Adversarial Learning of Semantic Relevance in Text to Image Synthesis

We describe a new approach that improves the training of generative adversarial nets (GANs) for synthesizing diverse images from a text input. Our approach is based on the conditional version of GANs and expands on previous work leveraging an auxiliary task in the discriminator. Our generated images are not limited to certain classes and do not suffer from mode collapse while semantically matching the text input. A key to our training methods is how to form positive and negative training examples with respect to the class label of a given image. Instead of selecting random training examples, we perform negative sampling based on the semantic distance from a positive example in the class. We evaluate our approach using the Oxford-102 flower dataset, adopting the inception score and multi-scale structural similarity index (MS-SSIM) metrics to assess discriminability and diversity of the generated images. The empirical results indicate greater diversity in the generated images, especially when we gradually select more negative training examples closer to a positive example in the semantic space

Adversarial nets with perceptual losses for text-to-image synthesis

Recent approaches in generative adversarial networks (GANs) can automatically synthesize realistic images from descriptive text. Despite the overall fair quality, the generated images often expose visible flaws that lack structural definition for an object of interest. In this paper, we aim to extend state of the art for GAN-based text-to-image synthesis by improving perceptual quality of generated images. Differentiated from previous work, our synthetic image generator optimizes on perceptual loss functions that measure pixel, feature activation, and texture differences against a natural image. We present visually more compelling synthetic images of birds and flowers generated from text descriptions in comparison to some of the most prominent existing work

Multimodal Sparse Coding for Event Detection

Unsupervised feature learning methods have proven effective for classification tasks based on a single modality. We present multimodal sparse coding for learning feature representations shared across multiple modalities. The shared representations are applied to multimedia event detection (MED) and evaluated in comparison to unimodal counterparts, as well as other feature learning methods such as GMM supervectors and sparse RBM. We report the cross-validated classification accuracy and mean average precision of the MED system trained on features learned from our unimodal and multimodal settings for a subset of the TRECVID MED 2014 dataset.Comment: Multimodal Machine Learning Workshop at NIPS 201

Optimizing Media Access Strategy for Competing Cognitive Radio Networks

This paper describes an adaptation of cognitive radio technology for tactical wireless networking. We introduce Competing Cognitive Radio Network (CCRN) featuring both communicator and jamming cognitive radio nodes that strategize in taking actions on an open spectrum under the presence of adversarial threats. We present the problem in the Multi-armed Bandit (MAB) framework and develop the optimal media access strategy consisting of mixed communicator and jammer actions in a Bayesian setting for Thompson sampling based on extreme value theory. Empirical results are promising that the proposed strategy seems to outperform Lai & Robbins and UCB, some of the most important MAB algorithms known to date.Engineering and Applied Science

Crystal structure of a Fanconi anemia-associated nuclease homolog bound to 5′ flap DNA: basis of interstrand cross-link repair by FAN1

Fanconi anemia (FA) is an autosomal recessive genetic disorder caused by defects in any of 15 FA genes responsible for processing DNA interstrand cross-links (ICLs). The ultimate outcome of the FA pathway is resolution of cross-links, which requires structure-selective nucleases. FA-associated nuclease 1 (FAN1) is believed to be recruited to lesions by a monoubiquitinated FANCI–FANCD2 (ID) complex and participates in ICL repair. Here, we determined the crystal structure of Pseudomonas aeruginosa FAN1 (PaFAN1) lacking the UBZ (ubiquitin-binding zinc) domain in complex with 5′ flap DNA. All four domains of the right-hand-shaped PaFAN1 are involved in DNA recognition, with each domain playing a specific role in bending DNA at the nick. The six-helix bundle that binds the junction connects to the catalytic viral replication and repair (VRR) nuclease (VRR nuc) domain, enabling FAN1 to incise the scissile phosphate a few bases distant from the junction. The six-helix bundle also inhibits the cleavage of intact Holliday junctions. PaFAN1 shares several conserved features with other flap structure-selective nucleases despite structural differences. A clamping motion of the domains around the wedge helix, which acts as a pivot, facilitates nucleolytic cleavage. The PaFAN1 structure provides insights into how archaeal Holliday junction resolvases evolved to incise 5′ flap substrates and how FAN1 integrates with the FA complex to participate in ICL repair

Endogenous bioelectric currents promote differentiation of the mammalian lens

Acknowledgements We are grateful to Kevin S. Mackenzie in our imaging core facility. This work was supported by the University of Aberdeen (at which the majority of the experimental work was conducted). The work was supported by Action Medical Research (GN2299) and Fight for Sight (RG13315-10).Peer reviewedPublisher PD