Evolutionary algorithms in artificial intelligence: a comparative study through applications

For many years research in artificial intelligence followed a symbolic paradigm which required a level of knowledge described in terms of rules. More recently subsymbolic approaches have been adopted as a suitable means for studying many problems. There are many search mechanisms which can be used to manipulate subsymbolic components, and in recent years general search methods based on models of natural evolution have become increasingly popular. This thesis examines a hybrid symbolic/subsymbolic approach and the application of evolutionary algorithms to a problem from each of the fields of shape representation (finding an iterated function system for an arbitrary shape), natural language dialogue (tuning parameters so that a particular behaviour can be achieved) and speech recognition (selecting the penalties used by a dynamic programming algorithm in creating a word lattice). These problems were selected on the basis that each should have a fundamentally different interactions at the subsymbolic level. Results demonstrate that for the experiments conducted the evolutionary algorithms performed well in most cases. However, the type of subsymbolic interaction that may occur influences the relative performance of evolutionary algorithms which emphasise either top-down (evolutionary programming - EP) or bottom-up (genetic algorithm - GA) means of solution discovery. For the shape representation problem EP is seen to perform significantly better than a GA, and reasons for this disparity are discussed. Furthermore, EP appears to offer a powerful means of finding solutions to this problem, and so the background and details of the problem are discussed at length. Some novel constraints on the problem's search space are also presented which could be used in related work. For the dialogue and speech recognition problems a GA and EP produce good results with EP performing slightly better. Results achieved with EP have been used to improve the performance of a speech recognition system

Detection of Trending Topic Communities: Bridging Content Creators and Distributors

The rise of a trending topic on Twitter or Facebook leads to the temporal emergence of a set of users currently interested in that topic. Given the temporary nature of the links between these users, being able to dynamically identify communities of users related to this trending topic would allow for a rapid spread of information. Indeed, individual users inside a community might receive recommendations of content generated by the other users, or the community as a whole could receive group recommendations, with new content related to that trending topic. In this paper, we tackle this challenge, by identifying coherent topic-dependent user groups, linking those who generate the content (creators) and those who spread this content, e.g., by retweeting/reposting it (distributors). This is a novel problem on group-to-group interactions in the context of recommender systems. Analysis on real-world Twitter data compare our proposal with a baseline approach that considers the retweeting activity, and validate it with standard metrics. Results show the effectiveness of our approach to identify communities interested in a topic where each includes content creators and content distributors, facilitating users' interactions and the spread of new information.Comment: 9 pages, 4 figures, 2 tables, Hypertext 2017 conferenc

A comparison of clustering and modification based graph anonymization methods with constraints

In this paper a comparison is performed on two of the key methods for graph anonymization and their behavior is evaluated when constraints are incorporated into the anonymization process. The two methods tested are node clustering and node modification and are applied to online social network (OSN) graph datasets. The constraints implement user defined utility requirements for the community structure of the graph and major hub nodes. The methods are benchmarked using three real OSN datasets and different levels of k?anonymity. The results show that the constraints reduce the information loss while incurring an acceptable disclosure risk. Overall, it is found that the modification method with constraints gives the best results for information loss and risk of disclosure.This research is partially supported by the Spanish MEC (projects ARES CONSOLIDER INGENIO 2010 CSD2007-00004 -- eAEGIS TSI2007-65406-C03-02 -- and HIPERGRAPH TIN2009-14560-C03-01)Peer Reviewe

A System Dynamics Model Approach for Simulating Hyper-inflammation in Different COVID-19 Patient Scenarios

The exceptionally high virulence of COVID-19 and the patients' precondition seem to constitute primary factors in how pro-inflammatory cytokines production evolves during the course of an infection. We present a System Dynamics Model approach for simulating the patient reaction using two key control parameters (i) virulence, which can be moderate or high and (ii) patient precondition, which can be healthy, not so healthy or serious preconditions. In particular, we study the behaviour of Inflammatory (M1) Alveolar Macrophages, IL6 and Active Adaptive Immune system as indicators of the immune system response, together with the COVID viral load over time. The results show that it is possible to build an initial model of the system to explore the behaviour of the key attributes involved in the patient condition, virulence and response. The model suggests aspects that need further study so that it can then assist in choosing the correct immunomodulatory treatment, for instance the regime of application of an Interleukin 6 (IL-6) inhibitor (tocilizumab) that corresponds to the projected immune status of the patients. We introduce machine learning techniques to corroborate aspects of the model and propose that a dynamic model and machine learning techniques could provide a decision support tool to ICU physicians

Genes and Small RNA Transcripts Exhibit Dosage-Dependent Expression Pattern in Maize Copy-Number Alterations

