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3. Odnosi med sledilci in vplivneži na Instagramu v vplivnostnem marketingu : doktorska disertacijaBranka Bizjak Zabukovec, 2025, doktorska disertacija Ključne besede: vplivnostni marketing, sledilci, Insta odnos, avtentičnost, konstruktivistična utemeljena teorija, merska lestvica, parasocialni odnos, parasocialna interakcija
Objavljeno v RUP: 11.02.2026; Ogledov: 29; Prenosov: 0
 Celotno besedilo (2,00 MB) |
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5. O prostorih gladkih zlepkov in izogeometričnih metodah za reševanje parcialnih diferencialnih enačb višjih redov nad večdelnimi domenami : doktorska disertacijaAljaž Kosmač, 2026, doktorska disertacija Ključne besede: isogeometric analysis, partial differential equations, multi-patch domains, splines, Cs-smoothnes, Galerkin method, collocation, mixed degree and regularity spline spaces
Objavljeno v RUP: 09.02.2026; Ogledov: 54; Prenosov: 0
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7. Death and burial of a set of fraternal twins from Tragurium : an osteobiographical approachAnna J. Osterholtz, Mario Novak, Mario Carić, Lujana Paraman, 2025, izvirni znanstveni članek Ključne besede: bioarchaeology, osteobiography, Croatia, paleopathology, isotopic analysis, Early Roman Period
Objavljeno v RUP: 09.02.2026; Ogledov: 69; Prenosov: 2
Celotno besedilo (16,09 MB)
Gradivo ima več datotek! Več... |
8. The ontogeny of the human calcaneus : insights from morphological and trabecular changes during postnatal growthCarla Figus, Kristian J. Carlson, Eugenio Bortolini, Jaap Saers, Francesca Seghi, Rita Sorrentino, Federico Bernardini, Antonino Vazzana, Igor Erjavec, Mario Novak, Claudio Tuniz, Maria Giovanna Belcastro, Jay T. Stock, Timothy M. Ryan, Stefano Benazzi, 2025, izvirni znanstveni članek Ključne besede: calcaneal ontogeny, geometric morphometrics, locomotion, trabecular analysis, calcaneus, bipedal locomotion, micro-CT
Objavljeno v RUP: 09.02.2026; Ogledov: 81; Prenosov: 2
Celotno besedilo (1,60 MB)
Gradivo ima več datotek! Več... |
9. Predictable artificial intelligenceLexin Zhou, Pablo A. M. Casares, Fernando Martínez-Plumed, John Burden, Ryan Burnell, Lucy Cheke, Cèsar Ferri, Alexandru Marcoci, Behzad Mehrbakhsh, Yael Moros-Daval, Danaja Rutar, 2026, izvirni znanstveni članek Opis: Many areas of artificial intelligence, and machine learning in particular, aim at being probably correct, i.e., valid on average, rather than pursuing the idealistic goal of being provably valid for all inputs. However, AI systems could still be predictably valid, such as an imperfect robot deliverer for which we can reliably and precisely predict the task instances for which it is correct and safe, its valid operating range. “Predictable AI” is a nascent research area that explores ways of anticipating key validity indicators (e.g., performance, safety) of present and future AI ecosystems. We argue that achieving predictability is crucial for fostering trust, liability, control, alignment and safety of AI, and thus should be prioritised over performance. We formally characterise predictability, explore its most relevant components, illustrate what can be predicted, describe alternative candidates for predictors, as well as the trade-offs between maximising validity and predictability. To illustrate these concepts, we bring an array of illustrative examples covering diverse ecosystem configurations. “Predictable AI” is related to other areas of technical and non-technical AI research, but have distinctive questions, hypotheses, techniques and challenges. This paper aims to elucidate them, calls for identifying paths towards a landscape of predictably valid AI systems and outlines the potential impact of this emergent field.
Ključne besede: predictable AI, general-purpose AI, AI safety
Objavljeno v RUP: 09.02.2026; Ogledov: 63; Prenosov: 3
Celotno besedilo (4,86 MB)
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