Bayesian optimisation for premise selection in automated theorem proving (student abstract)

Modern theorem provers utilise a wide array of heuristics to control the search space explosion, thereby requiring optimisation of a large set of parameters. An exhaustive search in this multi-dimensional parameter space is intractable in most cases, yet the performance of the provers is highly dependent on the parameter assignment. In this work, we introduce a principled probabilistic framework for heuristic optimisation in theorem provers. We present results using a heuristic for premise selection and the Archive of Formal Proofs (AFP) as a case study.</jats:p

Applying Dijkstras Algorithm in Routing Process

Network is defined as a combination of two or more nodes which are connected with each other. It allows nodes to exchange data from each other along the data connections. Routing is a process of finding the path between source and destination upon request of data transmission. There are various routing algorithms which helps in determining the path and distance over the network traffic. For routing of nodes, we can use many routing protocols. Dijkstrarsquos algorithm is one of the best shortest path search algorithms. Our focus and aim is to find the shortest path from source node to destination node. For finding the minimum path this algorithm uses the connection matrix and weight matrix Thus, a matrix consisting of paths from source node to each node is formed. We then choose a column of destination from path matrix formed and we get the shortest path. In a similar way, we choose a column from a mindis matrix for finding the minimum distance from source node to destination node. It has been applied in computer networking for routing of systems and in google maps to find the shortest possible path from one location to another location.nbs

Modelling outcomes after paediatric brain injury with admission laboratory values: a machine-learning approach.

BACKGROUND: Severe traumatic brain injury (TBI) is a leading cause of mortality in children, but the accurate prediction of outcomes at the point of admission remains very challenging. Admission laboratory results are a promising potential source of prognostic data, but have not been widely explored in paediatric cohorts. Herein, we use machine-learning methods to analyse 14 different serum parameters together and develop a prognostic model to predict 6-month outcomes in children with severe TBI. METHODS: A retrospective review of patients admitted to Cambridge University Hospital's Paediatric Intensive Care Unit between 2009 and 2013 with a TBI. The data for 14 admission serum parameters were recorded. Logistic regression and a support vector machine (SVM) were trained with these data against dichotimised outcomes from the recorded 6-month Glasgow Outcome Scale. RESULTS: Ninety-four patients were identified. Admission levels of lactate, H+, and glucose were identified as being the most informative of 6-month outcomes. Four different models were produced. The SVM using just the three most informative parameters was the best able to predict favourable outcomes at 6 months (sensitivity = 80%, specificity = 99%). CONCLUSIONS: Our results demonstrate the potential for highly accurate outcome prediction after severe paediatric TBI using admission laboratory data

Challenges for adopting and implementing IoT in smart cities: An integrated MICMAC-ISM approach

YesThe wider use of Internet of Things (IoT) makes it possible to create smart cities. The purpose of this paper is to identify key IoT challenges and understand the relationship between these challenges to support the development of smart cities. Design/methodology/approach: Challenges were identified using literature review, and prioritised and elaborated by experts. The contextual interactions between the identified challenges and their importance were determined using Interpretive Structural Modelling (ISM). To interrelate the identified challenges and promote IoT in the context of smart cities, the dynamics of interactions of these challenges were analysed using an integrated Matrice d’Impacts Croisés Multiplication Appliqués à un Classement (MICMAC)-ISM approach. MICMAC is a structured approach to categorise variables according to their driving power and dependence. Findings: Security and privacy, business models, data quality, scalability, complexity and governance were found to have strong driving power and so are key challenges to be addressed in sustainable cities projects. The main driving challenges are complexity and lack of IoT governance. IoT adoption and implementation should therefore focus on breaking down complexity in manageable parts, supported by a governance structure. Practical implications: This research can help smart city developers in addressing challenges in a phase-wise approach by first ensuring solid foundations and thereafter developing other aspects. Originality/value: A contribution originates from the integrated MICMAC-ISM approach. ISM is a technique used to identify contextual relationships among definite elements, whereas MICMAC facilitates the classification of challenges based on their driving and dependence power. The other contribution originates from creating an overview of challenges and theorising the contextual relationships and dependencies among the challenges

