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Geländer erhöhen – sinnvoll oder übertrieben?
Get PDFGeländer und Brüstungen werden teilweise um wenige Zentimeter erhöht. Besteht dafür aus risikoorientierter Sicht eine Rechtfertigung
The AVIRIS-4 airborne imaging spectrometer
Get PDFThe Airborne Visible/Infrared Imaging Spectrometer-4 (AVIRIS-4) represents the next generation in the series of airborne imaging spectrometers built by National Aeronautics and Space Administration (NASA) Jet Propulsion Laboratory (JPL). Commissioned by the Swiss Airborne Research Facility for the Earth System (ARES) research consortium, AVIRIS-4 is geared toward delivering cutting-edge imaging spectroscopy data for scientific and practical applications as a replacement for its predecessor Airborne Prism EXperiment (APEX). AVIRIS-4 is based on a Dyson-type imaging spectrometer design, also employed by NASA-operated AVIRIS-3 and Earth Surface Mineral Dust Source Investigation (EMIT), and integrates a scaled two-mirror telescope housed in a compact vacuum vessel. This enables airborne measurements in unpressurized aircraft at altitudes ranging from 500 to 7620 m, achieving image resolutions between 0.3 and 4.5 m with a field of view of 40.2° in 1241 spatial pixels. AVIRIS-4 surpasses previous state-of-the-art sensor heads in signal-to-noise ratio (SNR) performance and features a spectral range of 375–2504 nm and 7.4-nm spectral sampling. The operation, data capture, and mission control hardware as well as the calibration and data processing software are developed by UZH, EPFL, and ZHAW. This letter outlines the design and calibration strategies implemented in AVIRIS-4’s development and highlights its performance during its first year of operation in 2024
Reflections on writing and generative AI : a symposium
Get PDFThis symposium is an extension of a plenary forum on generative AI (hereafter GenAI) held at the EATAW Conference at Zurich University of Applied Sciences in Winterthur, Switzerland, in June 2023. Since the conference, AI – particularly the large language models (LLMs) shaping GenAI such as OpenAI’s ChatGPT – continue to develop rapidly with extensive integration and usage across disciplines and career sectors with educational and societal impacts.
Given these developments, we recognize the central role that writing instruction has in fostering critical literacies and engaged usage and, at times, non-usage of GenAI. Just as we have adapted our teaching and learning to other technological developments, so too are we now at a time of transition and adaptation. Our initial discussion at EATAW was wide-ranging, intentionally so because (1) there is so much to explore in relation to GenAI, and (2) the EATAW membership is diverse, coming from a range of academic backgrounds. Thus in our original plenary and here in this symposium we have raised issues ranging from specific pedagogical approaches to questions of program and institutional administration, to broader public issues and conversations about the relationship of humans to machines. Here in this written symposium we each raise a different issue related to GenAI and writing with the aim to foster dialogue and discussion about GenAI in writing-related contexts
Certify this!
Get PDFKlein, unscheinbar, aber äusserst wirkungsvoll ist das digitale Zertifikat – das Ausweisdokument, ohne das wir uns in Zukunft nicht mehr in der digitalen Welt bewegen können
Persuasive prompting : the case of digital health
No full textChatbots are being introduced to fill the gap between consultations, with the motivation of supporting adherence to treatment, especially in the case of chronic diseases. Such a task requires chatbots to have a persuasive ability to influence patient attitudes or behaviour, which is achievable through the ad-vancement of Large Language Models (LLMs). By means of prompting engineering, we aim to accomplish the design goal of persuasive chatbots powered by LLMs that can systematically apply persuasion strategies and dynamically adapt such strategies to the user. We have converted well-founded persuasion strategies into guidelines for prompt crafting to enhance the persuasiveness of LLM-based chatbots. This paper contributes to the design of persuasive chatbots by providing a list of guidance on prompt crafting, meta-prompts, and a proof-of-concept of the developed guidance from a digital health application. This paper also initiates the discourse on prompt guidance as a new type of design artefact within Design Science Research (DSR)
Eine Stadt stellt sich dem Wandel : Luzerns Handbuch für klimaangepasste Grünflächen
