arXiv subjects

Long Wang

Publications and source records attributed to Long Wang.

At least 217 records · Page 12Linked to original sources

arXiv2019

Angle-based Shape Determination Theory of Planar Graphs with Application to Formation Stabilization

This paper presents an angle-based approach for distributed formation shape stabilization of multi-agent systems in the plane. We develop an angle rigidity theory to study whether a planar framework can be determined by angles between segments uniquely up to translations, rotations, scalings and reflections. The proposed angle rigidity theory is applied to the formation stabilization problem, where multiple single-integrator modeled agents cooperatively achieve an angle-constrained formation. During the formation process, the global coordinate system is unknown for each agent and wireless communications between agents are not required. Moreover, by utilizing the advantage of high degrees of freedom, we propose a distributed control law for agents to stabilize a target formation shape with desired orientation and scale. Simulation examples are performed for illustrating effectiveness of the proposed control strategies.

eess.SY↗

arXiv2019

Social power evolution in influence networks with stubborn individuals

This paper studies the evolution of social power in influence networks with stubborn individuals. Based on the Friedkin-Johnsen opinion dynamics and the reflected appraisal mechanism, two models are proposed over issue sequences and over a single issue, respectively. These models generalize the original DeGroot-Friedkin (DF) model by including stubbornness. To the best of our knowledge, this paper is the first attempt to investigate the social power evolution of stubborn individuals basing on the reflected appraisal mechanism. Properties of equilibria and convergence are provided. We show that the models have same equilibrium social power and convergence property, where the equilibrium social power depends only upon interpersonal influence and individuals' stubbornness. Roughly speaking, more stubborn individual has more equilibrium social power. Moreover, unlike the DF model without stubbornness, we prove that for the models with stubbornness, autocracy can never be achieved, while democracy can be achieved under any network topology.

eess.SY↗

arXiv2019

Direct N-body simulation of the Galactic centre

We study the dynamics and evolution of the Milky Way nuclear star cluster performing a high resolution direct one-million-body simulation. Focusing on the interactions between such stellar system and the central supermassive black hole, we find that different stellar components adapt their overall distribution differently. After 5 Gyr, stellar mass black holes are characterized by a spatial distribution with power-slope $-1.75$, fully consistent with the prediction of Bahcall-Wolf pioneering work. Using the vast amount of data available, we infer the rate for tidal disruption events, being $4 \times 10^{-6}$ per yr, and estimate the number of objects that emit gravitational waves during the phases preceding the accretion onto the super-massive black hole, $\sim 270$ per Gyr. We show that some of these sources could form extreme mass-ratio inspirals. We follow the evolution of binary stars population, showing that the initial binary fraction of $5\%$ drops down to $2.5\%$ inside the inner parsec. Also, we explored the possible formation of binary systems containing a compact object, discussing the implications for millisecond pulsars formation and the development of Ia Supernovae.

astro-ph.GA↗

arXiv2019

Merging black holes in young star clusters

Searching for distinctive signatures, which characterize different formation channels of binary black holes (BBHs), is a crucial step towards the interpretation of current and future gravitational wave detections. Here, we investigate the demography of merging BBHs in young star clusters (SCs), which are the nursery of massive stars. We performed $4\times{} 10^3$ N-body simulations of SCs with metallicity $Z=0.002$, initial binary fraction $0.4$ and fractal initial conditions, to mimic the clumpiness of star forming regions. Our simulations include a novel population-synthesis approach based on the code MOBSE. We find that SC dynamics does not affect the merger rate significantly, but leaves a strong fingerprint on the properties of merging BBHs. More than 50 % of merging BBHs in young SCs form by dynamical exchanges in the first few Myr. Dynamically formed merging BBHs are significantly heavier than merging BBHs in isolated binaries: merging BBHs with total mass up to $\sim{}120$ M$_\odot$ form in young SCs, while the maximum total mass of merging BBHs in isolated binaries with the same metallicity is only $\sim{}70$ M$_\odot$. Merging BBHs born via dynamical exchanges tend to have smaller mass ratios than BBHs in isolated binaries. Furthermore, SC dynamics speeds up the merger: the delay time between star formation and coalescence is significantly shorter in young SCs. In our simulations, massive systems such as GW170729 form only via dynamical exchanges. Finally $\sim{}2$ % of merging BBHs in young SCs have mass in the pair-instability mass gap ($\sim{}60-120$ M$_\odot$). This represents a unique fingerprint of merging BBHs in SCs.

