Construction of equilibrium networks with an energy function

We construct equilibrium networks by introducing an energy function depending on the degree of each node as well as the product of neighboring degrees. With this topological energy function, networks constitute a canonical ensemble, which follows the Boltzmann distribution for given temperature. It is observed that the system undergoes a topological phase transition from a random network to a star or a fully-connected network as the temperature is lowered. Both mean-field analysis and numerical simulations reveal strong first-order phase transitions at temperatures which decrease logarithmically with the system size. Quantitative discrepancies of the simulation results from the mean-field prediction are discussed in view of the strong first-order nature.Comment: To appear in J. Phys.

Three Major Instructional Approaches for Requirements Engineering

In this talk, we report on our findings from the paper A Survey of Instructional Approaches in the Requirements Engineering Education Literature [DGT21], which has been accepted at and published in the proceedings of the 2021 IEEE International Conference on Requirements Engineering. The paper reports the findings of a systematic literature review to define and investigate the current state of research on requirements engineering education

Slow relaxation in the Ising model on a small-world network with strong long-range interactions

We consider the Ising model on a small-world network, where the long-range interaction strength $J_2$ is in general different from the local interaction strength $J_1$, and examine its relaxation behaviors as well as phase transitions. As $J_2/J_1$ is raised from zero, the critical temperature also increases, manifesting contributions of long-range interactions to ordering. However, it becomes saturated eventually at large values of $J_2/J_1$ and the system is found to display very slow relaxation, revealing that ordering dynamics is inhibited rather than facilitated by strong long-range interactions. To circumvent this problem, we propose a modified updating algorithm in Monte Carlo simulations, assisting the system to reach equilibrium quickly.Comment: 5 pages, 5 figure

Bidirectional Gradients of Stimulus Generalization: A Comparison of Normals and Retardates on a Visual-spatial Task

Psycholog

A Systematic Literature Review of Requirements Engineering Education

Requirements engineering (RE) has established itself as a core software engineering discipline. It is well acknowledged that good RE leads to higher quality software and considerably reduces the risk of failure or budget-overspending of software development projects. It is of vital importance to train future software engineers in RE and educate future requirements engineers to adequately manage requirements in various projects. To this date, there exists no central concept of what RE education shall comprise. To lay a foundation, we report on a systematic literature review of the feld and provide a systematic map describing the current state of RE education. Doing so allows us to describe how the educational landscape has changed over the last decade. Results show that only a few established author collaborations exist and that RE education research is predominantly published in venues other than the top RE research venues (i.e., in venues other than the RE conference and journal). Key trends in RE instruction of the past decade include involvement of real or realistic stakeholders, teaching predominantly elicitation as an RE activity, and increasing student factors such as motivation or communication skills. Finally, we discuss open opportunities in RE education, such as training for security requirements and supply chain risk management, as well as developing a pedagogical foundation grounded in evidence of effective instructional approaches

Learning robust policies for object manipulation with robot swarms

Swarm robotics investigates how a large population of robots with simple actuation and limited sensors can collectively solve complex tasks. One particular interesting application with robot swarms is autonomous object assembly. Such tasks have been solved successfully with robot swarms that are controlled by a human operator using a light source. In this paper, we present a method to solve such assembly tasks autonomously based on policy search methods. We split the assembly process in two subtasks: generating a high-level assembly plan and learning a low-level object movement policy. The assembly policy plans the trajectories for each object and the object movement policy controls the trajectory execution. Learning the object movement policy is challenging as it depends on the complex state of the swarm which consists of an individual state for each agent. To approach this problem, we introduce a representation of the swarm which is based on Hilbert space embeddings of distributions. This representation is invariant to the number of agents in the swarm as well as to the allocation of an agent to its position in the swarm. These invariances make the learned policy robust to changes in the swarm and also reduce the search space for the policy search method significantly. We show that the resulting system is able to solve assembly tasks with varying object shapes in multiple simulation scenarios and evaluate the robustness of our representation to changes in the swarm size. Furthermore, we demonstrate that the policies learned in simulation are robust enough to be transferred to real robots

Robust learning of object assembly tasks with an invariant representation of robot swarms

— Swarm robotics investigates how a large population of robots with simple actuation and limited sensors can collectively solve complex tasks. One particular interesting application with robot swarms is autonomous object assembly. Such tasks have been solved successfully with robot swarms that are controlled by a human operator using a light source. In this paper, we present a method to solve such assembly tasks autonomously based on policy search methods. We split the assembly process in two subtasks: generating a high-level assembly plan and learning a low-level object movement policy. The assembly policy plans the trajectories for each object and the object movement policy controls the trajectory execution. Learning the object movement policy is challenging as it depends on the complex state of the swarm which consists of an individual state for each agent. To approach this problem, we introduce a representation of the swarm which is based on Hilbert space embeddings of distributions. This representation is invariant to the number of agents in the swarm as well as to the allocation of an agent to its position in the swarm. These invariances make the learned policy robust to changes in the swarm and also reduce the search space for the policy search method significantly. We show that the resulting system is able to solve assembly tasks with varying object shapes in multiple simulation scenarios and evaluate the robustness of our representation to changes in the swarm size. Furthermore, we demonstrate that the policies learned in simulation are robust enough to be transferred to real robots

1/f spectrum and memory function analysis of solvation dynamics in a room-temperature ionic liquid

To understand the non-exponential relaxation associated with solvation dynamics in the ionic liquid 1-ethyl-3-methylimidazolium hexafluorophosphate, we study power spectra of the fluctuating Franck-Condon energy gap of a diatomic probe solute via molecular dynamics simulations. Results show 1/f dependence in a wide frequency range over 2 to 3 decades, indicating distributed relaxation times. We analyze the memory function and solvation time in the framework of the generalized Langevin equation using a simple model description for the power spectrum. It is found that the crossover frequency toward the white noise plateau is directly related to the time scale for the memory function and thus the solvation time. Specifically, the low crossover frequency observed in the ionic liquid leads to a slowly-decaying tail in its memory function and long solvation time. By contrast, acetonitrile characterized by a high crossover frequency and (near) absence of 1/f behavior in its power spectra shows fast relaxation of the memory function and single-exponential decay of solvation dynamics in the long-time regime.Comment: 10 pages, 4 figure