The availability of computing power now allows for computation methods that seemed expensive in the past. This enables the exploring of synthetic population characteristics while running simulations at the individual level. Specifically, it is now possible to generate synthetic populations that mimic population statistics of published epidemiological data and to explore hypothetical scenarios. This work shows how Evolutionary Computation (EC) techniques can be used to create a population occurrence model that projects possible treatment effects on population outcomes using characteristics that are intrinsic to the population. We demonstrate how EC is used to extend a previous solution to the population disease occurrence model and generalize it. This exploration reveals the need for epidemiological experts to provide additional information to accompany publication of population statistics to support machine comprehension. ABOUT THE AUTHORS Olaf Dammann is Professor of Public Health and Community Medicine at Tufts University School of Medicine. He is interested in the elucidation of perinatal risk scenarios in the context of maternal intrauterine infection and inflammatory responses of the mother, fetus, and newborn. The main outcomes researched are perinatal brain damage and retinopathy of prematurity. He is also interested in theory of risk and causation. Also see: https://medicine.tufts.edu/faculty/olaf-dammann Anselm Blumer is an Associate Professor Emeritus in Computer Science at Tufts University. His primary research interest is in machine learning, particularly in applications to biology and medicine. He has also done work on data compression and algorithms for indexing and search. Also see: https://engineering.tufts.edu/cs/people/faculty/anselm-blumer Jacob Barhak specializes in population modeling and specifically in chronic disease modeling with emphasis on using computational technological solutions. Dr. Barhak has diverse international background in engineering and computing science. The Reference Model for disease progression was independently self-developed by Dr. Barhak in 2012. He is the developer of the MIcro Simulation Tool (MIST). See: http://sites.google.com/site/jacobbarhak/ Aaron Garrett is an Assistant Professor in computer science at Wofford College. His interests include evolutionary computation and machine learning. He is the author of INSPYRED, a software library that includes biologically-inspired computation and encompasses a broad range of algorithms including evolutionary computation, swarm intelligence, and neural networks. For additional information please visit http://sites.wofford.edu/garrettal/ 2019 Paper No. 0014 Page 1 of 10

I use heuristic analytic approximations to consider the robustness of the Quadratic Voting (QV) mechanism proposed by (Lalley and Weyl, 2015) to collusion and variations in voter behavior. I also consider some examples of aggregate uncertainty in the value distribution and common values. While these variations typically reduce the efficiency of QV, in plausible cases the impacts are small and variations in voter behavior may actually improve QV’s rate of convergence towards efficiency. I contrast these results with other (approximately) efficient mechanism proposed by economists which are highly fragile along these dimensions.

MOTIVATION:With the growing availability of high-throughput protein-protein interaction (PPI) data, it has become possible to consider how a protein's local or global network characteristics predict its function. RESULTS:We introduce a graph-theoretic approach that identifies key regulatory proteins in an organism by analyzing proteins' local PPI network structure. We apply the method to the yeast genome and describe several properties of the resulting set of regulatory hubs. Finally, we demonstrate how the identified hubs and putative target gene sets can be used to identify causative, functional regulators of differential gene expression linked to human disease. AVAILABILITY:Code is available at http://bcb.cs.tufts.edu/hubcomps. CONTACT:fox.andrew.d@gmail.com; slonim@cs.tufts.edu SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.

Previous results on non-learnability of visual concepts relied on the assumption that such concepts are represented as sets of pixels [1]. This report uses an approach developed by Haussler [2] to show that, under an alternative, feature-based representation, recognition is PAC-learnable from a feasible number of examples in a distribution-free manner.

