OCTOPUS: Efficient query execution on dynamic mesh datasets

Tauheed, Farhan; Heinis, Thomas; Schürmann, Felix; Markram, Henry; Ailamaki, Anastasia

doi:10.1109/icde.2014.6816718

Cited by 2 publications

(3 citation statements)

References 25 publications

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“…CNN image directly convolves non-Euclidean structures such as graphs, which can both effectively obtain important information from the edge and reduce the computational complexity. Because of these characteristics, it has recently been widely applied in mesh datasets [16,17] and 3D face datasets [18,19]. Meanwhile, as a texture decoder, GAN has recently shown superpowers in fidelity textures and structural features where they do not exist in the diagram.…”

Section: Introductionmentioning

confidence: 99%

FRGAN: A Blind Face Restoration with Generative Adversarial Networks

Wei

Li²,

Chen³

et al. 2021

Mathematical Problems in Engineering

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Recent works based on deep learning and facial priors have performed well in superresolving severely degraded facial images. However, due to the limitation of illumination, pixels of the monitoring probe itself, focusing area, and human motion, the face image is usually blurred or even deformed. To address this problem, we properly propose Face Restoration Generative Adversarial Networks to improve the resolution and restore the details of the blurred face. They include the Head Pose Estimation Network, Postural Transformer Network, and Face Generative Adversarial Networks. In this paper, we employ the following: (i) Swish-B activation function that is used in Face Generative Adversarial Networks to accelerate the convergence speed of the cross-entropy cost function, (ii) a special prejudgment monitor that is added to improve the accuracy of the discriminant, and (iii) the modified Postural Transformer Network that is used with 3D face reconstruction network to correct faces at different expression pose angles. Our method improves the resolution of face image and performs well in image restoration. We demonstrate how our method can produce high-quality faces, and it is superior to the most advanced methods on the reconstruction task of blind faces for in-the-wild images; especially, our 8 × SR SSIM and PSNR are, respectively, 0.078 and 1.16 higher than FSRNet in AFLW.

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Section: Introductionmentioning

confidence: 99%

FRGAN: A Blind Face Restoration with Generative Adversarial Networks

Wei

Li²,

Chen³

et al. 2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…Rebuilding the complete index or updating nearly all the objects at every simulation step is a considerable time investment that substantially slows down the simulation and is difficult to amortize over a join operation [36]. Both the nested loop join and the plane sweep join [29] thus do not maintain auxiliary data structures but at the same time their join process is prohibitively slow.…”

Section: Iterative Static Spatial Joinmentioning

confidence: 99%

“…The ST2B-Tree [7] maps all objects on a uniform grid and indexes each object along with its identifier in a B+-Tree (cell identifiers are assigned based on a space-filling curve). OCTOPUS [36] on the other hand avoids an index and the associated maintenance cost when the data changes over time. It is, however, only applicable for mesh spatial datasets as it relies on the mesh connectivity to retrieve query results.…”

Section: Joining Moving Objectsmentioning

confidence: 99%

Thermal-Join

Tauheed¹,

Heinis

Ailamaki

2015

Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data

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Simulations have become ubiquitous in many domains of science. Today scientists study natural phenomena by first building massive three-dimensional spatial models and then by simulating the models at discrete intervals of time to mimic the behavior of natural phenomena. One frequently occurring challenge during simulations is the repeated computation of spatial self-joins of the model at each simulation time step. The join is performed to access a group of neighboring spatial objects (groups of particles, molecules or cosmological objects) so that scientists can calculate the cumulative effect (like gravitational force) on an object.Computing a self-join even in memory, soon becomes a performance bottleneck in simulation applications. The problem becomes even worse as scientists continue to improve the precision of simulations by increasing the number as well as the size (3D extent) of the objects. This leads to an exponential increase in join selectivity that challenges the performance and scalability of state-of-the-art approaches.We propose THERMAL-JOIN, a novel spatial self-join algorithm for dynamic memory-resident workloads. The algorithm groups objects in spatial proximity together into hot spots. Hot spots minimize the cost of computing join as objects assigned to a hot spot are guaranteed to overlap with each other. Using a nested spatial grid, THERMAL-JOIN partitions and indexes the dataset to locate hot spots. With experiments we show that our approach provides a speedup between 8 to 12× compared to the state of the art and also scales as scientists improve the precision of their simulations.

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OCTOPUS: Efficient query execution on dynamic mesh datasets

Cited by 2 publications

References 25 publications

FRGAN: A Blind Face Restoration with Generative Adversarial Networks

FRGAN: A Blind Face Restoration with Generative Adversarial Networks

Thermal-Join

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