53 research outputs found
Federated Sufficient Dimension Reduction Through High-Dimensional Sparse Sliced Inverse Regression
Federated learning has become a popular tool in the big data era nowadays. It
trains a centralized model based on data from different clients while keeping
data decentralized. In this paper, we propose a federated sparse sliced inverse
regression algorithm for the first time. Our method can simultaneously estimate
the central dimension reduction subspace and perform variable selection in a
federated setting. We transform this federated high-dimensional sparse sliced
inverse regression problem into a convex optimization problem by constructing
the covariance matrix safely and losslessly. We then use a linearized
alternating direction method of multipliers algorithm to estimate the central
subspace. We also give approaches of Bayesian information criterion and
hold-out validation to ascertain the dimension of the central subspace and the
hyper-parameter of the algorithm. We establish an upper bound of the
statistical error rate of our estimator under the heterogeneous setting. We
demonstrate the effectiveness of our method through simulations and real world
applications
Online Kernel Sliced Inverse Regression
Online dimension reduction is a common method for high-dimensional streaming
data processing. Online principal component analysis, online sliced inverse
regression, online kernel principal component analysis and other methods have
been studied in depth, but as far as we know, online supervised nonlinear
dimension reduction methods have not been fully studied. In this article, an
online kernel sliced inverse regression method is proposed. By introducing the
approximate linear dependence condition and dictionary variable sets, we
address the problem of increasing variable dimensions with the sample size in
the online kernel sliced inverse regression method, and propose a reduced-order
method for updating variables online. We then transform the problem into an
online generalized eigen-decomposition problem, and use the stochastic
optimization method to update the centered dimension reduction directions.
Simulations and the real data analysis show that our method can achieve close
performance to batch processing kernel sliced inverse regression
FISEdit: Accelerating Text-to-image Editing via Cache-enabled Sparse Diffusion Inference
Due to the recent success of diffusion models, text-to-image generation is
becoming increasingly popular and achieves a wide range of applications. Among
them, text-to-image editing, or continuous text-to-image generation, attracts
lots of attention and can potentially improve the quality of generated images.
It's common to see that users may want to slightly edit the generated image by
making minor modifications to their input textual descriptions for several
rounds of diffusion inference. However, such an image editing process suffers
from the low inference efficiency of many existing diffusion models even using
GPU accelerators. To solve this problem, we introduce Fast Image Semantically
Edit (FISEdit), a cached-enabled sparse diffusion model inference engine for
efficient text-to-image editing. The key intuition behind our approach is to
utilize the semantic mapping between the minor modifications on the input text
and the affected regions on the output image. For each text editing step,
FISEdit can automatically identify the affected image regions and utilize the
cached unchanged regions' feature map to accelerate the inference process.
Extensive empirical results show that FISEdit can be and
faster than existing methods on NVIDIA TITAN RTX and A100 GPUs
respectively, and even generates more satisfactory images.Comment: 12 pages, 7 figure
Experimental Investigations on the Inner Flow Behavior of Centrifugal Pumps under Inlet Air-Water Two-Phase Conditions
Centrifugal pumps are widely used and are known to be sensitive to inlet air-water two-phase flow conditions. The pump performance degradation mainly depends on the changes in the two-phase flow behavior inside the pump. In the present paper, experimental overall pump performance tests were performed for two different rotational speeds and several inlet air void fractions (αi) up to pump shut-off condition. Visualizations were also performed on the flow patterns of a whole impeller passage and the volute tongue area to physically understand pump performance degradation. The results showed that liquid flow modification does not follow head modification as described by affinity laws, which are only valid for homogeneous bubbly flow regimes. Three-dimensional effects were more pronounced when inlet void fraction increased up to 3%. Bubbly flow with low mean velocities were observed close to the volute tongue for all αi, and returned back to the impeller blade passages. The starting point of pump break down was related to a strong inward reverse flow that occurred in the vicinity of the shroud gap between the impeller and volute tongue area
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