323,970 research outputs found
Tenenbaum, Mrs Zisla, [No Service Number]
No full textThis record was harvested from a previous catalogue system and will be withdrawn in 2025. Information in this record may be superseded or incomplete. Visit this record in UMA's new catalogue at: https://archives.library.unimelb.edu.au/nodes/view/420869Surname: TENENBAUM. Given Name(s) or Initials: MRS ZISLA. Military Service Number or Last Known Location: [No Registration Number]. Missing, Wounded and Prisoner of War Enquiry Card Index Number: 44666.245579
Item: [2016.0049.53130] "Tenenbaum, Mrs Zisla, [No Service Number]
A comparison between IMDD and DPSK on the impact of MPI in all-Raman dispersion-compensated links
No full textWhat are you trying to tell me? A Bayesian model of how toddlers can simultaneously infer property extension and sampling processes
No full textURL to paper on conference site.Young human learners possess a remarkable ability to make inductive inferences from sparse data. Recent research suggests that children’s generalizations are sensitive to the process by which data are generated (i.e., teacher-driven vs. learner-driven sampling; Xu & Tenenbaum, 2007). In general, sampling process and properties of objects are tightly coupled; knowing how the data were sampled can inform your inference about property extensions, and vice versa. In real-world situations, however, both the extension of novel properties and the sampling process may be ambiguous. These situations commonly arise when children are learning socially from adults. How do children confront the challenge of simultaneously inferring both the property extension and the sampling process from a small amount of data? Here we present a Bayesian model showing how this joint inference problem can be solved. Consistent with the predictions of the model, two behavioral experiments suggest that toddlers (mean: 16 months) can use the relationship between a sample and a population to infer both the sampling process and the extent to which a non-obvious object property should be generalized.National Science Foundation (U.S.) (Faculty Early Career Development Award)Templeton Foundation (Award)James S. McDonnell Foundation (Collaborative Interdisciplinary Grant on Causal Reasoning
Fast Method To Obtain Insights On The λ0 Fluctuations In Optical Fibers
No full textStructures are observed in the noise spectrum propagated in dispersion-shifted fibers in the presence of two lasers. FWM interactions provide gain or increased loss at particular wavelengths that permits identification of the various fiber zero-dispersion wavelengths.355357Chávez Boggio, J.M., Tenenbaum, S., Guimarães, A., Fragnito, H.L., Observation of a Dip in the spectrum of noise after propagation in na Optical Fiber (2000) CLEO/EUROPEMazzali, C., Grosz, D.F., Fragnito, H.L., Simple method for measuring dispersion and nonlinear coefficient near the zero-dispersion wavelength of optical fibers (1999) Phot. Technol. Lett., 11, p. 25
Locality Sensitive Hashing for Set-Queries, Motivated by Group Recommendations
No full textLocality Sensitive Hashing (LSH) is an effective method to index a set of points such that we can efficiently find the nearest neighbors of a query point. We extend this method to our novel Set-query LSH (SLSH), such that it can find the nearest neighbors of a set of points, given as a query.
Let s(x,y) be the similarity between two points x and y. We define a similarity between a set Q and a point x by aggregating the similarities s(p,x) for all p∈ Q. For example, we can take s(p,x) to be the angular similarity between p and x (i.e., 1-(∠(x,p)/π)), and aggregate by arithmetic or geometric averaging, or taking the lowest similarity.
We develop locality sensitive hash families and data structures for a large set of such arithmetic and geometric averaging similarities, and analyze their collision probabilities. We also establish an analogous framework and hash families for distance functions. Specifically, we give a structure for the euclidean distance aggregated by either averaging or taking the maximum.
We leverage SLSH to solve a geometric extension of the approximate near neighbors problem. In this version, we consider a metric for which the unit ball is an ellipsoid and its orientation is specified with the query.
An important application that motivates our work is group recommendation systems. Such a system embeds movies and users in the same feature space, and the task of recommending a movie for a group to watch together, translates to a set-query Q using an appropriate similarity
On the Impact of Multipath Interference Noise in All-Raman Dispersion-Compensated Links
No full textJournal Pape
Four Wave Mixing Induced Changes In The Noise Spectrum In An Optical Fiber
No full textStructures are observed in the noise spectrum propagated in dispersion-shifted fibers in the presence of two lasers. FWM interactions provide gain or increased loss at particular wavelengths which permits identification of the various fiber zero-dispersion wavelengths.543WDD24/1WDD24/4Chávez Boggio, J.M., Tenenbaum, S., Guimarães, A., Fragnito, H.L., Observation of a dip in the spectrum of noise after propagation in an optical fiber CLEO/EUROPE 2000Mazzali, C., Grosz, D.F., Fragnito, H.L., Simple method for measuring dispersion and nonlinear coefficient near the zero-dispersion wavelength of optical fibers (1999) Phot. Technol. Lett., 11, p. 25
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