148 research outputs found
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 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 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
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