Synthetic Dataset Generation for Adversarial Machine Learning Research

Existing adversarial example research focuses on digitally inserted perturbations on top of existing natural image datasets. This construction of adversarial examples is not realistic because it may be difficult, or even impossible, for an attacker to deploy such an attack in the real-world due to sensing and environmental effects. To better understand adversarial examples against cyber-physical systems, we propose approximating the real-world through simulation. In this paper we describe our synthetic dataset generation tool that enables scalable collection of such a synthetic dataset with realistic adversarial examples. We use the CARLA simulator to collect such a dataset and demonstrate simulated attacks that undergo the same environmental transforms and processing as real-world images. Our tools have been used to collect datasets to help evaluate the efficacy of adversarial examples, and can be found at https://github.com/carla-simulator/carla/pull/4992

Formal Venture Capital Acquisition: Can Entrepreneurs Compensate for the Spatial Proximity Benefits of South East England and ‘Star’ Golden-Triangle Universities?

Building on the resource-based view of the firm and signalling theory, we challenge the traditional perspective that spatial proximity benefits can be leveraged by university spin-outs (USOs) located in the South East of England (particularly those drawn from 'star' golden-triangle universities with additional reputational benefits), and that USOs located elsewhere will be constrained from obtaining first formal venture capital (VC) required for venture development. With the aid of a longitudinal database of 134 USOs involving unique archival and survey data, event-history analysis identified, counter to the traditional perspective, that USOs located outside the South East of England were significantly more likely to obtain formal VC. Also, counter to the spatial proximity benefits view, star golden-triangle USOs were not significantly more likely to obtain VC. Our evidence supports a spatial mismatch view between investors and investees. Resource-combination signals sent by USOs and favourably received by VC firms were found to differ according to USO location context: USOs located outside the South East of England and star golden-triangle universities that signal the credible presence of habitual founders were more likely to obtain VC. USOs located outside star golden-triangle universities that had previously obtained publicly backed equity finance were also more likely to obtain VC. However, USOs located in the South East of England with reputable management teams were most likely to obtain VC

Energy harvesting for active implants: powering a ruminal pH-monitoring system

Energy harvesting is a feasible method to prolong service life of implanted devices. We present a thermal energy harvesting approach for a ruminal pH-monitoring probe in cattle. Thermoelectric generators utilize the temperature gradient between the probe and the ruminal fluid during water intake. The in vivo experiment yielded a maximum electric power of 32 μW

Broad Down, Devon: archaeological and other stories

publication-status: PublishedThis is a post-print, author-produced version of an article accepted for publication Journal of Material Culture, 2010, Vol. 15, Issue 3, pp. 345 - 367. Copyright © 2010 SAGE Publications. The definitive publisher-authenticated version is available online at http://mcu.sagepub.com/content/15/3/345.shortThis article explores the knowledge construction process of an archaeological site in East Devon, UK. Bouncing off an oral historical account of the site that seems to run against scientific truth claims, the author investigates the story of how knowledge of the site has developed over the last two centuries. Building on previous work that explores the history and practice of archaeology, the article opens up questions of what counts as evidence. Then, taking a cue from more recent work that suggests a more dynamic and open-ended engagement with the landscape, the article turns to examine how the meaning of a site can be made and remade. As part of this endeavour, questions of what as well as who can ‘speak’ are examined and some space is opened up for the agency of ‘minor figures’, both human and non-human

Genome-wide analysis identifies 12 loci influencing human reproductive behavior.

The genetic architecture of human reproductive behavior-age at first birth (AFB) and number of children ever born (NEB)-has a strong relationship with fitness, human development, infertility and risk of neuropsychiatric disorders. However, very few genetic loci have been identified, and the underlying mechanisms of AFB and NEB are poorly understood. We report a large genome-wide association study of both sexes including 251,151 individuals for AFB and 343,072 individuals for NEB. We identified 12 independent loci that are significantly associated with AFB and/or NEB in a SNP-based genome-wide association study and 4 additional loci associated in a gene-based effort. These loci harbor genes that are likely to have a role, either directly or by affecting non-local gene expression, in human reproduction and infertility, thereby increasing understanding of these complex traits

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Genome-wide association meta-analysis of 78,308 individuals identifies new loci and genes influencing human intelligence

Intelligence is associated with important economic and health-related life outcomes1. Despite intelligence having substantial heritability2 (0.54) and a confirmed polygenic nature, initial genetic studies were mostly underpowered3,4,5. Here we report a meta-analysis for intelligence of 78,308 individuals. We identify 336 associated SNPs (METAL P < 5 × 10−8) in 18 genomic loci, of which 15 are new. Around half of the SNPs are located inside a gene, implicating 22 genes, of which 11 are new findings. Gene-based analyses identified an additional 30 genes (MAGMA P < 2.73 × 10−6), of which all but one had not been implicated previously. We show that the identified genes are predominantly expressed in brain tissue, and pathway analysis indicates the involvement of genes regulating cell development (MAGMA competitive P = 3.5 × 10−6). Despite the well-known difference in twin-based heritability2 for intelligence in childhood (0.45) and adulthood (0.80), we show substantial genetic correlation (rg = 0.89, LD score regression P = 5.4 × 10−29). These findings provide new insight into the genetic architecture of intelligence