In Drosophila, repeat-associated small interfering RNAs (rasiRNAs) are produced in the germ line by a Dicer-independent pathway and function through the PIWI subfamily of Argonautes to ensure silencing of retrotransposons. We sequenced small RNAs associated with the PIWI subfamily member AGO3. Although other members of PIWI, Aubergine (Aub) and Piwi, associated with rasiRNAs derived mainly from the antisense strand of retrotransposons, AGO3-associated rasiRNAs arose mainly from the sense strand. Aub- and Piwi-associated rasiRNAs showed a strong preference for uracil at their 5' ends, and AGO3-associated rasiRNAs showed a strong preference for adenine at nucleotide 10. Comparisons between AGO3- and Aub-associated rasiRNAs revealed pairs of rasiRNAs showing complementarities in their first 10 nucleotides. Aub and AGO3 exhibited Slicer activity in vitro. These data support a model in which formation of a 5' terminus within rasiRNA precursors is guided by rasiRNAs originating from transcripts of the other strand in concert with the Slicer activity of PIWI.

In this work, we present a method for unsupervised domain adaptation. Many adversarial learning methods train domain classifier networks to distinguish the features as either a source or target and train a feature generator network to mimic the discriminator. Two problems exist with these methods. First, the domain classifier only tries to distinguish the features as a source or target and thus does not consider task-specific decision boundaries between classes. Therefore, a trained generator can generate ambiguous features near class boundaries. Second, these methods aim to completely match the feature distributions between different domains, which is difficult because of each domain's characteristics.To solve these problems, we introduce a new approach that attempts to align distributions of source and target by utilizing the task-specific decision boundaries. We propose to maximize the discrepancy between two classifiers' outputs to detect target samples that are far from the support of the source. A feature generator learns to generate target features near the support to minimize the discrepancy. Our method outperforms other methods on several datasets of image classification and semantic segmentation. The codes are available at https

We propose an approach for unsupervised adaptation of object detectors from label-rich to label-poor domains which can significantly reduce annotation costs associated with detection. Recently, approaches that align distributions of source and target images using an adversarial loss have been proven effective for adapting object classifiers. However, for object detection, fully matching the entire distributions of source and target images to each other at the global image level may fail, as domains could have distinct scene layouts and different combinations of objects. On the other hand, strong matching of local features such as texture and color makes sense, as it does not change category level semantics. This motivates us to propose a novel method for detector adaptation based on strong local alignment and weak global alignment. Our key contribution is the weak alignment model, which focuses the adversarial alignment loss on images that are globally similar and puts less emphasis on aligning images that are globally dissimilar. Additionally, we design the strong domain alignment model to only look at local receptive fields of the feature map. We empirically verify the effectiveness of our method on four datasets comprising both large and small domain shifts. Our code is available at https://github.com/ VisionLearningGroup/DA_Detection.

In Drosophila, Piwi (P-element-induced wimpy testis), which encodes a protein of the Argonaute family, is essential for germ stem cell self-renewal. Piwi has recently been shown to be a nuclear protein involved in gene silencing of retrotransposons and controlling their mobilization in the male germline. However, little is known about the molecular mechanisms of Piwi-dependent gene silencing. Here we show that endogenous Piwi immunopurified from ovary specifically associates with small RNAs of 25-29 nucleotides in length. Piwi-associated small RNAs were identified by cloning and sequencing as repeat-associated small interfering RNAs (rasiRNAs) derived from repetitive regions, such as retrotransposon and heterochromatic regions, in the Drosophila genome. Northern blot analyses revealed that in vivo Piwi does not associate with microRNAs (miRNAs) and that guide siRNA was not loaded onto Piwi when siRNA duplex was added to ovary lysate. In vitro, recombinant Piwi exhibits target RNA cleavage activity. These data together imply that Piwi functions in nuclear RNA silencing as Slicer by associating specifically with rasiRNAs originating from repetitive targets.[Keywords: Piwi; retrotransposon; rasiRNA; Slicer; Drosophila] Supplemental material is available at http://www.genesdev.org.

Contemporary domain adaptation methods are very effective at aligning feature distributions of source and target domains without any target supervision. However, we show that these techniques perform poorly when even a few labeled examples are available in the target domain. To address this semi-supervised domain adaptation (SSDA) setting, we propose a novel Minimax Entropy (MME) approach that adversarially optimizes an adaptive few-shot model. Our base model consists of a feature encoding network, followed by a classification layer that computes the features' similarity to estimated prototypes (representatives of each class). Adaptation is achieved by alternately maximizing the conditional entropy of unlabeled target data with respect to the classifier and minimizing it with respect to the feature encoder. We empirically demonstrate the superiority of our method over many baselines, including conventional feature alignment and few-shot methods, setting a new state of the art for SSDA. Our code is available at