119092 research outputs found
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ImmersiveAutochrome: Enhanced Digitized Mono-Autochrome Viewing through Deep Learning and Virtual Reality
No full textResulting from the earliest impactful color photography process developed by the Lumière Brothers, autochromes are invaluable historical artifacts with a high sensitivity and susceptibility to degradation over time. We investigate the potential of deep learning to enhance the experience of historical autochromes through 3D photography and Virtual Reality (VR). Our proposed pipeline utilizes single-image depth estimation and depth-aware inpainting to transform digitized mono-autochromes into layered depth images and corresponding 3D meshes, enabling immersive 3D visualization via a WebXR application. Our user study shows the effectiveness of VR-based experience of autochromes compared to traditional visualization methods, revealing that participants found the VR experience as pleasant as analog exploration despite the latter’s authenticity. Thereby, our findings suggest that 3D VR technology could play a crucial role in digitally preserving and revitalizing these culturally significant artifacts and foster investigations with more samples and more types of user interfaces to mature these insights
PeerQA: A Scientific Question Answering Dataset from Peer Reviews
No full textWe present PeerQA, a real-world, scientific, document-level Question Answering (QA) dataset. PeerQA questions have been sourced from peer reviews, which contain questions that reviewers raised while thoroughly examining the scientific article. Answers have been annotated by the original authors of each paper. The dataset contains 579 QA pairs from 208 academic articles, with a majority from ML and NLP, as well as a subset of other scientific communities like Geoscience and Public Health.PeerQA supports three critical tasks for developing practical QA systems: Evidence retrieval, unanswerable question classification, and answer generation. We provide a detailed analysis of the collected dataset and conduct experiments establishing baseline systems for all three tasks. Our experiments and analyses reveal the need for decontextualization in document-level retrieval, where we find that even simple decontextualization approaches consistently improve retrieval performance across architectures. On answer generation, PeerQA serves as a challenging benchmark for long-context modeling, as the papers have an average size of 12k tokens
OpenFactCheck: Building, Benchmarking Customized Fact-Checking Systems and Evaluating the Factuality of Claims and LLMs
No full textThe increased use of large language models (LLMs) across a variety of real-world applications calls for mechanisms to verify the fac- tual accuracy of their outputs. Difficulties lie in assessing the factuality of free-form responses in open domains. Also, different pa- pers use disparate evaluation benchmarks and measurements, which renders them hard to compare and hampers future progress. To mitigate these issues, we propose OpenFactCheck, a unified framework for building customized automatic fact-checking systems, benchmarking their accuracy, evaluating factuality of LLMs, and verifying claims in a document. OpenFactCheck consists of three modules: (i) CUSTCHECKER allows users to easily customize an automatic fact-checker and verify the factual correctness of documents and claims, (ii) LLMEVAL, a unified evaluation framework assesses LLM’s factuality ability from various perspectives fairly, and (iii) CHECKEREVAL is an extensible solution for gauging the reliability of automatic fact-checkers’ verification results using human-annotated datasets. Data and code are publicly available at https: //github.com/yuxiaw/openfactcheck
An Efficient Quantum Classifier Based on Hamiltonian Representations
No full textQuantum machine learning (QML) is a discipline that seeks to transfer the advantages of quantum computing to data-driven tasks. However, many studies rely on toy datasets or heavy feature reduction, raising concerns about their scalability. Progress is further hindered by hardware limitations and the significant costs of encoding dense vector representations on quantum devices. To address these challenges, we propose an efficient approach called Hamiltonian classifier that circumvents the costs associated with data encoding by mapping inputs to a finite set of Pauli strings and computing predictions as their expectation values. In addition, we introduce two classifier variants with different scaling in terms of parameters and sample complexity. We evaluate our approach on text and image classification tasks, against well-established classical and quantum models. The Hamiltonian classifier delivers performance comparable to or better than these methods. Notably, our method achieves logarithmic complexity in both qubits and quantum gates, making it well-suited for large-scale, real-world applications. We make our implementation available on GitHub
Cultural Learning-Based Culture Adaptation of Language Models
No full textAdapting large language models (LLMs) to diverse cultural values is a challenging task, as existing LLMs often reflect the values of specific groups by default, and potentially causing harm to others. In this paper, we present CLCA, a novel framework for enhancing LLM alignment with cultural values based on cultural learning. The framework leverages simulated social interactions to generate conversations in which LLMs engage in role-playing within culturally adapted social scenarios, capturing implicit cultural norms for model fine-tuning. CLCA improves cultural value alignment across various model architectures measured using World Value Survey data, demonstrating the effectiveness of our proposed approach. Our results provide early evidence that understanding intent and social interactions can enhance cultural value adaptation in LLMs, highlighting the promise of training approaches based on cultural learning