astro-ph.HE↗

arXiv2018

Role of the effective payoff function in evolutionary game dynamics

In most studies regarding evolutionary game dynamics, the effective payoff, a quantity that translates the payoff derived from game interactions into reproductive success, is usually assumed to be a specific function of the payoff. Meanwhile, the effect of different function forms of effective payoff on evolutionary dynamics is always left in the basket. With introducing a generalized mapping that the effective payoff of individuals is a non-negative function of two variables on selection intensity and payoff, we study how different effective payoff functions affect evolutionary dynamics in a symmetrical mutation-selection process. For standard two-strategy two-player games, we find that under weak selection the condition for one strategy to dominate the other depends not only on the classical σ-rule, but also on an extra constant that is determined by the form of the effective payoff function. By changing the sign of the constant, we can alter the direction of strategy selection. Taking the Moran process and pairwise comparison process as specific models in well-mixed populations, we find that different fitness or imitation mappings are equivalent under weak selection. Moreover, the sign of the extra constant determines the direction of one-third law and risk-dominance for sufficiently large populations. This work thus helps to elucidate how the effective payoff function as another fundamental ingredient of evolution affect evolutionary dynamics.

q-bio.PE↗

arXiv2018

Simple property of heterogeneous aspiration dynamics: Beyond weak selection

How individuals adapt their behavior in cultural evolution remains elusive. Theoretical studies have shown that the update rules chosen to model individual decision making can dramatically modify the evolutionary outcome of the population as a whole. This hints at the complexities of considering the personality of individuals in a population, where each one uses its own rule. Here, we investigate whether and how heterogeneity in the rules of behavior update alters the evolutionary outcome. We assume that individuals update behaviors by aspiration-based self-evaluation and they do so in their own ways. Under weak selection, we analytically reveal a simple property that holds for any two-strategy multi-player games in well-mixed populations and on regular graphs: the evolutionary outcome in a population with heterogeneous update rules is the weighted average of the outcomes in the corresponding homogeneous populations, and the associated weights are the frequencies of each update rule in the heterogeneous population. Beyond weak selection, we show that this property holds for public goods games. Our finding implies that heterogeneous aspiration dynamics is additive. This additivity greatly reduces the complexity induced by the underlying individual heterogeneity. Our work thus provides an efficient method to calculate evolutionary outcomes under heterogeneous update rules.

physics.soc-ph↗

arXiv2018

A MODEST review

We present an account of the state of the art in the fields explored by the research community invested in 'Modeling and Observing DEnse STellar systems'. For this purpose, we take as a basis the activities of the MODEST-17 conference, which was held at Charles University, Prague, in September 2017. Reviewed topics include recent advances in fundamental stellar dynamics, numerical methods for the solution of the gravitational N-body problem, formation and evolution of young and old star clusters and galactic nuclei, their elusive stellar populations, planetary systems, and exotic compact objects, with timely attention to black holes of different classes of mass and their role as sources of gravitational waves. Such a breadth of topics reflects the growing role played by collisional stellar dynamics in numerous areas of modern astrophysics. Indeed, in the next decade, many revolutionary instruments will enable the derivation of positions and velocities of individual stars in the Milky Way and its satellites and will detect signals from a range of astrophysical sources in different portions of the electromagnetic and gravitational spectrum, with an unprecedented sensitivity. On the one hand, this wealth of data will allow us to address a number of long-standing open questions in star cluster studies; on the other hand, many unexpected properties of these systems will come to light, stimulating further progress of our understanding of their formation and evolution.

astro-ph.GA↗

arXiv2018

Complete ejection of OB stars from very young star clusters and the formation of multiple populations

