85,380 research outputs found
Focal-plane wavefront sensing with high-order adaptive optics systems
We investigate methods to calibrate the non-common path aberrations at an
adaptive optics system having a wavefront-correcting device working at an
extremely high resolution (larger than 150x150). We use focal-plane images
collected successively, the corresponding phase-diversity information and
numerically efficient algorithms to calculate the required wavefront updates.
The wavefront correction is applied iteratively until the algorithms converge.
Different approaches are studied. In addition of the standard Gerchberg-Saxton
algorithm, we test the extension of the Fast & Furious algorithm that uses
three images and creates an estimate of the pupil amplitudes. We also test
recently proposed phase-retrieval methods based on convex optimisation. The
results indicate that in the framework we consider, the calibration task is
easiest with algorithms similar to the Fast & Furious.Comment: 11 pages, 7 figures, published in SPIE proceeding
Adaptive Document Retrieval for Deep Question Answering
State-of-the-art systems in deep question answering proceed as follows: (1)
an initial document retrieval selects relevant documents, which (2) are then
processed by a neural network in order to extract the final answer. Yet the
exact interplay between both components is poorly understood, especially
concerning the number of candidate documents that should be retrieved. We show
that choosing a static number of documents -- as used in prior research --
suffers from a noise-information trade-off and yields suboptimal results. As a
remedy, we propose an adaptive document retrieval model. This learns the
optimal candidate number for document retrieval, conditional on the size of the
corpus and the query. We report extensive experimental results showing that our
adaptive approach outperforms state-of-the-art methods on multiple benchmark
datasets, as well as in the context of corpora with variable sizes.Comment: EMNLP 201
Multilingual adaptive search for digital libraries
This paper describes a framework for Adaptive Multilingual Information Retrieval (AMIR) which allows multilingual resource discovery and delivery using on-the-fly machine translation of documents and queries. Result documents
are presented to the user in a contextualised manner. Challenges and affordances of both Adaptive and Multilingual IR, with a particular focus on Digital Libraries, are detailed. The framework components are motivated by a series of results from experiments on query logs and documents from The European Library. We conclude that factoring adaptivity and multilinguality aspects into the search process can enhance the user’s experience with online Digital Libraries
Topic-based mixture language modelling
This paper describes an approach for constructing a mixture of language models based on simple statistical notions of semantics using probabilistic models developed for information retrieval. The approach encapsulates corpus-derived semantic information and is able to model varying styles of text. Using such information, the corpus texts are clustered in an unsupervised manner and a mixture of topic-specific language models is automatically created. The principal contribution of this work is to characterise the document space resulting from information retrieval techniques and to demonstrate the approach for mixture language modelling.
A comparison is made between manual and automatic clustering in order to elucidate how the global content information is expressed in the space. We also compare (in terms of association with manual clustering and language modelling accuracy) alternative term-weighting schemes and the effect of singular value decomposition dimension reduction (latent semantic analysis). Test set perplexity results using the British National Corpus indicate that the approach can improve the potential of statistical language modelling. Using an adaptive procedure, the conventional model may be tuned to track text data with a slight increase in computational cost
Factory of realities: on the emergence of virtual spatiotemporal structures
The ubiquitous nature of modern Information Retrieval and Virtual World give
rise to new realities. To what extent are these "realities" real? Which
"physics" should be applied to quantitatively describe them? In this essay I
dwell on few examples. The first is Adaptive neural networks, which are not
networks and not neural, but still provide service similar to classical ANNs in
extended fashion. The second is the emergence of objects looking like
Einsteinian spacetime, which describe the behavior of an Internet surfer like
geodesic motion. The third is the demonstration of nonclassical and even
stronger-than-quantum probabilities in Information Retrieval, their use.
Immense operable datasets provide new operationalistic environments, which
become to greater and greater extent "realities". In this essay, I consider the
overall Information Retrieval process as an objective physical process,
representing it according to Melucci metaphor in terms of physical-like
experiments. Various semantic environments are treated as analogs of various
realities. The readers' attention is drawn to topos approach to physical
theories, which provides a natural conceptual and technical framework to cope
with the new emerging realities.Comment: 21 p
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Adaptive Frequency Neural Networks for Dynamic Pulse and Metre Perception.
Beat induction, the means by which humans listen to music and perceive a steady pulse, is achieved via a perceptualand cognitive process. Computationally modelling this phenomenon is an open problem, especially when processing expressive shaping of the music such as tempo change.To meet this challenge we propose Adaptive Frequency Neural Networks (AFNNs), an extension of Gradient Frequency Neural Networks (GFNNs).GFNNs are based on neurodynamic models and have been applied successfully to a range of difficult music perception problems including those with syncopated and polyrhythmic stimuli. AFNNs extend GFNNs by applying a Hebbian learning rule to the oscillator frequencies. Thus the frequencies in an AFNN adapt to the stimulus through an attraction to local areas of resonance, and allow for a great dimensionality reduction in the network.Where previous work with GFNNs has focused on frequency and amplitude responses, we also consider phase information as critical for pulse perception. Evaluating the time-based output, we find significantly improved re-sponses of AFNNs compared to GFNNs to stimuli with both steady and varying pulse frequencies. This leads us to believe that AFNNs could replace the linear filtering methods commonly used in beat tracking and tempo estimationsystems, and lead to more accurate methods
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Estimation of physical variables from multichannel remotely sensed imagery using a neural network: Application to rainfall estimation
Satellite-based remotely sensed data have the potential to provide hydrologically relevant information about spatially and temporally varying physical variables. A methodology for estimating such variables from multichannel remotely sensed data is presented; the approach is based on a modified counterpropagation neural network (MCPN) and is both effective and efficient at building complex nonlinear input-output function mappings from large amounts of data. An application to high-resolution estimation of the spatial and temporal variation of surface rainfall using geostationary satellite infrared and visible imagery is presented. Test results also indicate that spatially and temporally sparse ground-based observations can be assimilated via an adaptive implementation of the MCPN method, thereby allowing on-line improvement of the estimates
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