Natural visual cues eliciting predator avoidance in fiddler crabs

To efficiently provide an animal with relevant information, the design of its visual system should reflect the distribution of natural signals and the animal’s tasks. In many behavioural contexts, however, we know comparatively little about the moment-to-moment information-processing challenges animals face in their daily lives. In predator avoidance, for instance, we lack an accurate description of the natural signal stream and its value for risk assessment throughout the prey’s defensive behaviour.We characterized the visual signals generated by real, potentially predatory events by video-recording bird approaches towards an Uca vomeris colony. Using four synchronized cameras allowed us to simultaneously monitor predator avoidance responses of crabs. We reconstructed the signals generated by dangerous and non-dangerous flying animals, identified the cues that triggered escape responses and compared them with those triggering responses to dummy predators. Fiddler crabs responded to a combination of multiple visual cues (including retinal speed, elevation and visual flicker) that reflect the visual signatures of distinct bird and insect behaviours. This allowed crabs to discriminate between dangerous and non-dangerous events. The results demonstrate the importance of measuring natural sensory signatures of biologically relevant events in order to understand biological information processing and its effects on behavioural organization

The emergence of proto‐institutions in the new normal business landscape : dialectic institutional work and the dutch drone industry

In the current business landscape, in which technology‐enabled entrepreneurship is part of the New Normal, regulatory institutional structures are in constant flux. Previous studies have framed the challenges facing entrepreneurs in mature organizational fields as avoiding the power of overbearing regulators long enough to establish the legitimacy of their ventures. In fields typified by New Normal conditions, however, regulatory frameworks for evaluating new technology‐enabled ventures are often still lacking. Regulators may choose to actively reach out to entrepreneurs to arrive at a better understanding of the radical technological changes and high‐frequency entrepreneurial behavioral adaptations that occur in these settings. To grasp how novel regulatory institutional structures come about in the New Normal business landscape, we conducted a processual study of the emergence of a new technology that is the Dutch remotely‐piloted aircraft systems (drone) industry between 2000 and 2018. Our findings show that regulatory proto‐institutions result from dialectic institutional work in the form of structured interactions between entrepreneurs and regulators. Specifically, we present a process model that reveals how new regulatory structures evolve in contexts where high levels of technological and behavioral change induce systemic uncertainty, and enlarge the interdependence between entrepreneurs and regulators. We suggest that our process theory of proto‐institutional emergence generalizes towards other organizational fields in which technology‐enabled entrepreneurship has become the main driver of growth. Theoretically, our findings speak to the literatures on institutional work, proto‐institutional emergence, and the New Normal business landscape

Using Covariance Matrix Adaptation Evolutionary Strategy to boost the search accuracy in hierarchic memetic computations

Many global optimization problems arising naturally in science and engineering exhibit some form of intrinsic ill-posedness, such as multimodality and insensitivity. Severe ill-posedness precludes the use of standard regularization techniques and necessitates more specialized approaches, usually comprised of two separate stages - global phase, that determines the problem's modality and provides rough approximations of the solutions, and a local phase, which re fines these approximations. In this work, we attempt to improve one of the most efficient currently known approaches - Hierarchic Memetic Strategy (HMS) - by incorporating the Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) into its local phase. CMA-ES is a stochastic optimization algorithm that in some sense mimics the behavior of population-based evolutionary algorithms without explicitly evolving the population. This way, it avoids, to an extent, the associated cost of multiple evaluations of the objective function. We compare the performance of the HMS on relatively simple multimodal benchmark problems and on an engineering problem. To do so, we consider two con gurations: the CMA-ES and the standard SEA (Simple Evolutionary Algorithm). The results demonstrate that the HMS with CMA-ES in the local phase requires less objective function evaluations to provide the same accuracy, making this approach more efficient than the standard SEA

Flight Testing of the Gulfstream Quiet Spike(TradeMark) on a NASA F-15B

Gulfstream Aerospace has long been interested in the development of an economically viable supersonic business jet (SBJ). A design requirement for such an aircraft is the ability for unrestricted supersonic flight over land. Although independent studies continue to substantiate that a market for a SBJ exists, regulatory and public acceptance challenges still remain for supersonic operation over land. The largest technical barrier to achieving this goal is sonic boom attenuation. Gulfstream's attention has been focused on fundamental research into sonic boom suppression for several years. This research was conducted in partnership with the NASA Aeronautics Research Mission Directorate (ARMD) supersonic airframe cruise efficiency technical challenge. The Quiet Spike, a multi-stage telescopic nose boom and a Gulfstream-patented design (references 1 and 2), was developed to address the sonic boom attenuation challenge and validate the technical feasibility of a morphing fuselage. The Quiet Spike Flight Test Program represents a major step into supersonic technology development for sonic boom suppression. The Gulfstream Aerospace Quiet Spike was designed to reduce the sonic boom signature of the forward fuselage for an aircraft flying at supersonic speeds. In 2004, the Quiet Spike Flight Test Program was conceived by Gulfstream and NASA to demonstrate the feasibility of sonic boom mitigation and centered on the structural and mechanical viability of the translating test article design. Research testing of the Quiet Spike consisted of numerous ground and flight operations. Each step in the process had unique objectives, and involved numerous test team members from the NASA Dryden Flight Research Center (DFRC) and Gulfstream Aerospace. Flight testing of the Quiet Spike was conducted at the NASA Dryden Flight Research Center on an F-15B aircraft from August, 2006, to February, 2007. During this period, the Quiet Spike was flown at supersonic speeds up to Mach 1.8 at the maximum design dynamic pressure of 685 pounds per square foot. Extension and retraction tests were conducted at speeds up to Mach 1.4. The design of the Quiet Spike to shape the forward shock wave environment of the aircraft was confirmed during near-field shock wave probing at Mach 1.4. Thirty-two flights were performed without incident and all project objectives were achieved. The success of the Quiet Spike Flight Test Program represents an important step towards developing commercial aircraft capable of supersonic flight over land within the continental United States and in international airspace

