378 research outputs found

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Trade-off between Time, Space, and Workload: the case of the Self-stabilizing Unison

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    We present a self-stabilizing algorithm for the (asynchronous) unison problem which achieves an efficient trade-off between time, workload, and space in a weak model. Precisely, our algorithm is defined in the atomic-state model and works in anonymous networks in which even local ports are unlabeled. It makes no assumption on the daemon and thus stabilizes under the weakest one: the distributed unfair daemon. In a nn-node network of diameter DD and assuming a period B2D+2B \geq 2D+2, our algorithm only requires O(logB)O(\log B) bits per node to achieve full polynomiality as it stabilizes in at most 2D22D-2 rounds and O(min(n2B,n3))O(\min(n^2B, n^3)) moves. In particular and to the best of our knowledge, it is the first self-stabilizing unison for arbitrary anonymous networks achieving an asymptotically optimal stabilization time in rounds using a bounded memory at each node. Finally, we show that our solution allows to efficiently simulate synchronous self-stabilizing algorithms in an asynchronous environment. This provides a new state-of-the-art algorithm solving both the leader election and the spanning tree construction problem in any identified connected network which, to the best of our knowledge, beat all existing solutions of the literature.Comment: arXiv admin note: substantial text overlap with arXiv:2307.0663

    COVID-19 Booster Vaccine Acceptance in Ethnic Minority Individuals in the United Kingdom: a mixed-methods study using Protection Motivation Theory

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    Background: Uptake of the COVID-19 booster vaccine among ethnic minority individuals has been lower than in the general population. However, there is little research examining the psychosocial factors that contribute to COVID-19 booster vaccine hesitancy in this population.Aim: Our study aimed to determine which factors predicted COVID-19 vaccination intention in minority ethnic individuals in Middlesbrough, using Protection Motivation Theory (PMT) and COVID-19 conspiracy beliefs, in addition to demographic variables.Method: We used a mixed-methods approach. Quantitative data were collected using an online survey. Qualitative data were collected using semi-structured interviews. 64 minority ethnic individuals (33 females, 31 males; mage = 31.06, SD = 8.36) completed the survey assessing PMT constructs, COVID-19conspiracy beliefs and demographic factors. 42.2% had received the booster vaccine, 57.6% had not. 16 survey respondents were interviewed online to gain further insight into factors affecting booster vaccineacceptance.Results: Multiple regression analysis showed that perceived susceptibility to COVID-19 was a significant predictor of booster vaccination intention, with higher perceived susceptibility being associated with higher intention to get the booster. Additionally, COVID-19 conspiracy beliefs significantly predictedintention to get the booster vaccine, with higher conspiracy beliefs being associated with lower intention to get the booster dose. Thematic analysis of the interview data showed that barriers to COVID-19 booster vaccination included time constraints and a perceived lack of practical support in the event ofexperiencing side effects. Furthermore, there was a lack of confidence in the vaccine, with individuals seeing it as lacking sufficient research. Participants also spoke of medical mistrust due to historical events involving medical experimentation on minority ethnic individuals.Conclusion: PMT and conspiracy beliefs predict COVID-19 booster vaccination in minority ethnic individuals. To help increase vaccine uptake, community leaders need to be involved in addressing people’s concerns, misassumptions, and lack of confidence in COVID-19 vaccination

    Convex Optimization for Machine Learning

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    This book covers an introduction to convex optimization, one of the powerful and tractable optimization problems that can be efficiently solved on a computer. The goal of the book is to help develop a sense of what convex optimization is, and how it can be used in a widening array of practical contexts with a particular emphasis on machine learning. The first part of the book covers core concepts of convex sets, convex functions, and related basic definitions that serve understanding convex optimization and its corresponding models. The second part deals with one very useful theory, called duality, which enables us to: (1) gain algorithmic insights; and (2) obtain an approximate solution to non-convex optimization problems which are often difficult to solve. The last part focuses on modern applications in machine learning and deep learning. A defining feature of this book is that it succinctly relates the “story” of how convex optimization plays a role, via historical examples and trending machine learning applications. Another key feature is that it includes programming implementation of a variety of machine learning algorithms inspired by optimization fundamentals, together with a brief tutorial of the used programming tools. The implementation is based on Python, CVXPY, and TensorFlow. This book does not follow a traditional textbook-style organization, but is streamlined via a series of lecture notes that are intimately related, centered around coherent themes and concepts. It serves as a textbook mainly for a senior-level undergraduate course, yet is also suitable for a first-year graduate course. Readers benefit from having a good background in linear algebra, some exposure to probability, and basic familiarity with Python

