3,454 research outputs found
Reconstructing the gravitational field of the local universe
Tests of gravity at the galaxy scale are in their infancy. As a first step to
systematically uncovering the gravitational significance of galaxies, we map
three fundamental gravitational variables -- the Newtonian potential,
acceleration and curvature -- over the galaxy environments of the local
universe to a distance of approximately 200 Mpc. Our method combines the
contributions from galaxies in an all-sky redshift survey, halos from an N-body
simulation hosting low-luminosity objects, and linear and quasi-linear modes of
the density field. We use the ranges of these variables to determine the extent
to which galaxies expand the scope of generic tests of gravity and are capable
of constraining specific classes of model for which they have special
significance. Finally, we investigate the improvements afforded by upcoming
galaxy surveys.Comment: 12 pages, 4 figures; revised to match MNRAS accepted versio
Export Tourism: Rejoinder to Palmer. Quarterly Economic Commentary, November 1983
There appear to be three central points in Noel Palmer's comments. These
pertain to:
I: The treatment of the government sector - should it be regarded as
exogenous ( as in Norton, 1982) or endogenous?
II: An assumtion of the input-output model used by me, namely, its failure
to distinguish oetween average and marginal values of parameters.
III: The relative capital intensity of export tourism
Galaxy morphology rules out astrophysically relevant Hu-Sawicki gravity
is a paradigmatic modified gravity theory that typifies extensions to
General Relativity with new light degrees of freedom and hence screened fifth
forces between masses. These forces produce observable signatures in galaxy
morphology, caused by a violation of the weak equivalence principle due to a
differential impact of screening among galaxies' mass components. We compile
statistical datasets of two morphological indicators -- offsets between stars
and gas in galaxies and warping of stellar disks -- and use them to constrain
the strength and range of a thin-shell-screened fifth force. This is achieved
by applying a comprehensive set of upgrades to past work (Desmond et al
2018a,b): we construct a robust galaxy-by-galaxy Bayesian forward model for the
morphological signals, including full propagation of uncertainties in the input
quantities and marginalisation over an empirical model describing astrophysical
noise. Employing more stringent data quality cuts than previously we find no
evidence for a screened fifth force of any strength in
the Compton wavelength range Mpc, setting a bound of at Mpc that strengthens to at Mpc. These are the tightest
bounds to date beyond the Solar System by over an order of magnitude. For the
Hu-Sawicki model of with we require a background scalar field
value , forcing practically all astrophysical
objects to be screened. We conclude that this model can have no relevance to
astrophysics or cosmology.Comment: 15 pages, 6 figures; minor revision, matches PRD accepted versio
CONTEST : a Controllable Test Matrix Toolbox for MATLAB
Large, sparse networks that describe complex interactions are a common feature across a number of disciplines, giving rise to many challenging matrix computational tasks. Several random graph models have been proposed that capture key properties of real-life networks. These models provide realistic, parametrized matrices for testing linear system and eigenvalue solvers. CONTEST (CONtrollable TEST matrices) is a random network toolbox for MATLAB that implements nine models. The models produce unweighted directed or undirected graphs; that is, symmetric or unsymmetric matrices with elements equal to zero or one. They have one or more parameters that affect features such as sparsity and characteristic pathlength and all can be of arbitrary dimension. Utility functions are supplied for rewiring, adding extra shortcuts and subsampling in order to create further classes of networks. Other utilities convert the adjacency matrices into real-valued coefficient matrices for naturally arising computational tasks that reduce to sparse linear system and eigenvalue problems
Ensemble approach combining multiple methods improves human transcription start site prediction
Dineen DG, Schroeder M, Higgins DG, Cunningham P. Ensemble approach combining multiple methods improves human transcription start site prediction. BMC Genomics. 2010;11(1): 677.Background: The computational prediction of transcription start sites is an important unsolved problem. Some recent progress has been made, but many promoters, particularly those not associated with CpG islands, are still difficult to locate using current methods. These methods use different features and training sets, along with a variety of machine learning techniques and result in different prediction sets. Results: We demonstrate the heterogeneity of current prediction sets, and take advantage of this heterogeneity to construct a two-level classifier ('Profisi Ensemble') using predictions from 7 programs, along with 2 other data sources. Support vector machines using 'full' and 'reduced' data sets are combined in an either/or approach. We achieve a 14% increase in performance over the current state-of-the-art, as benchmarked by a third-party tool. Conclusions: Supervised learning methods are a useful way to combine predictions from diverse sources
Exhaustive Symbolic Regression
Symbolic Regression (SR) algorithms learn analytic expressions which both
accurately fit data and, unlike traditional machine-learning approaches, are
highly interpretable. Conventional SR suffers from two fundamental issues which
we address in this work. First, since the number of possible equations grows
exponentially with complexity, typical SR methods search the space
