A Neural Attention Model for Categorizing Patient Safety Events

Medical errors are leading causes of death in the US and as such, prevention of these errors is paramount to promoting health care. Patient Safety Event reports are narratives describing potential adverse events to the patients and are important in identifying and preventing medical errors. We present a neural network architecture for identifying the type of safety events which is the first step in understanding these narratives. Our proposed model is based on a soft neural attention model to improve the effectiveness of encoding long sequences. Empirical results on two large-scale real-world datasets of patient safety reports demonstrate the effectiveness of our method with significant improvements over existing methods.Comment: ECIR 201

Label-Dependencies Aware Recurrent Neural Networks

In the last few years, Recurrent Neural Networks (RNNs) have proved effective on several NLP tasks. Despite such great success, their ability to model \emph{sequence labeling} is still limited. This lead research toward solutions where RNNs are combined with models which already proved effective in this domain, such as CRFs. In this work we propose a solution far simpler but very effective: an evolution of the simple Jordan RNN, where labels are re-injected as input into the network, and converted into embeddings, in the same way as words. We compare this RNN variant to all the other RNN models, Elman and Jordan RNN, LSTM and GRU, on two well-known tasks of Spoken Language Understanding (SLU). Thanks to label embeddings and their combination at the hidden layer, the proposed variant, which uses more parameters than Elman and Jordan RNNs, but far fewer than LSTM and GRU, is more effective than other RNNs, but also outperforms sophisticated CRF models.Comment: 22 pages, 3 figures. Accepted at CICling 2017 conference. Best Verifiability, Reproducibility, and Working Description awar

Phase Analysis of Particles Nano Licoo2 as Cathode Materials of Rechargeable Battery Using X-ray Diffractometer

Research of the analysis of particle nano LiCoO2 phase as cathode material of lithium ion based batteries rechargeable using XRD has been done. Particle Nano LiCoO2 are synthesized using planetary milling technique followed by sonication. The morphology of particle nano LiCoO2 are characterized by using Scanning Electron Microscope (SEM) dan Transmission Electron Microscope (TEM), the phase of particle nano LiCoO2 have been analyzed using XRD. The results show that the size of the particle nano LiCoO2 isare 20-40 nm, the phase of n-particles LiCoO2 is rhombohedral, R-3m, with a = b = 2.82 Å and c = 14.08 Å, where LiCo formed octahedral symmetry, 3-3m, and CO2 to formed tetrahedral symmetry, 63m

A computationally and cognitively plausible model of supervised and unsupervised learning

Author version made available in accordance with the publisher's policy. "The final publication is available at link.springer.com”The issue of chance correction has been discussed for many decades in the context of statistics, psychology and machine learning, with multiple measures being shown to have desirable properties, including various definitions of Kappa or Correlation, and the psychologically validated ΔP measures. In this paper, we discuss the relationships between these measures, showing that they form part of a single family of measures, and that using an appropriate measure can positively impact learning

Using Regular Languages to Explore the Representational Capacity of Recurrent Neural Architectures

The presence of Long Distance Dependencies (LDDs) in sequential data poses significant challenges for computational models. Various recurrent neural architectures have been designed to mitigate this issue. In order to test these state-of-the-art architectures, there is growing need for rich benchmarking datasets. However, one of the drawbacks of existing datasets is the lack of experimental control with regards to the presence and/or degree of LDDs. This lack of control limits the analysis of model performance in relation to the specific challenge posed by LDDs. One way to address this is to use synthetic data having the properties of subregular languages. The degree of LDDs within the generated data can be controlled through the k parameter, length of the generated strings, and by choosing appropriate forbidden strings. In this paper, we explore the capacity of different RNN extensions to model LDDs, by evaluating these models on a sequence of SPk synthesized datasets, where each subsequent dataset exhibits a longer degree of LDD. Even though SPk are simple languages, the presence of LDDs does have significant impact on the performance of recurrent neural architectures, thus making them prime candidate in benchmarking tasks.Comment: International Conference of Artificial Neural Networks (ICANN) 201

PlaNet - Photo Geolocation with Convolutional Neural Networks

Is it possible to build a system to determine the location where a photo was taken using just its pixels? In general, the problem seems exceptionally difficult: it is trivial to construct situations where no location can be inferred. Yet images often contain informative cues such as landmarks, weather patterns, vegetation, road markings, and architectural details, which in combination may allow one to determine an approximate location and occasionally an exact location. Websites such as GeoGuessr and View from your Window suggest that humans are relatively good at integrating these cues to geolocate images, especially en-masse. In computer vision, the photo geolocation problem is usually approached using image retrieval methods. In contrast, we pose the problem as one of classification by subdividing the surface of the earth into thousands of multi-scale geographic cells, and train a deep network using millions of geotagged images. While previous approaches only recognize landmarks or perform approximate matching using global image descriptors, our model is able to use and integrate multiple visible cues. We show that the resulting model, called PlaNet, outperforms previous approaches and even attains superhuman levels of accuracy in some cases. Moreover, we extend our model to photo albums by combining it with a long short-term memory (LSTM) architecture. By learning to exploit temporal coherence to geolocate uncertain photos, we demonstrate that this model achieves a 50% performance improvement over the single-image model

Modified Streamline Diffusion Schemes for Convection-Diffusion Problems

We consider the design of robust and accurate finite element approximation methods for solving convection--diffusion problems. We develop some two--parameter streamline diffusion schemes with piecewise bilinear (or linear) trial functions and show that these schemes satisfy the necessary conditions for $L^{2}$-uniform convergence of order greater than $1/2$ introduced by Stynes and Tobiska. For smooth problems, the schemes satisfy error bounds of the form $O(h)|u|_{2}$ in an energy norm. In addition, extensive numerical experiments show that they effectively reproduce boundary layers and internal layers caused by discontinuities on relatively coarse grids, without any requirements on alignment of flow and grid. (Also cross-referenced as UMIACS-TR-97-71

Microglial Activation Correlates with Disease Progression and Upper Motor Neuron Clinical Symptoms in Amyotrophic Lateral Sclerosis

We evaluated clinicopathological correlates of upper motor neuron (UMN) damage in amyotrophic lateral sclerosis (ALS), and analyzed if the presence of the C9ORF72 repeat expansion was associated with alterations in microglial inflammatory activity.Microglial pathology was assessed by IHC with 2 different antibodies (CD68, Iba1), myelin loss by Kluver-Barrera staining and myelin basic protein (MBP) IHC, and axonal loss by neurofilament protein (TA51) IHC, performed on 59 autopsy cases of ALS including 9 cases with C9ORF72 repeat expansion.Microglial pathology as depicted by CD68 and Iba1 was significantly more extensive in the corticospinal tract (CST) of ALS cases with a rapid progression of disease. Cases with C9ORF72 repeat expansion showed more extensive microglial pathology in the medulla and motor cortex which persisted after adjusting for disease duration in a logistic regression model. Higher scores on the clinical UMN scale correlated with increasing microglial pathology in the cervical CST. TDP-43 pathology was more extensive in the motor cortex of cases with rapid progression of disease.This study demonstrates that microglial pathology in the CST of ALS correlates with disease progression and is linked to severity of UMN deficits