6,854 research outputs found
Robust interventions in network epidemiology
Which individual should we vaccinate to minimize the spread of a disease? Designing optimal interventions of this kind can be formalized as an optimization problem on networks, in which we have to select a budgeted number of dynamically important nodes to receive treatment that optimizes a dynamical outcome. Describing this optimization problem requires specifying the network, a model of the dynamics, and an objective for the outcome of the dynamics. In real-world contexts, these inputs are vulnerable to misspecification---the network and dynamics must be inferred from data, and the decision-maker must operationalize some (potentially abstract) goal into a mathematical objective function. Moreover, the tools to make reliable inferences---on the dynamical parameters, in particular---remain limited due to computational problems and issues of identifiability. Given these challenges, models thus remain more useful for building intuition than for designing actual interventions. This thesis seeks to elevate complex dynamical models from intuition-building tools to methods for the practical design of interventions.
First, we circumvent the inference problem by searching for robust decisions that are insensitive to model misspecification.If these robust solutions work well across a broad range of structural and dynamic contexts, the issues associated with accurately specifying the problem inputs are largely moot. We explore the existence of these solutions across three facets of dynamic importance common in network epidemiology.
Second, we introduce a method for analytically calculating the expected outcome of a spreading process under various interventions. Our method is based on message passing, a technique from statistical physics that has received attention in a variety of contexts, from epidemiology to statistical inference.We combine several facets of the message-passing literature for network epidemiology.Our method allows us to test general probabilistic, temporal intervention strategies (such as seeding or vaccination). Furthermore, the method works on arbitrary networks without requiring the network to be locally tree-like .This method has the potential to improve our ability to discriminate between possible intervention outcomes.
Overall, our work builds intuition about the decision landscape of designing interventions in spreading dynamics. This work also suggests a way forward for probing the decision-making landscape of other intervention contexts. More broadly, we provide a framework for exploring the boundaries of designing robust interventions with complex systems modeling tools
UMSL Bulletin 2023-2024
The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp
Digitalization and Development
This book examines the diffusion of digitalization and Industry 4.0 technologies in Malaysia by focusing on the ecosystem critical for its expansion. The chapters examine the digital proliferation in major sectors of agriculture, manufacturing, e-commerce and services, as well as the intermediary organizations essential for the orderly performance of socioeconomic agents.
The book incisively reviews policy instruments critical for the effective and orderly development of the embedding organizations, and the regulatory framework needed to quicken the appropriation of socioeconomic synergies from digitalization and Industry 4.0 technologies. It highlights the importance of collaboration between government, academic and industry partners, as well as makes key recommendations on how to encourage adoption of IR4.0 technologies in the short- and long-term.
This book bridges the concepts and applications of digitalization and Industry 4.0 and will be a must-read for policy makers seeking to quicken the adoption of its technologies
Combined Nutrition and Exercise Interventions in Community Groups
Diet and physical activity are two key modifiable lifestyle factors that influence health across the lifespan (prevention and management of chronic diseases and reduction of the risk of premature death through several biological mechanisms). Community-based interventions contribute to public health, as they have the potential to reach high population-level impact, through the focus on groups that share a common culture or identity in their natural living environment. While the health benefits of a balanced diet and regular physical activity are commonly studied separately, interventions that combine these two lifestyle factors have the potential to induce greater benefits in community groups rather than strategies focusing only on one or the other. Thus, this Special Issue entitled “Combined Nutrition and Exercise Interventions in Community Groups” is comprised of manuscripts that highlight this combined approach (balanced diet and regular physical activity) in community settings. The contributors to this Special Issue are well-recognized professionals in complementary fields such as education, public health, nutrition, and exercise. This Special Issue highlights the latest research regarding combined nutrition and exercise interventions among different community groups and includes research articles developed through five continents (Africa, Asia, America, Europe and Oceania), as well as reviews and systematic reviews
Security Aspects in Web of Data Based on Trust Principles. A brief of Literature Review
Within scientific community, there is a certain consensus to define "Big Data" as a global set, through a complex integration that embraces several dimensions from using of research data, Open Data, Linked Data, Social Network Data, etc. These data are scattered in different sources, which suppose a mix that respond to diverse philosophies, great diversity of structures, different denominations, etc. Its management faces great technological and methodological challenges: The discovery and selection of data, its extraction and final processing, preservation, visualization, access possibility, greater or lesser structuring, between other aspects, which allow showing a huge domain of study at the level of analysis and implementation in different knowledge domains. However, given the data availability and its possible opening: What problems do the data opening face? This paper shows a literature review about these security aspects
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
UMSL Bulletin 2022-2023
The 2022-2023 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1087/thumbnail.jp
Medical Image Analysis using Deep Relational Learning
In the past ten years, with the help of deep learning, especially the rapid
development of deep neural networks, medical image analysis has made remarkable
progress. However, how to effectively use the relational information between
various tissues or organs in medical images is still a very challenging
problem, and it has not been fully studied. In this thesis, we propose two
novel solutions to this problem based on deep relational learning. First, we
propose a context-aware fully convolutional network that effectively models
implicit relation information between features to perform medical image
segmentation. The network achieves the state-of-the-art segmentation results on
the Multi Modal Brain Tumor Segmentation 2017 (BraTS2017) and Multi Modal Brain
Tumor Segmentation 2018 (BraTS2018) data sets. Subsequently, we propose a new
hierarchical homography estimation network to achieve accurate medical image
mosaicing by learning the explicit spatial relationship between adjacent
frames. We use the UCL Fetoscopy Placenta dataset to conduct experiments and
our hierarchical homography estimation network outperforms the other
state-of-the-art mosaicing methods while generating robust and meaningful
mosaicing result on unseen frames.Comment: arXiv admin note: substantial text overlap with arXiv:2007.0778
A Comprehensive Survey on Applications of Transformers for Deep Learning Tasks
Transformer is a deep neural network that employs a self-attention mechanism
to comprehend the contextual relationships within sequential data. Unlike
conventional neural networks or updated versions of Recurrent Neural Networks
(RNNs) such as Long Short-Term Memory (LSTM), transformer models excel in
handling long dependencies between input sequence elements and enable parallel
processing. As a result, transformer-based models have attracted substantial
interest among researchers in the field of artificial intelligence. This can be
attributed to their immense potential and remarkable achievements, not only in
Natural Language Processing (NLP) tasks but also in a wide range of domains,
including computer vision, audio and speech processing, healthcare, and the
Internet of Things (IoT). Although several survey papers have been published
highlighting the transformer's contributions in specific fields, architectural
differences, or performance evaluations, there is still a significant absence
of a comprehensive survey paper encompassing its major applications across
various domains. Therefore, we undertook the task of filling this gap by
conducting an extensive survey of proposed transformer models from 2017 to
2022. Our survey encompasses the identification of the top five application
domains for transformer-based models, namely: NLP, Computer Vision,
Multi-Modality, Audio and Speech Processing, and Signal Processing. We analyze
the impact of highly influential transformer-based models in these domains and
subsequently classify them based on their respective tasks using a proposed
taxonomy. Our aim is to shed light on the existing potential and future
possibilities of transformers for enthusiastic researchers, thus contributing
to the broader understanding of this groundbreaking technology
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