It is known that the hardness of the (two-way) number partitioning problem (NPP) variant of the subset-sum problem (SSP) depends on the number and distribution of bits in the set of numbers, but beyond this, it is relatively unexplained for the SSP itself. Thus, we look at the solution space of various problem instances of the SSP using fractal analysis. Two methods to determine the dimension are used. Plotting the fractal dimension over the range and distributions of informational bits, we find that it is correlated with this linear model and also moderately correlated to the hardness of the NPP. This suggests that fractal analysis might be a useful tool in understanding the complexity of combinatorial problems and, we believe, may help further understand the hardness in NP. Finally, we introduce a thought experiment derived from the famous Hilbert’s hotel, which we call Hilbert’s hotel with elevators, to intuitively illustrate how the complexity of the solutions space and the computational hardness may relate across combinatorial problems.

The hardness of the partition problem does not only depend on the number of integers in the problem instance, but also on their magnitude, measured in informational bits. In this work, we will show that also the exact distribution of informational bits among the integers influences the hardness for at least one exact and three heuristic algorithms.

This paper discusses the problem of marrying structural similarity with semantic relatedness for Information Extraction from text. Aiming at accurate recognition of relations, we introduce local alignment kernels and explore various possibilities of using them for this task. We give a definition of a local alignment (LA) kernel based on the Smith-Waterman score as a sequence similarity measure and proceed with a range of possibilities for computing similarity between elements of sequences. We show how distributional similarity measures obtained from unlabeled data can be incorporated into the learning task as semantic knowledge. Our experiments suggest that the LA kernel yields promising results on various biomedical corpora outperforming two baselines by a large margin. Additional series of experiments have been conducted on the data sets of seven general relation types, where the performance of the LA kernel is comparable to the current state-of-the-art results.

BACKGROUND: Hypothesis generation in molecular and cellular biology is an empirical process in which knowledge derived from prior experiments is distilled into a comprehensible model. The requirement of automated support is exemplified by the difficulty of considering all relevant facts that are contained in the millions of documents available from PubMed. Semantic Web provides tools for sharing prior knowledge, while information retrieval and information extraction techniques enable its extraction from literature. Their combination makes prior knowledge available for computational analysis and inference. While some tools provide complete solutions that limit the control over the modeling and extraction processes, we seek a methodology that supports control by the experimenter over these critical processes. RESULTS: We describe progress towards automated support for the generation of biomolecular hypotheses. Semantic Web technologies are used to structure and store knowledge, while a workflow extracts knowledge from text. We designed minimal proto-ontologies in OWL for capturing different aspects of a text mining experiment: the biological hypothesis, text and documents, text mining, and workflow provenance. The models fit a methodology that allows focus on the requirements of a single experiment while supporting reuse and posterior analysis of extracted knowledge from multiple experiments. Our workflow is composed of services from the 'Adaptive Information Disclosure Application' (AIDA) toolkit as well as a few others. The output is a semantic model with putative biological relations, with each relation linked to the corresponding evidence. CONCLUSION: We demonstrated a 'do-it-yourself' approach for structuring and extracting knowledge in the context of experimental research on biomolecular mechanisms. The methodology can be used to bootstrap the construction of semantically rich biological models using the results of knowledge extraction processes. Models specific to particular experiments can be constructed that, in turn, link with other semantic models, creating a web of knowledge that spans experiments. Mapping mechanisms can link to other knowledge resources such as OBO ontologies or SKOS vocabularies. AIDA Web Services can be used to design personalized knowledge extraction procedures. In our example experiment, we found three proteins (NF-Kappa B, p21, and Bax) potentially playing a role in the interplay between nutrients and epigenetic gene regulation.

The notion of meaningful information seems to be associated with the sweet spot between order and chaos. This form of meaningfulness of information, which is primarily what science is interested in, is not captured by both Shannon information and Kolmogorov complexity. In this paper I develop a theoretical framework that can be seen as a first approximation to a study of meaningful information. In this context I introduce the notion of facticity of a data set. I discuss the relation between thermodynamics and algorithmic complexity theory in the context of this problem. I prove that, under adequate measurement conditions, the free energy of a system in the world is associated with the randomness deficiency of a data set with observations about this system. These insights suggest an explanation of the efficiency of human intelligence in terms of helpful distributions. Finally I give a critical discussion of Schmidhuber’s views specifically his notion of low complexity art, I defend the view that artists optimize facticity instead.

