7,267 research outputs found
A robust verification of the quantum nature of light
We present a conditional experiment involving a parametric amplifier and an
avalanche photodetector to generate highly nonclassical states of the radiation
field. The nonclassicality is robust against amplifier gain, detector
efficiency and dark counts. At the output all the generalized Wigner functions
have negative values, and this is exploited in order to reveal the
nonclassicality through quantum homodyne tomography
Learning Barrier Functions for Constrained Motion Planning with Dynamical Systems
Stable dynamical systems are a flexible tool to plan robotic motions in
real-time. In the robotic literature, dynamical system motions are typically
planned without considering possible limitations in the robot's workspace. This
work presents a novel approach to learn workspace constraints from human
demonstrations and to generate motion trajectories for the robot that lie in
the constrained workspace. Training data are incrementally clustered into
different linear subspaces and used to fit a low dimensional representation of
each subspace. By considering the learned constraint subspaces as zeroing
barrier functions, we are able to design a control input that keeps the system
trajectory within the learned bounds. This control input is effectively
combined with the original system dynamics preserving eventual asymptotic
properties of the unconstrained system. Simulations and experiments on a real
robot show the effectiveness of the proposed approach
Characterizing the time variability in magnetized neutrino--cooled accretion disks: signatures of the gamma-ray burst central engine
The central engine of Gamma Ray Bursts is hidden from direct probing with
photons mainly due to the high densities involved. Inferences on their
properties are thus made from their cosmological setting, energetics,
low-energy counterparts and variability. If GRBs are powered by hypercritical
accretion onto compact objects, on small spatial scales the flow will exhibit
fluctuations, which could in principle be reflected in the power output of the
central engine and ultimately in the high energy prompt emission. Here we
address this issue by characterizing the variability in neutrino cooled
accretion flows through local shearing box simulations with magnetic fields,
and then convolving them on a global scale with large scale dynamical
simulations of accretion disks. The resulting signature is characteristic, and
sensitive to the details of the cooling mechanism, providing in principle a
discriminant for GRB central engine properties.Comment: Accepted for publication in ApJ Letter
PRETZEL: Opening the Black Box of Machine Learning Prediction Serving Systems
Machine Learning models are often composed of pipelines of transformations.
While this design allows to efficiently execute single model components at
training time, prediction serving has different requirements such as low
latency, high throughput and graceful performance degradation under heavy load.
Current prediction serving systems consider models as black boxes, whereby
prediction-time-specific optimizations are ignored in favor of ease of
deployment. In this paper, we present PRETZEL, a prediction serving system
introducing a novel white box architecture enabling both end-to-end and
multi-model optimizations. Using production-like model pipelines, our
experiments show that PRETZEL is able to introduce performance improvements
over different dimensions; compared to state-of-the-art approaches PRETZEL is
on average able to reduce 99th percentile latency by 5.5x while reducing memory
footprint by 25x, and increasing throughput by 4.7x.Comment: 16 pages, 14 figures, 13th USENIX Symposium on Operating Systems
Design and Implementation (OSDI), 201
Explicit upper bounds for the number of primes simultaneously representable by any set of irreducible polynomials
Using an explicit version of Selberg's upper sieve, we obtain explicit upper
bounds for the number of such that a non-empty set of irreducible
polynomials with integer coefficients are simultaneously prime; this
set can contain as many polynomials as desired. To demonstrate, we present
computations for some irreducible polynomials and obtain an explicit upper
bound for the number of Sophie Germain primes up to , which have practical
applications in cryptography.Comment: 18 pages, one table, comments welcome
Being Realist about Bayes, and the Predictive Processing Theory of Mind
Some naturalistic philosophers of mind subscribing to the predictive processing theory of mind have adopted a realist attitude towards the results of Bayesian cognitive science. In this paper, we argue that this realist attitude is unwarranted. The Bayesian research program in cognitive science does not possess special epistemic virtues over alternative approaches for explaining mental phenomena involving uncertainty. In particular, the Bayesian approach is not simpler, more unifying, or more rational than alternatives. It is also contentious that the Bayesian approach is overall better supported by the empirical evidence. So, to develop philosophical theories of mind on the basis of a realist interpretation of results from Bayesian cognitive science is unwarranted. Naturalistic philosophers of mind should instead adopt an anti-realist attitude towards these results and remain agnostic as to whether Bayesian models are true. For continuing on with an exclusive focus and praise of Bayes within debates about the predictive processing theory will impede progress in philosophical understanding of scientific practice in computational cognitive science as well as of the architecture of the mind
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