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Qualifying parabolic mirrors with deflectometry [PDF]
Phase-measuring deflectometry is a full-field gradient technique that lends itself very well to testing reflective optical surfaces. In the past, the industry’s interest has been focussed mainly on the detection of defects and ripples, since it is easy ...
Burke J.+4 more
doaj +1 more source
PSYCHOPHYSICS OF REMEMBERING: TO BIAS OR NOT TO BIAS? [PDF]
Delayed matching to sample is typically a two‐alternative forced‐choice procedure with two sample stimuli. In this task the effects of varying the probability of reinforcers for correct choices and the resulting receiver operating characteristic are symmetrical.
K. Geoffrey White, John T. Wixted
openaire +3 more sources
RoB 2: a revised tool for assessing risk of bias in randomised trials
Assessment of risk of bias is regarded as an essential component of a systematic review on the effects of an intervention. The most commonly used tool for randomised trials is the Cochrane risk-of-bias tool. We updated the tool to respond to developments
J. Sterne+27 more
semanticscholar +1 more source
Despite a major increase in the range and number of software offerings now available to help researchers produce evidence syntheses, there is currently no generic tool for producing figures to display and explore the risk‐of‐bias assessments that ...
L. McGuinness, J. Higgins
semanticscholar +1 more source
Language (Technology) is Power: A Critical Survey of “Bias” in NLP [PDF]
We survey 146 papers analyzing “bias” in NLP systems, finding that their motivations are often vague, inconsistent, and lacking in normative reasoning, despite the fact that analyzing “bias” is an inherently normative process.
Su Lin Blodgett+3 more
semanticscholar +1 more source
Bias In, Bias Out? Evaluating the Folk Wisdom [PDF]
We evaluate the folk wisdom that algorithmic decision rules trained on data produced by biased human decision-makers necessarily reflect this bias.
Rambachan, Ashesh, Roth, Jonathan
core +2 more sources
AbstractIn marketing and finance, surprisingly simple models sometimes predict more accurately than more complex, sophisticated models. Here, we address the question of when and why simple models succeed — or fail — by framing the forecasting problem in terms of the bias–variance dilemma.
Henry Brighton, Gerd Gigerenzer
openaire +2 more sources
Salmon: fast and bias-aware quantification of transcript expression using dual-phase inference
We introduce Salmon, a lightweight method for quantifying transcript abundance from RNA–seq reads. Salmon combines a new dual-phase parallel inference algorithm and feature-rich bias models with an ultra-fast read mapping procedure.
Robert Patro+4 more
semanticscholar +1 more source
BBQ: A hand-built bias benchmark for question answering [PDF]
It is well documented that NLP models learn social biases, but little work has been done on how these biases manifest in model outputs for applied tasks like question answering (QA).
Alicia Parrish+7 more
semanticscholar +1 more source
ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions
Non-randomised studies of the effects of interventions are critical to many areas of healthcare evaluation, but their results may be biased. It is therefore important to understand and appraise their strengths and weaknesses. We developed ROBINS-I (“Risk
J. Sterne+34 more
semanticscholar +1 more source