From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

An updated genome-scale reconstruction of the metabolic network in Escherichia coli K-12 MG1655 is presented. This updated metabolic reconstruction includes: (1) an alignment with the latest genome annotation and the metabolic content of EcoCyc leading to the inclusion of the activities of 1260 ORFs, (2) characterization and quantification of the biomass components and maintenance requirements associated with growth of E. coli and (3) thermodynamic information for the included chemical reactions. The conversion of this metabolic network reconstruction into an in silico model is detailed. A new step in the metabolic reconstruction process, termed thermodynamic consistency analysis, is introduced, in which reactions were checked for consistency with thermodynamic reversibility estimates. Applications demonstrating the capabilities of the genome-scale metabolic model to predict high-throughput experimental growth and gene deletion phenotypic screens are presented. The increased scope and computational capability using this new reconstruction is expected to broaden the spectrum of both basic biology and applied systems biology studies of E. coli metabolism.

https://www.embopress.org/doi/pdf/10.1038/msb4100155

A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information.

Swarm robotics is an approach to collective robotics that takes inspiration from the self-organized behaviors of social animals. Through simple rules and local interactions, swarm robotics aims at designing robust, scalable, and flexible collective behaviors for the coordination of large numbers of robots. In this paper, we analyze the literature from the point of view of swarm engineering: we focus mainly on ideas and concepts that contribute to the advancement of swarm robotics as an engineering field and that could be relevant to tackle real-world applications. Swarm engineering is an emerging discipline that aims at defining systematic and well founded procedures for modeling, designing, realizing, verifying, validating, operating, and maintaining a swarm robotics system. We propose two taxonomies: in the first taxonomy, we classify works that deal with design and analysis methods; in the second taxonomy, we classify works according to the collective behavior studied. We conclude with a discussion of the current limits of swarm robotics as an engineering discipline and with suggestions for future research directions.

/pdf/swarm-robotics-a-review-from-the-swarm-engineering-46mfc058e6.pdf

Swarm robotics: a review from the swarm engineering perspective

The number and scope of methods developed to interrogate and use metabolic network reconstructions has significantly expanded over the past 15 years. In particular, Escherichia coli metabolic network reconstruction has reached the genome scale and been utilized to address a broad spectrum of basic and practical applications in five main categories: metabolic engineering, model-directed discovery, interpretations of phenotypic screens, analysis of network properties and studies of evolutionary processes. Spurred on by these accomplishments, the field is expected to move forward and further broaden the scope and content of network reconstructions, develop new and novel in silico analysis tools, and expand in adaptation to uses of proximal and distal causation in biology. Taken together, these efforts will solidify a mechanistic genotype-phenotype relationship for microbial metabolism.

The growing scope of applications of genome-scale metabolic reconstructions using Escherichia coli

http://radio.cuci.udg.mx/bch/ES/papers/MembraneModel_BBA_2013-epub.pdf

The Fluid—Mosaic Model of Membrane Structure: Still relevant to understanding the structure, function and dynamics of biological membranes after more than 40 years

We present a scalable approach to dynamically allocating a swarm of homogeneous robots to multiple tasks, which are to be performed in parallel, following a desired distribution. We employ a decentralized strategy that requires no communication among robots. It is based on the development of a continuous abstraction of the swarm obtained by modeling population fractions and defining the task allocation problem as the selection of rates of robot ingress and egress to and from each task. These rates are used to determine probabilities that define stochastic control policies for individual robots, which, in turn, produce the desired collective behavior. We address the problem of computing rates to achieve fast redistribution of the swarm subject to constraint(s) on switching between tasks at equilibrium. We present several formulations of this optimization problem that vary in the precedence constraints between tasks and in their dependence on the initial robot distribution. We use each formulation to optimize the rates for a scenario with four tasks and compare the resulting control policies using a simulation in which 250 robots redistribute themselves among four buildings to survey the perimeters.