Fibrous proteins such as collagen, silk, and elastin play critical biological roles, yet they have been the subject of few projects that use computational techniques to predict either their class or their structure. In this article, we present FiberID, a simple yet effective method for identifying and distinguishing three fibrous protein subclasses from their primary sequences. Using a combination of amino acid composition and fast Fourier measurements, FiberID can classify fibrous proteins belonging to these subclasses with high accuracy by using two standard machine learning techniques (decision trees and Naïve Bayesian classifiers). After presenting our results, we present several fibrous sequences that are regularly misclassified by FiberID as sequences of potential interest for further study. Finally, we analyze the decision trees developed by FiberID for potential insights regarding the structure of these proteins. Proteins 2007. © 2006 Wiley‐Liss, Inc.

MOTIVATION:Modularity analysis is a powerful tool for studying the design of biological networks, offering potential clues for relating the biochemical function(s) of a network with the 'wiring' of its components. Relatively little work has been done to examine whether the modularity of a network depends on the physiological perturbations that influence its biochemical state. Here, we present a novel modularity analysis algorithm based on edge-betweenness centrality, which facilitates the use of directional information and measurable biochemical data.

We experiment with two types of clustering, K-medians and a dimensionreduction technique known as approximate distance clustering (ADC) [Cowen and Priebe 19971, for classifying lung adenocarcinomas into high-risk and low-risk groups according to gene expression values from microarray data. The microarrays were Affymetrix oligonucleotide arrays used in studies at Michigan and Harvard, with 12,600 and 7129 probesets respectively. We show that we can obtain accurate classification based on a reduced set of genes obtained by nearest shrunken mean (NSM) [Tibshirani et al. 20021 or a combination of a variance-based approach with hierarchical clustering. The quality of the clustering is measured by using the p-values from log-rank tests, and the results are confirmed using cross- validation and by using the reduced set of genes obtained from one dataset to cluster the other.

Let a partial deterministic finite automaton be a DFA in which each state need not have a transition edge for each letter of the alphabet. We demonstrate that the minimal partial DFA for the set of all subwords of a given word w, |w| > 2, has at most 2|w| − 2 states and 3|w| − 4 transition edges, independently of the alphabet size. We give an algorithm to build this minimal partial DFA from the input w on-line in linear time.

This paper surveys several models of learnability proposed and investigated by computational learning theorists during the past few years. Computational learning theory is the study of learning as seen from a computational complexity point of view. In addition to the usual space and time complexity, computational learning theory studies the sample complexity, the number of examples seen by the learner. (In a statistical setting, this is known as the sample size.) This paper will cover those models of learnability where ideas from Vapnik-Chervonenkis combinatorics have had the greatest impact. There are a few short proofs to give a flavor of some of the ideas involved, but most of the proofs are too long to be included here. The focus is on giving an idea of the variety of models and the relationships between them. For more complete surveys of computational learning theory see (1988), (1990), (1991), (1992), or the proceedings of the annual Workshop on Computational Learning Theory published by Morgan Kaufmann. Some attempt has been made to keep the notation consistent within this paper, which means that it will be inconsistent with a large subset of the references.

A string compression technique can compress well only if it has an accurate model of the data source. For a source with statistically independent characters, Huffman or arithmetic codes give optimal compression [11]. In this case it is straightforward to use a fixed source model if the statistics are known in advance, or to adapt the model to unknown or changing statistics. For the many sources which produce dependent characters, a more sophisticated source model can provide much better compression at the expense of the extra space and time for storing and maintaining the model. The space required by a straightforward implementation of a Markov model grows exponentially in the order of the model. The Directed Acyclic Word Graph (DAWG) can be built in linear time and space, and provides the information needed to obtain compression equal to that obtained using a Markov model of high order. This paper presents two algorithms for string compression using DAWGs. The first is a very simple idea which generalizes run-length coding. It obtains good compression in many cases, but is provably non-optimal. The second combines the main idea of the first with arithmetic coding, resulting in a great improvement in performance.

