3,058 research outputs found
Optical Excitation of a Nanoparticle Cu/p-NiO Photocathode Improves Reaction Selectivity for COâ‚‚ Reduction in Aqueous Electrolytes
We report the light-induced modification of catalytic selectivity for photoelectrochemical COâ‚‚ reduction in aqueous media using copper (Cu) nanoparticles dispersed onto p-type nickel oxide (p-NiO) photocathodes. Optical excitation of Cu nanoparticles generates hot electrons available for driving COâ‚‚ reduction on the Cu surface, while charge separation is accomplished by hot-hole injection from the Cu nanoparticles into the underlying p-NiO support. Photoelectrochemical studies demonstrate that optical excitation of plasmonic Cu/p-NiO photocathodes imparts increased selectivity for COâ‚‚ reduction over hydrogen evolution in aqueous electrolytes. Specifically, we observed that plasmon-driven COâ‚‚ reduction increased the production of carbon monoxide and formate, while simultaneously reducing the evolution of hydrogen. Our results demonstrate an optical route toward steering the selectivity of artificial photosynthetic systems with plasmon-driven photocathodes for photoelectrochemical COâ‚‚ reduction in aqueous media
Reduced-order modeling of large-scale network systems
Large-scale network systems describe a wide class of complex dynamical
systems composed of many interacting subsystems. A large number of subsystems
and their high-dimensional dynamics often result in highly complex topology and
dynamics, which pose challenges to network management and operation. This
chapter provides an overview of reduced-order modeling techniques that are
developed recently for simplifying complex dynamical networks. In the first
part, clustering-based approaches are reviewed, which aim to reduce the network
scale, i.e., find a simplified network with a fewer number of nodes. The second
part presents structure-preserving methods based on generalized balanced
truncation, which can reduce the dynamics of each subsystem.Comment: Chapter 11 in the book Model Order Reduction: Volume 3 Application
Regularizing Face Verification Nets For Pain Intensity Regression
Limited labeled data are available for the research of estimating facial
expression intensities. For instance, the ability to train deep networks for
automated pain assessment is limited by small datasets with labels of
patient-reported pain intensities. Fortunately, fine-tuning from a
data-extensive pre-trained domain, such as face verification, can alleviate
this problem. In this paper, we propose a network that fine-tunes a
state-of-the-art face verification network using a regularized regression loss
and additional data with expression labels. In this way, the expression
intensity regression task can benefit from the rich feature representations
trained on a huge amount of data for face verification. The proposed
regularized deep regressor is applied to estimate the pain expression intensity
and verified on the widely-used UNBC-McMaster Shoulder-Pain dataset, achieving
the state-of-the-art performance. A weighted evaluation metric is also proposed
to address the imbalance issue of different pain intensities.Comment: 5 pages, 3 figure; Camera-ready version to appear at IEEE ICIP 201
Deep Convolutional Pooling Transformer for Deepfake Detection
Recently, Deepfake has drawn considerable public attention due to security
and privacy concerns in social media digital forensics. As the wildly spreading
Deepfake videos on the Internet become more realistic, traditional detection
techniques have failed in distinguishing between real and fake. Most existing
deep learning methods mainly focus on local features and relations within the
face image using convolutional neural networks as a backbone. However, local
features and relations are insufficient for model training to learn enough
general information for Deepfake detection. Therefore, the existing Deepfake
detection methods have reached a bottleneck to further improve the detection
performance. To address this issue, we propose a deep convolutional Transformer
to incorporate the decisive image features both locally and globally.
Specifically, we apply convolutional pooling and re-attention to enrich the
extracted features and enhance efficacy. Moreover, we employ the barely
discussed image keyframes in model training for performance improvement and
visualize the feature quantity gap between the key and normal image frames
caused by video compression. We finally illustrate the transferability with
extensive experiments on several Deepfake benchmark datasets. The proposed
solution consistently outperforms several state-of-the-art baselines on both
within- and cross-dataset experiments.Comment: Accepted to be published in ACM TOM
Metasomatized lithospheric mantle for Mesozoic giant gold deposits in the North China craton
The origin of giant lode gold deposits of Mesozoic age in the North China craton (NCC) is enigmatic because high-grade metamorphic ancient crust would be highly depleted in gold. Instead, lithospheric mantle beneath the crust is the likely source of the gold, which may have been anomalously enriched by metasomatic processes. However, the role of gold enrichment and metasomatism in the lithospheric mantle remains unclear. Here, we present comprehensive data on gold and platinum group element contents of mantle xenoliths (n = 28) and basalts (n = 47) representing the temporal evolution of the eastern NCC. The results indicate that extensive mantle metasomatism and hydration introduced some gold (<1–2 ppb) but did not lead to a gold-enriched mantle. However, volatile-rich basalts formed mainly from the metasomatized lithospheric mantle display noticeably elevated gold contents as compared to those from the asthenosphere. Combined with the significant inheritance of mantle-derived volatiles in auriferous fluids of ore bodies, the new data reveal that the mechanism for the formation of the lode gold deposits was related to the volatile-rich components that accumulated during metasomatism and facilitated the release of gold during extensional craton destruction and mantle melting. Gold-bearing, hydrous magmas ascended rapidly along translithospheric fault zones and evolved auriferous fluids to form the giant deposits in the crust
Towards Generalizable Deepfake Detection by Primary Region Regularization
The existing deepfake detection methods have reached a bottleneck in
generalizing to unseen forgeries and manipulation approaches. Based on the
observation that the deepfake detectors exhibit a preference for overfitting
the specific primary regions in input, this paper enhances the generalization
capability from a novel regularization perspective. This can be simply achieved
by augmenting the images through primary region removal, thereby preventing the
detector from over-relying on data bias. Our method consists of two stages,
namely the static localization for primary region maps, as well as the dynamic
exploitation of primary region masks. The proposed method can be seamlessly
integrated into different backbones without affecting their inference
efficiency. We conduct extensive experiments over three widely used deepfake
datasets - DFDC, DF-1.0, and Celeb-DF with five backbones. Our method
demonstrates an average performance improvement of 6% across different
backbones and performs competitively with several state-of-the-art baselines.Comment: 12 pages. Code and Dataset: https://github.com/xaCheng1996/PRL
Robust Identity Perceptual Watermark Against Deepfake Face Swapping
Notwithstanding offering convenience and entertainment to society, Deepfake
face swapping has caused critical privacy issues with the rapid development of
deep generative models. Due to imperceptible artifacts in high-quality
synthetic images, passive detection models against face swapping in recent
years usually suffer performance damping regarding the generalizability issue.
Therefore, several studies have been attempted to proactively protect the
original images against malicious manipulations by inserting invisible signals
in advance. However, the existing proactive defense approaches demonstrate
unsatisfactory results with respect to visual quality, detection accuracy, and
source tracing ability. In this study, we propose the first robust identity
perceptual watermarking framework that concurrently performs detection and
source tracing against Deepfake face swapping proactively. We assign identity
semantics regarding the image contents to the watermarks and devise an
unpredictable and unreversible chaotic encryption system to ensure watermark
confidentiality. The watermarks are encoded and recovered by jointly training
an encoder-decoder framework along with adversarial image manipulations.
Extensive experiments demonstrate state-of-the-art performance against Deepfake
face swapping under both cross-dataset and cross-manipulation settings.Comment: Submitted for revie
- …