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Eksik veri içeren zaman serilerinin işlenmesi için gürbüz ve etkin öğrenme yöntemleri
No full textCataloged from PDF version of article.Includes bibliographical references (leaves 84-90).We study nonlinear regression and classification problems on multivariate time series data that suffer from missing entries and variable sequence lengths, an issue that frequently arises in real-world applications due to sensor failures, communication delays, or data corruption. To address this challenge, we propose two complementary deep learning architectures tailored to different missingness scenarios. First, we introduce the FSI-LSTM architecture, a modular and end-to-end trainable framework that performs feature-specific imputation by dynamically selecting the most appropriate imputation model for each input dimension. The selection mechanism is formulated as a multi-armed bandit problem, enabling the framework to optimize imputation strategies and network parameters jointly based on their contribution to the final task performance. The proposed architecture supports a diverse set of imputation models—including both naive and neural techniques—and can be seamlessly integrated into various recurrent backbones such as the LSTM and GRU networks. Second, we present the Hierarchical-LSTM architecture for completely missing input patterns, where imputation becomes unreliable or infeasible. Instead of relying on statistical assumptions, this architecture exploits presence-patterns in the observed inputs by training a set of expert LSTM networks, each specialized for a specific configuration of missingness. The final prediction is obtained through adaptive expert selection based on the observed pattern at each time step. Both approaches are evaluated on multiple real-world datasets across diverse missingness regimes. Experimental results show that our methods significantly outperform state-of-the-art baselines in terms of both predictive accuracy and robustness. We also provide computational complexity analyses and share the source code to facilitate reproducibility and future research.by Safa Onur Şahi
Meme kanserinde erbin gen ifadesinin mikroRNA’ler tarafından düzenlenmesi
Full text linkCataloged from PDF version of article.Includes bibliographical references (leaves 84-106).Breast cancer is a heterogeneous disease characterized by distinct molecular subtypes, defined by specific gene markers and mutations associated with different prognoses and treatment responses. The ERBIN (ERBB2IP) gene encodes a basolateral scaffold/adaptor protein that maintains epithelial cell polarity and adhesion, while also regulating key signaling pathways, including MAPK and TGF-β signaling. Its expression changes across diseases, including HER2-positive breast cancer. However, the mechanisms underlying aberrant ERBIN expression levels remain poorly investigated. This study aimed to identify specific miRNAs regulating ERBIN expression in distinct breast cancer subtypes and to understand further regulatory interactions within the miRNA-ERBIN axis. ERBIN expression patterns in publicly available breast cancer patient datasets were analyzed across molecular subtypes, TP53 mutation status, and hormone receptor status to address this aim. Expression correlation analysis between ERBIN and miRNA expression, and the results from miRNA target prediction tools, identified potential miRNAs that may regulate ERBIN expression in breast cancer. These candidates were further analyzed for their expression in aggressive subgroups to evaluate their inverse association with ERBIN, considering clinical characteristics. In addition, miRNA-seq analysis following ERBIN knockdown in MDA-MB-231 cells revealed potential regulatory feedback loops between ERBIN and selected miRNAs. Overall, the results provide comprehensive insight into the post-transcriptional regulation of ERBIN in breast cancer and suggest that the miRNA-ERBIN relationships provide a potential biomarker or therapeutic target.by Özge Yükse
İkili genlik modülasyonu ile hızlı ve verimli optik alan kontrolü: optik holografi ve benek uyarlama uygulamaları
Full text linkCataloged from PDF version of article.Includes bibliographical references (leaves 65-73).Speckle patterns, which arise from the scattering of coherent light by disordered media, play a central role in applications such as imaging, sensing, and optical communication. However, controlling speckle patterns with both high speed and high efficiency remains a major challenge. Liquid crystal spatial light modulators provide precise phase control but operate at frame rates of only 50–100 Hz, while Digital Micromirror Devices (DMDs) can reach kHz speeds but typically suffer from low diffraction efficiency. This thesis introduces Diffuser-integrated Binary Amplitude Modulation (DiBAM), a framework that combines the kHz switching speed of DMDs with the strong mode mixing of a diffuser to enable rapid, efficient, and accurate speckle pattern engineering. Through purely binary amplitude modulation, DiBAM supports a wide range of wavefront-shaping applications, including point focusing, high-efficiency holography, and non-Rayleigh speckle customization. The diffuser ensures that each micromirror contributes to all output channels, enabling complex field synthesis with high modulation fidelity. Numerical and experimental demonstrations show that DiBAM achieves point focusing without phase modulators, surpasses state-of-the-art holographic methods in balancing efficiency and fidelity, and enables fast speckle customization with tailored intensity probability density functions. These findings establish DiBAM as a versatile, high-performance platform for next-generation adaptive optics and computational imaging.by Hakan Alıc
