Grammar Variational Autoencoder

Deep generative models have been wildly successful at learning coherent latent representations for continuous data such as video and audio. However, generative modeling of discrete data such as arithmetic expressions and molecular structures still poses significant challenges. Crucially, state-of-the-art methods often produce outputs that are not valid. We make the key observation that frequently, discrete data can be represented as a parse tree from a context-free grammar. We propose a variational autoencoder which encodes and decodes directly to and from these parse trees, ensuring the generated outputs are always valid. Surprisingly, we show that not only does our model more often generate valid outputs, it also learns a more coherent latent space in which nearby points decode to similar discrete outputs. We demonstrate the effectiveness of our learned models by showing their improved performance in Bayesian optimization for symbolic regression and molecular synthesis

Examining Different Reasons Why People Accept Or Reject Scientific Claims

The current project was designed to examine how cognitive style, cultural worldview, and conspiracy ideation correspond to various levels of agreement with scientific claims. Additionally, the kinds of justifications people provide for their position on scientific issues and the kinds of possible refutations of their scientific beliefs people are able to generate were qualitatively coded and analyzed. Participants were presented with a short survey asking about their level of agreement with scientific claims about biological evolution, anthropogenic climate change, pediatric vaccines, and genetically modified foods. Participants were asked two open-ended questions about each topic, one prompting participants to justify their level-of-agreement rating and the other prompting participants to generate possible refutations to their belief. Participants also filled in measures of cognitive style, cultural worldview, and conspiracy ideation. I predicted that analytical thinking style would be associated with overall higher levels of agreement with scientific claims, intuitive thinking and conspiracy ideation would be associated with overall lower levels of agreement with scientific claims, and agreement with scientific claims would be a function of cultural worldview. Results showed that greater agreement with all four scientific claims is related to a greater predisposition to analytical thinking and stronger self-reported political liberalism. I further hypothesized that the frequency of distinct categories of justifications and refutations would be predicted by level of agreement with scientific claims. Broadly, justifications were coded as non-justifications, subjective, evidential, or deferential, and refutations were broadly coded as denials, subjective, evidential, or deferential. Results of chi-squared analysis revealed topic-specific patterns in participants’ reasoning, suggesting that people do not reason about scientific topics consistently. Different scientific claims appear, instead, to be accepted or rejected for different reasons. For example, evidence may be cited for one socio-scientific issue, but subjective experience or reasoning may be used to justify others. Regression analyses indicated further the nuanced relationship between explicit reasoning provided by participants and their agreement with scientific claims. Higher agreement with all scientific claims was related to a greater frequency of explicitly referencing evidence in some form, but other categories of belief justification and belief refutation showed topic-specific relationships. Generally, findings from this research provide a crucial next step for better understanding why individuals reject established science, as well as for developing more effective means of improving scientific literacy

Pandemic influenza control in Europe and the constraints resulting from incoherent public health laws

© 2010 Martin et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Background: With the emergence of influenza H1N1v the world is facing its first 21st century global pandemic. Severe Acute Respiratory Syndrome (SARS) and avian influenza H5N1 prompted development of pandemic preparedness plans. National systems of public health law are essential for public health stewardship and for the implementation of public health policy[1]. International coherence will contribute to effective regional and global responses. However little research has been undertaken on how law works as a tool for disease control in Europe. With co-funding from the European Union, we investigated the extent to which laws across Europe support or constrain pandemic preparedness planning, and whether national differences are likely to constrain control efforts. Methods: We undertook a survey of national public health laws across 32 European states using a questionnaire designed around a disease scenario based on pandemic influenza. Questionnaire results were reviewed in workshops, analysing how differences between national laws might support or hinder regional responses to pandemic influenza. Respondents examined the impact of national laws on the movements of information, goods, services and people across borders in a time of pandemic, the capacity for surveillance, case detection, case management and community control, the deployment of strategies of prevention, containment, mitigation and recovery and the identification of commonalities and disconnects across states. Results: Results of this study show differences across Europe in the extent to which national pandemic policy and pandemic plans have been integrated with public health laws. We found significant differences in legislation and in the legitimacy of strategic plans. States differ in the range and the nature of intervention measures authorized by law, the extent to which borders could be closed to movement of persons and goods during a pandemic, and access to healthcare of non-resident persons. Some states propose use of emergency powers that might potentially override human rights protections while other states propose to limit interventions to those authorized by public health laws. Conclusion: These differences could create problems for European strategies if an evolving influenza pandemic results in more serious public health challenges or, indeed, if a novel disease other than influenza emerges with pandemic potential. There is insufficient understanding across Europe of the role and importance of law in pandemic planning. States need to build capacity in public health law to support disease prevention and control policies. Our research suggests that states would welcome further guidance from the EU on management of a pandemic, and guidance to assist in greater commonality of legal approaches across states.Peer reviewe

Motion Detection by Microcontroller for Panning Cameras

Motion detection is the first essential process in the extraction of information regarding moving objects. The approaches based on background difference are the most used with fixed cameras to perform motion detection, because of the high quality of the achieved segmentation. However, real time requirements and high costs prevent most of the algorithms proposed in literature to exploit the background difference with panning cameras in real world applications. This paper presents a new algorithm to detect moving objects within a scene acquired by panning cameras. The algorithm for motion detection is implemented on a Raspberry Pi microcontroller, which enables the design and implementation of a low-cost monitoring system.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Predictive Complexity Priors

Specifying a Bayesian prior is notoriously difficult for complex models such as neural networks. Reasoning about parameters is made challenging by the high-dimensionality and over-parameterization of the space. Priors that seem benign and uninformative can have unintuitive and detrimental effects on a model's predictions. For this reason, we propose predictive complexity priors: a functional prior that is defined by comparing the model's predictions to those of a reference model. Although originally defined on the model outputs, we transfer the prior to the model parameters via a change of variables. The traditional Bayesian workflow can then proceed as usual. We apply our predictive complexity prior to high-dimensional regression, reasoning over neural network depth, and sharing of statistical strength for few-shot learning

Depth uncertainty in neural networks

Existing methods for estimating uncertainty in deep learning tend to require multiple forward passes, making them unsuitable for applications where computational resources are limited. To solve this, we perform probabilistic reasoning over the depth of neural networks. Different depths correspond to subnetworks which share weights and whose predictions are combined via marginalisation, yielding model uncertainty. By exploiting the sequential structure of feed-forward networks, we are able to both evaluate our training objective and make predictions with a single forward pass. We validate our approach on real-world regression and image classification tasks. Our approach provides uncertainty calibration, robustness to dataset shift, and accuracies competitive with more computationally expensive baselines

Scale Models Formulation of Switched Reluctance Generators for Low Speed Energy Converters

info:eu-repo/semantics/publishedVersio