Internal State Transitions Research Articles

Current transmission pathways in potassium intercalated graphene

Carbon-based conductors are the focus of considerable research investigating the material roles and transport mechanisms which determine current transmission performance. Ab initio analysis of the potassium–graphene system suggests that neither material constituent should be cast in a subordinate current transmission role. When the dopant density is low, the potassium atoms perform a conventional doping function, improving the conductivity of the current carrying carbon substrate via charge transfer. As the doping density increases, the potassium atoms begin to form a second transmission pathway, complimenting the carbon substrate pathway and contributing to the net current transport. At the highest modeled dopant density, corresponding to the well known KC8 structure studied in various experiments, the potassium atoms form a parallel and nominally independent current transmission network, whose contribution to electron transport approximately matches that of the underlying graphene. Consistent with published experimental data on doped nanowires, the modeling results show that conductivity is a sensitive function of potassium concentration. The sensitivity is due to an internal state transition, not subject to direct experimental measurement, which strongly influences nanocomposite conductor performance.

Probing the flexible internal state transition and low-dimensional manifold dynamics of human brain with acupuncture

BackgroundAcupuncture can improve the brain cognition and have therapeutic effects on neural disorders. However, the mechanism underlying the acupuncture for modulating functional brain activity is still unclear. Methods and objectivesElectroencephalography (EEG) signals of human subjects with different acupuncture manipulations at “Zusanli” were recorded. Machine learning and time-series analysis methods, including power spectral density, Gaussian Hidden Markov Model (GHMM), and Variational Auto-Encoder (VAE) were applied to extract dynamic features of brain activity, with the aim to probe the modulation effect of acupuncture on internal brain state and explain nonlinear dynamical response of the brain to acupuncture stimulations. ResultsSpatial and temporal analysis exhibited that brain activity in delta band (1–4 Hz) and alpha band (8–12 Hz) was enhanced significantly during acupuncture phase, especially in frontal and parietal lobe areas. Furthermore, internal states of the whole brain were inferred with GHMM, which transited along with acupuncture process. It was shown that acupuncture can activate new brain states and different acupuncture manipulations induced state transitions in independent pathways, which indicated the diversity of their regulation effects on brain activities. In addition, latent factors of multi-channel EEG signals were further estimated. Neural manifolds observed in a low-dimensional state space were periodic, which derived distinct attractor dynamics for different acupuncture manipulations and could explain the nonlinear dynamical response of the brain to external stimulation. SignificanceThe findings provide a new perspective to enhance the understanding of the dynamics of the human brain during acupuncture and improve its therapeutic effectiveness in clinical applications for neural disorders.

Physical Unclonable Function Based on the Internal State Transitions of a Fibonacci Ring Oscillator

This article introduces a new class of physical unclonable functions (PUFs) based on the Fibonacci ring oscillator (FIRO). The research conducted here proves that before reaching the desired randomness, the oscillator shows a certain degree of repeatability and uniqueness in the initial sequence of internal state transitions. The use of an FIRO in conjunction with the restart method makes it possible to obtain a set of short boot sequences, which are processed with an innovative feature extraction algorithm that enables reliable device identification. This approach ensures the reuse of the existing random number generator (RNG), rather than multiplying ring oscillators in a dedicated structure. Moreover, the algorithm for the recovery of the device key from the boot set can be successfully implemented in the authorizing center, thus significantly releasing the resources of authorized low-complexity devices. The proposed methodology provides an easily obtainable key with identifiability, which was proven experimentally on FPGAs from different manufacturers.

Internal state transition to switch behavioral strategies in cricket phonotaxis.

Animals employ multiple behavioral strategies for exploring food and mating partners based on both their internal state and external environment. Here, we examined how cricket phonotaxis, which was considered an innate reactive behavior of females to approach the calling song of conspecific males, depended on these internal and external conditions. Our observation revealed that the phonotaxis process consisted of two distinctive phases: wandering and approaching. In the latter phase, crickets moved directly towards the sound source. The transition into this phase, referred to as the 'approach phase', was based on changes in the animal's internal state. Moreover, retention of the approach phase required recognition of the calling song, while song loss downregulated cricket mobility and induced frequent stopping. This is a typical movement in local search behaviors. Our results indicate that phonotaxis is not only a reactive response but a complicated process including multiple behavioral strategies.

