A Biologically Motivated Software Retina for Robotic Sensors for ARM-Based Mobile Platform Technology

A key issue in designing robotics systems is the cost of an integrated camera sensor that meets the bandwidth/processing requirement for many advanced robotics applications, especially lightweight robotics applications, such as visual surveillance or SLAM in autonomous aerial vehicles. There is currently much work going on to adapt smartphones to provide complete robot vision systems, as the smartphone is so exquisitely integrated by having camera(s), inertial sensing, sound I/O and excellent wireless connectivity. Mass market production makes this a very low-cost platform and manufacturers from quadrotor drone suppliers to children’s toys, such as the Meccanoid robot [5], employ a smartphone to provide a vision system/control system [7,8]. Accordingly, many research groups are attempting to optimise image analysis, computer vision and machine learning libraries for the smartphone platform. However current approaches to robot vision remain highly demanding for mobile processors such as the ARM, and while a number of algorithms have been developed, these are very stripped down, i.e. highly compromised in function or performance. For example, the semi-dense visual odometry implementation of [1] operates on images of only 320x240pixels. In our research we have been developing biologically motivated foveated vision algorithms based on a model of the mammalian retina [2], potentially 100 times more efficient than their conventional counterparts. Accordingly, vision systems based on the foveated architectures found in mammals have also the potential to reduce bandwidth and processing requirements by about x100 - it has been estimated that our brains would weigh ~60Kg if we were to process all our visual input at uniform high resolution. We have reported a foveated visual architecture [2,3,4] that implements a functional model of the retina-visual cortex to produce feature vectors that can be matched/classified using conventional methods, or indeed could be adapted to employ Deep Convolutional Neural Nets for the classification/interpretation stage. Given the above processing/bandwidth limitations, a viable way forward would be to perform off-line learning and implement the forward recognition path on the mobile platform, returning simple object labels, or sparse hierarchical feature symbols, and gaze control commands to the host robot vision system and controller. We are now at the early stages of investigating how best to port our foveated architecture onto an ARM-based smartphone platform. To achieve the required levels of performance we propose to port and optimise our retina model to the mobile ARM processor architecture in conjunction with their integrated GPUs. We will then be in the position to provide a foveated smart vision system on a smartphone with the advantage of processing speed gains and bandwidth optimisations. Our approach will be to develop efficient parallelising compilers and perhaps propose new processor architectural features to support this approach to computer vision, e.g. efficient processing of hexagonally sampled foveated images. Our current goal is to have a foveated system running in real-time on at least a 1080p input video stream to serve as a front-end robot sensor for tasks such as general purpose object recognition and reliable dense SLAM using a commercial off-the-shelf smartphone. Initially this system would communicate a symbol stream to conventional hardware performing back-end visual classification/interpretation, although simple object detection and recognition tasks should be possible on-board the device. We propose that, as in Nature, foveated vision is the key to achieving the necessary data reduction to be able to implement complete visual recognition and learning processes on the smartphone itself

A Software Retina for Egocentric & Robotic Vision Applications on Mobile Platforms

We present work in progress to develop a low-cost highly integrated camera sensor for egocentric and robotic vision. Our underlying approach is to address current limitations to image analysis by Deep Convolutional Neural Networks, such as the requirement to learn simple scale and rotation transformations, which contribute to the large computational demands for training and opaqueness of the learned structure, by applying structural constraints based on known properties of the human visual system. We propose to apply a version of the retino-cortical transform to reduce the dimensionality of the input image space by a factor of ex100, and map this spatially to transform rotations and scale changes into spatial shifts. By reducing the input image size accordingly, and therefore learning requirements, we aim to develop compact and lightweight egocentric and robot vision sensor using a smartphone as the target platfor

