Minimal random code learning: Getting bits back from compressed model parameters

While deep neural networks are a highly successful model class, their large memory footprint puts considerable strain on energy consumption, communication bandwidth, and storage requirements. Consequently, model size reduction has become an utmost goal in deep learning. A typical approach is to train a set of deterministic weights, while applying certain techniques such as pruning and quantization, in order that the empirical weight distribution becomes amenable to Shannon-style coding schemes. However, as shown in this paper, relaxing weight determinism and using a full variational distribution over weights allows for more efficient coding schemes and consequently higher compression rates. In particular, following the classical bits-back argument, we encode the network weights using a random sample, requiring only a number of bits corresponding to the Kullback-Leibler divergence between the sampled variational distribution and the encoding distribution. By imposing a constraint on the Kullback-Leibler divergence, we are able to explicitly control the compression rate, while optimizing the expected loss on the training set. The employed encoding scheme can be shown to be close to the optimal information-theoretical lower bound, with respect to the employed variational family. Our method sets new state-of-the-art in neural network compression, as it strictly dominates previous approaches in a Pareto sense: On the benchmarks LeNet-5/MNIST and VGG-16/CIFAR-10, our approach yields the best test performance for a fixed memory budget, and vice versa, it achieves the highest compression rates for a fixed test performance

Compressing images by encoding their latent representations with relative entropy coding

Variational Autoencoders (VAEs) have seen widespread use in learned image compression. They are used to learn expressive latent representations on which downstream compression methods can operate with high efficiency. Recently proposed 'bits-back' methods can indirectly encode the latent representation of images with codelength close to the relative entropy between the latent posterior and the prior. However, due to the underlying algorithm, these methods can only be used for lossless compression, and they only achieve their nominal efficiency when compressing multiple images simultaneously; they are inefficient for compressing single images. As an alternative, we propose a novel method, Relative Entropy Coding (REC), that can directly encode the latent representation with codelength close to the relative entropy for single images, supported by our empirical results obtained on the Cifar10, ImageNet32 and Kodak datasets. Moreover, unlike previous bits-back methods, REC is immediately applicable to lossy compression, where it is competitive with the state-of-the-art on the Kodak dataset

Seasonal change of thyroid histomorphological structure and hormone production in yellowfin seabream (Acanthopagrus latus) in the Persian Gulf

Seasonal changes of the thyroid gland structure and hormones secretion was examined in yellowfin seabream (Acanthopagrus latus) in the northwest of Persian Gulf (Musa creek). Thyroid gland composed of follicles scattered around the ventral aorta, near the gills. Follicular cells varied according to secretion of the gland during warm and cold seasons. Thyroid hormones (Triidothyronine [T3] and Thyroxine [T4]) were detected in the fish serum in levels ranged from 4.09-1.30 ng/mL for T3 and from 1.10-0.21 ng/mL for (T4) in the warm and cold seasons, respectively. The results showed that the height of thyroid epithelium and plasma concentration of thyroid hormones (thyroid activity) in A. latus increased significantly during spring and summer. The peak of these factors occurred in midsummer (August). Then, the thyroid activity decreased significantly during autumn and early winter from October to December according to decrease of temperature. T3 and T4 increased significantly from January to April

Learning Colour Representations of Search Queries

Image search engines rely on appropriately designed ranking features that capture various aspects of the content semantics as well as the historic popularity. In this work, we consider the role of colour in this relevance matching process. Our work is motivated by the observation that a significant fraction of user queries have an inherent colour associated with them. While some queries contain explicit colour mentions (such as 'black car' and 'yellow daisies'), other queries have implicit notions of colour (such as 'sky' and 'grass'). Furthermore, grounding queries in colour is not a mapping to a single colour, but a distribution in colour space. For instance, a search for 'trees' tends to have a bimodal distribution around the colours green and brown. We leverage historical clickthrough data to produce a colour representation for search queries and propose a recurrent neural network architecture to encode unseen queries into colour space. We also show how this embedding can be learnt alongside a cross-modal relevance ranker from impression logs where a subset of the result images were clicked. We demonstrate that the use of a query-image colour distance feature leads to an improvement in the ranker performance as measured by users' preferences of clicked versus skipped images.Comment: Accepted as a full paper at SIGIR 202

Flexible test-bed for unusual behavior detection

Visual surveillance and activity analysis is an active research field of computer vision. As a result, there are several different algorithms produced for this purpose. To obtain more robust systems it is desirable to integrate the different algorithms. To help achieve this goal, we propose a flexible, distributed software collaboration framework and present a prototype system for automatic event analysis. Copyright 2007 ACM

Role of Mitofusin 2 in the Renal Stress Response

The role of mitofusin 2 (MFN2), a key regulator of mitochondrial morphology and function in the renal stress response is unknown. To assess its role, the MFN2 floxed gene was conditionally deleted in the kidney of mice (MFN2 cKO) by Pax2 promoter driven Cre expression (Pax2Cre). MFN2 cKO caused severe mitochondrial fragmentation in renal epithelial cells that are critical for normal kidney tubular function. However, despite a small (20%) decrease in nephron number, newborn cKO pups had organ or tubular function that did not differ from littermate Cre-negative pups. MFN2 deficiency in proximal tubule epithelial cells in primary culture induced mitochondrial fragmentation but did not significantly alter ATP turnover, maximal mitochondrial oxidative reserve capacity, or the low level of oxygen consumption during cyanide exposure. MFN2 deficiency also did not increase apoptosis of tubule epithelial cells under non-stress conditions. In contrast, metabolic stress caused by ATP depletion exacerbated mitochondrial outer membrane injury and increased apoptosis by 80% in MFN2 deficient vs. control cells. Despite similar stress-induced Bax 6A7 epitope exposure in MFN2 deficient and control cells, MFN2 deficiency significantly increased mitochondrial Bax accumulation and was associated with greater release of both apoptosis inducing factor and cytochrome c. In conclusion, MFN2 deficiency in the kidney causes mitochondrial fragmentation but does not affect kidney or tubular function during development or under non-stress conditions. However, MFN2 deficiency exacerbates renal epithelial cell injury by promoting Bax-mediated mitochondrial outer membrane injury and apoptosis