Publications with Fulltext
Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/6
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Publication Open Access The noisy channel mode for unsupervised word sense disambiguation(Massachusetts Institute of Technology (MIT) Press, 2010) Department of Computer Engineering; Yüret, Deniz; Yatbaz, Mehmet Ali; Faculty Member; PhD Student; Department of Computer Engineering; College of Engineering; 179996; 192506We introduce a generative probabilistic model, the noisy channel model, for unsupervised word sense disambiguation. In our model, each context C is modeled as a distinct channel through which the speaker intends to transmit a particular meaning S using a possibly ambiguous word W. To reconstruct the intended meaning the hearer uses the distribution of possible meanings in the given context P(S|C) and possible words that can express each meaning P(W|S). We assume P(W|S) is independent of the context and estimate it using WordNet sense frequencies. The main problem of unsupervised WSD is estimating context-dependent P(S|C) without access to any sense-tagged text. We show one way to solve this problem using a statistical language model based on large amounts of untagged text. Our model uses coarse-grained semantic classes for S internally and we explore the effect of using different levels of granularity on WSD performance. The system outputs fine-grained senses for evaluation, and its performance on noun disambiguation is better than most previously reported unsupervised systems and close to the best supervised systems.Publication Open Access Interlaced: fully decentralized churn stabilization for Skip Graph-based DHTs(Elsevier, 2021) Department of Computer Engineering; Hassanzadeh-Nazarabadi, Yahya; Küpçü, Alptekin; Özkasap, Öznur; PhD Student; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 168060; 113507As a distributed hash table (DHT) routing overlay, Skip Graph is used in a variety of peer-to-peer (P2P) systems including cloud storage. The overlay connectivity of P2P systems is negatively affected by the arrivals and departures of nodes to and from the system that is known as churn. Preserving connectivity of the overlay network (i.e., the reachability of every pair of nodes) under churn without compromising the overlay latency is a performance challenge in every P2P system including the Skip Graph-based ones. The existing decentralized churn stabilization solutions that are applicable to Skip Graphs mainly optimize the connectivity of the system under churn and do not consider routing latency of overlay as an optimization goal. Additionally, those existing solutions change the message complexity of Skip Graphs, distort its topology, or apply constant message overhead to the system. In this paper, we propose Interlaced, a fully decentralized churn stabilization mechanism for Skip Graphs that provides drastically stronger overlay connectivity and faster search queries without changing the asymptotic complexity of the Skip Graph in terms of storage, computation, and communication. We also propose the Sliding Window De Bruijn Graph (SWDBG ) as a tool to predict the availability of nodes with high accuracy. Our simulation results show that in comparison to the best existing DHT-based solutions, Interlaced improves the overlay connectivity of the Skip Graph under churn with the gain of about 1.73 times. Likewise, compared to the existing availability prediction approaches for P2P systems, SWDBG is about 1.26 times more accurate. A Skip Graph that benefits from Interlaced and SWDBG is about 2.47 times faster on average in routing the queries under churn compared to the best existing solutions. We also present an adaptive extension of Interlaced to be applied to other DHTs, for example, Kademlia.Publication Open Access Multiple shape correspondence by dynamic programming(Wiley, 2014) Sahillioğlu, Y.; Department of Computer Engineering; Yemez, Yücel; Faculty Member; Department of Computer Engineering; College of EngineeringWe present a multiple shape correspondence method based on dynamic programming, that computes consistent bijective maps between all shape pairs in a given collection of initially unmatched shapes. As a fundamental distinction from previous work, our method aims to explicitly minimize the overall distortion, i.e., the average isometric distortion of the resulting maps over all shape pairs. We cast the problem as optimal path finding on a graph structure where vertices are maps between shape extremities. We exploit as much context information as possible using a dynamic programming based algorithm to approximate the optimal solution. Our method generates coarse multiple correspondences between shape extremities, as well as denser correspondences as by-product. We assess the performance on various mesh sequences of (nearly) isometric shapes. Our experiments show that, for isometric shape collections with non-uniform triangulation and noise, our method can compute relatively dense correspondences reasonably fast and outperform state of the art in terms of accuracy.Publication Open Access Observation of