Recently, three stellar sequences separated in age by about 1 Myr were discovered in the Orion Nebula Cluster (ONC; Beccari et al. 2017). Kroupa et al. (2018) suggest that such small dense subpopulations eject all their OB stars via the decay of unstable few-body systems such that the gas can recombine and form new stars. This explains the multisequence phenomenon without introducing an extra mechanism into star formation theory. In this work, we apply the recently updated primordial binary distribution model (Belloni et al. 2017; implemented here in a new version of MCLUSTER) and perform a large set of direct N-body simulations to investigate the feasibility of this dynamical scenario. Our results suggest that if 3-4 OB stars in the ONC formed primordially mass-segregated in the cluster centre with a maximum separation of about 0.003 pc, all OB stars have a high chance (50-70 per cent) to escape from the centre and do not come back within 1 Myr and the dynamical ejection scenario is a viable channel to form short-age-interval multipopulation sequences as observed in the ONC. This is also consistent with self-regulated star formation.

astro-ph.SR↗

arXiv2018

Evolutionary multiplayer games on graphs with edge diversity

Evolutionary game dynamics in structured populations has been extensively explored in past decades. However, most previous studies assume that payoffs of individuals are fully determined by the strategic behaviors of interacting parties and social ties between them only serve as the indicator of the existence of interactions. This assumption neglects important information carried by inter-personal social ties such as genetic similarity, geographic proximity, and social closeness, which may crucially affect the outcome of interactions. To model these situations, we present a framework of evolutionary multiplayer games on graphs with edge diversity, where different types of edges describe diverse social ties. Strategic behaviors together with social ties determine the resulting payoffs of interactants. Under weak selection, we provide a general formula to predict the success of one behavior over the other. We apply this formula to various examples which cannot be dealt with using previous models, including the division of labor and relationship- or edge-dependent games. We find that labor division facilitates collective cooperation by decomposing a many-player game into several games of smaller sizes. The evolutionary process based on relationship-dependent games can be approximated by interactions under a transformed and unified game. Our work stresses the importance of social ties and provides effective methods to reduce the calculating complexity in analyzing the evolution of realistic systems.

q-bio.PE↗

arXiv2018

Aspiration dynamics generate robust predictions in structured populations

Evolutionary game dynamics in structured populations are strongly affected by updating rules. Previous studies usually focus on imitation-based rules, which rely on payoff information of social peers. Recent behavioral experiments suggest that whether individuals use such social information for strategy updating may be crucial to the outcomes of social interactions. This hints at the importance of considering updating rules without dependence on social peers' payoff information, which, however, is rarely investigated. Here, we study aspiration-based self-evaluation rules, with which individuals self-assess the performance of strategies by comparing own payoffs with an imaginary value they aspire, called the aspiration level. We explore the fate of strategies on population structures represented by graphs or networks. Under weak selection, we analytically derive the condition for strategy dominance, which is found to coincide with the classical condition of risk-dominance. This condition holds for all networks and all distributions of aspiration levels, and for individualized ways of self-evaluation. Our condition can be intuitively interpreted: one strategy prevails over the other if the strategy brings more satisfaction to individuals than the other does. Our work thus sheds light on the intrinsic difference between evolutionary dynamics induced by aspiration-based and imitation-based rules.

q-bio.PE↗

arXiv2018

Energy cost for controlling complex networks

The controllability of complex networks has received much attention recently, which tells whether we can steer a system from an initial state to any final state within finite time with admissible external inputs. In order to accomplish the control in practice at the minimum cost, we must study how much control energy is needed to reach the desired final state. At a given control distance between the initial and final states, existing results present the scaling behavior of lower bounds of the minimum energy in terms of the control time analytically. However, to reach an arbitrary final state at a given control distance, the minimum energy is actually dominated by the upper bound, whose analytic expression still remains elusive. Here we theoretically show the scaling behavior of the upper bound of the minimum energy in terms of the time required to achieve control. Apart from validating the analytical results with numerical simulations, our findings are feasible to the scenario with any number of nodes that receive inputs directly and any types of networks. Moreover, more precise analytical results for the lower bound of the minimum energy are derived in the proposed framework. Our results pave the way to implement realistic control over various complex networks with the minimum control cost.

