220 research outputs found
Traceable and Authenticable Image Tagging for Fake News Detection
To prevent fake news images from misleading the public, it is desirable not
only to verify the authenticity of news images but also to trace the source of
fake news, so as to provide a complete forensic chain for reliable fake news
detection. To simultaneously achieve the goals of authenticity verification and
source tracing, we propose a traceable and authenticable image tagging approach
that is based on a design of Decoupled Invertible Neural Network (DINN). The
designed DINN can simultaneously embed the dual-tags, \textit{i.e.},
authenticable tag and traceable tag, into each news image before publishing,
and then separately extract them for authenticity verification and source
tracing. Moreover, to improve the accuracy of dual-tags extraction, we design a
parallel Feature Aware Projection Model (FAPM) to help the DINN preserve
essential tag information. In addition, we define a Distance Metric-Guided
Module (DMGM) that learns asymmetric one-class representations to enable the
dual-tags to achieve different robustness performances under malicious
manipulations. Extensive experiments, on diverse datasets and unseen
manipulations, demonstrate that the proposed tagging approach achieves
excellent performance in the aspects of both authenticity verification and
source tracing for reliable fake news detection and outperforms the prior
works
Load Frequency Control in Isolated Micro-Grids with Electrical Vehicles Based on Multivariable Generalized Predictive Theory
In power systems, although the inertia energy in power sources can partly cover power unbalances caused by load disturbance or renewable energy fluctuation, it is still hard to maintain the frequency deviation within acceptable ranges. However, with the vehicle-to-grid (V2G) technique, electric vehicles (EVs) can act as mobile energy storage units, which could be a solution for load frequency control (LFC) in an isolated grid. In this paper, a LFC model of an isolated micro-grid with EVs, distributed generations and their constraints is developed. In addition, a controller based on multivariable generalized predictive control (MGPC) theory is proposed for LFC in the isolated micro-grid, where EVs and diesel generator (DG) are coordinated to achieve a satisfied performance on load frequency. A benchmark isolated micro-grid with EVs, DG, and wind farm is modeled in the Matlab/Simulink environment to demonstrate the effectiveness of the proposed method. Simulation results demonstrate that with MGPC, the energy stored in EVs can be managed intelligently according to LFC requirement. This improves the system frequency stability with complex operation situations including the random renewable energy resource and the continuous load disturbances
Auto-Focus Contrastive Learning for Image Manipulation Detection
Generally, current image manipulation detection models are simply built on
manipulation traces. However, we argue that those models achieve sub-optimal
detection performance as it tends to: 1) distinguish the manipulation traces
from a lot of noisy information within the entire image, and 2) ignore the
trace relations among the pixels of each manipulated region and its
surroundings. To overcome these limitations, we propose an Auto-Focus
Contrastive Learning (AF-CL) network for image manipulation detection. It
contains two main ideas, i.e., multi-scale view generation (MSVG) and trace
relation modeling (TRM). Specifically, MSVG aims to generate a pair of views,
each of which contains the manipulated region and its surroundings at a
different scale, while TRM plays a role in modeling the trace relations among
the pixels of each manipulated region and its surroundings for learning the
discriminative representation. After learning the AF-CL network by minimizing
the distance between the representations of corresponding views, the learned
network is able to automatically focus on the manipulated region and its
surroundings and sufficiently explore their trace relations for accurate
manipulation detection. Extensive experiments demonstrate that, compared to the
state-of-the-arts, AF-CL provides significant performance improvements, i.e.,
up to 2.5%, 7.5%, and 0.8% F1 score, on CAISA, NIST, and Coverage datasets,
respectively
Network analyses of upper and lower airway transcriptomes identify shared mechanisms among children with recurrent wheezing and school-age asthma
