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Publication Metadata only 3D shape correspondence by isometry-driven greedy optimization(IEEE Computer Soc, 2010) N/A; Department of Computer Engineering; Sahillioğlu, Yusuf; Yemez, Yücel; PhD Student; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of Engineering; 215195; 107907We present an automatic method that establishes 3D correspondence between isometric shapes. Our goal is to find an optimal correspondence between two given (nearly) isometric shapes, that minimizes the amount of deviation from isometry. We cast the problem as a complete surface correspondence problem. Our method first divides the given shapes to be matched into surface patches of equal area and then seeks for a mapping between the patch centers which we refer to as base vertices. Hence the correspondence is established in a fast and robust manner at a relatively coarse level as imposed by the patch radius. We optimize the isometry cost in two steps. in the first step, the base vertices are transformed into spectral domain based on geodesic affinity, where the isometry errors are minimized in polynomial time by complete bipartite graph matching. the resulting correspondence serves as a good initialization for the second step of optimization in which we explicitly minimize the isometry cost via an iterative greedy algorithm in the original 3D Euclidean space. We demonstrate the performance of our method on various isometric (or nearly isometric) pairs of shapes for some of which the ground-truth correspondence is available.Publication Metadata only 3D video tools(Springer Science and Business Media Deutschland GmbH, 2019) Dumic, Emil; Boussetta, Khaled; da Silva Cruz, Luis A.; Dagiuklas, Tasos; Liotta, Antonio; Politis, Ilias; Qiao, Yuansong; Torres Vega, Maria; Ye, Yuhang; Department of Electrical and Electronics Engineering; Tekalp, Ahmet Murat; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering; 26207This chapter presents an overview of different tools used in research and engineering of 3D video delivery systems. These include software tools for 3D video compression and streaming, 3D video players, and their interfaces. Other types of tools widely used in research studies and development of new networking solutions, such as network simulators, emulators, testbeds, and network analysis tools are also covered. In addition, several 3D video evaluation tools, which have been specifically designed for testing and evaluation of 3D video sequences subject to network impairments, are further described. The chapter also presents several examples of recent works that have been carried out based on one or more simulation, emulation, test, and/or evaluation tools in research studies or innovative solutions for relevant problems affecting 3D multimedia delivery.Publication Metadata only A classification of concurrency bugs in java benchmarks by developer intent(Association for Computing Machinery (ACM), 2006) Department of Computer Engineering; Department of Computer Engineering; N/A; Keremoğlu, M. Erkan; Taşıran, Serdar; Elmas, Tayfun; Researcher; Faculty Member; PhD Student; Department of Computer Engineering; College of Engineering; College of Engineering; Graduate School of Sciences and Engineering; N/A; N/A; N/AThis work addresses the issue of selecting the formal correctness criterion for a concurrent Java program that best corresponds to the developer's intent. We study a set of concurrency-related bugs detected in Java benchmarks reported in the literature. On these programs, we determine whether race-freedom, atomicity or refinement is the simplest and most appropriate criterion for program correctness. Our purpose is to demonstrate empirically the fact that the appropriate fix for a concurrency error and the selection of a program analysis tool for detecting such an error must be based on the proper expression of the designer's intent using a formal correctness criterion.Publication Metadata only A kernel-based multilayer perceptron framework to identify pathways related to cancer stages(Springer International Publishing Ag, 2023) Mokhtaridoost, Milad; Department of Industrial Engineering; Soleimanpoor, Marzieh; Gönen, Mehmet; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of EngineeringStandard machine learning algorithms have limited knowledge extraction capability in discriminating cancer stages based on genomic characterizations, due to the strongly correlated nature of high-dimensional genomic data. Moreover, activation of pathways plays a crucial role in the growth and progression of cancer from early-stage to latestage. That is why we implemented a kernel-based neural network framework that