1,720,974 research outputs found
One-Shot Multi-Path Planning Using Fully Convolutional Networks in a Comparison to Other Algorithms
No full textPath planning plays a crucial role in many applications in robotics for example for planning an arm movement or for navigation. Most of the existing approaches to solve this problem are iterative, where a path is generated by prediction of the next state from the current state. Moreover, in case of multi-agent systems, paths are usually planned for each agent separately (decentralized approach). In case of centralized approaches, paths are computed for each agent simultaneously by solving a complex optimization problem, which does not scale well when the number of agents increases. In contrast to this, we propose a novel method, using a homogeneous, convolutional neural network, which allows generation of complete paths, even for more than one agent, in one-shot, i.e., with a single prediction step. First we consider single path planning in 2D and 3D mazes. Here, we show that our method is able to successfully generate optimal or close to optimal (in most of the cases \u0026lt;10% longer) paths in more than 99.5% of the cases. Next we analyze multi-paths either from a single source to multiple end-points or vice versa. Although the model has never been trained on multiple paths, it is also able to generate optimal or near-optimal (\u0026lt;22% longer) paths in 96.4 and 83.9% of the cases when generating two and three paths, respectively. Performance is then also compared to several state of the art algorithms.Open-Access-Publikationsfonds 202
Distributional semantics of objects in visual scenes in comparison to text
No full textThe distributional hypothesis states that the meaning of a concept is defined through the contexts it occurs in. In practice, often word co-occurrence and proximity are analyzed in text corpora for a given word to obtain a real-valued semantic word vector, which is taken to (at least partially) encode the meaning of this word. Here we transfer this idea from text to images, where pre-assigned labels of other objects or activations of convolutional neural networks serve as context. We propose a simple algorithm that extracts and processes object contexts from an image database and yields semantic vectors for objects. We show empirically that these representations exhibit on par performance with state-of-the-art distributional models over a set of conventional objects. For this we employ well-known word benchmarks in addition to a newly proposed object-centric benchmark
Detection transformer-based approach for mapping trees outside forests on high resolution satellite imagery
Full text linkhttp://dx.doi.org/10.13039/501100004543 China Scholarship Councilhttp://dx.doi.org/10.13039/100014988 Institute of Science and Technology Austriahttp://dx.doi.org/10.13039/501100001659 Deutsche ForschungsgemeinschaftOpen-Access-Publikationsfonds 202
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
An unsupervised map of excitatory neuron dendritic morphology in the mouse visual cortex
No full textAbstract Neurons in the neocortex exhibit astonishing morphological diversity, which is critical for properly wiring neural circuits and giving neurons their functional properties. However, the organizational principles underlying this morphological diversity remain an open question. Here, we took a data-driven approach using graph-based machine learning methods to obtain a low-dimensional morphological “bar code” describing more than 30,000 excitatory neurons in mouse visual areas V1, AL, and RL that were reconstructed from the millimeter scale MICrONS serial-section electron microscopy volume. Contrary to previous classifications into discrete morphological types (m-types), our data-driven approach suggests that the morphological landscape of cortical excitatory neurons is better described as a continuum, with a few notable exceptions in layers 5 and 6. Dendritic morphologies in layers 2–3 exhibited a trend towards a decreasing width of the dendritic arbor and a smaller tuft with increasing cortical depth. Inter-area differences were most evident in layer 4, where V1 contained more atufted neurons than higher visual areas. Moreover, we discovered neurons in V1 on the border to layer 5, which avoided deeper layers with their dendrites. In summary, we suggest that excitatory neurons’ morphological diversity is better understood by considering axes of variation than using distinct m-types
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