English syllable confusion and imitation in Korean bilingual and monolingual children and adults

In the present study, Korean-English bilingual (KEB) and Korean monolingual (KM) children, between the ages of 8 and 13 years, and KEB adults, ages 18 and older, were examined with one speech perception task, called the Nonsense Syllable Confusion Matrix (NSCM) task (Allen, 2005), and two production tasks, called the Nonsense Syllable Imitation Task (NSIT) and the Nonword Repetition Task (NRT; Dollaghan & Campbell, 1998). The present study examined (a) which English sounds on the NSCM task were identified less well, presumably due to interference from Korean phonology, in bilinguals learning English as a second language (L2) and in monolinguals learning English as a foreign language (FL); (b) which English phonemes on the NSIT were more challenging for bilinguals and monolinguals to produce; (c) whether perception on the NSCM task is related to production on the NSIT, or phonological awareness, as measured by the NRT; and (d) whether perception and production differ in three age-language status groups (i.e., KEB children, KEB adults, and KM children) and in three proficiency subgroups of KEB children (i.e., English-dominant, ED; balanced, BAL; and Korean-dominant, KD). In order to determine English proficiency in each group, language samples were extensively and rigorously analyzed, using software, called Systematic Analysis of Language Transcripts (SALT). Length of samples in complete and intelligible utterances, number of different and total words (NDW and NTW, respectively), speech rate in words per minute (WPM), and number of grammatical errors, mazes, and abandoned utterances were measured and compared among the three initial groups and the three proficiency subgroups. Results of the language sample analysis (LSA) showed significant group differences only between the KEBs and the KM children, but not between the KEB children and adults. Nonetheless, compared to normative means (from a sample length- and age-matched database provided by SALT), the KEB adult group and the KD subgroup produced English at significantly slower speech rates than expected for monolingual, English-speaking counterparts. Two existing models of bilingual speech perception and production—the Speech Learning Model or SLM (Flege, 1987, 1992) and the Perceptual Assimilation Model or PAM (Best, McRoberts, & Sithole, 1988; Best, McRoberts, & Goodell, 2001)—were considered to see if they could account for the perceptual and production patterns evident in the present study. The selected English sounds for stimuli in the NSCM task and the NSIT were 10 consonants, /p, b, k, g, f, θ, s, z, ʧ, ʤ/, and 3 vowels /I, ɛ, æ/, which were used to create 30 nonsense syllables in a consonant-vowel structure. Based on phonetic or phonemic differences between the two languages, English sounds were categorized either as familiar sounds—namely, English sounds that are similar, but not identical, to L1 Korean, including /p, k, s, ʧ, ɛ/—or unfamiliar sounds—namely, English sounds that are new to L1, including /b, g, f, θ, z, ʤ, I, æ/. The results of the NSCM task showed that (a) consonants were perceived correctly more often than vowels, (b) familiar sounds were perceived correctly more often than unfamiliar ones, and (c) familiar consonants were perceived correctly more often than unfamiliar ones across the three age-language status groups and across the three proficiency subgroups; and (d) the KEB children perceived correctly more often than the KEB adults, the KEB children and adults perceived correctly more often than the KM children, and the ED and BAL subgroups perceived correctly more often than the KD subgroup. The results of the NSIT showed (a) consonants were produced more accurately than vowels, and (b) familiar sounds were produced more accurately than unfamiliar ones, across the three age-language status groups. Also, (c) familiar consonants were produced more accurately than unfamiliar ones in the KEB and KM child groups, and (d) unfamiliar vowels were produced more accurately than a familiar one in the KEB child group, but the reverse was true in the KEB adult and KM child groups. The KEB children produced sounds correctly significantly more often than the KM children and the KEB adults, though the percent correct differences were smaller than for perception. Production differences were not found among the three proficiency subgroups. Perception on the NSCM task was compared to production on the NSIT and NRT. Weak positive correlations were found between perception and production (NSIT) for unfamiliar consonants and sounds, whereas a weak negative correlation was found for unfamiliar vowels. Several correlations were significant for perceptual performance on the NSCM task and overall production performance on the NRT: for unfamiliar consonants, unfamiliar vowels, unfamiliar sounds, consonants, vowels, and overall performance on the NSCM task. Nonetheless, no significant correlation was found between production on the NSIT and NRT. Evidently these are two very different production tasks, where immediate imitation of single syllables on the NSIT results in high performance for all groups. Findings of the present study suggest that (a) perception and production of L2 consonants differ from those of vowels; (b) perception and production of L2 sounds involve an interaction of sound type and familiarity; (c) a weak relation exists between perception and production performance for unfamiliar sounds; and (d) L2 experience generally predicts perceptual and production performance. The present study yields several conclusions. The first is that familiarity of sounds is an important influence on L2 learning, as claimed by both SLM and PAM. In the present study, familiar sounds were perceived and produced correctly more often than unfamiliar ones in most cases, in keeping with PAM, though experienced L2 learners (i.e., the KEB children) produced unfamiliar vowels better than familiar ones, in keeping with SLM. Nonetheless, the second conclusion is that neither SLM nor PAM consistently and thoroughly explains the results of the present study. This is because both theories assume that the influence of L1 on the perception of L2 consonants and vowels works in the same way as for production of them. The third and fourth conclusions are two proposed arguments: that perception and production of consonants are different than for vowels, and that sound type interacts with familiarity and L2 experience. These two arguments can best explain the current findings. These findings may help us to develop educational curricula for bilingual individuals listening to and articulating English. Further, the extensive analysis of spontaneous speech in the present study should contribute to the specification of parameters for normal language development and function in Korean-English bilingual children and adults.

