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Cloud computing is evolved from grid computing with a key support from the rapidly expanding virtualization technology. We argue that clouding computing is particularly suitable for supporting emergency response and management (ERM) because of some of its unique features such as rapid setup and deployment on ad hoc basis, highly flexible platforms (PaaS: Platform as a Service) and application services (SaaS: Software as a Service) with little time-space constraints. ERM is one of the seven critical national infrastructures and services mandated to protect by the 1999 US President's Executive Order (PCCIP). The paradigm of survivability and survivable network systems was a response of academia to the president's executive order. We concur that survivability should be the lifeline of any ERM, including the cloud computing supported (CCS) ERM systems. In this article, we present a research agenda that is aimed at developing a survivability-centered architecture for evolving reliable and survivable CCS-ERM systems. The research agenda suggests that biological and computational evolutions should be rich sources of biological inspirations as well as powerful optimization algorithm for designing (evolving) the ERM systems. The proposed research agenda advocates the application of three-layer survivability analysis, dynamic hybrid fault models, and extended evolutionary game theory modeling developed by Ma & Krings IMa & Krings (2008a-e, 2011), Ma et al. (2009a), Ma (2008, 2009, 2010, 20lla,b). We use banking system survivability as an example to illustrate the proposed research agenda.

Many natural patterns, such as the distributions of blood particles in a blood sample, proteins on cell surfaces, biological populations in their habitat, galaxies in the universe, the sequence of human genes, and the fitness in evolutionary computing, have been found to follow power law. Taylor's power law (Taylor 1961: Nature, 189:732-) is well recognized as one of the fundamental models in population ecology. A fundamental property of biological populations, which Taylor's power law reveals, is the near universal heterogeneity of population abundance distribution in habitat. Obviously, the heterogeneity also exists at the community level, where not only the distributions of population abundances but also the proportions of the species composition in the community are often heterogeneous. Nevertheless, existing community diversity indexes such as Shannon index and Simpson index can only measure "local" or "static" diversity in the sense that they are computed for each habitat at a specific time point, and the indexes alone do not reflect the diversity changes. In this note, I propose to extend the application scope of Taylor's power law to the studies of human microbial communities, specifically, the community heterogeneity at both population and community levels. I further suggested that population dispersion models such as Taylor (1980: Nature, 286, 53-), which is known to generate population distribution patterns consistent with the power law, should also be very useful for analyzing the distribution patterns of human microbes within the human body. Overall, I hope that the approach to human microbial community with the power law offers an example that ecological theories can play an important role in the emerging medical ecology, which aims at studying the ecology of human microbiome and its implications to human diseases and health, as well as in personalized medicine.

The human being should be awarded the championship of navigation on the planet, on the virtues that they not only have invented and manufactured the compass, GPS, aircraft, vessel, spacecraft, but also have demonstrated exceptional non-instrumental navigation skills.12 The Austronesian expansion from the Asian mainland into the Pacific performed by the Pacific navigators, who eventually populated the most remote islands of the Pacific about 4000 years ago, is a vivid example (Wehner 1998). However, animals, especially flying birds and insects are strong contenders. Monarch butterflies can migrate up to 2000 miles from their reproductive sites in the eastern US and Canada to their over-wintering sites in the forests of Mexico, and it is postulated that they may possess a biological equivalent of a low-resolution GPS system that is based on the magnetic field of the earth. In fact, even the long-legged ants (Cataglyphis fortis) in the Saharan desert use the dead-reckoning navigation strategy, which is attributed to the Polynesians, but the ants apparently have acquired the capability much earlier, given the relative short evolutionary history of humans. In this article, we briefly review the state-of-the-art research on insect navigation and communication used in flight and communication, with the objective to inspire cross-disciplinary studies in aerospace engineering, biology and computer science.

The formal version of our work has been published in BMC Bioinformatics and can be found here: http://www.biomedcentral.com/1471-2105/13/S6/S1 Motivation: To tackle the problem of huge memory usage associated with de Bruijn graph-based algorithms, upon which some of the most widely used de novo genome assemblers have been built, we released SparseAssembler1. SparseAssembler1 can save as much as 90% memory consumption in comparison with the state-of-art assemblers, but it requires rounds of denoising to accurately assemble genomes. In this paper, we introduce a new general model for genome assembly that uses only sparse k-mers. The new model replaces the idea of the de Bruijn graph from the beginning, and achieves similar memory efficiency and much better robustness compared with our previous SparseAssembler1. Results: We demonstrate that the decomposition of reads of all overlapping k-mers, which is used in existing de Bruijn graph genome assemblers, is overly cautious. We introduce a sparse k-mer graph structure for saving sparse k-mers, which greatly reduces memory space requirements necessary for de novo genome assembly. In contrast with the de Bruijn graph approach, we devise a simple but powerful strategy, i.e., finding links between the k-mers in the genome and traversing following the links, which can be done by saving only a few k-mers. To implement the strategy, we need to only select some k-mers that may not even be overlapping ones, and build the links between these k-mers indicated by the reads. We can traverse through this sparse k-mer graph to build the contigs, and ultimately complete the genome assembly. Since the new sparse k-mers graph shares almost all advantages of de Bruijn graph, we are able to adapt a Dijkstra-like breadth-first search algorithm to circumvent sequencing errors and resolve polymorphisms., Comment: Corresponding authors: Zhanshan (Sam) Ma, ma@vandals.uidaho.edu; Mihai Pop, mpop@umiacs.umd.edu || Availability: Programs in both Windows and Linux are available at: https://sites.google.com/site/sparseassembler

