The use of Large Language Models (LLMs) has become ubiquitous, with abundant applications in computational creativity. One such application is fictional story generation. Fiction is a narrative that occurs in a story world that is slightly different than ours. With LLMs becoming writing partners, we question how suitable they are to generate fiction. This study investigates the ability of LLMs to maintain a state of world essential to generate fiction. Through a series of questions to nine LLMs, we find that only two models exhibit consistent worldview, while the rest are self-conflicting. Subsequent analysis of stories generated by four models revealed a strikingly uniform narrative pattern. This uniformity across models further suggests a lack of `state' necessary for fiction. We highlight the limitations of current LLMs in fiction writing and advocate for future research to test and create story worlds for LLMs to reside in. All code, dataset, and the generated responses can be found in https://github.com/tanny411/llm-reliability-and-consistency-evaluation.

Bicycle-sharing systems (BSS) have become a daily reality for many citizens of larger, wealthier cities in developed regions. However, planning the layout of bicycle-sharing stations usually requires expensive data gathering, surveying travel behavior and trip modelling followed by station layout optimization. Many smaller cities and towns, especially in developing areas, may have difficulty financing such projects. Planning a BSS also takes a considerable amount of time. Yet as the pandemic has shown us, municipalities will face the need to adapt rapidly to mobility shifts, which include citizens leaving public transport for bicycles. Laying out a bike sharing system quickly will become critical in addressing the increase in bike demand. This paper addresses the problem of cost and time in BSS layout design and proposes a new solution to streamline and facilitate the process of such planning by using spatial embedding methods. Based only on publicly available data from OpenStreetMap, and station layouts from 34 cities in Europe, a method has been developed to divide cities into micro-regions using the Uber H3 discrete global grid system and to indicate regions where it is worth placing a station based on existing systems in different cities using transfer learning. The result of the work is a mechanism to support planners in their decision making when planning a station layout with a choice of reference cities.

The founders of ARIC include Lufthansa Industry Solutions, Pilot Hamburg GmbH, the Hamburg Information Technology Center (HITeC) and Zapliance GmbH. "But also the Nordakademie to which we owe our premises, the IT alliance between Hamburg's universities'ahoi digital', the Innovations Kontakt Stelle and the Ministry of Economics and Innovation," Krtil added. "We see ourselves as an interface between application-orientated research and practical applications with the aim of promoting AI in the metropolitan region and remaining internationally competitive."

Inverse classification is the process of manipulating an instance such that it is more likely to conform to a specific class. Past methods that address such a problem have shortcomings. Greedy methods make changes that are overly radical, often relying on data that is strictly discrete. Other methods rely on certain data points, the presence of which cannot be guaranteed. In this paper we propose a general framework and method that overcomes these and other limitations. The formulation of our method can use any differentiable classification function. We demonstrate the method by using logistic regression and Gaussian kernel SVMs. We constrain the inverse classification to occur on features that can actually be changed, each of which incurs an individual cost. We further subject such changes to fall within a certain level of cumulative change (budget). Our framework can also accommodate the estimation of (indirectly changeable) features whose values change as a consequence of actions taken. Furthermore, we propose two methods for specifying feature-value ranges that result in different algorithmic behavior. We apply our method, and a proposed sensitivity analysis-based benchmark method, to two freely available datasets: Student Performance from the UCI Machine Learning Repository and a real world cardiovascular disease dataset. The results obtained demonstrate the validity and benefits of our framework and method.

For people who play the video game Counter Strike online, it's hard enough watching your back at the best of times. In the fast-paced first-person shooter, there are always players with quicker reflexes or a sharper eye. But at the height of its popularity a few years ago, people started to come up against other players with skills that were too good to be true. Games like Counter Strike and Half Life – another shooter that was very popular online – had a problem with players who used software cheats that steadied their aim or let them see through walls. So in 2006, when the stakes were raised by an online competition with cash prizes, an unusual pair of referees were called in.

For people who play the video game Counter Strike online, it's hard enough watching your back at the best of times. In the fast-paced first-person shooter, there are always players with quicker reflexes or a sharper eye. But at the height of its popularity a few years ago, people started to come up against other players with skills that were too good to be true. Games like Counter Strike and Half Life – another shooter that was very popular online – had a problem with players who used software cheats that steadied their aim or let them see through walls. So in 2006, when the stakes were raised by an online competition with cash prizes, an unusual pair of referees were called in.