Taskmaster is a British television show that combines comedic performance with a formal scoring system. Despite the appearance of structured competition, it remains unclear whether scoring dynamics contribute meaningfully to audience engagement. We conducted a statistical analysis of 162 episodes across 18 series, using fifteen episode-level metrics to quantify rank volatility, point spread, lead changes, and winner dominance. None of these metrics showed a significant association with IMDb ratings, even after controlling for series effects. Long-term trends suggest that average points have increased over time, while volatility has slightly declined and rank spread has remained stable. These patterns indicate an attempt to enhance competitive visibility without altering the show's structural equilibrium. We also analyzed contestant rank trajectories and identified five recurring archetypes describing performance styles. These patterns suggest that viewer interest is shaped more by contestant behavior than by game mechanics.

We present a unified, data-driven framework for quantifying and enhancing offensive momentum and scoring likelihood (expected goals, xG) in professional hockey. Leveraging a Sportlogiq dataset of 541,000 NHL event records, our end-to-end pipeline comprises five stages: (1) interpretable momentum weighting of micro-events via logistic regression; (2) nonlinear xG estimation using gradient-boosted decision trees; (3) temporal sequence modeling with Long Short-Term Memory (LSTM) networks; (4) spatial formation discovery through principal component analysis (PCA) followed by K-Means clustering on standardized player coordinates; and (5) use of an X-Learner causal inference estimator to quantify the average treatment effect (ATE) of adopting the identified "optimal" event sequences and formations. We observe an ATE of 0.12 (95% CI: 0.05-0.17, p < 1e-50), corresponding to a 15% relative gain in scoring potential. These results demonstrate that strategically structured sequences and compact formations causally elevate offensive performance. Our framework delivers real-time, actionable insights for coaches and analysts, advancing hockey analytics toward principled, causally grounded tactical optimization.

Dear Prudence is Slate's advice column. For this edition, Hillary Frey, Slate's editor-in-chief, will be filling in as Prudie. My partner of five years and I just got married after two years of extensive wedding planning and preparation. We had a very large guest list with a variety of needs that needed to be taken into account, such as international travel and physical limitations, and I feel grateful that my husband was very intentional about making sure the labor of wedding planning was split as equitably as possible between the two of us. We agreed that we wanted to write our own vows because we thought it was more meaningful than using traditional ones.

This article presents the results of a pilot study involving the reception of a fictional short story translated from English into Dutch under four conditions: machine translation (MT), post-editing (PE), human translation (HT) and original source text (ST). The aim is to understand how creativity and errors in different translation modalities affect readers, specifically regarding cognitive load. Eight participants filled in a questionnaire, read a story using an eye-tracker, and conducted a retrospective think-aloud (RTA) interview. The results show that units of creative potential (UCP) increase cognitive load and that this effect is highest for HT and lowest for MT; no effect of error was observed. Triangulating the data with RTAs leads us to hypothesize that the higher cognitive load in UCPs is linked to increases in reader enjoyment and immersion. The effect of translation creativity on cognitive load in different translation modalities at word-level is novel and opens up new avenues for further research. All the code and data are available at https://github.com/INCREC/Pilot_to_MT_or_not_to_MT

Intertextuality is a key concept in literary theory that challenges traditional notions of text, signification or authorship. It views texts as part of a vast intertextual network that is constantly evolving and being reconfigured. This paper argues that the field of computational literary studies is the ideal place to conduct a study of intertextuality since we have now the ability to systematically compare texts with each others. Specifically, we present a work on a corpus of more than 12.000 French fictions from the 18th, 19th and early 20th century. We focus on evaluating the underlying roles of two literary notions, sub-genres and the literary canon in the framing of textuality. The article attempts to operationalize intertextuality using state-of-the-art contextual language models to encode novels and capture features that go beyond simple lexical or thematic approaches. Previous research (Hughes, 2012) supports the existence of a literary "style of a time", and our findings further reinforce this concept. Our findings also suggest that both subgenres and canonicity play a significant role in shaping textual similarities within French fiction. These discoveries point to the importance of considering genre and canon as dynamic forces that influence the evolution and intertextual connections of literary works within specific historical contexts.

