10,494 research outputs found
On first-order expressibility of satisfiability in submodels
Let be regular cardinals, , let
be a sentence of the language in a given
signature, and let express the fact that holds
in a submodel, i.e., any model in the signature satisfies
if and only if some submodel of satisfies . It was shown in [1] that, whenever is in
in the signature having less than
functional symbols (and arbitrarily many predicate symbols), then
is equivalent to a monadic existential sentence in the
second-order language , and that for any
signature having at least one binary predicate symbol there exists in
such that is not equivalent
to any (first-order) sentence in . Nevertheless, in
certain cases are first-order expressible. In this note,
we provide several (syntactical and semantical) characterizations of the case
when is in and is
or a certain large cardinal
Editorial: technology in higher education and human performance
Improvement of learning and human development for sustainable development has been recognized as a key strategy for individuals and organizations to strengthen their competitive advantages. It becomes crucial to help adult learners and knowledge workers to improve their self-directed and life-long learning capabilities. Meanwhile, learning in this context has expanded from individual to community and organizational levels with new focuses on externalization of tacit knowledge, creation of new knowledge, retention of knowledge assets for continuous improvement, and cross-cultural communication. To adapt to these changes, technologies have played an increasingly important role in enhancing and transforming learning at individual, community, and organizational levels. Papers in this special issue are representative of ongoing research on integration of technology with learning for innovation and sustainable development in higher education institutions and organizational and community environments.published_or_final_versio
Maximum Entropy Linear Manifold for Learning Discriminative Low-dimensional Representation
Representation learning is currently a very hot topic in modern machine
learning, mostly due to the great success of the deep learning methods. In
particular low-dimensional representation which discriminates classes can not
only enhance the classification procedure, but also make it faster, while
contrary to the high-dimensional embeddings can be efficiently used for visual
based exploratory data analysis.
In this paper we propose Maximum Entropy Linear Manifold (MELM), a
multidimensional generalization of Multithreshold Entropy Linear Classifier
model which is able to find a low-dimensional linear data projection maximizing
discriminativeness of projected classes. As a result we obtain a linear
embedding which can be used for classification, class aware dimensionality
reduction and data visualization. MELM provides highly discriminative 2D
projections of the data which can be used as a method for constructing robust
classifiers.
We provide both empirical evaluation as well as some interesting theoretical
properties of our objective function such us scale and affine transformation
invariance, connections with PCA and bounding of the expected balanced accuracy
error.Comment: submitted to ECMLPKDD 201
Editorial: Technology for higher education, adult learning and human performance
This special issue is dedicated to technology-enabled approaches for improving higher education, adult learning, and human performance. Improvement of learning and human development for sustainable development has been recognized as a key strategy for individuals, institutions, and organizations to strengthen their competitive advantages. It is crucial to help adult learners and knowledge workers to improve their self-directed and life-long learning capabilities. Meanwhile, advances in technology have been increasingly enabling and facilitating learning and knowledge-related initiatives. They have largely extended learning opportunities through the provision of resource-rich and learner-centered environment, computer-based learning support, and expanded social interactions and networks. Papers in this special issue are representative of ongoing research on integration of technology with learning for innovative and sustainable development in higher education institutions and organizational and community environments.published_or_final_versio
Personalized Pancreatic Tumor Growth Prediction via Group Learning
Tumor growth prediction, a highly challenging task, has long been viewed as a
mathematical modeling problem, where the tumor growth pattern is personalized
based on imaging and clinical data of a target patient. Though mathematical
models yield promising results, their prediction accuracy may be limited by the
absence of population trend data and personalized clinical characteristics. In
this paper, we propose a statistical group learning approach to predict the
tumor growth pattern that incorporates both the population trend and
personalized data, in order to discover high-level features from multimodal
imaging data. A deep convolutional neural network approach is developed to
model the voxel-wise spatio-temporal tumor progression. The deep features are
