Joint Deep Image Restoration and Unsupervised Quality Assessment

Deep learning techniques have revolutionized the fields of image restoration and image quality assessment in recent years. While image restoration methods typically utilize synthetically distorted training data for training, deep quality assessment models often require expensive labeled subjective data. However, recent studies have shown that activations of deep neural networks trained for visual modeling tasks can also be used for perceptual quality assessment of images. Following this intuition, we propose a novel attention-based convolutional neural network capable of simultaneously performing both image restoration and quality assessment. We achieve this by training a JPEG deblocking network augmented with "quality attention" maps and demonstrating state-of-the-art deblocking accuracy, achieving a high correlation of predicted quality with human opinion scores.Comment: 4 Pages, 2 figures, 3 table

Topic-based influence computation in social networks under resource constraints

As social networks are constantly changing and evolving, methods to analyze dynamic social networks are becoming more important in understanding social trends. However, due to the restrictions imposed by the social network service providers, the resources available to fetch the entire contents of a social network are typically very limited. As a result, analysis of dynamic social network data requires maintaining an approximate copy of the social network for each time period, locally. In this paper, we study the problem of dynamic network and text fetching with limited probing capacities, for identifying and maintaining influential users as the social network evolves. We propose an algorithm to probe the relationships (required for global influence computation) as well as posts (required for topic-based influence computation) of a limited number of users during each probing period, based on the influence trends and activities of the users. We infer the current network based on the newly probed user data and the last known version of the network maintained locally. Additionally, we propose to use link prediction methods to further increase the accuracy of our network inference. We employ PageRank as the metric for influence computation. We illustrate how the proposed solution maintains accurate PageRank scores for computing global influence, and topic-sensitive weighted PageRank scores for topic-based influence. The latter relies on a topic-based network constructed via weights determined by semantic analysis of posts and their sharing statistics. We evaluate the effectiveness of our algorithms by comparing them with the true influence scores of the full and up-to-date version of the network, using data from the micro-blogging service Twitter. Results show that our techniques significantly outperform baseline methods (80% higher accuracy for network fetching and 77% for text fetching) and are superior to state-of-the-art techniques from the literature (21% higher accuracy)

Perceptual video quality assessment: the journey continues!

Perceptual Video Quality Assessment (VQA) is one of the most fundamental and challenging problems in the field of Video Engineering. Along with video compression, it has become one of two dominant theoretical and algorithmic technologies in television streaming and social media. Over the last 2 decades, the volume of video traffic over the internet has grown exponentially, powered by rapid advancements in cloud services, faster video compression technologies, and increased access to high-speed, low-latency wireless internet connectivity. This has given rise to issues related to delivering extraordinary volumes of picture and video data to an increasingly sophisticated and demanding global audience. Consequently, developing algorithms to measure the quality of pictures and videos as perceived by humans has become increasingly critical since these algorithms can be used to perceptually optimize trade-offs between quality and bandwidth consumption. VQA models have evolved from algorithms developed for generic 2D videos to specialized algorithms explicitly designed for on-demand video streaming, user-generated content (UGC), virtual and augmented reality (VR and AR), cloud gaming, high dynamic range (HDR), and high frame rate (HFR) scenarios. Along the way, we also describe the advancement in algorithm design, beginning with traditional hand-crafted feature-based methods and finishing with current deep-learning models powering accurate VQA algorithms. We also discuss the evolution of Subjective Video Quality databases containing videos and human-annotated quality scores, which are the necessary tools to create, test, compare, and benchmark VQA algorithms. To finish, we discuss emerging trends in VQA algorithm design and general perspectives on the evolution of Video Quality Assessment in the foreseeable future