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.

Analysis of non-Markovian coupling of a lattice-trapped atom to free space

Behavior analogous to that of spontaneous emission in photonic band gap materials has been predicted for an atom-optical system consisting of an atom confined in a well of a state-dependent optical lattice that is coupled to free space through an internal-state transition [de Vega et al., Phys. Rev. Lett. 101, 260404 (2008)]. Using the Weisskopf-Wigner approach and considering a one-dimensional geometry, we analyze the properties of this system in detail, including the evolution of the lattice-trapped population, the momentum distribution of emitted matter waves, and the detailed structure of an evanescent matter-wave state below the continuum boundary. We compare and contrast our findings for the transition from Markovian to non-Markovian behaviors to those previously obtained for three dimensions.

Neural substrate of dynamic Bayesian inference in the cerebral cortex.

Dynamic Bayesian inference allows a system to infer the environmental state under conditions of limited sensory observation. Using a goal-reaching task, we found that posterior parietal cortex (PPC) and adjacent posteromedial cortex (PM) implemented the two fundamental features of dynamic Bayesian inference: prediction of hidden states using an internal state transition model and updating the prediction with new sensory evidence. We optically imaged the activity of neurons in mouse PPC and PM layers 2, 3 and 5 in an acoustic virtual-reality system. As mice approached a reward site, anticipatory licking increased even when sound cues were intermittently presented; this was disturbed by PPC silencing. Probabilistic population decoding revealed that neurons in PPC and PM represented goal distances during sound omission (prediction), particularly in PPC layers 3 and 5, and prediction improved with the observation of cue sounds (updating). Our results illustrate how cerebral cortex realizes mental simulation using an action-dependent dynamic model.

SECURING WEB-BASED APPLICATIONS WITH PRIVACY PRESERVING TRAFFIC PADDING

Now-a-days, Web-based applications are gaining great reputation as they require less client side resources and are easier to deliver and maintain. But they also have new security and privacy challenges. The encrypted traffic of many popular Web applications may actually disclose highly sensitive data due to the side channel attack, and consequently lead to serious breaches of user privacy. An eavesdropper potentially identifies the applications’ internal state transitions and the corresponding users’ inputs based on packets’ sizes and/or timing analysis. The existing solution such as random padding and packet size rounding were proven to incur prohibitive overhead but were failing to assure sufficient privacy requirement. For preventing such side channel attack is to pad packets such that each packet will no longer map to a unique input. Padding packets results in additional communication and processing overhead. One extreme cases to pad all packets to the identical size, namely, maximizing. In the proposed system a similarity has been identified between the privacy preserving traffic padding (PPTP) issue and well studied problem privacy preserving data publishing (PPDP). Based on such similarities PPTP model encompassing the privacy requirements, padding costs, and padding methods, and then formulate problems under different application scenarios. These algorithms have been designed for solving the PPTP problems in polynomial time with acceptable overhead. Through experiments an attempt had been made to increases the effectiveness and efficiency of these algorithms than the existing solutions using the real world search engine.

Study of strong-coupling impurity bound polaron in a quantum pseudodot

In the present work, we have studied the first internal excited state energy and transition frequency of strong-coupling impurity bound polaron in a quantum pseudodot using the well-known Lee–Low–Pines (LLP) unitary transformation method. We show the effect of Coulomb bound potential, electron–phonon (e–p) coupling strength, the quantum dot radius and potential height on first internal excited state energy and the transition frequency of the impurity bound polaron. According to the results, it is found that the first internal excited state energy is decreased with increasing quantum dot radius. Also, this energy is increased with enhancing potential height. The transition frequency is increased with increasing the e–p coupling strength. Also, the first internal excited state energy is increased with decreasing the e–p coupling strength. The transition frequency is enhanced with increasing the Coulomb bound potential.

