Contributing to ongoing debates surrounding immersive experiences in museums, this essay proposes the aesthetic category of the sublime as a productive framework for immersive mediation and exhibition-making. How can immersive mediation be conceived to reconcile the experiential qualities of immersion with the often challenging demands of meaning-making and ethical reflection?
The potential of the sublime to address this challenge is explored through the example of the art exhibition Goya and Munch: Modern Prophecies (MUNCH, 2023-24). Drawing on a wide range of materials, I examine specific aspects of the sublime relevant to the development of the exhibition’s mediation. These include the sublime’s excessiveness, unpresentability, ethical dimension, and close ties to the ridiculous.
Carleson and sparse collections of sets play a central role in dyadic harmonic analysis. We employ methods from optimization theory to study such collections. First, we present a strongly polynomial algorithm to compute the Carleson constant of a collection of sets, improving on the recent approximation algorithm of Rey. Our algorithm is based on submodular function minimization. Second, we provide an algorithm showing that any Carleson collection is sparse, achieving optimal dependence of the respective constants and thus providing a constructive proof of a result of Hänninen. Our key insight is a reformulation of the duality between the Carleson condition and sparseness in terms of the duality between the maximum flow and the minimum cut in a weighted directed graph.
This research aims to map the practices surrounding the collection of media art in Portuguese museums, with a particular focus on the preservation strategies employed by relevant institutions. At the core of the study is a critical analysis of data gathered through an online survey, completed by the following institutions: Museu da Bienal de Cerveira, Museu de Arte Contemporânea de Serralves, Centro de Arte Oliva, Coleção de Arte Contemporânea do Estado, and Museu Nacional de Arte Contemporânea do Chiado. The findings indicate a significant shortage of specialized conservators in Portugal, along with a lack of standardized terminology in the preservation of media art. The brevity of the sample suggests a lack of transparency from the institutions and limited public access to digital documentation concerning our object of study. Nonetheless, this sample provides valuable insight into current practises that typically remain inaccessible to the public.
Resilience thinking refers to the need to be prepared for the unexpected and unknown. Museums have learned to adjust to societal changes, not least because of the recent global pandemic, which has necessitated the introduction of new ways of activating a diverse public. We discuss how resilience thinking can function as a promoter of the adaptive reuse of industrial heritage by including local heritage knowledge in the ongoing regeneration of former brownfield sites. The current sectoral barriers in the planning system prevent museums from being central participants, despite their well-established local anchoring. Intangible heritage can provide coherence and connection between old buildings, including technical structures and new buildings/infrastructure. This allows for options for museums to voice ongoing creative and critical input and appear as spokespersons for civic involvement. Museums’ ability to facilitate local involvement needs to be acknowledged in urban planning.
With the current proliferation of training opportunities available in digital preservation, this study asks: what are the most in demand digital preservation instruction topics? To answer this question, we did a qualitative content analysis of 168 Professional Development Support applications received by the Digital Preservation Outreach and Education Network (DPOE-N) between September 2020 and December 2023. The study finds that the management of digital records and metadata/cataloging standards were the most requested training topics, and that general and broadly applicable skills tend to be the most sought after. This indicates that there is a continuing need to provide education focusing on the core elements of digital preservation and knowledge, and that we have not moved on yet to a place where cultural heritage professionals are solely seeking skills in more advanced or specialized digital preservation topics.
In this letter, we systematically investigate the microscopic dynamics of collective vibrational excitations in simple liquids. The thermodynamic states of simple liquids are unified to the mean atomic free volume. Our results show that longitudinal acoustic collective vibrational excitations are always observed in simple liquids even when the liquids are viscous, in which the atomic free volume is larger than the cross point of the corresponding mean propagation length and the atomic diffusion limit. This is because some long-wavelength longitudinal acoustic collective vibrational excitations can still propagate in viscous liquids. However, transverse acoustic collective vibrational excitations in viscous liquids become localized since both short- and long-wavelength transverse acoustic collective vibrational excitations have propagation lengths smaller than the atomic diffusion limit. Therefore, transverse acoustic collective vibrational excitations may not be detected in simple liquids. The propagation length of macroscopic elastic and shear waves which are the mechanical response of long-wavelength longitudinal and transverse collective vibrational excitations, respectively, is further calculated to quickly determine the propagation-to-localization crossover of collective vibrational excitations in simple liquids. Our findings here advance the understanding of the microscopic dynamics of collective vibrational excitations in simple liquids.
