5,845 research outputs found
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
Profit allocation in agricultural supply chains: exploring the nexus of cooperation and compensation
In this paper, we focus on decentralized agricultural supply chains
consisting of multiple non-competing distributors satisfying the demand of
their respective markets. These distributors source a single product from a
farmer through an agricultural cooperative, operating in a single period. The
agents have the ability to coordinate their actions to maximize their profits,
and we use cooperative game theory to analyze cooperation among them. The
distributors can engage in joint ordering, increasing their order size, which
leads to a decrease in the price per kilogram. Additionally, distributors have
the opportunity to cooperate with the farmer, securing a reduced price per
kilogram at the cost price, while compensating the farmer for any kilograms not
acquired in the cooperation agreement. We introduce multidistributor-farmer
games and we prove that all the agents have incentives to cooperate. We
demonstrate the existence of stable allocations, where no subgroup of agents
can be better off by separating. Moreover, we propose and characterize a
distribution of the total profit that justly compensates the contribution of
the farmer in any group of distributors. Finally, we explore the conditions
under which the farmer can be compensated in order to maximize their revenues
when cooperating with all players
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Rigorous Experimentation For Reinforcement Learning
Scientific fields make advancements by leveraging the knowledge created by others to push the boundary of understanding. The primary tool in many fields for generating knowledge is empirical experimentation. Although common, generating accurate knowledge from empirical experiments is often challenging due to inherent randomness in execution and confounding variables that can obscure the correct interpretation of the results. As such, researchers must hold themselves and others to a high degree of rigor when designing experiments. Unfortunately, most reinforcement learning (RL) experiments lack this rigor, making the knowledge generated from experiments dubious. This dissertation proposes methods to address central issues in RL experimentation.
Evaluating the performance of an RL algorithm is the most common type of experiment in RL literature. Most performance evaluations are often incapable of answering a specific research question and produce misleading results. Thus, the first issue we address is how to create a performance evaluation procedure that holds up to scientific standards.
Despite the prevalence of performance evaluation, these types of experiments produce limited knowledge, e.g., they can only show how well an algorithm worked and not why, and they require significant amounts of time and computational resources. As an alternative, this dissertation proposes that scientific testing, the process of conducting carefully controlled experiments designed to further the knowledge and understanding of how an algorithm works, should be the primary form of experimentation.
Lastly, this dissertation provides a case study using policy gradient methods, showing how scientific testing can replace performance evaluation as the primary form of experimentation. As a result, this dissertation can motivate others in the field to adopt more rigorous experimental practices
Generalizable deep learning based medical image segmentation
Deep learning is revolutionizing medical image analysis and interpretation. However, its real-world deployment is often hindered by the poor generalization to unseen domains (new imaging modalities and protocols). This lack of generalization ability is further exacerbated by the scarcity of labeled datasets for training: Data collection and annotation can be prohibitively expensive in terms of labor and costs because label quality heavily dependents on the expertise of radiologists. Additionally, unreliable predictions caused by poor model generalization pose safety risks to clinical downstream applications.
To mitigate labeling requirements, we investigate and develop a series of techniques to strengthen the generalization ability and the data efficiency of deep medical image computing models. We further improve model accountability and identify unreliable predictions made on out-of-domain data, by designing probability calibration techniques.
In the first and the second part of thesis, we discuss two types of problems for handling unexpected domains: unsupervised domain adaptation and single-source domain generalization. For domain adaptation we present a data-efficient technique that adapts a segmentation model trained on a labeled source domain (e.g., MRI) to an unlabeled target domain (e.g., CT), using a small number of unlabeled training images from the target domain.
For domain generalization, we focus on both image reconstruction and segmentation. For image reconstruction, we design a simple and effective domain generalization technique for cross-domain MRI reconstruction, by reusing image representations learned from natural image datasets. For image segmentation, we perform causal analysis of the challenging cross-domain image segmentation problem. Guided by this causal analysis we propose an effective data-augmentation-based generalization technique for single-source domains. The proposed method outperforms existing approaches on a large variety of cross-domain image segmentation scenarios.
In the third part of the thesis, we present a novel self-supervised method for learning generic image representations that can be used to analyze unexpected objects of interest. The proposed method is designed together with a novel few-shot image segmentation framework that can segment unseen objects of interest by taking only a few labeled examples as references. Superior flexibility over conventional fully-supervised models is demonstrated by our few-shot framework: it does not require any fine-tuning on novel objects of interest. We further build a publicly available comprehensive evaluation environment for few-shot medical image segmentation.
In the fourth part of the thesis, we present a novel probability calibration model. To ensure safety in clinical settings, a deep model is expected to be able to alert human radiologists if it has low confidence, especially when confronted with out-of-domain data. To this end we present a plug-and-play model to calibrate prediction probabilities on out-of-domain data. It aligns the prediction probability in line with the actual accuracy on the test data. We evaluate our method on both artifact-corrupted images and images from an unforeseen MRI scanning protocol. Our method demonstrates improved calibration accuracy compared with the state-of-the-art method.
