous-time SI model with a contagious incubation period to simulate the asymptomatic spread on networks, where the length of the incubation period is assumed to be an independent and identically distributed exponential distribution. In doing so, we formulate the source identification problem as a likelihood maximization problem and solve it using the Monte Carlo approximation and importance sampling. Finally, we validate the performance of our method on both synthetic and real-world networks with different experimental settings. The results show that our method can achieve higher identification accuracy than several benchmark methods.

Sequential Decision Making With Limited Observation Capability: Application to Wireless Networks

This paper studies a generalized class of restless multi-armed bandits with hidden states and allow cumulative feedback, as opposed to the conventional instantaneous feedback. We call them lazy restless bandits (LRBs) as the events of decision making are sparser than the events of state transition. Hence, feedback after each decision event is the cumulative effect of the following state transition events. The states of arms are hidden from the decision maker and rewards for actions are state dependent. The decision maker needs to choose one arm in each decision interval, such that the long-term cumulative reward is maximized. As the states are hidden, the decision maker maintains and updates its belief about them. It is shown that LRBs admit an optimal policy which has threshold structure in belief space. The Whittle-index policy for solving the LRB problem is analyzed; indexability of LRBs is shown. Further, the closed-form index expressions are provided for two sets of special cases; for more general cases, an algorithm for index computation is provided. An extensive simulation study is presented; Whittle-index, modified Whittle-index, and myopic policies are compared. The Lagrangian relaxation of the problem provides an upper bound on the optimal value function; it is used to assess the degree of sub-optimality various policies.

Low-Power (1T1N) Skyrmionic Synapses for Spiking Neuromorphic Systems

In this paper, we propose a “1-transistor 1-nanotrack” (1T1N) synapse based on the movement of magnetic skyrmions using spin-polarized current pulses. The proposed synaptic bit-cell has four terminals and fully decoupled spike transmission and programming paths. With careful tuning of programming parameters, we ensure multi-level non-volatile conductance evolution in the proposed skyrmionic synapse. Through micromagnetic simulations, we studied in detail the impact of the programming conditions (current density and pulse width) on synaptic performance parameters, such as the number of conductance levels and energy per transition. The programming parameters used for all further analysis gave rise to a synapse with 7 distinct conductance states and 1.2 fJ per conductance state transition event. Exploiting bidirectional conductance modulation, the 1T1N synapse is able to undergo long-term potentiation and depression according to a simplified variant of the biological spike timing-dependent plasticity rule. We present a subthreshold CMOS spike generator circuit which when coupled with a well-known subthreshold integrator circuit produces custom pre- and post-neuronal spike shapes responsible for implementing unsupervised learning with the proposed 1T1N synaptic bit-cell and consuming ~0.25 pJ/event. A spiking neural network incorporating the characteristics of the 1T1N synapse was simulated for two separate applications: pattern extraction from noisy video streams and handwritten digit classification.

Modeling the Epigenetic Landscape in Plant Development.

Computational mechanistic models enable a systems-level understanding of plant development by integrating available molecular experimental data and simulating their collective dynamical behavior. Boolean gene regulatory network dynamical models have been extensively used as a qualitative modeling framework for such purpose. More recently, network modeling protocols have been extended to model the epigenetic landscape associated with gene regulatory networks. In addition to understanding the concerted action of interconnected genes, epigenetic landscape models aim to uncover the patterns of cell state transition events that emerge under diverse genetic and environmental background conditions. In this chapter we present simple protocols that naturally extend gene regulatory network modeling and demonstrate their use in modeling plant developmental processes under the epigenetic landscape framework. We focus on conceptual clarity and practical implementation, providing directions to the corresponding technical literature. The protocols presented here can be applied to any well-characterized gene regulatory network in plants, animals, or human disease.

Mining task post-conditions: Automating the acquisition of process semantics

Semantic annotation of business process model in the business process designs has been addressed in a large and growing body of work, but these annotations can be difficult and expensive to acquire. This paper presents a data-driven approach to mining and validating these annotations (and specifically context-independent semantic annotations). We leverage event objects in process execution histories which describe both activity execution events (typically represented as process events) and state update events (represented as object state transition events). We present an empirical evaluation, which suggests that the approach provides generally reliable results.

Inference of spatiotemporal effects on cellular state transitions from time-lapse microscopy.

