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Fig. 1: Our algorithm learns to detect and localize image manipulations (splices), despite being trained only on unmanipulated images. The two input images above might look plausible, but our model correctly determined that they have been manipulated because they lack self-consistency: the visual information within the predicted splice region was found to be inconsistent with the rest of the image. IMAGE CREDITS: automatically created splice from Hays and Efros [1] (top), manual splice from Reddit user /u/Name-Albert Einstein (bottom).Abstract. Advances in photo editing and manipulation tools have made it significantly easier to create fake imagery. Learning to detect such manipulations, however, remains a challenging problem due to the lack of sufficient amounts of manipulated training data. In this paper, we propose a learning algorithm for detecting visual image manipulations that is trained only using a large dataset of real photographs. The algorithm uses the automatically recorded photo EXIF metadata as supervisory signal for training a model to determine whether an image is self-consistent -that is, whether its content could have been produced by a single imaging pipeline. We apply this self-consistency model to the task of detecting and localizing image splices. The proposed method obtains state-ofthe-art performance on several image forensics benchmarks, despite never seeing any manipulated images at training. That said, it is merely a step in the long quest for a truly general purpose visual forensics tool. Fig. 2: Anatomy of a splice: One of the most common ways of creative fake images is splicing together content from two different real source images. The insight explored in this paper is that patches from a spliced image are typically produced by different imaging pipelines, as indicated by the EXIF meta-data of the two source images. The problem is that in practice, we never have access to these source images at test time. 1

Autoregressive (AR) model and differential model for human prediction behavior of time series resulting from a discrete linear dynamic system were compared in terms of Akaike's information criterion (AIC). Comparison of the minimum AIC values revealed that the AR model was the better fitting model than the differential model. This result suggests that the AR information is a more practical means for the subject to predict future states than using the prior knowledge of the system dynamics, and that the subjects tend to make predictions based on only two or three preceding states of the time series. Next, the frequency characteristics of human prediction were compared with tracking behavior which involves motor constraints as well as human prediction. While amplitude ratio decreases and phase lag increases with frequency in tracking behavior, only the phase lag varies with frequency in prediction behavior. This is a characteristic feature of human prediction behavior.

T he scientific, academic, medical and data science communities have come together in the face of the COVID-19 pandemic crisis to rapidly assess novel paradigms in artificial intelligence (AI) that are rapid and secure, and potentially incentivize data sharing and model training and testing without the usual privacy and data ownership hurdles of conventional collaborations 1,2 . Healthcare providers, researchers and industry have pivoted their focus to address unmet and critical clinical needs created by the crisis, with remarkable results [3][4][5][6][7][8][9] . Clinical trial recruitment has been expedited and facilitated by national regulatory bodies and an international cooperative spirit 10-12 . The data analytics and AI disciplines have always fostered open

BackgroundAlthough diabetic kidney disease demonstrates both familial clustering and single nucleotide polymorphism heritability, the specific genetic factors influencing risk remain largely unknown.MethodsTo identify genetic variants predisposing to diabetic kidney disease, we performed genome-wide association study (GWAS) analyses. Through collaboration with the Diabetes Nephropathy Collaborative Research Initiative, we assembled a large collection of type 1 diabetes cohorts with harmonized diabetic kidney disease phenotypes. We used a spectrum of ten diabetic kidney disease definitions based on albuminuria and renal function.ResultsOur GWAS meta-analysis included association results for up to 19,406 individuals of European descent with type 1 diabetes. We identified 16 genome-wide significant risk loci. The variant with the strongest association (rs55703767) is a common missense mutation in the collagen type IV alpha 3 chain (COL4A3) gene, which encodes a major structural component of the glomerular basement membrane (GBM). Mutations in COL4A3 are implicated in heritable nephropathies, including the progressive inherited nephropathy Alport syndrome. The rs55703767 minor allele (Asp326Tyr) is protective against several definitions of diabetic kidney disease, including albuminuria and ESKD, and demonstrated a significant association with GBM width; protective allele carriers had thinner GBM before any signs of kidney disease, and its effect was dependent on glycemia. Three other loci are in or near genes with known or suggestive involvement in this condition (BMP7) or renal biology (COLEC11 and DDR1).ConclusionsThe 16 diabetic kidney disease–associated loci may provide novel insights into the pathogenesis of this condition and help identify potential biologic targets for prevention and treatment.