Approximation of the optimal two-part minimum description length (MDL) code for given data, through successive monotonically length-decreasing two-part MDL codes, has the following properties: (i) computation of each step may take arbitrarily long; (ii) we may not know when we reach the optimum, or whether we will reach the optimum at all; (iii) the sequence of models generated may not monotonically improve the goodness of fit; but (iv) the model associated with the optimum has (almost) the best goodness of fit. To express the practically interesting goodness of fit of individual models for individual data sets we have to rely on Kolmogorov complexity.

Most of the existing scientific workflow management systems (SWMS) are driven by applications from specific domains and are developed in academic projects. It is challenging to introduce an existing SWMS to a new domain; not only the workflow model and description language do not easily fit in new problem domains, but also the unstable development state of existing systems does not provide all functionality required by the new applications and thus gives high risk for the development. Aggregating different workflow systems as one generic environment enables the sharing on both components and processes between experiments, and promotes the knowledge transfer between domains. A workflow bus approach is to integrate different e-science workflow engines via a software bus. In this paper, we present the basic idea of workflow bus, and discuss how Jade agent framework can be used to prototype the runtime infrastructure of a workflow bus.

We point out a potential weakness in the application of the celebrated minimum description length (MDL) principle for model selection. Specifically, it is shown that (although the index of the model class which actually minimizes a two-part code has many desirable properties) a model which has a shorter two- part code-length than another is not necessarily better (unless of course it achieves the global minimum). This is illustrated by an application to infer a grammar (DFA) from positive examples. We also analyze computability issues, and robustness under receding of the data. Generally, the classical approach is inadequate to express the goodness-of-fit of individual models for individual data sets. In practice however, this is precisely what we are interested in: both to express the goodness of a procedure and where and how it can fail. To achieve this practical goal, we paradoxically have to use the, supposedly impractical, vehicle of Kolmogorov complexity.

We propose a method for reconstruction of human brain states directly from functional neuroimaging data. The method extends the traditional multivariate regression analysis of discretized fMRI data to the domain of stochastic functional measurements, facilitating evaluation of brain responses to naturalistic stimuli and boosting the power of functional imaging. The method searches for sets of voxel timecourses that optimize a multivariate functional linear model in terms of Rsquare-statistic. Population based incremental learning is used to search for spatially distributed voxel clusters, taking into account the variation in Haemodynamic lag across brain areas and among subjects by voxel-wise non-linear registration of stimuli to fMRI data. The method captures spatially distributed brain responses to naturalistic stimuli without attempting to localize function. Application of the method for prediction of naturalistic stimuli from new and unknown fMRI data shows that the approach is capable of identifying distributed clusters of brain locations that are highly predictive of a specific stimuli.

In this paper we present a general domain for the analysis of workflows and workflow components based on the notion of a collection of Turing machines sharing a set of tapes. We show that computationally equivalent workflows can be evaluated in terms of two dimensions: data complexity and process complexity. We show that this approach allows for the evaluation of various workflow architectures. Using this formal framework we prove that maximal simplicity, generality and consistency are mutually exclusive. Simplicity of and generality of workflow components leads to complexity of data structures and computational processes. This is an issue that deserves more attention from designers and users of workflow communication protocols. We define a formal version of the General Workflow Design Problem and show that this problem is decidable in the case of a finite number of topologies. Thus, automatic composition of workflows is possible in limited domains. Decidability for an infinite number of topologies remains an open question. We show how our findings from the formal framework manifest themselves in real world e-Science workflow environments.

In this article, we present experiences with the Crew Availability Planning and Training System (CAPTAINS). CAPTAINS is a complex planning‐aid system that assists professional career planners. This article describes the learning component of CAPTAINS—the Learning Classifier System (LCS)—which predicts the bids on functions of pilots. We also present experiments with the LCS and their results.