Lipid Food Preference and Transportation Using Tools by an Indian Ant Species Monomorium pharaonis (Hymenoptera: Formicidae): A Field Study

Besides proteins and carbohydrates, the Pharaoh ant (Monomoroium pharaonis) prefers lipid foods especially when it becomes limiting in the colony. We used four different food-grade lipids such as mustard oil, sunflower oil, coconut oil and butter for seven consecutive days for a specified time frame (11.15 hr - 14.15 hr). There was a specific time zone in which the highest number of ants accumulated at all four food baits with varying peak values. Sunflower oil was the most preferred food, and mustard oil showed the lowest like. The differential number of ants in all four lipid food baits under observation can be attributed to the physical features and nutrient content. We also noted an interesting feature of the foraging activity: the major role of gathering and transporting food was performed primarily by two castes, intermediate and large, among the four distinct foraging castes. Most tools used in the present investigation were <0.5mm and of naturally available sand particles. They used the smallest tools more often as compared with the size of the other two categories. Our ant (M. pharaonis) transported only the butter-soaked smallest tools back to the nest. M. pharaonis used the smallest tools more often and this could be because the particular-sized tools were efficiently managed by the ant species under study. Our ants transported only the butter-soaked smallest tools back to the nest. It is revealed that butter was the most important lipid food for the particular ant colony of M. pharaonis.

Billiards with Spatial Memory

Many classes of active matter develop spatial memory by encoding information in space, leading to complex pattern formation. It has been proposed that spatial memory can lead to more efficient navigation and collective behaviour in biological systems and influence the fate of synthetic systems. This raises important questions about the fundamental properties of dynamical systems with spatial memory. We present a framework based on mathematical billiards in which particles remember their past trajectories and react to them. Despite the simplicity of its fundamental deterministic rules, such a system is strongly non-ergodic and exhibits highly-intermittent statistics, manifesting in complex pattern formation. We show how these self-memory-induced complexities emerge from the temporal change of topology and the consequent chaos in the system. We study the fundamental properties of these billiards and particularly the long-time behaviour when the particles are self-trapped in an arrested state. We exploit numerical simulations of several millions of particles to explore pattern formation and the corresponding statistics in polygonal billiards of different geometries. Our work illustrates how the dynamics of a single-body system can dramatically change when particles feature spatial memory and provide a scheme to further explore systems with complex memory kernels.Comment: 11 pages, 6 figure

Displacement experiments provide evidence for path integration in Drosophila

Like many other animals, insects are capable of returning to previously visited locations using path integration, which is a memory of travelled direction and distance. Recent studies suggest that Drosophila can also use path integration to return to a food reward. However, the existing experimental evidence for path integration in Drosophila has a potential confound: pheromones deposited at the site of reward might enable flies to find previously rewarding locations even without memory. Here, we show that pheromones can indeed cause naïve flies to accumulate where previous flies had been rewarded in a navigation task. Therefore, we designed an experiment to determine if flies can use path integration memory despite potential pheromonal cues by displacing the flies shortly after an optogenetic reward. We found that rewarded flies returned to the location predicted by a memory-based model. Several analyses are consistent with path integration as the mechanism by which flies returned to the reward. We conclude that although pheromones are often important in fly navigation and must be carefully controlled for in future experiments, Drosophila may indeed be capable of performing path integration

X-Machines for Agent-Based Modeling

This book discusses various aspects of agent-based modeling and simulation using FLAME (Flexible Large-scale Agent-Based Modeling Environment) which is a popular agent-based modeling environment that enables automatic parallelization of models. Along with a focus on the software engineering principles in building agent-based models, the book comprehensively discusses how models can be written for various domains including biology, economics and social networks. The book also includes examples to guide readers on how to write their own models

An investigation into XSets of primitive behaviours for emergent behaviour in stigmergic and message passing antlike agents

