7,415 research outputs found

    Structure-from-motion using convolutional neural networks

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    Abstract. There is an increasing interest in the research community to 3D scene reconstruction from monocular RGB cameras. Conventionally, structure from motion or special hardware such as depth sensors or LIDAR systems were used to reconstruct the point clouds of complex scenes. However, structure from motion technique usually fails to create the dense point cloud, while particular sensors are inconvenient and more expensive than RGB cameras. Recent advances in deep learning research have presented remarkable results in many computer vision tasks. Nevertheless, complete solution for large-scale dense 3D point cloud reconstruction still remains untouched. This thesis introduces a deep-learning-based structure-from-motion pipeline for the dense 3D scene reconstruction problem. Several deep neural networks models were trained to predict the single view depth maps, and relative camera poses from RGB video frames. First, the obtained depth values were sequentially scaled to the first depth map. Next, the iterative closest point algorithm was utilized to further align the estimated camera poses. From these two processed cues, the point clouds of the scene were reconstructed by simple concatenation of 3D points. Although the final point cloud results are encouraging and in certain aspects preferable to the conventional structure from motion method, the system is just tackling the 3D reconstruction problem to some extent. The prediction outputs still have errors, especially in the camera orientation estimation. This system can be seen as the initial study that opens up lots of research questions and improvements in the future. Besides, the study also signified the positive intimation for using unsupervised deep learning scheme to address the 3D scene reconstruction task

    The ART of IAM: The Winning Strategy for the 2006 Competition

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    In many dynamic open systems, agents have to interact with one another to achieve their goals. Here, agents may be self-interested, and when trusted to perform an action for others, may betray that trust by not performing the actions as required. In addition, due to the size of such systems, agents will often interact with other agents with which they have little or no past experience. This situation has led to the development of a number of trust and reputation models, which aim to facilitate an agent's decision making in the face of uncertainty regarding the behaviour of its peers. However, these multifarious models employ a variety of different representations of trust between agents, and measure performance in many different ways. This has made it hard to adequately evaluate the relative properties of different models, raising the need for a common platform on which to compare competing mechanisms. To this end, the ART Testbed Competition has been proposed, in which agents using different trust models compete against each other to provide services in an open marketplace. In this paper, we present the winning strategy for this competition in 2006, provide an analysis of the factors that led to this success, and discuss lessons learnt from the competition about issues of trust in multiagent systems in general. Our strategy, IAM, is Intelligent (using statistical models for opponent modelling), Abstemious (spending its money parsimoniously based on its trust model) and Moral (providing fair and honest feedback to those that request it)

    Self-Compassion Mediates the Link Between Attachment Security and Intimate Relationship Quality for Couples Navigating Pregnancy

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    Millions of couples navigate the transition from pregnancy to postpartum in a given year, and this period of change and adjustment in the family is associated with elevated risk for intimate relationship dysfunction. Self-compassion has the potential to promote skills that are essential for healthy adaptation (e.g., emotion regulation, greater openness and flexibility, more awareness of the needs of oneself and one’s partner). The overarching goal of the present study was to investigate the role of self-compassion in intimate relationship quality during pregnancy. A sample of 159 couples completed semi-structured interviews and questionnaires. Parents engaging in more compassionate self-responding during pregnancy had higher quality intimate relationships as measured across multiple facets – the degree of emotional intimacy and closeness in the relationship, adaptive conflict management and resolution, high quality support in response to stress, and a high degree of respect and acceptance directed toward each other. Further, compassionate self-responding emerged as a mediator of the link between attachment security and intimate relationship quality. Specifically, mothers who were higher in attachment anxiety reported lower levels of compassionate self-responding which, in turn, undermined multiple dimensions of the intimate relationship. Further, fathers who were higher in attachment avoidance practiced less self-compassion, which had deleterious consequences for the couple. These results provide implications that can inform conceptual frameworks of intimate relationship quality and clinical implications for interventions targeting the transition into parenthood

    Radio Galaxy Zoo: Knowledge Transfer Using Rotationally Invariant Self-Organising Maps

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    With the advent of large scale surveys the manual analysis and classification of individual radio source morphologies is rendered impossible as existing approaches do not scale. The analysis of complex morphological features in the spatial domain is a particularly important task. Here we discuss the challenges of transferring crowdsourced labels obtained from the Radio Galaxy Zoo project and introduce a proper transfer mechanism via quantile random forest regression. By using parallelized rotation and flipping invariant Kohonen-maps, image cubes of Radio Galaxy Zoo selected galaxies formed from the FIRST radio continuum and WISE infrared all sky surveys are first projected down to a two-dimensional embedding in an unsupervised way. This embedding can be seen as a discretised space of shapes with the coordinates reflecting morphological features as expressed by the automatically derived prototypes. We find that these prototypes have reconstructed physically meaningful processes across two channel images at radio and infrared wavelengths in an unsupervised manner. In the second step, images are compared with those prototypes to create a heat-map, which is the morphological fingerprint of each object and the basis for transferring the user generated labels. These heat-maps have reduced the feature space by a factor of 248 and are able to be used as the basis for subsequent ML methods. Using an ensemble of decision trees we achieve upwards of 85.7% and 80.7% accuracy when predicting the number of components and peaks in an image, respectively, using these heat-maps. We also question the currently used discrete classification schema and introduce a continuous scale that better reflects the uncertainty in transition between two classes, caused by sensitivity and resolution limits

