Fundamentals

Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters

LIPIcs, Volume 261, ICALP 2023, Complete Volume

LIPIcs, Volume 261, ICALP 2023, Complete Volum

Fundamentals

Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters

Dynamical systems via domains:Toward a unified foundation of symbolic and non-symbolic computation

Non-symbolic computation (as, e.g., in biological and artificial neural networks) is astonishingly good at learning and processing noisy real-world data. However, it lacks the kind of understanding we have of symbolic computation (as, e.g., specified by programming languages). Just like symbolic computation, also non-symbolic computation needs a semantics—or behavior description—to achieve structural understanding. Domain theory has provided this for symbolic computation, and this thesis is about extending it to non-symbolic computation. Symbolic and non-symbolic computation can be described in a unified framework as state-discrete and state-continuous dynamical systems, respectively. So we need a semantics for dynamical systems: assigning to a dynamical system a domain—i.e., a certain mathematical structure—describing the system’s behavior. In part 1 of the thesis, we provide this domain-theoretic semantics for the ‘symbolic’ state-discrete systems (i.e., labeled transition systems). And in part 2, we do this for the ‘non-symbolic’ state-continuous systems (known from ergodic theory). This is a proper semantics in that the constructions form functors (in the sense of category theory) and, once appropriately formulated, even adjunctions and, stronger yet, equivalences. In part 3, we explore how this semantics relates the two types of computation. It suggests that non-symbolic computation is the limit of symbolic computation (in the ‘profinite’ sense). Conversely, if the system’s behavior is fairly stable, it may be described as realizing symbolic computation (here the concepts of ergodicity and algorithmic randomness are useful). However, the underlying concept of stability is limited by a no-go result due to a novel interpretation of Fitch’s paradox. This also has implications for AI-safety and, more generally, suggests fruitful applications of philosophical tools in the non-symbolic computation of modern AI

Semiannual progress report no. 1, 16 November 1964 - 30 June 1965

Summary reports of research in bioelectronics, electron streams and interactions, plasmas, quantum and optical electronics, radiation and propagation, and solid-state electronic

An analysis of age-related alterations in functional memory networks

The human brain is the most complex organ of the human body and many aspects of its functioning have not yet been understood. One of the most fascinating abilities of the human brain is the skill to store and retrieve information, which is what we refer to as memory. One attempt to get a deeper insight into the functioning of memory is to analyze the complex activity pattern of the human brain that emerges while a memory task is being processed. The understanding of memory is epistemologically very intriguing since it is this ability that enables us to collect, to store and to recall ideas, emotions and thoughts - hence, it builds our own identity. This thesis analyses age-related changes in functional connectivity networks related to episodic and working memory processing. The data for this study were measured using fMRI technique and the sample set consisted of healthy individuals aging from 20 up to over 80 years. Based on the fMRI data we construct correlation networks by correlating pairwisely the measured voxel activity, the nodes of the network being brain voxels, the edges being correlations. These networks are thresholded, anatomically clustered and analyzed by computing statistical network measures, using spectral methods, computing network entropy and calculating persistent homology. The main findings are: elderly individuals exhibit expanded neural networks with less differentiation between episodic and working memory tasks. However, we observe compensatory mechanisms that accompany this dedifferentiation process. Network synchronizability is higher for elderly individuals. Network entropy increases as well with age, yielding a lower network vulnerability for elderly individuals. Aging processes leave traces in the homology pattern of the networks, whereas all brain networks exhibit different persistent homology features