60 research outputs found
Semantic Segmentation using Neural Ordinary Differential Equations
The idea of neural Ordinary Differential Equations (ODE) is to approximate
the derivative of a function (data model) instead of the function itself. In
residual networks, instead of having a discrete sequence of hidden layers, the
derivative of the continuous dynamics of hidden state can be parameterized by
an ODE. It has been shown that this type of neural network is able to produce
the same results as an equivalent residual network for image classification. In
this paper, we design a novel neural ODE for the semantic segmentation task. We
start by a baseline network that consists of residual modules, then we use the
modules to build our neural ODE network. We show that our neural ODE is able to
achieve the state-of-the-art results using 57% less memory for training, 42%
less memory for testing, and 68% less number of parameters. We evaluate our
model on the Cityscapes, CamVid, LIP, and PASCAL-Context datasets
Local Neighborhood Features for 3D Classification
With advances in deep learning model training strategies, the training of
Point cloud classification methods is significantly improving. For example,
PointNeXt, which adopts prominent training techniques and InvResNet layers into
PointNet++, achieves over 7% improvement on the real-world ScanObjectNN
dataset. However, most of these models use point coordinates features of
neighborhood points mapped to higher dimensional space while ignoring the
neighborhood point features computed before feeding to the network layers. In
this paper, we revisit the PointNeXt model to study the usage and benefit of
such neighborhood point features. We train and evaluate PointNeXt on ModelNet40
(synthetic), ScanObjectNN (real-world), and a recent large-scale, real-world
grocery dataset, i.e., 3DGrocery100. In addition, we provide an additional
inference strategy of weight averaging the top two checkpoints of PointNeXt to
improve classification accuracy. Together with the abovementioned ideas, we
gain 0.5%, 1%, 4.8%, 3.4%, and 1.6% overall accuracy on the PointNeXt model
with real-world datasets, ScanObjectNN (hardest variant), 3DGrocery100's
Apple10, Fruits, Vegetables, and Packages subsets, respectively. We also
achieve a comparable 0.2% accuracy gain on ModelNet40
CIS UDEL Working Notes on ImageCLEF 2015: Compound figure detection task
Abstract. Figures that are included in biomedical publications play an important role in understanding essential aspects of the paper. Much work over the past few years has focused on figure analysis and classification in biomedical documents. As many of the figures appearing in biomedical documents comprise multiple panels (subfigures), the first step in the analysis requires identification of compound figures and their segmentation into subfigures. There is a wide variety ways to detect compound figures. In this paper, we utilize only visual information to identify compound vs non-compound figures. We have tested the proposed approach on the ImageCLEF 2015 benchmark of 10, 434 images; our approach has achieved an accuracy of 82.82%, thus demonstrating the best performance when compared to other systems that use only visual information for addressing the compound figure detection task
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Stromule extension along microtubules coordinated with actin-mediated anchoring guides perinuclear chloroplast movement during innate immunity.
Dynamic tubular extensions from chloroplasts called stromules have recently been shown to connect with nuclei and function during innate immunity. We demonstrate that stromules extend along microtubules (MTs) and MT organization directly affects stromule dynamics since stabilization of MTs chemically or genetically increases stromule numbers and length. Although actin filaments (AFs) are not required for stromule extension, they provide anchor points for stromules. Interestingly, there is a strong correlation between the direction of stromules from chloroplasts and the direction of chloroplast movement. Stromule-directed chloroplast movement was observed in steady-state conditions without immune induction, suggesting it is a general function of stromules in epidermal cells. Our results show that MTs and AFs may facilitate perinuclear clustering of chloroplasts during an innate immune response. We propose a model in which stromules extend along MTs and connect to AF anchor points surrounding nuclei, facilitating stromule-directed movement of chloroplasts to nuclei during innate immunity
Towards an Integrated Multiscale Simulation of Turbulent Clouds on PetaScale Computers
The development of precipitating warm clouds is affected by several effects of small-scale air turbulence including enhancement of droplet-droplet collision rate by turbulence, entrainment and mixing at the cloud edges, and coupling of mechanical and thermal energies at various scales. Large-scale computation is a viable research tool for quantifying these multiscale processes. Specifically, top-down large-eddy simulations (LES) of shallow convective clouds typically resolve scales of turbulent energy-containing eddies while the effects of turbulent cascade toward viscous dissipation are parameterized. Bottom-up hybrid direct numerical simulations (HDNS) of cloud microphysical processes resolve fully the dissipation-range flow scales but only partially the inertial subrange scales. it is desirable to systematically decrease the grid length in LES and increase the domain size in HDNS so that they can be better integrated to address the full range of scales and their coupling. In this paper, we discuss computational issues and physical modeling questions in expanding the ranges of scales realizable in LES and HDNS, and in bridging LES and HDNS. We review our on-going efforts in transforming our simulation codes towards PetaScale computing, in improving physical representations in LES and HDNS, and in developing better methods to analyze and interpret the simulation results
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