Modenhetsmodell For Måling Av Datadrevenhet i Organisasjoner

Med en økende grad av tilgjengelig data skapes det en forventing til organisasjoner om at de skal bli datadrevne. En datadreven organisasjon kjennetegnes av at de lykkes i å bruke data for å ta valg og skape verdi for organisasjonen. Organisasjoner har ofte en forståelse av hvorfor man burde være datadrevne. Det er allikevel uklart hvordan man skal gå frem for å skape en datadreven organisasjon. Vi har utviklet en modenhetsmodell som kan kartlegge organisasjoners modenhet, når det kommer til å ta i bruk data og analyse i beslutningstagning. Modellen er utviklet etter forskningsmetoden "Design Science Research" og evaluert grundig i samarbeid med en casebedrift. Formålet til modellen er å gi en indikasjon på graden av analytisk modenhet innenfor ulike enheter i en organisasjon, som kan brukes for å utvikle et overordnet veikart for videre utvikling

Nostril-Specific Olfactory Modulation of Visual Perception in Binocular Rivalry

It is known that olfaction and vision can work in tandem to represent object identities. What is yet unclear is the stage of the sensory processing hierarchy at which the two types of inputs converge. Here we study this issue through a well established visual phenomenon termed binocular rivalry. We show that smelling an odor from one nostril significantly enhances the dominance time of the congruent visual image in the contralateral visual field, relative to that in the ipsilateral visual field. Moreover, such lateralization-based enhancement extends to category selective regions so that when two images of words and human body, respectively, are engaged in rivalry in the central visual field, smelling natural human body odor from the right nostril increases the dominance time of the body image compared with smelling it from the left nostril. Semantic congruency alone failed to produce this effect in a similar setting. These results, taking advantage of the anatomical and functional lateralizations in the olfactory and visual systems, highlight the functional dissociation of the two nostrils and provide strong evidence for an object-based early convergence of olfactory and visual inputs in sensory representations

A novel approach to phylogenetic tree construction using stochastic optimization and clustering

BACKGROUND: The problem of inferring the evolutionary history and constructing the phylogenetic tree with high performance has become one of the major problems in computational biology. RESULTS: A new phylogenetic tree construction method from a given set of objects (proteins, species, etc.) is presented. As an extension of ant colony optimization, this method proposes an adaptive phylogenetic clustering algorithm based on a digraph to find a tree structure that defines the ancestral relationships among the given objects. CONCLUSION: Our phylogenetic tree construction method is tested to compare its results with that of the genetic algorithm (GA). Experimental results show that our algorithm converges much faster and also achieves higher quality than GA

Comprehensive phylogeny of ray-finned fishes (Actinopterygii) based on transcriptomic and genomic data

Our understanding of phylogenetic relationships among bony fishes has been transformed by analysis of a small number of genes, but uncertainty remains around critical nodes. Genomescale inferences so far have sampled a limited number of taxa and genes. Here we leveraged 144 genomes and 159 transcriptomes to investigate fish evolution with an unparalleled scale of data: >0.5 Mb from 1,105 orthologous exon sequences from 303 species, representing 66 out of 72 ray-finned fish orders. We apply phylogenetic tests designed to trace the effect of whole-genome duplication events on gene trees and find paralogy-free loci using a bioinformatics approach. Genome-wide data support the structure of the fish phylogeny, and hypothesis-testing procedures appropriate for phylogenomic datasets using explicit gene genealogy interrogation settle some long-standing uncertainties, such as the branching order at the base of the teleosts and among early euteleosts, and the sister lineage to the acanthomorph and percomorph radiations. Comprehensive fossil calibrations date the origin of all major fish lineages before the end of the Cretaceous.Fil: Hughes, Lily C.. National Museum of Natural History; Estados Unidos. The George Washington University; Estados UnidosFil: Ortí, Guillermo. National Museum of Natural History; Estados Unidos. The George Washington University; Estados UnidosFil: Huang, Yu. Beijing Genomics Institute; China. Chinese Academy of Sciences; República de ChinaFil: Sun, Ying. China National Genebank; China. Beijing Genomics Institute; ChinaFil: Baldwin, Carole C.. National Museum of Natural History; Estados UnidosFil: Thompson, Andrew W.. National Museum of Natural History; Estados Unidos. The George Washington University; Estados UnidosFil: Arcila, Dahiana. National Museum of Natural History; Estados Unidos. The George Washington University; Estados UnidosFil: Betancur, Ricardo. National Museum of Natural History; Estados Unidos. Universidad de Puerto Rico, Recinto de Rio Piedras; Puerto RicoFil: Li, Chenhong. Shanghai Ocean University; ChinaFil: Becker, Leandro Anibal. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto Andino Patagónico de Tecnologías Biológicas y Geoambientales. Universidad Nacional del Comahue. Instituto Andino Patagónico de Tecnologías Biológicas y Geoambientales.; Argentina. Universidad Nacional del Comahue. Centro Regional Universitario Bariloche; ArgentinaFil: Bellora, Nicolás. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto Andino Patagónico de Tecnologías Biológicas y Geoambientales. Universidad Nacional del Comahue. Instituto Andino Patagónico de Tecnologías Biológicas y Geoambientales.; Argentina. Universidad Nacional del Comahue. Centro Regional Universitario Bariloche; ArgentinaFil: Zhao, Xiaomeng. Chinese Academy of Sciences; República de China. Beijing Genomics Institute; ChinaFil: Li, Xiaofeng. Chinese Academy of Sciences; República de China. Beijing Genomics Institute; ChinaFil: Wang, Min. Beijing Genomics Institute; ChinaFil: Fang, Chao. Chinese Academy of Sciences; República de ChinaFil: Xie, Bing. Bgi-shenzhen; ChinaFil: Zhoui, Zhuocheng. China Fisheries Association; ChinaFil: Huang, Hai. Hainan Tropical Ocean University; ChinaFil: Chen, Songlin. Yellow Sea Fisheries Research Institute Chinese Academy Of Fishery Science; ChinaFil: Venkatesh, Byrappa. A-star, Institute Of Molecular And Cell Biology;Fil: Shi, Qiong. Chinese Academy of Sciences; República de Chin

