A Real-Time Unsupervised Neural Network for the Low-Level Control of a Mobile Robot in a Nonstationary Environment

This article introduces a real-time, unsupervised neural network that learns to control a two-degree-of-freedom mobile robot in a nonstationary environment. The neural controller, which is termed neural NETwork MObile Robot Controller (NETMORC), combines associative learning and Vector Associative Map (YAM) learning to generate transformations between spatial and velocity coordinates. As a result, the controller learns the wheel velocities required to reach a target at an arbitrary distance and angle. The transformations are learned during an unsupervised training phase, during which the robot moves as a result of randomly selected wheel velocities. The robot learns the relationship between these velocities and the resulting incremental movements. Aside form being able to reach stationary or moving targets, the NETMORC structure also enables the robot to perform successfully in spite of disturbances in the enviroment, such as wheel slippage, or changes in the robot's plant, including changes in wheel radius, changes in inter-wheel distance, or changes in the internal time step of the system. Finally, the controller is extended to include a module that learns an internal odometric transformation, allowing the robot to reach targets when visual input is sporadic or unreliable.Sloan Fellowship (BR-3122), Air Force Office of Scientific Research (F49620-92-J-0499

Formal Derivation of Concurrent Garbage Collectors

Concurrent garbage collectors are notoriously difficult to implement correctly. Previous approaches to the issue of producing correct collectors have mainly been based on posit-and-prove verification or on the application of domain-specific templates and transformations. We show how to derive the upper reaches of a family of concurrent garbage collectors by refinement from a formal specification, emphasizing the application of domain-independent design theories and transformations. A key contribution is an extension to the classical lattice-theoretic fixpoint theorems to account for the dynamics of concurrent mutation and collection.Comment: 38 pages, 21 figures. The short version of this paper appeared in the Proceedings of MPC 201

EU accession and Poland's external trade policy

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Expanding the Direct and Indirect Effects Model of Writing (DIEW) : Reading–writing relations, and dynamic relations as a function of measurement/dimensions of written composition

Within the context of the Direct and Indirect Effects Model of Writing (Kim & Park, 2019), we examined a dynamic relations hypothesis, which contends that the relations of component skills, including reading comprehension, to written composition vary as a function of dimensions of written composition. Specifically, we investigated (a) whether higher-order cognitive skills (i.e., inference, perspective taking, and monitoring) are differentially related to three dimensions of written composition—writing quality, writing productivity, and correctness in writing; (b) whether reading comprehension is differentially related to the three dimensions of written composition after accounting for oral language, cognition, and transcription skills, and whether reading comprehension mediates the relations of discourse oral language and lexical literacy to the three dimensions of written composition; and (c) whether total effects of oral language, cognition, transcription, and reading comprehension vary for the three dimensions of written composition. Structural equation model results from 350 English-speaking second graders showed that higher-order cognitive skills were differentially related to the three dimensions of written composition. Reading comprehension was related only to writing quality, but not to writing productivity or correctness in writing, and reading comprehension differentially mediated the relations of discourse oral language and lexical literacy to writing quality. Total effects of language, cognition, transcription, and reading comprehension varied largely for the three dimensions of written composition. These results support the dynamic relation hypothesis, role of reading in writing, and the importance of accounting for dimensions of written composition in a theoretical model of writing. (PsycInfo Database Record (c) 2022 APA, all rights reserved

Causality, Information and Biological Computation: An algorithmic software approach to life, disease and the immune system

Biology has taken strong steps towards becoming a computer science aiming at reprogramming nature after the realisation that nature herself has reprogrammed organisms by harnessing the power of natural selection and the digital prescriptive nature of replicating DNA. Here we further unpack ideas related to computability, algorithmic information theory and software engineering, in the context of the extent to which biology can be (re)programmed, and with how we may go about doing so in a more systematic way with all the tools and concepts offered by theoretical computer science in a translation exercise from computing to molecular biology and back. These concepts provide a means to a hierarchical organization thereby blurring previously clear-cut lines between concepts like matter and life, or between tumour types that are otherwise taken as different and may not have however a different cause. This does not diminish the properties of life or make its components and functions less interesting. On the contrary, this approach makes for a more encompassing and integrated view of nature, one that subsumes observer and observed within the same system, and can generate new perspectives and tools with which to view complex diseases like cancer, approaching them afresh from a software-engineering viewpoint that casts evolution in the role of programmer, cells as computing machines, DNA and genes as instructions and computer programs, viruses as hacking devices, the immune system as a software debugging tool, and diseases as an information-theoretic battlefield where all these forces deploy. We show how information theory and algorithmic programming may explain fundamental mechanisms of life and death.Comment: 30 pages, 8 figures. Invited chapter contribution to Information and Causality: From Matter to Life. Sara I. Walker, Paul C.W. Davies and George Ellis (eds.), Cambridge University Pres