„Ich weiß, dass das traumtänzerisch ist“. Ein Beitrag zum PlurCur®-Gesamtsprachencurriculum
No full textGenSwarm: Scalable Multi-Robot Code-Policy Generation and Deployment via Language Models
No full textThe development of control policies for multi-robot systems traditionally follows a complex and labor-intensive process, often lacking the flexibility to adapt to dynamic tasks. This has motivated research on methods to automatically create control policies. However, these methods require iterative processes of manually crafting and refining objective functions, thereby prolonging the development cycle. This work introduces GenSwarm, an end-to-end system that leverages large language models to automatically generate and deploy control policies for multi-robot tasks based on simple user instructions in natural language. As a multi-languageagent system, GenSwarm achieves zero-shot learning, enabling rapid adaptation to altered or unseen tasks. The white-box nature of the code policies ensures strong reproducibility and interpretability. With its scalable software and hardware architectures, GenSwarm supports efficient policy deployment on both simulated and real-world multi-robot systems, realizing an instruction-to-execution end-to-end functionality that could prove valuable for robotics specialists and non-specialists alike. The code of the proposed GenSwarm system is available online: https://github.com/WindyLab/GenSwarm
Montanarchäologische Befunde als Quellengattung - ein Überblick: Relikte der Montanwirtschaft und ihr Spiegelbild in der Flussaue
No full textDie Montanarchäologie beschäftigt sich mit den technischen, wirtschaftlichen und sozialen Aspekten der Nutzung natürlicher Rohstoffvorkommen durch den Menschen im Wandel der Zeiten. Dabei werden mittels zerstörungsfreier Prospektionsmethoden und/oder durch Grabungen die materiellen Hinterlassenschaften an Gewinnungsorten einerseits und an Lokationen der Weiterverarbeitung andererseits untersucht. Beide sind eingebettet in die historische Kulturlandschaft sowie deren Siedlungs- und Infrastruktur. Ausgangspunkt für die interdisziplinär-naturwissenschaftliche Beurteilung der Befunde und Funde sind die geologisch-lagerstättenkundliche Betrachtung der Rohstoffvorkommen und die geographische Analyse der weiteren natürlichen Ressourcen im Kontext des Montansektors. Bergbau, Steingewinnung und die Verarbeitung von mineralischen Rohstoffen hinterlassen direkte und indirekte Spuren in der Landschaft, die als Teil der fluvialen Anthroposphäre oder als ihr weiteres Einzugsgebiet interpretiert werden können
Die umwelthistorische Interpretation archäologischer Quellen
No full textBei der archäologischen Forschung müssen wir drei Ebenen differenzieren, die alle über ihre eigenen spezifischen Methoden verfügen, nämlich die Ebenen der Erschließung, der Analyse der Quellen und schließlich auch deren Interpretation. Nicht immer sind die Grenzen zwischen Analyse und Interpretation klar zu definieren, denn sie hängen nicht zuletzt von Ziel und Fragestellung ab. Diese haben sich in den vergangenen Jahren jedoch generell verschoben. An die Stelle einer klassischen Siedlungs- und Landschaftsarchäologie tritt in den vergangenen Jahren verstärkt eine Umweltarchäologie. Die Quellen der Archäologie sind die materiellen Hinterlassenschaften, die aus ihrem Nutzungskontext ausgeschieden und zumeist als Bodenfunde überliefert sind. Man differenziert Funde und Befunde. Das eine sind bewegliche Objekte, das andere meist bauliche ortsfeste Strukturen. In Bezug auf eine Archäologie der Aue wären Angel- oder Flößerhaken, aber auch Einbäume und Fischreusen als Funde, Uferbefestigungen, Kanäle, Brückenpfeiler oder Gebäudereste von Mühlen als Befunde zu nennen. Mehr aus Erwägungen der Denkmalpflege definiert man Fundstellen, an denen direkt menschliche Aktivitäten stattgefunden haben. Am konkreten Beispiel einer Kalktufflandschaft der Schwäbischen Alb werden in diesem Beitrag die Perspektiven und Methoden, aber auch einige Grundprinzipien der archäologischen Interpretation und Quellenkritik innerhalb von Flusslandschaften aufgezeigt
Ag-only inner electrode Na₀.₅Bi₀.₅TiO₃-based X9R MLCC: achieving high performance and cost efficiency
No full textThe demand for high-power electronic applications is set to drive the necessity for robust components like multi-layer ceramic capacitors (MLCCs). These MLCCs must endure a broad temperature range and withstand high electric fields. Simultaneously, the production cost of these components is a crucial concern for manufacturers. The regularly used Ag/Pd inner electrodes constitute the most significant cost factor. Hence, this study showcases the fabrication of a sodium bismuth titanate (NBT)-based MLCC using only Ag inner electrodes. This could be achieved by reducing the sintering temperatures with the help of sintering aids, but still maintaining excellent dielectric properties of the ceramic. This MLCC demonstrates an exceptional operational temperature range (− 90 to 310 °C), high energy density (up to 5.1 J/cm³), higher efficiency (92%) at 217 kV/cm, and robust capacitance stability (variation less than 10%) even under high temperatures and electric fields