Analysis of charged particle emission sources and coalescence in E/A = 61 MeV 36^{36}Ar + 27^{27}Al, 112^{112}Sn and 124^{124}Sn collisions

Single-particle kinetic energy spectra and two-particle small angle correlations of protons ($p$), deuterons ($d$) and tritons ($t$) have been measured simultaneously in 61A MeV $^{36}$Ar + $^{27}$Al, $^{112}$Sn and $^{124}$Sn collisions. Characteristics of the emission sources have been derived from a ``source identification plot'' ($\beta_{source}$--$E_{CM}$ plot), constructed from the single-particle invariant spectra, and compared to the complementary results from two-particle correlation functions. Furthermore, the source identification plot has been used to determine the conditions when the coalescence mechanism can be applied for composite particles. In our data, this is the case only for the Ar + Al reaction, where $p$, $d$ and $t$ are found to originate from a common source of emission (from the overlap region between target and projectile). In this case, the coalescence model parameter, $\tilde{p}_0$ -- the radius of the complex particle emission source in momentum space, has been analyzed.Comment: 20 pages, 5 figures, submitted to Nuclear Physics

Facilitating Access to Health Coverage and Care by Advancing Health Insurance Literacy

Although Massachusetts currently has the highest rate of health insurance coverage in the nation, reports suggest health care consumers do not fully understand how their insurance works. Thus, the insured and uninsured populations alike need ongoing support in order to develop health insurance literacy, defined as the degree to which individuals obtain, process, and understand information about health insurance in order to make informed decisions about choosing and using their coverage, which in turn can lead to positive health outcomes. Educating consumers and giving them tools and resources are strategies that advance health insurance literacy. Since 2001, the Blue Cross Blue Shield of Massachusetts Foundation (the Foundation) has awarded over $5 million to community health centers and community-based organizations throughout Massachusetts, through its Connecting Consumers with Care (CCC) grant program, to conduct outreach, provide education and help consumers enroll in health insurance and access primary care. In 2015, the Foundation focused its CCC grant activities to improve health insurance literacy and engage consumers to utilize the health care system more effectively. Grantees have collected data on common measures, using adaptable data collection tools (e.g., brief client surveys), to assess changes in clients\u27 knowledge, confidence, and/or preparedness to better navigate complex systems of coverage and care. The poster presentation will discuss: - the importance of health insurance literacy and its relevance to improving population and community health - strategies currently used to increase health insurance literacy among diverse populations, including successes and challenges - how the impact of these strategies was measured - how assessments were designed to reflect consumers\u27 voices

Zero-Shot Hashing via Transferring Supervised Knowledge

Hashing has shown its efficiency and effectiveness in facilitating large-scale multimedia applications. Supervised knowledge e.g. semantic labels or pair-wise relationship) associated to data is capable of significantly improving the quality of hash codes and hash functions. However, confronted with the rapid growth of newly-emerging concepts and multimedia data on the Web, existing supervised hashing approaches may easily suffer from the scarcity and validity of supervised information due to the expensive cost of manual labelling. In this paper, we propose a novel hashing scheme, termed \emph{zero-shot hashing} (ZSH), which compresses images of "unseen" categories to binary codes with hash functions learned from limited training data of "seen" categories. Specifically, we project independent data labels i.e. 0/1-form label vectors) into semantic embedding space, where semantic relationships among all the labels can be precisely characterized and thus seen supervised knowledge can be transferred to unseen classes. Moreover, in order to cope with the semantic shift problem, we rotate the embedded space to more suitably align the embedded semantics with the low-level visual feature space, thereby alleviating the influence of semantic gap. In the meantime, to exert positive effects on learning high-quality hash functions, we further propose to preserve local structural property and discrete nature in binary codes. Besides, we develop an efficient alternating algorithm to solve the ZSH model. Extensive experiments conducted on various real-life datasets show the superior zero-shot image retrieval performance of ZSH as compared to several state-of-the-art hashing methods.Comment: 11 page

