Building bulk geometry from the tensor Radon transform

Using the tensor Radon transform and related numerical methods, we study how bulk geometries can be explicitly reconstructed from boundary entanglement entropies in the specific case of AdS₃/CFT₂. We find that, given the boundary entanglement entropies of a 2d CFT, this framework provides a quantitative measure that detects whether the bulk dual is geometric in the perturbative (near AdS) limit. In the case where a well-defined bulk geometry exists, we explicitly reconstruct the unique bulk metric tensor once a gauge choice is made. We then examine the emergent bulk geometries for static and dynamical scenarios in holography and in many-body systems. Apart from the physics results, our work demonstrates that numerical methods are feasible and effective in the study of bulk reconstruction in AdS/CFT

Enhanced surface transfer doping of diamond by V2O5 with improved thermal stability

Surface transfer doping of hydrogen-terminated diamond has been achieved utilising V2O5 as a surface electron accepting material. Contact between the oxide and diamondsurface promotes the transfer of electrons from the diamond into the V2O5 as revealed by the synchrotron-based high resolution photoemission spectroscopy. Electrical characterization by Hall measurement performed before and after V2O5 deposition shows an increase in hole carrier concentration in the diamond from 3.0 × 1012 to 1.8 × 1013 cm−2 at room temperature. High temperature Hall measurements performed up to 300 °C in atmosphere reveal greatly enhanced thermal stability of the hole channel produced using V2O5 in comparison with an air-induced surface conduction channel. Transfer doping of hydrogen-terminated diamond using high electron affinity oxides such as V2O5 is a promising approach for achieving thermally stable, high performance diamond based devices in comparison with air-induced surface transfer dopin

Single-shot compressed ultrafast photography: a review

Compressed ultrafast photography (CUP) is a burgeoning single-shot computational imaging technique that provides an imaging speed as high as 10 trillion frames per second and a sequence depth of up to a few hundred frames. This technique synergizes compressed sensing and the streak camera technique to capture nonrepeatable ultrafast transient events with a single shot. With recent unprecedented technical developments and extensions of this methodology, it has been widely used in ultrafast optical imaging and metrology, ultrafast electron diffraction and microscopy, and information security protection. We review the basic principles of CUP, its recent advances in data acquisition and image reconstruction, its fusions with other modalities, and its unique applications in multiple research fields

Adversarial Camouflage for Node Injection Attack on Graphs

Node injection attacks against Graph Neural Networks (GNNs) have received emerging attention as a practical attack scenario, where the attacker injects malicious nodes instead of modifying node features or edges to degrade the performance of GNNs. Despite the initial success of node injection attacks, we find that the injected nodes by existing methods are easy to be distinguished from the original normal nodes by defense methods and limiting their attack performance in practice. To solve the above issues, we devote to camouflage node injection attack, i.e., camouflaging injected malicious nodes (structure/attributes) as the normal ones that appear legitimate/imperceptible to defense methods. The non-Euclidean nature of graph data and the lack of human prior brings great challenges to the formalization, implementation, and evaluation of camouflage on graphs. In this paper, we first propose and formulate the camouflage of injected nodes from both the fidelity and diversity of the ego networks centered around injected nodes. Then, we design an adversarial CAmouflage framework for Node injection Attack, namely CANA, to improve the camouflage while ensuring the attack performance. Several novel indicators for graph camouflage are further designed for a comprehensive evaluation. Experimental results demonstrate that when equipping existing node injection attack methods with our proposed CANA framework, the attack performance against defense methods as well as node camouflage is significantly improved

Conditional GANs with Auxiliary Discriminative Classifier

Conditional generative models aim to learn the underlying joint distribution of data and labels to achieve conditional data generation. Among them, the auxiliary classifier generative adversarial network (AC-GAN) has been widely used, but suffers from the problem of low intra-class diversity of the generated samples. The fundamental reason pointed out in this paper is that the classifier of AC-GAN is generator-agnostic, which therefore cannot provide informative guidance for the generator to approach the joint distribution, resulting in a minimization of the conditional entropy that decreases the intra-class diversity. Motivated by this understanding, we propose a novel conditional GAN with an auxiliary discriminative classifier (ADC-GAN) to resolve the above problem. Specifically, the proposed auxiliary discriminative classifier becomes generator-aware by recognizing the class-labels of the real data and the generated data discriminatively. Our theoretical analysis reveals that the generator can faithfully learn the joint distribution even without the original discriminator, making the proposed ADC-GAN robust to the value of the coefficient hyperparameter and the selection of the GAN loss, and stable during training. Extensive experimental results on synthetic and real-world datasets demonstrate the superiority of ADC-GAN in conditional generative modeling compared to state-of-the-art classifier-based and projection-based conditional GANs.Comment: ICML 202

Nanofluid impact on fluid interaction and migration characteristics for enhanced oil recovery in Baikouquan tight glutenite

Nanofluids have broad prospects in enhancing the oil recovery of reservoirs with low porosity, low permeability, high capillary pressure and low oil recovery. However, the modification effects of nanofluids on tight glutenite reservoirs remain unknown. In this paper, nanofluids with different proportions of silica nanoparticles and sodium dodecyl sulfate were prepared and characterized by zeta potential and particle size distribution. Then, the effects of nanofluids on interfacial tension and reservoir wettability were examined. Next, a computational fluid dynamics method was adopted to further investigate the effects of nanofluids and injection pressure on enhancing oil recovery of the Baikouquan Formation at the pore scale. The experimental results showed that all prepared nanofluids are stable systems with uniform dispersion. The interfacial tension between the nanofluids and oil was reduced by up to 8.01% compared with water, and the reservoir wettability was changed from intermediate-wet to strong hydrophilicity. The simulation results revealed that the water and nanofluid flooding processes could be divided into two stages: the initial channel establishment stage and the channel expansion stage. In the initial stage, the nanofluids hardly showed an enhanced oil recovery effect due to the faster and sharper migration fronts. In the channel expansion stage, the nanofluids clearly showed an enhanced oil recovery effect, as the nanofluids could displace the oil in the relative dead pores during water flooding. After 10 pore volume injection of displacement fluid at an injection pressure of 1 MPa, the oil recovery using NF5 was highest at 76.58%. In addition, a higher injection pressure led to the extraction of relative dead oil at a lower injection pressure near the inlet with a smaller sweep area near the outlet; the effect on recovery has both advantages and disadvantages.Document Type: Original articleCited as: Cao, X., Li, Q., Myers, M., Xu, L., Chen, Q., Tan, Y. Nanofluid impact on fluid interaction and migration characteristics for enhanced oil recovery in Baikouquan tight glutenite. Advances in Geo-Energy Research, 2023, 9(2): 94-105. https://doi.org/10.46690/ager.2023.08.0