We study active data collection strategies for operator learning when the target operator is linear and the input functions are drawn from a mean-zero stochastic process with continuous covariance kernels. With an active data collection strategy, we establish an error convergence rate in terms of the decay rate of the eigenvalues of the covariance kernel. We can achieve arbitrarily fast error convergence rates with sufficiently rapid eigenvalue decay of the covariance kernels. This contrasts with the passive (i.i.d.) data collection strategies, where the convergence rate is never faster than linear decay ($\sim n^{-1}$). In fact, for our setting, we show a \emph{non-vanishing} lower bound for any passive data collection strategy, regardless of the eigenvalues decay rate of the covariance kernel. Overall, our results show the benefit of active data collection strategies in operator learning over their passive counterparts.
The collective schedules problem consists in computing a schedule of tasks shared between individuals. Tasks may have different duration, and individuals have preferences over the order of the shared tasks. This problem has numerous applications since tasks may model public infrastructure projects, events taking place in a shared room, or work done by co-workers. Our aim is, given the preferred schedules of individuals (voters), to return a consensus schedule. We propose an axiomatic study of the collective schedule problem, by using classic axioms in computational social choice and new axioms that take into account the duration of the tasks. We show that some axioms are incompatible, and we study the axioms fulfilled by three rules: one which has been studied in the seminal paper on collective schedules (Pascual et al. 2018), one which generalizes the Kemeny rule, and one which generalizes Spearman's footrule. From an algorithmic point of view, we show that these rules solve NP-hard problems, but that it is possible to solve optimally these problems for small but realistic size instances, and we give an efficient heuristic for large instances. We conclude this paper with experiments.
Giorgio Audrito, Roberto Casadei, Ferruccio Damiani
et al.
Recent trends like the Internet of Things (IoT) suggest a vision of dense and multi-scale deployments of computing devices in nearly all kinds of environments. A prominent engineering challenge revolves around programming the collective adaptive behaviour of such computational ecosystems. This requires abstractions able to capture concepts like ensembles (dynamic groups of cooperating devices) and collective tasks (joint activities carried out by ensembles). In this work, we consider collections of devices interacting with neighbours and that execute in nearly-synchronised sense-compute-interact rounds, where the computation is given by a single program mapping sensing values and incoming messages to output and outcoming messages. To support programming whole computational collectives, we propose the abstraction of a distributed collective process, which can be used to define at once the ensemble formation logic and its collective task. We formalise the abstraction in the eXchange Calculus (XC), a core functional language based on neighbouring values (maps from neighbours to values) where state and interaction is handled through a single primitive, exchange, and provide a corresponding implementation in the FCPP language. Then, we exercise distributed collective processes using two case studies: multi-hop message propagation and distributed monitoring of spatial properties. Finally, we discuss the features of the abstraction and its suitability for different kinds of distributed computing applications.
The explosion of machine learning model size has led to its execution on distributed clusters at a very large scale. Many works have tried to optimize the process of producing collective algorithms and running collective communications, which act as a bottleneck to distributed machine learning. However, different works use their own collective algorithm representation, pushing away from co-optimizing collective communication and the rest of the workload. The lack of a standardized collective algorithm representation has also hindered interoperability between collective algorithm producers and consumers. Additionally, tool-specific conversions and modifications have to be made for each pair of tools producing and consuming collective algorithms which adds to engineering efforts. In this position paper, we propose a standardized workflow leveraging a common collective algorithm representation. Upstream producers and downstream consumers converge to a common representation format based on Chakra Execution Trace, a commonly used graph based representation of distributed machine learning workloads. Such a common representation enables us to view collective communications at the same level as workload operations and decouple producer and consumer tools, enhance interoperability, and relieve the user from the burden of having to focus on downstream implementations. We provide a proof-of-concept of this standardized workflow by simulating collective algorithms generated by the MSCCLang domain-specific language through the ASTRA-sim distributed machine learning simulator using various network configurations.
