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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/3
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Publication Metadata only Spatial and thermal aware methods for efficient workload management in distributed data centers(Elsevier B.V., 2024) N/A; Department of Computer Engineering; Ali, Ahsan; Özkasap, Öznur; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of EngineeringGeographically distributed data centers provide facilities for users to fulfill the demand of storage and computations, where most of the operational cost is due to electricity consumption. In this study, we address the problem of energy consumption of cloud data centers and identify key characteristics of techniques proposed for reducing operational costs, carbon emissions, and financial penalties due to service level agreement (SLA) violations. By considering computer room air condition (CRAC) units that utilize outside air for cooling purposes as well as temperature and space-varying properties, we propose the energy cost model which takes into account temperature ranges for cooling purposes and operations of CRAC units. Then, we propose spatio-thermal-aware algorithms to manage workload using the variation of electricity price, locational outside and within the data center temperature, where the aim is to schedule the incoming workload requests with minimum SLA violations, cooling cost, and energy consumption. We analyzed the performance of our proposed algorithms and compared the experimental results with the benchmark algorithms for metrics of interest including SLA violations, cooling cost, and overall operations cost. Modeling, experiments, and verification conducted on CloudSim with realistic data center scenarios and workload traces show that the proposed algorithms result in reduced SLA violations, save between 15% to 75% of cooling cost and between 3.89% to 39% of the overall operational cost compared to the existing solutions.Publication Metadata only Physics-informed and data-driven modeling of an industrial wastewater treatment plant with actual validation(PERGAMON-ELSEVIER SCIENCE LTD, 2024) Esenboga, Elif Ecem; Cosgun, Ahmet; Kusoglu, Gizem; Department of Chemical and Biological Engineering; Köksal, Ece Serenat; Asrav, Tuse; Aydın, Erdal; Department of Chemical and Biological Engineering; Koç University Tüpraş Energy Center (KUTEM) / Koç Üniversitesi Tüpraş Enerji Merkezi (KÜTEM); Graduate School of Sciences and Engineering; College of EngineeringData-driven modeling is essential in chemical engineering, especially in complex systems like wastewater treatment plants. Recurrent neural networks are effective for modeling parameters in wastewater treatment process such as dissolved oxygen concentration and chemical oxygen demand due to their nonlinear adaptability. However, traditional models face challenges such as the requirement for larger datasets and more frequent sampling, noisy measurements, and overfitting. To address this, physics-informed neural networks integrate physical knowledge for improved performance. In our study, we apply both approaches to a wastewater treatment plant, enhancing prediction performance. Our results demonstrate that physics-informed models perform successfully in offline and online validation, especially when standard methods fail. They maintain effectiveness without frequent updates. Yet, integrating physics-informed knowledge can introduce noise when standard methods suffice. This result points out the need for careful consideration of model choice in different scenarios.Publication Metadata only Multi-scale deformable alignment and content-adaptive inference for flexible-rate bi-directional video compression(IEEE Computer Society, 2023) Department of Electrical and Electronics Engineering; Yılmaz, Mustafa Akın; Ulaş, Ökkeş Uğur; Tekalp, Ahmet Murat; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; College of EngineeringThe lack of ability to adapt the motion compensation model to video content is an important limitation of current end-to-end learned video compression models. This paper advances the state-of-the-art by proposing an adaptive motion-compensation model for end-to-end rate-distortion optimized hierarchical bi-directional video compression. In particular, we propose two novelties: i) a multi-scale deformable alignment scheme at the feature level combined with multi-scale conditional coding, ii) motion-content adaptive inference. In addition, we employ a gain unit, which enables a single model to operate at multiple rate-distortion operating points. We also exploit the gain unit to control bit allocation among intra-coded vs. bi-directionally coded frames by fine tuning corresponding models for truly flexible-rate learned video coding. Experimental results demonstrate state-of-the-art rate-distortion performance exceeding those of all prior art in learned video coding1.Publication Metadata only An institutional perspective: how gatekeepers on a higher education interact for the organization of access(Springer, 2023) Department of Media and Visual Arts; Yıldız, Zeynep; Subaşı, Özge; Department of Media and Visual Arts; Graduate School of Social Sciences and HumanitiesThere is growing research on how collaborative systems could support equity in shaping access for marginalized communities in different contexts. Higher education institutions are essential contexts for examining issues around equity-based organization of access for diverse populations, including people with disabilities. However, there is a shortage of research in CSCW investigating equal access in higher education settings. To address this gap, in this case study, we aim to have a closer look at how gatekeepers (people who are responsible for accessibility) in a higher education institution organize access for members with disabilities. Gatekeeping has long been discussed in disability justice to examine systemic and institutional barriers for people with disabilities. We reveal how gatekeepers interact and collaborate around existing institutional communication channels to collect access-related requests and distribute access in the higher education setting. Our data shows that existing practices come with institutional challenges hindering equity and inclusion for members with disabilities. Key issues revealed through our findings are (1) communication tools and non-shared definitions around access, (2) lack of tools for experience documentation, (3) ineffective feedback loops around access requests, (4) impact-based prioritization for access requests. We discuss how our analysis contributes to equity-oriented system design for future collaboration around organizing higher education access at the institutional level.Publication Metadata only Virtual collaboration tools for mixed-ability workspaces: a cross disability solidarity case from Turkey(Assoc Computing Machinery, 2023) Department of Media and Visual Arts; Yıldız, Zeynep; Subaşı, Özge; Department of Media and Visual Arts; Graduate School of