Processing of Electronic Health Records using Deep Learning: A review

Availability of large amount of clinical data is opening up new research avenues in a number of fields. An exciting field in this respect is healthcare, where secondary use of healthcare data is beginning to revolutionize healthcare. Except for availability of Big Data, both medical data from healthcare institutions (such as EMR data) and data generated from health and wellbeing devices (such as personal trackers), a significant contribution to this trend is also being made by recent advances on machine learning, specifically deep learning algorithms

Deep Learning for Case-Based Reasoning through Prototypes: A Neural Network that Explains Its Predictions

Deep neural networks are widely used for classification. These deep models often suffer from a lack of interpretability -- they are particularly difficult to understand because of their non-linear nature. As a result, neural networks are often treated as "black box" models, and in the past, have been trained purely to optimize the accuracy of predictions. In this work, we create a novel network architecture for deep learning that naturally explains its own reasoning for each prediction. This architecture contains an autoencoder and a special prototype layer, where each unit of that layer stores a weight vector that resembles an encoded training input. The encoder of the autoencoder allows us to do comparisons within the latent space, while the decoder allows us to visualize the learned prototypes. The training objective has four terms: an accuracy term, a term that encourages every prototype to be similar to at least one encoded input, a term that encourages every encoded input to be close to at least one prototype, and a term that encourages faithful reconstruction by the autoencoder. The distances computed in the prototype layer are used as part of the classification process. Since the prototypes are learned during training, the learned network naturally comes with explanations for each prediction, and the explanations are loyal to what the network actually computes.Comment: The first two authors contributed equally, 8 pages, accepted in AAAI 201

Beyond Zipf's Law: The Lavalette Rank Function and its Properties

Although Zipf's law is widespread in natural and social data, one often encounters situations where one or both ends of the ranked data deviate from the power-law function. Previously we proposed the Beta rank function to improve the fitting of data which does not follow a perfect Zipf's law. Here we show that when the two parameters in the Beta rank function have the same value, the Lavalette rank function, the probability density function can be derived analytically. We also show both computationally and analytically that Lavalette distribution is approximately equal, though not identical, to the lognormal distribution. We illustrate the utility of Lavalette rank function in several datasets. We also address three analysis issues on the statistical testing of Lavalette fitting function, comparison between Zipf's law and lognormal distribution through Lavalette function, and comparison between lognormal distribution and Lavalette distribution.Comment: 15 pages, 4 figure

Strengthening of Aluminum Wires Treated with A206/Alumina Nanocomposites.

This study sought to characterize aluminum nanocomposite wires that were fabricated through a cold-rolling process, having potential applications in TIG (tungsten inert gas) welding of aluminum. A206 (Al-4.5Cu-0.25Mg) master nanocomposites with 5 wt % γAl₂O₃ nanoparticles were first manufactured through a hybrid process combining semi-solid mixing and ultrasonic processing. A206/1 wt % γAl₂O₃ nanocomposites were fabricated by diluting the prepared master nanocomposites with a monolithic A206 alloy, which was then added to a pure aluminum melt. The fabricated Al-γAl₂O₃ nanocomposite billet was cold-rolled to produce an Al nanocomposite wire with a 1 mm diameter and a transverse area reduction of 96%. Containing different levels of nanocomposites, the fabricated samples were mechanically and electrically characterized. The results demonstrate a significantly higher strength of the aluminum wires with the nanocomposite addition. Further, the addition of alumina nanoparticles affected the wires' electrical conductivity compared with that of pure aluminum and aluminum-copper alloys. The overall properties of the new material demonstrate that these wires could be an appealing alternative for fillers intended for aluminum welding

RAPTOR: Routing Attacks on Privacy in Tor

The Tor network is a widely used system for anonymous communication. However, Tor is known to be vulnerable to attackers who can observe traffic at both ends of the communication path. In this paper, we show that prior attacks are just the tip of the iceberg. We present a suite of new attacks, called Raptor, that can be launched by Autonomous Systems (ASes) to compromise user anonymity. First, AS-level adversaries can exploit the asymmetric nature of Internet routing to increase the chance of observing at least one direction of user traffic at both ends of the communication. Second, AS-level adversaries can exploit natural churn in Internet routing to lie on the BGP paths for more users over time. Third, strategic adversaries can manipulate Internet routing via BGP hijacks (to discover the users using specific Tor guard nodes) and interceptions (to perform traffic analysis). We demonstrate the feasibility of Raptor attacks by analyzing historical BGP data and Traceroute data as well as performing real-world attacks on the live Tor network, while ensuring that we do not harm real users. In addition, we outline the design of two monitoring frameworks to counter these attacks: BGP monitoring to detect control-plane attacks, and Traceroute monitoring to detect data-plane anomalies. Overall, our work motivates the design of anonymity systems that are aware of the dynamics of Internet routing

FDI, service intensity, and international marketing agility

Purpose The purpose of this paper is to provide a nuanced understanding of international marketing agility by connecting organizational capability literature with that of standardization and adaptation. The focus of the research is to clarify whether managing the tension between product standardization and service customization generates an extra premium in international markets. Design/methodology/approach Two disaggregated Chinese data sets, the Annual Survey of Industrial Enterprises and the China Customs Database, are used for developing an econometric model. Export quality improvement is the outcome variable in reflecting the effect of international marketing agility on performance. Findings International marketing agility is reached through upstream FDI intensity, particularly in the context of service FDI. Manufacturing sectors with higher service intensity have more agility, being more likely to generate export quality. Research limitations/implications This study makes three theoretical contributions by clarifying the concept of international marketing agility as an organizational capability generated by manufacturing standardization and service customization; investigating the influence of upstream FDI intensity for export quality while taking into account the industry contexts; and obtaining an enhanced understanding of the service intensity of manufacturing firms on export quality. Originality/value The authors offer a nuanced and contextualized understanding of international marketing agility and explore the complex relationships between FDI, service intensity and export quality