DATA ON ARTS ORGANIZATIONS: A REVIEW AND NEEDS ASSESSMENT, WITH DESIGN IMPLICATIONS

This project describes the data resources on arts organizations that are currently available to inform the efforts of policy makers, arts managers, and researchers working in the arts field. It assesses the adequacy of different data sources for identifying the population of arts and cultural organizations in a community. The report is based on a review of more than a dozen sources of information about arts and cultural organizations, interviews with researchers and data specialists, and an empirical study of arts organizations in three metropolitan areas - Philadelphia, Dallas-Fort Worth, and Minneapolis-St. Paul. The report concludes with recommendations for improving data quality and for establishing an ongoing national database on the arts sector.

All the World's a (Hyper)Graph: A Data Drama

We introduce Hyperbard, a dataset of diverse relational data representationsderived from Shakespeare's plays. Our representations range from simple graphscapturing character co-occurrence in single scenes to hypergraphs encodingcomplex communication settings and character contributions as hyperedges withedge-specific node weights. By making multiple intuitive representationsreadily available for experimentation, we facilitate rigorous representationrobustness checks in graph learning, graph mining, and network analysis,highlighting the advantages and drawbacks of specific representations.Leveraging the data released in Hyperbard, we demonstrate that many solutionsto popular graph mining problems are highly dependent on the representationchoice, thus calling current graph curation practices into question. As anhomage to our data source, and asserting that science can also be art, wepresent all our points in the form of a play.<br

Mixed Order Hyper-Networks for Function Approximation and Optimisation

Many systems take inputs, which can be measured and sometimes controlled, and outputs, which can also be measured and which depend on the inputs. Taking numerous measurements from such systems produces data, which may be used to either model the system with the goal of predicting the output associated with a given input (function approximation, or regression) or of finding the input settings required to produce a desired output (optimisation, or search). Approximating or optimising a function is central to the field of computational intelligence. There are many existing methods for performing regression and optimisation based on samples of data but they all have limitations. Multi layer perceptrons (MLPs) are universal approximators, but they suffer from the black box problem, which means their structure and the function they implement is opaque to the user. They also suffer from a propensity to become trapped in local minima or large plateaux in the error function during learning. A regression method with a structure that allows models to be compared, human knowledge to be extracted, optimisation searches to be guided and model complexity to be controlled is desirable. This thesis presents such as method. This thesis presents a single framework for both regression and optimisation: the mixed order hyper network (MOHN). A MOHN implements a function f:{-1,1}^n ->R to arbitrary precision. The structure of a MOHN makes the ways in which input variables interact to determine the function output explicit, which allows human insights and complexity control that are very difficult in neural networks with hidden units. The explicit structure representation also allows efficient algorithms for searching for an input pattern that leads to a desired output. A number of learning rules for estimating the weights based on a sample of data are presented along with a heuristic method for choosing which connections to include in a model. Several methods for searching a MOHN for inputs that lead to a desired output are compared. Experiments compare a MOHN to an MLP on regression tasks. The MOHN is found to achieve a comparable level of accuracy to an MLP but suffers less from local minima in the error function and shows less variance across multiple training trials. It is also easier to interpret and combine from an ensemble. The trade-off between the fit of a model to its training data and that to an independent set of test data is shown to be easier to control in a MOHN than an MLP. A MOHN is also compared to a number of existing optimisation methods including those using estimation of distribution algorithms, genetic algorithms and simulated annealing. The MOHN is able to find optimal solutions in far fewer function evaluations than these methods on tasks selected from the literature

Model of models -- Part 1

This paper proposes a new cognitive model, acting as the main component of an AGI agent. The model is introduced in its mature intelligence state, and as an extension of previous models, DENN, and especially AKREM, by including operational models (frames/classes) and will. This model's core assumption is that cognition is about operating on accumulated knowledge, with the guidance of an appropriate will. Also, we assume that the actions, part of knowledge, are learning to be aligned with will, during the evolution phase that precedes the mature intelligence state. In addition, this model is mainly based on the duality principle in every known intelligent aspect, such as exhibiting both top-down and bottom-up model learning, generalization verse specialization, and more. Furthermore, a holistic approach is advocated for AGI designing, and cognition under constraints or efficiency is proposed, in the form of reusability and simplicity. Finally, reaching this mature state is described via a cognitive evolution from infancy to adulthood, utilizing a consolidation principle. The final product of this cognitive model is a dynamic operational memory of models and instances. Lastly, some examples and preliminary ideas for the evolution phase to reach the mature state are presented.Comment: arXiv admin note: text overlap with arXiv:2301.1355