Adaptive data compression techniques can be viewed as consisting of a model specified by a database common to the encoder and decoder, an encoding rule and a rule for updating the model to ensure that the encoder and decoder always agree on the interpretation of the next transmission. The techniques which fit this framework range from run-length coding, to adaptive Huffman and arithmetic coding, to the string-matching techniques of Lempel and Ziv. The compression obtained by arithmetic coding is dependent on the generality of the source model. For many sources, an independent-letter model is clearly insufficient. Unfortunately, a straightforward implementation of a Markov model requires an amount of space exponential in the number of letters remembered. The Directed Acyclic Word Graph (DAWG) can be constructed in time and space proportional to the text encoded, and can be used to estimate the probabilities required for arithmetic coding based on an amount of memory which varies naturally depending on the encoded text. The tail of that portion of the text which was encoded is the longest suffix that has occurred previously. The frequencies of letters following these previous occurrences can be used to estimate the probability distribution of the next letter. Experimental results indicate that compression is often far better than that obtained using independent-letter models, and sometimes also significantly better than other non-independent techniques.

: A new digital data tape format has been developed to be used for data from the WHOI/Brown CTD microprofiler. CTD-78 Version 1 as detailed in this report is designed as a flexible and expandable internal data format adapted to the Hewlett-Packard 2100/21MX series 16-bit mini-computers currently used to transcribe and process CTD data at sea. The ten record types presently used in this multifile tape format store CTD and associated water sample data and the labeling information needed to convert the data to physical units. Record types are identified by unique keywords. (Author)

Suffix trees, directed acyclic word graphs (DAWGs) and related data structures are useful for text retrieval and analysis. Linear upper and lower bounds on their sizes are known. Constructing these data structures for random strings, one observes that the size does not increase smoothly, but oscillates between these bounds. We use Mellin transforms to obtain size estimates as integrals of meromorphic functions. Poles on the real axis lead to exact formulae for the average sizes, while poles with nonzero imaginary part lead to very good estimates of the oscillations.

We investigate the sample size needed to infer a separating line between two convex planar regions using Valiant's model of the complexity of learning from random examples [4]. A theorem proved in [1] using the Vapnik-Chervonenkis dimension gives an O((1/ε)ln(1/ε)) upper bound on the sample size sufficient to infer a separating line with error less than ε between two convex planar regions. This theorem requires that with high probability any separating line consistent with such a sample have small error. The present paper gives a lower bound showing that under this requirement the sample size cannot be improved. It is further shown that if this requirement is weakened to require only that a particular line which is tangent to the convex hulls of the sample points in the two regions have small error then the ln(1/ε) term can be eliminated from the upper bound.

Valiant's learnability model is extended to learning classes of concepts defined by regions in Euclidean space E n . The methods in this paper lead to a unified treatment of some of Valiant's results, along with previous results on distribution-free convergence of certain pattern recognition algorithms. It is shown that the essential condition for distribution-free learnability is finiteness of the Vapnik-Chervonenkis dimension, a simple combinatorial parameter of the class of concepts to be learned. Using this parameter, the complexity and closure properties of learnable classes are analyzed, and the necessary and sufficient conditions are provided for feasible learnability.

Given a finite set of texts S = { w 1, … , w k } over some fixed finite alphabet Σ, a complete inverted file for S is an abstract data type that provides the functions find ( w ), which returns the longest prefix of w that occurs (as a subword of a word) in S ; freq ( w ), which returns the number of times w occurs in S ; and locations ( w ), which returns the set of positions where w occurs in S . A data structure that implements a complete inverted file for S that occupies linear space and can be built in linear time, using the uniform-cost RAM model, is given. Using this data structure, the time for each of the above query functions is optimal. To accomplish this, techniques from the theory of finite automata and the work on suffix trees are used to build a deterministic finite automaton that recognizes the set of all subwords of the set S . This automaton is then annotated with additional information and compacted to facilitate the desired query functions. The result is a data structure that is smaller and more flexible than the suffix tree.

We show that a polynomial learning algorithm, as defined by Valiant (1984), is obtained whenever there exists a polynomial-time method of producing, for any sequence of observations, a nearly minimum hypothesis that is consistent with these observations.