Purpose This paper aims to seek to develop a digital twin (DT)–based framework for identifying systemic delivery risk in construction projects through latent performance–loss states. Conventional monitoring approaches rely primarily on observable indicators such as productivity or schedule deviation, which often reveal...
Joas Serugga· Construction Innovation· 0 citations
Abstract Background Pancreatic cancer is associated with high morbidity and mortality. Gemcitabine plus nab-paclitaxel (GA), along with FOLFIRINOX and NALIRIFOX, is a standard first-line treatment for advanced/metastatic pancreatic ductal adenocarcinoma (mPDAC). However, the standard GA schedule (days 1, 8, and 15 of a...
Celine Hoyek, Cody R. Eslinger, Angelo Pirozzi et al.· BMC Cancer· 0 citations
This poster was presented on the TRA Workshop "Great Data, Great Science" at the University of Bonn on 06 October 2026. --- Agricultural research increasingly depends on the integration, standardization, and sustainable reuse of heterogeneous research data. Data are generated across disciplines such as agroecology, bre...
Lucia Vedder, Julian Schneider, Gabriel Schneider· Zenodo (CERN European Organi...· 0 citations
This poster was presented on the TRA Workshop "Great Data, Great Science" at the University of Bonn on 06 October 2026. --- Agricultural research increasingly depends on the integration, standardization, and sustainable reuse of heterogeneous research data. Data are generated across disciplines such as agroecology, bre...
Lucia Vedder, Julian Schneider, Gabriel Schneider· Zenodo (CERN European Organi...· 0 citations
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Version v4. Language edit (Rubriq) merged into the manuscript: commas, spelling variants and wording-neutral substitutions only; no change to results or claims. A computational study of a phenomenological model. No experiment was performed and the model is not calibrated to any conductor. Prepared for submission to Sup...
Leon Sandler· Zenodo (CERN European Organi...· 0 citations
Version v3. Adds a subsection on experimental validation using fission-reactor irradiation (Section 4.5), softens two statements of the abstract, and removes a reference to separate work. A computational study of a phenomenological model. No experiment was performed and the model is not calibrated to any conductor. Pre...
Leon Sandler· Zenodo (CERN European Organi...· 0 citations
This poster was presented on the TRA Workshop "Great Data, Great Science" at the University of Bonn on 06 October 2026. --- In computational biology, the availability and careful handling of high-quality digital data are essential for obtaining reliable and biologically meaningful results. In silico analyses depend on...
Lucia Vedder, Heiko Schoof· Zenodo (CERN European Organi...· 0 citations
Sustainable development requires robust analytical tools to address complex socio-ecological challenges. Statistics, data science, and computing are increasingly applied, yet the literature remains fragmented across disciplines. A systematic literature review was conducted following PRISMA guidelines. Scopus was search...
Nur Jannah Tuasikal, Fendi A. Taib, Ananda Muhammad Arbick et al.· F1000Research· 1 citation
The repetition count that works best for a small language model may not remain best at a larger scale. We study this effect in pretraining with a finite target corpus mixed with generic data at a fixed target fraction. On Wikipedia-derived data and Proof-Pile-2, the ranking of measured repetition counts changes with mo...
Modern protein representation models support tasks such as enzyme design and drug discovery, but their reliance on static data such as sequence and native structure limits their ability to capture the conformational dynamics that drive protein function. We investigate whether persistent homology (PH) can provide descri...
Dominik Geng, Florian Graf, Martin Uray et al.· 0 citations
We study the problem of multiclass PAC learning with bandit feedback in the realizable setting. In this framework, there is an unknown data distribution over an instance space $\mathcal{X}$ and a label space $\mathcal{Y}$, as in classical multiclass PAC learning, but the learner does not observe the labels of the i.i.d...
Steve Hanneke, Qinglin Meng, Shay Moran et al.· 0 citations
Online learning from a stream of data is a defining feature of intelligence, yet modern machine learning systems often struggle in this setting, especially under distributional shift. To understand its basic properties, we study the relationship between online and offline learning in the context of kernel regression by...
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
