This paper initiates the reverse mathematics of social choice theory, studying Arrow’s impossibility theorem and related results including Fishburn’s possibility theorem and the Kirman–Sondermann theorem within the framework of reverse mathematics. We formalise fundamental notions of social choice theory in second-order arithmetic, yielding a definition of countable society which is tractable in ${\mathsf {RCA}}_0$. We then show that the Kirman–Sondermann analysis of social welfare functions can be carried out in ${\mathsf {RCA}}_0$. This approach yields a proof of Arrow’s theorem in ${\mathsf {RCA}}_0$, and thus in $\mathrm {PRA}$, since Arrow’s theorem can be formalised as a $\Pi ^0_1$ sentence. Finally we show that Fishburn’s possibility theorem for countable societies is equivalent to ${\mathsf {ACA}}_0$ over ${\mathsf {RCA}}_0$.

The unanticipated product of a survey involving 190 non-professional readers, this first-report paper looks at the way memories from different source media overlap, along with the potential consequences of this phenomenon for existing approaches to reader behaviour.

The paper begins with a focus on how everyday readers articulate their recollection of literary works, in particular those moments they found most memorable. We identify a common situation in which participants ‘mix up’ recollections of a book's content with memories of their respective film or TV adaptations. We offer the term spontaneous transmedia co-location to describe this form of effortless recall involving memories of literary texts which spontaneously trigger memories of other, visual media. We outline five preliminary modes of spontaneous transmedia co-location (STC) and explain what they consist of.

Finally, we elaborate how STC ties into wider theories of how readers and other consumers interact with media, and how they tend to remember and otherwise connect them in a transmedia space.

Carbon credits from the reducing emissions from deforestation and degradation (REDD+) projects have been criticized for issuing junk carbon credits due to invalid ex-ante baselines. Recently, the concept of ex-post baseline has been discussed to overcome the criticism, while ex-ante baseline is still necessary for project financing and risk assessment. To address this issue, we propose a Bayesian state-space model that integrates ex-ante baseline projection and ex-post dynamic baseline updating in a unified manner. Our approach provides a tool for appropriate risk assessment and performance evaluation of REDD+ projects. We apply the proposed model to a REDD+ project in Brazil and show that it may have had a small, positive effect but has been overcredited. We also demonstrate that the 90% predictive interval of the ex-ante baseline includes the ex-post baseline, implying that our ex-ante estimation can work effectively.

This article aims to track and tackle the #ŠtoTeNema hashtag to analyse the meanings generated by Twitter end-users who employed #ŠtoTeNema together with other hashtags, texts, visuals, hyperlinks, and metadata. ŠTO TE NEMA (Why are you not here?) first appeared as an alternative commemorative practice (in 2006) to remember the victims of the Srebrenica genocide (1995). In 2012, the #ŠtoTeNema hashtag emerged to commemorate human loss on Twitter and provide even more comprehensive access to this space of memory and suffering. Using multimodal discourse analysis, I examine how Aida Šehović, the artist behind ŠTO TE NEMA, with her team and Twitter's end-users, portrayed the Srebrenica genocide by employing #ŠtoTeNema. I argue that ŠTO TE NEMA has become an influential and recognisable representation of the Srebrenica genocide not only on-site but also online. This research concludes that #ŠtoTeNema gained momentum during the global pandemic peak (2020), creating inclusive access to commemorate the 25th anniversary of the genocide locally, regionally, and transnationally.

Safe and socially compliant navigation in a crowded environment is essential for social robots. Numerous research efforts have shown the advantages of deep reinforcement learning techniques in training efficient policies, while most of them ignore fast-moving pedestrians in the crowd. In this paper, we present a novel design of safety measure, named Risk-Area, considering collision theory and motion characteristics of different robots and humans. The geometry of Risk-Area is formed based on the real-time relative positions and velocities of the agents in the environment. Our approach perceives risk in the environment and encourages the robot to take safe and socially compliant navigation behaviors. The proposed method is verified with three existing well-known deep reinforcement learning models in densely populated environments. Experiment results demonstrate that our approach combined with the reinforcement learning techniques can efficiently perceive risk in the environment and navigate the robot with high safety in the crowds with fast-moving pedestrians.

The rise of digital technology has led to fundamental changes in how individual and collective perspectives on the past are transmitted and engaged. An immediate implication of these changes relates to the shift away from human communication as a single form of communication about memory towards multiple models which involve non-human (or robotic) agents. These non-human agents are primarily constituted by artificial intelligence (AI)-driven systems, such as search engines and conversational agents, which retrieve information about the past for human users and are increasingly used to generate memory-related content. To account for the growing complexity of memory-related digital communication, the article introduces three agency-based models of such communication: (1) human-to-human; (2) human-to-robot; and (3) robot-to-robot. It discusses examples of communication practices enabled by these models and scrutinises their implications for individual and collective memory transmission. The article concludes by outlining several directions for memory communication research increasingly shaped by non-human agents.

