Recurrent neural networks for hydrodynamic imaging using a 2D-sensitive artificial lateral line

The lateral line is a mechanosensory organ found in fish and amphibians that allows them to sense and act on their near-field hydrodynamic environment. We present a 2D-sensitive Artificial lateral line (ALL) comprising eight all-optical flow sensors, which we use to measure hydrodynamic velocity profiles along the sensor array in response to a moving object in its vicinity. We then use the measured velocity profiles to reconstruct the objects location, via two types of neural networks: feed-forward and recurrent. Several implementations of feed-forward neural networks for ALL source localisation exist, while recurrent neural networks may be more appropriate for this task. The performance of a recurrent neural network (the Long Short-Term Memory, LSTM) is compared to that of a feed-forward neural network (the Online-Sequential Extreme Learning Machine, OS-ELM) via localizing a 6 cm sphere moving at 13 cm/s. Results show that, in a 62 cm × 9.5 cm area of interest, the LSTM outperforms the OS-ELM with an average localisation error of 0.72 cm compared to 4.27 cm respectively. Furthermore, the recurrent network is relatively less affected by noise, indicating that recurrent connections can be beneficial for hydrodynamic object localisation

Three-dimensional multi-source localization of underwater objects using convolutional neural networks for artificial lateral lines

This research focuses on the signal processing required for a sensory system that can simultaneously localize multiple moving underwater objects in a three-dimensional (3D) volume by simulating the hydrodynamic flow caused by these objects. We propose a method for localization in a simulated setting based on an established hydrodynamic theory founded in fish lateral line organ research. Fish neurally concatenate the information of multiple sensors to localize sources. Similarly, we use the sampled fluid velocity via two parallel lateral lines to perform source localization in three dimensions in two steps. Using a convolutional neural network, we first estimate a two-dimensional image of the probability of a present source. Then we determine the position of each source, via an automated iterative 3D-aware algorithm. We study various neural network architectural designs and different ways of presenting the input to the neural network; multi-level amplified inputs and merged convolutional streams are shown to improve the imaging performance. Results show that the combined system can exhibit adequate 3D localization of multiple sources

Bio-inspired all-optical artificial neuromast for 2D flow sensing

We present the design, fabrication and testing of a novel all-optical 2D flow velocity sensor, inspired by a fish lateral line neuromast. This artificial neuromast consists of optical fibres inscribed with Bragg gratings supporting a fluid force recipient sphere. Its dynamic response is modelled based on the Stokes solution for unsteady flow around a sphere and found to agree with experimental results. Tuneable mechanical resonance is predicted, allowing a deconvolution scheme to accurately retrieve fluid flow speed and direction from sensor readings. The optical artificial neuromast achieves a low frequency threshold flow sensing of 5 mm s(-1) and 5 mu m s(-1) at resonance, with a typical linear dynamic range of 38 dB at 100 Hz sampling. Furthermore, the optical artificial neuromast is shown to determine flow direction within a few degrees

Front Stability in Mean Field Models of Diffusion Limited Growth

We present calculations of the stability of planar fronts in two mean field models of diffusion limited growth. The steady state solution for the front can exist for a continuous family of velocities, we show that the selected velocity is given by marginal stability theory. We find that naive mean field theory has no instability to transverse perturbations, while a threshold mean field theory has such a Mullins-Sekerka instability. These results place on firm theoretical ground the observed lack of the dendritic morphology in naive mean field theory and its presence in threshold models. The existence of a Mullins-Sekerka instability is related to the behavior of the mean field theories in the zero-undercooling limit.Comment: 26 pp. revtex, 7 uuencoded ps figures. submitted to PR

Strangeness Production in the HSD Transport Approach from SIS to SPS energies

We study systematically the production of strangeness in nuclear reactions from SIS to SPS energies within the covariant hadronic transport approach HSD. Whereas the proton and pion rapidity distributions as well as pion transverse momentum spectra are well described in the hadronic transport model from 2-200 AGeV, the $K^+$ and $K^-$ spectra are noticeably underestimated at AGS energies while the $K^+$ spectra match well at SIS and SPS energies with the experimental data. We conclude that the failure of the hadronic model at AGS energies points towards a nonhadronic phase during the collision of heavy systems around 10 AGeV.Comment: 25 pages, 19 figure

The Hilbertian Tensor Norm and Entangled Two-Prover Games

We study tensor norms over Banach spaces and their relations to quantum information theory, in particular their connection with two-prover games. We consider a version of the Hilbertian tensor norm $\gamma_2$ and its dual $\gamma_2^*$ that allow us to consider games with arbitrary output alphabet sizes. We establish direct-product theorems and prove a generalized Grothendieck inequality for these tensor norms. Furthermore, we investigate the connection between the Hilbertian tensor norm and the set of quantum probability distributions, and show two applications to quantum information theory: firstly, we give an alternative proof of the perfect parallel repetition theorem for entangled XOR games; and secondly, we prove a new upper bound on the ratio between the entangled and the classical value of two-prover games.Comment: 33 pages, some of the results have been obtained independently in arXiv:1007.3043v2, v2: an error in Theorem 4 has been corrected; Section 6 rewritten, v3: completely rewritten in order to improve readability; title changed; references added; published versio

Thermoregulation in African green pigeons (Treron calvus) and a re-analysis of insular effects on basal metabolic rate and heterothermy in columbid birds

