118 research outputs found
Large Deviations for Non-Markovian Diffusions and a Path-Dependent Eikonal Equation
This paper provides a large deviation principle for Non-Markovian, Brownian
motion driven stochastic differential equations with random coefficients.
Similar to Gao and Liu \cite{GL}, this extends the corresponding results
collected in Freidlin and Wentzell \cite{FreidlinWentzell}. However, we use a
different line of argument, adapting the PDE method of Fleming \cite{Fleming}
and Evans and Ishii \cite{EvansIshii} to the path-dependent case, by using
backward stochastic differential techniques. Similar to the Markovian case, we
obtain a characterization of the action function as the unique bounded solution
of a path-dependent version of the Eikonal equation. Finally, we provide an
application to the short maturity asymptotics of the implied volatility surface
in financial mathematics
DRS: Dynamic Resource Scheduling for Real-Time Analytics over Fast Streams
In a data stream management system (DSMS), users register continuous queries,
and receive result updates as data arrive and expire. We focus on applications
with real-time constraints, in which the user must receive each result update
within a given period after the update occurs. To handle fast data, the DSMS is
commonly placed on top of a cloud infrastructure. Because stream properties
such as arrival rates can fluctuate unpredictably, cloud resources must be
dynamically provisioned and scheduled accordingly to ensure real-time response.
It is quite essential, for the existing systems or future developments, to
possess the ability of scheduling resources dynamically according to the
current workload, in order to avoid wasting resources, or failing in delivering
correct results on time. Motivated by this, we propose DRS, a novel dynamic
resource scheduler for cloud-based DSMSs. DRS overcomes three fundamental
challenges: (a) how to model the relationship between the provisioned resources
and query response time (b) where to best place resources; and (c) how to
measure system load with minimal overhead. In particular, DRS includes an
accurate performance model based on the theory of \emph{Jackson open queueing
networks} and is capable of handling \emph{arbitrary} operator topologies,
possibly with loops, splits and joins. Extensive experiments with real data
confirm that DRS achieves real-time response with close to optimal resource
consumption.Comment: This is the our latest version with certain modificatio
Normalization Enhances Generalization in Visual Reinforcement Learning
Recent advances in visual reinforcement learning (RL) have led to impressive
success in handling complex tasks. However, these methods have demonstrated
limited generalization capability to visual disturbances, which poses a
significant challenge for their real-world application and adaptability. Though
normalization techniques have demonstrated huge success in supervised and
unsupervised learning, their applications in visual RL are still scarce. In
this paper, we explore the potential benefits of integrating normalization into
visual RL methods with respect to generalization performance. We find that,
perhaps surprisingly, incorporating suitable normalization techniques is
sufficient to enhance the generalization capabilities, without any additional
special design. We utilize the combination of two normalization techniques,
CrossNorm and SelfNorm, for generalizable visual RL. Extensive experiments are
conducted on DMControl Generalization Benchmark and CARLA to validate the
effectiveness of our method. We show that our method significantly improves
generalization capability while only marginally affecting sample efficiency. In
particular, when integrated with DrQ-v2, our method enhances the test
performance of DrQ-v2 on CARLA across various scenarios, from 14% of the
training performance to 97%
Few-photon single ionization of cold rubidium in the over-the-barrier regime
Photoionization of the rubidium (Rb) atoms cooled in a magneto-optical trap,
characterized by the coexistence of the ground 5 and the excited
5 states, is investigated experimentally and theoretically with the
400 nm femtosecond laser pulses at intensities of W/cm -
W/cm. Recoil-ion momentum distribution (RIMD) of Rb
exhibits rich ring-like structures and their energies correspond to one-photon
ionization of the 5 state, two-photon and three-photon ionizations of
the 5 state, respectively. With the increasing of , we find that
experimental signals near zero-momentum (NZM) in RIMDs resulted from the
5 state enhance dramatically and its peaked Rb momenta dwindle
obviously while that from the 5 state is maintained. Meanwhile, the
ion-yield ratio of the 5 over the 5 states varies from to
as increases. These features indicate a transition from
perturbative ionization to strong-perturbative ionization for the 5
state. Numerical simulations by solving the time-dependent Schr\"odinger
equation (TDSE) can qualitatively explain the measurements of RIMD, photoion
angular distributions, as well as ion-yield ratio. However, some discrepancies
still exist, especially for the NZM dip, which could stem from the
electron-electron correlation that is neglected in the present TDSE simulations
since we have adopted the single-active-electron approximation
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