Transition Graph Research Articles

Structuring Mobility Transition With an Adaptive Graph Representation

Modeling human mobility is a critical task in fields such as urban planning, ecology, and epidemiology. Given the current use of mobile phones, there is an abundance of data that can be used to create models of high reliability. Existing techniques can reveal the macropatterns of crowd movement or analyze the trajectory of a person; however, they typically focus on geographical characteristics. This paper presents a graph-based approach for structuring crowd mobility transition over multiple granularities in the context of social behavior. The key to our approach is an adaptive data representation, the adaptive mobility transition graph (AMTG), which is globally generated from citywide human mobility data by defining the temporal trends of human mobility and the interleaved transitions between different mobility patterns. We describe the design, creation, and manipulation of the AMTG and introduce a visual analysis system that supports the multifaceted exploration of citywide human mobility patterns.

BASIC PRINCIPLES OF BUILDING BLACK-BOX VULNERABILITY SCANNER OF WEB RESOURCE

The article discusses the main provisions for constructing a vulnerability scanner for a web resource based on the «black box» technique. The definitions of the Mealy automaton are introduced, applicable to the web application. An example of the use of the Mealy automaton for constructing transition graphs on the links of a web resource is given. An algorithm for constructing a graph based on sent requests and received answers is proposed. Describe the alleged problems that arise in the scanner. There are proposed ways of solving the problems obtained with the use of additional mechanisms and work algorithms. When the algorithm is running and transitions are added, the new vertex is colored with one of the colors, if a repeated state is detected. As a result, we have a colored graph, which may collapse in the same colors. The user in the browser moving inside the web application performs sequential actions, the scanning technique is as close as possible to the real actions of the average user and provides a top-down view of the content, as well as the probability of clicking on the link. Thus determined group of sequential requests that change the state of the scanner.

Dynamics of complex feedback architectures in metabolic pathways

Cellular metabolism contains intricate arrays of feedback control loops. These are a key mechanism by which cells adapt and survive environmental disturbances. Gene regulation, in particular, typically displays complex architectures that combine positive and negative feedback loops between metabolites and enzymatic genes. Yet because of strong nonlinearities and high-dimensionality, it is challenging to determine the closed-loop dynamics of a given feedback architecture. Here we present a novel technique for the analysis of metabolic pathways under gene regulation. Our theory blends ideas from timescale separation and piecewise affine dynamical systems, applied to a wide class of unbranched metabolic pathways under steep nonlinear feedback. We propose a systematic method to construct a state transition graph for a given regulatory architecture, from where candidate closed-loop dynamics can be singled out for further analysis. The method recasts a high-dimensional nonlinear system into a piecewise affine system defined on a polytopic partition of the state space. In its most general setup, our theory allows to characterize the dynamics of pathways of arbitrary length, with any number of regulators, and under any combination of positive and negative feedback loops. We illustrate our results on an exemplar system that displays a stable limit cycle and bistable dynamics. We also discuss the implications of our results for the design of gene circuits in synthetic biology and metabolic engineering.

Development of automated control systems of sluice gate at mine of second Solikamsky potash-mine department

The article analyzes the statistics of accidents at workplaces of PJSC Uralkali. The mine of the second Solikamsk potash-mine department is the object of the research. Analysis of accidents revealed that fatalities were the results of pinching down by the sluice gate. The devices of the gateway door drive and its electric drive control post have been studied. In order to eliminate accidents related to injuries and deaths of people at the sluice gate, an improved ladder diagram has been developed and recommendations for the installation of additional equipment have been worked out. To describe the graph of sluice gate operation, the necessary logical elements are introduced. The graph of states and transitions of cargo gate control is shown. The use of software and hardware based on the OMRON microcontroller is proposed with the development of a ladder diagram for automation of the sluice gate control system. The safety of miners when working in the mine will be improved as a result of its introduction. It is shown that the failure time in the developed ladder diagram to control the sluice gate motor will be significantly less than in the previously existing scheme.

Boolean dynamics revisited through feedback interconnections

Boolean models of physical or biological systems describe the global dynamics of the system and their attractors typically represent asymptotic behaviors. In the case of large networks composed of several modules, it may be difficult to identify all the attractors. To explore Boolean dynamics from a novel viewpoint, we will analyse the dynamics emerging from the composition of two known Boolean modules. The state transition graphs and attractors for each of the modules can be combined to construct a new asymptotic graph which will (1) provide a reliable method for attractor computation with partial information; (2) illustrate the differences in dynamical behavior induced by the updating strategy (asynchronous, synchronous, or mixed); and (3) show the inherited organization/structure of the original network’s state transition graph.