Coupled internal-state and center-of-mass dynamics of Rydberg atoms in a magnetic guide

We study cold rubidium Rydberg atoms, initially prepared in state 59D5/2, guided along a two-wire magnetic atom guide. The evolution of the atoms is driven by the combined effects of internal-state transitions and dipole forces acting on the center-of-mass degree of freedom. State-selective field ionization, applied at a variable delay time, is used to investigate the evolution of the internal-state distribution. We observe a broadening of the field ionization spectrum caused by population transfer between Rydberg states. At late times, the distribution of the remaining Rydberg atoms becomes biased toward states with high principal quantum numbers. The population transfer is attributed to thermal transitions and, to a lesser extent, initial state mixing due to Rydberg-Rydberg collisions. Characteristic components in spatially and temporally resolved distributions of the ion signal are interpreted in the context of the underlying physics. The system is simulated with a model in which the centerof-mass dynamics are treated classically, while the internal-state dynamics are treated quantum mechanically. The simulation qualitatively reproduces most experimental findings and provides experimentally inaccessible information.

Finite State Machine to Optimize Multi-Tasking Concurrence Technology for Real Time Operating System

An new approach to multi-task programming using Finite State Machine (FSM) integrated with the multi-task concurrence schedule mechanism of embedded real time operating system was proposed in this paper. Detailed procedures of the approach and two years’ application in an urban heating network were also presented. The approach exhibits a promising prospect in those industrial control systems where more than one highly demanding real-time task are controled concurrently. Both the calls between tasks and the internal state transitions are implemented in the form of concurrent, so that the efficiency of CPU is increased with reasonable arrangement and configuration of CPU.

SSPQL: Stochastic shortest path-based Q-learning

Reinforcement learning (RL) has been widely used as a mechanism for autonomous robots to learn state-action pairs by interacting with their environment. However, most RL methods usually suffer from slow convergence when deriving an optimum policy in practical applications. To solve this problem, a stochastic shortest path-based Q-learning (SSPQL) is proposed, combining a stochastic shortest path-finding method with Q-learning, a well-known model-free RL method. The rationale is, if a robot has an internal state-transition model which is incrementally learnt, then the robot can infer the local optimum policy by using a stochastic shortest path-finding method. By increasing state-action pair values comprising of these local optimum policies, a robot can then reach a goal quickly and as a result, this process can enhance convergence speed. To demonstrate the validity of this proposed learning approach, several experimental results are presented in this paper.

Modeling, Verification and Testing of Web Applications Using Model Checker

The number of Web applications handling online transaction is increasing, but verification of the correctness of Web application development has been done manually. This paper proposes a method for modeling, verifying and testing Web applications. In our method, a Web application is modeled using two finite-state automata, i.e., a page automaton which specifies Web page transitions, and an internal state automaton which specifies internal state transitions of the Web application. General properties for checking the Web application design are presented in LTL formulae and they are verified using the model checker Spin. Test cases examining the behavior of the Web application are also generated by utilizing the counterexamples obtained as the result of model checking. We applied our method to an example Web application to confirm its effectiveness.

A Low Complexity Low Power Signal Transition Detector Design for Self-Timed Circuits

A novel signal transition detector design using as few as 8 transistors is presented. The proposed design cleverly exploits the property of a specific internal state transition to mitigate the voltage degradation problem by employing only one extra transistor. It is thus capable of supporting level intact output signals and eliminating DC power consumption in the trailing buffer. The proposed design, featuring low circuit complexity and low power consumption, is considered useful for applications in self-timed circuits. Simulation results show that, when compared with other pass transistor logic based counterpart designs, as much as 46% savings in power and 28% in area can be achieved by the proposed design.

Quantum gates with atomic ensembles on an atom chip

We propose a scheme for implementing quantum computation with atomic ensembles on an atom chip, where a single qubit is carried by an atomic ensemble instead of a single atom. Two electronic ground states of the atoms are involved, one state with one or no atom is used to represent the qubit and the other state is used to hold the residual atoms. One and two-qubit gates are implemented by internal state transition with laser pulse sequences in the presence of the exciting blockade mechanism. A scalable quantum computer can be realized by one-dimensional or two-dimensional atomic lattices on an atom chip.

