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As dialogue agents become increasingly human-like in their performance, we must develop effective ways to describe their behaviour in high-level terms without falling into the trap of anthropomorphism. Here we foreground the concept of role play. Casting dialogue-agent behaviour in terms of role play allows us to draw on familiar folk psychological terms, without ascribing human characteristics to language models that they in fact lack. Two important cases of dialogue-agent behaviour are addressed this way, namely, (apparent) deception and (apparent) self-awareness., (© 2023. Springer Nature Limited.)

During social encounters, people tend to reproduce the facial expressions of others, termed "facial mimicry," which is believed to underlie many important social cognitive functions. Clinically, atypical mimicry is closely associated with serious social dysfunction. However, findings regarding the facial mimicry ability of children with autism spectrum disorder (ASD) are inconsistent; it is necessary to test whether deficits in facial mimicry are core defects of autism and explore the potential mechanism underlying this process. Using quantitative analysis, this study investigated voluntary and automatic facial mimicry performance of six basic expressions in children with and without ASD. There was no significant group difference in mimicry accuracy, but children with ASD showed less intensity in voluntary and automatic mimicry than typically developing children; they also presented less voluntary mimicry intensity for happy, sad, and fearful expressions. Performance on voluntary and automatic mimicry was significantly correlated with the level of autistic symptoms (r >-.43) and theory of mind (r >.34). Furthermore, theory of mind mediated the relationship between autistic symptoms and the intensity of facial mimicry. These results suggest that individuals with ASD show atypical facial mimicry (i.e., less intensity for both voluntary and automatic mimicry, mainly for voluntary mimicry of happiness, sadness, and fear), which might offer a potential cognitive marker for quantifying syndrome manifestations in children with ASD. These findings suggest that theory of mind plays a mediating role in facial mimicry, which may provide insight into the theoretical mechanism of social dysfunction in children with autism., (© 2023 International Society for Autism Research and Wiley Periodicals LLC.)

This paper defines the nature of collective irrationality that flourished during the COVID-19 pandemic and lays out specific individual and shared traits and dispositions that facilitate it. Drawing on the example of globally experienced phenomenon of panicked toilet paper buying and hoarding during the COVID-19 pandemic and resources from philosophy, psychology, sociology, and economics this paper identifies four essential features of collective irrationality: weak shared mentality; non-cognitive and immediate mimicry; affective contagion; and psychosocial adaptivity. After (I) initially pointing out conceptual problems around benchmarking collectivity and irrationality, this paper (II) identifies weak mentality as serving the goals of “group” recognition internally and externally. It is argued that (III) the non-cognitive and immediate mimicry and emotional contagion are shared and individual dispositional conditions that facilitate collective irrationality in environments affected by uncertainty (IV). The human mimetic faculty and susceptibility to emotional contagion are presented as enabling and augmenting conditions under which collective irrationality flourishes. Finally, (IV) presenting collective irrationality in the context of psychosocial adaptivity, the paper provides evolutionary reasons for engaging in irrational behaviors, rendering collective irrationality as an adaptive strategy. [ABSTRACT FROM AUTHOR]

The enduring effects of COVID-19 have called into question many of the assumptions upon which media and cultural studies rest, including a fundamental mode of perception: the sense of smell. In dialog with the field of sensory studies, this paper traces digital smell loss (anosmia) communities from pre-pandemic Facebook groups to mid-pandemic TikTok challenges. This article considers digital smell loss communities on TikTok as imitation publics characterized by repetition. Via replicable TikTok challenges, digital smell-loss communities reckoned with the unmooring effects of a seemingly mild symptom. By exploring how formulaic smell-loss challenges generated support and facilitated community-building, this article demands greater attention to a sense often considered 'disposable'. [ABSTRACT FROM AUTHOR]

