Confidentiality Protection in the 2020 US Census of Population and Housing

In an era where external data and computational capabilities far exceed statistical agencies' own resources and capabilities, they face the renewed challenge of protecting the confidentiality of underlying microdata when publishing statistics in very granular form and ensuring that these granular data are used for statistical purposes only. Conventional statistical disclosure limitation methods are too fragile to address this new challenge. This article discusses the deployment of a differential privacy framework for the 2020 US Census that was customized to protect confidentiality, particularly the most detailed geographic and demographic categories, and deliver controlled accuracy across the full geographic hierarchy.Comment: Version 2 corrects a few transcription errors in Tables 2, 3 and 5. Version 3 adds final journal copy edits to the preprin

Infiltration of tobacco leaf tissue

Method for transient expression in tobacco (N. tobacum and N. benthamiana) leaf lower epidermal cell

Oh What Did You Give, When Your Country Called?

Photogrpah of a soldier on crutches beside a child holding a sign that reads Heroes in war time, Pests in peace time.https://scholarsjunction.msstate.edu/cht-sheet-music/3117/thumbnail.jp

Effect of the National Resident Assessment Instrument on Selected Health Conditions and Problems

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/111253/1/j.1532-5415.1997.tb02972.x.pd

Main principles sports training

In this article we present and evaluate a system which allows a mobile robot to autonomously detect, model and re-recognize objects in everyday environments. Whilst other systems have demonstrated one of these elements, to our knowledge we present the first system which is capable of doing all of these things, all without human interaction, in normal indoor scenes. Our system detects objects to learn by modelling the static part of the environment and extracting dynamic elements. It then creates and executes a view plan around a dynamic element to gather additional views for learning. Finally these views are fused to create an object model. The performance of the system is evaluated on publicly available datasets as well as on data collected by the robot in both controlled and uncontrolled scenarios.QC 20160411STRAND

The agronomic performance and nutritional content of oat and barley varieties grown in a northern maritime environment depends on variety and growing conditions

Funding for this research came from the Scottish Government's Rural and Environment Science and Analytical Services Division (RESAS) through their support for this Strategic Partnership project. We are also grateful to Ingvar Andersson at Lantmännen SW Seed AB for supplying seed of the Scandinavian varieties for the trials each year and to the seed merchant William Shearer (Kirkwall) for importing it. We are indebted to Grietje Holtrop from Biomathematics and Statistics Scotland for her help with statistical analysis. Andy Beer (The Royal Zoological Society, Edinburgh) performed all NIRS analysis and Gill Campbell (Rowett Institute of Nutrition and Health) performed the mineral content analysis. The Centre for Sustainable Cropping platform is supported through Scottish Government Underpinning Capacity funding. The Agronomy Institute acknowledges support from the Northern Periphery and Arctic Programme's Northern Cereals project in preparing this publication.Peer reviewedPostprin