4,570 research outputs found
Improving Teaching and Learning for English Language Learners
While there has been tremendous growth in the numbers of Hispanics and
English language learners (ELLs) in our public schools, there has been a lack of
educational opportunities offered to these students resulting in low educational
achievement and attainment. Additionally, increases in the linguistic and cultural
diversity of the student population have not been accompanied by diversification of the
corps of teachers and many teachers who serve ELLs are not certified or prepared
adequately to meet the linguistic and academic needs of second language learners. This
context, in which the potential of our nation's ELLs is not being met by our education
system, calls for research focusing on the education of ELLs. The three studies that
constitute this dissertation address two critical areas, reading and mathematics, by
documenting the cognitive reading strategies that middle school Hispanic ELLs utilize
and evaluating professional development activities for teachers of ELLs.
The mixed methods studies used student self-report data from the Reading
Strategies Questionnaire (RSQ) as well as observational and survey data from a professional development program. Means and standard deviations were reported from
the RSQ. Data from the observations of the professional development program were
coded to determine the topics that were addressed in the program. Results from the RSQ
indicate that Hispanic ELLs, in general, do not consistently adopt a strategic approach to
reading in English. Additionally, while professional development is one avenue to
improve the instruction that Hispanic ELLs receive, results from the observations
indicate that teachers receive professional development of limited quality and that little
of the professional development is connected to instruction for ELLs. Findings from this
series of studies can be utilized to inform reading instruction for ELLs and to enhance
professional development opportunities for teachers of ELLs
Deep Multi-Modal Classification of Intraductal Papillary Mucinous Neoplasms (IPMN) with Canonical Correlation Analysis
Pancreatic cancer has the poorest prognosis among all cancer types.
Intraductal Papillary Mucinous Neoplasms (IPMNs) are radiographically
identifiable precursors to pancreatic cancer; hence, early detection and
precise risk assessment of IPMN are vital. In this work, we propose a
Convolutional Neural Network (CNN) based computer aided diagnosis (CAD) system
to perform IPMN diagnosis and risk assessment by utilizing multi-modal MRI. In
our proposed approach, we use minimum and maximum intensity projections to ease
the annotation variations among different slices and type of MRIs. Then, we
present a CNN to obtain deep feature representation corresponding to each MRI
modality (T1-weighted and T2-weighted). At the final step, we employ canonical
correlation analysis (CCA) to perform a fusion operation at the feature level,
leading to discriminative canonical correlation features. Extracted features
are used for classification. Our results indicate significant improvements over
other potential approaches to solve this important problem. The proposed
approach doesn't require explicit sample balancing in cases of imbalance
between positive and negative examples. To the best of our knowledge, our study
is the first to automatically diagnose IPMN using multi-modal MRI.Comment: Accepted for publication in IEEE International Symposium on
Biomedical Imaging (ISBI) 201
Synchronization and Redundancy: Implications for Robustness of Neural Learning and Decision Making
Learning and decision making in the brain are key processes critical to
survival, and yet are processes implemented by non-ideal biological building
blocks which can impose significant error. We explore quantitatively how the
brain might cope with this inherent source of error by taking advantage of two
ubiquitous mechanisms, redundancy and synchronization. In particular we
consider a neural process whose goal is to learn a decision function by
implementing a nonlinear gradient dynamics. The dynamics, however, are assumed
to be corrupted by perturbations modeling the error which might be incurred due
to limitations of the biology, intrinsic neuronal noise, and imperfect
measurements. We show that error, and the associated uncertainty surrounding a
learned solution, can be controlled in large part by trading off
synchronization strength among multiple redundant neural systems against the
noise amplitude. The impact of the coupling between such redundant systems is
quantified by the spectrum of the network Laplacian, and we discuss the role of
network topology in synchronization and in reducing the effect of noise. A
range of situations in which the mechanisms we model arise in brain science are
discussed, and we draw attention to experimental evidence suggesting that
cortical circuits capable of implementing the computations of interest here can
be found on several scales. Finally, simulations comparing theoretical bounds
to the relevant empirical quantities show that the theoretical estimates we
derive can be tight.Comment: Preprint, accepted for publication in Neural Computatio
Delay-induced multiple stochastic resonances on scale-free neuronal networks
We study the effects of periodic subthreshold pacemaker activity and
time-delayed coupling on stochastic resonance over scale-free neuronal
networks. As the two extreme options, we introduce the pacemaker respectively
to the neuron with the highest degree and to one of the neurons with the lowest
degree within the network, but we also consider the case when all neurons are
exposed to the periodic forcing. In the absence of delay, we show that an
intermediate intensity of noise is able to optimally assist the pacemaker in
imposing its rhythm on the whole ensemble, irrespective to its placing, thus
providing evidences for stochastic resonance on the scale-free neuronal
networks. Interestingly thereby, if the forcing in form of a periodic pulse
train is introduced to all neurons forming the network, the stochastic
resonance decreases as compared to the case when only a single neuron is paced.