Identification of a Topological Characteristic Responsible for the Biological Robustness of Regulatory Networks

Attribution of biological robustness to the specific structural properties of a regulatory network is an important yet unsolved problem in systems biology. It is widely believed that the topological characteristics of a biological control network largely determine its dynamic behavior, yet the actual mechanism is still poorly understood. Here, we define a novel structural feature of biological networks, termed ‘regulation entropy’, to quantitatively assess the influence of network topology on the robustness of the systems. Using the cell-cycle control networks of the budding yeast (Saccharomyces cerevisiae) and the fission yeast (Schizosaccharomyces pombe) as examples, we first demonstrate the correlation of this quantity with the dynamic stability of biological control networks, and then we establish a significant association between this quantity and the structural stability of the networks. And we further substantiate the generality of this approach with a broad spectrum of biological and random networks. We conclude that the regulation entropy is an effective order parameter in evaluating the robustness of biological control networks. Our work suggests a novel connection between the topological feature and the dynamic property of biological regulatory networks

Reply to: Contribution of carbon inputs to soil carbon accumulation cannot be neglected

In the accompanying Comment1, He et al. argue that the determinant role of microbial carbon use efficiency in global soil organic carbon (SOC) storage shown in Tao et al. (2023)2 was overestimated because carbon inputs were neglected in our data analysis while they suggest that our model-based analysis could be biased and model-dependent. Their argument is based on a different choice of independent variables in the data analysis and a sensitivity analysis of two process-based models other than that used in our study. We agree that both carbon inputs and outputs (as mediated by microbial processes) matter when predicting SOC storage – the question is their relative contributions. While we encourage further studies to examine how the evaluation of the relative importance of CUE to global SOC storage may vary with different model structures, He et al.’s claims about Tao et al. (2023) need to be taken as an alternative, unproven hypothesis until empirical data support their specific parameterization. Here we show that an additional literature assessment of global data does not support He et al.’s argument, in contrast to our study, and that further study on this topic is essential

Reply to: Beyond microbial carbon use efficiency

In their commentary, Xiao et al. cautioned that the conclusions on the critical role of microbial carbon use efficiency (CUE) in global soil organic carbon (SOC) storage in a paper by Tao et al. (2023) might be too simplistic. They claimed that Tao et al.’s study lacked mechanistic consideration of SOC formation and excluded important datasets. Xiao et al. brought up important points, which can be largely reconciled with our findings by understanding the differences in expressing processes in empirical studies and in models