Tempered Adversarial Networks

Generative adversarial networks (GANs) have been shown to produce realistic samples from high-dimensional distributions, but training them is considered hard. A possible explanation for training instabilities is the inherent imbalance between the networks: While the discriminator is trained directly on both real and fake samples, the generator only has control over the fake samples it produces since the real data distribution is fixed by the choice of a given dataset. We propose a simple modification that gives the generator control over the real samples which leads to a tempered learning process for both generator and discriminator. The real data distribution passes through a lens before being revealed to the discriminator, balancing the generator and discriminator by gradually revealing more detailed features necessary to produce high-quality results. The proposed module automatically adjusts the learning process to the current strength of the networks, yet is generic and easy to add to any GAN variant. In a number of experiments, we show that this can improve quality, stability and/or convergence speed across a range of different GAN architectures (DCGAN, LSGAN, WGAN-GP).Comment: accepted to ICML 201

“On-The-Fly” Fabrication of Highly-Ordered Interconnected Cylindrical and Spherical Porous Microparticles via Dual Polymerization Zone Microfluidics

A microfluidic platform with dual photopolymerization zones has been developed for production of novel uniform interconnected porous particles with shapes imposed either by the geometry of the external capillary or by the thermodynamic minimisation of interfacial area. Double w/o/w drops with well-defined internal droplet size and number were produced and then exposed to online photopolymerization to create the porous particles. Cylindrical interconnected porous particles were produced in a segmented flow where the drops took the shape of the capillary. The microfluidic set up included an extension capillary where the drops relaxed and conformed to their thermodynamically favoured morphology. Window opening of the particles occurred “on-the-fly” during UV polymerization without using any offline auxiliary methods. A distinction was made between critically and highly packed arrangements in double drops. The window opening occurred consistently for highly packed spherical drops, but only for critically packed drops containing more than 6 internal cores at internal phase ratio as low as 0.35. The size and number of cores, shape and structure of double drops could be precisely tuned by the flowrate and by packing structure of the inner droplets

Evaluation of the anxiolytic effect of Nepeta persica Boiss. in mice

The aim of the present study was to evaluate the anxiolytic effects of hydroalcoholic extract (HE) of Nepeta persica Boiss. (Lamiaceae) on the elevated plus-maze (EPM) model of anxiety. The extract of arial parts of the plant was administered intraperitoneally to male NMRI mice, at various doses, 30 min before behavioural evaluation. The HE extract of N. persica at the dose of 50 mg kg−1 significantly increased the percentage of time spent and percentage of arm entries in the open arms of the EPM. This dose of plant extract affected neither animal's locomotor activity nor ketamine-induced sleeping time. The 50 mg kg−1 dose of the plant extract seemed to be the optimal dose in producing the anxiolytic effects, lower or higher doses of the plant produce either sedative or stimulant effects. At 100 mg kg−1, the plant extract increased the locomotor activity. These results suggested that the extract of N. persica at dose of 50 mg kg−1 possess anxiolytic effect with less sedative and hypnotic effects than that of diazepam and causes a non-specific stimulation at 100 mg kg−1

RePAST: Relative Pose Attention Scene Representation Transformer

The Scene Representation Transformer (SRT) is a recent method to render novel views at interactive rates. Since SRT uses camera poses with respect to an arbitrarily chosen reference camera, it is not invariant to the order of the input views. As a result, SRT is not directly applicable to large-scale scenes where the reference frame would need to be changed regularly. In this work, we propose Relative Pose Attention SRT (RePAST): Instead of fixing a reference frame at the input, we inject pairwise relative camera pose information directly into the attention mechanism of the Transformers. This leads to a model that is by definition invariant to the choice of any global reference frame, while still retaining the full capabilities of the original method. Empirical results show that adding this invariance to the model does not lead to a loss in quality. We believe that this is a step towards applying fully latent transformer-based rendering methods to large-scale scenes

Frame-Recurrent Video Super-Resolution

Recent advances in video super-resolution have shown that convolutional neural networks combined with motion compensation are able to merge information from multiple low-resolution (LR) frames to generate high-quality images. Current state-of-the-art methods process a batch of LR frames to generate a single high-resolution (HR) frame and run this scheme in a sliding window fashion over the entire video, effectively treating the problem as a large number of separate multi-frame super-resolution tasks. This approach has two main weaknesses: 1) Each input frame is processed and warped multiple times, increasing the computational cost, and 2) each output frame is estimated independently conditioned on the input frames, limiting the system's ability to produce temporally consistent results. In this work, we propose an end-to-end trainable frame-recurrent video super-resolution framework that uses the previously inferred HR estimate to super-resolve the subsequent frame. This naturally encourages temporally consistent results and reduces the computational cost by warping only one image in each step. Furthermore, due to its recurrent nature, the proposed method has the ability to assimilate a large number of previous frames without increased computational demands. Extensive evaluations and comparisons with previous methods validate the strengths of our approach and demonstrate that the proposed framework is able to significantly outperform the current state of the art.Comment: Accepted at CVPR 201

Binary effect of fly ash and palm oil fuel ash on heat of hydration aerated concrete

The binary effect of pulverized fuel ash (PFA) and palm oil fuel ash (POFA) on heat of hydration of aerated concrete was studied. Three aerated concrete mixes were prepared, namely, concrete containing 100% ordinary Portland cement (control sample or Type I), binary concrete made from 50% POFA (Type II), and ternary concrete containing 30% POFA and 20% PFA (Type III). It is found that the temperature increases due to heat of hydration through all the concrete specimens especially in the control sample. However, the total temperature rises caused by the heat of hydration through both of the new binary and ternary concrete were significantly lower than the control sample.The obtained results reveal that the replacement of Portland cement with binary and ternary materials is beneficial, particularly for mass concrete where thermal cracking due to extreme heat rise is of great concern