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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/3
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Publication Metadata only Self-supervised object-centric learning for videos(Neural information processing systems foundation, 2023) Xie, Weidi; Department of Computer Engineering; Aydemir, Görkay; Güney, Fatma; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of EngineeringUnsupervised multi-object segmentation has shown impressive results on images by utilizing powerful semantics learned from self-supervised pretraining. An additional modality such as depth or motion is often used to facilitate the segmentation in video sequences. However, the performance improvements observed in synthetic sequences, which rely on the robustness of an additional cue, do not translate to more challenging real-world scenarios. In this paper, we propose the first fully unsupervised method for segmenting multiple objects in real-world sequences. Our object-centric learning framework spatially binds objects to slots on each frame and then relates these slots across frames. From these temporally-aware slots, the training objective is to reconstruct the middle frame in a high-level semantic feature space. We propose a masking strategy by dropping a significant portion of tokens in the feature space for efficiency and regularization. Additionally, we address over-clustering by merging slots based on similarity. Our method can successfully segment multiple instances of complex and high-variety classes in YouTube videos.Publication Metadata only How to detect an inadvertent pregnancy during random start stimulations(Elsevier Inc., 2024) Lawrenz, B; Fatemi, H.M.; Ata, Mustafa Barış; School of MedicineN/APublication Metadata only Event-triggered reinforcement learning based joint resource allocation for ultra-reliable low-latency V2X communications(Institute of Electrical and Electronics Engineers Inc., 2024) Department of Electrical and Electronics Engineering; Ergen, Sinem Çöleri; Khan, Nasir; Department of Electrical and Electronics Engineering; College of Engineering; Graduate School of Sciences and EngineeringFuture 6G-enabled vehicular networks face the challenge of ensuring ultra-reliable low-latency communication (URLLC) for delivering safety-critical information in a timely manner. Existing resource allocation schemes for vehicle-toeverything (V2X) communication systems primarily rely on traditional optimization-based algorithms. However, these methods often fail to guarantee the strict reliability and latency requirements of URLLC applications in dynamic vehicular environments due to the high complexity and communication overhead of the solution methodologies. This paper proposes a novel deep reinforcement learning (DRL) based framework for the joint power and block length allocation to minimize the worst-case decoding-error probability in the finite block length (FBL) regime for a URLLC-based downlink V2X communication system. The problem is formulated as a non-convex mixed-integer nonlinear programming problem (MINLP). Initially, an algorithm grounded in optimization theory is developed based on deriving the joint convexity of the decoding error probability in the block length and transmit power variables within the region of interest. Subsequently, an efficient event-triggered DRL based algorithm is proposed to solve the joint optimization problem. Incorporating event-triggered learning into the DRL framework enables assessing whether to initiate the DRL process, thereby reducing the number of DRL process executions while maintaining reasonable reliability performance. The DRL framework consists of a twolayered structure. In the first layer, multiple deep Q-networks (DQNs) are established at the central trainer for block length optimization. The second layer involves an actor-critic network and utilizes the deep deterministic policy-gradient (DDPG)-based algorithm to optimize the power allocation. Simulation results demonstrate that the proposed event-triggered DRL scheme can achieve 95% of the performance of the joint optimization scheme while reducing the DRL executions by up to 24% for different network settings.Publication Metadata only Contrast improvement through a Generative Adversarial Network (GAN) by utilizing a dataset obtained from a line-scanning confocal microscope(SPIE, 2024) Department of Physics; Kiraz, Alper; Morova, Berna; Bavili, Nima; Ketabchi, Amir Mohammad; Department of Physics; Koç University Research Center for Translational Medicine (KUTTAM) / Koç Üniversitesi Translasyonel Tıp Araştırma Merkezi (KUTTAM); College of Sciences; Graduate School of Sciences and EngineeringConfocal microscopy offers enhanced image contrast and signal-to-noise ratio compared to wide-field illumination microscopy, achieved by effectively eliminating out-of-focus background noise. In our study, we initially showcase the functionality of a line-scanning confocal microscope aligned through the utilization of a Digital Light Projector (DLP) and a rolling shutter CMOS camera. In this technique, a sequence of illumination lines is projected onto a sample using a DLP and focusing objective (50X, NA=0.55). The reflected light is imaged with the camera. Line-scanning confocal imaging is accomplished by synchronizing the illumination lines with the rolling