We study a labeled variant of the classical Coupon Collector Problem (CCP), recently introduced by Tan et al., where coupons arrive in groups and only the set of labels is revealed. The goal is to determine the expected number of group drawings required to uniquely identify the labeling of all coupons. We focus on the case where groups consist of pairs ($k=2$), and provide rigorous proofs for two conjectures posed by Tan et al.
A. J. Alves Junior, M. J. B. B. Davi, R. A. S. Fernandes
et al.
Accurate fault location is essential for operational reliability and fast restoration in wind farm collector networks. However, the growing integration of inverter-based resources changes the current and voltage behavior during faults, challenging the effectiveness of traditional phasor-based diagnostic methods. In this context, the present paper introduces an advanced machine-learning solution that enhances a deterministic fault distance estimator by incorporating a correction model driven by a Gated Residual Network, specifically designed to minimize residual fault location errors. Through comprehensive feature engineering and selection processes, an improved predictor was developed and trained on a diverse set of fault scenarios simulated in a PSCAD-based real-world wind farm model, including variations in fault type, resistance, location, inception angle, and generation penetration. Hyperparameter optimization was performed using the Optuna framework, and the robustness of the method was statistically validated. Results show a significant improvement in accuracy, with a 76% overall decrease in fault location error compared to state-of-the-art approaches. The proposed method demonstrates strong scalability and adaptability to topological and operational changes. This approach advances the deployment of data-driven fault location frameworks for modern power systems.
We generalize the well-known Coupon Collector Problem (CCP) in combinatorics. Our problem is to find the minimum and expected number of draws, with replacement, required to recover $n$ distinctly labeled coupons, with each draw consisting of a random subset of $k$ different coupons and a random ordering of their associated labels. We specify two variations of the problem, Type-I in which the set of labels is known at the start, and Type-II in which the set of labels is unknown at the start. We show that our problem can be viewed as an extension of the separating system problem introduced by Rényi and Katona, provide a full characterization of the minimum, and provide a numerical approach to finding the expectation using a Markov chain model, with special attention given to the case where two coupons are drawn at a time.
Unearthed in the Amp{è}re Museum near Lyon, France, a genuine version of the Einstein-de Haas experiment apparatus offers a rare glimpse into Einstein's experimental interests. This remarkable find not only connects us to a crucial epoch in history of science but also highlights Einstein's rare tangible legacy in the realm of experimental physics.
As former chief curator of the fifth Lisbon Triennial of architecture in 2019, I will first discuss about utility of such events to allow architecture to meet a wider audience as the academic or professional ones. This issue is also about form of such events in terms of overall organizations: didactic exhibitions conceived by curators vs. conceptual installations made by architects. I will explain why I chose the first option in Lisbon, and how this choice influenced the form of the exhibitions. Additionally, I will explore in which way the fact that I was an “intellectual practitioner” allowed me to give specific answers to a series of questions regarding architecture itself but as well the art of exhibition: in which way form can improve thought.
Ada-Astrid Balauca, Danda Pani Paudel, Kristina Toutanova
et al.
CLIP is a powerful and widely used tool for understanding images in the context of natural language descriptions to perform nuanced tasks. However, it does not offer application-specific fine-grained and structured understanding, due to its generic nature. In this work, we aim to adapt CLIP for fine-grained and structured -- in the form of tabular data -- visual understanding of museum exhibits. To facilitate such understanding we (a) collect, curate, and benchmark a dataset of 200K+ image-table pairs, and (b) develop a method that allows predicting tabular outputs for input images. Our dataset is the first of its kind in the public domain. At the same time, the proposed method is novel in leveraging CLIP's powerful representations for fine-grained and tabular understanding. The proposed method (MUZE) learns to map CLIP's image embeddings to the tabular structure by means of a proposed transformer-based parsing network (parseNet). More specifically, parseNet enables prediction of missing attribute values while integrating context from known attribute-value pairs for an input image. We show that this leads to significant improvement in accuracy. Through exhaustive experiments, we show the effectiveness of the proposed method on fine-grained and structured understanding of museum exhibits, by achieving encouraging results in a newly established benchmark. Our dataset and source-code can be found at: https://github.com/insait-institute/MUZE
Ivan D. Changoluisa Caiza, Ana Milas, Marco A. Montes Grova
et al.
Autonomous exploration using unmanned aerial vehicles (UAVs) is essential for various tasks such as building inspections, rescue operations, deliveries, and warehousing. However, there are two main limitations to previous approaches: they may not be able to provide a complete map of the environment and assume that the map built during exploration is accurate enough for safe navigation, which is usually not the case. To address these limitations, a novel exploration method is proposed that combines frontier-based exploration with a collector strategy that achieves global exploration and complete map creation. In each iteration, the collector strategy stores and validates frontiers detected during exploration and selects the next best frontier to navigate to. The collector strategy ensures global exploration by balancing the exploitation of a known map with the exploration of unknown areas. In addition, the online path replanning ensures safe navigation through the map created during motion. The performance of the proposed method is verified by exploring 3D simulation environments in comparison with the state-of-the-art methods. Finally, the proposed approach is validated in a real-world experiment.
La imagen de la Segobriga altoimperial se verá alterada a partir del siglo iv con la ocupación
de los edificios públicos y el entramado viario. En el foro augusteo se producirá la ocupación de los pórticos, tabernae y basílica en el mismo momento en el que se construirán estructuras domésticas en el tramo del kardo maximus por el que se accedía. Este proceso convirtió al espacio público –locus celeberrimus– de los segobrigenses en un lugar de habitación y trabajo. Entre el material mueble hallado en su excavación se encuentran algunos recipientes de vajilla broncínea –acetres/ sítulas, cuenco y pátera– utilizados en el servicio de mesa, que presentamos aquí.
