47,351 research outputs found
Noise Robustness of a Combined Phase Retrieval and Reconstruction Method for Phase-Contrast Tomography
Classical reconstruction methods for phase-contrast tomography consist of two
stages: phase retrieval and tomographic reconstruction. A novel algebraic
method combining the two was suggested by Kostenko et al. (Opt. Express, 21,
12185, 2013) and preliminary results demonstrating improved reconstruction
compared to a two-stage method given. Using simulated free-space propagation
experiments with a single sample-detector distance, we thoroughly compare the
novel method with the two-stage method to address limitations of the
preliminary results. We demonstrate that the novel method is substantially more
robust towards noise; our simulations point to a possible reduction in counting
times by an order of magnitude
On Relaxed Averaged Alternating Reflections (RAAR) Algorithm for Phase Retrieval from Structured Illuminations
In this paper, as opposed to the random phase masks, the structured
illuminations with a pixel-dependent deterministic phase shift are considered
to derandomize the model setup. The RAAR algorithm is modified to adapt to two
or more diffraction patterns, and the modified RAAR algorithm operates in
Fourier domain rather than space domain. The local convergence of the RAAR
algorithm is proved by some eigenvalue analysis. Numerical simulations is
presented to demonstrate the effectiveness and stability of the algorithm
compared to the HIO (Hybrid Input-Output) method. The numerical performances
show the global convergence of the RAAR in our tests.Comment: 17 pages, 26 figures, submitting to Inverse Problem
Cortical free association dynamics: distinct phases of a latching network
A Potts associative memory network has been proposed as a simplified model of
macroscopic cortical dynamics, in which each Potts unit stands for a patch of
cortex, which can be activated in one of S local attractor states. The internal
neuronal dynamics of the patch is not described by the model, rather it is
subsumed into an effective description in terms of graded Potts units, with
adaptation effects both specific to each attractor state and generic to the
patch. If each unit, or patch, receives effective (tensor) connections from C
other units, the network has been shown to be able to store a large number p of
global patterns, or network attractors, each with a fraction a of the units
active, where the critical load p_c scales roughly like p_c ~ (C S^2)/(a
ln(1/a)) (if the patterns are randomly correlated). Interestingly, after
retrieving an externally cued attractor, the network can continue jumping, or
latching, from attractor to attractor, driven by adaptation effects. The
occurrence and duration of latching dynamics is found through simulations to
depend critically on the strength of local attractor states, expressed in the
Potts model by a parameter w. Here we describe with simulations and then
analytically the boundaries between distinct phases of no latching, of
transient and sustained latching, deriving a phase diagram in the plane w-T,
where T parametrizes thermal noise effects. Implications for real cortical
dynamics are briefly reviewed in the conclusions
Automated user modeling for personalized digital libraries
Digital libraries (DL) have become one of the most typical ways of accessing any kind of digitalized information. Due to this key role, users welcome any improvements on the services they receive from digital libraries. One trend used to
improve digital services is through personalization. Up to now, the most common approach for personalization in digital libraries has been user-driven. Nevertheless, the design of efficient personalized services has to be done, at least in part, in
an automatic way. In this context, machine learning techniques automate the process of constructing user models. This paper proposes a new approach to construct digital libraries that satisfy user’s necessity for information: Adaptive Digital Libraries, libraries that automatically learn user preferences and goals and personalize their interaction using this information
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