Learning a Mixture of Deep Networks for Single Image Super-Resolution

Single image super-resolution (SR) is an ill-posed problem which aims to recover high-resolution (HR) images from their low-resolution (LR) observations. The crux of this problem lies in learning the complex mapping between low-resolution patches and the corresponding high-resolution patches. Prior arts have used either a mixture of simple regression models or a single non-linear neural network for this propose. This paper proposes the method of learning a mixture of SR inference modules in a unified framework to tackle this problem. Specifically, a number of SR inference modules specialized in different image local patterns are first independently applied on the LR image to obtain various HR estimates, and the resultant HR estimates are adaptively aggregated to form the final HR image. By selecting neural networks as the SR inference module, the whole procedure can be incorporated into a unified network and be optimized jointly. Extensive experiments are conducted to investigate the relation between restoration performance and different network architectures. Compared with other current image SR approaches, our proposed method achieves state-of-the-arts restoration results on a wide range of images consistently while allowing more flexible design choices. The source codes are available in http://www.ifp.illinois.edu/~dingliu2/accv2016

Single Image Super-Resolution Using Multi-Scale Convolutional Neural Network

Methods based on convolutional neural network (CNN) have demonstrated tremendous improvements on single image super-resolution. However, the previous methods mainly restore images from one single area in the low resolution (LR) input, which limits the flexibility of models to infer various scales of details for high resolution (HR) output. Moreover, most of them train a specific model for each up-scale factor. In this paper, we propose a multi-scale super resolution (MSSR) network. Our network consists of multi-scale paths to make the HR inference, which can learn to synthesize features from different scales. This property helps reconstruct various kinds of regions in HR images. In addition, only one single model is needed for multiple up-scale factors, which is more efficient without loss of restoration quality. Experiments on four public datasets demonstrate that the proposed method achieved state-of-the-art performance with fast speed

Economic and natural effects of nitrate pollution of agricultural origin, in particular the aquatic enviroment

The whole area of Hungary is the gathering ground of our principal rivers (Duna, Tisza) and some bigger lakes, like Balaton, Fertő lake and Velencei lake. The water isn’t only staff of life; it is one of the most sensitive biotope of world. We suppose to protect our aquatic environment from environmental pollution as such nitrate pollution or eutrophication. Trough agricultural production the nutrient rate increases in water. The weeds begin to pullulate, they are taking up more oxygen from the water, they are necrosis, the depth of warp increases faster so the eutrophication drowns on, and the nitrate rate of rivers increases

A degenerate pseudo-parabolic equation with memory

We prove the existence and uniqueness for a degenerate pseudo-parabolic problem with memory. This kind of problem arises in the study of the homogenization of some differential systems involving the Laplace-Beltrami operator and describes the effective behaviour of the electrical conduction in some composite materials

Applying artificial intelligence to assess the impact of orthognathic treatment on facial attractiveness and estimated age

This observational study aimed to use artificial intelligence to describe the impact of orthognathic treatment on facial attractiveness and age appearance. Pre- and post-treatment photographs (n=2164) of 146 consecutive orthognathic patients were collected for this longitudinal retrospective single-centre study. Every image was annotated with patient-related data (age; sex; malocclusion; performed surgery). For every image, facial attractiveness (score: 0-100) and apparent age were established with dedicated convolutional neural networks trained on >0.5million images for age estimation and with >17million ratings for attractiveness. Results for pre- and post-treatment photographs were averaged for every patient separately, and apparent age compared to real age (appearance). Changes in appearance and facial attractiveness were statistically examined. Analyses were performed on the entire sample and subgroups (sex; malocclusion; performed surgery). According to the algorithms, most patients' appearance improved with treatment (66.4%), resulting in younger appearance of nearly 1year [mean change: -0.93years (95% confidence interval (CI): -1.50; -0.36); p=0.002), especially after profile-altering surgery. Orthognathic treatment had similarly a beneficial effect on attractiveness in 74.7% [mean difference: 1.22 (95% CI: 0.81; 1.63); p<0.001], especially after lower jaw surgery. This investigation illustrates that artificial intelligence might be considered to score facial attractiveness and apparent age in orthognathic patients