Mission Analysis and Operational Optimization of Adaptive Cycle Microturbofan Engine in Surveillance and Firefighting Scenarios

The current work focuses on mission-based evaluation of a novel engine architecture arising from the conversion of a microturbojet to a microturbofan via introduction of a variable speed fan and bypass nozzle. The solution significantly improves maximum thrust by 260%, reduces fuel consumption by as much as 60% through maintaining the core independently running at its optimum, and enables a wider operational range, all the meanwhile preserving a simple single spool configuration. Particularly, the introduction of a variable-speed fan enables real-time optimization for both high-speed cruise and low-speed loitering. In order to characterize the performance of the adaptive cycle engine with increased number of controls (engine speed, gear ratio, bypass opening), a component map-based thermodynamic study is used to contrast it against other similar propulsion systems with incrementally reduced input variables. In the following, a shortest path-based optimization is conducted over the locally minimum fuel consumption operating points, based on a set of gradient driven connectivity constraints for changes in gear ratio and bypass nozzle area. The resultant state transition graphs provide global optimum for fuel consumption at a thrust range in a given altitude and Mach flight envelope. Then, the engine model is coupled to a flight mechanics solver supplied with a conceptual design for a representative multipurpose unmanned aerial vehicle (UAV). Finally, the associated mission benefits are demonstrated in surveillance and firefighting scenarios.

Combinatorial representations for the scheme of allocations of distinguishable particles into indistinguishable cells

Abstract For a scheme of allocation of distinguishable particles into indistinguishable cells we describe types of representation, numbering and enumerating of its outcomes in terms of the transition graph; this graph allows, in particular, to find the distribution on the set of outcomes. Several methods of statistical simulation of scheme outcomes are described.

Synthesis of Σ-Automata Specified in the First Order Logical Languages LP and LF

This paper presents methods for synthesizing Σ-automata from specifications in the language LP with deterministic semantics and in the language LF with nondeterministic semantics. These methods are based on the equivalent transformation of a formula of the form ∀tF (t) into the so-called normal form whose structure corresponds to the state transition graph of a specified Σ-automaton.

Static window transition graphs for Android

Static window transition graphs for Android

Analysis Tools for Interconnected Boolean Networks With Biological Applications.

Boolean networks with asynchronous updates are a class of logical models particularly well adapted to describe the dynamics of biological networks with uncertain measures. The state space of these models can be described by an asynchronous state transition graph, which represents all the possible exits from every single state, and gives a global image of all the possible trajectories of the system. In addition, the asynchronous state transition graph can be associated with an absorbing Markov chain, further providing a semi-quantitative framework where it becomes possible to compute probabilities for the different trajectories. For large networks, however, such direct analyses become computationally untractable, given the exponential dimension of the graph. Exploiting the general modularity of biological systems, we have introduced the novel concept of asymptotic graph, computed as an interconnection of several asynchronous transition graphs and recovering all asymptotic behaviors of a large interconnected system from the behavior of its smaller modules. From a modeling point of view, the interconnection of networks is very useful to address for instance the interplay between known biological modules and to test different hypotheses on the nature of their mutual regulatory links. This paper develops two new features of this general methodology: a quantitative dimension is added to the asymptotic graph, through the computation of relative probabilities for each final attractor and a companion cross-graph is introduced to complement the method on a theoretical point of view.

DSGRN: Examining the Dynamics of Families of Logical Models.

We present a computational tool DSGRN for exploring the dynamics of a network by computing summaries of the dynamics of switching models compatible with the network across all parameters. The network can arise directly from a biological problem, or indirectly as the interaction graph of a Boolean model. This tool computes a finite decomposition of parameter space such that for each region, the state transition graph that describes the coarse dynamical behavior of a network is the same. Each of these parameter regions corresponds to a different logical description of the network dynamics. The comparison of dynamics across parameters with experimental data allows the rejection of parameter regimes or entire networks as viable models for representing the underlying regulatory mechanisms. This in turn allows a search through the space of perturbations of a given network for networks that robustly fit the data. These are the first steps toward discovering a network that optimally matches the observed dynamics by searching through the space of networks.