This paper examines the use of performative verbs meaning “I beg, I ask” and pragmatic markers meaning “please” in the 6th-century Liber vitae patrum by Gregory of Tours. It discusses the frequency of each verb and the possible factors that may have influenced its selection, such as imitation of previous authors, the social status of the communication agents, the requested things, and any difficulty in fulfilling the request. For example, the fact that the highest frequency was found for quaeso (which was perceived as an archaism already in Cicero’s time) suggests that Gregory followed the model of learned men from the 4th and 5th centuries CE who “resuscitated” this verb after its decline in the 2nd and 3rd centuries CE. The paper also examines other additional devices, such as a formula with digneris, and forms of address. [ABSTRACT FROM AUTHOR]

Expert demonstrations in imitation learning often contain different behavioral modes, e.g., driving modes such as driving on the left, keeping the lane, and driving on the right in the driving tasks. Although most existing multi-modal imitation learning methods allow learning from demonstrations of multiple modes, they have strict constraints on the data of each mode, generally requiring a near data ratio of all modes. Otherwise, it tends to fall into a mode collapse or only learn the data distribution of the mode that has the largest data volume. To address the problem, an algorithm that balances real-fake loss and classification loss by modifying the output of the discriminator, referred to as BAlanced Generative Adversarial Imitation Learning (BAGAIL), is proposed. With this modification, the generator is only rewarded for generating real trajectories with correct modes. BAGAIL is therefore able to deal with imbalanced expert demonstrations and carry out efficient learning for each mode. The learning process of BAGAIL is divided into a pre-training stage and an imitation learning stage. During the pre-training stage, BAGAIL initializes the generator parameters by means of conditional Behavioral Cloning, laying the foundation for the direction of parameter optimization. During the imitation learning stage, BAGAIL optimizes the parameters by using the adversary between the generator and the modified discriminator so that the finally obtained policy can successfully learn the distribution of imbalanced expert data. The experiments showed that BAGAIL accurately distinguished different behavioral modes with imbalanced demonstrations. What is more, the learning result of each mode is close to the expert standard and more stable than other multi-modal imitation learning methods., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The purpose of this paper is to offer a new coding system to capture preschool playful behaviours based on the imitative, exploratory and imaginative aspects underlying playful behaviours (CIEIPO checklist: Imitation, Exploration, Imagination, for Play Observation). A series of focus group sessions (four in total) involving experts in the field were conducted to identify the most typical preschool play behaviours. In addition, a thorough literature review allowed for the operational definition of the items. The number of participants consisted of 280 preschool-aged children, aged 3 to 5 years (Mage = 4.5, SDage = 1.8), 130 of whom were girls. The most frequently observed behaviours were exploratory (150 times) and imaginative (136 times), while imitative behaviours occurred 58 times. The index of agreement between raters was 0.89 (Cohen's Kappa). The results show important implications for promoting play skills in preschool children, with particular relevance to the developmental functions involved (exploratory, symbolic and imitative skills). [ABSTRACT FROM AUTHOR]

Unrealistic optimism bias appears when a person perceives oneself – in comparison to peers – as less at risk from threats. This bias has been widely reported and the consequences are clear: it puts one's health in danger. The existing body of literature proposes egocentrism as a mechanism leading to a reduction in this bias. The present paper tests a novel mechanism orienting a person toward others – thus linked with egocentrism – i.e., mimicry. Results showed directly opposing effects: mimicry induced a stronger tendency to perceive oneself as less threatened. This result is not only surprising but especially alarming since mimicry may be used in patient-doctor dialogue which may backfire, leading to resistance to medical recommendations provided by the doctor. [ABSTRACT FROM AUTHOR]

Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

This paper attempts to develop a constructive dialogue between moral bioenhancement and Reformed theology of sanctification. According to Reformed theology, human beings are first sanctified by God objectively (passive sanctification) and consequently seek subjectively for their own growth in moral life (active sanctification). Moral bioenhancement and sanctification, thus, share a commonality, that is, emphasizing moral betterment. As such, to the extent that passive sanctification is not obliterated but rather prioritized, moral bioenhancement can be viewed as a consequence of a good work performed freely by the sanctified person to seek for moral growth in the imitation of Christ. [ABSTRACT FROM AUTHOR]

Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Adversarial imitation learning (AIL) is a powerful method for automated decision systems due to training a policy efficiently by mimicking expert demonstrations. However, implicit bias is present in the reward function of these algorithms, which leads to sample inefficiency. To solve this issue, an algorithm, referred to as Mutual Information Generative Adversarial Imitation Learning (MI-GAIL), is proposed to correct the biases. In this study, we propose two guidelines for designing an unbiased reward function. Based on these guidelines, we shape the reward function from the discriminator by adding auxiliary information from a potential-based reward function. The primary insight is that the potential-based reward function provides more accurate rewards for actions identified in the two guidelines. We compare our algorithm with SOTA imitation learning algorithms on a family of continuous control tasks. Experiments results show that MI-GAIL is able to address the issue of bias in AIL reward functions and further improve sample efficiency and training stability., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Volumetric representation is a technique used to express 3D objects in various fields, such as medical applications. On the other hand, tomography images for reconstructing volumetric data have limited utilization because they contain personal information. Existing GAN-based medical image generation techniques can produce virtual tomographic images for volume reconstruction while preserving the patient's privacy. Nevertheless, these images often do not consider vertical correlations between the adjacent slices, leading to erroneous results in 3D reconstruction. Furthermore, while volume generation techniques have been introduced, they often focus on surface modeling, making it challenging to represent the internal anatomical features accurately. This paper proposes volumetric imitation GAN (VI-GAN), which imitates a human anatomical model to generate volumetric data. The primary goal of this model is to capture the attributes and 3D structure, including the external shape, internal slices, and the relationship between the vertical slices of the human anatomical model. The proposed network consists of a generator for feature extraction and up-sampling based on a 3D U-Net and ResNet structure and a 3D-convolution-based LFFB (local feature fusion block). In addition, a discriminator utilizes 3D convolution to evaluate the authenticity of the generated volume compared to the ground truth. VI-GAN also devises reconstruction loss, including feature and similarity losses, to converge the generated volumetric data into a human anatomical model. In this experiment, the CT data of 234 people were used to assess the reliability of the results. When using volume evaluation metrics to measure similarity, VI-GAN generated a volume that realistically represented the human anatomical model compared to existing volume generation methods. [ABSTRACT FROM AUTHOR]

With the rapid development of autonomous driving technology, both self-driven and human-driven vehicles will share roads in the future and complex information exchange among vehicles will be required. Therefore, autonomous vehicles need to behave as similar to human drivers as possible, to ensure that their behavior can be effectively understood by the drivers of other vehicles and be more in line with the cognition of humans on driving behavior. Therefore, this paper studies the evaluation function of human drivers, using the method of inverse reinforcement learning, aiming for the learned behavior to better imitate the behavior of human drivers. At the same time, this paper proposes a semi-Markov model, to extract the intentions of surrounding related vehicles and divides them into defensive and cooperative, leading the vehicle to adopt a reasonable response to different types of driving scenarios. [ABSTRACT FROM AUTHOR]

Digitalization, virtualization, commercialization, loss of integrity, polystylistics, liberation from any norms are the latest trends that determine the development of contemporary art. They influence the functioning of communication models in the postmodern era that evolve in accordance with the achievements of technology and acquire mobility, variability and interactivity. Interaction between social processes and scientific and technological achievements is increasing, the essence of communication in the space of postmodern culture is being rethought, particularly, the boundary between the types of art is being levelled. The latter phenomenon leads to the emergence of new instruments and methods of artistic creativity and expands the possibilities of artistic selfexpression. One of the promising areas of artistic exploration is the creation of sound interactive installations. This paper proposes a classification of communication models that are based on the analysis of interactive sound installations. The classification is based on determining the degree to which audiences actions influence the final sound result of the creative action. The paper claims that the parameters of the exhibition space are important components of the installation's structure and form. A dialogue with nature by way of incorporating sounds of nature, their imitations, and interpretations in art objects is found to be a current trend in interactive sound installations. [ABSTRACT FROM AUTHOR]

