Hasil untuk "Museums. Collectors and collecting"

Naomi Haywood

Museum visits by school groups contribute towards life-long science engagement. This is likely to be particularly true for young children who have low science capital. There is only limited research on how museums can support low science capital school groups to engage and learn, particularly in object-rich galleries. The current research addresses this gap. It considers how a simple resource supports playful learning in two object-rich galleries. The resource invites children to find objects and interact with the objects through observation, movement and wondering. Some 92 children (4–7 years) and 15 adults in seven school groups were observed using the resource and then subsequently interviewed. Findings show that encouraging children to search for objects, move in relation to objects and wonder about objects supports learning. Movement and gestures were central to how children experienced the objects as these embodied forms of cognition did not require specific understanding or vocabulary. These findings show that museums can support low science capital young children to learn from objects through a focus on bodily experiences. The theoretical implications of these findings include the value of embodied cognition theory in supporting object engagement and learning, particularly for young children with low science capital.

Biliakovych Liana, Derman Lilia, Oborska Svitlana et al.

In the modern world, the fashion industry is constantly evolving, and digital technologies are having an increasingly significant impact on its development. Therefore, the research relevance is determined by the need to explore the features of modern digital fashion concerning the commercial aspect, ethical issues and concept of sustainable fashion. This research aims to predict the prospects for the development of digital fashion in the fashion and design industry, and is based on the following methods: analytical, comparative, generalisation and systematisation methods. The study results showed that digital fashion is becoming increasingly popular among well-known brands, designers and fashion houses, while the commercial use of digital fashion is expanding thanks to the non-fungible token (NFT) platform and other digital tools. At the same time, the research also identifies ethical issues related to the protection of intellectual property in digital reality. Furthermore, the research analyses the segmentation of the digital fashion market and forecasts the development of this industry until 2030 and beyond. In terms of the relationship with the concept of sustainable fashion, it is found that digital fashion can contribute to sustainable development by reducing the use of physical resources and preserving the environment.

Federico Cappadona

Archaeological Open-Air Museums (AOAMs) are well established in the international museum landscape, and today more than 350 of these sites can be counted in Europe alone. These museums differ considerably from one another, and each of them presents specific and unique features. Their identity strongly depends on several occurrences, such as the period in which they were founded, the stakeholders who conceived them (private, public, entrepreneurs, academics, amateurs, et cetera), and the cultural paradigms on which they rely. Given the different circumstances that shape them, it is not easy to draw a prototype of an AOAM, and many typologies of sites can easily follow under this name. Starting from these premises, this contribution intends to introduce the first AOAM's classification, based on two main attributes, such as the chronological periods displayed and the location of the AOAM. The article aims to propose a shared terminology to easily describe the AOAMs and refer to their main characteristics with a short and straightforward formula.

Lukas Schulthess, Tiago Salzmann, Christian Vogt et al.

Nowadays, cities provide much more than shopping opportunities or working spaces. Individual locations such as parks and squares are used as meeting points and local recreation areas by many people. To ensure that they remain attractive in the future, the design of such squares must be regularly adapted to the needs of the public. These utilization trends can be derived using public data collection. The more diverse and rich the data sets are, the easier it is to optimize public space design through data analysis. Traditional data collection methods such as questionnaires, observations, or videos are either labor intensive or cannot guarantee to preserve the individual's privacy. This work presents a privacy-preserving, low-power, and low-cost smart sensing system that is capable of anonymously collecting data about public space utilization by analyzing the occupancy distribution of public seating. To support future urban planning the sensor nodes are capable of monitoring environmental noise, chair utilization, and their position, temperature, and humidity and provide them over a city-wide Long Range Wide Area Network (LoRaWAN). The final sensing system's robust operation is proven in a trial run at two public squares in a city with 16 sensor nodes over a duration of two months. By consuming 33.65 mWh per day with all subsystems enabled, including sitting detection based on a continuous acceleration measurement operating on a robust and simple threshold algorithm, the custom-designed sensor node achieves continuous monitoring during the 2-month trial run. The evaluation of the experimental results clearly shows how the two locations are used, which confirms the practicability of the proposed solution. All data collected during the field trial is publicly available as open data.

Michael Bendersky, Donald Metzler, Marc Najork et al.

This article describes the history of information retrieval on personal document collections.

