Explore Spatiotemporal and Demographic Characteristics of Human Mobility via Twitter: A Case Study of Chicago

Characterizing human mobility patterns is essential for understanding human behaviors and the interactions with socioeconomic and natural environment. With the continuing advancement of location and Web 2.0 technologies, location-based social media (LBSM) have been gaining widespread popularity in the past few years. With an access to locations of users, profiles and the contents of the social media posts, the LBSM data provided a novel modality of data source for human mobility study. By exploiting the explicit location footprints and mining the latent demographic information implied in the LBSM data, the purpose of this paper is to investigate the spatiotemporal characteristics of human mobility with a particular focus on the impact of demography. We first collect geo-tagged Twitter feeds posted in the conterminous United States area, and organize the collection of feeds using the concept of space-time trajectory corresponding to each Twitter user. Commonly human mobility measures, including detected home and activity centers, are derived for each user trajectory. We then select a subset of Twitter users that have detected home locations in the city of Chicago as a case study, and apply name analysis to the names provided in user profiles to learn the implicit demographic information of Twitter users, including race/ethnicity, gender and age. Finally we explore the spatiotemporal distribution and mobility characteristics of Chicago Twitter users, and investigate the demographic impact by comparing the differences across three demographic dimensions (race/ethnicity, gender and age). We found that, although the human mobility measures of different demographic groups generally follow the generic laws (e.g., power law distribution), the demographic information, particular the race/ethnicity group, significantly affects the urban human mobility patterns

Optimal Allocation of Resources for Suppressing Epidemic Spreading on Networks

Efficient allocation of limited medical resources is crucial for controlling epidemic spreading on networks. Based on the susceptible-infected-susceptible model, we solve an optimization problem as how best to allocate the limited resources so as to minimize the prevalence, providing that the curing rate of each node is positively correlated to its medical resource. By quenched mean-field theory and heterogeneous mean-field (HMF) theory, we prove that epidemic outbreak will be suppressed to the greatest extent if the curing rate of each node is directly proportional to its degree, under which the effective infection rate $\lambda$ has a maximal threshold $\lambda_c^{opt}=1/\left\langle k \right\rangle$ where $\left\langle k \right\rangle$ is average degree of the underlying network. For weak infection region ($\lambda\gtrsim\lambda_c^{opt}$), we combine a perturbation theory with Lagrange multiplier method (LMM) to derive the analytical expression of optimal allocation of the curing rates and the corresponding minimized prevalence. For general infection region ($\lambda>\lambda_c^{opt}$), the high-dimensional optimization problem is converted into numerically solving low-dimensional nonlinear equations by the HMF theory and LMM. Counterintuitively, in the strong infection region the low-degree nodes should be allocated more medical resources than the high-degree nodes to minimize the prevalence. Finally, we use simulated annealing to validate the theoretical results.Comment: 7 pages for two columns, 2 figure

On the Structure of Compatible Rational Functions

A finite number of rational functions are compatible if they satisfy the compatibility conditions of a first-order linear functional system involving differential, shift and q-shift operators. We present a theorem that describes the structure of compatible rational functions. The theorem enables us to decompose a solution of such a system as a product of a rational function, several symbolic powers, a hyperexponential function, a hypergeometric term, and a q-hypergeometric term. We outline an algorithm for computing this product, and present an application

Discontinuous phase transition in an annealed multi-state majority-vote model

In this paper, we generalize the original majority-vote (MV) model with noise from two states to arbitrary $q$ states, where $q$ is an integer no less than two. The main emphasis is paid to the comparison on the nature of phase transitions between the two-state MV (MV2) model and the three-state MV (MV3) model. By extensive Monte Carlo simulation and mean-field analysis, we find that the MV3 model undergoes a discontinuous order-disorder phase transition, in contrast to a continuous phase transition in the MV2 model. A central feature of such a discontinuous transition is a strong hysteresis behavior as noise intensity goes forward and backward. Within the hysteresis region, the disordered phase and ordered phase are coexisting.Comment: 12 pages, 6 figure

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

). Size Dependency of the Elastic Modulus of ZnO Nanowires: Surface Stress Effect