Phytophthora blight of Pigeonpea [Cajanus cajan (L.) Millsp.]: An updating review of biology, pathogenicity and disease management

Phytophthora blight (PB), Phytophthora drechsleri Tucker f.sp. cajani (Pal et al.) Kannaiyan et al. is reoccurring as an economically important disease of pigeonpea [Cajanus cajan (L.) Millsp.], especially when excessive rains fall with in short span of time and hot and humid weather persists during the crop season. A few years after the initial reviews of Kanniyan et al. (1984), the disease was coming to halt. Despite earlier investigations on pathological and physiological characteristics of P. drechsleri f. sp. cajani, the nature of infection process and genetic basis of pathogen variability have not been clearly established. Therefore, information on the biology and survival of the pathogen is needed to devise effective management strategies. Attempts have been made to develop green-house and field screening techniques three decades ago for identification of HPR. However, only few pigeonpea germplasm and breeding lines belonging to cultivated and wild Cajanus spp. were found tolerant to PB. The recent frequent recurrence of PB epidemics in the major pigeonpea growing areas prioritized the search for higher levels of disease resistance. There is a need to study the biology of the pathogen, epidemiology of the disease and refinement of the resistance screening techniques and develop integrated disease management (IDM) technology for the disease. In this review, the symptomatology of the disease, biology of pathogen including its variability, epidemiology, sources of resistance, other management options, and available information on biochemical and genetic basis of disease resistance have been updated and discussed with the identification of future research priorities

Effects of the soil microbiome on the demography of two annual prairie plants

This work is licensed under a Creative Commons Attribution 4.0 International License.1. Both mutualistic and pathogenic soil microbes are known to play important roles in shaping the fitness of plants, likely affecting plants at different life cycle stages. 2. In order to investigate the differential effects of native soil mutualists and pathogens on plant fitness, we compared survival and reproduction of two annual tallgrass prairie plant species (Chamaecrista fasciculata and Coreopsis tinctoria) in a field study using 3 soil inocula treatments containing different compositions of microbes. The soil inocula types included fresh native whole soil taken from a remnant prairie containing both native mutualists and pathogens, soil enhanced with arbuscular mycorrhizal (AM) fungi derived from remnant prairies, and uninoculated controls. 3. For both species, plants inoculated with native prairie AM fungi performed much better than those in uninoculated soil for all parts of the life cycle. Plants in the native whole prairie soil were either generally similar to plants in the uninoculated soil or had slightly higher survival or reproduction. 4. Overall, these results suggest that native prairie AM fungi can have important positive effects on the fitness of early successional plants. As inclusion of prairie AM fungi and pathogens decreased plant fitness relative to prairie AM fungi alone, we expect that native pathogens also can have large effects on fitness of these annuals. Our findings support the use of AM fungi to enhance plant establishment in prairie restorations.National Science Foundation (NSF DEB‐1556664)National Science Foundation (DEB‐1738041)National Science Foundation (OIA 1656006

Turnover, account value and diversification of real traders: evidence of collective portfolio optimizing behavior

Despite the availability of very detailed data on financial market, agent-based modeling is hindered by the lack of information about real trader behavior. This makes it impossible to validate agent-based models, which are thus reverse-engineering attempts. This work is a contribution to the building of a set of stylized facts about the traders themselves. Using the client database of Swissquote Bank SA, the largest on-line Swiss broker, we find empirical relationships between turnover, account values and the number of assets in which a trader is invested. A theory based on simple mean-variance portfolio optimization that crucially includes variable transaction costs is able to reproduce faithfully the observed behaviors. We finally argue that our results bring into light the collective ability of a population to construct a mean-variance portfolio that takes into account the structure of transaction costsComment: 26 pages, 9 figures, Fig. 8 fixe