/pdf/optimized-stochastic-policies-for-task-allocation-in-swarms-1z9tg5djrh.pdf

Optimized Stochastic Policies for Task Allocation in Swarms of Robots

We present a biologically inspired approach to the dynamic assignment and reassignment of a homogeneous swarm of robots to multiple locations, which is relevant to applications like search and rescue, environmental monitoring, and task allocation. Our work is inspired by experimental studies of ant house hunting and empirical models that predict the behavior of the colony that is faced with a choice between multiple candidate nests. We design quorum based stochastic control policies that enable the team of agents to distribute themselves among multiple candidate sites in a specified ratio, and compare our results to the linear stochastic policies described in (Halasz et al., in Proceedings of the International Conference on Intelligent Robots and Systems (IROS’07), pp. 2320–2325, 2007). We show how our quorum model consistently performs better than the linear models while minimizing computational requirements and now it can be implemented without the use of inter-agent wireless communication.

Biologically inspired redistribution of a swarm of robots among multiple sites

In this paper, we present a comprehensive framework for stochastic modeling, model abstraction, and controller design for a biological system. The first half of the paper concerns modeling and model abstraction of the system. Most models in systems biology are deterministic models with ordinary differential equations in the concentration variables. We present a stochastic hybrid model of the lactose regulation system of E. coli bacteria that capture important phenomena which cannot be described by continuous deterministic models. We then show that the resulting stochastic hybrid model can be abstracted into a much simpler model, a two-state continuous-time Markov chain. The second half of the paper discusses controller design for a specific architecture. The architecture consists of measurement of a global quantity in a colony of bacteria as an output feedback and manipulation of global environmental variables as control actuation. We show that controller design can be performed on the abstracted (Markov chain) model and implementation on the real model yields the desired result.

/pdf/stochastic-modeling-and-control-of-biological-systems-the-esl6xtv077.pdf

Stochastic Modeling and Control of Biological Systems: The Lactose Regulation System of Escherichia Coli

We present a methodology for characterizing and synthesizing swarm behaviors using both a macroscopic model that represents a swarm as a continuum and a microscopic model that represents individual robots. We develop a systematic approach for synthesizing behaviors at the macroscopic level that can be realized on individual robots at the microscopic level. Our methodology is inspired by a dynamical model of ant house hunting [1], a decentralized process in which a colony attempts to emigrate to the best site among several alternatives. The model is hybrid because the colony switches between different sets of behaviors, or modes, during this process. At the macroscopic level, we are able to synthesize controllers that result in the deployment of a robotic swarm in a predefined ratio between distinct sites. We then derive hybrid controllers for individual robots using only local interactions and no communication that respect the specifications of the global continuous behavior. Our simulations demonstrate that our synthesis procedure yields a correct microscopic model from the macroscopic description with guarantees on performance at both levels

/pdf/bio-inspired-group-behaviors-for-the-deployment-of-a-swarm-x7mzylw4db.pdf

Bio-Inspired Group Behaviors for the Deployment of a Swarm of Robots to Multiple Destinations

We present an approach for the dynamic assignment and reassignment of a large team of homogeneous robotic agents to multiple locations with applications to search and rescue, reconnaissance and exploration missions Our work is inspired by experimental studies of ant house hunting and empirical models that predict the behavior of the colony that is faced with a choice between multiple candidate nests We design stochastic control policies that enable the team of agents to distribute themselves between multiple candidate sites in a specified ratio Additionally, we present an extension to our model to enable fast convergence via switching behaviors based on quorum sensing The stability and convergence properties of these control policies are analyzed and simulation results are presented

http://wiki.grasp.upenn.edu/~graspdoc/wiki/swarms/uploads/Main/BermanIROS07.pdf

Ádám M. Halász

Papers

Optimized Stochastic Policies for Task Allocation in Swarms of Robots

Biologically inspired redistribution of a swarm of robots among multiple sites

Stochastic Modeling and Control of Biological Systems: The Lactose Regulation System of Escherichia Coli

Bio-Inspired Group Behaviors for the Deployment of a Swarm of Robots to Multiple Destinations

Dynamic redistribution of a swarm of robots among multiple sites