6,075 research outputs found
Simple Parsimonious Types and Logarithmic Space
We present a functional characterization of deterministic logspace-computable predicates based on a variant (although not a subsystem) of propositional linear logic, which we call parsimonious logic. The resulting calculus is simply-typed and contains no primitive besides those provided by the underlying logical system, which makes it one of the simplest higher-order languages capturing logspace currently known. Completeness of the calculus uses the descriptive complexity characterization of logspace (we encode first-order logic with deterministic closure), whereas soundness is established by executing terms on a token machine (using the geometry of interaction)
Efficient inference of parsimonious phenomenological models of cellular dynamics using S-systems and alternating regression
The nonlinearity of dynamics in systems biology makes it hard to infer them
from experimental data. Simple linear models are computationally efficient, but
cannot incorporate these important nonlinearities. An adaptive method based on
the S-system formalism, which is a sensible representation of nonlinear
mass-action kinetics typically found in cellular dynamics, maintains the
efficiency of linear regression. We combine this approach with adaptive model
selection to obtain efficient and parsimonious representations of cellular
dynamics. The approach is tested by inferring the dynamics of yeast glycolysis
from simulated data. With little computing time, it produces dynamical models
with high predictive power and with structural complexity adapted to the
difficulty of the inference problem.Comment: 14 pages, 2 figure
Latent tree models
Latent tree models are graphical models defined on trees, in which only a
subset of variables is observed. They were first discussed by Judea Pearl as
tree-decomposable distributions to generalise star-decomposable distributions
such as the latent class model. Latent tree models, or their submodels, are
widely used in: phylogenetic analysis, network tomography, computer vision,
causal modeling, and data clustering. They also contain other well-known
classes of models like hidden Markov models, Brownian motion tree model, the
Ising model on a tree, and many popular models used in phylogenetics. This
article offers a concise introduction to the theory of latent tree models. We
emphasise the role of tree metrics in the structural description of this model
class, in designing learning algorithms, and in understanding fundamental
limits of what and when can be learned
Inference of Ancestral Recombination Graphs through Topological Data Analysis
The recent explosion of genomic data has underscored the need for
interpretable and comprehensive analyses that can capture complex phylogenetic
relationships within and across species. Recombination, reassortment and
horizontal gene transfer constitute examples of pervasive biological phenomena
that cannot be captured by tree-like representations. Starting from hundreds of
genomes, we are interested in the reconstruction of potential evolutionary
histories leading to the observed data. Ancestral recombination graphs
represent potential histories that explicitly accommodate recombination and
mutation events across orthologous genomes. However, they are computationally
costly to reconstruct, usually being infeasible for more than few tens of
genomes. Recently, Topological Data Analysis (TDA) methods have been proposed
as robust and scalable methods that can capture the genetic scale and frequency
of recombination. We build upon previous TDA developments for detecting and
quantifying recombination, and present a novel framework that can be applied to
hundreds of genomes and can be interpreted in terms of minimal histories of
mutation and recombination events, quantifying the scales and identifying the
genomic locations of recombinations. We implement this framework in a software
package, called TARGet, and apply it to several examples, including small
migration between different populations, human recombination, and horizontal
evolution in finches inhabiting the Gal\'apagos Islands.Comment: 33 pages, 12 figures. The accompanying software, instructions and
example files used in the manuscript can be obtained from
https://github.com/RabadanLab/TARGe
Using The Censored Gamma Distribution for Modeling Fractional Response Variables with an Application to Loss Given Default
Regression models for limited continuous dependent variables having a
non-negligible probability of attaining exactly their limits are presented. The
models differ in the number of parameters and in their flexibility. Fractional
data being a special case of limited dependent data, the models also apply to
variables that are a fraction or a proportion. It is shown how to fit these
models and they are applied to a Loss Given Default dataset from insurance to
which they provide a good fit
Time and Space Bounds for Reversible Simulation
We prove a general upper bound on the tradeoff between time and space that
suffices for the reversible simulation of irreversible computation. Previously,
only simulations using exponential time or quadratic space were known.
The tradeoff shows for the first time that we can simultaneously achieve
subexponential time and subquadratic space.
The boundary values are the exponential time with hardly any extra space
required by the Lange-McKenzie-Tapp method and the ()th power time with
square space required by the Bennett method. We also give the first general
lower bound on the extra storage space required by general reversible
simulation. This lower bound is optimal in that it is achieved by some
reversible simulations.Comment: 11 pages LaTeX, Proc ICALP 2001, Lecture Notes in Computer Science,
Vol xxx Springer-Verlag, Berlin, 200
- …