integrates pathways and gene expression data using multiple kernels and discriminates early- and late-stages of cancers. Our goal is to identify the relevant molecular mechanisms of the biological processes which might be driving cancer progression. As the input of developed multilayer perceptron (MLP), we constructed kernel matrices on multiple views of expression profiles of primary tumors extracted from pathways. We used Hallmark and Pathway Interaction Database (PID) datasets to restrict the search area to interpretable solutions. We applied our algorithm to 12 cancer cohorts from the Cancer Genome Atlas (TCGA), including more than 5100 primary tumors. The results showed that our algorithm could extract meaningful and disease-specific mechanisms of cancers. We tested the predictive performance of our MLP algorithm and compared it against three existing classification algorithms, namely, random forests, support vector machines, and multiple kernel learning. Our MLP method obtained better or comparable predictive performance against these algorithms.Publication Metadata only A novel test coverage metric for concurrently-accessed software components (A work-in-progress paper)(Springer-Verlag Berlin, 2006) N/A; Department of Computer Engineering; N/A; Department of Computer Engineering; Department of Computer Engineering; Taşıran, Serdar; Elmas, Tayfun; Bölükbaşı, Güven; Keremoğlu, M. Erkan; Faculty Member; PhD Student; Undergraduate Student; Reseacher; Department of Computer Engineering; College of Engineering; Graduate School of Sciences and Engineering; College of Engineering, College of Engineering; N/A; N/A; N/A; N/AWe propose a novel, practical coverage metric called "location pairs" (LP) for concurrently-accessed software components. The LP metric captures well common concurrency errors that lead to atomicity or refinement violations. We describe a software tool for measuring LP coverage and outline an inexpensive application of predicate abstraction and model checking for ruling out infeasible coverage targets.Publication Metadata only A prediction framework for fast sparse triangular solves(Springer International Publishing Ag, 2020) N/A; N/A; Department of Computer Engineering; Ahmad, Najeeb; Yılmaz, Buse; Erten, Didem Unat; PhD Student; N/A; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; N/A; College of Engineering; N/A; N/A; 219274Sparse triangular solve (SpTRSV) is an important linear algebra kernel, finding extensive uses in numerical and scientific computing. The parallel implementation of SpTRSV is a challenging task due to the sequential nature of the steps involved. This makes it, in many cases, one of the most time-consuming operations in an application. Many approaches for efficient SpTRSV on CPU and GPU systems have been proposed in the literature. However, no single implementation or platform (CPU or GPU) gives the fastest solution for all input sparse matrices. In this work, we propose a machine learning-based framework to predict the SpTRSV implementation giving the fastest execution time for a given sparse matrix based on its structural features. The framework is tested with six SpTRSV implementations on a state-of-the-art CPU-GPU machine (Intel Xeon Gold CPU, NVIDIA V100 GPU). Experimental results, with 998 matrices taken from the SuiteSparse Matrix Collection, show the classifier prediction accuracy of 87% for the fastest SpTRSV algorithm for a given input matrix. Predicted SpTRSV implementations achieve average speedups (harmonic mean) in the range of 1.4-2.7x against the six SpTRSV implementations used in the evaluation.Publication Metadata only Adaptive level binning: a new algorithm for solving sparse triangular systems(Information Processing Society of Japan (IPSJ), 2020) Department of Computer Engineering; Department of Computer Engineering; N/A; Department of Computer Engineering; Erten, Didem Unat; Yılmaz, Buse; Ahmad, Najeeb; Sipahioğlu, Buğra; Faculty Member; Researcher; PhD Student; Undergraduate Student; Department of Computer Engineering; College of Engineering; College of Engineering; Graduate School of Sciences and Engineering; College of Engineering; 219274; N/A; N/A; N/ASparse triangular solve (SpTRSV) is an important scientific kernel used in several applications such as preconditioners for Krylov methods. Parallelizing SpTRSV on multi-core systems is challenging since it exhibits limited parallelism due to computational dependencies and introduces high parallelization overhead due to finegrained and unbalanced nature of workloads. We propose a novel method, named Adaptive Level Binning (ALB), that addresses these challenges by eliminating redundant synchronization points and adapting the