This paper investigates the role of motorized three-wheelers (MTW) in urban mobility within popular transport, a demand-responsive and unscheduled mode of transportation provided by self-organized small operators frequently operating in grey areas of regulation. Although popular transport is the primary mobility option for millions worldwide, knowledge about its users, operation, and environmental and social impacts remains scarce. This paper sheds light on some of the features and impacts of popular MTW, focusing on two case studies in the Caribbean with different scales and urban trajectories: Puerto Viejo, Costa Rica, and Soledad in Colombia. We explored the relationship between MTW and fragmentation–(in)accessibility–exclusion in these cities, drawing on a framework connecting these concepts in the Latin American and Caribbean context. Using primary data from qualitative and quantitative methods, the paper examines the distribution of inhibitors or enablers of accessibility within the context of unequal, splintered, and fragmented transport and communication infrastructures. Additionally, the environmental impact of MTW in terms of CO2 and PM2.5 emissions is assessed using field data from low-cost sensors. The paper argues that planning for just urban mobility necessitates considering the ecological consequences of various transportation modes and their social consequences and potential for participation and inclusion. The applied methodology introduces low-cost, replicable, and scalable data production and analysis techniques, contributing to future research on sustainable and just mobility in resource-limited urban areas.

The current LiDAR-inertial odometry is prone to cumulative Z-axis error when it runs for a long time. This error can easily lead to the failure to detect the loop-closing in the correct scenario. In this paper, a ground-constrained LiDAR-inertial SLAM is proposed to solve this problem. Reasonable constraints on the ground motion of the mobile robot are incorporated to limit the Z-axis drift error. At the same time, considering the influence of initial positioning error on navigation, a keyframe selection strategy is designed to effectively improve the flatness and accuracy of positioning and the efficiency of loop detection. If GNSS is available, the GNSS factor is added to eliminate the cumulative error of the trajectory. Finally, a large number of experiments are carried out on the self-developed robot platform to verify the effectiveness of the algorithm. The results show that this method can effectively improve location accuracy in outdoor environments, especially in environments of feature degradation and large scale.

This study offers a comprehensive bibliometric analysis of artificial intelligence (AI) applications in the field of second language (L2) teaching and applied linguistics, spanning from the early developments in 1995 to 2022. It aims to uncover current trends, prominent themes, and influential authors, documents, and sources. A total of 185 relevant articles published in Social Sciences Citation Index (SSCI) indexed journals were analyzed using the VOSviewer bibliometric software tool. Our investigation reveals a highly multidisciplinary and interconnected field, with four main clusters identified: AI, natural language processing (NLP), robot-assisted language learning, and chatbots. Notable themes include the increasing use of intelligent tutoring systems, the importance of syntactic complexity and vocabulary in L2 learning, and the exploration of robots and gamification in language education. The study also highlights the potential of NLP and AI technologies to enhance personalized feedback and instruction for language learners. The findings emphasize the growing interest in AI applications in L2 teaching and applied linguistics, as well as the need for continued research to advance the field and improve language instruction and assessment. By providing a quantitative and rigorous overview of the literature, this study contributes valuable insights into the current state of research in AI-assisted L2 teaching and applied linguistics and identifies key areas for future exploration and development.

Previous research on audiovisual input attests to a significant effect of on-screen text and proficiency on learning gains. However, there is scarce research on whether these factors affect viewers’ feeling of learning, a variable that can affect overall second language (L2) learning outcomes (Ellis, 2008). Moreover, there is a lack of research exploring whether viewing experience prompts viewers to switch from one viewing mode (subtitles, captions, no on-screen text) to another and what factors affect those choices. This study explores learners’ perspectives on learning from audiovisual input and their preferred viewing mode before and after participating in a prolonged viewing intervention. A total of 136 participants of varying L2 English proficiency levels (from A1 to C2) completed pre-viewing and post-viewing questionnaires. The results show that vocabulary and expressions were perceived to be learnt the most. The elementary proficiency group were more likely to be positive about learning from the intervention than higher proficiency students. Concerning the preferred viewing mode outside of the classroom, the participants favoured no on-screen text or first language (L1) subtitles over L2 captions. At the end of the intervention, the elementary-level participants found that viewing without any L1 support was too challenging for leisure viewing, while the intermediate- and advanced-level students gained confidence in watching without any textual support.

Recent studies have shown that watching videos with dual subtitles can promote vocabulary learning. This study investigated the extent to which vocabulary learning may be enhanced through repeated viewings of dual-subtitled videos. A 3x3 counterbalanced experimental design was adopted to examine English as a foreign language (EFL) learners’ immediate vocabulary gains and retention under different learning conditions across three experimental sessions, including (a) immediate repeated viewing, (b) spaced repeated viewing, and (c) no repeated viewing. Participants were 60 Chinese-speaking lower-intermediate university EFL learners. They were divided into three groups and given each of the three treatments in each experimental session. ANOVA results revealed that viewing dual-subtitled videos with repetition allowed learners to achieve greater vocabulary gains than viewing with no repetition, with evidence indicating the superiority of immediate repetitions over spaced repetitions.