shutter of the sensor, leading to a substantial enhancement of approximately 50% in image contrast. Subsequently, this setup is employed to create a dataset comprising 500 pairs of images of paper tissue. This dataset is employed for training a Generative Adversarial Network (cGAN). Roughly 45% contrast improvement was measured in the test images for the trained network, in comparison to the ground-truth images.Publication Metadata only Immunotherapy for lung cancer(Turkiye Klinikleri, 2023) Karadurmuş,N.; Akyürek,N.; Aydiner,A.; Savaş,R.; Sönmez,Ö.; Şendur,M.A.N.; Oyan,B.; Yalman,D.; Yilmaz,M.U.; Yilmaz,Ü.; Göker,E.; Yumuk, Perran Fulden; School of MedicineLung cancer is one of the leading causes of cancer-related deaths in men and women. Similar to the approach with other cancer types, lung cancer staging is crucial in planning an effective treatment plan and predicting patient prognosis. Effective immunotherapies for patients with non-small cell lung cancer and non-genomic driver mutations are rapidly evolving. Moreover, anti-programmed death re-ceptor-1 (PD-1)/programmed death ligand 1 (PD-L1)-based treatments have become the first-line standard of care. Despite shortcomings, PD-L1 expression level seems currently to be a relatively reliable predictor of the clinical efficacy of treatment with anti-PD-1/PD-L1 anti-bodies. However, additional biomarkers are required to better personalize treatment options for these patients. This review aimed to increase awareness of lung cancer and immunotherapy treatment options, depending on patient and disease stage characteristics.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 Indomethacin affects the inflammatory response via interaction with the rhoa-actin cytoskeleton in THP-1 cells(Istanbul University Press, 2023) Aldogan, Ebru Haciosmanoglu; Bulut, Seyma; Yapislar, Hande; Guncer, Basak; Bektas, Muhammet; Yöntem, Fulya Dal; Koç University Research Center for Translational Medicine (KUTTAM) / Koç Üniversitesi Translasyonel Tıp Araştırma Merkezi (KUTTAM); School of MedicineObjective: Inflammation is a complex reaction present in numerous disorders. Indomethacin, a compound possessing an indoline core, is a Nonsteroidal Anti-Inflammatory Drug (NSAID) that is commonly prescribed for inflammation and pain. The actin network, plays a major role in cellular activities and it’s regulated by by Rho GTPases has important implications for cellular dynamics and orientation. In this research, we explore the effects of indomethacin on the inflammatory response as mediated via RhoA and pyrin inflammatory complexes using an inflammatory disease model with relation actin cytoskeleton. Materials and Methods: This study used Western blotting to examine the impact of indomethacin on the assembly processes related to the pyrin inflammasome complex and the RhoA signaling pathway in Lipopolysaccharide-stimulated THP-1 cells. Actin-indomethacin interaction was analyzed by Differential Scanning Fluorimetry (DSF). Results: We found that while the expression levels of pyrin decreased, phosphorylated-RhoA increased but overall RhoA levels did not change. The equilibrium dissociation constant (KD) for the G-actin-indomethacin complex was calculated to be 9.591± 1.608 ng/mL (R2= 0.8582) using ∆Tm measurements of indomethacin by DSF. Conclusion: Moreover, the effects of indomethacin on inflammation pathways may provide insight into the molecular mechanisms of pyrin inflammasome formation in various autoimmune diseases.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 Mri-powered magnetic miniature capsule robot with hifu-controlled on-demand drug delivery(Institute of Electrical and Electronics Engineers Inc., 2023) Tiryaki, Mehmet Efe; Dogangun, Fatih; Dayan, Cem Balda; Wrede, Paul; Department of Mechanical Engineering; Sitti, Metin; Department of Mechanical Engineering; College of Engineering; School of MedicineMagnetic resonance imaging (MRI)-guided robotic systems offer great potential for new minimally invasive medical tools, including MRI-powered miniature robots. By re-purposing the imaging hardware of an MRI scanner, the magnetic miniature robot could be navigated into the remote part of the patient's body without needing tethered endoscopic tools. However, state-of-art MRI-powered magnetic miniature robots have limited functionality besides navigation. Here, we propose an MRI-powered magnetic miniature capsule robot benefiting from acoustic streaming forces generated by MRI-guided high-intensity focus ultrasound (HIFU) for controlled drug release. Our design comprises a polymer capsule shell with a submillimeter-diameter drug-release hole that captures an air bubble functioning as a stopper. We use the HIFU pulse to initiate drug release by removing the air bubble once the capsule robot reaches the target location. By controlling acoustic pressure, we also regulate the drug release rate for multiple locations targeting during navigation. We demonstrated that the proposed magnetic capsule robot could travel at high speed, up to 1.13 cm/s in ex vivo porcine small intestine, and release drug to multiple target sites in a single operation, using a combination of MRI-powered actuation and HIFU-controlled release. The proposed MRI-guided microrobotic drug release system will greatly impact minimally invasive medical procedures by allowing on-demand targeted drug delivery.