Ahmad-Reza Ehyaei, Ali Shirali, Samira Samadi

Counterfactual explanations provide individuals with cost-optimal recommendations to achieve their desired outcomes. However, when a significant number of individuals seek similar state modifications, this individual-centric approach can inadvertently create competition and introduce unforeseen costs. Additionally, disregarding the underlying data distribution may lead to recommendations that individuals perceive as unusual or impractical. To address these challenges, we propose a novel framework that extends standard counterfactual explanations by incorporating a population dynamics model. This framework penalizes deviations from equilibrium after individuals follow the recommendations, effectively mitigating externalities caused by correlated changes across the population. By balancing individual modification costs with their impact on others, our method ensures more equitable and efficient outcomes. We show how this approach reframes the counterfactual explanation problem from an individual-centric task to a collective optimization problem. Augmenting our theoretical insights, we design and implement scalable algorithms for computing collective counterfactuals, showcasing their effectiveness and advantages over existing recourse methods, particularly in aligning with collective objectives.

Suvir Mirchandani, Suneel Belkhale, Joey Hejna et al.

A long-standing goal in robot learning is to develop methods for robots to acquire new skills autonomously. While reinforcement learning (RL) comes with the promise of enabling autonomous data collection, it remains challenging to scale in the real-world partly due to the significant effort required for environment design and instrumentation, including the need for designing reset functions or accurate success detectors. On the other hand, imitation learning (IL) methods require little to no environment design effort, but instead require significant human supervision in the form of collected demonstrations. To address these shortcomings, recent works in autonomous IL start with an initial seed dataset of human demonstrations that an autonomous policy can bootstrap from. While autonomous IL approaches come with the promise of addressing the challenges of autonomous RL as well as pure IL strategies, in this work, we posit that such techniques do not deliver on this promise and are still unable to scale up autonomous data collection in the real world. Through a series of real-world experiments, we demonstrate that these approaches, when scaled up to realistic settings, face much of the same scaling challenges as prior attempts in RL in terms of environment design. Further, we perform a rigorous study of autonomous IL methods across different data scales and 7 simulation and real-world tasks, and demonstrate that while autonomous data collection can modestly improve performance, simply collecting more human data often provides significantly more improvement. Our work suggests a negative result: that scaling up autonomous data collection for learning robot policies for real-world tasks is more challenging and impractical than what is suggested in prior work. We hope these insights about the core challenges of scaling up data collection help inform future efforts in autonomous learning.

Tong Qin, Changze Li, Haoyang Ye et al.

Recently, Neural Radiance Fields (NeRF) achieved impressive results in novel view synthesis. Block-NeRF showed the capability of leveraging NeRF to build large city-scale models. For large-scale modeling, a mass of image data is necessary. Collecting images from specially designed data-collection vehicles can not support large-scale applications. How to acquire massive high-quality data remains an opening problem. Noting that the automotive industry has a huge amount of image data, crowd-sourcing is a convenient way for large-scale data collection. In this paper, we present a crowd-sourced framework, which utilizes substantial data captured by production vehicles to reconstruct the scene with the NeRF model. This approach solves the key problem of large-scale reconstruction, that is where the data comes from and how to use them. Firstly, the crowd-sourced massive data is filtered to remove redundancy and keep a balanced distribution in terms of time and space. Then a structure-from-motion module is performed to refine camera poses. Finally, images, as well as poses, are used to train the NeRF model in a certain block. We highlight that we present a comprehensive framework that integrates multiple modules, including data selection, sparse 3D reconstruction, sequence appearance embedding, depth supervision of ground surface, and occlusion completion. The complete system is capable of effectively processing and reconstructing high-quality 3D scenes from crowd-sourced data. Extensive quantitative and qualitative experiments were conducted to validate the performance of our system. Moreover, we proposed an application, named first-view navigation, which leveraged the NeRF model to generate 3D street view and guide the driver with a synthesized video.

Dóra Bárdfalvy, Viktor Škultéty, Cesare Nardini et al.

