234 research outputs found
pramod2022_CNNfoveation
No full textData and code for "Human peripheral blur is optimal for object recognition" by RT Pramod, Harish Katti & SP Aru
pramod2022_CNNfoveation
No full textData and code for "Human peripheral blur is optimal for object recognition" by RT Pramod, Harish Katti & SP Aru
pramod2022_CNNfoveation
No full textData and code for "Human peripheral blur is optimal for object recognition" by RT Pramod, Harish Katti & SP Aru
Decoding predicted future states from the brain’s ‘physics engine’
No full textData used in the paper: Pramod et al - Decoding predicted future states from the brain’s ‘physics engine
IISc-DIO
No full textData and codes used in 'Pramod, R. T., & Arun, S. P. (2016). Do computational models differ systematically from human object perception?. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1601-1609)
IISc-DIO
No full textData and codes used in 'Pramod, R. T., & Arun, S. P. (2016). Do computational models differ systematically from human object perception?. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1601-1609)
Neural geolocation prediction in Twitter
No full textInferring the location of a user has been a valuable step for many applications that leverage social media, such as marketing, security monitoring and recommendation systems. Motivated by the recent success of Deep Learning techniques for many tasks such as computer vision, speech recognition, and natural language processing, we study the application of neural models to the problem of geolocation prediction and experiment with multiple techniques to analyze neural networks for geolocation inference based solely on text. Experimental results on the dataset suggest that choosing appropriate network architecture can all increase performance on this task and demonstrate a promising extension of neural network based models for geolocation prediction. Our systematic extensive study of four supervised and three unsupervised tweet representations reveal that Convolutional Neural Networks (CNNs) and fastText best encode the the textual and geoloca- tional properties of tweets respectively. fastText emerges as the best model for low resource settings, providing very little degradation with reduction in embedding size.Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2019-05-01The student, Pramod Srinivasan, accepted the attached license on 2017-04-25 at 12:15.The student, Pramod Srinivasan, submitted this Thesis for approval on 2017-04-25 at 12:51.This Thesis was approved for publication on 2017-04-25 at 18:42.DSpace SAF Submission Ingestion Package generated from Vireo submission #11043 on 2017-08-10 at 14:32:36Made available in DSpace on 2017-08-10T19:52:23Z (GMT). No. of bitstreams: 2
SRINIVASAN-THESIS-2017.pdf: 1215687 bytes, checksum: 96dbc159bb19eab4d69b3df1dfcffd17 (MD5)
LICENSE.txt: 4214 bytes, checksum: 6d429007259258d1f9571b8e0eac0cf7 (MD5)
Previous issue date: 2017-04-25Embargo set by: Colleen Fallaw for item 102685
Lift date: 2019-08-10T21:25:30Z
Reason: Author requested closed access (OA after 2yrs) in Vireo ETD systemLimited Restriction Lifted for Item 102685 on 2019-08-11T09:15:17Z
Excited-state intramolecular proton transfer of 2-acetylindan-1,3-dione studied by ultrafast absorption and fluorescence spectroscopy
No full textWe employ transient absorption from the deep-UV to the visible region and fluorescence upconversion to investigate the photoinduced excited-state intramolecular proton-transfer dynamics in a biologically relevant drug molecule, 2-acetylindan- 1,3-dione. The molecule is a ß-diketone which in the electronic ground state exists as exocyclic enol with an intramolecular H-bond. Upon electronic excitation at 300 nm, the first excited state of the exocyclic enol is initially populated, followed by ultrafast proton transfer (±160 fs) to form the vibrationally hot endocyclic enol. Subsequently, solvent-induced vibrational relaxation takes place (±10 ps) followed by decay (±390 ps) to the corresponding ground state. © 2015 Author(s)1561sciescopu
Supersymmetric many-body systems from partial symmetries — integrability, localization and scrambling
No full textPartial symmetries are described by generalized group structures known as symmetric inverse semigroups. We use the algebras arising from these structures to realize supersymmetry in (0+1) dimensions and to build many-body quantum systems on a chain. This construction consists in associating appropriate supercharges to chain sites, in analogy to what is done in spin chains. For simple enough choices of supercharges, we show that the resulting states have a finite non-zero Witten index, which is invariant under perturbations, therefore defining supersymmetric phases of matter protected by the index. The Hamiltonians we obtain are integrable and display a spectrum containing both product and entangled states. By introducing disorder and studying the out-of-time-ordered correlators (OTOC), we find that these systems are in the many-body localized phase and do not thermalize. Finally., we reformulate a theorem relating the growth of the second Rényi entropy to the OTOC on a thermal state in terms of partial symmetries. © 2017, The Author(s)2211Nsciescopu
Validation for gene expression pattern by RT-PCR.
No full text<p>Validation for gene expression pattern by RT-PCR.</p
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