Deep Markov Random Field for Image Modeling

Markov Random Fields (MRFs), a formulation widely used in generative image modeling, have long been plagued by the lack of expressive power. This issue is primarily due to the fact that conventional MRFs formulations tend to use simplistic factors to capture local patterns. In this paper, we move beyond such limitations, and propose a novel MRF model that uses fully-connected neurons to express the complex interactions among pixels. Through theoretical analysis, we reveal an inherent connection between this model and recurrent neural networks, and thereon derive an approximated feed-forward network that couples multiple RNNs along opposite directions. This formulation combines the expressive power of deep neural networks and the cyclic dependency structure of MRF in a unified model, bringing the modeling capability to a new level. The feed-forward approximation also allows it to be efficiently learned from data. Experimental results on a variety of low-level vision tasks show notable improvement over state-of-the-arts.Comment: Accepted at ECCV 201

Concept of Suicide: Neurophysiological/Genetic Theories and Possible Oxytocin Relevance

The suicidal behavior is regarded as the act by which a person seeks to take his life, being aware of the consequences of his action. In our review, besides describing the main introductory aspects for the concept of suicide, we focus our attention on the main neurophysiological and genetical mechanisms relevant for this extremely difficult to manage and controversial behavior. Moreover, considering the latest interests in the current literature on the relevance of central oxytocin to various superior cognitive behaviors, we will also make a short description on how important effects of oxytocin could be in the context of suicidal behavior.Суїцидальна поведінка – це дії, в результаті яких особа намагається позбавити себе життя, усвідомлюючи наслідки таких дій. У даному огляді, окрім опису основних загальних аспектів концепції суїциду, ми концентрували увагу на основних нейрофізіологічних та генетичних аспектах, котрі мають відношення до цього вкрай важко контрольованого та повного протиріч типу поведінки. Окрім того, враховуючи велику цікавість, яку викликає в сучасній літературі задіяність центральної окситоцинової системи в контроль когнітивної поведінки вищих типів, ми надали короткий опис того, наскільки ефекти окситоцину можуть бути важливими в контексті суїцидальної поведінки

ZipNet-GAN: Inferring Fine-grained Mobile Traffic Patterns via a Generative Adversarial Neural Network

Large-scale mobile traffic analytics is becoming essential to digital infrastructure provisioning, public transportation, events planning, and other domains. Monitoring city-wide mobile traffic is however a complex and costly process that relies on dedicated probes. Some of these probes have limited precision or coverage, others gather tens of gigabytes of logs daily, which independently offer limited insights. Extracting fine-grained patterns involves expensive spatial aggregation of measurements, storage, and post-processing. In this paper, we propose a mobile traffic super-resolution technique that overcomes these problems by inferring narrowly localised traffic consumption from coarse measurements. We draw inspiration from image processing and design a deep-learning architecture tailored to mobile networking, which combines Zipper Network (ZipNet) and Generative Adversarial neural Network (GAN) models. This enables to uniquely capture spatio-temporal relations between traffic volume snapshots routinely monitored over broad coverage areas (`low-resolution') and the corresponding consumption at 0.05 km $^2$ level (`high-resolution') usually obtained after intensive computation. Experiments we conduct with a real-world data set demonstrate that the proposed ZipNet(-GAN) infers traffic consumption with remarkable accuracy and up to 100$\times$ higher granularity as compared to standard probing, while outperforming existing data interpolation techniques. To our knowledge, this is the first time super-resolution concepts are applied to large-scale mobile traffic analysis and our solution is the first to infer fine-grained urban traffic patterns from coarse aggregates.Comment: To appear ACM CoNEXT 201