On first-order expressibility of satisfiability in submodels

Let $\kappa,\lambda$ be regular cardinals, $\lambda\le\kappa$, let $\varphi$ be a sentence of the language $\mathcal L_{\kappa,\lambda}$ in a given signature, and let $\vartheta(\varphi)$ express the fact that $\varphi$ holds in a submodel, i.e., any model $\mathfrak A$ in the signature satisfies $\vartheta(\varphi)$ if and only if some submodel $\mathfrak B$ of $\mathfrak A$ satisfies $\varphi$. It was shown in [1] that, whenever $\varphi$ is in $\mathcal L_{\kappa,\omega}$ in the signature having less than $\kappa$ functional symbols (and arbitrarily many predicate symbols), then $\vartheta(\varphi)$ is equivalent to a monadic existential sentence in the second-order language $\mathcal L^{2}_{\kappa,\omega}$, and that for any signature having at least one binary predicate symbol there exists $\varphi$ in $\mathcal L_{\omega,\omega}$ such that $\vartheta(\varphi)$ is not equivalent to any (first-order) sentence in $\mathcal L_{\infty,\omega}$. Nevertheless, in certain cases $\vartheta(\varphi)$ are first-order expressible. In this note, we provide several (syntactical and semantical) characterizations of the case when $\vartheta(\varphi)$ is in $\mathcal L_{\kappa,\kappa}$ and $\kappa$ is $\omega$ 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