2,872 research outputs found
The Pastor After the Heart of God
In the first of his Pastoral Letters (1 Tim. 3:1) Paul calls the office of a bishop (ἐπιοχοπή) a good work, χαλὸν ε̈ογον. That is a brief but beautiful and most significant characterization of the Christian ministry
Brief Studies
In spite of many fine Bible translations the pastor will constantly turn to his Greek New Testament, for no translation can reproduce fully the many fine shades of meaning in the original. A careful study of the original will frequently bring the exact meaning of a text or a word into sharper focus, change wholly or in part the meaning conveyed in the translation, or may even correct a misleading and inadequate translation. A few samples chosen at random will illustrate this
Conformal Anomaly Of Submanifold Observables In AdS/CFT Correspondence
We analyze the conformal invariance of submanifold observables associated
with -branes in the AdS/CFT correspondence. For odd , the resulting
observables are conformally invariant, and for even , they transform with a
conformal anomaly that is given by a local expression which we analyze in
detail for Comment: 11 p
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
Testing Conditional Independence of Discrete Distributions
We study the problem of testing \emph{conditional independence} for discrete
distributions. Specifically, given samples from a discrete random variable on domain , we want to distinguish,
with probability at least , between the case that and are
conditionally independent given from the case that is
-far, in -distance, from every distribution that has this
property. Conditional independence is a concept of central importance in
probability and statistics with a range of applications in various scientific
domains. As such, the statistical task of testing conditional independence has
been extensively studied in various forms within the statistics and
econometrics communities for nearly a century. Perhaps surprisingly, this
problem has not been previously considered in the framework of distribution
property testing and in particular no tester with sublinear sample complexity
is known, even for the important special case that the domains of and
are binary.
The main algorithmic result of this work is the first conditional
independence tester with {\em sublinear} sample complexity for discrete
distributions over . To complement our upper
bounds, we prove information-theoretic lower bounds establishing that the
sample complexity of our algorithm is optimal, up to constant factors, for a
number of settings. Specifically, for the prototypical setting when , we show that the sample complexity of testing conditional
independence (upper bound and matching lower bound) is
\[
\Theta\left({\max\left(n^{1/2}/\epsilon^2,\min\left(n^{7/8}/\epsilon,n^{6/7}/\epsilon^{8/7}\right)\right)}\right)\,.
\
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
Embedding Principal Component Analysis for Data Reductionin Structural Health Monitoring on Low-Cost IoT Gateways
Principal component analysis (PCA) is a powerful data reductionmethod for
Structural Health Monitoring. However, its computa-tional cost and data memory
footprint pose a significant challengewhen PCA has to run on limited capability
embedded platformsin low-cost IoT gateways. This paper presents a
memory-efficientparallel implementation of the streaming History PCA
algorithm.On our dataset, it achieves 10x compression factor and 59x
memoryreduction with less than 0.15 dB degradation in the
reconstructedsignal-to-noise ratio (RSNR) compared to standard PCA. More-over,
the algorithm benefits from parallelization on multiple cores,achieving a
maximum speedup of 4.8x on Samsung ARTIK 710
Difficult Lessons on Social Prediction from Wisconsin Public Schools
Early warning systems (EWS) are prediction algorithms that have recently
taken a central role in efforts to improve graduation rates in public schools
across the US. These systems assist in targeting interventions at individual
students by predicting which students are at risk of dropping out. Despite
significant investments and adoption, there remain significant gaps in our
understanding of the efficacy of EWS. In this work, we draw on nearly a
decade's worth of data from a system used throughout Wisconsin to provide the
first large-scale evaluation of the long-term impact of EWS on graduation
outcomes.
We present evidence that risk assessments made by the prediction system are
highly accurate, including for students from marginalized backgrounds. Despite
the system's accuracy and widespread use, we find no evidence that it has led
to improved graduation rates. We surface a robust statistical pattern that can
explain why these seemingly contradictory insights hold. Namely, environmental
features, measured at the level of schools, contain significant signal about
dropout risk. Within each school, however, academic outcomes are essentially
independent of individual student performance. This empirical observation
indicates that assigning all students within the same school the same
probability of graduation is a nearly optimal prediction.
Our work provides an empirical backbone for the robust, qualitative
understanding among education researchers and policy-makers that dropout is
structurally determined. The primary barrier to improving outcomes lies not in
identifying students at risk of dropping out within specific schools, but
rather in overcoming structural differences across different school districts.
Our findings indicate that we should carefully evaluate the decision to fund
early warning systems without also devoting resources to interventions tackling
structural barriers
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