2,394 research outputs found
Fast marginal likelihood estimation of penalties for group-adaptive elastic net
Nowadays, clinical research routinely uses omics data, such as gene
expression, for predicting clinical outcomes or selecting markers.
Additionally, so-called co-data are often available, providing complementary
information on the covariates, like p-values from previously published studies
or groups of genes corresponding to pathways. Elastic net penalisation is
widely used for prediction and covariate selection. Group-adaptive elastic net
penalisation learns from co-data to improve the prediction and covariate
selection, by penalising important groups of covariates less than other groups.
Existing methods are, however, computationally expensive. Here we present a
fast method for marginal likelihood estimation of group-adaptive elastic net
penalties for generalised linear models. We first derive a low-dimensional
representation of the Taylor approximation of the marginal likelihood and its
first derivative for group-adaptive ridge penalties, to efficiently estimate
these penalties. Then we show by using asymptotic normality of the linear
predictors that the marginal likelihood for elastic net models may be
approximated well by the marginal likelihood for ridge models. The ridge group
penalties are then transformed to elastic net group penalties by using the
variance function. The method allows for overlapping groups and unpenalised
variables. We demonstrate the method in a model-based simulation study and an
application to cancer genomics. The method substantially decreases computation
time and outperforms or matches other methods by learning from co-data.Comment: 16 pages, 6 figures, 1 tabl
Compression of sub-relativistic space-charge-dominated electron bunches for single-shot femtosecond electron diffraction
We demonstrate compression of 95 keV, space-charge-dominated electron bunches
to sub-100 fs durations. These bunches have sufficient charge (200 fC) and are
of sufficient quality to capture a diffraction pattern with a single shot,
which we demonstrate by a diffraction experiment on a polycrystalline gold
foil. Compression is realized by means of velocity bunching as a result of a
velocity chirp, induced by the oscillatory longitudinal electric field of a 3
GHz radio-frequency cavity. The arrival time jitter is measured to be 80 fs
Fast cross-validation for multi-penalty ridge regression
High-dimensional prediction with multiple data types needs to account for
potentially strong differences in predictive signal. Ridge regression is a
simple model for high-dimensional data that has challenged the predictive
performance of many more complex models and learners, and that allows inclusion
of data type specific penalties. The largest challenge for multi-penalty ridge
is to optimize these penalties efficiently in a cross-validation (CV) setting,
in particular for GLM and Cox ridge regression, which require an additional
estimation loop by iterative weighted least squares (IWLS). Our main
contribution is a computationally very efficient formula for the multi-penalty,
sample-weighted hat-matrix, as used in the IWLS algorithm. As a result, nearly
all computations are in low-dimensional space, rendering a speed-up of several
orders of magnitude. We developed a flexible framework that facilitates
multiple types of response, unpenalized covariates, several performance
criteria and repeated CV. Extensions to paired and preferential data types are
included and illustrated on several cancer genomics survival prediction
problems. Moreover, we present similar computational shortcuts for maximum
marginal likelihood and Bayesian probit regression. The corresponding
R-package, multiridge, serves as a versatile standalone tool, but also as a
fast benchmark for other more complex models and multi-view learners
Vectorwise: a Vectorized Analytical DBMS
Vectorwise is a new entrant in the analytical database marketplace whose technology comes straight from innovations in the database research community in the past years. The product has since made waves due to its excellent performance in analytical customer workloads as well as benchmarks.
We describe the history of Vectorwise, as well as its basic architecture and the experiences in turning a technology developed in an academic context into a commercial-grade product. Finally, we turn our attention to recent performance results, most notably on the TPC-H benchmark at various sizes
The Kondo Effect in the Unitary Limit
We observe a strong Kondo effect in a semiconductor quantum dot when a small
magnetic field is applied. The Coulomb blockade for electron tunneling is
overcome completely by the Kondo effect and the conductance reaches the
unitary-limit value. We compare the experimental Kondo temperature with the
theoretical predictions for the spin-1/2 Anderson impurity model. Excellent
agreement is found throughout the Kondo regime. Phase coherence is preserved
when a Kondo quantum dot is included in one of the arms of an Aharonov-Bohm
ring structure and the phase behavior differs from previous results on a
non-Kondo dot.Comment: 10 page
Gate-tunable band structure of the LaAlO-SrTiO interface
The 2-dimensional electron system at the interface between LaAlO and
SrTiO has several unique properties that can be tuned by an externally
applied gate voltage. In this work, we show that this gate-tunability extends
to the effective band structure of the system. We combine a magnetotransport
study on top-gated Hall bars with self-consistent Schr\"odinger-Poisson
calculations and observe a Lifshitz transition at a density of
cm. Above the transition, the carrier density of one
of the conducting bands decreases with increasing gate voltage. This surprising
decrease is accurately reproduced in the calculations if electronic
correlations are included. These results provide a clear, intuitive picture of
the physics governing the electronic structure at complex oxide interfaces.Comment: 14 pages, 4 figure
Dynamic root growth in response to depth-varying soil moisture availability:a rhizobox study
Plant roots are highly adaptable, but their adaptability is not included in crop and land surface models. They rely on a simplified representation of root growth, which is independent of soil moisture availability. Data of subsurface processes and interactions, needed for model setup and validation, are scarce. Here we investigated soil-moisture-driven root growth. To this end, we installed subsurface drip lines and small soil moisture sensors (0.2 L measurement volume) inside rhizoboxes (length × width × height of 45 × 7.5 × 45 cm). The development of the vertical soil moisture and root growth profiles is tracked with a high spatial and temporal resolution. The results confirm that root growth is predominantly driven by vertical soil moisture distribution, while influencing soil moisture at the same time. Besides support for the functional relationship between the soil moisture and the root density growth rate, the experiments also suggest that the extension of the maximum rooting depth will stop if the soil moisture at the root tip drops below a threshold value. We show that even a parsimonious one-dimensional water balance model, driven by the water input flux (irrigation), can be convincingly improved by implementing root growth driven by soil moisture availability
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