    Artificial Dendritic Neuron: A Model of Computation and Learning Algorithm

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    Dendrites are root-like extensions from the neuron cell body and have long been thought to serve as the predominant input structures of neurons. Since the early twentieth century, neuroscience research has attempted to define the dendrite’s contribution to neural computation and signal integration. This body of experimental and modeling research strongly indicates that dendrites are not just input structures but are crucial to neural processing. Dendritic processing consists of both active and passive elements that utilize the spatial, electrical and connective properties of the dendritic tree. This work presents a neuron model based around the structure and properties of dendrites. This research assesses the computational benefits and requirements of adding dendrites to a spiking artificial neuron model. A list of the computational properties of actual dendrites that have shaped this work is given. An algorithm capable of generating and training a network of dendritic neurons is created as an investigative tool through which computational challenges and attributes are explored. This work assumes that dendrites provide a necessary and beneficial function to biological intelligence (BI) and their translation into the artificial intelligence (AI) realm would broaden the capabilities and improve the realism of artificial neural network (ANN) research. To date there have been only a few instances in which neural network-based AI research has ventured beyond the point neuron; therefore, the work presented here should be viewed as exploratory. The contribution to AI made by this work is an implementation of the artificial dendritic (AD) neuron model and an algorithm for training AD neurons with spatially distributed inputs with dendrite-like connectivity

    Differential evolution of non-coding DNA across eukaryotes and its close relationship with complex multicellularity on Earth

