Investigating the structural diversity within a committee of classifiers and their generalization performance

This study investigates the measures of diversity within ensembles of classifiers. The use of neural networks is carried out in measuring ensemble diversity by the use of statistical and ecological methods and to some extent information theory. A new way of looking at ensemble diversity is proposed. This ensemble diversity is called ensemble structural diversity, for this study is concerned with the diversity within the structure of the individual classifiers forming an ensemble and not via the outcomes of the individual classifiers. Ensemble structural diversity was also induced within the ensemble by varying the structural parameters (learning parameters) of the artificial machines (classifiers). The importance or the use of these measures was judged by comparing the measure of structural diversity and the ensemble generalization performance. This was done so that comparisons can be made on the robustness of the idea of structural diversity and its relationship with ensemble generalization performance. It was found that diversity could be induced by having ensembles with different structural and implicit (e.g learning) parameters and that this diversity does influence the predictive ability of the ensemble. This was concurrent with literature even though within literature ensemble diversity was viewed from the output as opposed to the structure of the individual classifiers. As the structural diversity increased so did the generalization performance. However there was a point where structural diversity decreased the generalization performance of the ensemble, where from that point onwards as the structural diversity increased the generalization performance decreased. This makes sense because too much of diversity within the ensemble might mean no consensus is reached at all. The disadvantages of comparing structural diversity and the generalization performance (accuracy) of the ensemble are that: an ensemble can be structurally diverse even though all the classifiers within the ensemble approximate the same function which means in this case structural diversity is meaningless in terms of improving the accuracy of the ensemble. The use of ensemble structural diversity measures in developing efficient ensembles still remains to be explored. This study, however, has also shown that diversity can be measured from the structural parameters and moreover reducing the abstractness of diversity by being able to quantify structural diversity making it possible to map a relationship between structural diversity and accuracy. It was observed that structural diversity does improve the accuracy of the ensemble, however, within a limited region of structural diversity

Comparing Methods to Constrain Future European Climate Projections Using a Consistent Framework

Political decisions, adaptation planning, and impact assessments need reliable estimates of future climate change and related uncertainties. To provide these estimates, different approaches to constrain, filter, or weight climate model projections into probabilistic distributions have been proposed. However, an assessment of multiple such methods to, for example, expose cases of agreement or disagreement, is often hindered by a lack of coordination, with methods focusing on a variety of variables, time periods, regions, or model pools. Here, a consistent framework is developed to allow a quantitative comparison of eight different methods; focus is given to summer temperature and precipitation change in three spatial regimes in Europe in 2041–60 relative to 1995–2014. The analysis draws on projections from several large ensembles, the CMIP5 multimodel ensemble, and perturbed physics ensembles, all using the high-emission scenario RCP8.5. The methods’ key features are summarized, assumptions are discussed, and resulting constrained distributions are presented. Method agreement is found to be dependent on the investigated region but is generally higher for median changes than for the uncertainty ranges. This study, therefore, highlights the importance of providing clear context about how different methods affect the assessed uncertainty—in particular, the upper and lower percentiles that are of interest to risk-averse stakeholders. The comparison also exposes cases in which diverse lines of evidence lead to diverging constraints; additional work is needed to understand how the underlying differences between methods lead to such disagreements and to provide clear guidance to users.ISSN:0894-8755ISSN:1520-044

Biochemical Networks Across Planets and Scales

abstract: Biochemical reactions underlie all living processes. Their complex web of interactions is difficult to fully capture and quantify with simple mathematical objects. Applying network science to biology has advanced our understanding of the metabolisms of individual organisms and the organization of ecosystems, but has scarcely been applied to life at a planetary scale. To characterize planetary-scale biochemistry, I constructed biochemical networks using global databases of annotated genomes and metagenomes, and biochemical reactions. I uncover scaling laws governing biochemical diversity and network structure shared across levels of organization from individuals to ecosystems, to the biosphere as a whole. Comparing real biochemical reaction networks to random reaction networks reveals the observed biological scaling is not a product of chemistry alone, but instead emerges due to the particular structure of selected reactions commonly participating in living processes. I perform distinguishability tests across properties of individual and ecosystem-level biochemical networks to determine whether or not they share common structure, indicative of common generative mechanisms across levels. My results indicate there is no sharp transition in the organization of biochemistry across distinct levels of the biological hierarchy—a result that holds across different network projections. Finally, I leverage these large biochemical datasets, in conjunction with planetary observations and computational tools, to provide a methodological foundation for the quantitative assessment of biology’s viability amongst other geospheres. Investigating a case study of alkaliphilic prokaryotes in the context of Enceladus, I find that the chemical compounds observed on Enceladus thus far would be insufficient to allow even these extremophiles to produce the compounds necessary to sustain a viable metabolism. The environmental precursors required by these organisms provides a reference for the compounds which should be prioritized for detection in future planetary exploration missions. The results of this framework have further consequences in the context of planetary protection, and hint that forward contamination may prove infeasible without meticulous intent. Taken together these results point to a deeper level of organization in biochemical networks than what has been understood so far, and suggests the existence of common organizing principles operating across different levels of biology and planetary chemistry.Dissertation/ThesisDoctoral Dissertation Geological Sciences 201

A universal understanding of thermal quenching in Eu2+ and Ce3+ doped phosphors

Phosphor-converted white light-emitting diodes (pc-WLEDs) have been widely adopted for next-generation solid-state lighting mainly. However, our understanding of thermal-quenching (TQ), i.e., the loss of photoluminescence (PL) intensity with increase of temperature, remains incomplete, with several competing theories. In this work, we demonstrate the application of ab-initio molecular dynamics (AIMD) simulations as a novel approach to investigate the effect of temperature on Eu2+/Ce3+ LE. A statistical analysis of the activator LE is extracted from the atomic trajectories from AIMD at multiple temperatures revealing a clear relationship between the temperature-dependent activator LE, and the experimentally measured TQ in numerous well-known phosphor compounds. We have found that phosphors with low TQ show a small variability in their activator’s LE distribution within the operating temperature range. We propose a model where the activator LE fluctuations due to temperature lead to changes in the crystal field splitting around the activator, which in turn have a consequence on the photoionization barrier and TQ behavior. Finally, we propose structural descriptors based on these observations that can be used to rapidly screen novel materials for high quantum efficiency phosphors with excellent TQ resistance

Surrogate-Assisted Unified Optimization Framework for Investigating Marine Structural Design Under Information Uncertainty.

