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Department: search.filters.department.Department of Computer EngineeringSubject: search.filters.subject.Physics

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    PublicationOpen Access
    A diversity combination model incorporating an inward bias for interaural time-level difference cue integration in sound lateralization
    (Multidisciplinary Digital Publishing Institute (MDPI), 2020) N/A; Department of Computer Engineering; Mojtahedi, Sina; Erzin, Engin; Ungan, Pekcan; Faculty Member; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of Engineering; School of Medicine; N/A; 34503; N/A
    A sound source with non-zero azimuth leads to interaural time level differences (ITD and ILD). Studies on hearing system imply that these cues are encoded in different parts of the brain, but combined to produce a single lateralization percept as evidenced by experiments indicating trading between them. According to the duplex theory of sound lateralization, ITD and ILD play a more significant role in low-frequency and high-frequency stimulations, respectively. In this study, ITD and ILD, which were extracted from a generic head-related transfer functions, were imposed on a complex sound consisting of two low- and seven high-frequency tones. Two-alternative forced-choice behavioral tests were employed to assess the accuracy in identifying a change in lateralization. Based on a diversity combination model and using the error rate data obtained from the tests, the weights of the ITD and ILD cues in their integration were determined by incorporating a bias observed for inward shifts. The weights of the two cues were found to change with the azimuth of the sound source. While the ILD appears to be the optimal cue for the azimuths near the midline, the ITD and ILD weights turn to be balanced for the azimuths far from the midline.
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    PublicationMetadata only
    A diversity combination model incorporating an inward bias for interaural time-level difference cue integration in sound lateralization
    (MDPI, 2020) N/A; N/A; Department of Computer Engineering; N/A; Mojtahedi, Sina; Erzin, Engin; Ungan, Pekcan; PhD Student; Faculty Member; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of Engineering; School of Medicine; N/A; 34503; N/A
    A sound source with non-zero azimuth leads to interaural time level differences (ITD and ILD). Studies on hearing system imply that these cues are encoded in different parts of the brain, but combined to produce a single lateralization percept as evidenced by experiments indicating trading between them. According to the duplex theory of sound lateralization, ITD and ILD play a more significant role in low-frequency and high-frequency stimulations, respectively. In this study, ITD and ILD, which were extracted from a generic head-related transfer functions, were imposed on a complex sound consisting of two low- and seven high-frequency tones. Two-alternative forced-choice behavioral tests were employed to assess the accuracy in identifying a change in lateralization. Based on a diversity combination model and using the error rate data obtained from the tests, the weights of the ITD and ILD cues in their integration were determined by incorporating a bias observed for inward shifts. The weights of the two cues were found to change with the azimuth of the sound source. While the ILD appears to be the optimal cue for the azimuths near the midline, the ITD and ILD weights turn to be balanced for the azimuths far from the midline.
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    PublicationMetadata only
    Quantum mechanical calculations of tryptophan and comparison with conformations in native proteins
    (amer Chemical Soc, 2006) Department of Chemistry; Department of Computer Engineering; Department of Chemical and Biological Engineering; Yurtsever, İsmail Ersin; Yüret, Deniz; Erman, Burak; Faculty Member; Faculty Member; Faculty Member; Department of Chemistry; Department of Computer Engineering; Department of Chemical and Biological Engineering; College of Sciences; College of Engineering; College of Engineering; 7129; 179996; 179997
    We report a detailed analysis of the potential energy surface of N-acetyl-L-tryptophan-N-methylamide, (NaTMa) both in the gas phase and in solution. the minima are identified using the density-functional-theory (DFT) with the 6-31g(d) basis set. the full potential energy surface in terms of torsional angles is spanned starting from various initial configurations. We were able to locate 77 distinct L-minima. the calculated energy maps correspond to the intrinsic conformational propensities of the individual NaTMa molecule. We show that these conformations are essentially similar to the conformations of tryptophan in native proteins. for this reason, we compare the results of DFT calculations in the gas and solution phases with native state conformations of tryptophan obtained from a protein library. in native proteins, tryptophan conformations have strong preferences for the, sheet, right-handed helix, tight turn, and bridge structures. the conformations calculated by DFT, the solution-phase results in particular, for the single tryptophan residue are in agreement with native state values obtained from the Protein Data Bank.
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    PublicationOpen 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; 192506
    We 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.