    Trabecular bone structure correlates with hand posture and use in hominoids

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    Bone is capable of adapting during life in response to stress. Therefore, variation in locomotor and manipulative behaviours across extant hominoids may be reflected in differences in trabecular bone structure. The hand is a promising region for trabecular analysis, as it is the direct contact between the individual and the environment and joint positions at peak loading vary amongst extant hominoids. Building upon traditional volume of interest-based analyses, we apply a whole-epiphysis analytical approach using high-resolution microtomographic scans of the hominoid third metacarpal to investigate whether trabecular structure reflects differences in hand posture and loading in knuckle-walking (Gorilla, Pan), suspensory (Pongo, Hylobates and Symphalangus) and manipulative (Homo) taxa. Additionally, a comparative phylogenetic method was used to analyse rates of evolutionary changes in trabecular parameters. Results demonstrate that trabecular bone volume distribution and regions of greatest stiffness (i.e., Young's modulus) correspond with predicted loading of the hand in each behavioural category. In suspensory and manipulative taxa, regions of high bone volume and greatest stiffness are concentrated on the palmar or distopalmar regions of the metacarpal head, whereas knuckle-walking taxa show greater bone volume and stiffness throughout the head, and particularly in the dorsal region; patterns that correspond with the highest predicted joint reaction forces. Trabecular structure in knuckle-walking taxa is characterised by high bone volume fraction and a high degree of anisotropy in contrast to the suspensory brachiators. Humans, in which the hand is used primarily for manipulation, have a low bone volume fraction and a variable degree of anisotropy. Finally, when trabecular parameters are mapped onto a molecular-based phylogeny, we show that the rates of change in trabecular structure vary across the hominoid clade. Our results support a link between inferred behaviour and trabecular structure in extant hominoids that can be informative for reconstructing behaviour in fossil primates

    A Novel Root-Knot Nematode Resistance QTL on Chromosome Vu01 in Cowpea.

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    The root-knot nematode (RKN) species Meloidogyne incognita and M. javanica cause substantial root system damage and suppress yield of susceptible cowpea cultivars. The narrow-based genetic resistance conferred by the Rk gene, present in some commercial cultivars, is not effective against Rk-virulent populations found in several cowpea production areas. The dynamics of virulence within RKN populations require a broadening of the genetic base of resistance in elite cowpea cultivars. As part of this goal, F1 and F2 populations from the cross CB46-Null (susceptible) x FN-2-9-04 (resistant) were phenotyped for M. javanica induced root-galling (RG) and egg-mass production (EM) in controlled growth chamber and greenhouse infection assays. In addition, F[Formula: see text] families of the same cross were phenotyped for RG on field sites infested with Rk-avirulent M. incognita and M. javanica The response of F1 to RG and EM indicated that resistance to RKN in FN-2-9-04 is partially dominant, as supported by the degree of dominance in the F2 and F[Formula: see text] populations. Two QTL associated with both RG and EM resistance were detected on chromosomes Vu01 and Vu04. The QTL on Vu01 was most effective against aggressive M. javanica, whereas both QTL were effective against avirulent M. incognita Allelism tests with CB46 x FN-2-9-04 progeny indicated that these parents share the same RKN resistance locus on Vu04, but the strong, broad-based resistance in FN-2-9-04 is conferred by the additive effect of the novel resistance QTL on Vu01. This novel resistance in FN-2-9-04 is an important resource for broadening RKN resistance in elite cowpea cultivars

    Spitzer 70 Micron Source Counts in GOODS-North

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    We present ultradeep Spitzer 70 μm observations of GOODS-North (Great Observatories Origins Deep Survey). For the first time, the turnover in the 70 μm Euclidean-normalized differential source counts is observed. We derive source counts down to a flux density of 1.2 mJy. From the measured source counts and fluctuation analysis, we estimate a power-law approximation of the faint 70 μm source counts of dN/dS ∝ S^−1.6, consistent with that observed for the faint 24 μm sources. An extrapolation of the 70 μm source counts to zero flux density implies a total extragalactic background light (EBL) of 7.4 ± 1.9 nW m^−2 sr^−1. The source counts above 1.2 mJy account for about 60% of the estimated EBL. From fluctuation analysis, we derive a photometric confusion level of σc = 0.30 ± 0.15 mJy (q = 5) for the Spitzer 70 μm band
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