Combined Effects of Knowledge About Others' Opinions and Anticipation of Group Discussion on Confirmatory Information Search

There is conclusive evidence that information search processes are typically biased in favor of the information seeker’s own opinion (confirmation bias). Less is known about how knowledge about others’ opinions affects this confirmatory information search. In the present study, the authors manipulated feedback about others’ opinions and anticipation of group interaction. As predicted, the effect of knowledge about others’ opinions on confirmatory information search depended on whether participants anticipated interacting with these others. Specifically, minority members anticipating a group discussion exhibited a particularly strong confirmation bias, whereas minority members who did not anticipate a discussion predominantly sought information opposing their opinion. For participants not anticipating group interaction, confidence about the correctness of one’s decision mediated the impact of knowledge about others’ opinions on confirmatory information search. Results are discussed with regard to the debiasing effect of preference heterogeneity on confirmatory information search in groups

Measurement and Process Control in Precision Hot Embossing

Microfluidic technologies hold a great deal of promise in advancing the medical field, but transitioning them from research to commercial production has proven problematic. We propose precision hot embossing as a process to produce high volumes of devices with low capital cost and a high degree of flexibility. Hot embossing has not been widely applied to precision forming of hard polymers at viable production rates. To this end we have developed experimental equipment capable of maintaining the necessary precision in forming parameters while minimizing cycle time. In addition, since equipment precision alone does not guarantee consistent product quality, our work also focuses on real-time sensing and diagnosis of the process. This paper covers both the basic details for a novel embossing machine, and the utilization of the force and displacement data acquired during the embossing cycle to diagnose the state of the material and process. The precision necessary in both the forming machine and the instrumentation will be covered in detail. It will be shown that variation in the material properties (e.g. thickness, glass transition temperature) as well as the degree of bulk deformation of the substrate can be detected from these measurements. If these data are correlated with subsequent downstream functional tests, a total measure of quality may be determined and used to apply closed-loop cycle-to-cycle control to the entire process. By incorporating automation and specialized precision equipment into a tabletop “microfactory” setting, we aim to demonstrate a high degree of process control and disturbance rejection for the process of hot embossing as applied at the micron scale.Singapore-MIT Alliance. Manufacturing Systems and Technology Programm

Orientability and energy minimization in liquid crystal models

Uniaxial nematic liquid crystals are modelled in the Oseen-Frank theory through a unit vector field $n$. This theory has the apparent drawback that it does not respect the head-to-tail symmetry in which $n$ should be equivalent to -$n$. This symmetry is preserved in the constrained Landau-de Gennes theory that works with the tensor $Q=s\big(n\otimes n- \frac{1}{3} Id\big)$.We study the differences and the overlaps between the two theories. These depend on the regularity class used as well as on the topology of the underlying domain. We show that for simply-connected domains and in the natural energy class $W^{1,2}$ the two theories coincide, but otherwise there can be differences between the two theories, which we identify. In the case of planar domains we completely characterise the instances in which the predictions of the constrained Landau-de Gennes theory differ from those of the Oseen-Frank theory

Google Votes: A Liquid Democracy Experiment on a Corporate Social Network

This paper introduces Google Votes, an experiment in liquid democracy built on Google\u27s internal corporate Google+ social network. Liquid democracy decision-making systems can scale to cover large groups by enabling voters to delegate their votes to other voters. This approach is in contrast to direct democracy systems where voters vote directly on issues, and representative democracy systems where voters elect representatives to vote on issues for them. Liquid democracy systems can provide many of the benefits of both direct and representative democracy systems with few of the weaknesses. Thus far, high implementation complexity and infrastructure costs have prevented widespread adoption. Google Votes demonstrates how the use of social-networking technology can overcome these barriers and enable practical liquid democracy systems. The case-study of Google Votes usage at Google over a 3 year timeframe is included, as well as a framework for evaluating vote visibility called the Golden Rule of Liquid Democracy

Global Solution to the Three-Dimensional Incompressible Flow of Liquid Crystals

The equations for the three-dimensional incompressible flow of liquid crystals are considered in a smooth bounded domain. The existence and uniqueness of the global strong solution with small initial data are established. It is also proved that when the strong solution exists, all the global weak solutions constructed in [16] must be equal to the unique strong solution

Private Multiplicative Weights Beyond Linear Queries

A wide variety of fundamental data analyses in machine learning, such as linear and logistic regression, require minimizing a convex function defined by the data. Since the data may contain sensitive information about individuals, and these analyses can leak that sensitive information, it is important to be able to solve convex minimization in a privacy-preserving way. A series of recent results show how to accurately solve a single convex minimization problem in a differentially private manner. However, the same data is often analyzed repeatedly, and little is known about solving multiple convex minimization problems with differential privacy. For simpler data analyses, such as linear queries, there are remarkable differentially private algorithms such as the private multiplicative weights mechanism (Hardt and Rothblum, FOCS 2010) that accurately answer exponentially many distinct queries. In this work, we extend these results to the case of convex minimization and show how to give accurate and differentially private solutions to *exponentially many* convex minimization problems on a sensitive dataset