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

Scaling network-based spectrum analyzer with constant communication cost

e propose a spectrum analyzer that leverages many networked commodity sensor nodes, each of which sam- ples its portion in a wideband spectrum. The sensors operate in parallel and transmit their measurements over a wireless network without performing any significant computations such as FFT. The measurements are forwarded to the backend of the system where spectrum analysis takes place. In particular, we propose a solution that compresses the raw measurements in a simple random linear projection and combines the compressed measurements from multiple sensors in-network. As a result, we achieve a substantial reduction in the network bandwidth requirement to operate the proposed system. We discover that the overall communication cost can be independent of the number of sensors and is affected only by sparsity of discretized spectrum under analysis. This principle founds the basis for a claim that our network-based spectrum analyzer can scale up the number of sensor nodes to process a very wide spectrum block potentially having a GHz bandwidth. We devise a novel recovery algorithm that systematically undoes compressive encoding and in-network combining done to the raw measurements, incorporating the least squares and l1-minimization decoding used in compressive sensing, and demonstrate that the algorithm can effectively restore an accurate estimate of the original data suitable for fine- rained spectrum analysis. We present mathematical analysis and empirical evaluation of the system with software-defined radios.Engineering and Applied Science

DISTROY: Detecting Integrated Circuit Trojans with Compressive Measurements

Detecting Trojans in an integrated circuit (IC) is an important but hard problem. A Trojan is malicious hardware it can be extremely small in size and dormant until triggered by some unknown circuit state. To allow wake-up, a Trojan could draw a minimal amount of power, for example, to run a clock or a state machine, or to monitor a triggering event. We introduce DISTROY (Discover Trojan), a new approach that can effciently and reliably detect extremely small background power leakage that a Trojan creates and as a result, we can detect the Trojan. We formulate our method based on compressive sensing, a recent advance in signal processing, which can recover a signal using the number of measurements approximately proportional to its sparsity rather than size. We argue that circuit states in which the Trojan background power consumption stands out are rare, and thus sparse, so that we can apply compressive sensing. We describe how this is done in DISTROY so as to afford suffcient measurement statistics to detect the presence of Trojans. Finally, we present our initial simulation results that validate DISTROY and discuss the impact of our work in the field of hardware security.Engineering and Applied Science

Compressive Sensing with Optimal Sparsifying Basis and Applications in Spectrum Sensing

We describe a method of integrating Karhunen-Loève Transform (KLT) into compressive sensing, which can as a result improve the compression ratio without affecting the accuracy of decoding. We present two complementary results: 1) by using KLT to find an optimal basis for decoding we can drastically reduce the number of measurements for compressive sensing used in applications such as radio spectrum analysis; 2) by using compressive sensing we can estimate and recover the KLT basis from compressive measurements of an input signal. In particular, we propose CS-KLT, an online estimation algorithm to cope with nonstationarity of wireless channels in reality. We validate our results with empirical data collected from a wideband UHF spectrum and eld experiments to detect multiple radio transmitters, using software-defined radios.Engineering and Applied Science

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

DefogGAN: Predicting Hidden Information in the StarCraft Fog of War with Generative Adversarial Nets

We propose DefogGAN, a generative approach to the problem of inferring state information hidden in the fog of war for real-time strategy (RTS) games. Given a partially observed state, DefogGAN generates defogged images of a game as predictive information. Such information can lead to create a strategic agent for the game. DefogGAN is a conditional GAN variant featuring pyramidal reconstruction loss to optimize on multiple feature resolution scales.We have validated DefogGAN empirically using a large dataset of professional StarCraft replays. Our results indicate that DefogGAN can predict the enemy buildings and combat units as accurately as professional players do and achieves a superior performance among state-of-the-art defoggers

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