History of the arts, Museums. Collectors and collecting
The Museum aan de Stroom (MAS) in Antwerp, Belgium, opened the doors of its long-anticipated exhibition, 100 X Congo, on 3 October 2020, highlighting the presence of a Congolese art collection that has been owned by the city for a hundred years (1920-2020). Tackling the often uneasy history of how these things ended up in museums in the colonial ‘motherland’, this exhibition signals a step forwards in museology in Belgium, away from mere aestheticism of Congolese and, by extension, African arts, towards, in contrast, a focus on provenance, context and cultural importance.
In 2013 a team of researchers in collaboration with the community in the municipality of Coronel José Dias, around Serra da Capivara National Park, Piauí-Brazil, created the Olho D 'Água Institute and since then has been developing research and didactic experiments with the communities. The project entitled Arte na Serra aims to engage communities in the archaeological universe through archaeological, ethnographic experimentation, museum exhibitions and guided tours of archaeological and historical sites.
Artykuł omawia zagadnienia związane z edukacją muzealną, w kontekście opracowywania projektów obejmujących rozmaite formy działalności połączonej wspólną tematyką. Autorka w pierwszej części tekstu uzasadnia potrzebę organizowania tego typu aktywności przytaczając wypowiedzi badaczy zagadnienia oraz samych edukatorów. W drugiej opisuje przykład „dobrej praktyki”, analizując działania związane z projektem: Nie tylko petanka… W kręgu haftów regionalnych, opracowanym i zrealizowanym przez Pracowników Muzeum Archeologicznego i Etnograficznego w Łodzi i Łódzkiego Domu Kultury przy współpracy wybranych trzech muzeów regionalnych województwa łódzkiego. Autorka podkreśla nie tylko złożoność i skuteczność przyjętych metod i zadań, ale także dobre efekty wieloletniej współpracy zaangażowanych w projekt instytucji. Artykuł kończy recenzja wystawy stanowiącej podsumowanie całość projektu.
Mohammad Hossein Ahmadi, Alireza Baghban, Milad Sadeghzadeh
et al.
Solar energy is a renewable resource of energy that is broadly utilized and has the least emissions among renewable energies. In this study, machine learning methods of artificial neural networks (ANNs), least squares support vector machines (LSSVM), and neuro-fuzzy are used for advancing prediction models for the thermal performance of a photovoltaic-thermal solar collector (PV/T). In the proposed models, the inlet temperature, flow rate, heat, solar radiation, and the sun heat have been considered as the inputs variables. Data set has been extracted through experimental measurements from a novel solar collector system. Different analyses are performed to examine the credibility of the introduced approaches and evaluate their performance. The proposed LSSVM model outperformed ANFIS and ANNs models. LSSVM model is reported suitable when the laboratory measurements are costly and time-consuming, or achieving such values requires sophisticated interpretations.
This paper is about the Coupon collector's problem. There are some coupons, or baseball cards, or other plastic knick-knacks that are put into bags of chips or under soda bottles, etc. A collector starts collecting these trinkets and wants to form a complete collection of all possible ones. Every time they buy the product however, they don't know which coupon they will "collect" until they open the product. How many coupons do they need to collect before they complete the collection? In this paper, we explore the mean and variance of this random variable, $N$ using various methods. Some of them work only for the special case with the coupons having equal probabilities of being collected, while others generalize to the case where the coupons are collected with unequal probabilities (which is closer to a real world scenario).
Several programming languages use garbage collectors (GCs) to automatically manage memory for the programmer. Such collectors must decide when to look for unreachable objects to free, which can have a large performance impact on some applications. In this preliminary work, we propose a design for a learned garbage collector that autonomously learns over time when to perform collections. By using reinforcement learning, our design can incorporate user-defined reward functions, allowing an autonomous garbage collector to learn to optimize the exact metric the user desires (e.g., request latency or queries per second). We conduct an initial experimental study on a prototype, demonstrating that an approach based on tabular Q learning may be promising.
Benjamin Gearey, Mark Griffiths, Brian Mac Domhnaill
et al.
This paper outlines The Pallasboy Project, which set out to craft a replica of the eponymous Irish Iron Age wooden vessel. We consider the process and progress of the project, as it developed in a number of slightly unusual directions. The paper includes a description of the experimental work, alongside personal reflections and comments by the people who became involved in the project as it progressed. The work was documented visually through photography, video and artistic responses and selections from this material are included. Unconventional (in experimental archaeological terms) ‘interactions’ are outlined, including a performance by musicians who ‘played’ the replica vessel. This paper is also intended as a guide to the project blog, which hosts the written pieces and other content linked to below. The project subsequently moved onto consider other wooden archaeological artefacts, also discussed on the blog, but in this paper we focus on the Pallasboy Vessel itself. The ethos embodied by the approach can be conceived as one of ‘multi-vocal understandings’, a concept inspired by the process known as ‘Deep Mapping’. Springett (2015, p.628) has described this as an approach through which: “There is no privileging or authorizing knowledge of one source of information over another and all agents have equal resonance ... at least philosophically.” In other words, the practical, experimental archaeological crafting is just one of the various different strands and ‘responses’ to ‘understanding’ the Pallasboy vessel as an Iron Age artefact, but also the replica as very much a contemporary object. We discuss how concepts of ‘multivocality’ may be of broader value for experimental archaeology.
When you are planning your experiment how many of you think of the climate and whether it matches the period that your experiment is based in? Depending on the type of experiment there are many factors that may influence the results. You need to consider parameters such as; humidity, ambient temperature, how effective your clothes are, burning the right firewood, keeping a shelter warm, or the quality of the grass that your animals are living on.