305,201 research outputs found
Ramalli, E, NX13595
No full textThis record was harvested from a previous catalogue system and will be withdrawn in 2025. Information in this record may be superseded or incomplete. Visit this record in UMA's new catalogue at: https://archives.library.unimelb.edu.au/nodes/view/412256Surname: RAMALLI. Given Name(s) or Initials: E. Military Service Number or Last Known Location: NX13595. Missing, Wounded and Prisoner of War Enquiry Card Index Number: 1487.228890
Item: [2016.0049.44519] "Ramalli, E, NX13595
Sustainability and Governance of Data Ecosystems
No full textData ecosystems are often the most promising choice to enhance the development of many areas, from industry to social science. Both academic and industry research departments, over time, have refined this technology, formalizing principles, methodologies, and approaches to better handle the initial development overhead in terms of challenges, design, and development. However, there is still space for improvement regarding the countermeasures to keep these projects alive in the long run. These platforms face a significant rate of early-stage failure, primarily due to low user engagement and high cost. Therefore, it is necessary to account for these challenges, particularly in the design and early stages of deployment. The reasons for this phenomenon are already under investigation at the business level, but how to technically account for these phenomena in the design phase and in the technology is still something to be discussed. Therefore, this paper first identifies the main reasons that threaten a long-term use and life of a data ecosystem. Then, discuss the mitigations at the design level and the possible technological solutions. This work moves a step toward a sustainable data ecosystem thanks to the appropriate data governance and technical design choices
Knowledge graph embedding for experimental uncertainty estimation
No full textPurpose: Experiments are the backbone of the development process of data-driven predictive models for scientific applications. The quality of the experiments directly impacts the model performance. Uncertainty inherently affects experiment measurements and is often missing in the available data sets due to its estimation cost. For similar reasons, experiments are very few compared to other data sources. Discarding experiments based on the missing uncertainty values would preclude the development of predictive models. Data profiling techniques are fundamental to assess data quality, but some data quality dimensions are challenging to evaluate without knowing the uncertainty. In this context, this paper aims to predict the missing uncertainty of the experiments. Design/methodology/approach: This work presents a methodology to forecast the experiments’ missing uncertainty, given a data set and its ontological description. The approach is based on knowledge graph embeddings and leverages the task of link prediction over a knowledge graph representation of the experiments database. The validity of the methodology is first tested in multiple conditions using synthetic data and then applied to a large data set of experiments in the chemical kinetic domain as a case study. Findings: The analysis results of different test case scenarios suggest that knowledge graph embedding can be used to predict the missing uncertainty of the experiments when there is a hidden relationship between the experiment metadata and the uncertainty values. The link prediction task is also resilient to random noise in the relationship. The knowledge graph embedding outperforms the baseline results if the uncertainty depends upon multiple metadata. Originality/value: The employment of knowledge graph embedding to predict the missing experimental uncertainty is a novel alternative to the current and more costly techniques in the literature. Such contribution permits a better data quality profiling of scientific repositories and improves the development process of data-driven models based on scientific experiments
Know Your Experiments: Interpreting Categories of Experimental Data and Their Coverage
No full textData management in scientific domains is more important than ever due to the increasing availability of experimental data. Automatically integrating and managing the information would significantly speed up their reuse and, in particular, the development of predictive models for a given domain. However, the diversity, ambiguity, and complexity of experimental data make it hard in practice. In this work, we propose a general approach to overcome these challenges, combining a human-in-the-loop process with a new methodology to understand automatically the semantics of experimental data, which can also be used as a data cleaning procedure. In addition, we focus on assessing the domain coverage of an experimental database using only categorical characteristics of the domain, which is essential for model validation or to understand if and where there is a need to perform additional experiments
Improving the Quality of Monostatic Synthetic-Aperture Ultrasound Imaging Through Deep-Learning-Based Beamforming
No full textIn synthetic aperture (SA) ultrasound imaging, either monostatic or multistatic approaches can be employed. In both cases, in transmission, a single element of the transducer array is used at each time. In reception, the same element is used for the monostatic approach, while the whole array is used for the multistatic one. Thus, the monostatic approach could be implemented using a simpler single-channel architecture, however at the expense of image quality, while the multistatic one provides a high quality image but requires a more complex N-channel system. In this work, we show that a deep neural network can be trained to reconstruct images with a high contrast, as in the multistatic SA case (considering a 128-element array), but starting from the pre-beamforming signals acquired through the monostatic SA approach. We implemented a U-net and trained it using 27200 simulated signal-sets and the corresponding target images generated with Field II, considering numerical phantoms with random elliptical targets. The deep neural network (DNN) output image quality was evaluated in terms of contrast on a test set made of 500 simulated images, and on experimental scans of a commercial phantom and of the carotid artery. The results show that, after training over 39 epochs, the DNN is able to provide images with a good quality starting from the radiofrequency signals obtained with a simple monostatic SA approach, potentially requiring a single-channel only
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
High-frame-rate coherence imaging of the heart with ultrasound diverging waves
No full textSeveral techniques have been proposed up to now to achieve higher temporal resolution in echocardiography. Among these, the use of diverging beams, which insonify a large region of interest, allows to significantly increase the frame-rate, but at the cost of a reduced signal-to-noise ratio. For this reason, in this paper we propose to combine high-frame-rate imaging, by transmitting diverging waves (DWs), to the Short-Lag Spatial Coherence (SLSC) technique in reception, which provides images of the coherence of backscattered echoes and is known to yield improved contrast in scenarios with high-clutter. We test this combined method first on phantom acquisitions and then on in vivo cardiac scans, i.e. on apical views of the heart. Results show that SLSC can provide improved contrast ratio (CR) and generalized contrast-to-noise ratio (GCNR) with respect to the classic Delay and Sum (DAS) as the number of transmitted DWs increases, particularly when clutter is present. Indeed, cardiac images show improved apex visibility and artifact suppression in the heart chambers with SLSC, achieving high contrast and high frame-rate at the same time
Dispelling the Myths Behind First-author Citation Counts
No full textWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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