Structural decisions made in the early stages of marine systems design can have a large impact on future acquisition, maintenance and life-cycle costs. However, owing to the unique nature of early stage marine system design, these critical structure decisions are often made on the basis of incomplete information or knowledge about the design. When coupled with design optimization analysis, the complex, uncertain early stage design environment makes it very difficult to deliver a quantified trade-off analysis for decision making. This work presents a novel decision support method that integrates design optimization, high-fidelity analysis, and modeling of information uncertainty for early stage design and analysis. To support this method this dissertation improves the design optimization methods for marine structures by proposing several novel surrogate modeling techniques and strategies. The proposed work treats the uncertainties that are sourced from limited information in a non-statistical interval uncertainty form. This interval uncertainty is treated as an objective function in an optimization framework in order to explore the impact of information uncertainty on structural design performance. In this examination, the potential structural weight penalty regarding information uncertainty can be quickly identified in early stage, avoiding costly redesign later in the design. This dissertation then continues to explore a balanced computational structure between fidelity and efficiency. A proposed novel variable fidelity approach can be applied to wisely allocate expensive high-fidelity computational simulations. In achieving the proposed capabilities for design optimization, several surrogate modeling methods are developed concerning worst-case estimation, clustered multiple meta-modeling, and mixed variable modeling techniques. These surrogate methods have been demonstrated to significantly improve the efficiency of optimizer in dealing with the challenges of early stage marine structure design.PhDNaval Architecture and Marine EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133365/1/yanliuch_1.pd

PICES Press, Vol. 18, No. 2, Summer 2010

•The 2010 Inter-sessional Science Board Meeting: A Note from the Science Board Chairman (pp. 1-3) •2010 Symposium on “Effects of Climate Change on Fish and Fisheries” (pp. 4-11) •2009 Mechanism of North Pacific Low Frequency Variability Workshop (pp. 12-14) •The Fourth China-Japan-Korea GLOBEC/IMBER Symposium (pp. 15-17, 23) •2010 Sendai Ocean Acidification Workshop (pp. 18-19, 31) •2010 Sendai Coupled Climate-to-Fish-to-Fishers Models Workshop (pp. 20-21) •2010 Sendai Salmon Workshop on Climate Change (pp. 22-23) •2010 Sendai Zooplankton Workshop (pp. 24-25, 28) •2010 Sendai Workshop on “Networking across Global Marine Hotspots” (pp. 26-28) •The Ocean, Salmon, Ecology and Forecasting in 2010 (pp. 29, 44) •The State of the Northeast Pacific during the Winter of 2009/2010 (pp. 30-31) •The State of the Western North Pacific in the Second Half of 2009 (pp. 32-33) •The Bering Sea: Current Status and Recent Events (pp. 34-35, 39) •PICES Seafood Safety Project: Guatemala Training Program (pp. 36-39) •The Pacific Ocean Boundary Ecosystem and Climate Study (POBEX) (pp. 40-43) •PICES Calendar (p. 44

Structural characterization and selective drug targeting of higher-order DNA G-quadruplex systems.

There is now substantial evidence that guanine-rich regions of DNA form non-B DNA structures known as G-quadruplexes in cells. G-quadruplexes (G4s) are tetraplex DNA structures that form amid four runs of guanines which are stabilized via Hoogsteen hydrogen bonding to form stacked tetrads. DNA G4s have roles in key genomic functions such as regulating gene expression, replication, and telomere homeostasis. Because of their apparent role in disease, G4s are now viewed as important molecular targets for anticancer therapeutics. To date, the structures of many important G4 systems have been solved by NMR or X-ray crystallographic techniques. Small molecules developed to target these structures have shown promising results in treating cancer in vitro and in vivo, however, these compounds commonly lack the selectivity required for clinical success. There is now evidence that long single-stranded G-rich regions can stack or otherwise interact intramolecularly to form G4-multimers, opening a new avenue for rational drug design. For a variety of reasons, G4 multimers are not amenable to NMR or X-ray crystallography. In the current dissertation, I apply a variety of biophysical techniques in an integrative structural biology (ISB) approach to determine the primary conformation of two disputed higher-order G4 systems: (1) the extended human telomere G-quadruplex and (2) the G4-multimer formed within the human telomerase reverse transcriptase (hTERT) gene core promoter. Using the higher-order human telomere structure in virtual drug discovery approaches I demonstrate that novel small molecule scaffolds can be identified which bind to this sequence in vitro. I subsequently summarize the current state of G-quadruplex focused virtual drug discovery in a review that highlights successes and pitfalls of in silico drug screens. I then present the results of a massive virtual drug discovery campaign targeting the hTERT core promoter G4 multimer and show that discovering selective small molecules that target its loops and grooves is feasible. Lastly, I demonstrate that one of these small molecules is effective in down-regulating hTERT transcription in breast cancer cells. Taken together, I present here a rigorous ISB platform that allows for the characterization of higher-order DNA G-quadruplex structures as unique targets for anticancer therapeutic discovery