817 research outputs found
Irreversible Markov chains in spin models: Topological excitations
We analyze the convergence of the irreversible event-chain Monte Carlo
algorithm for continuous spin models in the presence of topological
excitations. In the two-dimensional XY model, we show that the local nature of
the Markov-chain dynamics leads to slow decay of vortex-antivortex correlations
while spin waves decorrelate very quickly. Using a Frechet description of the
maximum vortex-antivortex distance, we quantify the contributions of
topological excitations to the equilibrium correlations, and show that they
vary from a dynamical critical exponent z \sim 2 at the critical temperature to
z \sim 0 in the limit of zero temperature. We confirm the event-chain
algorithm's fast relaxation (corresponding to z = 0) of spin waves in the
harmonic approximation to the XY model. Mixing times (describing the approach
towards equilibrium from the least favorable initial state) however remain much
larger than equilibrium correlation times at low temperatures. We also describe
the respective influence of topological monopole-antimonopole excitations and
of spin waves on the event-chain dynamics in the three-dimensional Heisenberg
model.Comment: 5 pages, 5 figure
Mixing and perfect sampling in one-dimensional particle systems
We study the approach to equilibrium of the event-chain Monte Carlo (ECMC)
algorithm for the one-dimensional hard-sphere model. Using the connection to
the coupon-collector problem, we prove that a specific version of this local
irreversible Markov chain realizes perfect sampling in O(N^2 log N) events,
whereas the reversible local Metropolis algorithm requires O(N^3 log N) time
steps for mixing. This confirms a special case of an earlier conjecture about
O(N^2 log N) scaling of mixing times of ECMC and of the forward Metropolis
algorithm, its discretized variant. We furthermore prove that sequential ECMC
(with swaps) realizes perfect sampling in O(N^2) events. Numerical simulations
indicate a cross-over towards O(N^2 log N) mixing for the sequential forward
swap Metropolis algorithm, that we introduce here. We point out open
mathematical questions and possible applications of our findings to
higher-dimensional statistical-physics models.Comment: 7 pages, 7 figure
Event-chain Monte Carlo with factor fields
International audienceWe study the dynamics of one-dimensional (1D) interacting particles simulated with the event-chain Monte Carlo algorithm (ECMC). We argue that previous versions of the algorithm suffer from a mismatch in the factor potential between different particle pairs (factors) and show that in 1D models, this mismatch is overcome by factor fields. ECMC with factor fields is motivated, in 1D, for the harmonic model, and validated for the Lennard-Jones model as well as for hard spheres. In 1D particle systems with short-range interactions, autocorrelation times generally scale with the second power of the system size for reversible Monte Carlo dynamics, and with its first power for regular ECMC and for molecular-dynamics. We show, using numerical simulations, that they grow only with the square root of the systems size for ECMC with factor fields. Mixing times, which bound the time to reach equilibrium from an arbitrary initial configuration, grow with the first power of the system size
Theoretical Explanation of Activation Sparsity through Flat Minima and Adversarial Robustness
A recent empirical observation of activation sparsity in MLP layers offers an
opportunity to drastically reduce computation costs for free. Despite several
works attributing it to training dynamics, the theoretical explanation of
activation sparsity's emergence is restricted to shallow networks, small
training steps well as modified training, even though the sparsity has been
found in deep models trained by vanilla protocols for large steps. To fill the
three gaps, we propose the notion of gradient sparsity as the source of
activation sparsity and a theoretical explanation based on it that explains
gradient sparsity and then activation sparsity as necessary steps to
adversarial robustness w.r.t. hidden features and parameters, which is
approximately the flatness of minima for well-learned models. The theory
applies to standardly trained LayerNorm-ed pure MLPs, and further to
Transformers or other architectures if noises are added to weights during
training. To eliminate other sources of flatness when arguing sparsities'
necessity, we discover the phenomenon of spectral concentration, i.e., the
ratio between the largest and the smallest non-zero singular values of weight
matrices is small. We utilize random matrix theory (RMT) as a powerful
theoretical tool to analyze stochastic gradient noises and discuss the
emergence of spectral concentration. With these insights, we propose two
plug-and-play modules for both training from scratch and sparsity finetuning,
as well as one radical modification that only applies to from-scratch training.
Another under-testing module for both sparsity and flatness is also immediate
from our theories. Validational experiments are conducted to verify our
explanation. Experiments for productivity demonstrate modifications'
improvement in sparsity, indicating further theoretical cost reduction in both
training and inference
Research of growth mechanism of ceramic coatings fabricated by micro-arc oxidation on magnesium alloys at high current mode
AbstractMicro-arc oxidation (MAO) coatings of ZK60 magnesium alloys were formed in a self-developed dual electrolyte composed of sodium silicate and phosphate at the high constant current of 1.8 A (15 A/dm2). The MAO process and growth mechanism were investigated by scanning electron microscopy (SEM) coupled with an energy dispersive spectrometer (EDS), confocal laser scanning microscopy and X-ray diffraction (XRD). The results indicate that the growth process of MAO coating mainly goes through “forming → puncturing → rapid growth of micro-arc oxidation →large arc discharge → self-repairing”. The coating grows inward and outward at the same time in the initial stage, but outward growth of the coating is dominant later. Mg, Mg2SiO4 and MgO are the main phases of ceramic coating
A compact robotic device for upper-limb reaching rehabilitation
This paper presents a compact linear-motion robotic device for upper-extremity reaching rehabilitation. Starting from conceptual design, the paper describes electronic circuit design and program development. The work develops a prototype that provides active and passive rehabilitation training. In active training, subjects actively move their arm with assistive or resistive force from the device to finish predefined displacement and force profiles. In passive training, subjects remain passive while the device moves the limb following the pre-defined displacement profile. Engineering specifications with adequate safety factor are determined and standard electronic and readily available mechanical components are exploited to keep the total cost low
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