the correlations between pair wise interaction and functional organization of the proteins, in the protein ınteraction network of saccaromyces cerevisiae(World Academy of Science, Engineering and Technology (WASET), 2008) Haliloğlu, T.; Department of Computer Engineering; Department of Chemical and Biological Engineering; Tunçbağ, Nurcan; Keskin, Özlem; Faculty Member; Department of Computer Engineering; Department of Chemical and Biological Engineering; College of Engineering; N/A; 26605Understanding the cell's large-scale organization is an interesting task in computational biology. Thus, protein-protein interactions can reveal important organization and function of the cell. Here, we investigated the correspondence between protein interactions and function for the yeast. We obtained the correlations among the set of proteins. Then these correlations are clustered using both the hierarchical and biclustering methods. The detailed analyses of proteins in each cluster were carried out by making use of their functional annotations. As a result, we found that some functional classes appear together in almost all biclusters. On the other hand, in hierarchical clustering, the dominancy of one functional class is observed. In brief, from interaction data to function, some correlated results are noticed about the relationship between interaction and function which might give clues about the organization of the proteins.Publication Open Access On the importance of hidden bias and hidden entropy in representational efficiency of the Gaussian-Bipolar Restricted Boltzmann Machines(Elsevier, 2018) Department of Computer Engineering; Isabekov, Altynbek; Erzin, Engin; Faculty Member; Department of Computer Engineering; College of Engineering; N/A; 34503In this paper, we analyze the role of hidden bias in representational efficiency of the Gaussian-Bipolar Restricted Boltzmann Machines (GBPRBMs), which are similar to the widely used Gaussian-Bernoulli RBMs. Our experiments show that hidden bias plays an important role in shaping of the probability density function of the visible units. We define hidden entropy and propose it as a measure of representational efficiency of the model. By using this measure, we investigate the effect of hidden bias on the hidden entropy and provide a full analysis of the hidden entropy as function of the hidden bias for small models with up to three hidden units. We also provide an insight into understanding of the representational efficiency of the larger scale models. Furthermore, we introduce Normalized Empirical Hidden Entropy (NEHE) as an alternative to hidden entropy that can be computed for large models. Experiments on the MNIST, CIFAR-10 and Faces data sets show that NEHE can serve as measure of representational efficiency and gives an insight on minimum number of hidden units required to represent the data.Publication Open Access Leveraging frequency based salient spatial sound localization to improve 360 degrees video saliency prediction(Institute of Electrical and Electronics Engineers (IEEE), 2021) Çökelek, Mert; İmamoğlu, Nevrez; Özçınar, Çağrı; Department of Computer Engineering; Erdem, Aykut; Faculty Member; Department of Computer Engineering; College of Engineering; 20331Virtual and augmented reality (VR/AR) systems dramatically gained in popularity with various application areas such as gaming, social media, and communication. It is therefore a crucial task to have the knowhow to efficiently utilize, store or deliver 360° videos for end-users. Towards this aim, researchers have been developing deep neural network models for 360° multimedia processing and computer vision fields. In this line of work, an important research direction is to build models that can learn and predict the observers' attention on 360° videos to obtain so-called saliency maps computationally. Although there are a few saliency models proposed for this purpose, these models generally consider only visual cues in video frames by neglecting audio cues from sound sources. In this study, an unsupervised frequency-based saliency model is presented for predicting the strength and location of saliency in spatial audio. The prediction of salient audio cues is then used as audio bias on the video saliency predictions of state-of-the-art models. Our experiments yield promising results and show that integrating the proposed spatial audio bias into the existing video saliency models consistently improves their performance.Publication Open Access HotRegion: a database of predicted hot spot clusters(Oxford University Press (OUP), 2012) N/A; Department of Computer Engineering; Department of Chemical and Biological Engineering; Çukuroğlu, Engin; Gürsoy, Attila; Keskin, Özlem; PhD Student; Faculty Member; Department of Computer Engineering; Department of Chemical and Biological Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 8745; 26605Hot spots are energetically important residues at protein interfaces and they are not randomly distributed across the interface but rather clustered. These clustered hot spots form hot regions. Hot regions are important for the stability of