86 research outputs found
Sources of Irreproducibility in Machine Learning: A Review
Lately, several benchmark studies have shown that the state of the art in
some of the sub-fields of machine learning actually has not progressed despite
progress being reported in the literature. The lack of progress is partly
caused by the irreproducibility of many model comparison studies. Model
comparison studies are conducted that do not control for many known sources of
irreproducibility. This leads to results that cannot be verified by third
parties. Our objective is to provide an overview of the sources of
irreproducibility that are reported in the literature. We review the literature
to provide an overview and a taxonomy in addition to a discussion on the
identified sources of irreproducibility. Finally, we identify three lines of
further inquiry
Spatial methods for event reconstruction in CLEAN
In CLEAN (Cryogenic Low Energy Astrophysics with Noble gases), a proposed
neutrino and dark matter detector, background discrimination is possible if one
can determine the location of an ionizing radiation event with high accuracy.
We simulate ionizing radiation events that produce multiple scintillation
photons within a spherical detection volume filled with liquid neon. We
estimate the radial location of a particular ionizing radiation event based on
the observed count data corresponding to that event. The count data are
collected by detectors mounted at the spherical boundary of the detection
volume. We neglect absorption, but account for Rayleigh scattering. To account
for wavelength-shifting of the scintillation light, we assume that photons are
absorbed and re-emitted at the detectors. Here, we develop spatial Maximum
Likelihood methods for event reconstruction, and study their performance in
computer simulation experiments. We also study a method based on the centroid
of the observed count data. We calibrate our estimates based on training data
Optimal Proton Trapping in a Neutron Lifetime Experiment
In a neutron lifetime experiment conducted at the National Institute of
Standards and Technology, protons produced by neutron decay events are confined
in a Penning trap. In each run of the experiment, there is a trapping stage of
duration . After the trapping stage, protons are purged from the trap. A
proton detector provides incomplete information because it goes dead after
detecting the first of any purged protons. Further, there is a dead time
between the end of the trapping stage in one run and the beginning of
the next trapping stage in the next run. Based on the fraction of runs where a
proton is detected, I estimate the trapping rate by the method of
maximum likelihood. I show that the expected value of the maximum likelihood
estimate is infinite. To obtain a maximum likelihood estimate with a finite
expected value and a well-defined and finite variance, I restrict attention to
a subsample of all realizations of the data. This subsample excludes an
exceedingly rare realization that yields an infinite-valued estimate of
. I present asymptotically valid formulas for the bias,
root-mean-square prediction error, and standard deviation of the maximum
likelihood estimate of for this subsample. Based on nominal values of
and the dead time , I determine the optimal duration of the
trapping stage by minimizing the root-mean-square prediction error of
the estimate.Comment: 21 pages, 4 figures This is a revised version of "Optimal Proton
Trapping". Based on a review, some aspects of the techical argument were
refine
Nanostructuring Titania by Embossing with Polymer Molds Made from Anodic Alumina Templates
Dysregulated RasGRP1 Responds to Cytokine Receptor Input in T Cell Leukemogenesis
Enhanced signaling by the small guanosine triphosphatase Ras is common in T cell acute lymphoblastic leukemia/lymphoma (T-ALL), but the underlying mechanisms are unclear. We identified the guanine nucleotide exchange factor RasGRP1 (Rasgrp1 in mice) as a Ras activator that contributes to leukemogenesis. We found increased RasGRP1 expression in many pediatric T-ALL patients, which is not observed in rare early T cell precursor T-ALL patients with KRAS and NRAS mutations, such as K-Ras[superscript G12D]. Leukemia screens in wild-type mice, but not in mice expressing the mutant K-Ras[superscript G12D] that encodes a constitutively active Ras, yielded frequent retroviral insertions that led to increased Rasgrp1 expression. Rasgrp1 and oncogenic K-Ras[superscript G12D] promoted T-ALL through distinct mechanisms. In K-Ras[superscript G12D] T-ALLs, enhanced Ras activation had to be uncoupled from cell cycle arrest to promote cell proliferation. In mouse T-ALL cells with increased Rasgrp1 expression, we found that Rasgrp1 contributed to a previously uncharacterized cytokine receptor–activated Ras pathway that stimulated the proliferation of T-ALL cells in vivo, which was accompanied by dynamic patterns of activation of effector kinases downstream of Ras in individual T-ALLs. Reduction of Rasgrp1 abundance reduced cytokine-stimulated Ras signaling and decreased the proliferation of T-ALL in vivo. The position of RasGRP1 downstream of cytokine receptors as well as the different clinical outcomes that we observed as a function of RasGRP1 abundance make RasGRP1 an attractive future stratification marker for T-ALL.National Institutes of Health (U.S.). Pioneer AwardNational Cancer Institute (U.S.). Physical Sciences-Oncology Center (U54CA143874)National Institutes of Health (U.S.). (P01 AI091580
Does modifying competition affect the frequency of technical skills in junior rugby league?
The technical demands of games can be affected by changing the number of players, pitch size and rules. This controlled trial compared the frequency of technical skills between a 'traditional' and newly introduced systematically 'modified' game of primary rugby league. A total of 475 primary rugby league players (Under 7s - 9s) were filmed playing traditional (n=49) and modified (n= 249) formats. Notational analysis examined the frequency of technical skills (e.g., number of passes) within 'traditional' and 'modified' games. At each age category, multivariate analysis of variance indicated the clear superiority of the 'modified' game for the frequency of technical skills (e.g., Under 7s total skill opportunities - 'traditional' = 342.9±47.0; 'modified' = 449.4±93.3, d=1.44, p<0.001). Systematically modifying the competitive game is an effective way to increase skill opportunities for children within rugby league. Future research should examine the outcomes of modifying games in optimizing skill development in youth sport
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