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

Calibration of Plastic Phoswich Detectors for Charged Particle Detection

The response of an array of plastic phoswich detectors to ions of $1\le Z\le 18$ has been measured from $E/A$=12 to 72 MeV. The detector response has been parameterized by a three parameter fit which includes both quenching and high energy delta-ray effects. The fits have a mean variation of $\le 4\%$ with respect to the data.Comment: 17 pages, 5 figure

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

A Quasi-Classical Model of Intermediate Velocity Particle Production in Asymmetric Heavy Ion Reactions

The particle emission at intermediate velocities in mass asymmetric reactions is studied within the framework of classical molecular dynamics. Two reactions in the Fermi energy domain were modelized, $^{58}$Ni+C and $^{58}$Ni+Au at 34.5 MeV/nucleon. The availability of microscopic correlations at all times allowed a detailed study of the fragment formation process. Special attention was paid to the physical origin of fragments and emission timescales, which allowed us to disentangle the different processes involved in the mid-rapidity particle production. Consequently, a clear distinction between a prompt pre- equilibrium emission and a delayed aligned asymmetric breakup of the heavier partner of the reaction was achieved.Comment: 8 pages, 7 figures. Final version: figures were redesigned, and a new section discussing the role of Coulomb in IMF production was include

Fusion of radioactive 132^{132}Sn with 64^{64}Ni

Evaporation residue and fission cross sections of radioactive $^{132}$Sn on $^{64}$Ni were measured near the Coulomb barrier. A large sub-barrier fusion enhancement was observed. Coupled-channel calculations including inelastic excitation of the projectile and target, and neutron transfer are in good agreement with the measured fusion excitation function. When the change in nuclear size and shift in barrier height are accounted for, there is no extra fusion enhancement in $^{132}$Sn+$^{64}$Ni with respect to stable Sn+$^{64}$Ni. A systematic comparison of evaporation residue cross sections for the fusion of even $^{112-124}$Sn and $^{132}$Sn with $^{64}$Ni is presented.Comment: 9 pages, 11 figure

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