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    Here, I elaborate on the hypothesis that complex multicellularity (CM, sensu Knoll) is a major evolutionary transition (sensu Szathmary), which has convergently evolved a few times in Eukarya only: within red and brown algae, plants, animals, and fungi. Paradoxically, CM seems to correlate with the expansion of non-coding DNA (ncDNA) in the genome rather than with genome size or the total number of genes. Thus, I investigated the correlation between genome and organismal complexities across 461 eukaryotes under a phylogenetically controlled framework. To that end, I introduce the first formal definitions and criteria to distinguish ‘unicellularity’, ‘simple’ (SM) and ‘complex’ multicellularity. Rather than using the limited available estimations of unique cell types, the 461 species were classified according to our criteria by reviewing their life cycle and body plan development from literature. Then, I investigated the evolutionary association between genome size and 35 genome-wide features (introns and exons from protein-coding genes, repeats and intergenic regions) describing the coding and ncDNA complexities of the 461 genomes. To that end, I developed ‘GenomeContent’, a program that systematically retrieves massive multidimensional datasets from gene annotations and calculates over 100 genome-wide statistics. R-scripts coupled to parallel computing were created to calculate >260,000 phylogenetic controlled pairwise correlations. As previously reported, both repetitive and non-repetitive DNA are found to be scaling strongly and positively with genome size across most eukaryotic lineages. Contrasting previous studies, I demonstrate that changes in the length and repeat composition of introns are only weakly or moderately associated with changes in genome size at the global phylogenetic scale, while changes in intron abundance (within and across genes) are either not or only very weakly associated with changes in genome size. Our evolutionary correlations are robust to: different phylogenetic regression methods, uncertainties in the tree of eukaryotes, variations in genome size estimates, and randomly reduced datasets. Then, I investigated the correlation between the 35 genome-wide features and the cellular complexity of the 461 eukaryotes with phylogenetic Principal Component Analyses. Our results endorse a genetic distinction between SM and CM in Archaeplastida and Metazoa, but not so clearly in Fungi. Remarkably, complex multicellular organisms and their closest ancestral relatives are characterized by high intron-richness, regardless of genome size. Finally, I argue why and how a vast expansion of non-coding RNA (ncRNA) regulators rather than of novel protein regulators can promote the emergence of CM in Eukarya. As a proof of concept, I co-developed a novel ‘ceRNA-motif pipeline’ for the prediction of “competing endogenous” ncRNAs (ceRNAs) that regulate microRNAs in plants. We identified three candidate ceRNAs motifs: MIM166, MIM171 and MIM159/319, which were found to be conserved across land plants and be potentially involved in diverse developmental processes and stress responses. Collectively, the findings of this dissertation support our hypothesis that CM on Earth is a major evolutionary transition promoted by the expansion of two major ncDNA classes, introns and regulatory ncRNAs, which might have boosted the irreversible commitment of cell types in certain lineages by canalizing the timing and kinetics of the eukaryotic transcriptome.:Cover page Abstract Acknowledgements Index 1. The structure of this thesis 1.1. Structure of this PhD dissertation 1.2. Publications of this PhD dissertation 1.3. Computational infrastructure and resources 1.4. Disclosure of financial support and information use 1.5. Acknowledgements 1.6. Author contributions and use of impersonal and personal pronouns 2. Biological background 2.1. The complexity of the eukaryotic genome 2.2. The problem of counting and defining “genes” in eukaryotes 2.3. The “function” concept for genes and “dark matter” 2.4. Increases of organismal complexity on Earth through multicellularity 2.5. Multicellularity is a “fitness transition” in individuality 2.6. The complexity of cell differentiation in multicellularity 3. Technical background 3.1. The Phylogenetic Comparative Method (PCM) 3.2. RNA secondary structure prediction 3.3. Some standards for genome and gene annotation 4. What is in a eukaryotic genome? GenomeContent provides a good answer 4.1. Background 4.2. Motivation: an interoperable tool for data retrieval of gene annotations 4.3. Methods 4.4. Results 4.5. Discussion 5. The evolutionary correlation between genome size and ncDNA 5.1. Background 5.2. Motivation: estimating the relationship between genome size and ncDNA 5.3. Methods 5.4. Results 5.5. Discussion 6. The relationship between non-coding DNA and Complex Multicellularity 6.1. Background 6.2. Motivation: How to define and measure complex multicellularity across eukaryotes? 6.3. Methods 6.4. Results 6.5. Discussion 7. The ceRNA motif pipeline: regulation of microRNAs by target mimics 7.1. Background 7.2. A revisited protocol for the computational analysis of Target Mimics 7.3. Motivation: a novel pipeline for ceRNA motif discovery 7.4. Methods 7.5. Results 7.6. Discussion 8. Conclusions and outlook 8.1. Contributions and lessons for the bioinformatics of large-scale comparative analyses 8.2. Intron features are evolutionarily decoupled among themselves and from genome size throughout Eukarya 8.3. “Complex multicellularity” is a major evolutionary transition 8.4. Role of RNA throughout the evolution of life and complex multicellularity on Earth 9. Supplementary Data Bibliography Curriculum Scientiae Selbständigkeitserklärung (declaration of authorship

    Proceedings of the 29th International Symposium on Analytical and Environmental Problems

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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

    Making local algorithms efficiently self-stabilizing in arbitrary asynchronous environments

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    This paper deals with the trade-off between time, workload, and versatility in self-stabilization, a general and lightweight fault-tolerant concept in distributed computing.In this context, we propose a transformer that provides an asynchronous silent self-stabilizing version Trans(AlgI) of any terminating synchronous algorithm AlgI. The transformed algorithm Trans(AlgI) works under the distributed unfair daemon and is efficient both in moves and rounds.Our transformer allows to easily obtain fully-polynomial silent self-stabilizing solutions that are also asymptotically optimal in rounds.We illustrate the efficiency and versatility of our transformer with several efficient (i.e., fully-polynomial) silent self-stabilizing instances solving major distributed computing problems, namely vertex coloring, Breadth-First Search (BFS) spanning tree construction, k-clustering, and leader election
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