Suggesting Cooking Recipes Through Simulation and Bayesian Optimization

Cooking typically involves a plethora of decisions about ingredients and tools that need to be chosen in order to write a good cooking recipe. Cooking can be modelled in an optimization framework, as it involves a search space of ingredients, kitchen tools, cooking times or temperatures. If we model as an objective function the quality of the recipe, several problems arise. No analytical expression can model all the recipes, so no gradients are available. The objective function is subjective, in other words, it contains noise. Moreover, evaluations are expensive both in time and human resources. Bayesian Optimization (BO) emerges as an ideal methodology to tackle problems with these characteristics. In this paper, we propose a methodology to suggest recipe recommendations based on a Machine Learning (ML) model that fits real and simulated data and BO. We provide empirical evidence with two experiments that support the adequacy of the methodology

Germination at low osmotic potential as a selection criteria for drought stress tolerance in sweet corn

Water stress can affect germination by decreasing the percentage of germination. A study was undertaken to evaluate the influence of different osmotic potentials (MPa) on proline content and percentage seed germination of corn. The experiment was conducted in factorial with a randomized complete block design (RCBD) with three replications. Seeds of two open pollinated varieties (Masmadu and Thai super sweet) and three hybrids (968, 969 and 926) sweet corn were germinated at 0, -0.2, -0.5, -0.7, -1.2 and -1.4 MPa osmotic potentials, respectively. Results show that the percentage of germination and coefficient of velocity (CVG) decreased with decrease in osmotic potential while proline content and mean germination time (MGT) increased. Polyethylene glycol (PEG) increased root length (RL) and length per volume (LPV) at low osmotic potential (-0.2 MPa) but decreased at more than -0.7 MPa. Seedling proline content appears not to be related to percentage germination but appears to be related to the decline in osmotic potential in germination media. Seed germination test at -0.7 to -1.2 MPa has the potential to be used as a vigor test in sweet corn.Keywords: Osmotic potential, germination, polyethylene glycol, corn, proline contentAfrican Journal of Biotechnology, Vol. 13(2), pp. 294-300, 8 January, 201

A Deep Neural Network as Surrogate Model for Forward Simulation of Borehole Resistivity Measurements

Inverse problems appear in multiple industrial applications. Solving such inverse problems require the repeated solution of the forward problem. This is the most time-consuming stage when employing inversion techniques, and it constitutes a severe limitation when the inversion needs to be performed in real-time. In here, we focus on the real-time inversion of resistivity measurements for geosteering. We investigate the use of a deep neural network (DNN) to approximate the forward function arising from Maxwell's equations, which govern the electromagnetic wave propagation through a media. By doing so, the evaluation of the forward problems is performed offline, allowing for the online real-time evaluation (inversion) of the DNN

Immunoreactivity analysis of Toxoplasma gondii recombinant antigen rSAG3 in sera from immunized BALB/c mice and tox-oplasmosis patients

Background: The coccidian protozoa Toxoplasma gondii is an obligate intracellular parasite of humans and other warm-blooded animals. Diagnosis of toxoplasmosis is of considerable medical importance for human, especially pregnant women and immunocompromised individuals. The apply of an Escherichia coli recombinant antigen(s) would be signifi-cantly useful in developing standardization of the diagnostic tests and reducing their costs. In this study, immunoreac-tivity of recombinant SAG3 against sera from immunized mice and human anti-T. gondii IgG positive patients was evaluated by western-blotting and enzyme immunoassay (EIA) in Department of Parasitology and Mycology, School of Medicine, Shiraz University of Medical Sciences in 2013. Methods: Three inbreed BALB/c female mice were obtained. Two mice were injected with rSAG3 and one was re-mained untreated, as control. Sera from immunized mice and also pooled sera from IgG positive toxoplasmosis cases were evaluated with western-blotting. IgG antibody responses to recombinant SAG3 was measured by indirect ELISA against the negative control group. Results: The rSAG3 protein reacted with sera of immunized mice and sera from patients with anti-Toxoplasma IgG antibodies in western-blot analysis. The result of ELISA showed that, there was marked differences in the absorbance values between the recombinant SAG3 immunized mice and control group. Conclusion: The rSAG3 showed IgG reactivity with sera from immunized mice and anti-Toxoplasma IgG patients. © 2016, Iranian Journal of Public Health. All rights reserved

Bayesian optimization of the PC algorithm for learning Gaussian Bayesian networks

The PC algorithm is a popular method for learning the structure of Gaussian Bayesian networks. It carries out statistical tests to determine absent edges in the network. It is hence governed by two parameters: (i) The type of test, and (ii) its significance level. These parameters are usually set to values recommended by an expert. Nevertheless, such an approach can suffer from human bias, leading to suboptimal reconstruction results. In this paper we consider a more principled approach for choosing these parameters in an automatic way. For this we optimize a reconstruction score evaluated on a set of different Gaussian Bayesian networks. This objective is expensive to evaluate and lacks a closed-form expression, which means that Bayesian optimization (BO) is a natural choice. BO methods use a model to guide the search and are hence able to exploit smoothness properties of the objective surface. We show that the parameters found by a BO method outperform those found by a random search strategy and the expert recommendation. Importantly, we have found that an often overlooked statistical test provides the best over-all reconstruction results

Identification of Turnip mosaic virus isolated from canola in northeast area of Iran

