74,615 research outputs found
Realistic Cost for the Model of Coherent Computing
For the model of so-called coherent computing recently proposed by Yamamoto et al. [Y. Yamamoto et al., New Gen. Comput. 30 (2012) 327-355], a theoretical analysis of the success probability is given. Although it was claimed as their prospect that the Ising spin configuration problem would be efficiently solvable in the model, here it is shown that the probability of finding a desired spin configuration decreases exponentially in the number of spins for certain hard instances. The model is thus physically unfeasible for solving the problem within a polynomial cost
Optimized Blind Gamma-ray Pulsar Searches at Fixed Computing Budget
The sensitivity of blind gamma-ray pulsar searches in multiple years worth of
photon data, as from the Fermi LAT, is primarily limited by the finite
computational resources available. Addressing this "needle in a haystack"
problem, we here present methods for optimizing blind searches to achieve the
highest sensitivity at fixed computing cost. For both coherent and semicoherent
methods, we consider their statistical properties and study their search
sensitivity under computational constraints. The results validate a multistage
strategy, where the first stage scans the entire parameter space using an
efficient semicoherent method and promising candidates are then refined through
a fully coherent analysis. We also find that for the first stage of a blind
search incoherent harmonic summing of powers is not worthwhile at fixed
computing cost for typical gamma-ray pulsars. Further enhancing sensitivity, we
present efficiency-improved interpolation techniques for the semicoherent
search stage. Via realistic simulations we demonstrate that overall these
optimizations can significantly lower the minimum detectable pulsed fraction by
almost 50% at the same computational expense.Comment: 22 pages, 13 figures; includes ApJ proof correction
Empirically extending the range of validity of parameter-space metrics for all-sky searches for gravitational-wave pulsars
All-sky searches for gravitational-wave pulsars are generally limited in
sensitivity by the finite availability of computing resources. Semicoherent
searches are a common method of maximizing search sensitivity given a fixed
computing budget. The work of Wette and Prix [Phys. Rev. D 88, 123005 (2013)]
and Wette [Phys. Rev. D 92, 082003 (2015)] developed a semicoherent search
method which uses metrics to construct the banks of pulsar signal templates
needed to search the parameter space of interest. In this work we extend the
range of validity of the parameter-space metrics using an empirically-derived
relationship between the resolution (or mismatch) of the template banks and the
mismatch of the overall search. This work has important consequences for the
optimization of metric-based semicoherent searches at fixed computing cost.Comment: 14 pages, 5 figures, 4 table
Visual Servoing from Deep Neural Networks
We present a deep neural network-based method to perform high-precision,
robust and real-time 6 DOF visual servoing. The paper describes how to create a
dataset simulating various perturbations (occlusions and lighting conditions)
from a single real-world image of the scene. A convolutional neural network is
fine-tuned using this dataset to estimate the relative pose between two images
of the same scene. The output of the network is then employed in a visual
servoing control scheme. The method converges robustly even in difficult
real-world settings with strong lighting variations and occlusions.A
positioning error of less than one millimeter is obtained in experiments with a
6 DOF robot.Comment: fixed authors lis
Lagrangian Data-Driven Reduced Order Modeling of Finite Time Lyapunov Exponents
There are two main strategies for improving the projection-based reduced
order model (ROM) accuracy: (i) improving the ROM, i.e., adding new terms to
the standard ROM; and (ii) improving the ROM basis, i.e., constructing ROM
bases that yield more accurate ROMs. In this paper, we use the latter. We
propose new Lagrangian inner products that we use together with Eulerian and
Lagrangian data to construct new Lagrangian ROMs. We show that the new
Lagrangian ROMs are orders of magnitude more accurate than the standard
Eulerian ROMs, i.e., ROMs that use standard Eulerian inner product and data to
construct the ROM basis. Specifically, for the quasi-geostrophic equations, we
show that the new Lagrangian ROMs are more accurate than the standard Eulerian
ROMs in approximating not only Lagrangian fields (e.g., the finite time
Lyapunov exponent (FTLE)), but also Eulerian fields (e.g., the streamfunction).
We emphasize that the new Lagrangian ROMs do not employ any closure modeling to
model the effect of discarded modes (which is standard procedure for
low-dimensional ROMs of complex nonlinear systems). Thus, the dramatic increase
in the new Lagrangian ROMs' accuracy is entirely due to the novel Lagrangian
inner products used to build the Lagrangian ROM basis
Compressive Sensing for Spread Spectrum Receivers
With the advent of ubiquitous computing there are two design parameters of
wireless communication devices that become very important power: efficiency and
production cost. Compressive sensing enables the receiver in such devices to
sample below the Shannon-Nyquist sampling rate, which may lead to a decrease in
the two design parameters. This paper investigates the use of Compressive
Sensing (CS) in a general Code Division Multiple Access (CDMA) receiver. We
show that when using spread spectrum codes in the signal domain, the CS
measurement matrix may be simplified. This measurement scheme, named
Compressive Spread Spectrum (CSS), allows for a simple, effective receiver
design. Furthermore, we numerically evaluate the proposed receiver in terms of
bit error rate under different signal to noise ratio conditions and compare it
with other receiver structures. These numerical experiments show that though
the bit error rate performance is degraded by the subsampling in the CS-enabled
receivers, this may be remedied by including quantization in the receiver
model. We also study the computational complexity of the proposed receiver
design under different sparsity and measurement ratios. Our work shows that it
is possible to subsample a CDMA signal using CSS and that in one example the
CSS receiver outperforms the classical receiver.Comment: 11 pages, 11 figures, 1 table, accepted for publication in IEEE
Transactions on Wireless Communication
Can biological quantum networks solve NP-hard problems?
There is a widespread view that the human brain is so complex that it cannot
be efficiently simulated by universal Turing machines. During the last decades
the question has therefore been raised whether we need to consider quantum
effects to explain the imagined cognitive power of a conscious mind.
This paper presents a personal view of several fields of philosophy and
computational neurobiology in an attempt to suggest a realistic picture of how
the brain might work as a basis for perception, consciousness and cognition.
The purpose is to be able to identify and evaluate instances where quantum
effects might play a significant role in cognitive processes.
Not surprisingly, the conclusion is that quantum-enhanced cognition and
intelligence are very unlikely to be found in biological brains. Quantum
effects may certainly influence the functionality of various components and
signalling pathways at the molecular level in the brain network, like ion
ports, synapses, sensors, and enzymes. This might evidently influence the
functionality of some nodes and perhaps even the overall intelligence of the
brain network, but hardly give it any dramatically enhanced functionality. So,
the conclusion is that biological quantum networks can only approximately solve
small instances of NP-hard problems.
On the other hand, artificial intelligence and machine learning implemented
in complex dynamical systems based on genuine quantum networks can certainly be
expected to show enhanced performance and quantum advantage compared with
classical networks. Nevertheless, even quantum networks can only be expected to
efficiently solve NP-hard problems approximately. In the end it is a question
of precision - Nature is approximate.Comment: 38 page
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