Finally, we summarize the major contributions and limitations of our works. We also suggest future research directions that will benefit from the works in this thesis.Open Acces
Artificial Intelligence, Robots, and Philosophy
This book is a collection of all the papers published in the special issue “Artificial Intelligence, Robots, and Philosophy,” Journal of Philosophy of Life, Vol.13, No.1, 2023, pp.1-146. The authors discuss a variety of topics such as science fiction and space ethics, the philosophy of artificial intelligence, the ethics of autonomous agents, and virtuous robots. Through their discussions, readers are able to think deeply about the essence of modern technology and the future of humanity. All papers were invited and completed in spring 2020, though because of the Covid-19 pandemic and other problems, the publication was delayed until this year. I apologize to the authors and potential readers for the delay. I hope that readers will enjoy these arguments on digital technology and its relationship with philosophy. ***
Contents***
Introduction
: Descartes and Artificial Intelligence;
Masahiro Morioka***
Isaac Asimov and the Current State of Space Science Fiction
: In the Light of Space Ethics;
Shin-ichiro Inaba***
Artificial Intelligence and Contemporary Philosophy
: Heidegger, Jonas, and Slime Mold;
Masahiro Morioka***
Implications of Automating Science
: The Possibility of Artificial Creativity and the Future of Science;
Makoto Kureha***
Why Autonomous Agents Should Not Be Built for War;
István Zoltán Zárdai***
Wheat and Pepper
: Interactions Between Technology and Humans;
Minao Kukita***
Clockwork Courage
: A Defense of Virtuous Robots;
Shimpei Okamoto***
Reconstructing Agency from Choice;
Yuko Murakami***
Gushing Prose
: Will Machines Ever be Able to Translate as Badly as
Humans?;
Rossa Ó Muireartaigh**
Educar para a resiliência : um percurso com crianças e adolescentes surdos
A resiliência é um conceito que implica adaptação e sucesso perante os desafios que surgem diariamente e que colocam em risco o desenvolvimento harmonioso do indivíduo. Promover programas preventivos e promotores de resiliência é fundamental na infância e na adolescência, principalmente com crianças vulneráveis, nomeadamente crianças surdas, já que enfrentam quotidianamente desafios acrescidos a nível da comunicação e da linguagem. Estes desafios acarretam outros, por exemplo, a nível relacional e emocional. O Currículo Europeu para a Resiliência surge num consórcio europeu com o objetivo de promover competências cognitivas, sociais e emocionais associadas à resiliência. Consciente da importância desta ferramenta e das caraterísticas específicas de crianças e adolescentes surdos, este estudo tem como objetivo adaptar o RESCUR para esta população, implementá-lo e avaliá-lo sob a perspetiva dos alunos, respetivos pais e professores. As quatro investigações que integram este trabalho apresentam a seguinte sequência de estudos: (i) uma revisão compreensiva da literatura acerca da resiliência em crianças e adolescentes surdos; (ii) um estudo com as adaptações do RESCUR para a população surda, tendo em consideração a estrutura de cada sessão e respetivas atividades; (iii) dois estudos, um quantitativo e outro qualitativo, realizados em contexto escolar, em três Agrupamentos de Escolas de Referência Bilingue: no norte, centro e sul de Portugal, com 37 alunos surdos, do pré-escolar ao 3.º ciclo. Os resultados sob a perspetiva de pais, professores e alunos revelam melhorias ao nível da aquisição de competências relacionais, comunicacionais e académicas; ao nível do bem-estar individual e social. Ainda integrado neste estudo é apresentado o documento “Indicações para a aplicação do RESCUR com crianças/adolescentes surdos” com diretrizes específicas para o educador implementar este currículo em contexto escolar; são também apresentados exemplos de sessões adaptadas.Resilience is a concept involving adaptation and success in the face of challenges that arise daily and that endanger the harmonious development of the individual. Promoting preventive programs and promoting resilience is fundamental in childhood and adolescence, especially with vulnerable children, namely deaf children, as they face increased challenges in terms of communication and language daily. These challenges lead to others, for example, at a relational and emotional level. The European Curriculum for Resilience is part of a European consortium to promote cognitive, social, and emotional skills associated with resilience.
Aware of the importance of this tool and the specific characteristics of deaf children and adolescents, this study aims to adapt the RESCUR to this population, implement it and evaluate it from the perspective of students, their parents, and teachers. The four investigations that make up this work present the following sequence of studies: (i) a comprehensive review of the literature on resilience in children and adolescents with deafness; (ii) a study with the adaptations of RESCUR for the deaf population, taking into account the structure of each session and respective activities; (iii) two studies, one quantitative and the other qualitative, carried out in a school context, in three Groups of Bilingual Reference Schools: in the north, center, and south of Portugal, with 37 deaf students, from preschool to 3rd cycle.
The results from the perspective of parents, teachers, and students reveal improvements in terms of the acquisition of relational, communicational, and academic skills; at the level of individual and social well-being. Also included in this study is the document “Indications for the application of RESCUR with deaf children/adolescents” with specific guidelines for the educator to implement this curriculum in a school context; examples of adapted sessions are also presented
Intelligent computing : the latest advances, challenges and future
Computing is a critical driving force in the development of human civilization. In recent years, we have witnessed the emergence of intelligent computing, a new computing paradigm that is reshaping traditional computing and promoting digital revolution in the era of big data, artificial intelligence and internet-of-things with new computing theories, architectures, methods, systems, and applications. Intelligent computing has greatly broadened the scope of computing, extending it from traditional computing on data to increasingly diverse computing paradigms such as perceptual intelligence, cognitive intelligence, autonomous intelligence, and human computer fusion intelligence. Intelligence and computing have undergone paths of different evolution and development for a long time but have become increasingly intertwined in recent years: intelligent computing is not only intelligence-oriented but also intelligence-driven. Such cross-fertilization has prompted the emergence and rapid advancement of intelligent computing
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