Efficient modeling of entangled details for natural scenes

Proceedings of Pacific Graphics 2016 (Okinawa)International audienceDigital landscape realism often comes from the multitude of details that are hard to model such as fallen leaves, rock piles orentangled fallen branches. In this article, we present a method for augmenting natural scenes with a huge amount of details suchas grass tufts, stones, leaves or twigs. Our approach takes advantage of the observation that those details can be approximatedby replications of a few similar objects and therefore relies on mass-instancing. We propose an original structure, the GhostTile, that stores a huge number of overlapping candidate objects in a tile, along with a pre-computed collision graph. Detailsare created by traversing the scene with the Ghost Tile and generating instances according to user-defined density fields thatallow to sculpt layers and piles of entangled objects while providing control over their density and distribution

Sand transverse dune aerodynamics: 3D Coherent Flow Structures from a computational study

The engineering interest about dune fields is dictated by the their interaction with a number of human infrastructures in arid environments. Sand dunes dynamics is dictated by wind and its ability to induce sand erosion, transport and deposition. A deep understanding of dune aerodynamics serves then to ground effective strategies for the protection of human infrastructures from sand, the so-called sand mitigation. Because of their simple geometry and their frequent occurrence in desert area, transverse sand dunes are usually adopted in literature as a benchmark to investigate dune aerodynamics by means of both computational or experimental approaches, usually in nominally 2D setups. The present study aims at evaluating 3D flow features in the wake of a idealised transverse dune, if any, under different nominally 2D setup conditions by means of computational simulations and to compare the obtained results with experimental measurements available in literature

Modelling Grocery Retail Topic Distributions: Evaluation, Interpretability and Stability

Understanding the shopping motivations behind market baskets has high commercial value in the grocery retail industry. Analyzing shopping transactions demands techniques that can cope with the volume and dimensionality of grocery transactional data while keeping interpretable outcomes. Latent Dirichlet Allocation (LDA) provides a suitable framework to process grocery transactions and to discover a broad representation of customers' shopping motivations. However, summarizing the posterior distribution of an LDA model is challenging, while individual LDA draws may not be coherent and cannot capture topic uncertainty. Moreover, the evaluation of LDA models is dominated by model-fit measures which may not adequately capture the qualitative aspects such as interpretability and stability of topics. In this paper, we introduce clustering methodology that post-processes posterior LDA draws to summarise the entire posterior distribution and identify semantic modes represented as recurrent topics. Our approach is an alternative to standard label-switching techniques and provides a single posterior summary set of topics, as well as associated measures of uncertainty. Furthermore, we establish a more holistic definition for model evaluation, which assesses topic models based not only on their likelihood but also on their coherence, distinctiveness and stability. By means of a survey, we set thresholds for the interpretation of topic coherence and topic similarity in the domain of grocery retail data. We demonstrate that the selection of recurrent topics through our clustering methodology not only improves model likelihood but also outperforms the qualitative aspects of LDA such as interpretability and stability. We illustrate our methods on an example from a large UK supermarket chain.Comment: 20 pages, 9 figure

Becoming the Expert - Interactive Multi-Class Machine Teaching

Compared to machines, humans are extremely good at classifying images into categories, especially when they possess prior knowledge of the categories at hand. If this prior information is not available, supervision in the form of teaching images is required. To learn categories more quickly, people should see important and representative images first, followed by less important images later - or not at all. However, image-importance is individual-specific, i.e. a teaching image is important to a student if it changes their overall ability to discriminate between classes. Further, students keep learning, so while image-importance depends on their current knowledge, it also varies with time. In this work we propose an Interactive Machine Teaching algorithm that enables a computer to teach challenging visual concepts to a human. Our adaptive algorithm chooses, online, which labeled images from a teaching set should be shown to the student as they learn. We show that a teaching strategy that probabilistically models the student's ability and progress, based on their correct and incorrect answers, produces better 'experts'. We present results using real human participants across several varied and challenging real-world datasets.Comment: CVPR 201

The Partial Evaluation Approach to Information Personalization

Information personalization refers to the automatic adjustment of information content, structure, and presentation tailored to an individual user. By reducing information overload and customizing information access, personalization systems have emerged as an important segment of the Internet economy. This paper presents a systematic modeling methodology - PIPE (`Personalization is Partial Evaluation') - for personalization. Personalization systems are designed and implemented in PIPE by modeling an information-seeking interaction in a programmatic representation. The representation supports the description of information-seeking activities as partial information and their subsequent realization by partial evaluation, a technique for specializing programs. We describe the modeling methodology at a conceptual level and outline representational choices. We present two application case studies that use PIPE for personalizing web sites and describe how PIPE suggests a novel evaluation criterion for information system designs. Finally, we mention several fundamental implications of adopting the PIPE model for personalization and when it is (and is not) applicable.Comment: Comprehensive overview of the PIPE model for personalizatio

Optimization of ergosterol extraction from: Pleurotus mushrooms using response surface methodology