Self-propelled micron-size particles suspended in a fluid, like bacteria or synthetic microswimmers, are strongly non-equilibrium systems where particle motility breaks the microscopic detailed balance, often resulting in large-scale collective motion. Previous theoretical work has identified long-range hydrodynamic interactions as the main driver of collective motion in unbounded dilute suspension of rear-actuated ("pusher") microswimmers. In contrast, most experimental studies of collective motion in microswimmer suspensions have been carried out in quasi-2-dimensional geometries such as in thin films or near solid or fluid interfaces, where both the swimmers' motion and their long-range flow fields become altered due to the proximity of a boundary. Here, we study numerically a minimal model of microswimmers in such a restricted geometry, where the particles move in the midplane between two no-slip walls. For pushers, we demonstrate collective motion with only short-ranged order, in contrast with the long-ranged flows observed in unbounded systems. For front-actuated ("puller") microswimmers, we discover a long-wavelength density instability resulting in the formation of dense microswimmer clusters. Both types of collective motion are fundamentally different from their previously studied counterparts in unbounded domains. Our results illustrate that hydrodynamic screening due to the presence of a wall is subdominant in determining the collective state of the suspension, which is instead dictated by the geometrical restriction of the swimmers' motion.

Jacob Calvert

Natural collectives, despite comprising individuals who may not know their numerosity, can exhibit behaviors that depend sensitively on it. This paper proves that the collective behavior of number-oblivious individuals can even have a critical numerosity, above and below which it qualitatively differs. We formalize the concept of critical numerosity in terms of a family of zero--one laws and introduce a model of collective motion, called chain activation and transport (CAT), that has one. CAT describes the collective motion of $n \geq 2$ individuals as a Markov chain that rearranges $n$-element subsets of the $d$-dimensional grid, $m < n$ elements at a time. According to the individuals' dynamics, with each step, CAT removes $m$ elements from the set and then progressively adds $m$ elements to the boundary of what remains, in a way that favors the consecutive addition and removal of nearby elements. This paper proves that, if $d \geq 3$, then CAT has a critical numerosity of $n_c = 2m+2$ with respect to the behavior of its diameter. Specifically, if $n < n_c$, then the elements form one "cluster," the diameter of which has an a.s.--finite limit infimum. However, if $n \geq n_c$, then there is an a.s.--finite time at which the set consists of clusters of between $m+1$ and $2m+1$ elements, and forever after which these clusters grow apart, resulting in unchecked diameter growth. The existence of critical numerosities means that collectives can exhibit "phase transitions" that are governed purely by their numerosity and not, for example, their density or the strength of their interactions. This fact challenges prevalent beliefs about collective behavior and suggests new functionality for programmable matter. More broadly, it demonstrates an opportunity to explore the possible behaviors collectives through the study of random processes that rearrange sets.

Rajaa Abdulrazzaq Al Anbagi, Talib Owaid Al-Khesraji

The present study reports Morchella conica Pers.1818, which belongs to the family, Morchellaceae as a new record of Iraqi macromycota based on the morphological and molecular methods. During their short and often sporadic fruiting season, this fungal species was found in mixed forest unburned areas in Branan ranges (Suliamaniya Province, Northeast Iraq). Currently, M. conicais the second Morchellaspecies reported from Iraq.The current study aimed to introduce this new record, which is poorly studied in the Middle East. M. conicais morphologically described and phylogenetically confirmed. The relationship between this species and other species within the genus was studied using the nrDNA ITS sequences from different species and diverse geographical regions. Maximum likelihood (ML) analyses were also conducted to build the molecular phylogeny of this species. The results of the presented species are essential for assessing the genus geographic distribution and developing information about species of this highly prized edible, industrial medicinal fungus.

Lorenz Stangier, Ji-Ung Lee, Yuxi Wang et al.

Collecting and annotating task-oriented dialog data is difficult, especially for highly specific domains that require expert knowledge. At the same time, informal communication channels such as instant messengers are increasingly being used at work. This has led to a lot of work-relevant information that is disseminated through those channels and needs to be post-processed manually by the employees. To alleviate this problem, we present TexPrax, a messaging system to collect and annotate problems, causes, and solutions that occur in work-related chats. TexPrax uses a chatbot to directly engage the employees to provide lightweight annotations on their conversation and ease their documentation work. To comply with data privacy and security regulations, we use an end-to-end message encryption and give our users full control over their data which has various advantages over conventional annotation tools. We evaluate TexPrax in a user-study with German factory employees who ask their colleagues for solutions on problems that arise during their daily work. Overall, we collect 202 task-oriented German dialogues containing 1,027 sentences with sentence-level expert annotations. Our data analysis also reveals that real-world conversations frequently contain instances with code-switching, varying abbreviations for the same entity, and dialects which NLP systems should be able to handle.

