237 research outputs found

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Towards Object-Centric Scene Understanding

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    Visual perception for autonomous agents continues to attract community attention due to the disruptive technologies and the wide applicability of such solutions. Autonomous Driving (AD), a major application in this domain, promises to revolutionize our approach to mobility while bringing critical advantages in limiting accident fatalities. Fueled by recent advances in Deep Learning (DL), more computer vision tasks are being addressed using a learning paradigm. Deep Neural Networks (DNNs) succeeded consistently in pushing performances to unprecedented levels and demonstrating the ability of such approaches to generalize to an increasing number of difficult problems, such as 3D vision tasks. In this thesis, we address two main challenges arising from the current approaches. Namely, the computational complexity of multi-task pipelines, and the increasing need for manual annotations. On the one hand, AD systems need to perceive the surrounding environment on different levels of detail and, subsequently, take timely actions. This multitasking further limits the time available for each perception task. On the other hand, the need for universal generalization of such systems to massively diverse situations requires the use of large-scale datasets covering long-tailed cases. Such requirement renders the use of traditional supervised approaches, despite the data readily available in the AD domain, unsustainable in terms of annotation costs, especially for 3D tasks. Driven by the AD environment nature and the complexity dominated (unlike indoor scenes) by the presence of other scene elements (mainly cars and pedestrians) we focus on the above-mentioned challenges in object-centric tasks. We, then, situate our contributions appropriately in fast-paced literature, while supporting our claims with extensive experimental analysis leveraging up-to-date state-of-the-art results and community-adopted benchmarks

    Rethinking FPGA Architectures for Deep Neural Network applications

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    The prominence of machine learning-powered solutions instituted an unprecedented trend of integration into virtually all applications with a broad range of deployment constraints from tiny embedded systems to large-scale warehouse computing machines. While recent research confirms the edges of using contemporary FPGAs to deploy or accelerate machine learning applications, especially where the latency and energy consumption are strictly limited, their pre-machine learning optimised architectures remain a barrier to the overall efficiency and performance. Realizing this shortcoming, this thesis demonstrates an architectural study aiming at solutions that enable hidden potentials in the FPGA technology, primarily for machine learning algorithms. Particularly, it shows how slight alterations to the state-of-the-art architectures could significantly enhance the FPGAs toward becoming more machine learning-friendly while maintaining the near-promised performance for the rest of the applications. Eventually, it presents a novel systematic approach to deriving new block architectures guided by designing limitations and machine learning algorithm characteristics through benchmarking. First, through three modifications to Xilinx DSP48E2 blocks, an enhanced digital signal processing (DSP) block for important computations in embedded deep neural network (DNN) accelerators is described. Then, two tiers of modifications to FPGA logic cell architecture are explained that deliver a variety of performance and utilisation benefits with only minor area overheads. Eventually, with the goal of exploring this new design space in a methodical manner, a problem formulation involving computing nested loops over multiply-accumulate (MAC) operations is first proposed. A quantitative methodology for deriving efficient coarse-grained compute block architectures from benchmarks is then suggested together with a family of new embedded blocks, called MLBlocks

    Data analysis with merge trees

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    Today’s data are increasingly complex and classical statistical techniques need growingly more refined mathematical tools to be able to model and investigate them. Paradigmatic situations are represented by data which need to be considered up to some kind of trans- formation and all those circumstances in which the analyst finds himself in the need of defining a general concept of shape. Topological Data Analysis (TDA) is a field which is fundamentally contributing to such challenges by extracting topological information from data with a plethora of interpretable and computationally accessible pipelines. We con- tribute to this field by developing a series of novel tools, techniques and applications to work with a particular topological summary called merge tree. To analyze sets of merge trees we introduce a novel metric structure along with an algorithm to compute it, define a framework to compare different functions defined on merge trees and investigate the metric space obtained with the aforementioned metric. Different geometric and topolog- ical properties of the space of merge trees are established, with the aim of obtaining a deeper understanding of such trees. To showcase the effectiveness of the proposed metric, we develop an application in the field of Functional Data Analysis, working with functions up to homeomorphic reparametrization, and in the field of radiomics, where each patient is represented via a clustering dendrogram

    A Search for Role Clarity: A Critical Discourse Analysis of the RN and RPN Entry-to-Practice Competencies That Shape Nursing Curriculum in Ontario, Canada

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    There is confusion regarding the practice expectations of Registered Nurses (RN) and Registered Practical Nurses (RPN) for employers, educators, nurses, nursing students, and the public, in Ontario, Canada. As the entry-to-practice competencies (ETPC) serve as a guide to the curricular content of nursing programs, a critical discourse analysis of the entry-to-practice documents available to the public was performed to: 1) attempt to understand the meaning and intent of the ETPC, 2) to answer the question of what are the differences in practice expectations for RN versus RPN graduates, and 3) how can role clarity be improved through this process. Critical discourse analysis affords the opportunity to understand these documents, not just through the words on the page, but understand the social, cultural, political, and contextual forces and processes that led to their creation by the nursing regulators in Canada. However, there are competency interpretation documents available only to nursing educators embarking on the College of Nurses of Ontario (CNO) program approval process, which are not made readily available to nurses, the public or employers. These interpretation documents provide a clearer picture of the key differences and similarities between RN and RPN practice expectations. Despite this increased clarity, some language use and sentence construction confound even a seasoned educator as these words have different meanings depending on the context and common understanding of the meaning. As a useful tool for nursing practice, a table of comparison was made to guide nursing educators, employers, nurses, nursing students, and the public to make visible these differences and similarities in both the competencies and the interpretation documents. This analysis also suggests that the College of Nurses of Ontario make the interpretation documents available to a wider audience to support the link between nursing practice and nursing education to create a living curriculum that can be responsive to the ever-changing needs of the profession