In this study, heat-assisted extraction (HAE) was used to maximize ergosterol extraction from Pleurotus ostreatus (PO) and Pleurotus eryngii (PE) using response surface methodology (RSM). Different temperature (T) and time (t) conditions were applied to understand their influence on the extraction yield (Y1), ergosterol purity in the extracted material (mg g-1 R, Y2) and ergosterol content in the two Pleurotus species (mg per 100 g dw, Y3). A circumscribed central composite design was used to evaluate the interactive effects of extraction variables and the optimal conditions were determined using second-order polynomial mathematical models to describe the responses obtained. In all cases, the predicted responses showed satisfactory fitting between the predicted and experimental values (R2 values >0.8). The global optimum conditions predicted by the models were for PO at T = 54.3 °C and t = 150 min (yielded 7.25%, 33.32 mg E per g R and 244.25 mg E per 100 g dw), while for PE at T = 61.8 °C and t = 150 min (yielded 8.02%, 43.63 mg E per g R and 360.58 mg E per 100 g dw). The obtained results from the two Pleurotus mushroom species using HAE show the possibilities of using them as a production source of enriched extracts in ergosterol.The authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES to CIMO (UIDB/00690/2020); this work was also funded by the European Agricultural Fund for Rural Development (EAFRD), through the Rural Development Program (PDR2020), within the scope of the project MicoCoating (PDR2020-101-03147). L. Barros also acknowledges the national funding by FCT, P.I., through the institutional scientific employment program-contract for their contracts. MICINN is acknowledged for the financial support for the Ramón&Cajal researcher, M.A. Prieto.info:eu-repo/semantics/publishedVersio

Rafigh: A Living Media System for Motivating Target Application Use for Children

Digital living media systems combine living media such as plants, animals and fungi with computational components. In this dissertation, I respond to the question of how can digital living media systems better motivate children to use target applications (i.e., learning and/or therapeutic applications)? To address this question, I employed a participatory design approach where I incorporated input from children, parents, speech language pathologists and teachers into the design of a new system. Rafigh is a digital embedded system that uses the growth of a living mushrooms colony to provide positive reinforcements to children when they conduct target activities. The growth of the mushrooms is affected by the amount of water administered to them, which in turn corresponds to the time children spend on target applications. I used an iterative design process to develop and evaluate three Rafigh prototypes. The evaluations showed that the system must be robust, customizable, and should include compelling engagement mechanisms to keep the children interested. I evaluated Rafigh using two case studies conducted in participants homes. In each case study, two siblings and their parent interacted with Rafigh over two weeks and the parents identified a series of target applications that Rafigh should motivate the children to use. The study showed that Rafigh motivated the children to spend significantly more time on target applications during the intervention phase and that it successfully engaged one out of two child participants in each case study who showed signs of responsibility, empathy and curiosity towards the living media. The study showed that the majority of participants described the relationship between using target applications and mushrooms growth correctly. Further, Rafigh encouraged more communication and collaboration between the participants. Rafighs slow responsivity did not impact the engagement of one out of two child participants in each case study and might even have contributed to their investment in the project. Finally, Rafighs presence as an ambient physical object allowed users to interact with it freely and as part of their home environment

DEVELOPMENT OF ASSESSMENT TOOLS OF PACKAGE/PRODUCT SYSTEMS FOR A SUSTAINABLE FOOD CHAIN

Post-harvest life of fresh produce is limited due to high metabolic activity and microbial spoilage. Modified atmosphere packaging (MAP) has proven to be one of the most effective techniques to extend the shelf life of fresh produce commercially. Obtaining of an optimum concentration of oxygen and carbon dioxide inside the package depends upon the product properties, the environmental conditions of the cold chain, the permeable film, some of which are subjected to natural variability during the food distribution chain. This variability may generate produce that is out of specification that will lead to food waste. Uncertainty analysis of this problem may lead to relevant interventions to prevent these losses. The hypothesis of this work was to create a mathematical model that predicts key quality factors for MAP packaged fresh products in the supply chain distribution, which will help to assess the food losses in relation to quality thresholds. The model developed simulated the respiration rate as function of O2 and CO2 concentration and produce temperature using Michaelis-Menten equations. The exchange of gases (O2, CO2) and water vapour between the fruit surface, package atmosphere and external atmosphere was modelled taking into account the process of transpiration and condensation. In the transpiration model, the fresh produce surface was assumed to be perfectly saturated and the energy of respiration was used to evaporate surface water. Temperature changes in the headspace due to metabolic heat, convective heat transfer and heat exchange by gas transmission through the package were accounted for. The quality attributes of fresh produce included weight loss and colour change (L, a, and b values) for mushroom, from Botrytis and its fermentative activity for strawberry and weight loss and spoilage for tomato. ii These conditions were simulated for real and variable i) export cold chain and ii) retail display storage to evaluate the effect of cold chain variability (temperature and relative humidity) on the quality of fresh produce and associated waste generation. The prediction of propagation of biological variance on the quality of fresh produce during storage was obtained using a mathematical model. Sensitivity analysis of the stochastic MAP model pointed out the influence of input parameters on the quality of fresh produce. The conclusions of the study showed that the toolbox developed is able to interpret cold chain data: 1) mathematical prediction of quality; 2) simulation of cold chain conditions allowing for different variability components; 3) estimation of waste generation kinetics based in all quality criteria and thresholds; 4) sensitivity analysis to identify the most sensitive technological parameters; and 5) identification of interventions that affect the benchmarked technological parameters