11 research outputs found
Bootstrapping Multilingual AMR with Contextual Word Alignments
We develop high performance multilingualAbstract Meaning Representation (AMR)
sys-tems by projecting English AMR annotationsto other languages with weak
supervision. Weachieve this goal by bootstrapping transformer-based
multilingual word embeddings, in partic-ular those from cross-lingual RoBERTa
(XLM-R large). We develop a novel technique forforeign-text-to-English AMR
alignment, usingthe contextual word alignment between En-glish and foreign
language tokens. This wordalignment is weakly supervised and relies onthe
contextualized XLM-R word embeddings.We achieve a highly competitive
performancethat surpasses the best published results forGerman, Italian,
Spanish and Chinese
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Fluctuation analysis of nonequilibrium limit cycle oscillators : Application to hair cells
Mechanical detection of the auditory system displays exquisite sensitivity, with the inner ear capable of detecting pressure waves that result in Angstrom-scale displacements. Detection in the inner ear is performed by mechanically sensitive hair cells, named for the bundles of stereocilia that protrude from their surfaces. The inner ear is also highly nonlinear in nature, exhibiting sharply tuned frequency selectivity and compression of dynamic range. Several experiments have consistently shown that the hair cells and their hair bundles are not just passive sensory detectors, rather underlying their exemplary behavior is an internal active mechanical process which is also adaptive in nature. Furthermore, this active process leads to an inherent mechanical instability manifested in the spontaneous limit cycle oscillations of the hair bundles. A number of theories based on nonlinear dynamics have described these active bundles using complex biophysical models as well as using a relatively simple two-dimensional mathematical model that exhibits a supercritical Hopf bifurcation. In this dissertation which is theoretical in nature, with the spontaneously oscillating inner ear hair bundle as our model biological system, we study the effects of stochasticity on nonlinear oscillators driven by internal active processes. First, we develop a framework for the general interpretation of such dynamical systems near a limit cycle. We demonstrate that in the presence of noise the phase of the limit cycle oscillator diffuses while fluctuations in the directions locally orthogonal to the cycle display a Lorentzian power spectrum. Further we identify two mechanisms that underlie the complex frequency dependence of the diffusive dynamics. In the subsequent chapter, we detail how the effects of stochasticity maybe observed with respect to the change in shape and size of the mean limit cycle as well. In particular, we show that the noise-induced distortion of the limit cycle generically leads to its rounding through elimination of sharp features. Conversely, using a theoretical criterion one may identify limit cycle regions most susceptible to such distortion and obtain more meaningful parametric fits of dynamical models from experimental data. In the third chapter of this thesis we study fluctuation-dissipation relations in computationally-driven, nonequilibrium limit-cycle oscillators. A computational drive is loosely analogous to the adaptive internal activity that powers the spontaneous oscillations of the hair cells. It measures the current state of the system and modifies its power input accordingly. We observe that computationally-driven systems not only violate the equilibrium fluctuation-dissipation theorem (FDT) but also a generalized FDT. Thus in turn we propose that the breakdown of this generalized theorem may be used as a tool to broadly identify the presence and effect of such drives within biological systems. Lastly, by quantifying the computing ability of these drives we seek to derive a new generalized fluctuation-dissipation theorem which can be satisfied by complex computationally-driven biological systems such as the inner ear
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Fluctuation analysis of nonequilibrium limit cycle oscillators : Application to hair cells
Mechanical detection of the auditory system displays exquisite sensitivity, with the inner ear capable of detecting pressure waves that result in Angstrom-scale displacements. Detection in the inner ear is performed by mechanically sensitive hair cells, named for the bundles of stereocilia that protrude from their surfaces. The inner ear is also highly nonlinear in nature, exhibiting sharply tuned frequency selectivity and compression of dynamic range. Several experiments have consistently shown that the hair cells and their hair bundles are not just passive sensory detectors, rather underlying their exemplary behavior is an internal active mechanical process which is also adaptive in nature. Furthermore, this active process leads to an inherent mechanical instability manifested in the spontaneous limit cycle oscillations of the hair bundles. A number of theories based on nonlinear dynamics have described these active bundles using complex biophysical models as well as using a relatively simple two-dimensional mathematical model that exhibits a supercritical Hopf bifurcation. In this dissertation which is theoretical in nature, with the spontaneously oscillating inner ear hair bundle as our model biological system, we study the effects of stochasticity on nonlinear oscillators driven by internal active processes. First, we develop a framework for the general interpretation of such dynamical systems near a limit cycle. We demonstrate that in the presence of noise the phase of the limit cycle oscillator diffuses while fluctuations in the directions locally orthogonal to the cycle display a Lorentzian power spectrum. Further we identify two mechanisms that underlie the complex frequency dependence of the diffusive dynamics. In the subsequent chapter, we detail how the effects of stochasticity maybe observed with respect to the change in shape and size of the mean limit cycle as well. In particular, we show that the noise-induced distortion of the limit cycle generically leads to its rounding through elimination of sharp features. Conversely, using a theoretical criterion one may identify limit cycle regions most susceptible to such distortion and obtain more meaningful parametric fits of dynamical models from experimental data. In the third chapter of this thesis we study fluctuation-dissipation relations in computationally-driven, nonequilibrium limit-cycle oscillators. A computational drive is loosely analogous to the adaptive internal activity that powers the spontaneous oscillations of the hair cells. It measures the current state of the system and modifies its power input accordingly. We observe that computationally-driven systems not only violate the equilibrium fluctuation-dissipation theorem (FDT) but also a generalized FDT. Thus in turn we propose that the breakdown of this generalized theorem may be used as a tool to broadly identify the presence and effect of such drives within biological systems. Lastly, by quantifying the computing ability of these drives we seek to derive a new generalized fluctuation-dissipation theorem which can be satisfied by complex computationally-driven biological systems such as the inner ear
Using a Fluctuation Analysis of Limit Cycle Oscillations in Inner Ear Hair Bundles as a New Test of Low Dimensional Dynamical Models
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The Interplay of Relevance, Sensory Uncertainty and Statistical Learning Influences Auditory Categorization
Auditory perception requires categorizing sound sequences, such as speech, into classes, such as syllables. Such categorization depends not only on the sequences’ acoustic waveform, but also on the listener’s sensory uncertainty, any individual sound’s relevance to the task, and learning the temporal statistics of the acoustic environment. Although previous studies have explored the effects of these perceptual and cognitive factors in separation, whether and how their interplay shapes categorization is unknown. Here, we tested this interplay by measuring human participants’ performance on a multi-tone categorization task. Using a Bayesian framework, we found that task-relevant tones contributed more to category choice than task-irrelevant tones, confirming that participants combined information about sensory features with task relevance. Conversely, poor estimates of tones’ task relevance or high sensory uncertainty adversely impacted category choice. Learning temporal statistics of sound category also affected decisions – the magnitude of this effect correlated inversely with participants’ relevance estimates. These results demonstrate that humans differentially weigh sensory uncertainty, task relevance and statistical learning, providing a novel understanding of sensory decision-making under real-life behavioral demands