BackgroundPredicting which preschool children with recurrent wheezing (RW) will develop school-age asthma (SA) is difficult, highlighting the critical need to clarify the pathogenesis of RW and the mechanistic relationship between RW and SA. Despite shared environmental exposures and genetic determinants, RW and SA are usually studied in isolation. Based on network analysis of nasal and tracheal transcriptomes, we aimed to identify convergent transcriptomic mechanisms in RW and SA.MethodsRNA-sequencing data from nasal and tracheal brushing samples were acquired from the Gene Expression Omnibus. Combined with single-cell transcriptome data, cell deconvolution was used to infer the composition of 18 cellular components within the airway. Consensus weighted gene co-expression network analysis was performed to identify consensus modules closely related to both RW and SA. Shared pathways underlying consensus modules between RW and SA were explored by enrichment analysis. Hub genes between RW and SA were identified using machine learning strategies and validated using external datasets and quantitative reverse transcription-polymerase chain reaction (qRT-PCR). Finally, the potential value of hub genes in defining RW subsets was determined using nasal and tracheal transcriptome data.ResultsCo-expression network analysis revealed similarities in the transcriptional networks of RW and SA in the upper and lower airways. Cell deconvolution analysis revealed an increase in mast cell fraction but decrease in club cell fraction in both RW and SA airways compared to controls. Consensus network analysis identified two consensus modules highly associated with both RW and SA. Enrichment analysis of the two consensus modules indicated that fatty acid metabolism-related pathways were shared key signals between RW and SA. Furthermore, machine learning strategies identified five hub genes, i.e., CST1, CST2, CST4, POSTN, and NRTK2, with the up-regulated hub genes in RW and SA validated using three independent external datasets and qRT-PCR. The gene signatures of the five hub genes could potentially be used to determine type 2 (T2)-high and T2-low subsets in preschoolers with RW.ConclusionsThese findings improve our understanding of the molecular pathogenesis of RW and provide a rationale for future exploration of the mechanistic relationship between RW and SA
Intracerebral Hemorrhage Induces Cardiac Dysfunction in Mice Without Primary Cardiac Disease
Background: Intracerebral hemorrhage (ICH) is a life threatening stroke subtype and a worldwide health problem. In this study, we investigate brain-heart interaction after ICH in mice and test whether ICH induces cardiac dysfunction in the absence of primary cardiac disease. We also investigate underlying mechanisms such as oxidative stress and inflammatory responses in mediating cardiac dysfunction post-ICH in mice.Methods: Male, adult (3–4 m) C57BL/6J mice were subjected to sham surgery or ICH using an autologous blood injection model (n = 16/group). Cardiac function was evaluated at 7 and 28 days after ICH using echocardiography (n = 8/group per time point). Western blot and immunostaining analysis were employed to assess oxidative stress and inflammatory responses in the heart.Results: Mice subjected to ICH exhibited significantly decreased cardiac contractile function measured by left ventricular ejection fraction (LVEF) and left ventricular fractional shortening (LVFS) at 7 and 28 days after ICH compared to sham-control mice (p < 0.05). ICH induced cardiac dysfunction was significantly worse at 28 days than at 7 days after ICH (p < 0.05). ICH in mice significantly increased cardiomyocyte apoptosis, inflammatory factor expression and inflammatory cell infiltration in heart tissue, and induced cardiac oxidative stress at 7 days post-ICH compared to sham-control mice. Compared to sham-control mice, ICH-mice also exhibited significantly increased (p < 0.05) cardiomyocyte hypertrophy and cardiac fibrosis at 28 days after ICH.Conclusions: ICH induces significant and progressive cardiac dysfunction in mice. ICH increases cardiac oxidative stress and inflammatory factor expression in heart tissue which may play key roles in ICH-induced cardiac dysfunction
Geom-Erasing: Geometry-Driven Removal of Implicit Concept in Diffusion Models
Fine-tuning diffusion models through personalized datasets is an acknowledged
method for improving generation quality across downstream tasks, which,
however, often inadvertently generates unintended concepts such as watermarks
and QR codes, attributed to the limitations in image sources and collecting
methods within specific downstream tasks. Existing solutions suffer from
eliminating these unintentionally learned implicit concepts, primarily due to
the dependency on the model's ability to recognize concepts that it actually
cannot discern. In this work, we introduce Geom-Erasing, a novel approach that
successfully removes the implicit concepts with either an additional accessible
classifier or detector model to encode geometric information of these concepts
into text domain. Moreover, we propose Implicit Concept, a novel image-text
dataset imbued with three implicit concepts (i.e., watermarks, QR codes, and
text) for training and evaluation. Experimental results demonstrate that
Geom-Erasing not only identifies but also proficiently eradicates implicit
concepts, revealing a significant improvement over the existing methods. The
integration of geometric information marks a substantial progression in the
precise removal of implicit concepts in diffusion models
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