Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

1993 Annual report on scientific programs: A broad research program on the sciences of complexity

This report provides a summary of many of the research projects completed by the Santa Fe Institute (SFI) during 1993. These research efforts continue to focus on two general areas: the study of, and search for, underlying scientific principles governing complex adaptive systems, and the exploration of new theories of computation that incorporate natural mechanisms of adaptation (mutation, genetics, evolution)

Novel Hyper-heuristics Applied to the Domain of Bin Packing

Principal to the ideology behind hyper-heuristic research is the desire to increase the level of generality of heuristic procedures so that they can be easily applied to a wide variety of problems to produce solutions of adequate quality within practical timescales.This thesis examines hyper-heuristics within a single problem domain, that of Bin Packing where the benefits to be gained from selecting or generating heuristics for large problem sets with widely differing characteristics is considered. Novel implementations of both selective and generative hyper-heuristics are proposed. The former approach attempts to map the characteristics of a problem to the heuristic that best solves it while the latter uses Genetic Programming techniques to automate the heuristic design process. Results obtained using the selective approach show that solution quality was improved significantly when contrasted to the performance of the best single heuristic when applied to large sets of diverse problem instances. Although enforcing the benefits to be gained by selecting from a range of heuristics the study also highlighted the lack of diversity in human designed algorithms. Using Genetic Programming techniques to automate the heuristic design process allowed both single heuristics and collectives of heuristics to be generated that were shown to perform significantly better than their human designed counterparts. The thesis concludes by combining both selective and generative hyper-heuristic approaches into a novel immune inspired system where heuristics that cover distinct areas of the problem space are generated. The system is shown to have a number of advantages over similar cooperative approaches in terms of its plasticity, efficiency and long term memory. Extensive testing of all of the hyper-heuristics developed on large sets of both benchmark and newly generated problem instances enforces the utility of hyper-heuristics in their goal of producing fast understandable procedures that give good quality solutions for a range of problems with widely varying characteristics

A novel dual surface type-2 fuzzy logic controller for a micro robot

Over the last few years there has been an increasing interest in the area of type-2 fuzzy logic sets and systems in academic and industrial circles. Within robotic research the majority of type-2 fuzzy logic investigations has been centred on large autonomous mobile robots, where resource availability (memory and computing power) is not an issue. These large robots usually have a variation of a Unix operating system on board. This allows the implementation of complex fuzzy logic systems to control the motors. Specifically the implementation of interval and geometric type-2 fuzzy logic controllers is of interest as they are shown to outperform type-1 fuzzy logic controllers in uncertain environments. However when it comes to using micro robots it is not practical to use type-1 and type-2 fuzzy logic controllers, due to the lack of memory and the processor time needed to calculate a control output value. The choice of motor controller is usually either fixed pre-set values, a variable scaled value or a PID controller to generate wheel velocities. In this research novel ways of implementing type-1 and interval type-2 fuzzy logic controllers on micro robots with limited resources are investigated. The solution thatis being proposed is the use of pre-calculated 3D surfaces generated by an off-line Fuzzy Logic System covering the expected ranges of the input and output variables. The surfaces are then loaded into the memory of the micro robots and can be accessed by the motor controller. The aim of the research is to test if there is an advantage of using type-2 fuzzy logic controllers implemented as surfaces over type-1 and PID controllers on a micro robot with limited resources. Control surfaces were generated for both type-1 and average interval type-2 fuzzy logic controllers. Each control surface was then accessed using bilinear interpolation to provide the crisp output value that was used to control the motor. Previously when this method has been used a single surface was employed to hold the information. This thesis presents the novel approach of the dual surface type-2 fuzzy logic controller on micro robots. The lower and upper values that are averaged for the classic interval type-2 controller are generated as surfaces and installed on the micro robots. The advantage is that nuances and features of both the lower and upper surfaces are available to be exploited, rather than being lost due to the averaging process. Having conducted the experiments it is concluded that the best approach to controlling micro robots is to use fuzzy logic controllers over the classical PID controllers where ever possible. When fuzzy controllers are used then type-2 fuzzy controllers (dual or single surface) should be used over type-1 fuzzy controllers when applied as surfaces on micro robots. When a type-2 fuzzy controller is used then the novel dual surface type-2 fuzzy logic controller should be used over the classic average surface. The novel dual surface controller offers a dynamic, weighted, adaptive and superior response over all the other fuzzy controllers examined

Develpoment of Multiplexed Techniques Using 2D-HPLC, Protein Microarrays and Mass Spectrometry for Investigations in Protein Posttranslational Modifications and Disease Progression Pathways.