Unraveling the emergence of collective learning in systems of coupled artificial neural networks points to broader implications for machine learning, neuroscience, and society. Here we introduce a minimal model that condenses several recent decentralized algorithms by considering a competition between two terms: the local learning dynamics in the parameters of each neural network unit, and a diffusive coupling among units that tends to homogenize the parameters of the ensemble. We derive an effective theory for linear networks to show that the coarse-grained behavior of our system is equivalent to a deformed Ginzburg-Landau model with quenched disorder. This framework predicts depth-dependent disorder-order-disorder phase transitions in the parameters' solutions that reveal a depth-delayed onset of a collective learning phase and a low-rank microscopic learning path. We validate the theory in coupled ensembles of realistic neural networks trained on the MNIST dataset under privacy constraints. Interestingly, experiments confirm that individual networks -- trained on private data -- can fully generalize to unseen data classes when the collective learning phase emerges. Our work establishes the physics of collective learning and contributes to the mechanistic interpretability of deep learning in decentralized settings.
Thamires Nascimento Oliveira, Raimunda Fernanda dos Santos
Apresenta um breve histórico do surgimento dos Repositórios Digitais, seu papel e importância no âmbito da comunicação científica, bem como suas tipologias, dando ênfase aos Repositórios Institucionais das Universidades Federais brasileiras, que armazenam e preservam a produção intelectual das comunidades acadêmicas e permitem o acesso e uso dessas produções através da busca e recuperação da informação. Discorre acerca da indexação, suas modalidades e evidencia a necessidade de criação de Políticas de Indexação com diretrizes, que irão guiar o indexador nas tomadas de decisão e no processo de indexação, visando a eficácia da recuperação da informação em ambientes sistematizados como os Repositórios Institucionais. Tem como objetivo geral analisar as diretrizes concernentes às práticas de indexação em Repositórios Institucionais brasileiros. Especificamente visa: identificar as Instituições Federais de Ensino Superior do Brasil que possuem Repositórios Institucionais; verificar os documentos disponíveis nos Repositórios Institucionais identificados com vistas a constatar eventuais orientações acerca das práticas de indexação nesses ambientes; propor melhorias para a indexação nesses ambientes informacionais. Utiliza como metodologia as pesquisas bibliográfica, documental, exploratória e descritiva com abordagem qualitativa e quantitativa. Apresenta como resultados um panorama das orientações relacionadas à indexação nos Repositórios Institucionais analisados.
Diplomatics. Archives. Seals, Bibliography. Library science. Information resources
James Mansell, Alexander De Little, Annie Jamieson
This article reports on the experimental methodology and key findings of the AHRC-funded impact and engagement project Sonic Futures: Collecting, Curating and Engaging with Sound at the National Science and Media Museum (2020–21). The project undertook a series of listening-based public engagement activities – described here as staging listening – to identify new ways of engaging listening audiences with sound technology objects in museums. These activities led to the creation of three new interactive sounding exhibit prototypes created jointly with audiences. Because the project took place during periods of lockdown caused by the coronavirus pandemic in the UK in 2020–21, the exhibit prototypes were created digitally and tested via online interaction. The article argues that engaging with listening audiences can diversify and enrich museum listening scenarios, a term we use here to describe auditory situations which elicit different kinds of listening attention, interaction and learning. These listening scenarios produce divergent signatures of listening, a concept we develop here to describe the various kinds of learning and engagement we observed throughout the project.
History of scholarship and learning. The humanities, Museums. Collectors and collecting
Brijen Thananjeyan, Justin Kerr, Huang Huang
et al.
