48 research outputs found
Recommended from our members
A unified framework for resource-bounded autonomous agents interacting with unknown environments
No full textThe aim of this thesis is to present a mathematical framework for conceptualizing and constructing adaptive autonomous systems under resource constraints. The first part of this thesis contains a concise presentation of the foundations of classical agency: namely the formalizations of decision making and learning. Decision making includes: (a) subjective expected utility (SEU) theory, the framework of decision making under uncertainty; (b) the maximum SEU principle to choose the optimal solution; and (c) its application to the design of autonomous systems, culminating in the Bellman optimality equations. Learning includes: (a) Bayesian probability theory, the theory for reasoning under uncertainty that extends logic; and (b) Bayes-Optimal agents, the application of Bayesian probability theory to the design of optimal adaptive agents. Then, two major problems of the maximum SEU principle are highlighted: (a) the prohibitive computational costs and (b) the need for the causal precedence of the choice of the policy. The second part of this thesis tackles the two aforementioned problems. First, an information-theoretic notion of resources in autonomous systems is established. Second, a framework for resource-bounded agency is introduced. This includes: (a) a maximum bounded SEU principle that is derived from a set of axioms of utility; (b) an axiomatic model of probabilistic causality, which is applied for the formalization of autonomous systems having uncertainty over their policy and environment; and (c) the Bayesian control rule, which is derived from the maximum bounded SEU principle and the model of causality, implementing a stochastic adaptive control law that deals with the case where autonomous agents are uncertain about their policy and environment
Author Correction: Explainable AI approach with original vegetation data classifies spatio-temporal nitrogen in flows from ungauged catchments to the Great Barrier Reef
No full textCorrection to: Scientific Reports, published online 24 October 2023 The original version of this Article contained an error in the Results section, under the subheading ‘Verification of catchment classification for DIN similarities’, where two instances of the unit ‘mg/L’ were incorrectly stated as m/L and g/L , respectively. While simulated peaks were under estimated in all cases, a review of the raw data identified that the maximum nitrogen concentration in the dataset for Herbert Catchment was 1.8105 m/L, which is the highest historical record, plus two additional peaks ranging between 1.320 g/L and 1.694 mg/L. now reads: While simulated peaks were under estimated in all cases, a review of the raw data identified that the maximum nitrogen concentration in the dataset for Herbert Catchment was 1.8105 mg/L, which is the highest historical record, plus two additional peaks ranging between 1.320 mg/L and 1.694 mg/L. The original Article has been corrected. © 2023, The Author(s)
Who Invented MutadÄrik Meter in Arabic Prosody?
No full textAlthough the majority of the scholars of the Arabic prosody share the belief that MutadÄrik meter was devised and added to the Khalili's Fifteen Meters by Akhfash Owsat, historical and structural investigations show that this meter can not be his innovation. This article also attempts to introduce the real innovator of MutadÄrik meter, whom the author of this article believes to be Ibn HammÄd JawharÄ«. He innovated MutadÄrik meter to explain his own metrical theory
Scalable Graphical Models for Social Networks
No full textThe views and conclusions contained in this document are those of the author and should not be interpreted as representing the official policies, either expressed or implied, of any sponsoring institution, the U.S. government or any other entity
Dynamic probabilistic models for latent feature propagation in social networks
No full textCurrent Bayesian models for dynamic social network data have focused on modelling the influence of evolving unobserved structure on observed social interactions. However, an understanding of how observed social relationships from the past affect future unobserved structure in the network has been neglected. In this paper, we introduce a new probabilistic model for capturing this phenomenon, which we call latent feature propagation, in social networks. We demonstrate our model's capability for inferring such latent structure in varying types of social network datasets, and experimental studies show this structure achieves higher predictive performance on link prediction and forecasting tasks. Copyright 2013 by the author(s)
Correction to: Derivable maps at commutative products on Banach algebras (Acta Scientiarum Mathematicarum, (2024), 90, 1-2, (165-174), 10.1007/s44146-023-00104-8)
No full textThe publication of this article unfortunately contained mistakes. One co-author was missing. The corrected authorship is given in this erratum. The original article has been corrected
Approximate amenability of tensor products of Banach algebras