Enfeksiyon hastalıklarla mücadelede moleküler yaklaşımlar
No full textCataloged from PDF version of article.Includes bibliographical references (leaves 222-249).Synthetic biology enables the logical design of programmable, modular, and multifunctional molecular systems to combat infectious diseases, offering new solutions outside the limitations of conventional vaccines and antimicrobial therapies. This thesis takes advantage of synthetic biology to develop therapeutic and diagnostic platforms against two leading respiratory pathogens, which are severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and Mycobacterium tuberculosis (Mtb). In the first study, a phage display library of M13 was used to select 12-mer peptides with high binding affinity to inactivated SARS-CoV-2 particles. Selected peptides were synthesized and evaluated by quartz crystal microbalance (QCM) for Spike (S) protein interaction by enzyme-linked immunosorbent assay (ELISA) for S-ACE2 binding inhibition in wild-type as well as variant strains. Several candidates exhibited vigorous neutralization activity, suggesting their potential as antiviral drugs and versatile components of diagnostic systems. The second project addressed Mtb immune evasion by generating EsxA- and EsxB targeting nanobodies from a yeast surface display library and engineering chimeric Toll-like receptors (TLRs) to redirect immune recognition from TLR2 to engineered chimeric TLR4 signaling pathways. Also, in vitro error-prone PCR mutagenesis and in silico sequence diversification were complementarily utilized to create a TLR4 library for further high-throughput screening. Collectively, these approaches illustrate a synthetic biology-guided platform for developing modular therapeutic and diagnostic reagents that can be quickly tailored to varied and emerging pathogenic threats.by Cemile Elif Özçeli
Bayesçi eniyilemede gürbüz yeterlilik
No full textCataloged from PDF version of article.Includes bibliographical references (leaves 45-51).This thesis explores the use of a recent optimization framework called "robust satisficing", in the context of Bayesian optimization. Robust satisficing (RS) is an alternative to optimizing, where the goal is to find a solution to a problem that achieves a predefined threshold robustly, under environmental uncertainties. On the other hand Bayesian optimization (BO) is a well-established framework for optimizing difficult to evaluate black-box functions. Previously, the BO literature has mainly focused on optimization, robust optimization or pure satisficing. The goal of this work is to introduce RS in to the BO framework. We analyze the problem in the contextual GP setting where distribution shifts on the contextual variable introduces uncertainties, and also in an adversarial setting where actions chosen are subjected to a perturbation from an adversary. We develop novel algorithms in both settings, using both a known RS approach and a new modification of it. We prove regret bounds for our algorithms in both settings and do extensive simulations that show the advantages of our approach compared with other state-of-the-art methods such as distributionally robust optimization.by Artun Sada
Gradyan dizi sistemlerine yönelik modüler spektrometre mimarileri
Full text linkCataloged from PDF version of article.Includes bibliographical references (leaves 102-108).Gradient arrays are gaining popularity in the magnetic resonance imaging (MRI) community due to their superior performance and flexibility compared to conventional gradient coils. The technology can be used to excite multiple slices simultaneously, mitigate the B1+ inhomogeneity, reduce the power loss on the cryostat, and increase peripheral nerve stimulation (PNS) thresholds. However, despite their potential, fully standalone MRI systems based on gradient arrays are yet to be realized. With an increasing number of channels, scalable design methodologies become essential to resolve the hardware complexity. In this thesis, we present the development of modular spectrometer architectures for gradient array coils and parallel radio frequency (RF) transmit arrays to work towards a fully standalone gradient array magnetic resonance imaging system. Building upon previous work, the spectrometer submodules are implemented entirely on field programmable gate array (FPGA) hardware and consist of three main subsystems: a sequence parser, a gradient controller, and an RF controller. The sequence parser reads PulSeq-formatted conventional sequences and converts them into hardware-compatible memory structures. The gradient controller accesses these memory-mapped sequences and generates time-resolved gradient amplitudes, which are projected onto array channels and sent to an external driver chain. Lastly, the RF controller synthesizes a carrier clock and drives external RF power amplifiers using pulse-width-modulated (PWM) envelope signals. Unlike traditional RF synthesizers that rely on digital-to-analog converters (DACs) or mixers, the RF clock is generated entirely using FPGA primitives, simplifying the overall architecture and reducing scaling costs.by Mehmet Emin Öztür