During March and April of 2011, 436 samples showing viral disease symptoms were collected from canola fields in the Khorasan Razavi province. The samples were tested by double-antibody sandwich (DAS)-enzyme linked immunosorbent assay (ELISA) for the presence of Turnip mosaic virus (TuMV). Among the 436 samples, 117 samples were found to be infected with TuMV. One of the infected samples from Govareshk region (TuMV-IRN GSK) was selected for biological purification. Total RNA of this isolate were extracted and reverse transcriptase (RT)-PCR was performed with specific primers according to the coat protein gene. PCR products (986 bp) was first purified and then directly sequenced. Phylogenetic analyses based on ClustalW multiple alignments with previously reported 33 isolates indicated 88 to 98% similarity in nucleotide and 94 to 99% in amino acid levels among isolates. TuMV-IRN GSK represented the highest identity to another Iranian isolate (IRN TRa6). Phylogenetic tree clustered all sequences into four groups and IRN GSK fell into the basal-B group. Nucleotide and amino acid distances between IRN GSK and other isolates in the basal-B group showed that this isolate was closely related to another Iranian isolate IRN TRa6, and distinct from other isolates in the basal-B group. These results indicate that TuMV is a common pathogen of canola crops in the Khorasan Razavi province.Key words: Turnip mosaic virus (TuMV), canola, reverse-transcription polymerase chain reaction (RT-PCR), coat protein gene, sequence analysis

A Gray-Box Approach for Curriculum Learning

Curriculum learning is often employed in deep reinforcement learning to let the agent progress more quickly towards better behaviors. Numerical methods for curriculum learning in the literature provides only initial heuristic solutions, with little to no guarantee on their quality. We define a new gray-box function that, including a suitable scheduling problem, can be effectively used to reformulate the curriculum learning problem. We propose different efficient numerical methods to address this gray-box reformulation. Preliminary numerical results on a benchmark task in the curriculum learning literature show the viability of the proposed approach

Disruption of the plant-specific CFS1 gene impairs autophagosome turnover and triggers EDS1-dependent cell death

Cell death, autophagy and endosomal sorting contribute to many physiological, developmental and immunological processes in plants. They are mechanistically interconnected and interdependent, but the molecular basis of their mutual regulation has only begun to emerge in plants. Here, we describe the identification and molecular characterization of CELL DEATH RELATED ENDOSOMAL FYVE/SYLF PROTEIN 1 (CFS1). The CFS1 protein interacts with the ENDOSOMAL SORTING COMPLEX REQUIRED FOR TRANSPORT I (ESCRT-I) component ELCH (ELC) and is localized at ESCRT-I-positive late endosomes likely through its PI3P and actin binding SH3YL1 Ysc84/Lsb4p Lsb3p plant FYVE (SYLF) domain. Mutant alleles of cfs1 exhibit auto-immune phenotypes including spontaneous lesions that show characteristics of hypersensitive response (HR). Autoimmunity in cfs1 is dependent on ENHANCED DISEASE SUSCEPTIBILITY 1 (EDS1)-mediated effector-triggered immunity (ETI) but independent from salicylic acid. Additionally, cfs1 mutants accumulate the autophagy markers ATG8 and NBR1 independently from EDS1. We hypothesize that CFS1 acts at the intersection of autophagosomes and endosomes and contributes to cellular homeostasis by mediating autophagosome turnover

Evolution of Scikit-Learn Pipelines with Dynamic Structured Grammatical Evolution

The deployment of Machine Learning (ML) models is a difficult and time-consuming job that comprises a series of sequential and correlated tasks that go from the data pre-processing, and the design and extraction of features, to the choice of the ML algorithm and its parameterisation. The task is even more challenging considering that the design of features is in many cases problem specific, and thus requires domain-expertise. To overcome these limitations Automated Machine Learning (AutoML) methods seek to automate, with few or no human-intervention, the design of pipelines, i.e., automate the selection of the sequence of methods that have to be applied to the raw data. These methods have the potential to enable non-expert users to use ML, and provide expert users with solutions that they would unlikely consider. In particular, this paper describes AutoML-DSGE - a novel grammar-based framework that adapts Dynamic Structured Grammatical Evolution (DSGE) to the evolution of Scikit-Learn classification pipelines. The experimental results include comparing AutoML-DSGE to another grammar-based AutoML framework, Resilient ClassificationPipeline Evolution (RECIPE), and show that the average performance of the classification pipelines generated by AutoML-DSGE is always superior to the average performance of RECIPE; the differences are statistically significant in 3 out of the 10 used datasets.Comment: EvoApps 202

A Suite of Computationally Expensive Shape Optimisation Problems Using Computational Fluid Dynamics

This is the author accepted manuscript. The final version is available from Springer via the DOI in this record.PPSN2018: 15th International Conference on Parallel Problem Solving from Nature, 8-12 September 2018, Coimbra, PortugalIn many product design and development applications, Computational Fluid Dynamics (CFD) has become a useful tool for analysis. This is particularly because of the accuracy of CFD simulations in predicting the important flow attributes for a given design. On occasions when design optimisation is applied to real-world engineering problems using CFD, the implementation may not be available for examination. As such, in both the CFD and optimisation communities, there is a need for a set of computationally expensive benchmark test problems for design optimisation using CFD. In this paper, we present a suite of three computationally expensive real-world problems observed in different fields of engineering. We have developed Python software capable of automatically constructing geometries from a given decision vector, running appropriate simulations using the CFD code OpenFOAM, and returning the computed objective values. Thus, users may easily evaluate a decision vector and perform optimisation of these design problems using their optimisation methods without developing custom CFD code. For comparison, we provide the objective values for the base geometries and typical computation times for the test cases presented here.This work was supported by the UK Engineering and Physical Sciences Research Council (EPSRC) grant (reference number: EP/M017915/1)