Large-scale semantic image annotation is a significant challenge for learning-based perception systems in robotics. Current approaches often rely on human labelers, which can be expensive, or simulation data, which can visually or physically differ from real data. This paper proposes Labels from UltraViolet (LUV), a novel framework that enables rapid, labeled data collection in real manipulation environments without human labeling. LUV uses transparent, ultraviolet-fluorescent paint with programmable ultraviolet LEDs to collect paired images of a scene in standard lighting and UV lighting to autonomously extract segmentation masks and keypoints via color segmentation. We apply LUV to a suite of diverse robot perception tasks to evaluate its labeling quality, flexibility, and data collection rate. Results suggest that LUV is 180-2500 times faster than a human labeler across the tasks. We show that LUV provides labels consistent with human annotations on unpainted test images. The networks trained on these labels are used to smooth and fold crumpled towels with 83% success rate and achieve 1.7mm position error with respect to human labels on a surgical needle pose estimation task. The low cost of LUV makes it ideal as a lightweight replacement for human labeling systems, with the one-time setup costs at $300 equivalent to the cost of collecting around 200 semantic segmentation labels on Amazon Mechanical Turk. Code, datasets, visualizations, and supplementary material can be found at https://sites.google.com/berkeley.edu/luv
Linda Hurcombe and Peter Inker gave the closing talk at EAC12 amazing conference. They did it in the same way as at the conference: Peter was online in the United States and Linda was online in the United Kingdom. The conversations between the two of them have been running throughout the live conference. They have both talked about the need to do things differently in a world where there is climate change to think about. The new format has been both a challenge and a chance for new opportunities.
To date, formal models of collective intelligence have lacked a plausible mathematical description of the relationship between local-scale interactions between highly autonomous sub-system components (individuals) and global-scale behavior of the composite system (the collective). In this paper we use the Active Inference Formulation (AIF), a framework for explaining the behavior of any non-equilibrium steady state system at any scale, to posit a minimal agent-based model that simulates the relationship between local individual-level interaction and collective intelligence (operationalized as system-level performance). We explore the effects of providing baseline AIF agents (Model 1) with specific cognitive capabilities: Theory of Mind (Model 2); Goal Alignment (Model 3), and Theory of Mind with Goal Alignment (Model 4). These stepwise transitions in sophistication of cognitive ability are motivated by the types of advancements plausibly required for an AIF agent to persist and flourish in an environment populated by other AIF agents, and have also recently been shown to map naturally to canonical steps in human cognitive ability. Illustrative results show that stepwise cognitive transitions increase system performance by providing complementary mechanisms for alignment between agents' local and global optima. Alignment emerges endogenously from the dynamics of interacting AIF agents themselves, rather than being imposed exogenously by incentives to agents' behaviors (contra existing computational models of collective intelligence) or top-down priors for collective behavior (contra existing multiscale simulations of AIF). These results shed light on the types of generic information-theoretic patterns conducive to collective intelligence in human and other complex adaptive systems.
The Collective Variables Dashboard is a software tool for real-time, seamless exploration of molecular structures and trajectories in a customizable space of collective variables. The Dashboard arises from the integration of the Collective Variables Module with the visualization software VMD, augmented with a fully discoverable graphical interface offering interactive workflows for the design and analysis of collective variables. Typical use cases include a priori design of collective variables for enhanced sampling and free energy simulations as well as post-mortem analysis of any type of simulation or collection of structures in a collective variable space. A combination of those cases commonly occurs when preliminary simulations, biased or unbiased, reveal that an optimized set of collective variables is necessary to improve sampling in further simulations. Then the Dashboard provides an efficient way to intuitively explore the space of likely collective variables, validate them on existing data, and use the resulting collective variable definitions directly in further biased simulations using the Collective Variables Module. We illustrate the use of the Dashboard on two applications: discovering coordinates to describe ligand unbinding from a protein binding site, and designing volume-based variables to bias the hydration of a transmembrane pore.
Abstract In view of the large seasonal temperature difference, and the large temperature difference between day and night, difficulty in supplying domestic hot water supply in high altitude area, and the contradiction between low efficiency and cost of single type solar collector system, a hybrid heat collection system based on two different types of solar collectors has been considered in this paper, and a multi-objective optimization model for the heat collecting area optimization of the hybrid solar collectors has also established to solve the above problems. Besides, a fast and effective multi-objective optimization method was proposed for solving of this multi-objective optimization model. This method is based on the fast non-dominated genetic algorithm and the multi-objective decision-making analytic hierarchy process, and was used to analyze the practical hot water supply in a farm in Guoluo zone. The research results shown that this proposed optimization algorithm can quickly and effectively complete the heat collecting area distribution of two different types of collectors in the combined heat collecting system given a certain weight of system cost and heat collecting area. Under the premise of guaranteeing sufficient hot water supply, the system cost is significantly reduced. Therefore, the problem of difficulty and high cost of hot water supply in the plateau region is solved, which has a good practical significance.