Social Sciences and HumanitiesA growing body of literature on mixed-ability teams within HCI investigates how disabled and non-disabled people collaborate. Still, how diferent disabilities can interact in a mixed-ability team is underexplored, especially for long commitments and in non-western contexts. As an emerging perspective in accessibility studies in HCI, disability justice emphasizes the importance of cross-disability collaborations. Collaborative access, interdependence, and crossdisability dialogue are keys to building accessible mixed-ability interactions. We conducted ten in-depth interviews with the members of a unique mixed-ability team (which includes people with diferent physical disabilities) using the same workspace with crossdisability interactions in Turkey. We aim to understand the requirements for an accessible mixed-ability virtual workspace and to identify practical design considerations for cross-disability solidarityoriented virtual collaboration tools. To ensure equal access in virtual workspaces, we suggest implications for centering collective access, balancing external power dynamics, and supporting language and cultural diversities.Publication Metadata only Ris-aided angular-based hybrid beamforming design in mmwave massive mimo systems(IEEE, 2022) Koc, Asil; Tho Le-Ngoc; Department of Electrical and Electronics Engineering; Yıldırım, İbrahim; Başar, Ertuğrul; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; College of EngineeringThis paper proposes a reconfigurable intelligent surface (RIS)-aided and angular-based hybrid beamforming (AB-HBF) technique for the millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems. The proposed RIS-AB-HBF architecture consists of three stages: (i) RF beam-former, (ii) baseband (BB) precoder/combiner, and (iii) RIS phase shift design. First, in order to reduce the number of RF chains and the channel estimation overhead, RF beamformers are designed based on the 3D geometry-based mmWave channel model using slow time-varying angular parameters of the channel. Second, a BB precoder/combiner is designed by exploiting the reduced-size effective channel seen from the BB stages. Then, the phase shifts of the RIS are adjusted to maximize the achievable rate of the system via the nature-inspired particle swarm optimization (PSO) algorithm. Illustrative simulation results demonstrate that the use of RISs in the AB-HBF systems has the potential to provide more promising advantages in terms of reliability and flexibility in system design.Publication Metadata only On maximal partial Latin hypercubes(Springer, 2023) Donovan, Diane M.; Grannell, Mike J.; Department of Mathematics; Yazıcı, Emine Şule; Department of Mathematics; College of SciencesA lower bound is presented for the minimal number of filled cells in a maximal partial Latin hypercube of dimension d and order n. The result generalises and extends previous results for d= 2 (Latin squares) and d= 3 (Latin cubes). Explicit constructions show that this bound is near-optimal for large n> d . For d> n , a connection with Hamming codes shows that this lower bound gives a related upper bound for the same quantity. The results can be interpreted in terms of independent dominating sets in certain graphs, and in terms of codes that have covering radius 1 and minimum distance at least 2.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 Virtual reality simulation-based training in otolaryngology(Springer London Ltd, 2023) N/A; Ünsaler, Selin; Hafız, Ayşenur Meriç; Gökler, Ozan; Özkaya, Yasemin Sıla; School of Medicine; Koç University HospitalVR simulators will gain wider place in medical education in order to ensure high quality surgical training. The integration of VR simulators into residency programs is actually required more than ever in the era after the pandemic. In this review, the literature is reviewed for articles that reported validation results of different VR simulators designed for the field of otolaryngology. A total of 213 articles searched from Pubmed and Web of Science databases with the key words "virtual reality simulation" and "otolaryngology" on January 2022 are retrieved. After removal of duplicates, 190 articles were reviewed by two independent authors. All the accessible articles in english and which report on validation studies of virtual reality systems are included in this review. There were 33 articles reporting validation studies of otolaryngology simulators. Twenty one articles reported on otology simulator validation studies, eight articles reported rhinology simulator validation studies and four articles reported on pharyngeal and laryngeal surgery simulators. Otology simulators are shown to increase the performance of the trainees. In some studies, efficacy of simulators has been found comparable to cadaveric bone dissections and trainees reported that VR simulators was very useful in facilitating the learning process and improved the learning curves. Rhinology simulators designed for endoscopic sinus surgery are shown to have the construct validity to differentiate the surgeons of different level of expertise. Simulators in temporal bone surgery and endoscopic sinus surgery can mimic the surgical environment and anatomy along with different surgical scenarios, thus can be more implemented in surgical training and evaluation of the trainees in the future. Currently there are no validated surgical simulators for pharyngeal and laryngeal surgery.Publication Metadata only Snoopie: a multi-GPU communication profiler and visualizer(Assoc Computing Machinery, 2024) Department of Computer Engineering; Issa, Mohammad Kefah Taha; Sasongko, Muhammad Aditya; Turimbetov, İlyas; Baydamirli, Javid; Sağbili, Doğan; Erten, Didem Unat; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of EngineeringWith data movement becoming one of the most expensive bottlenecks in computing, the need for profiling tools to analyze communication becomes crucial for effectively scaling multi-GPU applications. While existing profiling tools including first-party software by GPU vendors are robust and excel at capturing compute operations within a single GPU, support for monitoring GPU-GPU data transfers and calls issued by communication libraries is currently inadequate. To fill these gaps, we introduce Snoopie, an instrumentation-based multi-GPU communication profiling tool built on NVBit, capable of tracking peer-to-peer transfers and GPU-centric communication library calls. To increase programmer productivity, Snoopie can attribute data movement to the source code line and the data objects involved. It comes with multiple visualization modes at varying granularities, from a coarse view of the data movement in the system as a whole to specific instructions and addresses. Our case studies demonstrate Snoopie's effectiveness in monitoring data movement, locating performance bugs in applications, and understanding concrete data transfers abstracted beneath communication libraries. The tool is publicly available at https://github.com/ParCoreLab/snoopie.