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This study investigates the effects of prior intentions and intentions in action during the initial stage of a process of design in search of correlations between these cognitive processes and the quality of the design outcomes. We found that the quality of the outcomes was not influenced by sketching. Nevertheless, most of the participants said that they preferred sketching, and their verbal reports can be seen as indicative of the formation of prior intentions. Therefore, we argue that prior intentions have an important role even within a process of pure material engagement. This has important implications for understanding the active role of human agency and our capacity to form prior intentions with pragmatic effects in the world. [ABSTRACT FROM AUTHOR]

In this study, we investigated the prosody of domestic cat meows produced in different contexts. Prosodic cues – i.e., variation in intonation, duration, voice quality and fundamental frequency – in humans as well as in nonhuman animals carry information about idiosyncratic traits of the signaller, including sex, age, and physical and mental state. The duration, fundamental frequency (F0) and intonation in a sample of 969 meows recorded in seven different contexts (i.e., cuddle , door , food , greeting , lifting , play , cat carrier) were analysed using linear mixed effects regression and generalized additive models. In this, we controlled for cat age and sex, as meows produced by old cats had lower mean F0 than those produced by young cats, and female cats produced meows with higher mean F0 than male cats. We found significant effects of context on duration and mean F0, but not on F0 range. Furthermore, the results showed that the intonation of meows produced by cats in a cat carrier displayed a falling pattern, while that of meows produced in cuddle and door contexts was relatively level, and that of meows produced in the other contexts consisted of combinations of rising and falling. The average slope of meows produced in cat carrier and play contexts was negative, while that of meows produced in the other contexts was positive. We argue that this prosodic variation reflects the cats' mental or emotional state, because of valence and arousal differences associated with the various contexts that were included in the study. Further studies will need to confirm this. In addition, we also plan additional analyses of spectral and voice quality parameters in meows and other cat vocalisation types. • The vocal prosody of domestic cat meows varies with the physical context. • Domestic cat meows vary in duration, mean fundamental frequency (F0) and intonation. • Young and female cat meows have higher F0 than old and male cat meows. • Meows in cat carriers have falling intonation, but is more varied in other contexts. [ABSTRACT FROM AUTHOR]

Gender inflection for animated nouns in Italian presents challenges influenced by societal pressures and linguistic structure, especially in morphologically complex words like compounds. The study investigates gender inflection distribution in compounds with capo- compared to other nouns (i.e., occupations traditionally performed by women, by men, and the word capo in isolation), exploring the interplay of social, etymological and morphological factors. 192 native Italian speakers inflected masculine nouns to feminine forms after hearing the stimulus. Results reveal that respondents’ attitudes towards gender-fair language significantly determine the use of feminine, indicating a complex interplay between linguistic structures and social perceptions. Despite historical resistance, the word capa in isolation shows increasing acceptance, challenging entrenched norms. In compounds, capo- element’s gender inflection appears more resistant due to morphological complexity, with an interaction with number. This study advances our understanding of gender inflection, with implications for broader conversations about gender representation and language inclusivity. [ABSTRACT FROM AUTHOR]

Given the often enormous effort required to train GANs, both computationally as well as in dataset collection, the re-use of pretrained GANs largely increases the potential impact of generative models. Therefore, we propose a novel knowledge transfer method for generative models based on mining the knowledge that is most beneficial to a specific target domain, either from a single or multiple pretrained GANs. This is done using a miner network that identifies which part of the generative distribution of each pretrained GAN outputs samples closest to the target domain. Mining effectively steers GAN sampling towards suitable regions of the latent space, which facilitates the posterior finetuning and avoids pathologies of other methods, such as mode collapse and lack of flexibility. Furthermore, to prevent overfitting on small target domains, we introduce sparse subnetwork selection, that restricts the set of trainable neurons to those that are relevant for the target dataset. We perform comprehensive experiments on several challenging datasets using various GAN architectures (BigGAN, Progressive GAN, and StyleGAN) and show that the proposed method, called MineGAN, effectively transfers knowledge to domains with few target images, outperforming existing methods. In addition, MineGAN can successfully transfer knowledge from multiple pretrained GANs. MineGAN. [ABSTRACT FROM AUTHOR]

In continual learning applications on-the-edge multiple self-centered devices (SCD) learn different local tasks independently, with each SCD only optimizing its own task. Can we achieve (almost) zero-cost collaboration between different devices? We formalize this problem as a Distributed Continual Learning (DCL) scenario, where SCDs greedily adapt to their own local tasks and a separate continual learning (CL) model perform a sparse and asynchronous consolidation step that combines the SCD models sequentially into a single multi-task model without using the original data. Unfortunately, current CL methods are not directly applicable to this scenario. We propose Data-Agnostic Consolidation (DAC), a novel double knowledge distillation method which performs distillation in the latent space via a novel Projected Latent Distillation loss. Experimental results show that DAC enables forward transfer between SCDs and reaches state-of-the-art accuracy on Split CIFAR100, CORe50 and Split TinyImageNet, both in single device and distributed CL scenarios. Somewhat surprisingly, a single out-of-distribution image is sufficient as the only source of data for DAC. • We formalize distributed continual learning. • We design data-agnostic consolidation (DAC). • DAC achieves SotA performance in the single device and distribution settings. • We highlight the benefits of model consolidation for continual learning. • We show that DAC provides forward transfer at (almost) zero cost for new devices. [ABSTRACT FROM AUTHOR]