6,608 research outputs found
A customer relationship management ecosystem that utilizes multiple sources and types of information conjointly
No full textIn the current economic, budget tightening and competitive times, organizations need to be customer focused and provide customized service to customers to ensure their loyalty. To achieve this, Customer Relationship Management (CRM) systems help organizations to deal with and answer various customer queries. However with a change in the type of information being created (for example from structured to semistructured), CRM systems have to make effective use of such information which may be in multiple information sources for effective knowledge management and knowledge synthesis in order to provide customized services to the customers. In this paper, we propose a Customer Relationship Management ecosystem that conjointly utilizes multiple information sources and information types to achieve this. We explain the architecture of the proposed CRM ecosystems framework and demonstrate its application in the real estate domain
Event handling for distributed real-time cyber-physical systems
No full textCyber-Physical Systems (CPS) provides a smart infrastructure connecting abstract computational artifacts with the physical world. This paper presents some challenges for developing distributed real-time Cyber-Physical Systems. The focus is on one particular challenge, namely event modelling in distributed real-time CPS. A Web-of-Things based CPS framework for event handling and processing is proposed. To illustrate the application of the proposed framework, a case study for achieving demand response in a smart home is provided
Exploring differential effects of product and service innovations on industrial firms' financial performance
No full textEggert A, Thiesbrummel C, Deutscher C. Exploring differential effects of product and service innovations on industrial firms' financial performance . In: Rindfleisch A, ed. Challenging the bounds of marketing thought. AMA Winter Marketing Educators' Conference 2013 ; AMA educators' proceedings Volume 24 ; Las Vegas, Nevada, USA, 15 - 17 February 2013. Red Hook, NY: Curran Associates; 2013: 119-120
Microfluidic device for control and sensing of dynamic oxygen levels during cell cultivation
No full textKaganovitch E, Krischer M, Probst C, et al. Microfluidic device for control and sensing of dynamic oxygen levels during cell cultivation. In: 20th International Conference on Miniaturized Systems for Chemistry and Life Sciences (MicroTAS 2016). Red Hook, NY: Curran Associates, Inc.; 2017: 355-356
Closing the gap between microfluidic single-cell analysis and bioprocess development for microbial organisms
No full textProbst C, Freier C, Mahr R, et al. Closing the gap between microfluidic single-cell analysis and bioprocess development for microbial organisms. In: 19th International Conference on Miniaturized Systems for Chemistry and Life Sciences (MicroTAS 2015). Red Hook, NY: Curran Associates, Inc.; 2016: 537-539
Capacity Allocation and Pricing Policies for Cloud Computing Service Providers
No full text##nofultext##Özgür Özlük (MEF Author)The cloud computing is regarded as a paradigm shift in today’s IT world. As cloud computing resources behave like perishable products, revenue management techniques can be applied to increase cloud service provider's total revenue. In this paper, we propose various methods for pricing and capacity allocation. We consider three types of instances offered by the service provider; subscription, on-demand and spot instances. We introduce three allocation and pricing policies and propose different models. We simulate these models on a randomly generated dataset and evaluate the models for different capacities. The results we obtain indicate the sensitivity of revenue to varying policies and demonstrate the potential profit increase for cloud service providers. © 2018, Curran Associates Inc. All rights reserved
Approximate Inference Turns Deep Networks into Gaussian Processes
No full textKhan MEE, Immer A, Abedi E, Korzepa M. Approximate Inference Turns Deep Networks into Gaussian Processes. In: Wallach H, Larochelle H, Beygelzimer A, d'Alché-Buc F, Fox E, Garnett R, eds. 32nd Conference on Neural Information Processing Systems (NeurIPS 2019) : Vancouver, Canada, 8-14 December 2019. Vol. 4. Advances in Neural Information Processing Systems. Vol 32. Red Hook, NY: Curran Associates, Inc.; 2019: 3071-3081
End-to-end differentiable physics for learning and control
No full text© 2018 Curran Associates Inc.All rights reserved. We present a differentiable physics engine that can be integrated as a module in deep neural networks for end-to-end learning. As a result, structured physics knowledge can be embedded into larger systems, allowing them, for example, to match observations by performing precise simulations, while achieves high sample efficiency. Specifically, in this paper we demonstrate how to perform backpropagation analytically through a physical simulator defined via a linear complementarity problem. Unlike traditional finite difference methods, such gradients can be computed analytically, which allows for greater flexibility of the engine. Through experiments in diverse domains, we highlight the system's ability to learn physical parameters from data, efficiently match and simulate observed visual behavior, and readily enable control via gradient-based planning methods. Code for the engine and experiments is included with the paper
DeepProbLog: Neural Probabilistic Logic Programming
No full text© 2018 Curran Associates Inc..All rights reserved. We introduce DeepProbLog, a probabilistic logic programming language that incorporates deep learning by means of neural predicates. We show how existing inference and learning techniques can be adapted for the new language. Our experiments demonstrate that DeepProbLog supports (i) both symbolic and subsymbolic representations and inference, (ii) program induction, (iii) probabilistic (logic) programming, and (iv) (deep) learning from examples. To the best of our knowledge, this work is the first to propose a framework where general-purpose neural networks and expressive probabilistic-logical modeling and reasoning are integrated in a way that exploits the full expressiveness and strengths of both worlds and can be trained end-to-end based on examples.sponsorship: RM is a SB PhD fellow at FWO (1S61718N). SD is supported by the Research Fund KU Leuven (GOA/13/010) and Research Foundation - Flanders (G079416N) (FWO|1S61718N, Research Fund KU Leuven|GOA/13/010, Research Foundation - Flanders|G079416N)status: Published onlin
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