No full textExamples constructed by the first author and Charles Read make it clear that many of the hereditary properties of amenability no longer hold for approximate amenability. These and earlier results of the authors also show that the presence of a bounded approximate identity often entails positive results. Here we show that the tensor product of approximately amenable algebras need not be approximately amenable, and investigate conditions under which A and B being approximately amenable implies, or is implied by, A⊗ˆB or A#⊗ˆB# being approximately amenable. Once again, the rôle of having a bounded approximate identity comes to the fore. Our methods also enable us to prove that if A⊗ˆB is amenable, then so are A and B, a result proved by Barry Johnson in 1996 under an additional assumptio
Minority Community Resilience and Cultural Heritage Preservation: A Case Study of Gullah Geechee Community
No full textThe Gullah Geechee community of the south-eastern United States endures today as a minority group with a significant cultural heritage. However, little research has been conducted to explore this community’s resilience in the face of climate change and other environmental impacts. The database Web of Science was searched and 109 publications on the Gullah Geechee community were identified. Using quantitative and qualitative methods, we analyzed the publications to identify patterns and primary research themes related to the Gullah Geechee community’s resilience. Findings revealed that Gullah Geechee‘s cultural heritage is vulnerable to climatic and societal changes, but can also be a source for enhancing community resilience and promoting more sustainable community-led heritage and tourism developments. A framework is proposed for building community resilience in the context of minority and/or marginalized communities (e.g., Gullah Geechee). This study highlights the urgent need to not only better understand and incorporate a community’s economic dimensions and losses in various decision- and policy-making processes but also their cultural and social dimensions and losses. This systematic analysis can help inform both heritage preservation and community-led tourism practices and policies related to the Gullah Geechee community, as well as help direct new research efforts focusing on minority and/or marginalized community resilience.History, Form & Aesthetic
Bayesian Sets
No full textInspired by "Google Sets", we consider the problem of retrieving items from a concept or cluster, given a query consisting of a few items from that cluster. We formulate this as a Bayesian inference problem and describe a very simple algorithm for solving it. Our algorithm uses a modelbased concept of a cluster and ranks items using a score which evaluates the marginal probability that each item belongs to a cluster containing the query items. For exponential family models with conjugate priors this marginal probability is a simple function of sufficient statistics. We focus on sparse binary data and show that our score can be evaluated exactly using a single sparse matrix multiplication, making it possible to apply our algorithm to very large datasets. We evaluate our algorithm on three datasets: retrieving movies from EachMovie, finding completions of author sets from the NIPS dataset, and finding completions of sets of words appearing in the Grolier encyclopedia. We compare to Google Sets and show that Bayesian Sets gives very reasonable set completions
Classification of Microarray Data with Factor Mixture Models.
No full textThe classification of few tissue samples on a very large
number of genes represents a non-standard problem in statistics but a
usual one in microarray expression data analysis. In fact, the dimension
of the feature space (the number of genes) is typically much greater
than the number of tissues. We consider high-density oligonucleotide
microarray data, where the expression level is associated to an ‘abso-
lute call’, which represents a qualitative indication of whether or not a
transcript is detected within a sample. The ‘absolute call’ is generally
not taken in consideration in analyses.
Results: In contrast to frequently used cluster analysis methods to
analyze gene expression data, we consider a problem of classification
of tissues and of the variables selection. We adopted methodologies
formulated by Ghahramani and Hinton and Rocci and Vichi for simul-
taneous dimensional reduction of genes and classification of tissues;
trying to identify genes (denominated ‘markers’) that are able to distin-
guish between two known different classes of tissue samples. In this
respect, we propose a generalization of the approach proposed by
McLachlan et al. by advising to estimate the distribution of log LR statis-
tic for testing one versus two component hypothesis in the mixture
model for each gene considered individually, using a parametric
bootstrap approach. We compare conditional (on ‘absolute call’) and
unconditional analyses performed on dataset described in Golub et al.
We show that the proposed techniques improve the results of classi-
fication of tissue samples with respect to known results on the same
benchmark dataset.
Availability: The software of Ghahramani and Hinton is written in
Matlab and available in ‘Mixture of Factor Analyzers’ on http://www.
gatsby.ucl.ac.uk/zoubin/software.html while the software of Rocci
and Vichi is available upon request from the author
