25,546 research outputs found
Probably Safe or Live
This paper presents a formal characterisation of safety and liveness
properties \`a la Alpern and Schneider for fully probabilistic systems. As for
the classical setting, it is established that any (probabilistic tree) property
is equivalent to a conjunction of a safety and liveness property. A simple
algorithm is provided to obtain such property decomposition for flat
probabilistic CTL (PCTL). A safe fragment of PCTL is identified that provides a
sound and complete characterisation of safety properties. For liveness
properties, we provide two PCTL fragments, a sound and a complete one. We show
that safety properties only have finite counterexamples, whereas liveness
properties have none. We compare our characterisation for qualitative
properties with the one for branching time properties by Manolios and Trefler,
and present sound and complete PCTL fragments for characterising the notions of
strong safety and absolute liveness coined by Sistla
Unconventional machine learning of genome-wide human cancer data
Recent advances in high-throughput genomic technologies coupled with
exponential increases in computer processing and memory have allowed us to
interrogate the complex aberrant molecular underpinnings of human disease from
a genome-wide perspective. While the deluge of genomic information is expected
to increase, a bottleneck in conventional high-performance computing is rapidly
approaching. Inspired in part by recent advances in physical quantum
processors, we evaluated several unconventional machine learning (ML)
strategies on actual human tumor data. Here we show for the first time the
efficacy of multiple annealing-based ML algorithms for classification of
high-dimensional, multi-omics human cancer data from the Cancer Genome Atlas.
To assess algorithm performance, we compared these classifiers to a variety of
standard ML methods. Our results indicate the feasibility of using
annealing-based ML to provide competitive classification of human cancer types
and associated molecular subtypes and superior performance with smaller
training datasets, thus providing compelling empirical evidence for the
potential future application of unconventional computing architectures in the
biomedical sciences
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Identification and separation of DNA mixtures using peak area information (Updated version of Statistical Research Paper No. 25)
We introduce a new methodology, based upon probabilistic expert systems, for analysing forensic identification problems involving DNA mixture traces using quantitative peak area information. Peak area is modelled with conditional Gaussian distributions. The expert system can be used for ascertaining whether individuals, whose profiles have been measured, have contributed to the mixture, but also to predict DNA profiles of unknown contributors by separating the mixture into its individual components. The potential of our probabilistic methodology is illustrated on case data examples and compared with alternative approaches. The advantages are that identification and separation issues can be handled in a unified way within a single probabilistic model and the uncertainty associated with the analysis is quantified. Further work, required to bring the methodology to a point where it could be applied to the routine analysis of casework, is discussed
Predicting Genetic Regulatory Response Using Classification
We present a novel classification-based method for learning to predict gene
regulatory response. Our approach is motivated by the hypothesis that in simple
organisms such as Saccharomyces cerevisiae, we can learn a decision rule for
predicting whether a gene is up- or down-regulated in a particular experiment
based on (1) the presence of binding site subsequences (``motifs'') in the
gene's regulatory region and (2) the expression levels of regulators such as
transcription factors in the experiment (``parents''). Thus our learning task
integrates two qualitatively different data sources: genome-wide cDNA
microarray data across multiple perturbation and mutant experiments along with
motif profile data from regulatory sequences. We convert the regression task of
predicting real-valued gene expression measurement to a classification task of
predicting +1 and -1 labels, corresponding to up- and down-regulation beyond
the levels of biological and measurement noise in microarray measurements. The
learning algorithm employed is boosting with a margin-based generalization of
decision trees, alternating decision trees. This large-margin classifier is
sufficiently flexible to allow complex logical functions, yet sufficiently
simple to give insight into the combinatorial mechanisms of gene regulation. We
observe encouraging prediction accuracy on experiments based on the Gasch S.
cerevisiae dataset, and we show that we can accurately predict up- and
down-regulation on held-out experiments. Our method thus provides predictive
hypotheses, suggests biological experiments, and provides interpretable insight
into the structure of genetic regulatory networks.Comment: 8 pages, 4 figures, presented at Twelfth International Conference on
Intelligent Systems for Molecular Biology (ISMB 2004), supplemental website:
http://www.cs.columbia.edu/compbio/geneclas
In light of the theory of Special Relativity is a Passage of Time and the argument of the Presentist untenable?
In light of the Special Theory of Relativity and the Minkowski creation of ‘spacetime’, the universe is taken to be a four-dimensional entity which postulates bodies as existing within a temporally extended reality. The Special Theory of Relativity’s implications liken the nature of the universe to a ‘block’ within which all events coexist equally in spacetime. Such a view strikes against the very essence of presentism, which holds that all that exists is the instantaneous state of objects in the present moment. With respect to the present moment, events have a clear division into the past or future, however such regions do not exist in reality and the universe is a three-dimensional entity. The consequences of a four-dimensional universe are disturbing to say the least for our everyday human experience, with once objective facts about reality becoming dependent upon an observer’s relative motion and the debate over the extent of true free will in a Block Universe. This paper will look at arguments which seek to rescue the presentist view in light of Special Relativity so such four-dimensionalist implications do not have to be accepted. Two approaches will be considered. The first accepts that presentism is incompatible with Special Relativity, and seeks to show that the theory is ultimately false. The second holds that it is the Block Universe interpretation of Special Relativity that is wrong, and a version of presentism can be reconciled with Special Relativity. The paper will expound and critically examine both of these approaches to review whether the case for the three-dimensionalist and a fundamental passage of time can be made
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