stochastically and hence do not necessarily find the best function. In many
cases, the target problems of SR are sufficiently simple that a brute-force
approach is not only feasible, but desirable. Second, the criteria used to
select the equation which optimally balances accuracy with simplicity have been
variable and poorly motivated. To address these issues we introduce a new
method for SR -- Exhaustive Symbolic Regression (ESR) -- which systematically
and efficiently considers all possible equations and is therefore guaranteed to
find not only the true optimum but also a complete function ranking. Utilising
the minimum description length principle, we introduce a principled method for
combining these preferences into a single objective statistic. To illustrate
the power of ESR we apply it to a catalogue of cosmic chronometers and the
Pantheon+ sample of supernovae to learn the Hubble rate as a function of
redshift, finding 40 functions (out of 5.2 million considered) that fit
the data more economically than the Friedmann equation. These low-redshift data
therefore do not necessarily prefer a CDM expansion history, and
traditional SR algorithms that return only the Pareto-front, even if they found
this successfully, would not locate CDM. We make our code and full
equation sets publicly available.Comment: 14 pages, 6 figures, 2 tables. Submitted to IEEE Transactions on
Pattern Analysis and Machine Intelligenc
Perceptions of a Bible Belt State\u27s Proposed Casino Gaming Legislation by Religious Affiliation: The Case of Kentucky Residents
This study seeks to explore whether differences exist among Kentucky residents\u27 perception of casino gaming based on religious affiliation. A survey was conducted to sample 600 residents regarding currently a widely debated introduction of land-based casinos in the state, yielding a response rate of 38.4%. The results support earlier studies regarding the impact religion has on people\u27s attitudes toward gaming. The findings suggest that Catholics have a more positive attitude toward the legalization of gambling than persons of Protestant faiths
Priors for symbolic regression
When choosing between competing symbolic models for a data set, a human will
naturally prefer the "simpler" expression or the one which more closely
resembles equations previously seen in a similar context. This suggests a
non-uniform prior on functions, which is, however, rarely considered within a
symbolic regression (SR) framework. In this paper we develop methods to
incorporate detailed prior information on both functions and their parameters
into SR. Our prior on the structure of a function is based on a -gram
language model, which is sensitive to the arrangement of operators relative to
one another in addition to the frequency of occurrence of each operator. We
also develop a formalism based on the Fractional Bayes Factor to treat
numerical parameter priors in such a way that models may be fairly compared
though the Bayesian evidence, and explicitly compare Bayesian, Minimum
Description Length and heuristic methods for model selection. We demonstrate
the performance of our priors relative to literature standards on benchmarks
and a real-world dataset from the field of cosmology.Comment: 8+2 pages, 2 figures. Submitted to The Genetic and Evolutionary
Computation Conference (GECCO) 2023 Workshop on Symbolic Regressio
Cross-platform comparison and visualisation of gene expression data using co-inertia analysis
BACKGROUND: Rapid development of DNA microarray technology has resulted in different laboratories adopting numerous different protocols and technological platforms, which has severely impacted on the comparability of array data. Current cross-platform comparison of microarray gene expression data are usually based on cross-referencing the annotation of each gene transcript represented on the arrays, extracting a list of genes common to all arrays and comparing expression data of this gene subset. Unfortunately, filtering of genes to a subset represented across all arrays often excludes many thousands of genes, because different subsets of genes from the genome are represented on different arrays. We wish to describe the application of a powerful yet simple method for cross-platform comparison of gene expression data. Co-inertia analysis (CIA) is a multivariate method that identifies trends or co-relationships in multiple datasets which contain the same samples. CIA simultaneously finds ordinations (dimension reduction diagrams) from the datasets that are most similar. It does this by finding successive axes from the two datasets with maximum covariance. CIA can be applied to datasets where the number of variables (genes) far exceeds the number of samples (arrays) such is the case with microarray analyses. RESULTS: We illustrate the power of CIA for cross-platform analysis of gene expression data by using it to identify the main common relationships in expression profiles on a panel of 60 tumour cell lines from the National Cancer Institute (NCI) which have been subjected to microarray studies using both Affymetrix and spotted cDNA array technology. The co-ordinates of the CIA projections of the cell lines from each dataset are graphed in a bi-plot and are connected by a line, the length of which indicates the divergence between the two datasets. Thus, CIA provides graphical representation of consensus and divergence between the gene expression profiles from different microarray platforms. Secondly, the genes that define the main trends in the analysis can be easily identified. CONCLUSIONS: CIA is a robust, efficient approach to coupling of gene expression datasets. CIA provides simple graphical representations of the results making it a particularly attractive method for the identification of relationships between large datasets
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