Airport gates are the main places for aircraft to receive ground services. With the increased number of flights, limited gate resources near to the terminal make the gate assignment work more complex. Traditional solution methods based on mathematical programming models and iterative algorithms are usually used to solve these static situations, lacking learning and real-time decision-making abilities. In this paper, a two-stage hybrid algorithm based on imitation learning and genetic algorithm (IL-GA) is proposed to solve the gate assignment problem. First of all, the problem is defined from a mathematical model to a Markov decision process (MDP), with the goal of maximizing the number of flights assigned to contact gates and the total gate preferences. In the first stage of the algorithm, a deep policy network is created to obtain the gate selection probability of each flight. This policy network is trained by imitating and learning the assignment trajectory data of human experts, and this process is offline. In the second stage of the algorithm, the policy network is used to generate a good initial population for the genetic algorithm to calculate the optimal solution for an online instance. The experimental results show that the genetic algorithm combined with imitation learning can greatly shorten the iterations and improve the population convergence speed. The flight rate allocated to the contact gates is 14.9% higher than the manual allocation result and 4% higher than the traditional genetic algorithm. Learning the expert assignment data also makes the allocation scheme more consistent with the preference of the airport, which is helpful for the practical application of the algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a series of experiments in collective social robotics, spanning more than 10 years, with the long-term aim of building embodied models of (aspects of) cultural evolution. Initial experiments demonstrated the emergence of behavioural traditions in a group of social robots programmed to imitate each other's behaviours (we call these Copybots). These experiments show that the noisy (i.e. less than perfect fidelity) imitation that comes for free with real physical robots gives rise naturally to variation in social learning. More recent experimental work extends the robots' cognitive capabilities with simulation-based internal models, equipping them with a simple artificial theory of mind. With this extended capability we explore, in our current work, social learning not via imitation but robot–robot storytelling, in an effort to model this very human mode of cultural transmission. In this paper, we give an account of the methods and inspiration for these experiments, the experiments and their results, and an outline of possible directions for this programme of research. It is our hope that this paper stimulates not only discussion but suggestions for hypotheses to test with the Storybots. This article is part of a discussion meeting issue 'The emergence of collective knowledge and cumulative culture in animals, humans and machines'. [ABSTRACT FROM AUTHOR]

Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Competing Interests: Declaration of Competing Interest The author declares that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Generative adversarial imitation learning (GAIL) regards imitation learning (IL) as a distribution matching problem between the state-action distributions of the expert policy and the learned policy. In this paper, we focus on the generalization and computational properties of policy classes. We prove that the generalization can be guaranteed in GAIL when the class of policies is well controlled. With the capability of policy generalization, we introduce distributional reinforcement learning (RL) into GAIL and propose the greedy distributional soft gradient (GDSG) algorithm to solve GAIL. The main advantages of GDSG can be summarized as: (1) Q-value overestimation, a crucial factor leading to the instability of GAIL with off-policy training, can be alleviated by distributional RL. (2) By considering the maximum entropy objective, the policy can be improved in terms of performance and sample efficiency through sufficient exploration. Moreover, GDSG attains a sublinear convergence rate to a stationary solution. Comprehensive experimental verification in MuJoCo environments shows that GDSG can mimic expert demonstrations better than previous GAIL variants., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