An Agent-Oriented Hierarchic Strategy for Solving Inverse Problems

The paper discusses the complex, agent-oriented hierarchic memetic strategy (HMS) dedicated to solving inverse parametric problems. The strategy goes beyond the idea of two-phase global optimization algorithms. The global search performed by a tree of dependent demes is dynamically alternated with local, steepest descent searches. The strategy offers exceptionally low computational costs, mainly because the direct solver accuracy (performed by the hp-adaptive finite element method) is dynamically adjusted for each inverse search step. The computational cost is further decreased by the strategy employed for solution inter-processing and fitness deterioration. The HMS efficiency is compared with the results of a standard evolutionary technique, as well as with the multi-start strategy on benchmarks that exhibit typical inverse problems' difficulties. Finally, an HMS application to a real-life engineering problem leading to the identification of oil deposits by inverting magnetotelluric measurements is presented. The HMS applicability to the inversion of magnetotelluric data is also mathematically verified

A hybrid method for inversion of 3D DC resistivity logging measurements

This paper focuses on the application of hp hierarchic genetic strategy (hp-HGS) for solution of a challenging problem, the inversion of 3D direct current (DC) resistivity logging measurements. The problem under consideration has been formulated as the global optimization one, for which the objective function (misfit between computed and reference data) exhibits multiple minima. In this paper, we consider the extension of the hp-HGS strategy, namely we couple the hp-HGS algorithm with a gradient based optimization method for a local search. Forward simulations are performed with a self-adaptive hp finite element method, hp-FEM. The computational cost of misfit evaluation by hp-FEM depends strongly on the assumed accuracy. This accuracy is adapted to the tree of populations generated by the hp-HGS algorithm, which makes the global phase significantly cheaper. Moreover, tree structure of demes as well as branch reduction and conditional sprouting mechanism reduces the number of expensive local searches up to the number of minima to be recognized. The common (direct and inverse) accuracy control, crucial for the hp-HGS efficiency, has been motivated by precise mathematical considerations. Numerical results demonstrate the suitability of the proposed method for the inversion of 3D DC resistivity logging measurements

Can a connectionist model explain the processing of regularly and irregularly inflected words in German as L1 and L2?

The connectionist model is a prevailing model of the structure and functioning of the cognitive system of the processing of morphology. According to this model, the morphology of regularly and irregularly inflected words (e.g., verb participles and noun plurals) is processed in the same cognitive network. A validation of the connectionist model of the processing of morphology in German as L2 has yet to be achieved. To investigate L2-specific aspects, we compared a group of L1 speakers of German with speakers of German as L2. L2 and L1 speakers of German were assigned to their respective group by their reaction times in picture naming prior to the central task. The reaction times in the lexical decision task of verb participles and noun plurals were largely consistent with the assumption of the connectionist model. Interestingly, speakers of German as L2 showed a specific advantage for irregular compared with regular verb participles

Critical behavior at Mott-Anderson transition: a TMT-DMFT perspective

We present a detailed analysis of the critical behavior close to the Mott-Anderson transition. Our findings are based on a combination of numerical and analytical results obtained within the framework of Typical-Medium Theory (TMT-DMFT) - the simplest extension of dynamical mean field theory (DMFT) capable of incorporating Anderson localization effects. By making use of previous scaling studies of Anderson impurity models close to the metal-insulator transition, we solve this problem analytically and reveal the dependence of the critical behavior on the particle-hole symmetry. Our main result is that, for sufficiently strong disorder, the Mott-Anderson transition is characterized by a precisely defined two-fluid behavior, in which only a fraction of the electrons undergo a "site selective" Mott localization; the rest become Anderson-localized quasiparticles.Comment: 4+ pages, 4 figures, v2: minor changes, accepted for publication in Phys. Rev. Let

Teaser: Individualized benchmarking and optimization of read mapping results for NGS data

Mapping reads to a genome remains challenging, especially for non-model organisms with lower quality assemblies, or for organisms with higher mutation rates. While most research has focused on speeding up the mapping process, little attention has been paid to optimize the choice of mapper and parameters for a user's dataset. Here, we present Teaser, a software that assists in these choices through rapid automated benchmarking of different mappers and parameter settings for individualized data. Within minutes, Teaser completes a quantitative evaluation of an ensemble of mapping algorithms and parameters. We use Teaser to demonstrate how Bowtie2 can be optimized for different data