math.OC↗

arXiv2018

On Synchronization of Dynamical Systems over Directed Switching Topologies: An Algebraic and Geometric Perspective

In this paper, we aim to investigate the synchronization problem of dynamical systems, which can be of generic linear or Lipschitz nonlinear type, communicating over directed switching network topologies. A mild connectivity assumption on the switching topologies is imposed, which allows them to be directed and jointly connected. We propose a novel analysis framework from both algebraic and geometric perspectives to justify the attractiveness of the synchronization manifold. Specifically, it is proven that the complementary space of the synchronization manifold can be spanned by certain subspaces. These subspaces can be the eigenspaces of the nonzero eigenvalues of Laplacian matrices in linear case. They can also be subspaces in which the projection of the nonlinear self-dynamics still retains the Lipschitz property. This allows to project the states of the dynamical systems into these subspaces and transform the synchronization problem under consideration equivalently into a convergence one of the projected states in each subspace. Then, assuming the joint connectivity condition on the communication topologies, we are able to work out a simple yet effective and unified convergence analysis for both types of dynamical systems. More specifically, for partial-state coupled generic linear systems, it is proven that synchronization can be reached if an extra condition, which is easy to verify in several cases, on the system dynamics is satisfied. For Lipschitz-type nonlinear systems with positive-definite inner coupling matrix, synchronization is realized if the coupling strength is strong enough to stabilize the evolution of the projected states in each subspace under certain conditions.

eess.SY↗

arXiv2018

Medical Technologies and Challenges of Robot Assisted Minimally Invasive Intervention and Diagnostics

Emerging paradigms furthering the reach of medical technology deeper into human anatomy present unique modeling, control and sensing problems. This paper discusses a brief history of medical robotics leading to the current trend of minimally invasive intervention and diagnostics in confined spaces. Robotics for natural orifice and single port access surgery, capsule and magnetically actuated robotics and microrobotics are discussed with the aim of elucidating the state of the art. Works on modeling, sensing and control of mechanical architectures of robots for natural orifice and single port access surgery are discussed, followed by a presentation of works on magnetic actuation, sensing and localization for capsule robotics and microrobotics. Finally challenges and open problems in each one of these areas are presented.

cs.RO↗

arXiv2018

The optimal trajectory to control complex networks

Controllability, a basic property of various networked systems, has gained profound theoretical applications in complex social, technological, biological, and brain networks. Yet, little attention has been given to the control trajectory (route), along which a controllable system can be controlled from any initial to any final state, hampering the implementation of practical control. Here we systematically uncover the fundamental relations between control trajectory and several other key factors, such as the control distance between initial and final states ($δ$), number of driver nodes, and the control time. The length ($\mathcal{L}$) and maximum distance to the initial state ($\mathcal{R}$) are employed to quantify the locality and globality of control trajectories. We analyze how the scaling behavior of the averaged $\mathcal{L}$ and $\mathcal{R}$ changes with increasing $δ$ for different initial states. After showing the scaling behavior for each trajectory, we also provide the distributions of $\mathcal{L}$ and $\mathcal{R}$. Further attention is given to the control time $t_{f}$ and its influence on $\mathcal{L}$ and $\mathcal{R}$. Our results provide comprehensive insights in understanding control trajectories for complex networks, and pave the way to achieve practical control in various real systems.

math.OC↗

arXiv2018

Phenotype affinity mediated interactions can facilitate the evolution of cooperation

We study the coevolutionary dynamics of the diversity of phenotype expression and the evolution of cooperation in the Prisoner's Dilemma game. Rather than pre-assigning zero-or-one interaction rate, we diversify the rate of interaction by associating it with the phenotypes shared in common. Individuals each carry a set of potentially expressible phenotypes and expresses a certain number of phenotypes at a cost proportional to the number. The number of expressed phenotypes and thus the rate of interaction is an evolvable trait. Our results show that nonnegligible cost of expressing phenotypes restrains phenotype expression, and the evolutionary race mainly proceeds on between cooperative strains and defective strains who express a very few phenotypes. It pays for cooperative strains to express a very few phenotypes. Though such a low level of expression weakens reciprocity between cooperative strains, it decelerates rate of interaction between cooperative strains and defective strains to a larger degree, leading to the predominance of cooperative strains over defective strains. We also find that evolved diversity of phenotype expression can occasionally destabilize due to the invasion of defective mutants, implying that cooperation and diversity of phenotype expression can mutually reinforce each other. Therefore, our results provide new insights into better understanding the coevolution of cooperation and the diversity of phenotype expression.