Studies of Protein-Protein and Protein-Water Interactions by Small Angle X-Ray Scattering, Terahertz Spectroscopy, ASMOS, And Computer Simulation

The protein folding problem has been one of the most challenging subjects in biological physics due to its complexity. Energy landscape theory based on statistical mechanics provides a thermodynamic interpretation of the protein folding process. We have been working to answer fundamental questions about protein-protein and protein-water interactions, which are very important for describing the energy landscape surface of proteins correctly. At first, we present a new method for computing protein-protein interaction potentials of solvated proteins directly from SAXS data. An ensemble of proteins was modeled by Metropolis Monte Carlo and Molecular Dynamics simulations, and the global X-ray scattering of the whole model ensemble was computed at each snapshot of the simulation. The interaction potential model was optimized and iterated by a Levenberg-Marquardt algorithm. Secondly, we report that terahertz spectroscopy directly probes hydration dynamics around proteins and determines the size of the dynamical hydration shell. We also present the sequence and pH-dependence of the hydration shell and the effect of the hydrophobicity. On the other hand, kinetic terahertz absorption (KITA) spectroscopy is introduced to study the refolding kinetics of ubiquitin and its mutants. KITA results are compared to small angle X-ray scattering, tryptophan fluorescence, and circular dichroism results. We propose that KITA monitors the rearrangement of hydrogen bonding during secondary structure formation. Finally, we present development of the automated single molecule operating system (ASMOS) for a high throughput single molecule detector, which levitates a single protein molecule in a 10 µm diameter droplet by the laser guidance. I also have performed supporting calculations and simulations with my own program codes.

Shape and chemically anisotropic colloidal particles: A theoretical study of their structure, phase behavior, and slow dynamics