    東北大学電通談話会記録 第92巻 第1号

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    XVI Agricultural Science Congress 2023: Transformation of Agri-Food Systems for Achieving Sustainable Development Goals

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    The XVI Agricultural Science Congress being jointly organized by the National Academy of Agricultural Sciences (NAAS) and the Indian Council of Agricultural Research (ICAR) during 10-13 October 2023, at hotel Le Meridien, Kochi, is a mega event echoing the theme “Transformation of Agri-Food Systems for achieving Sustainable Development Goals”. ICAR-Central Marine Fisheries Research Institute takes great pride in hosting the XVI ASC, which will be the perfect point of convergence of academicians, researchers, students, farmers, fishers, traders, entrepreneurs, and other stakeholders involved in agri-production systems that ensure food and nutritional security for a burgeoning population. With impeding challenges like growing urbanization, increasing unemployment, growing population, increasing food demands, degradation of natural resources through human interference, climate change impacts and natural calamities, the challenges ahead for India to achieve the Sustainable Development Goals (SDGs) set out by the United Nations are many. The XVI ASC will provide an interface for dissemination of useful information across all sectors of stakeholders invested in developing India’s agri-food systems, not only to meet the SDGs, but also to ensure a stable structure on par with agri-food systems around the world. It is an honour to present this Book of Abstracts which is a compilation of a total of 668 abstracts that convey the results of R&D programs being done in India. The abstracts have been categorized under 10 major Themes – 1. Ensuring Food & Nutritional Security: Production, Consumption and Value addition; 2. Climate Action for Sustainable Agri-Food Systems; 3. Frontier Science and emerging Genetic Technologies: Genome, Breeding, Gene Editing; 4. Livestock-based Transformation of Food Systems; 5. Horticulture-based Transformation of Food Systems; 6. Aquaculture & Fisheries-based Transformation of Food Systems; 7. Nature-based Solutions for Sustainable AgriFood Systems; 8. Next Generation Technologies: Digital Agriculture, Precision Farming and AI-based Systems; 9. Policies and Institutions for Transforming Agri-Food Systems; 10. International Partnership for Research, Education and Development. This Book of Abstracts sets the stage for the mega event itself, which will see a flow of knowledge emanating from a zeal to transform and push India’s Agri-Food Systems to perform par excellence and achieve not only the SDGs of the UN but also to rise as a world leader in the sector. I thank and congratulate all the participants who have submitted abstracts for this mega event, and I also applaud the team that has strived hard to publish this Book of Abstracts ahead of the event. I wish all the delegates and participants a very vibrant and memorable time at the XVI ASC

    LIPIcs, Volume 274, ESA 2023, Complete Volume

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    LIPIcs, Volume 274, ESA 2023, Complete Volum

    Corporate strategies for sustainable development and adoption of new technologies

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    Technological advancements might have positive or negative impacts on sustainability. It’s essential to understand the adoption of these technologies to achieve better sustainability. The United Nations 2030 Agenda and the associated SDGs have been promoted as tools suitable to alleviate poverty, protect Planet Earth, and contribute to worldwide prosperity (UN, 2015; Tsalis, 2020). But governments alone cannot achieve sustainable development; they must be supported by the private sector, which plays a colossal role in advancing and achieving the SDGs. Specifically, the private sector can integrate the ‘green’ principles into their corporate strategies. This integration depends on, and requires, an effective approach to green development and the knowledge generation of SDGs as embedded in the companies’ functions, values, and day-to-day operations. The papers in this special issue investigate the role of corporate strategies for sustainable green development and knowledge generation in the implementation of the SDGs or principles by Asian and Eastern European companies from Malaysia, Vietnam, Indonesia, Emirates, Zimbabwe and Russia. Hence, there is a need to expand the research in further studies to gauge the contribution of corporate strategies towards the achievement of the SDGs in a wider group of countries. These further studies could also focus on a comparative cross-country analysis to provide insights into how institutional differences among countries influence the implementation and achievement of the SDGs. In addition, there is also a need to understand the role of other corporate strategies, including integrated reporting and long-term value, in the achievement of the SDGs. It is a matter of great importance for companies to explain how businesses create value for their key stakeholders in the long term by implementing the SDGs. The insights drawn from this special issue contribute to the existing literature and provide valuable practical information for practitioners, policymakers, and developers. Practitioners can rely on the insights provided in this special issue to make informed decisions that consider both the short-term and long-term impacts of technology solutions and their adoption in organizations. They need to consider the opportunities and challenges associated with technology adoption and develop plans to mitigate the negative impacts and maximize the positive effects of technology adoption. Additionally, policymakers can use the findings of the eight papers to establish policies and regulations that encourage the adoption of sustainable technologies that serve society while minimizing the negative impacts on the environment, economy, and the general public. Further, developers can consider the barriers identified in the analysis to develop more effective solutions. They can also incorporate sustainable practices into the development process to ensure their technologies align with sustainable development principles
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