q-bio.PE↗

arXiv2018

Trajectory-Optimized Sensing for Active Search of Tissue Abnormalities in Robotic Surgery

In this work, we develop an approach for guiding robots to automatically localize and find the shapes of tumors and other stiff inclusions present in the anatomy. Our approach uses Gaussian processes to model the stiffness distribution and active learning to direct the palpation path of the robot. The palpation paths are chosen such that they maximize an acquisition function provided by an active learning algorithm. Our approach provides the flexibility to avoid obstacles in the robot's path, incorporate uncertainties in robot position and sensor measurements, include prior information about location of stiff inclusions while respecting the robot-kinematics. To the best of our knowledge this is the first work in literature that considers all the above conditions while localizing tumors. The proposed framework is evaluated via simulation and experimentation on three different robot platforms: 6-DoF industrial arm, da Vinci Research Kit (dVRK), and the Insertable Robotic Effector Platform (IREP). Results show that our approach can accurately estimate the locations and boundaries of the stiff inclusions while reducing exploration time.

cs.RO↗

arXiv2018

Scaled Gaussian Stochastic Process for Computer Model Calibration and Prediction

We consider the problem of calibrating an imperfect computer model using experimental data. To compensate the misspecification of the computer model and make more accurate predictions, a discrepancy function is often included and modeled via a Gaussian stochastic process (GaSP). The calibrated computer model alone, however, sometimes fits the experimental data poorly, as the calibration parameters become unidentifiable. In this work, we propose the scaled Gaussian stochastic process (S-GaSP), a novel stochastic process that bridges the gap between two predominant methods, namely the $L_2$ calibration and the GaSP calibration. It is shown that our approach performs well in both calibration and prediction. A computationally feasible approach is introduced for this new model under the Bayesian paradigm. Compared with the GaSP calibration, the S-GaSP calibration enables the calibrated computer model itself to predict the reality well, based on the posterior distribution of the calibration parameters. Numerical comparisons of the simulated and real data are provided to illustrate the connections and differences between the proposed S-GaSP and other alternative approaches.

stat.ME↗

arXiv2018

Fortran interface layer of the framework for developing particle simulator FDPS

Numerical simulations based on particle methods have been widely used in various fields including astrophysics. To date, simulation softwares have been developed by individual researchers or research groups in each field, with a huge amount of time and effort, even though numerical algorithms used are very similar. To improve the situation, we have developed a framework, called FDPS, which enables researchers to easily develop massively parallel particle simulation codes for arbitrary particle methods. Until version 3.0, FDPS have provided API only for C++ programing language. This limitation comes from the fact that FDPS is developed using the template feature in C++, which is essential to support arbitrary data types of particle. However, there are many researchers who use Fortran to develop their codes. Thus, the previous versions of FDPS require such people to invest much time to learn C++. This is inefficient. To cope with this problem, we newly developed a Fortran interface layer in FDPS, which provides API for Fortran. In order to support arbitrary data types of particle in Fortran, we design the Fortran interface layer as follows. Based on a given derived data type in Fortran representing particle, a Python script provided by us automatically generates a library that manipulates the C++ core part of FDPS. This library is seen as a Fortran module providing API of FDPS from the Fortran side and uses C programs internally to interoperate Fortran with C++. In this way, we have overcome several technical issues when emulating `template' in Fortran. By using the Fortran interface, users can develop all parts of their codes in Fortran. We show that the overhead of the Fortran interface part is sufficiently small and a code written in Fortran shows a performance practically identical to the one written in C++.

astro-ph.IM↗