S3-TM: scalable streaming short text matching

Micro-blogging services have become major venues for information creation, as well as channels of information dissemination. Accordingly, monitoring them for relevant information is a critical capability. This is typically achieved by registering content-based subscriptions with the micro-blogging service. Such subscriptions are long-running queries that are evaluated against the stream of posts. Given the popularity and scale of micro-blogging services like Twitter and Weibo, building a scalable infrastructure to evaluate these subscriptions is a challenge. To address this challenge, we present the S3-TM system for streaming short text matching. S3-TM is organized as a stream processing application, in the form of a data parallel flow graph designed to be run on a data center environment. It takes advantage of the structure of the publications (posts) and subscriptions to perform the matching in a scalable manner, without broadcasting publications or subscriptions to all of the matcher instances. The basic design of S$$^3$$3-TM uses a scoped multicast for publications and scoped anycast for subscriptions. To further improve throughput, we introduce publication routing algorithms that aim at minimizing the scope of the multicasts. First set of algorithms we develop are based on partitioning the word co-occurrence frequency graph, with the aim of routing posts that include commonly co-occurring words to a small set of matchers. While effective, these algorithms fell short in balancing the load. To address this, we develop the SALB algorithm, which provides better load balance by modeling the load more accurately using the word-to-post bipartite graph. We also develop a subscription placement algorithm, called LASP, to group together similar subscriptions, in order to minimize the subscription matching cost. Furthermore, to achieve good scalability for increasing number of nodes, we introduce techniques to handle workload skew. Finally, we introduce load shedding techniques for handling unexpected load spikes with small impact on the accuracy. Our experimental results show that S3-TM is scalable. Furthermore, the SALB algorithm provides more than 2.5× throughput compared to the baseline multicast and outperforms the graph partitioning-based approaches. © 2015, Springer-Verlag Berlin Heidelberg

Development and ın-vıtro evaluatıon of self emulsıfyıng system for biopharmaceutıcal classıfıcation system class II drug substance

Siklosporin organ nakillerinin reddini önlemek için kullanılan immunosüpresan aktiviteye sahip bir peptittir. BCS Sınıf II kategorisinde yer alıp suda çözünmeyen ve düşük biyoyararlanım gösteren bir moleküldür. Suda çözünürlüğü ve biyoyararlanımı artırmak için kendiliğinden emülsifiye olabilen sistem geliştirilmiş ve geliştirilen sistem katı dozaj formuna dönüştürülüp in-vitro çalışmaları yapılmıştır. Kendiliğinden emülsifiye olabilen sistem, etkin madde siklosporin, yüzey etkin madde makrogolgliserol hidroksistearat, çözücü madde gliserol monolinoleat, yardımcı yüzey etkin madde propilen glikol ve PEG 300 kullanılarak elde edilmiştir. Hazırlanan sistemlerde partikül büyüklüğü, zeta potansiyel, çözünme hızı analizleri gibi karakterizasyon çalışmaları yapılmıştır. Partikül büyüklüğü ve zeta potansiyel sonuçları referans ürün Sandimmun Neroral® ile benzer bulunmuştur. Kendiliğinden emülsifiye olabilen sistemler, Neusilin® ve Fujicalin® üzerine adsorbe edilerek katı dozaj formları elde edilmiştir. Katı dozaj formları için karakterizasyon çalışmaları yapılmıştır. Elde edilen katı dozaj formlarının emülsiyon oluşturma özelliklerini koruduğu ve emülsiyon oluşturma sırasında Neusilin® ve Fujicalin®’ nin etkisinin olmadığı görülmüştür. Katı dozaj formları için 25 ± 2°C ve %60 ± 5ve 40 ± 2°C ve %75 ± 5’de 3 ay stabilite takibi yapılmıştır. Stabilite periyodunda sıcaklığın çözünme hızına etkisi olduğu bulunmuştur. Neusilin® ile hazırlanan katı dozaj formundan etkin maddenin daha hızlı serbestleştiği tespit edilmiştir.Cyclosporine is a peptide, with immunesupresant activity, used to prevent rejection of organ transplants. It is a water-insoluble and low bioavailable molecule that belongs to the BCS Class II category. In order to increase water solubility and bioavailability, a system that can be emulsified by itself has been developed, and the developed system has been converted into a solid dosage form and in-vitro studies have been carried out. For the self-emulsifying system, the active substance was obtained using cyclosporin, surfactant macrogolglycerol hydroxystearate, solvent glycerol monokinoleate, auxiliary surfactant propylene glycol and PEG300. Characterization studies such as particle size, zeta potential, dissolution rate, were performed for the systems. Particle size and zeta potential results were similar to that of reference product Sandimmun Neoral®. The self emulsifying system was adsorbed on Neusilin® and Fujicalin® to obtain solid dosage forms. Characterization studies were performed for the powder mixture ready to obtained solid dosage forms. It has been found that the resulting solid dosage forms retain their emulsifying properties and Neusilin® and Fujicalin® have no effect during emulsion formation. For solid dosage forms, stability was followed for 3 months at 25 ± 2 ° C and 60 ± 5% and 40 ± 2 ° C and 75 ± 5%. It was found that the temperature had an effect on the dissolution rate during the stability period. It was oberved that, from the solid dosage form prepared with Neusilin®, the active substance dissolves faster versus that of Fujicalin®