The compensation approach for walks with small steps in the quarter plane

This paper is the first application of the compensation approach to counting problems. We discuss how this method can be applied to a general class of walks in the quarter plane $Z_{+}^{2}$ with a step set that is a subset of $\{(-1,1),(-1,0),(-1,-1),(0,-1),(1,-1)\}$ in the interior of $Z_{+}^{2}$. We derive an explicit expression for the counting generating function, which turns out to be meromorphic and nonholonomic, can be easily inverted, and can be used to obtain asymptotic expressions for the counting coefficients.Comment: 22 pages, 5 figure

Analysis of the Karmarkar-Karp Differencing Algorithm

The Karmarkar-Karp differencing algorithm is the best known polynomial time heuristic for the number partitioning problem, fundamental in both theoretical computer science and statistical physics. We analyze the performance of the differencing algorithm on random instances by mapping it to a nonlinear rate equation. Our analysis reveals strong finite size effects that explain why the precise asymptotics of the differencing solution is hard to establish by simulations. The asymptotic series emerging from the rate equation satisfies all known bounds on the Karmarkar-Karp algorithm and projects a scaling $n^{-c\ln n}$, where $c=1/(2\ln2)=0.7213...$. Our calculations reveal subtle relations between the algorithm and Fibonacci-like sequences, and we establish an explicit identity to that effect.Comment: 9 pages, 8 figures; minor change

Dislocation of the ozurdex implant into the anterior chamber (case of reposition)

The purpose of the study is to report a case of migration of the dexamethasone Ozurdex implant into the anterior chamber in a patient with pseudophakia and avitria and the method of reposition.Цель исследования – cообщить о случае миграции имплантата дексаметазона Озурдекс в переднюю камеру у пациента с артифакией и авитрией и способе репозиции

ATHENA: A knowledge-based hybrid backpropagation-grammatical evolution neural network algorithm for discovering epistasis among quantitative trait Loci

<p>Abstract</p> <p>Background</p> <p>Growing interest and burgeoning technology for discovering genetic mechanisms that influence disease processes have ushered in a flood of genetic association studies over the last decade, yet little heritability in highly studied complex traits has been explained by genetic variation. Non-additive gene-gene interactions, which are not often explored, are thought to be one source of this "missing" heritability.</p> <p>Methods</p> <p>Stochastic methods employing evolutionary algorithms have demonstrated promise in being able to detect and model gene-gene and gene-environment interactions that influence human traits. Here we demonstrate modifications to a neural network algorithm in ATHENA (the Analysis Tool for Heritable and Environmental Network Associations) resulting in clear performance improvements for discovering gene-gene interactions that influence human traits. We employed an alternative tree-based crossover, backpropagation for locally fitting neural network weights, and incorporation of domain knowledge obtainable from publicly accessible biological databases for initializing the search for gene-gene interactions. We tested these modifications <it>in silico </it>using simulated datasets.</p> <p>Results</p> <p>We show that the alternative tree-based crossover modification resulted in a modest increase in the sensitivity of the ATHENA algorithm for discovering gene-gene interactions. The performance increase was highly statistically significant when backpropagation was used to locally fit NN weights. We also demonstrate that using domain knowledge to initialize the search for gene-gene interactions results in a large performance increase, especially when the search space is larger than the search coverage.</p> <p>Conclusions</p> <p>We show that a hybrid optimization procedure, alternative crossover strategies, and incorporation of domain knowledge from publicly available biological databases can result in marked increases in sensitivity and performance of the ATHENA algorithm for detecting and modelling gene-gene interactions that influence a complex human trait.</p

Deep learning based vehicle make-model classification

Bu çalışma, 04-07 Ekim 2018 tarihlerinde Rhodes[Yunanistan]’da düzenlenen 27. International Conference on Artificial Neural Networks (ICANN) Kongresi‘nde bildiri olarak sunulmuştur.This paper studies the problem of vehicle make & model classification. Some of the main challenges are reaching high classification accuracy and reducing the annotation time of the images. To address these problems, we have created a fine-grained database using online vehicle marketplaces of Turkey. A pipeline is proposed to combine an SSD (Single Shot Multibox Detector) model with a CNN (Convolutional Neural Network) model to train on the database. In the pipeline, we first detect the vehicles by following an algorithm which reduces the time for annotation. Then, we feed them into the CNN model. It is reached approximately 4% better classification accuracy result than using a conventional CNN model. Next, we propose to use the detected vehicles as ground truth bounding box (GTBB) of the images and feed them into an SSD model in another pipeline. At this stage, it is reached reasonable classification accuracy result without using perfectly shaped GTBB. Lastly, an application is implemented in a use case by using our proposed pipelines which detects the unauthorized vehicles by comparing their license plate numbers and make & models. It is assumed that license plates are readable

Polymer dynamics In an Interface-confined space: NMR study of poly(hexyl ethacrylate)-block-poly(acrylic acid) and poly(dodercyl methacrylate)-block-poly(acrylic acid) micelles in D2O

The mobility of distant methyl side groups in poly(hexyl methacrylate)-block-poly(sodium acrylate) (PHMA−PAANa) and poly(dodecyl methacrylate))-block-poly(sodium acrylate) (PLMA−PAANa) micelles, dispersed in D2O and characterized by SANS, was studied using 1H single and double quantum high-resolution and MAS NMR. Transverse and rotating-frame relaxation was studied at temperatures 320−345 K in the original micellar solutions and in the systems with their viscosity gradually increased by additions of high-molecular-weight poly(ethylene oxide) and with the micellar cores gradually swollen by chlorobenzene. The relaxation data were quantitatively evaluated using a pragmatic model based on Cohen-Addad's theory. The results obtained strongly indicate that the side groups are relatively immobilized to an even higher degree than in poly(2-ethylhexyl acrylate)-block-poly(acrylic acid) micelles at the same conditions. The most immobilized groups are at or near the interface. Possible underlying effects, including penetration resistance of the surrounding water molecules, fixation of the polymer backbone at the interface, and motional correlation between the distant side group and the backbone are discussed