Quantum Computing with Collective Ensembles of Multilevel Systems

We propose a new physical approach for encoding and processing of quantum information in ensembles of multilevel quantum systems, where the different bits are not carried by individual particles but associated with the collective population of different internal levels. One- and two-bit gates are implemented by collective internal state transitions taking place in the presence of an excitation blockade mechanism, which restricts the population of each internal state to the values zero and unity. Quantum computers with 10-20 bits can be built via this scheme in single trapped clouds of ground state atoms subject to the Rydberg excitation blockade mechanism, and the linear dependence between register size and the number of internal quantum states in atoms offers realistic means to reach larger registers.

Interorganizational Workflow Execution Based on Process Agents and ECA Rules

Flexibility, adaptation and distribution have been regarded as major challenges of modern interorganizational workflow. To address these issues, this paper proposes an interorganizational workflow execution framework based on process agents and ECA rules. In our framework, an interorganizational workflow is modeled as a multiagent system with a process agent for each organization. The whole execution is divided into two parts: the intra-execution, which means execution within a same organization, and the inter-execution, which represents interaction between organizations. For intra-execution, we use the method of transforming the graph-based local workflow into block-based workflow to design general ECA rules. ECA rules are used to control internal state transitions and process agents are used to control external state transitions of tasks in the local workflows. Inter-execution is realized by process agent interaction protocols. The proposed approach can provide flexible execution of interorganizational workflow with distributed organizational autonomy and adaptation. A case study of offshore software development is illustrated for the proposed approach.

Absorption line shape of a one-dimensional Bose gas.

We discuss the line shape for an internal state transition for bosonic atoms confined as a one-dimensional gas, using a method by Haldane. Typical line shape is an edge singularity due to the absence of Bose-Einstein condensation in such systems.

Dynamical Systems for Predictive Control of Autonomous Robots

Summary Regularities in the environment are accessible to an autonomous agents as reproducible relations between actions and perceptions and can be exploited by unsupervised learning. Our approach is based on the possibility to perform and to verify predictions about perceivable consequences of actions. It is implemented as a three-layer neural network that combines predictive perception, internal-state transitions and action selection into a loop which closes via the environment. In addition to minimizing prediction errors, the goal of network adaptation comprises also an optimization of the minimization rate such that new behaviors are favored over already learned ones, which would result in a vanishing improvement of predictability. Previously learned behaviors are reactivated or continued if triggering stimuli are available and an externally or otherwise given reward overcompensates the decay of the learning rate. In the model, behavior learning and learning behavior are brought about by the same mechanism, namely the drive to continuously experience learning success. Behavior learning comprises representation and storage of learned behaviors and finally their inhibition such that a further exploration of the environment is possible. Learning behavior, in contrast, detects the frontiers of the manifold of learned behaviors and provides estimates of the learnability of behaviors leading outwards the field of expertise. The network module has been implemented in a Khepera miniature robot. We also consider hierarchical architectures consisting of several modules in one agent as well as groups of several agents, which are controlled by such networks.

Calculation of reduced partial cross sections of molecules photodesorbing from a cold crystal surface with internal vibrations: Inclusion of curve-crossing effects

A Gaussian wave packet/path integral (GWD/PI) method is used to compute final internal state distributions for a molecule photodesorbing from the surface of a zero-temperature crystal with internal vibrations in the situation where nonadiabatic coupling between two excited state potential surfaces is significant. The internal state distributions of the desorbed molecule are influenced by vast numbers of internal vibrational state transitions in the crystal which are not resolved in the calculation (or in experiment). A correlation function technique, introduced previously for the case of direct photodissociation on a single excited potential surface, is generalized to systems where two or more excited potential surfaces are nonadiabatically coupled. The accuracy of the method is successfully tested on a two-dimensional model for which numerically exact results can be computed. The method is then applied to a collinear model of a diatomic molecule photodesorbing from a chain of atoms coupled by Hooke’s law springs. While exact results cannot be obtained in this case, sum rule checks suggest that the results of the GWD/PI are of acceptable accuracy (fractional error of several percent). It is found that for the class of problems under study, which feature nonadiabatic coupling that decays to zero along the photodesorption coordinate, only a few paths through the electronic state space have significant weight. This suggests that the method can be utilized to treat more complicated problems.