This paper reports on the history and use of man-made crystals for jewelry in a contemporary context. The cross-disciplinary contextual review traces human fascination with gemstones back centuries, and covers key developments in their "imitation." The infiltration of these man-made crystals in the market sparked the development of the discipline of gemology, through which they are often painted in a negative light. With innovations in the growth of man-made crystals revolutionizing other industries (engineering, technology, etc.), and their distinction from mined crystals increasingly more challenging, the paper reports on the terminology prescribed to the industry to describe man-made and mined crystals. These descriptions, which include "natural" and "real" versus "synthetic," are interrogated in relation to key texts and a need for the identification of an alternative terminology is proposed. The jewelry industry remains apprehensive about the influx of man-made crystals, and only a small number of man-made crystal suppliers are growing crystals for the purpose of creative exploration beyond the imitation of mined crystals for jewelry. Through the review of artifacts, where man-made crystals have been used, their ability to enable creativity is highlighted, and their further future creative possibilities explored. [ABSTRACT FROM AUTHOR]

In this paper, the design of a five degrees of freedom (5DoF) underwater manipulator system is presented and discussed. The forward and inverse kinematics problem are solved and shown. Additionally, a system that mimics the human arm movements using a manipulator is proposed. In order to imitate the movement of the human arm, the layout diagram of the IMU sensors and the method of first calibration were presented. The work also presents a device with a simple haptic feedback used to facilitate control of the gripper. The presented approach to the design of a robot control system is dedicated to an underwater robot with the main assumption of reducing the complexity of the system. Taking into account the authors' knowledge, this approach distinguishes the presented system from solutions known from the literature. [ABSTRACT FROM AUTHOR]

Human action recognition (HAR) is a rapidly growing field with numerous applications in various domains. HAR involves the development of algorithms and techniques to automatically identify and classify human actions from video data. Accurate recognition of human actions has significant implications in fields such as surveillance and sports analysis and in the health care domain. This paper presents a study on the design and development of an imitation detection system using an HAR algorithm based on deep learning. This study explores the use of deep learning models, such as a single-frame convolutional neural network (CNN) and pretrained VGG-16, for the accurate classification of human actions. The proposed models were evaluated using a benchmark dataset, KTH. The performance of these models was compared with that of classical classifiers, including K-Nearest Neighbors, Support Vector Machine, and Random Forest. The results showed that the VGG-16 model achieved higher accuracy than the single-frame CNN, with a 98% accuracy rate. [ABSTRACT FROM AUTHOR]

This paper argues that in Kierkegaard's works, in his upbuilding discourses and late journal entries in particular, meekness or gentleness (Danish: Sagtmodighed) is presented as a distinctive moral and spiritual quality that exhibits a number of characteristics that are usually regarded as attributes of a virtue. Following a "grammatical approach" to what counts as a virtue, rather than a specifically Aristotelian-Thomistic interpretation, it is argued that Kierkegaard presents meekness as an encompassing attitude, a character trait, which can be acquired through imitation of exemplary persons, Christ, in particular, which aims for the good life, is conducive of the good, and is for the benefit of others and the self. It is demonstrated that according to Kierkegaard, meekness differs from other virtues such as courage and patience by its forgiving attitude towards the wrongdoer and nonviolent resistance to injustice and evil. As a virtue that disposes a person to nonviolent resistance, meekness has socio-political implications: injustice is uncovered and criticized for the benefit of "the poor". A meek person does not confirm the world in its evil, but criticizes it, albeit in a way that is appropriate to meekness, i.e., in a forgiving and nonviolent way. [ABSTRACT FROM AUTHOR]

Human–Robot Collaboration (HRC) is an interdisciplinary research area that has gained attention within the smart manufacturing context. To address changes within manufacturing processes, HRC seeks to combine the impressive physical capabilities of robots with the cognitive abilities of humans to design tasks with high efficiency, repeatability, and adaptability. During the implementation of an HRC cell, a key activity is the robot programming that takes into account not only the robot restrictions and the working space, but also human interactions. One of the most promising techniques is the so-called Learning from Demonstration (LfD), this approach is based on a collection of learning algorithms, inspired by how humans imitate behaviors to learn and acquire new skills. In this way, the programming task could be simplified and provided by the shop floor operator. The aim of this work is to present a survey of this programming technique, with emphasis on collaborative scenarios rather than just an isolated task. The literature was classified and analyzed based on: the main algorithms employed for Skill/Task learning, and the human level of participation during the whole LfD process. Our analysis shows that human intervention has been poorly explored, and its implications have not been carefully considered. Among the different methods of data acquisition, the prevalent method is physical guidance. Regarding data modeling, techniques such as Dynamic Movement Primitives and Semantic Learning were the preferred methods for low-level and high-level task solving, respectively. This paper aims to provide guidance and insights for researchers looking for an introduction to LfD programming methods in collaborative robotics context and identify research opportunities. [ABSTRACT FROM AUTHOR]

