Training artificial neural networks to learn a nondeterministic game

It is well known that artificial neural networks (ANNs) can learn deterministic automata. Learning nondeterministic automata is another matter. This is important because much of the world is nondeterministic, taking the form of unpredictable or probabilistic events that must be acted upon. If ANNs are to engage such phenomena, then they must be able to learn how to deal with nondeterminism. In this project the game of Pong poses a nondeterministic environment. The learner is given an incomplete view of the game state and underlying deterministic physics, resulting in a nondeterministic game. Three models were trained and tested on the game: Mona, Elman, and Numenta's NuPIC.Comment: ICAI'15: The 2015 International Conference on Artificial Intelligence, Las Vegas, NV, USA, 201

Comparing the spatial distribution of DCD and urinary nitrogen on well-drained and poorly-drained soils : a thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Agricultural Science at Massey University, Manawatū, New Zealand

Nitrogen (N) losses from urine patches can be significant contributors to greenhouse gas emissions and water quality issues. Nitrification inhibitors may reduce these losses by slowing down the transformation of urine-N to nitrate. Technologies exist that can detect urine patches and target inhibitor applications specifically to the patch area, thereby avoiding the need to apply the inhibitor over the entire paddock. However, the potential time delay between the grazing event and the inhibitor application, and the small volumes of inhibitor used could result in only partial interception of the urine by the inhibitor in the soil. This would limit the potential effectiveness of the inhibitor. Two studies were undertaken to compare the movement of urine to the movement, and therefore potential interception, of the nitrification inhibitor dicyandiamide (DCD). In the first study, patches of urine were created by pouring three different volumes of urine (1, 2 and 3 L) onto two soils of contrasting drainage at two different moisture levels. In the second study, two volumes of DCD (the equivalent of 10 and 20 kg DCD/ha) were sprayed using a Spikey® spray unit onto urine (2 L volume) patches created within 80 cm diameter chambers in two soils of contrasting drainage at two different moisture levels. The variation in urine-N concentration both within and between individual urine patches was substantial. Total urine N recovery averaged 38%. On average, 67% of the recovered N was recovered from the top 5 cm, 14% from 5-10 cm and 19% from 10-20 cm. On average, 78% and 69% of the DCD applied at 30 mL and 60 mL, respectively was recovered from the soil. Of this, on average 67% was present in the 0-2 cm, 8% in 2-5 cm and 24% in 5-10 cm soil depths. DCD concentrations in the top 2 cm varied greatly and average concentrations of 15.5 and 11.4 mg DCD/kg soil were measured for 30 and 60 mL applications. There was little difference in DCD (1.45 mg DCD/kg soil) measured below 2 cm between application rates. Concentrations were significantly higher with a higher application rate at 0-2 cm on the Tokomaru soil but not on the Manawatū. After five days, following 24 mm rainfall, DCD recovery remained the same but its distribution and concentrations among the soil depths changed indicating its downward movement. About half of the recovered DCD remained in the 0-2 cm soil, one-third accumulated in 2-5 cm depth and the remainder was in 5-10 cm depth. The difference between urine and DCD distributions suggests that the DCD applications used in this experiment only intercepted 35-50% of the urine patch, without rainfall. With at least 24 mm of rainfall and 60 mL of DCD (13.8 kg DCD/ha) the DCD could be intercepting 80% of the urine-N. This will limit the effectiveness of DCD to reduce N leaching. It’s impact on N₂O emissions is less certain

Tree-formed Verification Data for Trusted Platforms

The establishment of trust relationships to a computing platform relies on validation processes. Validation allows an external entity to build trust in the expected behaviour of the platform based on provided evidence of the platform's configuration. In a process like remote attestation, the 'trusted' platform submits verification data created during a start up process. These data consist of hardware-protected values of platform configuration registers, containing nested measurement values, e.g., hash values, of loaded or started components. Commonly, the register values are created in linear order by a hardware-secured operation. Fine-grained diagnosis of components, based on the linear order of verification data and associated measurement logs, is not optimal. We propose a method to use tree-formed verification data to validate a platform. Component measurement values represent leaves, and protected registers represent roots of a hash tree. We describe the basic mechanism of validating a platform using tree-formed measurement logs and root registers and show an logarithmic speed-up for the search of faults. Secure creation of a tree is possible using a limited number of hardware-protected registers and a single protected operation. In this way, the security of tree-formed verification data is maintained.Comment: 15 pages, 11 figures, v3: Reference added, v4: Revised, accepted for publication in Computers and Securit

Patient-reported outcomes provide evidence for increased depressive symptoms and increased mental impairment in giant cell arteritis

ObjectivesThe spectrum of giant cell arteritis (GCA) and polymyalgia rheumatica (PMR) represents highly inflammatory rheumatic diseases. Patients mostly report severe physical impairment. Possible consequences for mental health have been scarcely studied. The aim of this study was to investigate psychological well-being in the context of GCA and PMR.MethodsCross-sectional study with N = 100 patients with GCA and/or PMR (GCA-PMR). Patient-reported outcomes (PROs) were measured using the Short Form 36 Version 2 (SF-36v2) and visual analog scale (VAS) assessment. Moreover, the Patient Health Questionnaire 9 (PHQ-9) was used in 35 of 100 patients to detect depression. To compare PROs with physician assessment, VAS was also rated from physician perspective. To assess a possible association with inflammation itself, serological parameters of inflammation (C-reactive protein [CRP], erythrocyte sedimentation rate [ESR]) were included.ResultsIn all scales of the SF-36v2 except General Health (GH) and in the physical and mental sum score (PCS, MCS), a significant impairment compared to the German reference collective was evident (MCS: d = 0.533, p < 0.001). In the PHQ-9 categorization, 14 of the 35 (40%) showed evidence of major depression disorder. VAS Patient correlated significantly with PHQ-9 and SF-36 in all categories, while VAS Physician showed only correlations to physical categories and not in the mental dimensions. Regarding inflammatory parameters, linear regression showed CRP to be a complementary significant positive predictor of mental health subscale score, independent of pain.ConclusionPRO show a relevant impairment of mental health up to symptoms of major depression disorder. The degree of depressive symptoms is also distinctly associated with the serological inflammatory marker CRP

OpenWorm: overview and recent advances in integrative biological simulation of Caenorhabditis elegans

The adoption of powerful software tools and computational methods from the software industry by the scientific research community has resulted in a renewed interest in integrative, large-scale biological simulations. These typically involve the development of computational platforms to combine diverse, process-specific models into a coherent whole. The OpenWorm Foundation is an independent research organization working towards an integrative simulation of the nematode Caenorhabditis elegans, with the aim of providing a powerful new tool to understand how the organism's behaviour arises from its fundamental biology. In this perspective, we give an overview of the history and philosophy of OpenWorm, descriptions of the constituent sub-projects and corresponding open-science management practices, and discuss current achievements of the project and future directions. This article is part of a discussion meeting issue ‘Connectome to behaviour: modelling C. elegans at cellular resolution’