work granularity with an efficient load balancing strategy. Similar to the commonly used level-set methods for solving SpTRSV, ALB constructs level-sets of rows, where each level can be computed in parallel. Differently, ALB bins rows to levels adaptively and reduces redundant dependencies between rows. On an Intel® Xeon® Gold 6148 processor and NVIDIA® Tesla V100 GPU, ALB obtains 1.83x speedup on average and up to 5.28x speedup over Intel MKL and, over NVIDIA cuSPARSE, an average speedup of 2.80x and a maximum speedup of 39.40x for 29 matrices selected from Suite Sparse Matrix Collection.Publication Metadata only Adaptive peer-to-peer video streaming with optimized flexible multiple description coding(IEEE, 2006) Akyol, Emrah; Department of Electrical and Electronics Engineering; Department of Electrical and Electronics Engineering; Tekalp, Ahmet Murat; Civanlar, Mehmet Reha; Faculty Member; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering; College of Engineering; 26207; 16372Efficient peer-to-peer (P2P) video streaming is a challenging task due to time-varying nature of both the number of available peers and network/channel conditions. To this effect, we propose a receiver driven P2P streaming system which utilizes a flexible scalable multiple description coding method [1], where the number of base and enhancement descriptions, and the rate and redundancy level of each description can be adapted on the fly. The optimization of the parameters of the proposed MDC scheme according to network conditions is discussed within the context of the proposed adaptive P2P streaming framework, where the number and quality of available streaming peers/paths are a priori unknown and vary in time. Experimental results, by means of NS-2 network simulation of a P2P video streaming system, show that adaptation of the number, type, and rate of descriptions and the redundancy level of each description according to network conditions yields significantly superior performance when compared to MDC schemes using a fixed number of descriptions/layers with fixed rate and redundancy level.Publication Metadata only Adaptive per-GOP bandwidth allocation for H.264 video transmission over differentiated services networks(Ieee, 2005) De Martin, JC; Department of Computer Engineering; N/A; Department of Electrical and Electronics Engineering; De Vito, Fabio; Yılmaz, Elif Merve; Tekalp, Ahmet Murat; Other; Researcher; Faculty Member; Department of Computer Engineering; Department of Electrical and Electronics Engineering; College of Engineering; Law School; College of Engineering; N/A; 267672; 26207While transmitting over differentiated services networks, in case of severe congestion also the most privileged classes may experience losses. In those cases, and especially in case of video transmission, protecting a higher fraction of traffic can have the effect of decreasing the quality, due to the overload of high-priority classes. We propose a method to compute, at source side, the allocation of video traffic over the available classes to ensure the lowest decoder-side distortion and provide traffic friendliness. To show this algorithm performance, the simple case of Poisson traffic with a bottleneck shared-buffer router is shown. The same approach can be extended to other traffic characteristics and router architectures.Publication Metadata only Affective burst detection from speech using Kernel-fusion dilated convolutional neural networks(IEEE, 2022) N/A; N/A; Department of Computer Engineering; Köprü, Berkay; Erzin, Engin; N/A; Faculty Member; Department of Computer Engineering; N/A; College of Engineering; N/A; 34503As speech interfaces are getting richer and widespread, speech emotion recognition promises more attractive applications. In the continuous emotion recognition (CER) problem, tracking changes across affective states is an essential and desired capability. Although CER studies widely use correlation metrics in evaluations, these metrics do not always capture all the high-intensity changes in the affective domain. In this paper, we define a novel affective burst detection problem to capture high-intensity changes of the affective attributes accurately. We formulate a two-class classification approach to isolate affective burst regions over the affective state contour for this problem. The proposed classifier is a kernel-fusion dilated convolutional neural network (KFDCNN) architecture driven by speech spectral features to segment the affective attribute contour into idle and burst sections. Experimental evaluations are performed on the RECOLA and CreativeIT datasets. The proposed KFDCNN outperforms baseline feedforward neural networks on both datasets.