Cross-Domain Local Characteristic Enhanced Deepfake Video Detection

As ultra-realistic face forgery techniques emerge, deepfake detection has attracted increasing attention due to security concerns. Many detectors cannot achieve accurate results when detecting unseen manipulations despite excellent performance on known forgeries. In this paper, we are motivated by the observation that the discrepancies between real and fake videos are extremely subtle and localized, and inconsistencies or irregularities can exist in some critical facial regions across various information domains. To this end, we propose a novel pipeline, Cross-Domain Local Forensics (XDLF), for more general deepfake video detection. In the proposed pipeline, a specialized framework is presented to simultaneously exploit local forgery patterns from space, frequency, and time domains, thus learning cross-domain features to detect forgeries. Moreover, the framework leverages four high-level forgery-sensitive local regions of a human face to guide the model to enhance subtle artifacts and localize potential anomalies. Extensive experiments on several benchmark datasets demonstrate the impressive performance of our method, and we achieve superiority over several state-of-the-art methods on cross-dataset generalization. We also examined the factors that contribute to its performance through ablations, which suggests that exploiting cross-domain local characteristics is a noteworthy direction for developing more general deepfake detectors

Selectively fluorinated citronellol analogues support a hydrogen bonding donor interaction with the human OR1A1 olfactory receptor

Authors thank the Chinese Scholarship Council for funding a Studentship (No. 202008060063) at the University of St. Andrews, U.K.C-2 fluorinated and methylated stereoisomers of the fragrance citronellol 1 and its oxalate esters were prepared from (R)-pulegone 11 and explored as agonists of the human olfactory receptor OR1A1 and assayed also against site-specific mutants. There were clear isomer preferences and C-2 difluorination as in 18 led to the most active compound suggesting an important hydrogen bond donor role for citronellol 1. C-2 methylation and the corresponding oxalate ester analogues were less active.Publisher PDFPeer reviewe

Fraudulent User Detection Via Behavior Information Aggregation Network (BIAN) On Large-Scale Financial Social Network

Financial frauds cause billions of losses annually and yet it lacks efficient approaches in detecting frauds considering user profile and their behaviors simultaneously in social network . A social network forms a graph structure whilst Graph neural networks (GNN), a promising research domain in Deep Learning, can seamlessly process non-Euclidean graph data . In financial fraud detection, the modus operandi of criminals can be identified by analyzing user profile and their behaviors such as transaction, loaning etc. as well as their social connectivity. Currently, most GNNs are incapable of selecting important neighbors since the neighbors' edge attributes (i.e., behaviors) are ignored. In this paper, we propose a novel behavior information aggregation network (BIAN) to combine the user behaviors with other user features. Different from its close "relatives" such as Graph Attention Networks (GAT) and Graph Transformer Networks (GTN), it aggregates neighbors based on neighboring edge attribute distribution, namely, user behaviors in financial social network. The experimental results on a real-world large-scale financial social network dataset, DGraph, show that BIAN obtains the 10.2% gain in AUROC comparing with the State-Of-The-Art models.Comment: 6 pages, 1 figur

A Deep Learning Framework for Hydrogen-fueled Turbulent Combustion Simulation

The high cost of high-resolution computational fluid/flame dynamics (CFD) has hindered its application in combustion related design, research and optimization. In this study, we propose a new framework for turbulent combustion simulation based on the deep learning approach. An optimized deep convolutional neural network (CNN) inspired from a U-Net architecture and inception module is designed for constructing the framework of the deep learning solver, named CFDNN. CFDNN is then trained on the simulation results of hydrogen combustion in a cavity with different inlet velocities. After training, CFDNN can not only accurately predict the flow and combustion fields within the range of the training set, but also shows an extrapolation ability for prediction outside the training set. The results from CFDNN solver show excellent consistency with the conventional CFD results in terms of both predicted spatial distributions and temporal dynamics. Meanwhile, two orders of magnitude of acceleration is achieved by using CFDNN solver compared to the conventional CFD solver. The successful development of such a deep learning-based solver opens up new possibilities of low-cost, high-accuracy simulations, fast prototyping, design optimization and real-time control of combustion systems such as gas turbines and scramjets

Emergently Alteration of Procedural Strategy During Transcatheter Aortic Valve Replacement to Prevent Coronary Occlusion: A Case Report

