LectureChat extends the WikiChat conversational AI system by integrating multilingual university lecture transcripts alongside

Wikipedia content. The demo showcases a dual retrieval architecture that combines structured encyclopedic knowledge with academic lecture

material, leveraging multiple segmentation strategies and cross index reconciliation to improve retrieval quality. The system maintains separate

citation spaces for Wikipedia (numeric) and lectures (alphabetic) and preserves temporal provenance for direct video navigation. We present

the overall architecture, interaction flow, implementation details, and a reproducibility plan.

Nonlinear optimisation techniques are commonly employed to minimise complex cost functions, with their effectiveness determined largely by the structure of the underlying error landscape. These methods require initial parameter values, and in the presence of multiple local minima, they are prone to becoming trapped in suboptimal regions. The likelihood of locating the global minimum increases substantially when the initialisation lies within its corresponding basin of attraction. Consequently, high-quality initial parameters are critical for successful optimisation. This technical report outlines a new strategy for selecting suitable initial parameters for a trigonometric model and unevenly sampled data, ensuring that the optimisation procedure starts sufficiently close to the global minimum. The proposed parameter estimation approach is strictly NI-based, interpretable, and explainable. It targets at complicated cases which include: samples with strong random noise, samples with only few covered periods, and samples which cover only a fraction of one period. Special attention is put on the frequency estimation. It can be shown that an estimation of initial parameters with sufficient accuracy is possible down to a signal-noise-ratio of 1.4 dB at much lower computational costs than the Lomb-Scargle-periodogram method requires.

Progress in artificial intelligence research, caused by the volume of available data on the World Wide Web, the development of affordable yet extremely efficient mathematical processors ("GPUs", graphical processing units), and the discovery of more effective training algorithms for very large models (such as "transformer" neural networks like Google's BERT and OpenAI's GPT, the technology behind ChatGPT.com) has recently led to a technological convergence that has begun to disrupt many other areas of scientific research, business and life. In this work, we explore some methodological concerns and boundary conditions when aspiring to apply such advanced technologies in order to advance the state of the art in software-implementable models for risk intelligence. We look at the potential of these technologies to assist open-ended 360˚ risk profiling, ethical and government questions such as dealing with the inherent bias in data, potentially unknown status of of information’s factuality of datasets, questionable provenance of datasets and other factors, such as sabotaging models. Borrowing from security engineering, we adopt the concept of the ‘attack surface‘ and introduce a variant of it as ‘risk surface‘: we posit that a good risk model should be supplemented by a model of its own risks in the form of making limitations like blind spots and known questionable behavior explicit. Model cards a proposed as a standard type of document to capture the risk profile of the risk model itself.

The escalating challenges of the 21st century demand a rethinking of design education, as traditional methods no longer prepare designers to address complexity or contribute to resilient societies. This paper introduces Interwoven Futures, an initiative from a product design department that emphasizes hands-on practice, critical reflection, and multi-stakeholder collaboration. Rather than focusing on linear problem-solving, it equips students to navigate uncertainty and systems-level change. The project has three components: a university-industry collaboration with VAUDE, a curated publication, and a public event. Together, these elements engage with ecological and societal issues while cultivating designers as agents of change who learn to stay with the trouble in real-world contexts. The paper discusses the structure, findings, and implications of Interwoven Futures, positioning it as a case study in adaptive, future- oriented design education that contributes to the evolving discourse on sustainability in design.