396 research outputs found

    The telecoupled sustainability impacts of global agricultural value chains:Assessing the cross-scale sustainability impacts of the cocoa sector

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    Agriculture is a major contributor to the global environmental crisis. Natural ecosystems are being replaced by agricultural land, which leads to the extinction of species and the release of tons of carbon emissions. Global agricultural value chains (GVCs) have grown due to the intensification of international trade. While GVCs have undeniably created economic opportunities for the agriculture sector, they have also led to the escalation of local environmental issues. Several initiatives have been implemented to reduce the negative impacts of agriculture, including government regulations, sustainability certification labels, and voluntary sustainability commitments. However, the effectiveness of these initiatives has been questioned due to several reasons, including the mismatches between the scale of the problem and the solution, the lack of monitoring and verification of sustainability actions, and their weak enforcement. Sustainability initiatives are informed by studies assessing the impacts of agriculture that often only focus on local impacts, while disregarding larger-scale – telecoupled– dynamics that can trigger impacts across geographic and temporal scales. This thesis aims to help bridge these knowledge gaps by examining the impacts of agricultural GVCs across scales, studying the role of GVC’s configuration in modulating these impacts and investigating the role of GVC actors in mitigating sustainability risks across scales. The global cocoa value chain is used as a case study. Chapter 2 examines various impact assessment methods and their ability to capture the effects caused by telecoupled dynamics across different scales. The study concludes that no single method is sufficient to capture all telecoupled cross-scale dynamics and that the integration of different methods is necessary to bridge gaps between methods and complement their scope. Chapter 3 implements the recommendations outlined in Chapter 2 by analyzing the impacts caused by cocoa agroforestry and cocoa full-sun production in Ghana. Impacts on carbon, biodiversity stocks, and environmental pollution were analyzed within and beyond the farm-level. This chapter reveals that findings drawn from farm-level assessments can contradict those from landscape-level assessments. Decision-makers focused should be wary of extrapolating farm-level assessment results to larger scales. Chapter 4 expands the scope to the global scale by examining the role of the cocoa GVC configuration on the capacity of the sector to address sustainability challenges across scales. The chapter identifies different types of cocoa traders, their market dominance, and sustainability commitments. The chapter highlights that to address the telecoupled impacts of the cocoa GVC, coordinated action between traders is required, along with government interventions to balance power asymmetries. Chapter 5 measured the degree to which cocoa traders, as identified in Chapter 4, are exposed to deforestation and climate change. This chapter highlights that sustainability challenges in agricultural value chains cannot be resolved in isolation as farming systems are constantly interacting with other farming systems and land competing sectors. To avoid displacing negative impacts across scales, it is necessary to have a coordinated and collaborative effort from stakeholders and sectors involved in making decisions related to land use. This thesis shows that addressing the telecoupled impacts caused by agricultural value chains needs a good understanding of the cause-effect dynamics at play. This requires the quantification of impacts caused by agriculture across scales and the characterization of the GVC network of actors modulating these impacts. Interdisciplinary methods need to be leveraged and integrated to generate actionable insights. The findings of this thesis can assist decision-makers and private actors in devising customized sustainability strategies, prioritizing action, and addressing the most vulnerable hotspots while being mindful of global teleconnections and avoiding spillovers

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well

    Adaptive dynamical networks

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    It is a fundamental challenge to understand how the function of a network is related to its structural organization. Adaptive dynamical networks represent a broad class of systems that can change their connectivity over time depending on their dynamical state. The most important feature of such systems is that their function depends on their structure and vice versa. While the properties of static networks have been extensively investigated in the past, the study of adaptive networks is much more challenging. Moreover, adaptive dynamical networks are of tremendous importance for various application fields, in particular, for the models for neuronal synaptic plasticity, adaptive networks in chemical, epidemic, biological, transport, and social systems, to name a few. In this review, we provide a detailed description of adaptive dynamical networks, show their applications in various areas of research, highlight their dynamical features and describe the arising dynamical phenomena, and give an overview of the available mathematical methods developed for understanding adaptive dynamical networks

    Conditional Invertible Generative Models for Supervised Problems

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    Invertible neural networks (INNs), in the setting of normalizing flows, are a type of unconditional generative likelihood model. Despite various attractive properties compared to other common generative model types, they are rarely useful for supervised tasks or real applications due to their unguided outputs. In this work, we therefore present three new methods that extend the standard INN setting, falling under a broader category we term generative invertible models. These new methods allow leveraging the theoretical and practical benefits of INNs to solve supervised problems in new ways, including real-world applications from different branches of science. The key finding is that our approaches enhance many aspects of trustworthiness in comparison to conventional feed-forward networks, such as uncertainty estimation and quantification, explainability, and proper handling of outlier data

    LIPIcs, Volume 261, ICALP 2023, Complete Volume

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    LIPIcs, Volume 261, ICALP 2023, Complete Volum

    Topological data analysis of organoids

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    Organoids are multi-cellular structures which are cultured in vitro from stem cells to resemble specific organs (e.g., colon, liver) in their three- dimensional composition. The gene expression and the tissue composition of organoids constantly affect each other. Dynamic changes in the shape, cellular composition and transcriptomic profile of these model systems can be used to understand the effect of mutations and treatments in health and disease. In this thesis, I propose new techniques in the field of topological data analysis (TDA) to analyse the gene expression and the morphology of organoids. I use TDA methods, which are inspired by topology, to analyse and quantify the continuous structure of single-cell RNA sequencing data, which is embedded in high dimensional space, and the shape of an organoid. For single-cell RNA sequencing data, I developed the multiscale Laplacian score (MLS) and the UMAP diffusion cover, which both extend and im- prove existing topological analysis methods. I demonstrate the utility of these techniques by applying them to a published benchmark single-cell data set and a data set of mouse colon organoids. The methods validate previously identified genes and detect additional genes with known involvement cancers. To study the morphology of organoids I propose DETECT, a rotationally invariant signature of dynamically changing shapes. I demonstrate the efficacy of this method on a data set of segmented videos of mouse small intestine organoid experiments and show that it outperforms classical shape descriptors. I verify the method on a synthetic organoid data set and illustrate how it generalises to 3D to conclude that DETECT offers rigorous quantification of organoids and opens up computationally scalable methods for distinguishing different growth regimes and assessing treatment effects. Finally, I make a theoretical contribution to the statistical inference of the method underlying DETECT

    Causality in complex systems: An inferentialist proposal

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    I argue for an inferentialist account of the meaning of causal claims, which draws on the writings of Sellars and Brandom. The account is meant to be widely applicable. In this work, it is motivated and defended with reference to complex systems sciences, i.e., sciences that study the behaviour of systems with many components interacting at various levels of organisation (e.g. cells, brain, social groups). Here are three, seemingly-uncontroversial platitudes about causality. (1) Causal relations are objective, mind-independent relations and, as such, analysable in objective, mind-independent terms. (2) There is a tight connection between our practice of predicting, explaining and controlling phenomena, and the use of causal notions. (3) The second platitude should be explained in terms of the first. Contrary to this widely-held stance, I suggest that we reverse the order of analysis, by taking our activities of agents as the raw material in terms of which to account for the obtaining of causal relations. To this end, I propose and defend an inferentialist account of causality. Causality is a ‘category’ that the knowing subject employs to ‘mediate’ between himself and the world. In inferentialist terms, this mediation is the result of the concept of cause figuring in a network of inferences, used in our practice of gathering evidence and using it to explain, predict and intervene. Complexity only makes the mediation more difficult, thereby rendering the meaning of causality more evident
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