Clustering Wi-Fi fingerprints for indoor–outdoor detection

This paper presents a method for continuous indoor-outdoor environment detection on mobile devices based solely on WiFi fingerprints. Detection of indoor outdoor switching is an important part of identifying a user's context, and it provides important information for upper layer context aware mobile applications such as recommender systems, navigation tools, etc. Moreover, future indoor positioning systems are likely to use Wi-Fi fingerprints, and therefore Wi-Fi receivers will be on most of the time. In contrast to existing research, we believe that these fingerprints should be leveraged, and they serve as the basis of the proposed method. Using various machine learning algorithms, we train a supervised classifier based on features extracted from the raw fingerprints, clusters, and cluster transition graph. The contribution of each of the features to the method is assessed. Our method assumes no prior knowledge of the environment, and a training set consisting of the data collected for just a few hours on a single device is sufficient in order to provide indoor-outdoor classification, even in an unknown location or when using new devices. We evaluate our method in an experiment involving 12 participants during their daily routine, with a total of 828 hours' worth of data collected by the participants. We report a predictive performance of the AUC (area under the curve) of 0.94 using the gradient boosting machine ensemble learning method. We show that our method can be used for other context detection tasks such as learning and recognizing a given building or room.

Effective search strategy via internal state transition graphs on onboard planning for deep space probes

As to support the mission of Mars exploration in China, automated onboard planning is required to enhance the security and robustness of deep space probes. Onboard planning here is a term that defines a complex set of activities or states aiming at deciding the daily tasks on a probe and at figuring out if mission goals are met. Deep space onboard planning requires modeling of complex operation constraints and focusing on intricate state transitions of involved subsystems. Also, devices of various operation modes and multiple functionalities, which are ubiquitous in physical systems, are intractable in onboard planning and have not been effectively handled. To cope with these difficulties, we introduce an approach of knowledge representation that explicitly establishes the mentioned features. The key techniques we build on are the notion of timeline-based planning tasks and heuristic estimate method designed on internal state transition graphs. Furthermore, state transitions have provided crucial information for search guidance, and a search algorithm joint with internal state transition graph heuristic method is proposed to avoid redundant work. Finally, we run comprehensive experiments on selected domains, and our techniques present an excellent performance compared to the algorithm in Europa2.

Observability of Boolean Control Networks Using Parallel Extension and Set Reachability.

This brief reviews various definitions of observability for Boolean control networks (BCNs) and proposes a new one: output-feedback observability. This new definition applies to all BCNs whose initial states can be identified from the history of output measurements. A technique called parallel extension is then proposed to facilitate observability analysis. Furthermore, a technique called state transition graph reconstruction is proposed for analyzing the set reachability of BCNs, based on which new criteria for observability, single-input sequence observability, and arbitrary-input observability, are obtained. Using the proposed techniques, this brief proves that the problem of output-feedback observability can be recast as that of stabilizing a logic dynamical system with output feedback. Then, a necessary and sufficient condition for static output feedback observability is proposed. The relationships between the different definitions of observability are discussed, and the main results are illustrated with examples.

High-Performance Architecture for Binary-Tree-Based Finite State Machines

A binary-tree-based finite state machine (BT-FSM) is a state machine with a 1-bit input signal whose state transition graph is a binary tree. BT-FSMs are useful in those application areas where searching in a binary tree is required, such as computer networks, compression, automatic control, or cryptography. This paper presents a new architecture for implementing BT-FSMs which is based on the model finite virtual state machine (FVSM). The proposed architecture has been compared with the general FVSM and conventional approaches by using both synthetic test benches and very large BT-FSMs obtained from a real application. In synthetic test benches, the average speed improvement of the proposed architecture respect to the best results of the other approaches achieves 41% (there are some cases in which the speed is more than double). In the case of the real application, the average speed improvement achieves 155%.

Enhancing Understanding through the Use of Structured Representations

Mathematical representations are an essential tool in the study of mathematics and problem solving. They are also used in word problems to facilitate the transformation from textual to symbolic information. We proposed a stepwise, blocked, structured state transition graph (STG) based on the principles of instructional message design. In this study, we adopted a posttest-only non-equivalent group design to compare the performance of students who used either STG or matrix-like tables to learn to solve word problems via transition matrices. We also took into account the student’s previous learning achievements in mathematics. The participants included four classes of senior students in a vocational high school, with two classes randomly designated as the experiment (STG) group and two designated as the control (Table) group. High-achieving students taught using STG outperformed their counterparts who were taught using matrix-like tables. The performance of low-achieving students appeared to be unaffected by the instructional method. These findings suggest that STG provides a clear representation of the relationships used in matrix calculation, which makes it easier to select and organize information. Nonetheless, alternative methods will be required to improve the performance of low-achieving students.

