Linking Light Scalar Modes with A Small Positive Cosmological Constant in String Theory

Based on the studies in Type IIB string theory phenomenology, we conjecture that a good fraction of the meta-stable de Sitter vacua in the cosmic stringy landscape tend to have a very small cosmological constant $\Lambda$ when compared to either the string scale $M_S$ or the Planck scale $M_P$, i.e., $\Lambda \ll M_S^4 \ll M_P^4$. These low lying de Sitter vacua tend to be accompanied by very light scalar bosons/axions. Here we illustrate this phenomenon with the bosonic mass spectra in a set of Type IIB string theory flux compactification models. We conjecture that small $\Lambda$ with light bosons is generic among de Sitter solutions in string theory; that is, the smallness of $\Lambda$ and the existence of very light bosons (may be even the Higgs boson) are results of the statistical preference for such vacua in the landscape. We also discuss a scalar field $\phi^3/\phi^4$ model to illustrate how this statistical preference for a small $\Lambda$ remains when quantum loop corrections are included, thus bypassing the radiative instability problem.Comment: 35 pages, 7 figures; added subsection: Finite Temperature and Phase Transitio

Evaluating Feature Extraction Methods for Biomedical Word Sense Disambiguation

Evaluating Feature Extraction Methods for Biomedical WSD Clint Cuffy, Sam Henry and Bridget McInnes, PhD Virginia Commonwealth University, Richmond, Virginia, USA Introduction. Biomedical text processing is currently a high active research area but ambiguity is still a barrier to the processing and understanding of these documents. Many word sense disambiguation (WSD) approaches represent instances of an ambiguous word as a distributional context vector. One problem with using these vectors is noise -- information that is overly general and does not contribute to the word’s representation. Feature extraction approaches attempt to compensate for sparsity and reduce noise by transforming the data from high-dimensional space to a space of fewer dimensions. Currently, word embeddings [1] have become an increasingly popular method to reduce the dimensionality of vector representations. In this work, we evaluate word embeddings in a knowledge-based word sense disambiguation method. Methods. Context requiring disambiguation consists of an instance of an ambiguous word, and multiple denotative senses. In our method, each word is replaced with its respective word embedding and either summed or averaged to form a single instance vector representation. This also is performed for each sense of an ambiguous word using the sense’s definition obtained from the Unified Medical Language System (UMLS). We calculate the cosine similarity between each sense and instance vectors, and assign the instance the sense with the highest value. Evaluation. We evaluate our method on three biomedical WSD datasets: NLM-WSD, MSH-WSD and Abbrev. The word embeddings were trained on the titles and abstracts from the 2016 Medline baseline. We compare using two word embedding models, Skip-gram and Continuous Bag of Words (CBOW), and vary the word vector representational lengths, from one-hundred to one-thousand, and compare differences in accuracy. Results. The overall outcome of this method demonstrates fairly high accuracy at disambiguating biomedical instance context from groups of denotative senses. The results showed the Skip-gram model obtained a higher disambiguation accuracy than CBOW but the increase was not significant for all of the datasets. Similarly, vector representations of differing lengths displayed minimal change in results, often differing by mere tenths in percentage. We also compared our results to current state-of-the-art knowledge-based WSD systems, including those that have used word embeddings, showing comparable or higher disambiguation accuracy. Conclusion. Although biomedical literature can be ambiguous, our knowledge-based feature extraction method using word embeddings demonstrates a high accuracy in disambiguating biomedical text while eliminating variations of associated noise. In the future, we plan to explore additional dimensionality reduction methods and training data. [1] T. Mikolov, I. Sutskever, K. Chen, G. Corrado and J. Dean, Distributed representations of words and phrases and their compositionality, Advances in neural information processing systems, pp. 3111-3119, 2013.https://scholarscompass.vcu.edu/uresposters/1278/thumbnail.jp

Extending the CRESST-II commissioning run limits to lower masses

Motivated by the recent interest in light WIMPs of mass ~O(10 GeV), an extension of the elastic, spin-independent WIMP-nucleon cross-section limits resulting from the CRESST-II commissioning run (2007) are presented. Previously, these data were used to set cross-section limits from 1000 GeV down to ~17 GeV, using tungsten recoils, in 47.9 kg-days of exposure of calcium tungstate. Here, the overlap of the oxygen and calcium bands with the acceptance region of the commissioning run data set is reconstructed using previously published quenching factors. The resulting elastic WIMP cross section limits, accounting for the additional exposure of oxygen and calcium, are presented down to 5 GeV.Comment: 4 pages, 4 figure

Indirect Relatedness, Evaluation, and Visualization for Literature Based Discovery

The exponential growth of scientific literature is creating an increased need for systems to process and assimilate knowledge contained within text. Literature Based Discovery (LBD) is a well established field that seeks to synthesize new knowledge from existing literature, but it has remained primarily in the theoretical realm rather than in real-world application. This lack of real-world adoption is due in part to the difficulty of LBD, but also due to several solvable problems present in LBD today. Of these problems, the ones in most critical need of improvement are: (1) the over-generation of knowledge by LBD systems, (2) a lack of meaningful evaluation standards, and (3) the difficulty interpreting LBD output. We address each of these problems by: (1) developing indirect relatedness measures for ranking and filtering LBD hypotheses; (2) developing a representative evaluation dataset and applying meaningful evaluation methods to individual components of LBD; (3) developing an interactive visualization system that allows a user to explore LBD output in its entirety. In addressing these problems, we make several contributions, most importantly: (1) state of the art results for estimating direct semantic relatedness, (2) development of set association measures, (3) development of indirect association measures, (4) development of a standard LBD evaluation dataset, (5) division of LBD into discrete components with well defined evaluation methods, (6) development of automatic functional group discovery, and (7) integration of indirect relatedness measures and automatic functional group discovery into a comprehensive LBD visualization system. Our results inform future development of LBD systems, and contribute to creating more effective LBD systems

MEMS 411: The Rock Collecting Rover Design Project

The rock collecting over project challenged groups to construct a small,, remote controlled rover that is able to move and collect dice