182,828 research outputs found
A Sparse Stress Model
Force-directed layout methods constitute the most common approach to draw
general graphs. Among them, stress minimization produces layouts of
comparatively high quality but also imposes comparatively high computational
demands. We propose a speed-up method based on the aggregation of terms in the
objective function. It is akin to aggregate repulsion from far-away nodes
during spring embedding but transfers the idea from the layout space into a
preprocessing phase. An initial experimental study informs a method to select
representatives, and subsequent more extensive experiments indicate that our
method yields better approximations of minimum-stress layouts in less time than
related methods.Comment: Appears in the Proceedings of the 24th International Symposium on
Graph Drawing and Network Visualization (GD 2016
Relaxation and reconstruction on (111) surfaces of Au, Pt, and Cu
We have theoretically studied the stability and reconstruction of (111)
surfaces of Au, Pt, and Cu. We have calculated the surface energy, surface
stress, interatomic force constants, and other relevant quantities by ab initio
electronic structure calculations using the density functional theory (DFT), in
a slab geometry with periodic boundary conditions. We have estimated the
stability towards a quasi-one-dimensional reconstruction by using the
calculated quantities as parameters in a one-dimensional Frenkel-Kontorova
model. On all surfaces we have found an intrinsic tensile stress. This stress
is large enough on Au and Pt surfaces to lead to a reconstruction in which a
denser surface layer is formed, in agreement with experiment. The
experimentally observed differences between the dense reconstruction pattern on
Au(111) and a sparse structure of stripes on Pt(111) are attributed to the
details of the interaction potential between the first layer of atoms and the
substrate.Comment: 8 pages, 3 figures, submitted to Physical Review
Control to flocking of the kinetic Cucker-Smale model
The well-known Cucker-Smale model is a macroscopic system reflecting
flocking, i.e. the alignment of velocities in a group of autonomous agents
having mutual interactions. In the present paper, we consider the mean-field
limit of that model, called the kinetic Cucker-Smale model, which is a
transport partial differential equation involving nonlocal terms. It is known
that flocking is reached asymptotically whenever the initial conditions of the
group of agents are in a favorable configuration. For other initial
configurations, it is natural to investigate whether flocking can be enforced
by means of an appropriate external force, applied to an adequate time-varying
subdomain.
In this paper we prove that we can drive to flocking any group of agents
governed by the kinetic Cucker-Smale model, by means of a sparse centralized
control strategy, and this, for any initial configuration of the crowd. Here,
"sparse control" means that the action at each time is limited over an
arbitrary proportion of the crowd, or, as a variant, of the space of
configurations; "centralized" means that the strategy is computed by an
external agent knowing the configuration of all agents. We stress that we do
not only design a control function (in a sampled feedback form), but also a
time-varying control domain on which the action is applied. The sparsity
constraint reflects the fact that one cannot act on the whole crowd at every
instant of time.
Our approach is based on geometric considerations on the velocity field of
the kinetic Cucker-Smale PDE, and in particular on the analysis of the particle
flow generated by this vector field. The control domain and the control
functions are designed to satisfy appropriate constraints, and such that, for
any initial configuration, the velocity part of the support of the measure
solution asymptotically shrinks to a singleton, which means flocking
Experimental study on dynamic deformation properties of muck soil under low frequency cyclic loading
A series of dynamic triaxial tests were performed to investigate the dynamic deformation properties of the muck soil, in the Pearl River Delta region of Shenzhen, China, under different consolidation ratios, loading frequencies and cyclic stress with SPAX-2000 triaxial testing system. The results showed that the initial stress-strain hysteresis curve of the muck soil under the low-frequency cyclic loading developed rapidly and the curve shape changes from sparse to tight and to slightly sparse. The cumulative plastic strain of muck soil increased nonlinearly with the dynamic stress amplitude, and there was a critical dynamic stress. As the dynamic stress amplitude reached its critical value, the strain increased sharply and the soil microstructure was destroyed. There was a frequency threshold between 0.25 Hz and 0.5 Hz, and the cumulative plastic strain development mode was from stable model to over-destructive model. The stiffness of the muck decreased gradually, and the plastic deformation increased as the number of cycles increased. Therefore, the lower the loading frequency developed, the greater the plastic deformation would be. The dynamic elastic modulus decreased as the plastic deformation increased, while the dynamic elastic modulus increased as the consolidation stress increased. Moreover, the empirical formulas of dynamic elastic modulus and plastic strain index were established with the consolidation stress ratio as the parameter, and the validity was verified by experimental data