BackgroundCoronary occlusion is an uncommon but fatal complication of transcatheter aortic valve replacement (TAVR) with a poor prognosis.Case PresentationA patient with symptomatic severe bicuspid aortic valve stenosis was admitted to a high-volume center specializing in transfemoral TAVR with self-expanding valves. No anatomical risk factors of coronary occlusion were identified on pre-procedural computed tomography analysis. The patient was scheduled for a transfemoral TAVR with a self-expanding valve. Balloon pre-dilatation prior to prosthesis implantation was routinely used for assessing the supra-annular structure and assessing the risk of coronary occlusion. Immediately after the tubular balloon inflation, fluoroscopy revealed that the right coronary artery was not visible, and the flow in the left coronary artery was reduced. The patient would be at high-risk of coronary occlusion if a long stent self-expanding valve was implanted. Therefore, our heart team decided to suspend the ongoing procedure. A transapical TAVR with a 23 mm J-valve was performed 3 days later. The prosthesis was deployed at a proper position without blocking the coronary ostia and the final fluoroscopy showed normal flow in bilateral coronary arteries with the same filling as preoperatively.DiscussionOur successful case highlights the importance of a comprehensive assessment of coronary risk and a thorough understanding of the TAVR procedure for the heart team. A short-stent prosthesis is feasible for patients at high risk of coronary occlusion. Most importantly TAVR should be called off even if the catheter has been introduced when an extremely high risk of coronary obstruction is identified during the procedure and no solution can be found

Y-DWMS - A digital watermark management system based on smart contracts

With the development of information technology, films, music, and other publications are inclined to be distributed in digitalized form. However, the low cost of data replication and dissemination leads to digital rights problems and brings huge economic losses. Up to now, existing digital rights management (DRM) schemes have been powerless to deter attempts of infringing digital rights and recover losses of copyright holders. This paper presents a YODA-based digital watermark management system (Y-DWMS), adopting non-repudiation of smart contract and blockchain, to implement a DRM mechanism to infinitely amplify the cost of infringement and recover losses copyright holders suffered once the infringement is reported. We adopt game analysis to prove that in Y-DWMS, the decision of non-infringement always dominates rational users, so as to fundamentally eradicate the infringement of digital rights, which current mainstream DRM schemes cannot reach

Spatial Transcriptomics-correlated Electron Microscopy maps transcriptional and ultrastructural responses to brain injury

Understanding the complexity of cellular function within a tissue necessitates the combination of multiple phenotypic readouts. Here, we developed a method that links spatially-resolved gene expression of single cells with their ultrastructural morphology by integrating multiplexed error-robust fluorescence in situ hybridization (MERFISH) and large area volume electron microscopy (EM) on adjacent tissue sections. Using this method, we characterized in situ ultrastructural and transcriptional responses of glial cells and infiltrating T-cells after demyelinating brain injury in male mice. We identified a population of lipid-loaded foamy microglia located in the center of remyelinating lesion, as well as rare interferon-responsive microglia, oligodendrocytes, and astrocytes that co-localized with T-cells. We validated our findings using immunocytochemistry and lipid staining-coupled single-cell RNA sequencing. Finally, by integrating these datasets, we detected correlations between full-transcriptome gene expression and ultrastructural features of microglia. Our results offer an integrative view of the spatial, ultrastructural, and transcriptional reorganization of single cells after demyelinating brain injury. To understand complexity of cellular function, multiple phenotypic readouts are needed. Here, authors devised an approach integrating location, transcriptome, ultrastructure, and lipid content to characterize single-cell states after brain injury

Robust strategies in nuclear-targeted cancer therapy based on functional nanomaterials

Nucleus, as the machinery for genome transcription, play prominent roles to support the fundamental cel-lular functions, the destruction of any of these specific parts would significantly modulate the cell function. Therefore, troumendous drug delivery systems with enhanced nucleus targeting ability have been studied for nucleus-related disease regulation. The purpose of this review is to sort out the fundamental nuclear tar-geting strategy, especially the active mechanism of various nuclear targeting ligands and their extensive applications based on cancer targeting therapy. Various nuclear targeting ligands are first introduced to understand their nuclear entry mechanism. Next, to overcome biological barriers and avoid the serum pro-tein absorption, diverse robust delivery strategies based on different nuclear targeting ligands are dis-cussed. Moreover, other sophisticated carrier systems with enhanced nuclear entry, while without nuclear targeting ligands are also assembled. At the end the challenges and future opportunities in the field of nuclear targeting nanotherapeutics are tentatively proposed, to speed up their clinical translation. (c) 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/)