Simple and Effective Multi-Paragraph Reading Comprehension

We consider the problem of adapting neural paragraph-level question answering models to the case where entire documents are given as input. Our proposed solution trains models to produce well calibrated confidence scores for their results on individual paragraphs. We sample multiple paragraphs from the documents during training, and use a shared-normalization training objective that encourages the model to produce globally correct output. We combine this method with a state-of-the-art pipeline for training models on document QA data. Experiments demonstrate strong performance on several document QA datasets. Overall, we are able to achieve a score of 71.3 F1 on the web portion of TriviaQA, a large improvement from the 56.7 F1 of the previous best system.Comment: 11 pages, updated a referenc

Greetings from an Old Friend...

Dear Foresters, February 10th, 2001 I arrived at Ali Al Salem Base in Kuwait with my National Guard Unit, C Company 1-168 Infantry out of Denison, Iowa. We are over here on a 120-day deployment to help support eh Army\u27s peacekeeping mission Operation Desert Spring. I am the A team leader, 4th squad of 2nd Platoon. Our platoon\u27s mission is to provide security for the Patriot Missile Site here on Ali Al Salem

Joint UK Land Environment Simulator (JULES) Version 2.2 User Manual

The Joint UK Land Environment Simulator (JULES) is a computer model that simulates many soil and vegetation processes. This document describes how to run version 2.2 of JULES. It primarily describes the format of the input and output files, and does not include detailed descriptions of the science and representation of the processes in the model. The first version of JULES was based on the Met Office Surface Exchange System (MOSES), the land surface model used in the Unified Model (UM) of the UK Met Office. After that initial split, the MOSES and JULES code bases evolved separately, but with JULES2.1 these differences were reconciled, so that all versions since v2.1 have had identical code in both the standalone version (as described here) and in the UM

Biofuels Boom, Aquifer’s Doom?

This paper seeks to estimate the impact of the emerging biofuels sector on groundwater consumption and cropping patterns in the Kansas portion of the High Plains Aquifer. Three case counties in western Kansas have been selected for analysis, which vary in their groundwater availability and historical cropping patterns

Laser induced ultrasonic phased array using Full Matrix Capture data acquisition and Total Focusing Method

Laser based ultrasound is a technique where a short pulsed laser is used to generate ultrasound and optical interferometry is used in order to detect the signal. Since both generation and detection of ultrasound is based on optical means, the technique is broadband, non-contact, and couplant free, suitable for large stand-off distances, inspection of components of complex geometries and hazardous environments. A data collection method (Full Matrix Capture) developed for ultrasonic arrays, is used for data collected using laser based ultrasound. In this method, a signal is captured from every possible transmitter-receiver array element combination. After the data capture, the imaging is done in post processing using the Total Focusing Method, in which the beam is focused, in both transmission and reception, on every pixel in the image to achieve improved defect detectability and very high spatial resolution. For the first time in laser ultrasonics, the beamforming and steering of the ultrasound is done during the post processing. In this way, a synthesised 1-D laser induced ultrasonic array operates, in post processing, as a phased array without the need of complicated optical setups, optical fibres or use of multiple laser beams. This work presents results from a non destructive laser ultrasonic inspection of aluminium samples with side drilled holes and slots at depths varying between 5 and 20mm from the surface.</p

Introduction to Inside the Insider Threat Minitrack

N/