Moreover, we show that finite delays in coupling can significantly affect the
stochastic resonance on scale-free neuronal networks. In particular,
appropriately tuned delays can induce multiple stochastic resonances
independently of the placing of the pacemaker, but they can also altogether
destroy stochastic resonance. Delay-induced multiple stochastic resonances
manifest as well-expressed maxima of the correlation measure, appearing at
every multiple of the pacemaker period. We argue that fine-tuned delays and
locally active pacemakers are vital for assuring optimal conditions for
stochastic resonance on complex neuronal networks.Comment: 7 two-column pages, 5 figures; accepted for publication in Chao
Dual Monte Carlo and Cluster Algorithms
We discuss the development of cluster algorithms from the viewpoint of
probability theory and not from the usual viewpoint of a particular model. By
using the perspective of probability theory, we detail the nature of a cluster
algorithm, make explicit the assumptions embodied in all clusters of which we
are aware, and define the construction of free cluster algorithms. We also
illustrate these procedures by rederiving the Swendsen-Wang algorithm,
presenting the details of the loop algorithm for a worldline simulation of a
quantum 1/2 model, and proposing a free cluster version of the
Swendsen-Wang replica method for the random Ising model. How the principle of
maximum entropy might be used to aid the construction of cluster algorithms is
also discussed.Comment: 25 pages, 4 figures, to appear in Phys.Rev.
Improving Reading Comprehension in Dual Language Programs
The low achievement levels and high dropout rates of English Language Learners (ELLs) continue to be a great challenge for educators. One area that can provide us with useful information on how to improve the education of ELLs is dual language programs. Research in this area indicates that native language development is important for academic success. The present study examined the cognitive reading strategies of students enrolled in a dual language program. The results indicate that both language groups of students were using successful cognitive reading strategies to comprehend text. The study also describes how the questionnaire used in the study can assist teachers in diagnosing the type(s) of cognitive reading strategies that students use
Cluster update and recognition
We present a fast and robust cluster update algorithm that is especially
efficient in implementing the task of image segmentation using the method of
superparamagnetic clustering. We apply it to a Potts model with spin
interactions that are are defined by gray-scale differences within the image.
Motivated by biological systems, we introduce the concept of neural inhibition
to the Potts model realization of the segmentation problem. Including the
inhibition term in the Hamiltonian results in enhanced contrast and thereby
significantly improves segmentation quality. As a second benefit we can - after
equilibration - directly identify the image segments as the clusters formed by
the clustering algorithm. To construct a new spin configuration the algorithm
performs the standard steps of (1) forming clusters and of (2) updating the
spins in a cluster simultaneously. As opposed to standard algorithms, however,
we share the interaction energy between the two steps. Thus the update
probabilities are not independent of the interaction energies. As a
consequence, we observe an acceleration of the relaxation by a factor of 10
compared to the Swendson and Wang procedure.Comment: 4 pages, 2 figure
A Complex Network Approach to Topographical Connections
The neuronal networks in the mammals cortex are characterized by the
coexistence of hierarchy, modularity, short and long range interactions,
spatial correlations, and topographical connections. Particularly interesting,
the latter type of organization implies special demands on the evolutionary and
ontogenetic systems in order to achieve precise maps preserving spatial
adjacencies, even at the expense of isometry. Although object of intensive
biological research, the elucidation of the main anatomic-functional purposes
of the ubiquitous topographical connections in the mammals brain remains an
elusive issue. The present work reports on how recent results from complex
network formalism can be used to quantify and model the effect of topographical
connections between neuronal cells over a number of relevant network properties
such as connectivity, adjacency, and information broadcasting. While the
topographical mapping between two cortical modules are achieved by connecting
nearest cells from each module, three kinds of network models are adopted for
implementing intracortical connections (ICC), including random,
preferential-attachment, and short-range networks. It is shown that, though
spatially uniform and simple, topographical connections between modules can
lead to major changes in the network properties, fostering more effective
intercommunication between the involved neuronal cells and modules. The
possible implications of such effects on cortical operation are discussed.Comment: 5 pages, 5 figure
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