de Bruijn graph-based algorithms are one of the two most widely used approaches for de novo genome assembly. A major limitation of this approach is the large computational memory space requirement to construct the de Bruijn graph, which scales with k-mer length and total diversity (N) of unique k-mers in the genome expressed in base pairs or roughly (2k+8)N bits. This limitation is particularly important with large-scale genome analysis and for sequencing centers that simultaneously process multiple genomes. We present a sparse de Bruijn graph structure, based on which we developed SparseAssembler that greatly reduces memory space requirements. The structure also allows us to introduce a novel method for the removal of substitution errors introduced during sequencing. The sparse de Bruijn graph structure skips g intermediate k-mers, therefore reducing the theoretical memory space requirement to ~(2k/g+8)N. We have found that a practical value of g=16 consumes approximately 10% of the memory required by standard de Bruijn graph-based algorithms but yields comparable results. A high error rate could potentially derail the SparseAssembler. Therefore, we developed a sparse de Bruijn graph-based denoising algorithm that can remove more than 99% of substitution errors from datasets with a \leq 2% error rate. Given that substitution error rates for the current generation of sequencers is lower than 1%, our denoising procedure is sufficiently effective to safeguard the performance of our algorithm. Finally, we also introduce a novel Dijkstra-like breadth-first search algorithm for the sparse de Bruijn graph structure to circumvent residual errors and resolve polymorphisms., Comment: Corresponding author: Douglas W. Yu, dougwyu@gmail.com

Conventional wisdom is that logic and language are tightly connected to logics in human cognition. However, recent studies have revealed that, in animal cognition, there exist logics that do not depend on languages. In other words, logical behavior is not human brain specific. At least four logics: perceptual logic, technical logic, social logic, and inference logic have been studied in animal cognition. Despite the obvious differences between animals and humans in using languages, recent studies confirm that both humans and animals utilize the socalled sensor brain maps for most sensory modalities: populations of neurons are selectively tuned to different stimulus features or feature combinations (Ewert 2005, Ma and Krings 2009). This commonality suggests that the studies of animal logics should also be insightful for understanding human logics. After briefly reviewing some of the recent advances in animal logics research, we turn to a more practical research field—the Brain Computer Interface (BCI) [also known as Brain Machine Interface (BMI)] in biomedicine. BCI promises to provide nonmuscular communication and control for people with severe motor disabilities. A fundamental goal of BCI is to translate thought or intent into action with brain activity only (Birbaumer 2006). If we recognize that logic is about the way of thinking and it is probably the most reliable and possibly most efficient way to understand thoughts, an interesting question could be: will the understanding of animal logics be very helpful for BCI research? The current BCI research is primarily targeted for rehabilitation applications. In this article, we also discuss the potential of using BCI techniques in aerospace systems and space explorations. One can imagine the potential that an astronaut operates a robot device by only thinking. Perhaps a revolutionary breakthrough from BCI technology can be the 'copiloting' of aerial vehicles by multiple pilots including some who stations at the groun

The study of the First Passage Time (FPT) problem (also known as first passage problem, FPP) started more than a century ago, but its diverse applications in science and engineering mostly emerged in the last two to three decades. Assuming that X(t) is a one-dimensional stochastic process, the First Passage Time is defined as the time (T) when X(t) first crosses a threshold. Engineering reliability is obviously a suitable application domain, and indeed applications such as optimal dam design in hydrology and analysis of structural failure in civil and mechanical engineering are typical examples. Although we envision that the FPT problem has great potential in network and software reliability, it should be more useful for network security and survivability because the approaches developed for the FPT problem are mostly analytical. The assumption for this inference is that in reliability analysis, experimental or historical data are often more readily available, which makes statistical approaches such as survival analysis more convenient and likely more realistic. In contrast, data is generally more difficult to obtain in security and survivability analyses, and analytical approaches can be leveraged to play more important roles. Furthermore, security and survivability often have to deal with malicious actions that may be driven by sophisticated cognition and behavioral processes, which are highly variable over time and very difficult to detect with short term data. If the behavior of an intruder can be characterized with some stochastic process such as Brownian motion, then the FPT approach may be applied to find the closedform solution of the probability density function (PDF) of the first passage time, which can be the time when the system breaks down or when the hacker is successful in compromising a network. In addition, the solutions to FPT depend on boundary and initial conditions of the corresponding partial differential equations, and they also describe the e