This paper is based on research conducted in February–April 2022. It describes and illuminates what was happening with tech-savvy educated people between 20 and 40 years old in Russia, while their usual digital tools and places for the autobiographical process were changing in the spring of 2022. Facing censorship of platforms, surveillance, and the inability to pay for services, people who were keeping important memories of their lives online were deleting their profiles, migrating to other platforms, censoring themselves, and creating archives of autobiographically meaningful materials. The paper examines these disruptions as a case that illuminates the role of online platforms in autobiographical memory and expands some concepts within autobiographical memory studies, such as evocative objects and autotopography.

We show that the conceptual distance between any two theories of first-order logic is the same as the generator distance between their Lindenbaum–Tarski algebras of concepts. As a consequence of this, we show that, for any two arbitrary mathematical structures, the generator distance between their meaning algebras (also known as cylindric set algebras) is the same as the conceptual distance between their first-order logic theories. As applications, we give a complete description for the distances between meaning algebras corresponding to structures having at most three elements and show that this small network represents all the possible conceptual distances between complete theories. As a corollary of this, we will see that there are only two non-trivial structures definable on three-element sets up to conceptual equivalence (i.e., up to elementary plus definitional equivalence).

For a subset $A$ of an abelian group $G$, given its size $|A|$, its doubling $\kappa =|A+A|/|A|$, and a parameter $s$ which is small compared to $|A|$, we study the size of the largest sumset $A+A'$ that can be guaranteed for a subset $A'$ of $A$ of size at most $s$. We show that a subset $A'\subseteq A$ of size at most $s$ can be found so that $|A+A'| = \Omega (\!\min\! (\kappa ^{1/3},s)|A|)$. Thus, a sumset significantly larger than the Cauchy–Davenport bound can be guaranteed by a bounded size subset assuming that the doubling $\kappa$ is large. Building up on the same ideas, we resolve a conjecture of Bollobás, Leader and Tiba that for subsets $A,B$ of $\mathbb{F}_p$ of size at most $\alpha p$ for an appropriate constant $\alpha \gt 0$, one only needs three elements $b_1,b_2,b_3\in B$ to guarantee $|A+\{b_1,b_2,b_3\}|\ge |A|+|B|-1$. Allowing the use of larger subsets $A'$, we show that for sets $A$ of bounded doubling, one only needs a subset $A'$ with $o(|A|)$ elements to guarantee that $A+A'=A+A$. We also address another conjecture and a question raised by Bollobás, Leader and Tiba on high-dimensional analogues and sets whose sumset cannot be saturated by a bounded size subset.

The rise in the number of automated robotic kitchens accelerated the need for advanced food handling system, emphasizing food analysis including ingredient classification pose recognition and assembling strategy. Selecting the optimal piece from a pile of similarly shaped food items is a challenge to automated meal assembling system. To address this, we present a constructive assembling algorithm, introducing a unique approach for food pose detection–Fast Image to Pose Detection (FI2PD), and a closed-loop packing strategy. Powered by a convolutional neural network (CNN) and a pose retrieval model, FI2PD is adept at constructing a 6D pose from only RGB images. The method employs a coarse-to-fine approach, leveraging the CNN to pinpoint object orientation and position, alongside a pose retrieval process for target selection and 6D pose derivation. Our closed-loop packing strategy, aided by the Item Arrangement Verifier, ensures precise arrangement and system robustness. Additionally, we introduce our FdIngred328 dataset of nine food categories ranging from fake foods to real foods, and the automatically generated data based on synthetic techniques. The performance of our method for object recognition and pose detection has been demonstrated to achieve a success rate of 97.9%. Impressively, the integration of a closed-loop strategy into our meal-assembly process resulted in a notable success rate of 90%, outperforming the results of systems lacking the closed-loop mechanism.

Design engineering education is increasingly challenge-based, which requires educators to form cohesive student teams capable of delivering desired outcomes while fostering learning and collaboration. An example is an international network in which students from different global universities collaborate. Student teams work on researching the problem space, re-framing their challenge and producing multiple prototypes. The challenge for the teaching teams is to be able to form multiple cohesive teams out of a pre-selected group of highly motivated students. Because of the exclusive nature of this educational program, it is a suitable case study for exploring student design team formation practices. The aim is to identify the methods, tools, theoretical underpinnings, challenges and limitations of student team formation. We interviewed teachers from seven universities about their practices. The interviewees had several years of experience in team building. The interviews were analyzed to contrast practices across universities as well as to the team formation literature. Our findings show that mixed methods that combine self-assessments and observer-assessment methods are the preferred means of forming teams. Our findings also show that current practices have evolved over time through trial and error, and are only partially grounded in different literatures and not necessarily in team formation literature.