Columbid birds represent a useful model taxon for examining adaptation in metabolic and thermal traits, including the effects of insularity. To test predictions concerning the role of insularity and low predation risk as factors selecting for the use of torpor, and the evolution of low basal metabolic rate in island species, we examined thermoregulation under laboratory and semi-natural conditions in a mainland species, the African Green-Pigeon (Treron calvus). Under laboratory conditions, rest-phase body temperature (Tb) was significantly and positively correlated with air temperature (Ta) between 0 °C and 35 °C, and the relationship between resting metabolic rate (RMR) and Ta differed from typical endothermic patterns. The minimum RMR, which we interpret as basal metabolic rate (BMR), was 0.825 ± 0.090 W. Green-pigeons responded to food restriction by significantly decreasing rest-phase Tb, but the reductions were small (at most ~ 5 °C below normothermic values), with a minimum Tb of 33.1 °C recorded in a food-deprived bird. We found no evidence of the large reductions in Tb and metabolic rate and the lethargic state characteristic of torpor. The absence of torpor in T. calvus lends support to the idea that species restricted to islands that are free of predators are more likely to use torpor than mainland species that face the risk of predation during the rest-phase. We also analysed interspecific variation in columbid BMR in a phylogenetically-informed framework, and verified the conclusions of an earlier study that found that BMR is significantly lower in island species compared to those that occur on mainlands.DST/NRF Centre of Excellence at the Percy FitzPatrick Institute, the University of Pretoria, and the National Science Foundation, USA (IOS-1122228).http://www.springer.com/life+sci/biochemistry/journal/360hb2013ab201

Characterization and calibration of the James Webb space telescope mirror actuators fine stage motion

The James Webb Space Telescope’s (Webb’s) deployable primary and secondary mirrors are actively controlled to achieve and maintain precise optical alignment on-orbit. Each of the 18 primary mirror segment assemblies (PMSAs) and the secondary mirror assembly (SMA) are controlled in six degrees of freedom by using six linear actuators in a hexapod arrangement. In addition, each PMSA contains a seventh actuator that adjusts radius of curvature (RoC). The actuators are of a novel stepper motor-based cryogenic two-stage design that is capable of sub-10 nm motion accuracy over a 20 mm range. The nm-level motion of the 132 actuators were carefully tested and characterized before integration into the mirror assemblies. Using these test results as an initial condition, knowledge of each actuator’s length (and therefore mirror position) has relied on software bookkeeping and configuration control to keep an accurate motor step count from which actuator position can be calculated. These operations have been carefully performed through years of Webb test operations using both ground support actuator control software as well as the flight Mirror Control Software (MCS). While the actuator’s coarse stage length is cross-checked using a linear variable differential transformer (LVDT), no on-board cross-check exists for the nm-level length changes of the actuators’ fine stage. To ensure that the software bookkeeping of motor step count is still accurate after years of testing and to test that the actuator position knowledge was properly handed off from the ground software to the flight MCS, a series of optical tests were devised and performed through the Center of Curvature (CoC) ambient optical test campaigns at the Goddard Space Flight Center (GSFC) and during the thermal-vacuum tests of the entire optical payload that were conducted in Chamber A at Johnson Space Center (JSC). In each test, the actuator Fine Step Count (FSC) value is compared to an external measurement provided by an optical metrology tool with the goal of either confirming the MCS database value, or providing a recommendation for an updated calibration if the measured FSC differs significantly from the MCS-based expectation. During ambient testing of the PMSA hexapods, the nm-level actuator length changes were measured with a custom laser deflectometer by measuring tilts of the PMSA. The PMSA RoC fine stage characterization was performed at JSC using multi-wave interferometric measurements with the CoC Optical Assembly (COCOA). Finally, the SMA hexapod fine stage characterization test was performed at JSC using the NIRCam instrument in the “pass-and-a-half” test configuration using a test source from the Aft-Optics System Source Plate Assembly (ASPA). In this paper, each of these three tests, subsequent data analyses, and uncertainty estimations will be presented. Additionally, a summary of the ensemble state of Webb’s actuator fine stages is provided, along with a comparison to a Wavefront Sensing and Control (WFSC)-based requirement for FSC errors as they relate to the optical alignment convergence of the telescope on-orbit

Can Light Signals Travel Faster than c in Nontrivial Vacuua in Flat space-time? Relativistic Causality II

In this paper we show that the Scharnhorst effect (Vacuum with boundaries or a Casimir type vacuum) cannot be used to generate signals showing measurable faster-than-c speeds. Furthermore, we aim to show that the Scharnhorst effect would violate special relativity, by allowing for a variable speed of light in vacuum, unless one can specify a small invariant length scale. This invariant length scale would be agreed upon by all inertial observers. We hypothesize the approximate scale of the invariant length.Comment: 12 pages no figure

Performance of neural networks for localizing moving objects with an artificial lateral line

Fish are able to sense water flow velocities relative to their body with their mechanoreceptive lateral line organ. This organ consists of an array of flow detectors distributed along the fish body. Using the excitation of these individual detectors, fish can determine the location of nearby moving objects. Inspired by this sensory modality, it is shown here how neural networks can be used to extract an object's location from simulated excitation patterns, as can be measured along arrays of stationary artificial flow velocity sensors. The applicability, performance and robustness with respect to input noise of different neural network architectures are compared. When trained and tested under high signal to noise conditions (46 dB), the Extreme Learning Machine architecture performs best with a mean Euclidean error of 0.4% of the maximum depth of the field D, which is taken half the length of the sensor array. Under lower signal to noise conditions Echo State Networks, having recurrent connections, enhance the performance while the Multilayer Perceptron is shown to be the most noise robust architecture. Neural network performance decreased when the source moves close to the sensor array or to the sides of the array. For all considered architectures, increasing the number of detectors per array increased localization performance and robustness