1,850 research outputs found

    Disqualification of Opinion-Drafting Attorneys under Federal Circuit Standards for Willful Infringement

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    Article published in the Michigan State University School of Law Student Scholarship Collection

    Development and integration of Honeywell’s One-Wireless network

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    The purpose of this project has been to develop upon the Honeywell One-Wireless network in the Murdoch University Pilot Plant and integrate it into the Distributed Control System. This will give future students exposure to developing process control schemes around industrial wireless technology in a small plant setting. Industrial Wireless is still on the cutting edge of technology and it will challenge the status quo in Industry with its many advantages. A brief review of Industrial wireless technology has been included in this thesis report to provide the reader a background to the communications technology. Also included is Honeywell’s One-Wireless Network solution which was used in this project. There, where significant challenges in getting the network operational, and as a result a systematic troubleshooting process was followed. Once the network was operational additional wireless instruments where added to expand the network and set up in the system. From here the One-Wireless network was integrated into the Distributive Control System which operates the pilot plant, this was done using Modbus TCP/IP. To determine the effectiveness of the network a post Radio Frequency assessment was carried out to determine the impact of the network and ensure that it was following best practices. Relevant documentation on the network was developed as a handover for future students to build upon the work carried out

    Studying Innovation in Businesses: New Research Possibilities

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    The rapid pace of globalization and technological change has created demand for more and better analysis to answer key policy questions about the role of businesses in innovation. This demand was codified into law in the America COMPETES Act. However, existing business datasets are not adequate to create an empirically based foundation for policy decisions. This paper argues that the existing IRS data infrastructure could be used in a number of ways to respond to the national imperative. It describes the legal framework within which such a response could take place, and outlines the organizational features that would be required to establish an IRS/researcher partnership. It concludes with a discussion of the role for the research policy community.Business microdata, innovation, confidentiality, researcher access, tax policy

    X-CNN: Cross-modal Convolutional Neural Networks for Sparse Datasets

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    In this paper we propose cross-modal convolutional neural networks (X-CNNs), a novel biologically inspired type of CNN architectures, treating gradient descent-specialised CNNs as individual units of processing in a larger-scale network topology, while allowing for unconstrained information flow and/or weight sharing between analogous hidden layers of the network---thus generalising the already well-established concept of neural network ensembles (where information typically may flow only between the output layers of the individual networks). The constituent networks are individually designed to learn the output function on their own subset of the input data, after which cross-connections between them are introduced after each pooling operation to periodically allow for information exchange between them. This injection of knowledge into a model (by prior partition of the input data through domain knowledge or unsupervised methods) is expected to yield greatest returns in sparse data environments, which are typically less suitable for training CNNs. For evaluation purposes, we have compared a standard four-layer CNN as well as a sophisticated FitNet4 architecture against their cross-modal variants on the CIFAR-10 and CIFAR-100 datasets with differing percentages of the training data being removed, and find that at lower levels of data availability, the X-CNNs significantly outperform their baselines (typically providing a 2--6% benefit, depending on the dataset size and whether data augmentation is used), while still maintaining an edge on all of the full dataset tests.Comment: To appear in the 7th IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2016), 8 pages, 6 figures. Minor revisions, in response to reviewers' comment

    EmBench: Quantifying Performance Variations of Deep Neural Networks across Modern Commodity Devices

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    In recent years, advances in deep learning have resulted in unprecedented leaps in diverse tasks spanning from speech and object recognition to context awareness and health monitoring. As a result, an increasing number of AI-enabled applications are being developed targeting ubiquitous and mobile devices. While deep neural networks (DNNs) are getting bigger and more complex, they also impose a heavy computational and energy burden on the host devices, which has led to the integration of various specialized processors in commodity devices. Given the broad range of competing DNN architectures and the heterogeneity of the target hardware, there is an emerging need to understand the compatibility between DNN-platform pairs and the expected performance benefits on each platform. This work attempts to demystify this landscape by systematically evaluating a collection of state-of-the-art DNNs on a wide variety of commodity devices. In this respect, we identify potential bottlenecks in each architecture and provide important guidelines that can assist the community in the co-design of more efficient DNNs and accelerators.Comment: Accepted at MobiSys 2019: 3rd International Workshop on Embedded and Mobile Deep Learning (EMDL), 201

    FDAPT: Federated Domain-adaptive Pre-training for Language Models

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    Combining Domain-adaptive Pre-training (DAPT) with Federated Learning (FL) can enhance model adaptation by leveraging more sensitive and distributed data while preserving data privacy. However, few studies have focused on this method. Therefore, we conduct the first comprehensive empirical study to evaluate the performance of Federated Domain-adaptive Pre-training (FDAPT). We demonstrate that FDAPT can maintain competitive downstream task performance to the centralized baseline in both IID and non-IID situations. Furthermore, we propose a novel algorithm, Frozen Federated Domain-adaptive Pre-training (FFDAPT). FFDAPT improves the computational efficiency by 12.1% on average and exhibits similar downstream task performance to standard FDAPT, with general performance fluctuations remaining less than 1%. Finally, through a critical evaluation of our work, we identify promising future research directions for this new research area.Comment: 6 page