Over the last few years cancer has replaced heart disease as the major killer in the US where the incidence of cancer is rising steadily. In order to understand the underlying biology of cancer and the role posttranslational modifications play in its development, several studies were performed to characterize human cancer cell lines and tissues. High throughput and multiplexed methods using reversed phase protein microarrays employing fractionated proteins from whole cell and tissue lysates were used to study the autoantibody response in cancer cells, the role of phosphorylation cascades in cancer development and mining disease response pathways in cancer. In these experiments, the cell and tissue lysates were fractionated using a 2D-HPLC based method where proteins were first separated according to their pI and then using non-porous reversed phase HPLC. Monolithic capillary HPLC methods were developed to increase the reliability of mass spectrometry based protein identifications employing peptide mass fingerprinting (PMF) and for high-throughput proteomics. In the first study, protein microarrays were used to study the global changes in phosphorylation following the inhibition of FGFR2 gene/protein in SUM-52PE human breast cancer cell lines. A small molecule universal phospho-sensor dye was used to detect phosphorylations on the microarrays. Peptide mass fingerprinting was used to identify the phospho-proteins and LC-MS/MS was used to validate the protein IDs. Further, human pancreatic cancer Panc-1 cell-lines and metastatic and localized prostate cancer tissue proteins were fractionated respectively and used as baits for obtaining auto-antibody response data with an aim of mining biomarkers for improved classification of disease including predicting disease progression stage. This study also used molecular concept modeling to obtain information pathways that are perturbed in the progression of cancer. The last two studies use monolithic-HPLC for increasing sequence coverage for peptide mass fingerprinting and ESI-MS/MS. Sequence coverage exceeding 85% could be obtained in 2D liquid fractionated human breast cancer CA1a.c11 and CA1d.c11 cell line proteins. An automated platform was developed for spotting of MALDI plates and used for on-plate digestion and proteins separated using monolithic capillary HPLC for PMF. These methods can often detect low abundance proteins and posttranslational modifications.Ph.D.ChemistryUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/57653/2/manojp_1.pd

Exploiting immunological metaphors in the development of serial, parallel and distributed learning algorithms

This thesis examines the use of immunological metaphors in building serial, parallel, and distributed learning algorithms. It offers a basic study in the development of biologically-inspired algorithms which merge inspiration from biology with known, standard computing technology to examine robust methods of computing. This thesis begins by detailing key interactions found within the immune system that provide inspiration for the development of a learning system. It then exploits the use of more processing power for the development of faster algorithms. This leads to the exploration of distributed computing resources for the examination of more biologically plausible systems. This thesis offers the following main contributions. The components of the immune system that exhibit the capacity for learning are detailed. A framework for discussing learning algorithms is proposed. Three properties of every learning algorithm-memory, adaptation, and decision-making-are identified for this framework, and traditional learning algorithms are placed in the context of this framework. An investigation into the use of immunological components for learning is provided. This leads to an understanding of these components in terms of the learning framework. A simplification of the Artificial Immune Recognition System (AIRS) immune-inspired learning algorithm is provided by employing affinity-dependent somatic hypermutation. A parallel version of the Clonal Selection Algorithm (CLONALG) immune learning algorithm is developed. It is shown that basic parallel computing techniques can provide computational benefits for this algorithm. Exploring this technology further, a parallel version of AIRS is offered. It is shown that applying these same parallel computing techniques to AIRS, while less scalable than when applied to CLONALG, still provides computational gains. A distributed approach to AIRS is offered, and it is argued that this approach provides a more biologically appealing model. The simple distributed approach is proposed in terms of an initial step toward a more complex, distributed system. Biological immune systems exhibit complex cellular interactions. The mechanisms of these interactions, while often poorly understood, hint at an extremely powerful information processing/problem solving system. This thesis demonstrates how the use of immunological principles coupled with standard computing technology can lead to the development of robust, biologically inspired learning algorithms.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Directed evolution and structural analysis of an OB-fold domain towards a specifc binding reagent