Dynamic Mathematics for Automated Machine Learning Techniques

Machine Learning and Neural Networks have been gaining popularity and are widely considered as the driving force of the Fourth Industrial Revolution. However, modern machine learning techniques such as backpropagation training was firmly established in 1986 while computer vision was revolutionised in 2012 with the introduction of AlexNet. Given all these accomplishments, why are neural networks still not an integral part of our society? ``Because they are difficult to implement in practice.'' I'd like to use machine learning, but I can't invest much time. The concept of Automated Machine Learning (AutoML) was first proposed by Professor Frank Hutter of the University of Freiburg. Machine learning is not simple; it requires a practitioner to have thorough understanding on the attributes of their data and the components which their model entails. AutoML is the effort to automate all tedious aspects of machine learning to form a clean data analysis pipeline. This thesis is our effort to develop and to understand ways to automate machine learning. Specifically, we focused on Recurrent Neural Networks (RNNs), Meta-Learning, and Continual Learning. We studied continual learning to enable a network to sequentially acquire skills in a dynamic environment; we studied meta-learning to understand how a network can be configured efficiently; and we studied RNNs to understand the consequences of consecutive actions. Our RNN-study focused on mathematical interpretability. We described a large variety of RNNs as one mathematical class to understand their core network mechanism. This enabled us to extend meta-learning beyond network configuration for network pruning and continual learning. This also provided insights for us to understand how a single network should be consecutively configured and led us to the creation of a simple generic patch that is compatible to several existing continual learning archetypes. This patch enhanced the robustness of continual learning techniques and allowed them to generalise data better. By and large, this thesis presented a series of extensions to enable AutoML to be made simple, efficient, and robust. More importantly, all of our methods are motivated with mathematical understandings through the lens of dynamical systems. Thus, we also increased the interpretability of AutoML concepts

A Unified Framework for Gradient-based Hyperparameter Optimization and Meta-learning

Machine learning algorithms and systems are progressively becoming part of our societies, leading to a growing need of building a vast multitude of accurate, reliable and interpretable models which should possibly exploit similarities among tasks. Automating segments of machine learning itself seems to be a natural step to undertake to deliver increasingly capable systems able to perform well in both the big-data and the few-shot learning regimes. Hyperparameter optimization (HPO) and meta-learning (MTL) constitute two building blocks of this growing effort. We explore these two topics under a unifying perspective, presenting a mathematical framework linked to bilevel programming that captures existing similarities and translates into procedures of practical interest rooted in algorithmic differentiation. We discuss the derivation, applicability and computational complexity of these methods and establish several approximation properties for a class of objective functions of the underlying bilevel programs. In HPO, these algorithms generalize and extend previous work on gradient-based methods. In MTL, the resulting framework subsumes classic and emerging strategies and provides a starting basis from which to build and analyze novel techniques. A series of examples and numerical simulations offer insight and highlight some limitations of these approaches. Experiments on larger-scale problems show the potential gains of the proposed methods in real-world applications. Finally, we develop two extensions of the basic algorithms apt to optimize a class of discrete hyperparameters (graph edges) in an application to relational learning and to tune online learning rate schedules for training neural network models, an old but crucially important issue in machine learning

Speculations

From the Editorial Introduction: "Since I am convinced that nobody reads editorials I will keep my remarks brief. Putting together the inaugural issue of Speculations has been an unusual experience. It has depended on the collusion of fellow speculative types, the help of many anonymous reviewers, the endless patience of designer Thomas Gokey, and more hours than someone in the final year of their PhD should ever spend on a project. Looking over the final product I think it has all been worth it. This is the first journal dedicated to speculative realism and despite the obscurity of that term I think we all understand it as a handy label under which weird realists, continental metaphysicians, object oriented ontologists, transcendental realists, vitalists, and Lovecraftians can unite. This is also, perhaps, the first time a journal can boast that each contributor is also a blogger. This is the reason why Speculations could only ever be an online, open-access journal. …

Characterising and modeling the co-evolution of transportation networks and territories

The identification of structuring effects of transportation infrastructure on territorial dynamics remains an open research problem. This issue is one of the aspects of approaches on complexity of territorial dynamics, within which territories and networks would be co-evolving. The aim of this thesis is to challenge this view on interactions between networks and territories, both at the conceptual and empirical level, by integrating them in simulation models of territorial systems.Comment: Doctoral dissertation (2017), Universit\'e Paris 7 Denis Diderot. Translated from French. Several papers compose this PhD thesis; overlap with: arXiv:{1605.08888, 1608.00840, 1608.05266, 1612.08504, 1706.07467, 1706.09244, 1708.06743, 1709.08684, 1712.00805, 1803.11457, 1804.09416, 1804.09430, 1805.05195, 1808.07282, 1809.00861, 1811.04270, 1812.01473, 1812.06008, 1908.02034, 2012.13367, 2102.13501, 2106.11996

An Initial Framework Assessing the Safety of Complex Systems

Trabajo presentado en la Conference on Complex Systems, celebrada online del 7 al 11 de diciembre de 2020.Atmospheric blocking events, that is large-scale nearly stationary atmospheric pressure patterns, are often associated with extreme weather in the mid-latitudes, such as heat waves and cold spells which have significant consequences on ecosystems, human health and economy. The high impact of blocking events has motivated numerous studies. However, there is not yet a comprehensive theory explaining their onset, maintenance and decay and their numerical prediction remains a challenge. In recent years, a number of studies have successfully employed complex network descriptions of fluid transport to characterize dynamical patterns in geophysical flows. The aim of the current work is to investigate the potential of so called Lagrangian flow networks for the detection and perhaps forecasting of atmospheric blocking events. The network is constructed by associating nodes to regions of the atmosphere and establishing links based on the flux of material between these nodes during a given time interval. One can then use effective tools and metrics developed in the context of graph theory to explore the atmospheric flow properties. In particular, Ser-Giacomi et al. [1] showed how optimal paths in a Lagrangian flow network highlight distinctive circulation patterns associated with atmospheric blocking events. We extend these results by studying the behavior of selected network measures (such as degree, entropy and harmonic closeness centrality)at the onset of and during blocking situations, demonstrating their ability to trace the spatio-temporal characteristics of these events.This research was conducted as part of the CAFE (Climate Advanced Forecasting of sub-seasonal Extremes) Innovative Training Network which has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 813844