Ants are fascinating creatures - not so much because they are intelligent on their own, but because as a group they display compelling emergent behaviour (the extent to which one observes features in a swarm which cannot be traced back to the actions of swarm members). What does each swarm member do which allows deliberate engineering of emergent behaviour? We investigate the development of a language for programming swarms of ant agents towards desired emergent behaviour. Five aspects of stigmergic (pheromone sensitive computational devices in which a non-symbolic form of communication that is indirectly mediated via the environment arises) and message passing ant agents (computational devices which rely on implicit communication spaces in which direction vectors are shared one-on-one) are studied. First, we investigate the primitive behaviours which characterize ant agents' discrete actions at individual levels. Ten such primitive behaviours are identified as candidate building blocks of the ant agent language sought. We then study mechanisms in which primitive behaviours are put together into XSets (collection of primitive behaviours, parameter values, and meta information which spells out how and when primitive behaviours are used). Various permutations of XSets are possible which define the search space for best performer XSets for particular tasks. Genetic programming principles are proposed as a search strategy for best performer XSets that would allow particular emergent behaviour to occur. XSets in the search space are evolved over various genetic generations and tested for abilities to allow path finding (as proof of concept). XSets are ranked according to the indices of merit (fitness measures which indicate how well XSets allow particular emergent behaviour to occur) they achieve. Best performer XSets for the path finding task are identifed and reported. We validate the results yield when best performer XSets are used with regard to normality, correlation, similarities in variation, and similarities between mean performances over time. Commonly, the simulation results yield pass most statistical tests. The last aspect we study is the application of best performer XSets to different problem tasks. Five experiments are administered in this regard. The first experiment assesses XSets' abilities to allow multiple targets location (ant agents' abilities to locate continuous regions of targets), and found out that best performer XSets are problem independent. However both categories of XSets are sensitive to changes in agent density. We test the influences of individual primitive behaviours and the effects of the sequences of primitive behaviours to the indices of merit of XSets and found out that most primitive behaviours are indispensable, especially when specific sequences are prescribed. The effects of pheromone dissipation to the indices of merit of stigmergic XSets are also scrutinized. Precisely, dissipation is not causal. Rather, it enhances convergence. Overall, this work successfully identify the discrete primitive behaviours of stigmergic and message passing ant-like devices. It successfully put these primitive behaviours together into XSets which characterize a language for programming ant-like devices towards desired emergent behaviour. This XSets approach is a new ant language representation with which a wider domain of emergent tasks can be resolved

Learning cognitive maps: Finding useful structure in an uncertain world

In this chapter we will describe the central mechanisms that influence how people learn about large-scale space. We will focus particularly on how these mechanisms enable people to effectively cope with both the uncertainty inherent in a constantly changing world and also with the high information content of natural environments. The major lessons are that humans get by with a less is more approach to building structure, and that they are able to quickly adapt to environmental changes thanks to a range of general purpose mechanisms. By looking at abstract principles, instead of concrete implementation details, it is shown that the study of human learning can provide valuable lessons for robotics. Finally, these issues are discussed in the context of an implementation on a mobile robot. © 2007 Springer-Verlag Berlin Heidelberg

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

Generating and auto-tuning parallel stencil codes

In this thesis, we present a software framework, Patus, which generates high performance stencil codes for different types of hardware platforms, including current multicore CPU and graphics processing unit architectures. The ultimate goals of the framework are productivity, portability (of both the code and performance), and achieving a high performance on the target platform. A stencil computation updates every grid point in a structured grid based on the values of its neighboring points. This class of computations occurs frequently in scientific and general purpose computing (e.g., in partial differential equation solvers or in image processing), justifying the focus on this kind of computation. The proposed key ingredients to achieve the goals of productivity, portability, and performance are domain specific languages (DSLs) and the auto-tuning methodology. The Patus stencil specification DSL allows the programmer to express a stencil computation in a concise way independently of hardware architecture-specific details. Thus, it increases the programmer productivity by disburdening her or him of low level programming model issues and of manually applying hardware platform-specific code optimization techniques. The use of domain specific languages also implies code reusability: once implemented, the same stencil specification can be reused on different hardware platforms, i.e., the specification code is portable across hardware architectures. Constructing the language to be geared towards a special purpose makes it amenable to more aggressive optimizations and therefore to potentially higher performance. Auto-tuning provides performance and performance portability by automated adaptation of implementation-specific parameters to the characteristics of the hardware on which the code will run. By automating the process of parameter tuning — which essentially amounts to solving an integer programming problem in which the objective function is the number representing the code's performance as a function of the parameter configuration, — the system can also be used more productively than if the programmer had to fine-tune the code manually. We show performance results for a variety of stencils, for which Patus was used to generate the corresponding implementations. The selection includes stencils taken from two real-world applications: a simulation of the temperature within the human body during hyperthermia cancer treatment and a seismic application. These examples demonstrate the framework's flexibility and ability to produce high performance code

2016 GREAT Day Program

SUNY Geneseo’s Tenth Annual GREAT Day.https://knightscholar.geneseo.edu/program-2007/1010/thumbnail.jp

A complex systems approach to education in Switzerland

The insights gained from the study of complex systems in biological, social, and engineered systems enables us not only to observe and understand, but also to actively design systems which will be capable of successfully coping with complex and dynamically changing situations. The methods and mindset required for this approach have been applied to educational systems with their diverse levels of scale and complexity. Based on the general case made by Yaneer Bar-Yam, this paper applies the complex systems approach to the educational system in Switzerland. It confirms that the complex systems approach is valid. Indeed, many recommendations made for the general case have already been implemented in the Swiss education system. To address existing problems and difficulties, further steps are recommended. This paper contributes to the further establishment complex systems approach by shedding light on an area which concerns us all, which is a frequent topic of discussion and dispute among politicians and the public, where billions of dollars have been spent without achieving the desired results, and where it is difficult to directly derive consequences from actions taken. The analysis of the education system's different levels, their complexity and scale will clarify how such a dynamic system should be approached, and how it can be guided towards the desired performance