BackgroundTime-lapse microscopy allows to monitor cell state transitions in a spatiotemporal context. Combined with single cell tracking and appropriate cell state markers, transition events can be observed within the genealogical relationship of a proliferating population. However, to infer the correlations between the spatiotemporal context and cell state transitions, statistical analysis with an appropriately large number of samples is required.ResultsHere, we present a method to infer spatiotemporal features predictive of the state transition events observed in time-lapse microscopy data. We first formulate a generative model, simulate different scenarios, such as time-dependent or local cell density-dependent transitions, and illustrate how to estimate univariate transition rates. Second, we formulate the problem in a machine-learning language using regularized linear models. This allows for a multivariate analysis and to disentangle indirect dependencies via feature selection. We find that our method can accurately recover the relevant features and reconstruct the underlying interaction kernels if a critical number of samples is available. Finally, we explicitly use the tree structure of the data to validate if the estimated model is sufficient to explain correlated transition events of sister cells.ConclusionsUsing synthetic cellular genealogies, we prove that our method is able to correctly identify features predictive of state transitions and we moreover validate the chosen model. Our approach allows to estimate the number of cellular genealogies required for the proposed spatiotemporal statistical analysis, and we thus provide an important tool for the experimental design of challenging single cell time-lapse microscopy assays.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-015-0208-5) contains supplementary material, which is available to authorized users.

Improving business process intelligence by observing object state transitions

During the execution of business processes several events happen that are recorded in the company's information systems. These events deliver insights into process executions so that process monitoring and analysis can be performed resulting, for instance, in prediction of upcoming process steps or the analysis of the run time of single steps. While event capturing is trivial when a process engine with integrated logging capabilities is used, manual process execution environments do not provide automatic logging of events, so that typically external devices, like bar code scanners, have to be used. As experience shows, these manual steps are error-prone and induce additional work. Therefore, we use object state transitions as additional monitoring information, so-called object state transition events. Based on these object state transition events, we reason about the enablement and termination of activities and provide the basis for process monitoring and analysis in terms of a large event log. In this paper, we present the concept to utilize information from these object state transition events for capturing process progress. Furthermore, we discuss a methodology to create the required design time artifacts that then are used for monitoring at run time. In a proof-of-concept implementation, we show how the design time and run time side work and prove applicability of the introduced concept of object state transition events.

MO2 - A Comparison Of State Transition And Discrete Event Modeling Approaches For Antiplatelet Use In The Secondary Prevention Of Thrombotic Events After Myocardial Infarction (MI)

MO2 - A Comparison Of State Transition And Discrete Event Modeling Approaches For Antiplatelet Use In The Secondary Prevention Of Thrombotic Events After Myocardial Infarction (MI)

Direct optimization of inference model for human activity and posture class recognition

The demand for ambulatory monitoring is rising due to the widespread adoption of home-based healthcare models for the increasing elderly population. Since monitoring patients makes it possible to provide remote medical services based on real-world contexts, context-aware technology is regarded as a crucial element in core module development for ubiquitous healthcare solutions. This paper describes the design and implementation of a class label tagging system for human activity recognition based on sensor data from a single tri-axial accelerometer attached to the waist of a human subject. Our human activity and posture classifier (APC) was designed to model more effectively an event that takes place over a period of time. Consequently, the APC problem becomes the process of tagging class labels onto sequential data from a single tri-axial accelerometer. This sequential tagging naturally led to our hypothesis that a linear combination of the classification model and the prior, approximated by observing a state transition event of human activity and posture (AP), may decrease the error rate of the system. Therefore, this paper aims at testing the aforementioned experimental hypothesis while explicating our method of direct optimization, also known as direct loss minimization. Our experimental hypothesis is based on the view that the task of labeling a series of class tags on a data object of the AP event should be supported by a statistical model capable of robustly capturing the probabilistic context influencing the generation of a set of features at a given discrete time. A radial basis function network algorithm was used for parameter estimation of a class likelihood inference model augmented by training a state transition model to capture the sequential nature of a posture or activity generation event. The linear combination requires our design step to have a further optimization, in addition to learning the parameters of each inference model, of the model parameters that represent the relative contribution to the decision process. Previous studies have elaborated on an effective approach to direct optimization of model parameters when an appropriate evaluation metric exists for a given problem domain. In our case, f-measure was used as an objective function of the direct optimization. We used local beam search to optimize directly the weight vector for the likelihood and the prior. Our method is tested on the set of nine-dimensional feature vectors contained in 69,376 lines of sequential AP data. The experiment shows that embedding prior probability into the recognition process decreases the error rate in a set of categories. Several experiments proved the efficiency of our proposed method and the usefulness of the system.

176 Model-based diagnosis - state transition events and constraints equations

176 Model-based diagnosis - state transition events and constraints equations

Model-Based Diagnosis - State Transition Events and Constraint Equations

Two different approaches to model-based on-line diagnosis are compared. The DMP method is based on quantitative constraint equations. MIDAS is based on signed directed graphs that are translated into an event graph consisting of the possible state transition events of the process and the failures that they are symptoms of. The methods, which both have been implemented in G2, have been applied to a real-time simulation model of a sterilization process.

Model-based diagnosis-state transition events and constraint equations

Two different approaches to modelbased on-line diagnosis are compared. The DMP method is based on quantitative constraint equations. MIDAS is based on signed directed graphs that are translated into an event graph consisting of the possible state transition events of the process and the failures that they are symptoms of. The methods, which both have been implemented in G2, have been applied to a real-time simulation model of a sterilization process.