Pressures for Asymptotically Sub-additive Potentials Under a Mistake Function

This paper defines the pressure for asymptotically subadditive potentials under a mistake function, including the measuretheoretical and the topological versions. Using the advanced techniques of ergodic theory and topological dynamics, we reveals a variational principle for the new defined topological pressure without any additional conditions on the potentials and the compact metric space.Comment: 13page

Image Forgery Localization Based on Multi-Scale Convolutional Neural Networks

In this paper, we propose to utilize Convolutional Neural Networks (CNNs) and the segmentation-based multi-scale analysis to locate tampered areas in digital images. First, to deal with color input sliding windows of different scales, a unified CNN architecture is designed. Then, we elaborately design the training procedures of CNNs on sampled training patches. With a set of robust multi-scale tampering detectors based on CNNs, complementary tampering possibility maps can be generated. Last but not least, a segmentation-based method is proposed to fuse the maps and generate the final decision map. By exploiting the benefits of both the small-scale and large-scale analyses, the segmentation-based multi-scale analysis can lead to a performance leap in forgery localization of CNNs. Numerous experiments are conducted to demonstrate the effectiveness and efficiency of our method.Comment: 7 pages, 6 figure

Weak Specification Properties and Large Deviations for Non-additive Potentials

We obtain large deviation bounds for the measure of deviation sets associated to asymptotically additive and sub-additive potentials under some weak specification properties. In particular a large deviation principle is obtained in the case of uniformly hyperbolic dynamical systems. Some examples in connection with the convergence of Lyapunov exponents are given.Comment: 25 pages; accepted by Ergodic Theory and Dynamical System

UVL: A Unified Framework for Video Tampering Localization

With the development of deep learning technology, various forgery methods emerge endlessly. Meanwhile, methods to detect these fake videos have also achieved excellent performance on some datasets. However, these methods suffer from poor generalization to unknown videos and are inefficient for new forgery methods. To address this challenging problem, we propose UVL, a novel unified video tampering localization framework for synthesizing forgeries. Specifically, UVL extracts common features of synthetic forgeries: boundary artifacts of synthetic edges, unnatural distribution of generated pixels, and noncorrelation between the forgery region and the original. These features are widely present in different types of synthetic forgeries and help improve generalization for detecting unknown videos. Extensive experiments on three types of synthetic forgery: video inpainting, video splicing and DeepFake show that the proposed UVL achieves state-of-the-art performance on various benchmarks and outperforms existing methods by a large margin on cross-dataset

On the topological pressure of random bundle transformations in sub-additive case

In this paper, we define the topological pressure for sub-additive potentials via separated sets in random dynamical systems and we give a proof of the relativized variational principle for the topological pressure.Comment: 16page

N6-Methyladenosine-Mediated Overexpression of Long Noncoding Rna ADAMTS9-AS2 Triggers Neuroblastoma Differentiation via Regulating lin28B/Let-7/MYCN Signaling

Neuroblastomas have shed light on the differentiation disorder that is associated with spontaneous regression or differentiation in the same tumor at the same time. Long noncoding RNAs (lncRNAs) actively participate in a broad spectrum of biological processes. However, the detailed molecular mechanisms underlying lncRNA regulation of differentiation in neuroblastomas remain largely unknown. Here, we sequenced clinical samples of ganglioneuroma, ganglioneuroblastoma, and neuroblastoma. We compared transcription profiles of neuroblastoma cells, ganglion cells, and intermediate state cells; verified the profiles in a retinoic acid-induced cell differentiation model and clinical samples; and screened out the lncRNA ADAMTS9 antisense RNA 2 (ADAMTS9-AS2), which contributed to neuroblastoma differentiation. ADAMTS9-AS2 upregulation in neuroblastoma cell lines inhibited proliferation and metastatic potential. Additional mechanistic studies illustrated that the interactions between ADAMTS9-AS2 and LIN28B inhibited the association between LIN28B and primary let-7 (pri-let-7) miRNA, then released pri-let-7 into cytoplasm to form mature let-7, resulting in the inhibition of oncogene MYCN activity that subsequently affected cancer stemness and differentiation. Furthermore, we showed that the observed differential expression of ADAMTS9-AS2 in neuroblastoma cells was due to N6-methyladenosine methylation. Finally, ADAMTS9-AS2 upregulation inhibited proliferation and cancer stem-like capabilities in vivo. Taken together, these results show that ADAMTS9-AS2 loss leads to malignant neuroblastoma by increasing metastasis and causing dysfunctional differentiation