432 research outputs found
LSTM-SDM: An integrated framework of LSTM implementation for sequential data modeling[Formula presented]
LSTM-SDM is a python-based integrated computational framework built on the top of Tensorflow/Keras and written in the Jupyter notebook. It provides several object-oriented functionalities for implementing single layer and multilayer LSTM models for sequential data modeling and time series forecasting. Multiple subroutines are blended to create a conducive user-friendly environment that facilitates data exploration and visualization, normalization and input preparation, hyperparameter tuning, performance evaluations, visualization of results, and statistical analysis. We utilized the LSTM-SDM framework in predicting the stock market index and observed impressive results. The framework can be generalized to solve several other real-world time series problems
Diffusion-assisted molecular beam epitaxy of CuCrO thin films
Using molecular beam epitaxy (MBE) to grow multi-elemental oxides (MEO) is
generally challenging, partly due to difficulty in stoichiometry control.
Occasionally, if one of the elements is volatile at the growth temperature,
stoichiometry control can be greatly simplified using adsorption-controlled
growth mode. Otherwise, stoichiometry control remains one of the main hurdles
to achieving high quality MEO film growths. Here, we report another kind of
self-limited growth mode, dubbed diffusion-assisted epitaxy, in which excess
species diffuses into the substrate and leads to the desired stoichiometry, in
a manner similar to the conventional adsorption-controlled epitaxy.
Specifically, we demonstrate that using diffusion-assisted epitaxy,
high-quality epitaxial CuCrO films can be grown over a wide growth window
without precise flux control using MBE.Comment: Accepted to the special edition of JVSTA on Thin Film Deposition for
Materials Discover
A test of the automaticity assumption of compliance tactics: discouraging undergraduate binge drinking by appealing to consistency and reciprocity
The mindfulness of compliance with requests making use of the commitment/consistency or the reciprocity principle was investigated. Participants (N = 129) received a foot-in-the-door (FITD) request (commitment/consistency application), a door-in-the-face (DITF) request (reciprocity application), or no request. Next, participants read either a weak or neutral message about the importance of moderate alcohol consumption then reported the likelihood of not drinking excessively for one week (target request). When accompanied by a weak message, the target request elicited less compliance if preceded by the DITF or FITD requests than by no initial request, suggesting compliance tactics sometimes increase thoughtfulness
Reducing model bias in a deep learning classifier using domain adversarial neural networks in the MINERvA experiment
We present a simulation-based study using deep convolutional neural networks
(DCNNs) to identify neutrino interaction vertices in the MINERvA passive
targets region, and illustrate the application of domain adversarial neural
networks (DANNs) in this context. DANNs are designed to be trained in one
domain (simulated data) but tested in a second domain (physics data) and
utilize unlabeled data from the second domain so that during training only
features which are unable to discriminate between the domains are promoted.
MINERvA is a neutrino-nucleus scattering experiment using the NuMI beamline at
Fermilab. -dependent cross sections are an important part of the physics
program, and these measurements require vertex finding in complicated events.
To illustrate the impact of the DANN we used a modified set of simulation in
place of physics data during the training of the DANN and then used the label
of the modified simulation during the evaluation of the DANN. We find that deep
learning based methods offer significant advantages over our prior track-based
reconstruction for the task of vertex finding, and that DANNs are able to
improve the performance of deep networks by leveraging available unlabeled data
and by mitigating network performance degradation rooted in biases in the
physics models used for training.Comment: 41 page
How Accessible Was Information about H1N1 Flu? Literacy Assessments of CDC Guidance Documents for Different Audiences
We assessed the literacy level and readability of online communications about H1N1/09 influenza issued by the Centers for Disease Control and Prevention (CDC) during the first month of outbreak. Documents were classified as targeting one of six audiences ranging in technical expertise. Flesch-Kincaid (FK) measure assessed literacy level for each group of documents. ANOVA models tested for differences in FK scores across target audiences and over time. Readability was assessed for documents targeting non-technical audiences using the Suitability Assessment of Materials (SAM). Overall, there was a main-effect by audience, F(5, 82) = 29.72, P<.001, but FK scores did not vary over time, F(2, 82) = .34, P>.05. A time-by-audience interaction was significant, F(10, 82) = 2.11, P<.05. Documents targeting non-technical audiences were found to be text-heavy and densely-formatted. The vocabulary and writing style were found to adequately reflect audience needs. The reading level of CDC guidance documents about H1N1/09 influenza varied appropriately according to the intended audience; sub-optimal formatting and layout may have rendered some text difficult to comprehend
Measurement of Total and Differential Cross Sections of Neutrino and Antineutrino Coherent Production on Carbon
Neutrino induced coherent charged pion production on nuclei,
, is a rare inelastic interaction in
which the four-momentum squared transfered to the nucleus is nearly zero,
leaving it intact. We identify such events in the scintillator of MINERvA by
reconstructing |t| from the final state pion and muon momenta and by removing
events with evidence of energetic nuclear recoil or production of other final
state particles. We measure the total neutrino and antineutrino cross sections
as a function of neutrino energy between 2 and 20 GeV and measure flux
integrated differential cross sections as a function of , and
. The dependence and equality of the neutrino and
anti-neutrino cross-sections at finite provide a confirmation of Adler's
PCAC hypothesis
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