172 research outputs found
'Yellow Peril': Contradictions of Race in James Ellroy's Perfidia
James Ellroy’s treatment of race continues to captivate and polarize both popular and academic opinion. Whilst some see the casual racism and often uncomfortable stereotypes in Ellroy’s work as a reflection of the author’s own political agenda, for others Ellroy’s work offers a complex deconstruction of both racial identity and white social power. Focusing on his novel Perfidia, this paper explores these contradictions and paradoxes in Ellroy’s representations of race, arguing that whilst the novel depicts and forcefully overemphasizes an historical moment fraught with a brand of physiognomic racism that persecutes individuals on the basis of biological difference, it simultaneously deconstructs such essentialist engenderings by foregrounding the performative dimensions of race as a category of identity. As a result, this paper argues that Ellroy’s novel “visibilizes” the socially and institutionally constructed nature of race, deconstructing and destabilizing the integrity and authority of white social power. Yet, this paper also suggests that through such an unyielding portrayal of white power, Perfidia only partly dislodges the authority and power of institutional whiteness, and can in fact be seen to validate the sustainment of such apparatus
MicroAST: Towards Super-Fast Ultra-Resolution Arbitrary Style Transfer
Arbitrary style transfer (AST) transfers arbitrary artistic styles onto
content images. Despite the recent rapid progress, existing AST methods are
either incapable or too slow to run at ultra-resolutions (e.g., 4K) with
limited resources, which heavily hinders their further applications. In this
paper, we tackle this dilemma by learning a straightforward and lightweight
model, dubbed MicroAST. The key insight is to completely abandon the use of
cumbersome pre-trained Deep Convolutional Neural Networks (e.g., VGG) at
inference. Instead, we design two micro encoders (content and style encoders)
and one micro decoder for style transfer. The content encoder aims at
extracting the main structure of the content image. The style encoder, coupled
with a modulator, encodes the style image into learnable dual-modulation
signals that modulate both intermediate features and convolutional filters of
the decoder, thus injecting more sophisticated and flexible style signals to
guide the stylizations. In addition, to boost the ability of the style encoder
to extract more distinct and representative style signals, we also introduce a
new style signal contrastive loss in our model. Compared to the state of the
art, our MicroAST not only produces visually superior results but also is 5-73
times smaller and 6-18 times faster, for the first time enabling super-fast
(about 0.5 seconds) AST at 4K ultra-resolutions. Code is available at
https://github.com/EndyWon/MicroAST.Comment: Accepted by AAAI 202
Generative Image Inpainting with Segmentation Confusion Adversarial Training and Contrastive Learning
This paper presents a new adversarial training framework for image inpainting
with segmentation confusion adversarial training (SCAT) and contrastive
learning. SCAT plays an adversarial game between an inpainting generator and a
segmentation network, which provides pixel-level local training signals and can
adapt to images with free-form holes. By combining SCAT with standard global
adversarial training, the new adversarial training framework exhibits the
following three advantages simultaneously: (1) the global consistency of the
repaired image, (2) the local fine texture details of the repaired image, and
(3) the flexibility of handling images with free-form holes. Moreover, we
propose the textural and semantic contrastive learning losses to stabilize and
improve our inpainting model's training by exploiting the feature
representation space of the discriminator, in which the inpainting images are
pulled closer to the ground truth images but pushed farther from the corrupted
images. The proposed contrastive losses better guide the repaired images to
move from the corrupted image data points to the real image data points in the
feature representation space, resulting in more realistic completed images. We
conduct extensive experiments on two benchmark datasets, demonstrating our
model's effectiveness and superiority both qualitatively and quantitatively.Comment: Accepted to AAAI2023, Ora
W-MAC: A Workload-Aware MAC Protocol for Heterogeneous Convergecast in Wireless Sensor Networks
The power consumption and latency of existing MAC protocols for wireless sensor networks (WSNs) are high in heterogeneous convergecast, where each sensor node generates different amounts of data in one convergecast operation. To solve this problem, we present W-MAC, a workload-aware MAC protocol for heterogeneous convergecast in WSNs. A subtree-based iterative cascading scheduling mechanism and a workload-aware time slice allocation mechanism are proposed to minimize the power consumption of nodes, while offering a low data latency. In addition, an efficient schedule adjustment mechanism is provided for adapting to data traffic variation and network topology change. Analytical and simulation results show that the proposed protocol provides a significant energy saving and latency reduction in heterogeneous convergecast, and can effectively support data aggregation to further improve the performance
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