34 research outputs found
Phonological Contrast in Bai
This dissertation presents an account of synchronic phonological contrast for the Bai language. Bai is a Sino-Tibetan language primarily spoken in Yunnan Province in Southwest China. There is a sizable amount of published research on this language due to the large amount of Chinese-related basic vocabulary in Bai, which is of considerable interest in the field of Sino-Tibetan historical linguistics. However, most of the available references prioritize the ability to transcribe the observed contrastive syllables as distinct from one another instead of offering synchronic phonological analysis of this language. The proposal I present in this dissertation intends to fill this gap in the literature with phonological analysis of the consonant, vowel, and tone systems of the Erhai (Dali), Jianchuan, and Heqing varieties of Bai.
My phonological analysis assumes articulator-based distinctive features, syllable structure, time slots, and other commonly assumed phonological architecture to generate all well-formed phonological representations in this language. The proposal fundamentally differs from prior descriptions in that pre-nuclear glides are consistently treated as constituents of the onset and not as constituents of the rime of the Bai syllable. Along with this fixed syllable structure, underspecification and economy in underlying representations are argued to optimize the ratio of attested-to-possible syllables within the space of predicted syllable types. Furthermore, these principles are suggested to limit the range of surface phonological variation attested across speakers. Specific phonemena addressed in detail include spreading processes (such as palatalization), identification of merged tone categories, representation of the rhotic vowel, and epenthetic segments. The generalizations I identify are supported by descriptions of word-based evidence and phonetic data – both from the literature and collected through lexical elicitation in the field. The Zhaozhuang variety is explored in thorough detail and a syllable inventory of this variety with lexical examples for each syllable type glossed in English and Chinese is included in the appendicies of this dissertationPHDLinguisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/137117/1/opper_1.pd
Equilibration through local information exchange in networks
We study the equilibrium states of energy functions involving a large set of
real variables, defined on the links of sparsely connected networks, and
interacting at the network nodes, using the cavity and replica methods. When
applied to the representative problem of network resource allocation, an
efficient distributed algorithm is devised, with simulations showing full
agreement with theory. Scaling properties with the network connectivity and the
resource availability are found.Comment: v1: 7 pages, 1 figure, v2: 4 pages, 2 figures, simplified analysis
and more organized results, v3: minor change
Inference and Optimization of Real Edges on Sparse Graphs - A Statistical Physics Perspective
Inference and optimization of real-value edge variables in sparse graphs are
studied using the Bethe approximation and replica method of statistical
physics. Equilibrium states of general energy functions involving a large set
of real edge-variables that interact at the network nodes are obtained in
various cases. When applied to the representative problem of network resource
allocation, efficient distributed algorithms are also devised. Scaling
properties with respect to the network connectivity and the resource
availability are found, and links to probabilistic Bayesian approximation
methods are established. Different cost measures are considered and algorithmic
solutions in the various cases are devised and examined numerically. Simulation
results are in full agreement with the theory.Comment: 21 pages, 10 figures, major changes: Sections IV to VII updated,
Figs. 1 to 3 replace
Optimal Resource Allocation in Random Networks with Transportation Bandwidths
We apply statistical physics to study the task of resource allocation in
random sparse networks with limited bandwidths for the transportation of
resources along the links. Useful algorithms are obtained from recursive
relations. Bottlenecks emerge when the bandwidths are small, causing an
increase in the fraction of idle links. For a given total bandwidth per node,
the efficiency of allocation increases with the network connectivity. In the
high connectivity limit, we find a phase transition at a critical bandwidth,
above which clusters of balanced nodes appear, characterised by a profile of
homogenized resource allocation similar to the Maxwell's construction.Comment: 28 pages, 11 figure
Dynamical and Stationary Properties of On-line Learning from Finite Training Sets
The dynamical and stationary properties of on-line learning from finite
training sets are analysed using the cavity method. For large input dimensions,
we derive equations for the macroscopic parameters, namely, the student-teacher
correlation, the student-student autocorrelation and the learning force
uctuation. This enables us to provide analytical solutions to Adaline learning
as a benchmark. Theoretical predictions of training errors in transient and
stationary states are obtained by a Monte Carlo sampling procedure.
Generalization and training errors are found to agree with simulations. The
physical origin of the critical learning rate is presented. Comparison with
batch learning is discussed throughout the paper.Comment: 30 pages, 4 figure
Construction algorithm for the parity-machine
An algorithm for the training of a special multilayered feed-forward neural network is presented. The strategy is very similar to the well-known tiling algorithm, yet the resulting architecture is completely different. Neurons are added in one layer only. The output of the network is given by the product of its k many hidden neurons, which is for ±1 units the result of the parity-operation. The capacity αc of a network trained according to the algorithm is estimated for the storage of randomly defined classifications. The asymptotic dependence is found to be αc ~ k ln k for k→∞. This is in agreement with recent analytic results for the algorithm-independent storage capacity of a parity-machine.