M. R. Mumpower, D. Nuedecker, H. Sasaki et al.

The pre-equilibrium reaction mechanism is considered in the context of the exciton model. A modification to the one-particle one-hole state density is studied which can be interpreted as a collective enhancement. The magnitude of the collective enhancement is set by simulating the Lawrence Livermore National Laboratory (LLNL) pulsed-spheres neutron-leakage spectra. The impact of the collective enhancement is explored in the context of the highly deformed actinide, 239-Pu. A consequence of this enhancement is the removal of fictitious levels in the Distorted-Wave Born Approximation often used in modern nuclear reaction codes.

Sergio Vidal Álvarez, Beatriz Campderá Gutiérrez, Solène de Pablos Hamon et al.

La exposición «Las artes del metal en al-Ándalus» (diciembre 2019-septiembre 2020) ha sido un proyecto elaborado por el Departamento de Antigüedades Medievales del Museo Arqueológico Nacional, en el que han colaborado especialistas y equipos de diversos ámbitos. El presente artículo analiza el proyecto desde su origen y creación de contenidos, diseño museográfico, gestión de contenidos, coordinación de préstamos y transporte de obras y montaje museográfico, dedicando un apartado a la acogida por parte del público, las actividades comunicativas y divulgativas, así como la confección del catálogo de la muestra.

Martina Tanga

Facing multiple unprecedented calamities throughout 2020—a global pandemic, economic upheaval, social turmoil, and climate crisis—museums shuttered, decimated their staff, and gutted their organizational structures. Now, they seem to struggle to maintain outward relevance in these bleak and uncertain times. What if, instead of being reactive, museums are proactive; instead of being defensive, they model social change? What if this change comes first from within? What if they rebuild differently, not guided by an insidious corporate model but one that places access, diversity, community, care, and people at its center? What if overhauling the internal staff structure—the static, hierarchical power dynamic, departmental silos, and over-bureaucratization of larger institutions—results in a museum that reflects twenty-first-century ideals of democracy? Let’s envision a different museum staff structure inspired by feminist theory, social entrepreneurship, and grassroots organizations.

Fernando Contreras Rodrigo

The cultural entity Sanisera, in accordance with its line of action, concentrated in the port of Sanitja (Menorca, Balearic Islands) with regard to heritage management activities and archaeological research, claiming the value of the Sanitja archaeological site to attract attention of different organizations and appropriate mechanisms are established that serve to propose plans that maintain the heritage value of its archaeological complex and preventive protection measures in the most unique heritage area in the north of Menorca. The asset management action of the public bodies of the different autonomous communities of Spain has directed its measures to the elaboration of master and special plans, serving as a basis, both for the political direction, as well as for the technical-administrative body, to adopt solutions that order the management of outstanding archaeological sites.

Linus Boes, Rachael Colley, Umberto Grandi et al.

Many important collective decision-making problems can be seen as multi-agent versions of discrete optimisation problems. Participatory budgeting, for instance, is the collective version of the knapsack problem; other examples include collective scheduling, and collective spanning trees. Rather than developing a specific model, as well as specific algorithmic techniques, for each of these problems, we propose to represent and solve them in the unifying framework of judgment aggregation with weighted issues. We provide a modular definition of collective discrete optimisation (CDO) rules based on coupling a set scoring function with an operator, and we show how they generalise several existing procedures developed for specific CDO problems. We also give an implementation based on integer linear programming (ILP) and test it on the problem of collective spanning trees.

Antoine Canaguier-Durand, Astrid Lambrecht, Serge Reynaud

We present a theoretical treatment of light scattering by an ensemble of N dipoles, taking into account recurrent multiple scattering. We study the intrinsic optical properties of collective dipolar systems without specifying a particular illumination condition. We apply this formalism to study the collective absorption modes for an ensemble of small nanoparticles and then to derive collective radiative corrections due to mutual interactions in dipolar ensembles, a topic of major importance in the development of collective nanophotonic systems or atomic networks used as optical clocks.

Vasile Diaconu

Experimental archaeology, as an educational means, has become a particularly useful practice in museum institutions in Romania, although there is no tradition in this field. Here, we present activities of the History and Ethnography Museum in Târgu Neamţ, where several experimental archaeology workshops were organised for pupils aged between 9 and 12 years. Participants were introduced to the prehistoric technologies of processing stone, producing ceramic vessels, and processing wood and bone. The schoolchildren made various tools, household objects and decorations by carving and polishing, they moulded small clay pots, and they processed cereals using ancient methods. The workshops were held in 2016 and 2017 during which 100 students, divided into several groups, attended the practical activities. Some of the objects made by students were later used in educational activities in the schools.