High-Dimensional Inference with the generalized Hopfield Model: Principal Component Analysis and Corrections
We consider the problem of inferring the interactions between a set of N
binary variables from the knowledge of their frequencies and pairwise
correlations. The inference framework is based on the Hopfield model, a special
case of the Ising model where the interaction matrix is defined through a set
of patterns in the variable space, and is of rank much smaller than N. We show
that Maximum Lik elihood inference is deeply related to Principal Component
Analysis when the amp litude of the pattern components, xi, is negligible
compared to N^1/2. Using techniques from statistical mechanics, we calculate
the corrections to the patterns to the first order in xi/N^1/2. We stress that
it is important to generalize the Hopfield model and include both attractive
and repulsive patterns, to correctly infer networks with sparse and strong
interactions. We present a simple geometrical criterion to decide how many
attractive and repulsive patterns should be considered as a function of the
sampling noise. We moreover discuss how many sampled configurations are
required for a good inference, as a function of the system size, N and of the
amplitude, xi. The inference approach is illustrated on synthetic and
biological data.Comment: Physical Review E: Statistical, Nonlinear, and Soft Matter Physics
(2011) to appea
A spectrum-driven damage identification by minimum constitutive relation error and sparse regularization
This paper proposes a novel model-based damage identification strategy based on minimum constitutive relation error and sparse regularization using the power spectrum density data. Firstly, the stationary random vibration problem is transformed into a series of harmonic vibrations by the pseudo excitation method and the error in constitutive relation is established by the admissible stress field and admissible displacement field. A much more general and simpler strategy so as to build the admissible stress field is addressed by requiring only an extra decomposition of the stiffness matrix. Then, the sparse regularization is added to the original constitutive relation error objective function to circumvent the ill-posedness of the inverse problem. Finally, the solution of this nonlinear optimization problem is solved by the alternating minimization method. The proposed method has the advantage that only measurement power spectrum density data from few limited sensors are needed in the inverse analysis. Numerical and experimental results show the effectiveness and robustness of this approach
The SPARSE model for the prediction of water stress and evapotranspiration components from thermal infra-red data and its evaluation over irrigated and rainfed wheat
Evapotranspiration is an important component of the water cycle, especially in semi-arid lands. A way to quantify the spatial distribution of evapotranspiration and water stress from remote-sensing data is to exploit the available surface temperature as a signature of the surface energy balance. Remotely sensed energy balance models enable one to estimate stress levels and, in turn, the water status of continental surfaces. Dual-source models are particularly useful since they allow derivation of a rough estimate of the water stress of the vegetation instead of that of a soil–vegetation composite. They either assume that the soil and the vegetation interact almost independently with the atmosphere (patch approach corresponding to a parallel resistance scheme) or are tightly coupled (layer approach corresponding to a series resistance scheme). The water status of both sources is solved simultaneously from a single surface temperature observation based on a realistic underlying assumption which states that, in most cases, the vegetation is unstressed, and that if the vegetation is stressed, evaporation is negligible. In the latter case, if the vegetation stress is not properly accounted for, the resulting evaporation will decrease to unrealistic levels (negative fluxes) in order to maintain the same total surface temperature. This work assesses the retrieval performances of total and component evapotranspiration as well as surface and plant water stress levels by (1) proposing a new dual-source model named Soil Plant Atmosphere and Remote Sensing Evapotranspiration (SPARSE) in two versions (parallel and series resistance networks) based on the TSEB (Two-Source Energy Balance model, Norman et al., 1995) model rationale as well as state-of-the-art formulations of turbulent and radiative exchange, (2) challenging the limits of the underlying hypothesis for those two versions through a synthetic retrieval test and (3) testing the water stress retrievals (vegetation water stress and moisture-limited soil evaporation) against in situ data over contrasted test sites (irrigated and rainfed wheat). We demonstrated with those two data sets that the SPARSE series model is more robust to component stress retrieval for this cover type, that its performance increases by using bounding relationships based on potential conditions (root mean square error lowered by up to 11 W m−2 from values of the order of 50–80 W m−2), and that soil evaporation retrieval is generally consistent with an independent estimate from observed soil moisture evolution
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