Algorithms Based upon Generalized Linear Programming for Stochastic Programs with Recourse

In this paper, the author discusses solution algorithms for a particular form of two-stage stochastic linear programs with recourse. The algorithms considered are based upon the generalized linear programming method of Wolfe. The author first gives an alternative formulation of the original problem and uses this to examine the relation between tenders and certainty equivalents. He then considers problems with simple recourse, discussing algorithms for two cases: (a) when the distribution is discrete and probabilities are known explicitly; (b) when the probability distribution is other than discrete or when it is only known implicitly through some simulation model. The latter case is especially useful because it makes possible the transition to general recourse. Some possible solution strategies based upon generalized programming for general recourse problems are then discussed. This paper is a product of the Adaptation and Optimization Project within the System and Decision Sciences Program

Chemical-Based Formulation Design: Virtual Experimentation

This paper presents a software, the virtual Product-Process Design laboratory (virtual PPD-lab) and the virtual experimental scenarios for design/verification of consumer oriented liquid formulated products where the software can be used. For example, the software can be employed for the design of the active ingredient-solvent mixture and/or their verification in terms of the product function. These consumer products are still primarily designed, developed and/or tested through experiment-based trial and error approaches. However, using the powerful methodologies and tools developed within the process system engineering community, it is possible now to replace, at least, some of the experimental steps with efficient and validated model-based approaches. For example, the search space can be significantly reduced through computer-aided screenings of the active ingredient (AI), the solvent mixture, the additives and/or their mixtures (formulations). Therefore, the experimental resources can focus on a few candidate product formulations to find the best product. The virtual PPD-lab allows various options for experimentations related to design and/or verification of the product. For example, the selection and verification of the functions of the AI; the design of solvent mixtures for the delivery of the AI; the stability test of the liquid formulated product; the selection of additives such as aroma compounds to be added to the products to enhance their quality; the generation of a list of candidate formulations; the addition of the missing chemicals to an incomplete formulation and the verification of the final product. The software is based on a framework that allows quick implementation of different design/verification work-flows and their associated models, methods, tools and data. The software contains a suite of databases with data of AIs used in different products (such as insect repellents), solvents classified in terms of special characteristics (such as solubility in water), and additives classified in terms of their application (such as aroma agents, wetting agents and preservatives). In addition, the software has built-in intelligence through implemented knowledge-bases related to transforming product attributes (consumer needs) to a set of physical-chemical properties; templates (work-flows) for specific product types are also available; guidance for property model (such as pure component properties and mixture properties) selection and adaptation is provided; the selection and use of models for product verification is also possible (such as stability of liquid and evaporation of the solvent after application of the product). Finally, the software has a collection of algorithms (such as CAMD, mixture design, model adaptation). All of the above helps to perform virtual experiments by blending chemicals together and observing their predicted behaviour. The paper will highlight the application of the virtual PPD-lab in the design and/or verification of different consumer products (paint formulation, hair spray, sunscreen lotion, insect repellent lotion). The results of the virtual experimentations will be illustrated through the (initial) base case designs that were obtained and their verification through real experiments and/or available product data analysis

Robust sound event detection in bioacoustic sensor networks

Bioacoustic sensors, sometimes known as autonomous recording units (ARUs), can record sounds of wildlife over long periods of time in scalable and minimally invasive ways. Deriving per-species abundance estimates from these sensors requires detection, classification, and quantification of animal vocalizations as individual acoustic events. Yet, variability in ambient noise, both over time and across sensors, hinders the reliability of current automated systems for sound event detection (SED), such as convolutional neural networks (CNN) in the time-frequency domain. In this article, we develop, benchmark, and combine several machine listening techniques to improve the generalizability of SED models across heterogeneous acoustic environments. As a case study, we consider the problem of detecting avian flight calls from a ten-hour recording of nocturnal bird migration, recorded by a network of six ARUs in the presence of heterogeneous background noise. Starting from a CNN yielding state-of-the-art accuracy on this task, we introduce two noise adaptation techniques, respectively integrating short-term (60 milliseconds) and long-term (30 minutes) context. First, we apply per-channel energy normalization (PCEN) in the time-frequency domain, which applies short-term automatic gain control to every subband in the mel-frequency spectrogram. Secondly, we replace the last dense layer in the network by a context-adaptive neural network (CA-NN) layer. Combining them yields state-of-the-art results that are unmatched by artificial data augmentation alone. We release a pre-trained version of our best performing system under the name of BirdVoxDetect, a ready-to-use detector of avian flight calls in field recordings.Comment: 32 pages, in English. Submitted to PLOS ONE journal in February 2019; revised August 2019; published October 201

Managing in conflict: How actors distribute conflict in an industrial network

IMP researchers have examined conflict as a threat to established business relationships and commercial exchanges, drawing on theories and concepts developed in organization studies. We examine cases of conflict in relationships from the oil and gas industry's service sector, focusing on conflicts of interest and resources, and conflict as experienced by actors. Through a comparative case study design, we propose an explanation of how actors manage conflict and manage in conflict given that they tend to value and maintain relationships beyond episodes of exchange. We consider conflicts in relationships from a network perspective, showing that actors experienced these while adapting to changes in their business setting, modifying their roles in that network. By identifying conflict with the organizing forms of relationship and network, we show how actors formulate conflict through pursuing and combining a number of strategies, distributing the conflict across an enlarged network