Copy-number alterations are widespread in animal and plant genomes, but their immediate impact on gene expression is still unclear. In animals, copy-number alterations usually exhibit dosage effects, except for sex chromosomes which tend to be dosage compensated. In plants, genes within small duplications (\u3c100 kb) often exhibit dosage-dependent expression, whereas large duplications (\u3e50 Mb) are more often dosage compensated. However, little or nothing is known about expression in moderately-sized (1–50 Mb) segmental duplications, and about the response of small RNAs to dosage change. Here, we compared maize (Zea mays) plants with two, three, and four doses of a 14.6-Mb segment of chromosome 1 that contains ∼300 genes. Plants containing the duplicated segment exhibit dosage-dependent effects on ear length and flowering time. Transcriptome analyses using GeneChip and RNA-sequencing methods indicate that most expressed genes and unique small RNAs within the duplicated segments exhibit dosage-dependent transcript levels. We conclude that dosage effect is the predominant regulatory response for both genes and unique small RNA transcripts in the segmental dosage series we tested. To our knowledge this is the first analysis of small RNA expression in plant gene dosage variants. Because segmental duplications comprise a significant proportion of eukaryotic genomes, these findings provide important new insight into the regulation of genes and small RNAs in response to dosage changes

Analysis of On-Line Social Networks Represented as Graphs -Extraction of an Approximation of Community Structure Using Sampling

Abstract. In this paper we benchmark two distinct algorithms for extracting community structure from social networks represented as graphs, considering how we can representatively sample an OSN graph while maintaining its community structure. We also evaluate the extraction algorithms' optimum value (modularity) for the number of communities using five well-known benchmarking datasets, two of which represent real online OSN data. Also we consider the assignment of the filtering and sampling criteria for each dataset. We find that the extraction algorithms work well for finding the major communities in the original and the sampled datasets. The quality of the results is measured using an NMI (Normalized Mutual Information) type metric to identify the grade of correspondence between the communities generated from the original data and those generated from the sampled data. We find that a representative sampling is possible which preserves the key community structures of an OSN graph, significantly reducing computational cost and also making the resulting graph structure easier to visualize. Finally, comparing the communities generated by each algorithm, we identify the grade of correspondence

Code-assisted discovery of TAL effector targets in bacterial leaf streak of rice reveals contrast with bacterial blight and a novel susceptibility gene

Citation: Cernadas RA, Doyle EL, Nin˜o-Liu DO, Wilkins KE, Bancroft T, et al. (2014) Code-Assisted Discovery of TAL Effector Targets in Bacterial Leaf Streak of Rice Reveals Contrast with Bacterial Blight and a Novel Susceptibility Gene. PLoS Pathog 10(2): e1003972. https://doi.org/10.1371/journal.ppat.1003972Bacterial leaf streak of rice, caused by Xanthomonas oryzae pv. oryzicola (Xoc) is an increasingly important yield constraint in this staple crop. A mesophyll colonizer, Xoc differs from X. oryzae pv. oryzae (Xoo), which invades xylem to cause bacterial blight of rice. Both produce multiple distinct TAL effectors, type III-delivered proteins that transactivate effector-specific host genes. A TAL effector finds its target(s) via a partially degenerate code whereby the modular effector amino acid sequence identifies nucleotide sequences to which the protein binds. Virulence contributions of some Xoo TAL effectors have been shown, and their relevant targets, susceptibility (S) genes, identified, but the role of TAL effectors in leaf streak is uncharacterized. We used host transcript profiling to compare leaf streak to blight and to probe functions of Xoc TAL effectors. We found that Xoc and Xoo induce almost completely different host transcriptional changes. Roughly one in three genes upregulated by the pathogens is preceded by a candidate TAL effector binding element. Experimental analysis of the 44 such genes predicted to be Xoc TAL effector targets verified nearly half, and identified most others as false predictions. None of the Xoc targets is a known bacterial blight S gene. Mutational analysis revealed that Tal2g, which activates two genes, contributes to lesion expansion and bacterial exudation. Use of designer TAL effectors discriminated a sulfate transporter gene as the S gene. Across all targets, basal expression tended to be higher than genome-average, and induction moderate. Finally, machine learning applied to real vs. falsely predicted targets yielded a classifier that recalled 92% of the real targets with 88% precision, providing a tool for better target prediction in the future. Our study expands the number of known TAL effector targets, identifies a new class of S gene, and improves our ability to predict functional targeting

Scanning microarrays at multiple intensities enhances discovery of differentially expressed genes

Motivation: Scanning parameters are often overlooked when optimizing microarray experiments. A scanning approach that extends the dynamic data range by acquiring multiple scans of different intensities has been developed. Results: Data from each of three scan intensities (low, medium, high) were analyzed separately using multiple scan and linear regression approaches to identify and compare the sets of genes that exhibit statistically significant differential expression. In the multiple scan approach only one-third of the differentially expressed genes were shared among the three intensities, and each scan intensity identified unique sets of differentially expressed genes. The set of differentially expressed genes from any one scan amounted to \u3c70% of the total number of genes identified in at least one scan. The average signal intensity of genes that exhibited statistically significant changes in expression was highest for the low-intensity scan and lowest for the high-intensity scan, suggesting that low-intensity scans may be best for detecting expression differences in high-signal genes, while high-intensity scans may be best for detecting expression differences in low-signal genes. Comparison of the differentially expressed genes identified in the multiple scan and linear regression approaches revealed that the multiple scan approach effectively identifies a subset of statistically significant genes that linear regression approach is unable to identify. Quantitative RT–PCR (qRT–PCR) tests demonstrated that statistically significant differences identified at all three scan intensities can be verified