985 research outputs found
Precision shooting: Sampling long transition pathways
The kinetics of collective rearrangements in solution, such as protein
folding and nanocrystal phase transitions, often involve free energy barriers
that are both long and rough. Applying methods of transition path sampling to
harvest simulated trajectories that exemplify such processes is typically made
difficult by a very low acceptance rate for newly generated trajectories. We
address this problem by introducing a new generation algorithm based on the
linear short-time behavior of small disturbances in phase space. Using this
``precision shooting'' technique, arbitrarily small disturbances can be
propagated in time, and any desired acceptance ratio of shooting moves can be
obtained. We demonstrate the method for a simple but computationally
problematic isomerization process in a dense liquid of soft spheres. We also
discuss its applicability to barrier crossing events involving metastable
intermediate states.Comment: 9 pages, 12 figures, submitted to J. Chem. Phy
Combining Adversarial Guarantees and Stochastic Fast Rates in Online Learning
We consider online learning algorithms that guarantee worst-case regret rates
in adversarial environments (so they can be deployed safely and will perform
robustly), yet adapt optimally to favorable stochastic environments (so they
will perform well in a variety of settings of practical importance). We
quantify the friendliness of stochastic environments by means of the well-known
Bernstein (a.k.a. generalized Tsybakov margin) condition. For two recent
algorithms (Squint for the Hedge setting and MetaGrad for online convex
optimization) we show that the particular form of their data-dependent
individual-sequence regret guarantees implies that they adapt automatically to
the Bernstein parameters of the stochastic environment. We prove that these
algorithms attain fast rates in their respective settings both in expectation
and with high probability
Adaptive Hedge
Most methods for decision-theoretic online learning are based on the Hedge
algorithm, which takes a parameter called the learning rate. In most previous
analyses the learning rate was carefully tuned to obtain optimal worst-case
performance, leading to suboptimal performance on easy instances, for example
when there exists an action that is significantly better than all others. We
propose a new way of setting the learning rate, which adapts to the difficulty
of the learning problem: in the worst case our procedure still guarantees
optimal performance, but on easy instances it achieves much smaller regret. In
particular, our adaptive method achieves constant regret in a probabilistic
setting, when there exists an action that on average obtains strictly smaller
loss than all other actions. We also provide a simulation study comparing our
approach to existing methods.Comment: This is the full version of the paper with the same name that will
appear in Advances in Neural Information Processing Systems 24 (NIPS 2011),
2012. The two papers are identical, except that this version contains an
extra section of Additional Materia
The Differential Impact of Retirement on Informal Caregiving, Volunteering, and Grandparenting: Results of a 3-Year Panel Study
Providing informal care next to paid work:explaining care-giving gratification, burden and stress among older workers
Metastability in pressure-induced structural transformations of CdSe/ZnS core/shell nanocrystals
The kinetics and thermodynamics of structural transformations under pressure
depend strongly on particle size due to the influence of surface free energy.
By suitable design of surface structure, composition, and passivation it is
possible, in principle, to prepare nanocrystals in structures inaccessible to
bulk materials. However, few realizations of such extreme size-dependent
behavior exist. Here we show with molecular dynamics computer simulation that
in a model of CdSe/ZnS core/shell nanocrystals the core high pressure structure
can be made metastable under ambient conditions by tuning the thickness of the
shell. In nanocrystals with thick shells, we furthermore observe a wurtzite to
NiAs transformation, which does not occur in the pure bulk materials. These
phenomena are linked to a fundamental change in the atomistic transformation
mechanism from heterogenous nucleation at the surface to homogenous nucleation
in the crystal core. Our results suggest a new route towards expanding the
range of available nanoscale materials
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