Relation between the elastic modulus and the diameter (D) of ZnOnanowires was elucidated using a model with the calculated ZnOsurface stresses as input. We predict for ZnOnanowires due to surface stress effect: (1) when D\u3e20nm, the elastic modulus would be lower than the bulk modulus and decrease with the decreasing diameter, (2) when 20nm\u3eD\u3e2nm, the nanowires with a longer length and a wurtzite crystal structure could be mechanically unstable, and (3) when D\u3c2nm, the elastic modulus would be higher than that of the bulk value and increase with a decrease in nanowire diameter

Phase transitions in a multistate majority-vote model on complex networks

We generalize the original majority-vote (MV) model from two states to arbitrary $p$ states and study the order-disorder phase transitions in such a $p$-state MV model on complex networks. By extensive Monte Carlo simulations and a mean-field theory, we show that for $p\geq3$ the order of phase transition is essentially different from a continuous second-order phase transition in the original two-state MV model. Instead, for $p\geq3$ the model displays a discontinuous first-order phase transition, which is manifested by the appearance of the hysteresis phenomenon near the phase transition. Within the hysteresis loop, the ordered phase and disordered phase are coexisting and rare flips between the two phases can be observed due to the finite-size fluctuation. Moreover, we investigate the type of phase transition under a slightly modified dynamics [Melo \emph{et al.} J. Stat. Mech. P11032 (2010)]. We find that the order of phase transition in the three-state MV model depends on the degree heterogeneity of networks. For $p\geq4$, both dynamics produce the first-order phase transitions.Comment: two-column 7 pages, 1 table and 7 figure

Predicting Young’s Modulus of Nanowires from First-Principles Calculations on their Surface and Bulk Materials

Using the concept of surface stress, we developed a model that is able to predict Young’s modulus of nanowires as a function of nanowire diameters from the calculated properties of their surface and bulk materials. We took both equilibrium strain effect and surface stress effect into consideration to account for the geometric size influence on the elastic properties of nanowires. In this work, we combined first-principles density functional theory calculations of material properties with linear elasticity theory of clamped-end three-point bending. Furthermore, we applied this computational approach to Ag, Au, and ZnOnanowires. For both Ag and Aunanowires, our theoretical predictions agree well with the experimental data in the literature. For ZnOnanowires, our predictions are qualitatively consistent with some of experimental data for ZnO nanostructures. Consequently, we found that surface stress plays a very important role in determining Young’s modulus of nanowires. Our finding suggests that the elastic properties of nanowires could be possibly engineered by altering the surface stress of their lateral surfaces

Robust Keyframe-based Dense SLAM with an RGB-D Camera

In this paper, we present RKD-SLAM, a robust keyframe-based dense SLAM approach for an RGB-D camera that can robustly handle fast motion and dense loop closure, and run without time limitation in a moderate size scene. It not only can be used to scan high-quality 3D models, but also can satisfy the demand of VR and AR applications. First, we combine color and depth information to construct a very fast keyframe-based tracking method on a CPU, which can work robustly in challenging cases (e.g.~fast camera motion and complex loops). For reducing accumulation error, we also introduce a very efficient incremental bundle adjustment (BA) algorithm, which can greatly save unnecessary computation and perform local and global BA in a unified optimization framework. An efficient keyframe-based depth representation and fusion method is proposed to generate and timely update the dense 3D surface with online correction according to the refined camera poses of keyframes through BA. The experimental results and comparisons on a variety of challenging datasets and TUM RGB-D benchmark demonstrate the effectiveness of the proposed system.Comment: 12 pages, 9 figure

The simulation of loss of U ions due to charge changing processes in the CSRm ring

Significant beam loss caused by the charge exchange processes and ions impact induced outgassing play a crucial role in the limitation of the maximum number of accumulated heavy ions during the high intensity operation in the accelerators. With the aim to control beam loss due to charge exchange processes and to confine the generated desorption gas, the tracking of the loss positions and installing the absorber blocks with low-desorption rate material at appropriate locations in the CSRm ring will be taken. The loss simulation of U ions having lost an electron will be presented in this report and the calculation of the collimation efficiency of the CSRm ring will be continued in the future.Comment: 4 pages, 5 figure