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 CuCrO2_2 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$_2$ 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. $A$-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

Direct Measurement of Nuclear Dependence of Charged Current Quasielastic-like Neutrino Interactions using MINERvA

Charged-current $\nu_{\mu}$ interactions on carbon, iron, and lead with a final state hadronic system of one or more protons with zero mesons are used to investigate the influence of the nuclear environment on quasielastic-like interactions. The transfered four-momentum squared to the target nucleus, $Q^2$, is reconstructed based on the kinematics of the leading proton, and differential cross sections versus $Q^2$ and the cross-section ratios of iron, lead and carbon to scintillator are measured for the first time in a single experiment. The measurements show a dependence on atomic number. While the quasielastic-like scattering on carbon is compatible with predictions, the trends exhibited by scattering on iron and lead favor a prediction with intranuclear rescattering of hadrons accounted for by a conventional particle cascade treatment. These measurements help discriminate between different models of both initial state nucleons and final state interactions used in the neutrino oscillation experiments