combined with the time intervals and the clinical factors to feed a process of
feature selection. Our predictive model is pretrained on a group data set and
personalized on the target patient data to estimate the future spatio-temporal
progression of the patient's tumor. Multimodal imaging data at multiple time
points are used in the learning, personalization and inference stages. Our
method achieves a Dice coefficient of 86.8% +- 3.6% and RVD of 7.9% +- 5.4% on
a pancreatic tumor data set, outperforming the DSC of 84.4% +- 4.0% and RVD
13.9% +- 9.8% obtained by a previous state-of-the-art model-based method
Controllability and controller-observer design for a class of linear time-varying systems
“The final publication is available at Springer via http://dx.doi.org/10.1007/s10852-012-9212-6"In this paper a class of linear time-varying control systems is considered. The time variation consists of a scalar time-varying coefficient multiplying the state matrix of an otherwise time-invariant system. Under very weak assumptions of this coefficient, we show that the controllability can be assessed by an algebraic rank condition, Kalman canonical decomposition is possible, and we give a method for designing a linear state-feedback controller and Luenberger observer
Effects of Ixeris Chinensis (Thunb.) Nakai boiling water extract on hepatitis B viral activity and hepatocellular carcinoma
Background: Hepatitis B virus (HBV) infection and hepatocellular carcinoma are major diseases that affect the Taiwanese population. Therefore, the development of an alternative herbal medicine that can effectively treat these diseases is a research target. In this study, we tested Ixeris Chinensis (Thunb.) Nakai boiling water extract (ICTN BWE) in vitro and analysed its effects on the HBV and liver cancer.Materials and Methods: We used a human liver cancer cell line (Hep3B, a cell line that continuously secretes HBV particles into a medium) as an experimental model for the screening of various ICTN BWE concentrations and their effects on the HBV in vitro.Results: Our results showed that 75 μg/mL ICTN BWE downregulated the relative expression of the hepatitis B virus surface antigens (HBsAg) to 77.1%. Using the human liver cancer cell lines HuH-7 and HepG2, and 3-(4,5- dimethylthiazol-zyl)-2,5-diphenyl tetrazolium bromide (MTT) and tumour clonogenic assays, we then showed that ICTN BWE inhibits hepatocellular carcinoma growth.Conclusion: Fluorescent microscopy of DAPI(4',6-Diamidino-2-phenylindole)-stained nuclei and DNA fragmentation assays confirmed the inhibitory effects of ICTN BWE on liver tumour cell growth through induction of apoptosis.Keywords: herbal medicine, Ixeris Chinensis (Thunb.) Nakai, antihepatocellular carcinoma, apoptosis, antihepatitis B viru
Space-efficient Feature Maps for String Alignment Kernels
String kernels are attractive data analysis tools for analyzing string data.
Among them, alignment kernels are known for their high prediction accuracies in
string classifications when tested in combination with SVM in various
applications. However, alignment kernels have a crucial drawback in that they
scale poorly due to their quadratic computation complexity in the number of
input strings, which limits large-scale applications in practice. We address
this need by presenting the first approximation for string alignment kernels,
which we call space-efficient feature maps for edit distance with moves
(SFMEDM), by leveraging a metric embedding named edit sensitive parsing (ESP)
and feature maps (FMs) of random Fourier features (RFFs) for large-scale string
analyses. The original FMs for RFFs consume a huge amount of memory
proportional to the dimension d of input vectors and the dimension D of output
vectors, which prohibits its large-scale applications. We present novel
space-efficient feature maps (SFMs) of RFFs for a space reduction from O(dD) of
the original FMs to O(d) of SFMs with a theoretical guarantee with respect to
concentration bounds. We experimentally test SFMEDM on its ability to learn SVM
for large-scale string classifications with various massive string data, and we
demonstrate the superior performance of SFMEDM with respect to prediction
accuracy, scalability and computation efficiency.Comment: Full version for ICDM'19 pape
The Early Bird Catches The Term: Combining Twitter and News Data For Event Detection and Situational Awareness
Twitter updates now represent an enormous stream of information originating
from a wide variety of formal and informal sources, much of which is relevant
to real-world events. In this paper we adapt existing bio-surveillance
algorithms to detect localised spikes in Twitter activity corresponding to real
events with a high level of confidence. We then develop a methodology to
automatically summarise these events, both by providing the tweets which fully
describe the event and by linking to highly relevant news articles. We apply
our methods to outbreaks of illness and events strongly affecting sentiment. In
both case studies we are able to detect events verifiable by third party
sources and produce high quality summaries
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