3,379 research outputs found
Multilingual Adaptation of RNN Based ASR Systems
In this work, we focus on multilingual systems based on recurrent neural
networks (RNNs), trained using the Connectionist Temporal Classification (CTC)
loss function. Using a multilingual set of acoustic units poses difficulties.
To address this issue, we proposed Language Feature Vectors (LFVs) to train
language adaptive multilingual systems. Language adaptation, in contrast to
speaker adaptation, needs to be applied not only on the feature level, but also
to deeper layers of the network. In this work, we therefore extended our
previous approach by introducing a novel technique which we call "modulation".
Based on this method, we modulated the hidden layers of RNNs using LFVs. We
evaluated this approach in both full and low resource conditions, as well as
for grapheme and phone based systems. Lower error rates throughout the
different conditions could be achieved by the use of the modulation.Comment: 5 pages, 1 figure, to appear in 2018 IEEE International Conference on
Acoustics, Speech and Signal Processing (ICASSP 2018
Performance Prediction of Nonbinary Forward Error Correction in Optical Transmission Experiments
In this paper, we compare different metrics to predict the error rate of
optical systems based on nonbinary forward error correction (FEC). It is shown
that the correct metric to predict the performance of coded modulation based on
nonbinary FEC is the mutual information. The accuracy of the prediction is
verified in a detailed example with multiple constellation formats, FEC
overheads in both simulations and optical transmission experiments over a
recirculating loop. It is shown that the employed FEC codes must be universal
if performance prediction based on thresholds is used. A tutorial introduction
into the computation of the threshold from optical transmission measurements is
also given.Comment: submitted to IEEE/OSA Journal of Lightwave Technolog
NetLSD: Hearing the Shape of a Graph
Comparison among graphs is ubiquitous in graph analytics. However, it is a
hard task in terms of the expressiveness of the employed similarity measure and
the efficiency of its computation. Ideally, graph comparison should be
invariant to the order of nodes and the sizes of compared graphs, adaptive to
the scale of graph patterns, and scalable. Unfortunately, these properties have
not been addressed together. Graph comparisons still rely on direct approaches,
graph kernels, or representation-based methods, which are all inefficient and
impractical for large graph collections.
In this paper, we propose the Network Laplacian Spectral Descriptor (NetLSD):
the first, to our knowledge, permutation- and size-invariant, scale-adaptive,
and efficiently computable graph representation method that allows for
straightforward comparisons of large graphs. NetLSD extracts a compact
signature that inherits the formal properties of the Laplacian spectrum,
specifically its heat or wave kernel; thus, it hears the shape of a graph. Our
evaluation on a variety of real-world graphs demonstrates that it outperforms
previous works in both expressiveness and efficiency.Comment: KDD '18: The 24th ACM SIGKDD International Conference on Knowledge
Discovery & Data Mining, August 19--23, 2018, London, United Kingdo
Efficient Portfolios in the Asset Liability Context
The set of efficient portfolios in an asset liability model is discussed in detail. The occurence of liabilities leads to a parallel shift of the efficient set. Under an appropriate assumption, the shift vector can be decomposed in different components. For the special case, where the investor is a pension fund, it is shown how shortfall constraints can be reconciled with efficiency. Finally, optimality conditions for the market portfolio are derive
Effect of large- and small- bodied zooplankton on phytoplankton in a eutrophic oxbow
Macrozooplankton and microzooplankton effects on the phytoplankton were measured in situ in a eutrophic lake. Indigenous phytoplankton were incubated for 5 days in 301 mesocosms with either the macro- and microzooplankton (complete), microzooplankton only (micro) or no zooplankton
(none). Changes in phytoplankton biovolume were investigated. Rotifer densities became significantly
higher in the 'micro' treatment than in the 'complete' and 'none' treatments. Total algal biovolume changed little in the 'complete' and 'none' treatments, but increased significantly in the 'micro' treatment. The results suggest that macrozooplankton (Daphnia magna) suppressed it and
microzooplankton (Keratella cochlearis) enhanced it. They had opposite net effects on the phytoplankton.
Suppression of microzooplankton by Daphnia probably had an indirect negative effect on the phytoplankton
ACon: A learning-based approach to deal with uncertainty in contextual requirements at runtime
Context: Runtime uncertainty such as unpredictable operational environment and failure of sensors that gather environmental data is a well-known challenge for adaptive systems.
Objective: To execute requirements that depend on context correctly, the system needs up-to-date knowledge about the context relevant to such requirements. Techniques to cope with uncertainty in contextual requirements are currently underrepresented. In this paper we present ACon (Adaptation of Contextual requirements), a data-mining approach to deal with runtime uncertainty affecting contextual requirements.
Method: ACon uses feedback loops to maintain up-to-date knowledge about contextual requirements based on current context information in which contextual requirements are valid at runtime. Upon detecting that contextual requirements are affected by runtime uncertainty, ACon analyses and mines contextual data, to (re-)operationalize context and therefore update the information about contextual requirements.
Results: We evaluate ACon in an empirical study of an activity scheduling system used by a crew of 4 rowers in a wild and unpredictable environment using a complex monitoring infrastructure. Our study focused on evaluating the data mining part of ACon and analysed the sensor data collected onboard from 46 sensors and 90,748 measurements per sensor.
Conclusion: ACon is an important step in dealing with uncertainty affecting contextual requirements at runtime while considering end-user interaction. ACon supports systems in analysing the environment to adapt contextual requirements and complements existing requirements monitoring approaches by keeping the requirements monitoring specification up-to-date. Consequently, it avoids manual analysis that is usually costly in today’s complex system environments.Peer ReviewedPostprint (author's final draft
A waveguide atom beamsplitter for laser-cooled neutral atoms
A laser-cooled neutral-atom beam from a low-velocity intense source is split
into two beams while guided by a magnetic-field potential. We generate our
multimode-beamsplitter potential with two current-carrying wires on a glass
substrate combined with an external transverse bias field. The atoms bend
around several curves over a -cm distance. A maximum integrated flux of
is achieved with a current density of
in the 100- diameter
wires. The initial beam can be split into two beams with a 50/50 splitting
ratio
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