Responding to Climate Change: The Economy and Economics - Part of the Problem and Solution

The Climate Change Starter’s Guide provides an introduction and overview for education planners and practitioners on the wide range of issues relating to climate change and climate change education, including causes, impacts, mitigation and adaptation strategies, as well as some broad political and economic principles. The aim of this guide is to serve as a starting point for mainstreaming climate change education into school curricula. It has been created to enable education planners and practitioners to understand the issues at hand, to review and analyse their relevance to particular national and local contexts, and to facilitate the development of education policies, curricula, programmes and lesson plans. The guide covers four major thematic areas: 1. the science of climate change, which explains the causes and observed changes; 2. the social and human aspects of climate change including gender, health, migration, poverty and ethics; 3. policy responses to climate change including measures for mitigation and adaptation; and 4. education approaches including education for sustainable development, disaster reduction and sustainable lifestyles. A selection of key resources in the form of publication titles or websites for further reading is provided after each of the thematic sections

Many-electron tunneling in atoms

A theoretical derivation is given for the formula describing N-electron ionization of atom by a dc field and laser radiation in tunneling regime. Numerical examples are presented for noble gases atoms.Comment: 11 pages, 1 EPS figure, submitted to JETP (Jan 99

Phase Decomposition and Chemical Inhomogeneity in Nd2-xCexCuO4

Extensive X-ray and neutron scattering experiments and additional transmission electron microscopy results reveal the partial decomposition of Nd2-xCexCuO4 (NCCO) in a low-oxygen-fugacity environment such as that typically realized during the annealing process required to create a superconducting state. Unlike a typical situation in which a disordered secondary phase results in diffuse powder scattering, a serendipitous match between the in-plane lattice constant of NCCO and the lattice constant of one of the decomposition products, (Nd,Ce)2O3, causes the secondary phase to form an oriented, quasi-two-dimensional epitaxial structure. Consequently, diffraction peaks from the secondary phase appear at rational positions (H,K,0) in the reciprocal space of NCCO. Additionally, because of neodymium paramagnetism, the application of a magnetic field increases the low-temperature intensity observed at these positions via neutron scattering. Such effects may mimic the formation of a structural superlattice or the strengthening of antiferromagnetic order of NCCO, but the intrinsic mechanism may be identified through careful and systematic experimentation. For typical reduction conditions, the (Nd,Ce)2O3 volume fraction is ~1%, and the secondary-phase layers exhibit long-range order parallel to the NCCO CuO2 sheets and are 50-100 angstromsthick. The presence of the secondary phase should also be taken into account in the analysis of other experiments on NCCO, such as transport measurements.Comment: 15 pages, 17 figures, submitted to Phys. Rev.

Variational Deep Semantic Hashing for Text Documents

As the amount of textual data has been rapidly increasing over the past decade, efficient similarity search methods have become a crucial component of large-scale information retrieval systems. A popular strategy is to represent original data samples by compact binary codes through hashing. A spectrum of machine learning methods have been utilized, but they often lack expressiveness and flexibility in modeling to learn effective representations. The recent advances of deep learning in a wide range of applications has demonstrated its capability to learn robust and powerful feature representations for complex data. Especially, deep generative models naturally combine the expressiveness of probabilistic generative models with the high capacity of deep neural networks, which is very suitable for text modeling. However, little work has leveraged the recent progress in deep learning for text hashing. In this paper, we propose a series of novel deep document generative models for text hashing. The first proposed model is unsupervised while the second one is supervised by utilizing document labels/tags for hashing. The third model further considers document-specific factors that affect the generation of words. The probabilistic generative formulation of the proposed models provides a principled framework for model extension, uncertainty estimation, simulation, and interpretability. Based on variational inference and reparameterization, the proposed models can be interpreted as encoder-decoder deep neural networks and thus they are capable of learning complex nonlinear distributed representations of the original documents. We conduct a comprehensive set of experiments on four public testbeds. The experimental results have demonstrated the effectiveness of the proposed supervised learning models for text hashing.Comment: 11 pages, 4 figure