Certain Adenylated Non-Coding RNAs, Including 5′ Leader Sequences of Primary MicroRNA Transcripts, Accumulate in Mouse Cells following Depletion of the RNA Helicase MTR4

RNA surveillance plays an important role in posttranscriptional regulation. Seminal work in this field has largely focused on yeast as a model system, whereas exploration of RNA surveillance in mammals is only recently begun. The increased transcriptional complexity of mammalian systems provides a wider array of targets for RNA surveillance, and, while many questions remain unanswered, emerging data suggest the nuclear RNA surveillance machinery exhibits increased complexity as well. We have used a small interfering RNA in mouse N2A cells to target the homolog of a yeast protein that functions in RNA surveillance (Mtr4p). We used high-throughput sequencing of polyadenylated RNAs (PA-seq) to quantify the effects of the mMtr4 knockdown (KD) on RNA surveillance. We demonstrate that overall abundance of polyadenylated protein coding mRNAs is not affected, but several targets of RNA surveillance predicted from work in yeast accumulate as adenylated RNAs in the mMtr4KD. microRNAs are an added layer of transcriptional complexity not found in yeast. After Drosha cleavage separates the pre-miRNA from the microRNA\u27s primary transcript, the byproducts of that transcript are generally thought to be degraded. We have identified the 5′ leading segments of pri-miRNAs as novel targets of mMtr4 dependent RNA surveillance

High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs

We present a new method for synthesizing high-resolution photo-realistic images from semantic label maps using conditional generative adversarial networks (conditional GANs). Conditional GANs have enabled a variety of applications, but the results are often limited to low-resolution and still far from realistic. In this work, we generate 2048x1024 visually appealing results with a novel adversarial loss, as well as new multi-scale generator and discriminator architectures. Furthermore, we extend our framework to interactive visual manipulation with two additional features. First, we incorporate object instance segmentation information, which enables object manipulations such as removing/adding objects and changing the object category. Second, we propose a method to generate diverse results given the same input, allowing users to edit the object appearance interactively. Human opinion studies demonstrate that our method significantly outperforms existing methods, advancing both the quality and the resolution of deep image synthesis and editing.Comment: v2: CVPR camera ready, adding more results for edge-to-photo example

Robust Learning of Deep Time Series Anomaly Detection Models with Contaminated Training Data

Time series anomaly detection (TSAD) is an important data mining task with numerous applications in the IoT era. In recent years, a large number of deep neural network-based methods have been proposed, demonstrating significantly better performance than conventional methods on addressing challenging TSAD problems in a variety of areas. Nevertheless, these deep TSAD methods typically rely on a clean training dataset that is not polluted by anomalies to learn the "normal profile" of the underlying dynamics. This requirement is nontrivial since a clean dataset can hardly be provided in practice. Moreover, without the awareness of their robustness, blindly applying deep TSAD methods with potentially contaminated training data can possibly incur significant performance degradation in the detection phase. In this work, to tackle this important challenge, we firstly investigate the robustness of commonly used deep TSAD methods with contaminated training data which provides a guideline for applying these methods when the provided training data are not guaranteed to be anomaly-free. Furthermore, we propose a model-agnostic method which can effectively improve the robustness of learning mainstream deep TSAD models with potentially contaminated data. Experiment results show that our method can consistently prevent or mitigate performance degradation of mainstream deep TSAD models on widely used benchmark datasets

Incomplete Augmented Lagrangian Preconditioner for Steady Incompressible Navier-Stokes Equations

An incomplete augmented Lagrangian preconditioner, for the steady incompressible Navier-Stokes equations discretized by stable finite elements, is proposed. The eigenvalues of the preconditioned matrix are analyzed. Numerical experiments show that the incomplete augmented Lagrangian-based preconditioner proposed is very robust and performs quite well by the Picard linearization or the Newton linearization over a wide range of values of the viscosity on both uniform and stretched grids