Highly cited biomarker studies often report larger effect estimates for postulated associations than are reported in subsequent meta-analyses evaluating the same associations.
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Our challenge is clear: The drive for performance and the end of voltage scaling have made power, and not the number of transistors, the principal factor limiting further improvements in computing performance. Continuing to scale compute performance will require the creation and effective use of new specialized compute engines, and will require the participation of application experts to be successful. If we play our cards right, and develop the tools that allow our customers to become part of the design process, we will create a new wave of innovative and efficient computing devices.
The distribution of effect sizes may offer insights about the research done and reported in a scientific field. We have evaluated 12 412 manually collected correlation effect sizes (Sample 1) and 31 157 computer-extracted correlation effect sizes (Sample 2) published in journals focused on social or developmental psychology. Sample 1 consisted of 243 studies from six journals published in 2010 and 2019. Sample 2 consisted of 5012 papers published in 10 journals between 2010 and 2019. The 25th, 50th and 75th effect size percentiles were 0.08, 0.17 and 0.33, and 0.17, 0.31 and 0.52 in Samples 1 and 2, respectively. Sample 2 percentiles were probably larger because Sample 2 only included effect sizes from the text but not from tables. In text authors may have emphasized larger correlations. Large sample sizes were associated with smaller reported correlations. In Sample 1 about 70% of studies specified a directional hypothesis. In 2010 no papers had power calculations, while in 2019 14% of papers had power calculations. These data offer empirical insights into the distribution of reported correlations and may inform the interpretation of effect sizes. They also demonstrate the importance of computation of statistical power and highlight potential reporting bias.
There is large diversity on whether and how analyses of primary outcomes are adjusted in randomized controlled trials and these choices can sometimes change the nominal significance of the results. Registered protocols should explicitly specify adjustments plans for main outcomes and analysis should follow these plans.
A new delay generator based on a series of coupled ring oscillators has been developed; it produces precise delays with subgate delay resolution for chip testing applications. It achieves a delay resolution equal to a buffer delay divided by the number of rings. The coupling employed forces the outputs of a linear array of ring oscillators oscillating at the same frequency to be uniformly offset in phase by a precise fraction of a buffer delay. The buffer stage used in the ring oscillators is based on a source-coupled pair and achieves high supply noise rejection while operating at low supply voltages. Experimental results from a 2- mu m N-well CMOS implementation of the delay generator demonstrate that it can achieve an output delay resolution of 101 ps while operating at 141 MHz with a peak error of 58 ps.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