A detailed non-equilibrium state diagram of shape-anisotropic particle fluids is constructed. The effects of particle shape are explored using Naive Mode Coupling Theory (NMCT), and a single particle Non-linear Langevin Equation (NLE) theory. The dynamical behavior of non-ergodic fluids are discussed. We employ a rotationally frozen approach to NMCT in order to determine a transition to center of mass (translational) localization. Both ideal and kinetic glass transitions are found to be highly shape dependent, and uniformly increase with particle dimensionality. The glass transition volume fraction of quasi 1- and 2- dimensional particles fall monotonically with the number of sites (aspect ratio), while 3-dimensional particles display a non-monotonic dependence of glassy vitrification on the number of sites. Introducing interparticle attractions results in a far more complex state diagram. The ideal non-ergodic boundary shows a glass-fluid-gel re-entrance previously predicted for spherical particle fluids. The non-ergodic region of the state diagram presents qualitatively different dynamics in different regimes. They are qualified by the different behaviors of the NLE dynamic free energy. The caging dominated, repulsive glass regime is characterized by long localization lengths and barrier locations, dictated by repulsive hard core interactions, while the bonding dominated gel region has short localization lengths (commensurate with the attraction range), and barrier locations. There exists a small region of the state diagram which is qualified by both glassy and gel localization lengths in the dynamic free energy. A much larger (high volume fraction, and high attraction strength) region of phase space is characterized by short gel-like localization lengths, and long barrier locations. The region is called the attractive glass and represents a 2-step relaxation process whereby a particle first breaks attractive physical bonds, and then escapes its topological cage. The dynamic fragility of fluids are highly particle shape dependent. It increases with particle dimensionality and falls with aspect ratio for quasi 1- and 2- dimentional particles. An ultralocal limit analysis of the NLE theory predicts universalities in the behavior of relaxation times, and elastic moduli. The equlibrium phase diagram of chemically anisotropic Janus spheres and Janus rods are calculated employing a mean field Random Phase Approximation. The calculations for Janus rods are corroborated by the full liquid state Reference Interaction Site Model theory. The Janus particles consist of attractive and repulsive regions. Both rods and spheres display rich phase behavior. The phase diagrams of these systems display fluid, macrophase separated, attraction driven microphase separated, repulsion driven microphase separated and crystalline regimes. Macrophase separation is predicted in highly attractive low volume fraction systems. Attraction driven microphase separation is charaterized by long length scale divergences, where the ordering length scale determines the microphase ordered structures. The ordering length scale of repulsion driven microphase separation is determined by the repulsive range. At the high volume fractions, particles forgo the enthalpic considerations of attractions and repulsions to satisfy hard core constraints and maximize vibrational entropy. This results in site length scale ordering in rods, and the sphere length scale ordering in Janus spheres, i.e., crystallization. A change in the Janus balance of both rods and spheres results in quantitative changes in spinodal temperatures and the position of phase boundaries. However, a change in the block sequence of Janus rods causes qualitative changes in the type of microphase ordered state, and induces prominent features (such as the Lifshitz point) in the phase diagrams of these systems. A detailed study of the number of nearest neighbors in Janus rod systems reflect a deep connection between this local measure of structure, and the structure factor which represents the most global measure of order.

Paths to peace: conflict management trajectories in militarized interstate disputes

When multiple third-parties (states, coalitions, and international organizations) intervene in the same conflict, do their efforts inform one another? Anecdotal evidence suggests such a possibility, but research to date has not attempted to model this interdependence directly. The current project breaks with that tradition. In particular, it proposes three competing explanations of how previous intervention efforts affect current intervention decisions: a cost model (and a variant on it, a limited commitments model), a learning model, and a random model. After using a series of Markov transition (regime-switching) models to evaluate conflict management behavior within militarized interstate disputes in the 1946-2001 period, this study concludes that third-party intervention efforts inform one another. More specifically, third-parties examine previous efforts and balance their desire to manage conflict with their need to minimize intervention costs (the cost and limited commitments models). As a result, third-parties intervene regularly using verbal pleas and mediation, but rely significantly less frequently on legal, administrative, or peace operations strategies. This empirical threshold to the intervention costs that third-parties are willing to bear has strong theoretical foundations and holds across different time periods and third-party actors. Furthermore, the analysis indicates that the first third-party to intervene in a conflict is most likely to use a strategy designed to help the disputants work toward a resolution of their dispute. After this initial intervention, the level of third-party involvement declines and often devolves into a series of verbal pleas for peace. Such findings cumulatively suggest that disputants hold the key to effective conflict management. If the disputants adopt and maintain an extreme bargaining position or fail to encourage third-parties to accept greater intervention costs, their dispute will receive little more than verbal pleas for negotiations and peace.