Get PDFDie Herausforderungen des Klimawandels machen auch vor der Stadt Luzern nicht halt. Steigende Temperaturen, häufigere Hitzetage, intensivere Starkniederschläge und schneeärmere Winter prägen die Klimaszenarien für die Stadt bis 2060 (NCCS, 2018). Diese Veränderungen erfordern eine grundlegende Anpassung urbaner Frei- und Grünräume, um die Lebensqualität der Bevölkerung zu sichern und gleichzeitig die Klimaresilienz, die Widerstandsfähigkeit von Ökosystemen gegenüber den Folgen des Klimawandels, zu stärken. Wie viele andere Gemeinden in der Schweiz reagiert die Stadt Luzern auf diese Herausforderungen mit einer zweigleisigen Strategie: Zum einen setzt sie auf die Reduktion von Treibhausgasen durch die Klima- und Energiestrategie (Stadt Luzern, 2021), zum anderen auf die Anpassung an unvermeidliche klimatische Veränderungen. Die vom Stadtrat verabschiedete Klimaanpassungsstrategie definiert konkrete Maßnahmen, darunter die Entwicklung klimaangepasster Pflanz- und Grünflächen (Stadt Luzern, 2020). Vor diesem Hintergrund wurde das Praxishandbuch „Klimaangepasste Pflanz- und Grünflächen“ entwickelt. Es dient als Leitfaden für Fachpersonen der kommunalen Verwaltung und externe Planende, die von der Stadt beauftragt werden. Ziel ist es, Grünflächen so zu gestalten und zu pflegen, dass sie gegenüber den Folgen des Klimawandels widerstandsfähig sind, gleichzeitig die Biodiversität fördern und die Lebensqualität in der Stadt erhöhen. Das Handbuch ist eng mit anderen städtischen Strategien und Werkzeugen verknüpft. So baut es auf der Biodiversitätsstrategie und dem Pflegehandbuch Grünflächenpflege auf und erweitert diese um spezifische Hinweise zur Klimaanpassung
Spatial Reasoner : a 3D inference pipeline for XR applications
Get PDFModern extended reality XR systems provide rich analysis of image data and fusion of sensor input and demand AR/VR applications that can reason about 3D scenes in a semantic manner. We present a spatial reasoning framework that bridges geometric facts with symbolic predicates and relations to handle key tasks such as determining how 3D objects are arranged among each other ('on', 'behind', 'near', etc.). Its foundation relies on oriented 3D bounding box representations, enhanced by a comprehensive set of spatial predicates, ranging from topology and connectivity to directionality and orientation, expressed in a formalism related to natural language. The derived predicates form a spatial knowledge graph and, in combination with a pipeline-based inference model, enable spatial queries and dynamic rule evaluation. Implementations for client- and server-side processing demonstrate the framework’s capability to efficiently translate geometric data into actionable knowledge, ensuring scalable and technology-independent spatial reasoning in complex 3D environments. The Spatial Reasoner framework is fostering the creation of spatial ontologies, and seamlessly integrates with and therefore enriches machine learning, natural language processing, and rule systems in XR applications
Automated segmentation for cortical thickness of the medial perirhinal cortex
Get PDFAlzheimer’s disease (AD) is characterized by a progressive spread of neurofibrillary tangles (NFT), beginning in the medial perirhinal cortex (mPRC), advancing to the entorhinal cortex (ERC), and subsequently involving the hippocampus, lateral perirhinal cortex (lPRC), and the rest of the brain. Given the close relationship between NFT accumulation and neuronal loss, the mPRC reflects a promising structural marker for early diagnosis of AD. However, only limited tools that automatically measure the cortical thickness of the mPRC are currently available. Utilizing the nnU-Net framework, we trained models on structural MRI of 126 adults, with manually segmented labels as ground truth. These models were then applied to an independent dataset of 103 adults (comprising patients with Alzheimer’s dementia, amnestic mild cognitive impairment (aMCI), and healthy controls). High agreement was observed between manual and automated measurements of cortical thickness. Furthermore, we found significant atrophy in the Alzheimer’s dementia group in the mPRC, ERC, and lPRC compared to healthy controls. Comparison of the aMCI group and healthy controls revealed significant differences in the ERC only. The results underscore the utility of our automated segmentation tool in advancing Alzheimer’s research
AI for real machines : bridging the research-industry gap
No full textWe interact with AI daily: writing emails, navigating traffic, or receiving personalized recommendations. AI is no longer just the future - it is the present. At the same time, industry is undergoing its own transformation, striving to embed AI into manufacturing, services, and business processes. But how far along are companies really? Have they even completed the previous industrial revolution?
Bringing cutting-edge AI research into real-world industrial environments reveals a number of persistent gaps. In this talk, I will present examples of such gaps and share practical AI use cases from collaborative research projects with industry partners, aimed at anomaly detection, predictive maintenance, product quality monitoring, and automatic sensor calibration. I will highlight key challenges that are often encountered, such as limited and noisy data, scalability of deployment across diverse systems, high variability in machine behaviour and operating conditions, uncertainty quantification, and the need for transparent, interpretable AI models. I will also discuss how combining AI techniques with physics-based models and domain expertise enables practical, robust, and trustworthy solutions—demonstrating AI that truly works for real machines