Connected devices are part of everyday life. The proliferation of connected portable devices such as mobile phones, laptop, smart watches, tablets, or non-portable connected devices such as TV, video game console saturates the environment with RF signals. In parallel to the reception of desired data from its communication partner(s), such connected devices receive also unwanted signals, so called interferers. The interferers, especially from Wi-Fi signals, can occur in a random manner in the form of a signal burst of variable duration and have a signal strength possibly much higher than the desired signal. Interferers with a high signal strength can cause saturation of the receiver preventing proper reception of the desired data. Some techniques tackle this issue by continuously monitoring the received signal strength and adjust immediately the receiver gain to avoid saturation whilst still maintaining the highest sensitivity level. However, when operating popular wireless communication protocols such as Wireless PAN (Bluetooth, BLE, Zigbee...), the receiver is not allowed to adjust the gain during the data payload. RF receivers for these communication protocols adjust then the gain during a time interval prior to the payload reception based on the real-time received signal and freeze the gain just before switching to the payload reception period. This is illustrated in figure 1. Due to the random nature in occurrence and strength level, interferers may appear during the data payload, receiver may saturate causing data loss.

The 38th AAAI Conference on Artificial Intelligence (AAAI-24) kicked off on Tuesday 20 February. The in-person event is being held in the Vancouver Convention Centre. We take a look at what the participants have been getting up to over the past few days. We are excited to welcome everyone to #AAAI24! pic.twitter.com/UN8RUzrq1C We will present a tutorial on "Knowledge Editing for Large Language Models" at #AAAI2024 from 2-6PM PST on Feb 20.

In maritime traffic surveillance, detecting illegal activities, such as illegal fishing or transshipment of illicit products is a crucial task of the coastal administration. In the open sea, one has to rely on Automatic Identification System (AIS) message transmitted by on-board transponders, which are captured by surveillance satellites. However, insincere vessels often intentionally shut down their AIS transponders to hide illegal activities. In the open sea, it is very challenging to differentiate intentional AIS shutdowns from missing reception due to protocol limitations, bad weather conditions or restricting satellite positions. This paper presents a novel approach for the detection of abnormal AIS missing reception based on self-supervised deep learning techniques and transformer models. Using historical data, the trained model predicts if a message should be received in the upcoming minute or not. Afterwards, the model reports on detected anomalies by comparing the prediction with what actually happens. Our method can process AIS messages in real-time, in particular, more than 500 Millions AIS messages per month, corresponding to the trajectories of more than 60 000 ships. The method is evaluated on 1-year of real-world data coming from four Norwegian surveillance satellites. Using related research results, we validated our method by rediscovering already detected intentional AIS shutdowns.

This article presents the results of a study involving the reception of a fictional story by Kurt Vonnegut translated from English into Catalan and Dutch in three conditions: machine-translated (MT), post-edited (PE) and translated from scratch (HT). 223 participants were recruited who rated the reading conditions using three scales: Narrative Engagement, Enjoyment and Translation Reception. The results show that HT presented a higher engagement, enjoyment and translation reception in Catalan if compared to PE and MT. However, the Dutch readers show higher scores in PE than in both HT and MT, and the highest engagement and enjoyments scores are reported when reading the original English version. We hypothesize that when reading a fictional story in translation, not only the condition and the quality of the translations is key to understand its reception, but also the participants reading patterns, reading language, and, perhaps language status in their own societies.

Artificial Intelligence (AI) is transforming all fields of knowledge and production. From surgery, autonomous driving, to image and video creation, AI seems to make possible hitherto unimaginable processes of automation and efficient creation. Media and communication are not an exception, and we are currently witnessing the dawn of powerful AI tools capable of creating artistic images from simple keywords, or to capture emotions from facial expression. These examples may be only the beginning of what can be in the future the engines for automatic AI real time creation of media content linked to the emotional and behavioural responses of individuals. Although it may seem we are still far from there, it is already the moment to adapt our theories about media to the hypothetical scenario in which content production can be done without human intervention, and governed by the controlled any reactions of the individual to the exposure to media content. Following that, I propose the definition of the Integrated Model of Artificial Intelligence-Mediated Communication Effects (IMAGINE), and its consequences on the way we understand media evolution (Scolari, 2012) and we think about media effects (Potter, 2010). The conceptual framework proposed is aimed to help scholars theorizing and doing research in a scenario of continuous real-time connection between AI measurement of people's responses to media, and the AI creation of content, with the objective of optimizing and maximizing the processes of influence. Parasocial interaction and real-time beautification are used as examples to model the functioning of the IMAGINE process.