Learning a Neural Semantic Parser from User Feedback

We present an approach to rapidly and easily build natural language interfaces to databases for new domains, whose performance improves over time based on user feedback, and requires minimal intervention. To achieve this, we adapt neural sequence models to map utterances directly to SQL with its full expressivity, bypassing any intermediate meaning representations. These models are immediately deployed online to solicit feedback from real users to flag incorrect queries. Finally, the popularity of SQL facilitates gathering annotations for incorrect predictions using the crowd, which is directly used to improve our models. This complete feedback loop, without intermediate representations or database specific engineering, opens up new ways of building high quality semantic parsers. Experiments suggest that this approach can be deployed quickly for any new target domain, as we show by learning a semantic parser for an online academic database from scratch.Comment: Accepted at ACL 201

Effect of raster angle on mechanical properties of 3D printed short carbon fiber reinforced acrylonitrile butadiene styrene

The most common additive manufacturing technique fused filament fabrication (FFF) suffers from inter-bead porosity that reduces mechanical properties. Inter-bead pores follow the raster angle, which causes anisotropic mechanical properties. Yet, the effects of raster angle on the mechanical behavior of short-carbon-fiber-reinforced (SCFR) thermoplastics are unclear. In this study, we performed tensile, flexural, and fracture toughness tests on SCFR acrylonitrile butadiene styrene (ABS). Raster angles of 0°, 15°, 30°, 45°, 60°, 75°, and 90° were investigated. Tensile strength and elastic modulus decreased by 22–35% for a change from 0° to 15°. Flexural strength and modulus were less sensitive to raster angle. Flexural strengths were at least 50% more than tensile strength for the same raster angle. Whereas flexural modulus is at least 15% less than elastic modulus. Fracture toughness showed a non-linear relationship with the raster angle. Maximum fracture toughness was observed at 0° and 60° rasters. Crack deflection was observed as the toughening mechanism

CLOUD BASED COMPILER

Compilers are used to run programs and convert them from a text format to executable format. A compiler that is to be installed manually on every system physically requires a lot of space and also configuring of it if not installed using default parameters. Also once a program is compiled it becomes platform dependent. It is also not easy to carry the same program code to multiple systems if situation doesn’t permit the usage of a single system. Another drawback is that we would need to install a different complier on each language on which we wish to work. We propose a solution to this in the form of a cloud based compiler. Cloud computing is a model for enabling convenient, on demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released with minimal management effort. Our project aims to create an online compiler which helps to reduce the problems of portability of storage and space by making use of the concept of cloud computing. The ability to use different compilers allows the programmer to pick up the fastest or the most convenient tool to compile the code and remove the errors. Moreover a web based application can be used remotely through any network connection which is platform independent. The errors/Output of the compiled program can be stored in a more convenient way. Also the trouble of installing a compiler on each computer is avoided. Thus these advantages make this application ideal for conducting online examinations. We would be implementing a private cloud on which the software would be hosted. The software would be provided to the end user using a SAAS cloud. The software would contain a system that has a text editor and a terminal. The user would be given an option to select the language in which he wants to compile the program. The software will compile the program and return the output to the user. Additional functionalities such as monitoring of the system, user usage, user forums, and collaborative development can be added as needed

Learning signals of adverse drug-drug interactions from the unstructured text of electronic health records.

Drug-drug interactions (DDI) account for 30% of all adverse drug reactions, which are the fourth leading cause of death in the US. Current methods for post marketing surveillance primarily use spontaneous reporting systems for learning DDI signals and validate their signals using the structured portions of Electronic Health Records (EHRs). We demonstrate a fast, annotation-based approach, which uses standard odds ratios for identifying signals of DDIs from the textual portion of EHRs directly and which, to our knowledge, is the first effort of its kind. We developed a gold standard of 1,120 DDIs spanning 14 adverse events and 1,164 drugs. Our evaluations on this gold standard using millions of clinical notes from the Stanford Hospital confirm that identifying DDI signals from clinical text is feasible (AUROC=81.5%). We conclude that the text in EHRs contain valuable information for learning DDI signals and has enormous utility in drug surveillance and clinical decision support

Profiling risk factors for chronic uveitis in juvenile idiopathic arthritis: a new model for EHR-based research.

BackgroundJuvenile idiopathic arthritis is the most common rheumatic disease in children. Chronic uveitis is a common and serious comorbid condition of juvenile idiopathic arthritis, with insidious presentation and potential to cause blindness. Knowledge of clinical associations will improve risk stratification. Based on clinical observation, we hypothesized that allergic conditions are associated with chronic uveitis in juvenile idiopathic arthritis patients.MethodsThis study is a retrospective cohort study using Stanford's clinical data warehouse containing data from Lucile Packard Children's Hospital from 2000-2011 to analyze patient characteristics associated with chronic uveitis in a large juvenile idiopathic arthritis cohort. Clinical notes in patients under 16 years of age were processed via a validated text analytics pipeline. Bivariate-associated variables were used in a multivariate logistic regression adjusted for age, gender, and race. Previously reported associations were evaluated to validate our methods. The main outcome measure was presence of terms indicating allergy or allergy medications use overrepresented in juvenile idiopathic arthritis patients with chronic uveitis. Residual text features were then used in unsupervised hierarchical clustering to compare clinical text similarity between patients with and without uveitis.ResultsPreviously reported associations with uveitis in juvenile idiopathic arthritis patients (earlier age at arthritis diagnosis, oligoarticular-onset disease, antinuclear antibody status, history of psoriasis) were reproduced in our study. Use of allergy medications and terms describing allergic conditions were independently associated with chronic uveitis. The association with allergy drugs when adjusted for known associations remained significant (OR 2.54, 95% CI 1.22-5.4).ConclusionsThis study shows the potential of using a validated text analytics pipeline on clinical data warehouses to examine practice-based evidence for evaluating hypotheses formed during patient care. Our study reproduces four known associations with uveitis development in juvenile idiopathic arthritis patients, and reports a new association between allergic conditions and chronic uveitis in juvenile idiopathic arthritis patients