Analyzing the Fine-Scale Dynamics of Two Dominant Species in a Polytrichum–Myrtillus Pine Forest. I. A Homogeneous Markov Chain and Cyclicity Indices

Using long-term direct observations in a Polytrichum-Myrtillus pine forest, we have constructed and verified a homogeneous Markov chain model for two dominant species (Vaccinium myrtillus and V. vitis-idaed) at the late stages of succession. The sampling design features a large sample size (2000 quadrats) on permanent transects, several re-examinations with the interval of 5 years, and the use of species rooted frequency. As a model of the process under concern, the discrete Markov chain accounts for the following four states: both species being absent on the quadrat, one of them being present alone, and the joint presence of the both; the model time step coincides with the time interval between observations. The model is calibrated on the data of two successive examinations and verified on that of one more examination. All possible transitions between the states are revealed to realize in quadrats for one time interval, as well as the absence of transitions at each state, which results in the complete digraph (directed graph) of transitions. Major model results are obtained by the formulae of finite Markov chain theory: the steady-state square distribution, cyclicity characteristics, and the mean durations of stages in the fine-scale dynamics. As a steady-state (stable) outcome of succession, the distribution among quadrats is expected where 30% of quadrats are occupied by V. myrtillus alone, 11% by V. vitis-idaea alone, both species are present on 18% of quadrats, and 41% of quadrats are 'empty'. This demonstrates a possibility for V. myrtillus and V. vitis-idaea to coexist stably at the latest stages of succession, with the clear predominance of V. myrtillus, yet without competitive exclusion. The quantitative characteristics of cyclicity and the durations of stages in the fine-scale dynamics enable us to estimate the total duration of secondary post-fire succession as about 45 years (to reach a distribution of states that differs less than 5% from the steady-state one). Out of the four states specified, the quadrats with V. vitis-idaea alone persist for the least time (8 years) on the average, while 'empty' ones persist for the greatest time (18 years). Forecasting the dynamics for one model time step forward and comparing the forecast with the real square distribution have revealed the measure of difference to be 5.4%. This illustrates the efficiency of the (time-)homogeneous Markov chain as a short-term forecast tool, yet leaves open the question whether the homogeneity hypothesis be true in the longer term.

Constructing Temporally Extended Actions through Incremental Community Detection.

Hierarchical reinforcement learning works on temporally extended actions or skills to facilitate learning. How to automatically form such abstraction is challenging, and many efforts tackle this issue in the options framework. While various approaches exist to construct options from different perspectives, few of them concentrate on options' adaptability during learning. This paper presents an algorithm to create options and enhance their quality online. Both aspects operate on detected communities of the learning environment's state transition graph. We first construct options from initial samples as the basis of online learning. Then a rule-based community revision algorithm is proposed to update graph partitions, based on which existing options can be continuously tuned. Experimental results in two problems indicate that options from initial samples may perform poorly in more complex environments, and our presented strategy can effectively improve options and get better results compared with flat reinforcement learning.

State-Based Encoding of Large Asynchronous Controllers

State encoding is one of the fundamental problems in the synthesis of asynchronous controllers. The requirement for a correct hazard-free implementation imposes severe constraints on the way encoding signals can be inserted in the specification of a controller. Even though some specification formalisms, such as burst-mode machines or signal transition graphs, enable the specification of behaviors at the event level, the state encoding methods that provide the best good-quality solutions work at the state level. This imposes a severe limitation on the size of the controllers that can be handled by these methods. This paper proposes a method to solve the encoding problem for large asynchronous controllers using state-based methods. It is based on an iterative process of projection and re-composition that reduces the size specification by hiding signals, partially solves the encoding problem at the state level, and re-composes the original specification using a synchronous product. The process iterates until all encoding conflicts have been solved. The method is proved to preserve the behavior of the specification (branching bisimilarity) and shown to be capable of providing good-quality solutions for controllers of more than 100 signals and 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sup> states.

High-Level Asynchronous Concepts at the Interface Between Analog and Digital Worlds

Asynchronous circuits are becoming increasingly important in system design for Internet of Things, where they orchestrate the interface between big synchronous computation components and the analog environment, which is inherently asynchronous and has high uncertainty with respect to power supply, temperature, and long-term aging effects. However, wide adoption of asynchronous circuits by industrial users is hindered by a steep learning curve for asynchronous control models, such as signal transition graphs (STGs), that are developed by the academic community for specification, verification, and synthesis of asynchronous circuits. In this paper, we introduce a novel high-level description language for asynchronous circuits, which is based on behavioral concepts —high-level descriptions of asynchronous circuit requirements, that can be shared, reused, and extended by users, and can be automatically translated to STGs for further processing by conventional asynchronous and synchronous electronic design automation tools, such as Petrify and Mpsat. Our aim is to simplify the process of capturing system requirements in the form of a formal specification, and to promote behavioral concepts as a means for design reuse. The proposed design flow is fully automated in open-source toolsuite Workcraft, and is applied to the development of an asynchronous power regulator.