protein complexes, as well as providing specificity to binding sites. We propose a database called HotRegion, which provides the hot region information of the interfaces by using predicted hot spot residues, and structural properties of these interface residues such as pair potentials of interface residues, accessible surface area (ASA) and relative ASA values of interface residues of both monomer and complex forms of proteins. Also, the 3D visualization of the interface and interactions among hot spot residues are provided.Publication Open Access Mechanistic differences of activation of Rac1(P29S) and Rac1(A159V)(American Chemical Society (ACS), 2021) Jang, Hyunbum; Nussinov, Ruth; N/A; Department of Chemical and Biological Engineering; Department of Computer Engineering; Şenyüz, Simge; Keskin, Özlem; Gürsoy, Attila; Faculty Member; Department of Chemical and Biological Engineering; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 26605; 8745Rac1 is a small GTPase that plays key roles in actin reorganization, cell motility, and cell survival/growth as well as in various cancer types and neurodegenerative diseases. Similar to other Ras superfamily GTPases, Rac1 switches between active GTP-bound and inactive GDP-bound states. Switch I and II regions open and close during GDP/GTP exchange. P29S and A159V (paralogous to K-Ras(A146)) mutations are the two most common somatic mutations of Rac1. Rac1(P2)(9S)( )is a known hotspot for melanoma, whereas Rac1(A159V) most commonly occurs in head and neck cancer. To investigate how these substitutions induce the Rac1 dynamics, we used atomistic molecular dynamics simulations on the wild-type Rac1 and two mutant systems (P29S and A159V) in the GTP bound state, and on the wild-type Rac1 and P29S mutated system in the GDP bound state. Here, we show that P29S and A159V mutations activate Rac1 with different mechanisms. In Rac1(P29S)-GTP, the substitution increases the flexibility of Switch I based on RMSF and dihedral angle calculations and leads to an open conformation. We propose that the open Switch I conformation is one of the underlying reasons for rapid GDP/GTP exchange of Rac1(P29S). On the other hand, in Rac1(A159V)-GTP, some of the contacts of the guanosine ring of GTP with Rac1 are temporarily lost, enabling the guanosine ring to move toward Switch I and subsequently close the switch. Rac1(A159V)-GTP adopts a Ras state 2 like conformation, where both switch regions are in closed conformation and Thr35 forms a hydrogen bond with the nucleotide.Publication Open Access Androgen receptor-mediated transcription in prostate cancer(Multidisciplinary Digital Publishing Institute (MDPI), 2022) Morova, Tunç; Department of Computer Engineering; Department of Chemical and Biological Engineering; Lack, Nathan Alan; Özturan, Doğancan; Faculty Member; PhD Student; Department of Computer Engineering; Department of Chemical and Biological Engineering; Koç University Research Center for Translational Medicine (KUTTAM) / Koç Üniversitesi Translasyonel Tıp Araştırma Merkezi (KUTTAM); School of Medicine; 120842; N/AAndrogen receptor (AR)-mediated transcription is critical in almost all stages of prostate cancer (PCa) growth and differentiation. This process involves a complex interplay of coregulatory proteins, chromatin remodeling complexes, and other transcription factors that work with AR at cis-regulatory enhancer regions to induce the spatiotemporal transcription of target genes. This enhancer-driven mechanism is remarkably dynamic and undergoes significant alterations during PCa progression. In this review, we discuss the AR mechanism of action in PCa with a focus on how cis-regulatory elements modulate gene expression. We explore emerging evidence of genetic variants that can impact AR regulatory regions and alter gene transcription in PCa. Finally, we highlight several outstanding questions and discuss potential mechanisms of this critical transcription factor.Publication Open Access Self-supervised monocular scene decomposition and depth estimation(IEEE Computer Society, 2021) Department of Computer Engineering; N/A; Güney, Fatma; Safadoust, Sadra; Department of Computer Engineering; Koç Üniversitesi İş Bankası Yapay Zeka Uygulama ve Araştırma Merkezi (KUIS AI)/ Koç University İş Bank Artificial Intelligence Center (KUIS AI); College of Engineering; Graduate School of Sciences and Engineering; 187939; N/ASelf-supervised monocular depth estimation approaches either ignore independently moving objects in the scene or need a separate segmentation step to identify them. We propose MonoDepthSeg to jointly estimate depth and segment moving objects from monocular video without using any ground-truth labels. We decompose the scene into a fixed number of components where each component corresponds to a region on the image with its own transformation matrix representing its motion. We estimate both the mask and the motion of each component efficiently with a shared encoder. We evaluate our method on three driving datasets and show that our model clearly improves depth estimation while decomposing the scene into separately moving components.