Opera album of young pianists : instructive fantasies for elementary and junior high school
No full textDonated by Melahat Oransay in memory of her husband Gültekin Oransay.Publisher's no.: Edition H.R. Krentzlin Nr. KR 295 II.Printed music also available as a digital reproductio
Led dizilerinde güç uyarlaması ile hassas görünür ışık konumlandırma
No full textCataloged from PDF version of article.Includes bibliographical references (leaves 41-50).In this thesis, we develop power allocation strategies for asynchronous visible light positioning (VLP) systems employing light-emitting diode (LED) arrays with the aim of improving localization accuracy. We first review the literature on visible light positioning in indoor scenarios. Then, we formulate an optimization problem for minimizing the Cramér-Rao lower bound (CRLB) related to the estimation of the receiver location subject to practical constraints on total and individual transmission powers and required illumination levels. Due to the nonconvexity of the proposed formulation, we devise a two-step methodology. First, a convex optimization problem is employed to determine the power allocation that maximizes the total received power. Subsequently, a projected gradient descent algorithm is utilized to refine the power allocation according to the CRLB metric under the power and illumination constraints. In addition, a framework for iterative power allocation and localization is developed for practical deployment, in which each step of the optimization process uses the most recent position estimate. Simulation results indicate that the proposed method can outperform both the uniform and power maximization based allocation schemes, resulting in improved localization accuracy over a wide range of operating conditions. Hence, it can provide robust and high-precision indoor positioning, even in circumstances involving asynchronous transmission and stringent illumination requirements.by Ramazan Ya
Müttefikler arasında bir başka kriz: haşhaş krizi ve silah ambargosu
No full textCataloged from PDF version of article.Includes bibliographical references (leaves 75-80).This thesis historically analyzes the opium issue and the arms embargo in 1975 between the Republic of Turkey and the United States of America through the theory of asymmetric relations. The central focus is American coercion over Turkey on the opium issue, and the arms embargo is not a result of American imperialism, but a consequence of asymmetry in the bilateral relations. This thesis is based on archival primary sources, such as diplomatic correspondence, secondary sources from the existing literature, and a theoretical framework. The main purpose of this thesis is to provide a deeper understanding of the Turkish-American relations and to prevent conceptual ambiguity between imperialism and asymmetric relations. The main argument is that American coercion is not an act of imperialism but an act of the senior partner over the junior partner in an asymmetric relationship.by Ufuk Yılmaz Sevi
Dikkat eksikliği ve hiperaktıvite bozukluğu (DEHB) olan öğrencilerin resmî olmayan öğrenme ortamlarındakı görsel-işitsel algılarının araştırılması
Full text linkCataloged from PDF version of article.Includes bibliographical references (leaves 70-77).This study explores the audiovisual perceptions of students in informal learning environments at the I.D. Bilkent University campus, with a particular focus on students diagnosed with Attention Deficit Hyperactivity Disorder (ADHD). Using a grounded theory approach, semi-structured interviews (ISO/TS 12913-2:2018 Method C) were conducted to gain in-depth insight into how students experience and interpret their sensory surroundings. A total of 20 participants, which are equally divided between ADHD and non-ADHD students, were selected through convenience sampling. Four informal learning environments on campus were selected based on their diverse acoustic and spatial characteristics, supporting both individual study and social interaction. To complement the qualitative data, structured questionnaires (Method A) and in-situ sound level measurements (LAeq) were also employed to capture perceptual attributes and objective acoustic conditions. The findings indicate that students diagnosed with ADHD evaluated informal learning environments more negatively, describing them as chaotic, annoying, and less calming. In contrast, non-ADHD participants reported greater comfort and satisfaction, particularly in acoustically balanced spaces. Significant perceptual differences between the two groups were observed in specific environments like the dormitory and Faculty of Fine Arts. Moreover, natural sounds were positively correlated with feelings of calmness, while human and technological noises contributed to negative perceptions. These perceptual differences highlight the need for inclusive design strategies that consider neurodiversity in learning environments.by Ceren Şahmara
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