Automated Discovery of Social Networks in Online Learning Communities

As a way to gain greater insights into the operation of online communities, this dissertation applies automated text mining techniques to text-based communication to identify, describe and evaluate underlying social networks among online community members. The main thrust of the study is to automate the discovery of social ties that form between community members, using only the digital footprints left behind in their online forum postings. Currently, one of the most common but time consuming methods for discovering social ties between people is to ask questions about their perceived social ties. However, such a survey is difficult to collect due to the high investment in time associated with data collection and the sensitive nature of the types of questions that may be asked. To overcome these limitations, the dissertation presents a new, content-based method for automated discovery of social networks from threaded discussions, referred to as ‘name network’. As a case study, the proposed automated method is evaluated in the context of online learning communities. The results suggest that the proposed ‘name network’ method for collecting social network data is a viable alternative to costly and time-consuming collection of users’ data using surveys. The study also demonstrates how social networks produced by the ‘name network’ method can be used to study online classes and to look for evidence of collaborative learning in online learning communities. For example, educators can use name networks as a real time diagnostic tool to identify students who might need additional help or students who may provide such help to others. Future research will evaluate the usefulness of the ‘name network’ method in other types of online communities.

Datasets, features, learning, and models in visual recognition

Visual recognition is a fundamental research topic in computer vision. This dissertation explores datasets, features, learning, and models used for visual recognition. In order to train visual models and evaluate different recognition algorithms, this dissertation develops an approach to collect object image datasets on web pages using an analysis of text around the image and of image appearance. This method exploits established online knowledge resources (Wikipedia pages for text; Flickr and Caltech data sets for images). The resources provide rich text and object appearance information. This dissertation describes results on two datasets. The first is Berg’s collection of 10 animal categories; on this dataset, we significantly outperform previous approaches. On an additional set of 5 categories, experimental results show the effectiveness of the method. Images are represented as features for visual recognition. This dissertation introduces a text-based image feature and demonstrates that it consistently improves performance on hard object classification problems. The feature is built using an auxiliary dataset of images annotated with tags, downloaded from the Internet. Image tags are noisy. The method obtains the text features of an unannotated image from the tags of its k-nearest neighbors in this auxiliary collection. A visual classifier presented with an object viewed under novel circumstances (say, a new viewing direction) must rely on its visual examples. This text feature may not change, because the auxiliary dataset likely contains a similar picture. While the tags associated with images are noisy, they are more stable when appearance changes. The performance of this feature is tested using PASCAL VOC 2006 and 2007 datasets. This feature performs well; it consistently improves the performance of visual object classifiers, and is particularly effective when the training dataset is small. With more and more collected training data, computational cost becomes a bottleneck, especially when training sophisticated classifiers such as kernelized SVM. This dissertation proposes a fast training algorithm called Stochastic Intersection Kernel Machine (SIKMA). This proposed training method will be useful for many vision problems, as it can produce a kernel classifier that is more accurate than a linear classifier, and can be trained on tens of thousands of examples in two minutes. It processes training examples one by one in a sequence, so memory cost is no longer the bottleneck to process large scale datasets. This dissertation applies this approach to train classifiers of Flickr groups with many group training examples. The resulting Flickr group prediction scores can be used to measure image similarity between two images. Experimental results on the Corel dataset and a PASCAL VOC dataset show the learned Flickr features perform better on image matching, retrieval, and classification than conventional visual features. Visual models are usually trained to best separate positive and negative training examples. However, when recognizing a large number of object categories, there may not be enough training examples for most objects, due to the intrinsic long-tailed distribution of objects in the real world. This dissertation proposes an approach to use comparative object similarity. The key insight is that, given a set of object categories which are similar and a set of categories which are dissimilar, a good object model should respond more strongly to examples from similar categories than to examples from dissimilar categories. This dissertation develops a regularized kernel machine algorithm to use this category dependent similarity regularization. Experiments on hundreds of categories show that our method can make significant improvement for categories with few or even no positive examples.