Interactions between proteins are a central concept in biology, and understanding and manipulation of these interactions is key to advancing biological science. Research into antibodies as customised binding molecules provided the foundation for development of the field of protein “scaffolds” for molecular recognition, where functional residues are mounted on to a stable protein platform. Consequently, the immunoglobulin domain has been describes as “nature’s paradigm” for a scaffold, and has been widely researched to make engineered antibodies better tools for specific applications. However, limitations in their use have lead to a number of non-immunoglobulin domains to be investigated as customisable scaffolds, to replace or complement antibodies. To be considered a scaffold, a protein domain must show an evolutionarily conserved hydrophobic core in diverse functional contexts. The study presented here investigated the oligosaccharide/oligonucleotide-binding (OB) fold as scaffold, which is a 5-standed β-barrel seen in diverse organisms with no sequence conservation. The term “Obody” was coined to describe engineered OB-folds. This thesis examined a previously engineered Obody with affinity for lysozyme (KD = 40 μM) in complex with its ligand by x-ray crystallography (resolution 2.75 Å) which revealed the atomic details of binding. Affinity maturation for lysozyme was undertaken by phage display directed evolution. Gene libraries were constructed by combinatorial PCR incorporating site-specific randomised codons identified by examination of the structure in complex with lysozyme, or by random generation of point mutations by error-prone PCR. Overall a 100-fold improvement in affinity was achieved (KD = 600 nM). To investigate the structural basis of the affinity maturation, two further Obody-lysozyme complexes were solved by x-ray crystallography, one at a KD of 5 μM (resolution 1.96 Å), one at 600 nM (resolution 1.86 Å). Analysis of the structures revealed changes in individual residue arrangements, as well as rigid-body changes in the relative orientation of the Obody and lysozyme molecules in complex. Directed evolution of Obodies as protein binding reagents remains a challenge, but this study demonstrates their potential. The structures presented here will contribute invaluable insights for the future design of improved Obodies

Investigation of artificial immune systems and variable selection techniques for credit scoring

Most lending institutions are aware of the importance of having a well-performing credit scoring model or scorecard and know that, in order to remain competitive in the credit industry, it is necessary to continuously improve their scorecards. This is because better scorecards result in substantial monetary savings that can be stated in terms of millions of dollars. Thus, there has been increasing interest in the application of new classifiers in credit scoring from both practitioners and researchers in the last few decades. Most of the recent work in this field has focused on the use of new and innovative techniques to classify applicants as either 'credit-worthy' or 'non-credit-worthy', with the aim of improving scorecard performance. In this thesis, we investigate the suitability of intelligent systems techniques for credit scoring. In particular, intelligent systems that use immunological metaphors are examined and used to build a learning and evolutionary classification algorithm. Our model, named Simple Artificial Immune System (SAIS), is based on the concepts of the natural immune system. The model uses applicants' credit details to classify them as either 'credit-worthy' or 'non-credit-worthy'. As part of the model development, we also investigate several techniques for selecting variables from the applicants' credit details. Variable selection is important as choosing the best set of variables can have a significant effect on the performance of scorecards. Interestingly, our results demonstrate that the traditional stepwise regression variable selection technique seems to perform better than many of the more recent techniques. A further contribution offered by this thesis is a detailed description of the scorecard development process. A detailed explanation of this process is not readily available in the literature and our description of the process is based on our own experiences and discussions with industry credit risk practitioners. We evaluate our model using both publicly available datasets as well as a very large set of real-world consumer credit scoring data obtained from a leading Australian bank. The evaluation results reveal that SAIS is a competitive classifier and is appropriate for developing scorecards which require a class decision as an outcome. Another conclusion reached is one confirmed by the existing literature, that even though more sophisticated scorecard development techniques, including SAIS, perform well compared to the traditional statistical methods, their performances are not statistically significantly different from the statistical methods. As with other intelligent systems techniques, SAIS is not explicitly designed to develop practical scorecards which require the generation of a score that represents the degree of confidence that an applicant will belong to a particular group. However, it is comparable to other intelligent systems techniques which are outperformed by statistical techniques for generating p ractical scorecards. Our final remark on this research is that even though SAIS does not seem to be quite suitable for developing practical scorecards, we still believe that there is room for improvement and that the natural immune system of the body has a number of avenues yet to be explored which could assist with the development of practical scorecards