Data management for production quality deep learning models: Challenges and solutions

Deep learning (DL) based software systems are difficult to develop and maintain in industrial settings due to several challenges. Data management is one of the most prominent challenges which complicates DL in industrial deployments. DL models are data-hungry and require high-quality data. Therefore, the volume, variety, velocity, and quality of data cannot be compromised. This study aims to explore the data management challenges encountered by practitioners developing systems with DL components, identify the potential solutions from the literature and validate the solutions through a multiple case study. We identified 20 data management challenges experienced by DL practitioners through a multiple interpretive case study. Further, we identified 48 articles through a systematic literature review that discuss the solutions for the data management challenges. With the second round of multiple case study, we show that many of these solutions have limitations and are not used in practice due to a combination of four factors: high cost, lack of skill-set and infrastructure, inability to solve the problem completely, and incompatibility with certain DL use cases. Thus, data management for data-intensive DL models in production is complicated. Although the DL technology has achieved very promising results, there is still a significant need for further research in the field of data management to build high-quality datasets and streams that can be used for building production-ready DL systems. Furthermore, we have classified the data management challenges into four categories based on the availability of the solutions.(c) 2022 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Pain, depression and anxiety in people with haemophilia from three Nordic countries : Cross-sectional survey data from the MIND study

Introduction People with haemophilia (PwH) may experience symptoms of haemophilia-related pain, depression or anxiety, which can negatively impact health-related quality of life. Aim To obtain the perspective of PwH and treaters from Sweden, Finland and Denmark on the management of haemophilia-related pain, depression and anxiety using cross-sectional survey data from the MIND study (NCT03276130). Methods PwH or their caregivers completed a survey about experiences of pain, depression and anxiety related to haemophilia, and the standard EQ-5D-5L instrument. Five investigators at haemophilia treatment centres (HTC) were sent a complementary survey containing questions about the management of pain and depression/anxiety. Results There were 343 PwH (mild: 103; moderate: 53; severe: 180; seven lacking severity information) and 71 caregiver responses. Experience of pain in the last 6 months was reported by 50% of PwH respondents and 46% of caregiver respondents. Anxiety/depression was reported by 28% of PwH respondents. Reporting of pain and anxiety/depression was associated with disease severity. Whilst 62% of PwH who had experienced pain at any time point (n = 242) felt this was adequately addressed and treated at their HTC, only 24% of those who had experienced depression/anxiety (n = 127) felt this was adequately addressed. Disease severity was negatively associated with EQ-5D-5L utility value (p < .001). In the HTC survey, 4/5 and 2/5 agreed that pain and depression/anxiety, respectively, are adequately addressed. Conclusions Pain and depression/anxiety occur more frequently with increasing haemophilia severity, with negative impacts on health-related quality of life. PwH with depression/anxiety or unaddressed pain could benefit from improved management strategies.Peer reviewe

Transit of H2O2 across the endoplasmic reticulum membrane is not sluggish

Cellular metabolism provides various sources of hydrogen peroxide (H2O2) in different organelles and compartments. The suitability of H2O2 as an intracellular signaling molecule therefore also depends on its ability to pass cellular membranes. The propensity of the membranous boundary of the endoplasmic reticulum (ER) to let pass H2O2 has been discussed controversially. In this essay, we challenge the recent proposal that the ER membrane constitutes a simple barrier for H2O2 diffusion and support earlier data showing that (i) ample H2O2 permeability of the ER membrane is a prerequisite for signal transduction, (ii) aquaporin channels are crucially involved in the facilitation of H2O2 permeation, and (iii) a proper experimental framework not prone to artifacts is necessary to further unravel the role of H2O2 permeation in signal transduction and organelle biology. © 2016 Elsevier Inc

Operational Research: Methods and Applications

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order

Going digital: Disruption and transformation in software-intensive embedded systems ecosystems

Digitalization is transforming industry to an extent that we have only seen the beginnings of. Across domains, companies experience rapid changes to their existing practices due to new technologies and new entrants that current businesses. While digitalization brings endless opportunities, it comes with challenges that require companies to strategically engage with partners in their surrounding ecosystems. In this paper, we study how companies in the embedded systems domain experience the process of transitioning from product-based companies to businesses where software, data, and artificial intelligence (AI) play an increasingly important role. To manage this, these companies need to evolve their existing ecosystems while at the same time create new ecosystems around new technologies. This involves maintaining existing technologies such as mechanics and electronics while at the same time expanding these with software, data, and AI. We provide a strategic decision framework that helps software-intensive embedded systems companies to successfully navigate the digital transformation. We do this in two steps. First, we present three models that provide the technical content of the strategic decision framework. Second, we provide an overview of the strategic alternatives that incumbents and new entrants have available when existing technologies are commoditizing and new technologies are introduced

Reducing retail supply chain costs of product returns using digital product fitting

Purpose - This paper investigate how fit uncertainty impacts product return costs in online retailing and how digital product fitting, a pre-sales fitting practice, can reduce fit uncertainty. Design/methodology/approach - The paper analyzes the current performance of a retailer's e-commerce and return operations by estimating costs generated by product returns, including product handling costs, tied-up capital, inventory holding costs, transportation costs, and order-picking costs. The estimated costs were built on 2,229 return transactions from a Scandinavian fashion footwear retailer. A digital product fitting technology was tested with the retailer's products and resulted in estimations on how such technology could affect product returns. Findings - The cost of a return is approximately 17% of the prime cost. The major cost elements are product handling costs and transportation costs, which together amount to 72% of the total costs. If well calibrated, the fitting technology can cut fit-related return costs by up to 80%. The findings show how customers reacted to the fitting technology: it was unable to verify fit every time, but it serves as a useful and effective support tool for customers when placing orders. Research limitations/implications - Virtual fit verification using digital product fitting is key to retailers to reduce fit-related returns. Digital product fitting using three-dimensional scanning is more appropriate for some products, but it is unsuitable for products that are difficult to measure and scan. Originality/value - The paper contributes an empirical estimate of retail supply chain costs associated with fit uncertainty, as well as theoretical understanding of the role of pre-sales fit verification in avoiding product returns.Peer reviewe

FINDING A CONSENSUS ON CREDIBLE FEATURES AMONG SEVERAL PALEOCLIMATE RECONSTRUCTIONS

We propose a method to merge several paleoclimate time series into one that exhibits a consensus on the features of the individual times series. The paleoclimate time series can be noisy, nonuniformly sampled and the dates at which the paleoclimate is reconstructed can have errors. Bayesian inference is used to model the various sources of uncertainty and smoothing of the posterior distribution of the consensus is used to capture its credible features in different time scales. The technique is demonstrated by analyzing a collection of six Holocene temperature reconstructions from Finnish Lapland based on various biological proxies. Although the paper focuses on paleoclimate time series, the proposed method can be applied in other contexts where one seeks to infer features that are jointly supported by an ensemble of irregularly sampled noisy time series.Comment: Published in at http://dx.doi.org/10.1214/12-AOAS540 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org