31,037 research outputs found
MeshfreeFlowNet: A Physics-Constrained Deep Continuous Space-Time Super-Resolution Framework
We propose MeshfreeFlowNet, a novel deep learning-based super-resolution
framework to generate continuous (grid-free) spatio-temporal solutions from the
low-resolution inputs. While being computationally efficient, MeshfreeFlowNet
accurately recovers the fine-scale quantities of interest. MeshfreeFlowNet
allows for: (i) the output to be sampled at all spatio-temporal resolutions,
(ii) a set of Partial Differential Equation (PDE) constraints to be imposed,
and (iii) training on fixed-size inputs on arbitrarily sized spatio-temporal
domains owing to its fully convolutional encoder. We empirically study the
performance of MeshfreeFlowNet on the task of super-resolution of turbulent
flows in the Rayleigh-Benard convection problem. Across a diverse set of
evaluation metrics, we show that MeshfreeFlowNet significantly outperforms
existing baselines. Furthermore, we provide a large scale implementation of
MeshfreeFlowNet and show that it efficiently scales across large clusters,
achieving 96.80% scaling efficiency on up to 128 GPUs and a training time of
less than 4 minutes.Comment: Supplementary Video: https://youtu.be/mjqwPch9gDo. Accepted to SC2
QueryProp: Object Query Propagation for High-Performance Video Object Detection
Video object detection has been an important yet challenging topic in
computer vision. Traditional methods mainly focus on designing the image-level
or box-level feature propagation strategies to exploit temporal information.
This paper argues that with a more effective and efficient feature propagation
framework, video object detectors can gain improvement in terms of both
accuracy and speed. For this purpose, this paper studies object-level feature
propagation, and proposes an object query propagation (QueryProp) framework for
high-performance video object detection. The proposed QueryProp contains two
propagation strategies: 1) query propagation is performed from sparse key
frames to dense non-key frames to reduce the redundant computation on non-key
frames; 2) query propagation is performed from previous key frames to the
current key frame to improve feature representation by temporal context
modeling. To further facilitate query propagation, an adaptive propagation gate
is designed to achieve flexible key frame selection. We conduct extensive
experiments on the ImageNet VID dataset. QueryProp achieves comparable accuracy
with state-of-the-art methods and strikes a decent accuracy/speed trade-off.
Code is available at https://github.com/hf1995/QueryProp.Comment: This paper is accepted to AAAI202
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
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