Social learning often occurs between groups with different levels of experience. Yet little is known about the ideal behavioral rules in such contexts. Existing insights only apply when individuals learn from each other in the same group. In this paper, we close this gap and consider two groups, novices and experienced. Experienced should not learn from novices. For novices learning from experienced, a particular form of probabilistic imitation is selected. Novices should imitate any experienced who is more successful, and sometimes but not always imitate an experienced who is less successful. [ABSTRACT FROM AUTHOR]

This paper proposes a novel perspective for thinking about ideophones, which are imitative words that communicate sensory perceptions and emotions with linguistic sounds and with so‐called "paralinguistic" features, especially gesture and intonation. By considering their performative and depictive qualities with concepts from mindfulness and meditative practices, it is argued that a contemplative, mindful impulse underlies their use by Kichwa‐speaking Runa living in Amazonian Ecuador. Using concepts from traditional meditation treatises, a contemporary guided meditation, and contemporary ethnographic research, the paper argues that, whatever else may motivate their articulations of ideophones, whether for expressive, humorous, or dramatic purposes, Kichwa speakers are mindfully and meditatively attending to the rising and falling of ordinary perceptions and giving a voice to the dynamic nature of their lived realities. By voicing the dynamic nature of sensory experiences, speakers are also able, momentarily, to become what they imitate, thereby expressing the perspectives of nonhuman beings according to their sounds, movements, appearances, and energies. [ABSTRACT FROM AUTHOR]

Empirical findings and theorizations of both imitation and selective trust offer different views on and interpretations of children's social learning mechanisms. The imitation literature provides ample documentation of children's behavioural patterns in the acquisition of socially appropriate norms and practices. The selective trust literature provides insights into children's cognitive processes of choosing credible informants and what information to learn in future interactions. In this paper, we place together findings from both fields and note that they share analogically similar theoretical underpinnings and offer explanations that are complementary to each other. We contend that children's imitative tendency may be due to their selection of in‐group members as cultural experts, who serve as reliable sources of conventional information. Moving forward, we note the importance of evaluating individual differences and cultural factors to provide a more holistic understanding of universality and variation in children's social learning mechanisms. [ABSTRACT FROM AUTHOR]

Imitation is a vital skill that humans leverage in various situations. Humans achieve imitation by observing others with apparent ease. Yet, in reality, it is computationally expensive to model on artificial agents (e.g., social robots) to acquire new skills by imitating an expert agent. Although learning through imitation has been extensively addressed in the robotic literature, most studies focus on answering the following questions: what to imitate and how to imitate. In this conceptual paper, we focus on one of the overlooked questions of imitation through observation: who to imitate. We present possible answers to the who-to-imitate question by exploring motivational factors documented in psychological research and their possible implementation in robotics. To this end, we focus on two critical instances of the who-to-imitate question that guide agents to prioritize one demonstrator over another: outcome expectancies, viewed as the anticipated learning gains, and efficacy expectations, viewed as the anticipated costs of performing actions, respectively. [ABSTRACT FROM AUTHOR]

Hybrid polymer composites are utilized in biomechanical design and orthopedic surgery training to imitate the thermomechanical behavior of human bones. Despite extensive research on the mechanics of hybrid composites in biomechanical design, information on their thermomechanical response during orthopedic drilling operations is scarce. This paper presents a new experimental study to characterize the force and temperature generated during the drilling of hybrid composites that simulate human bones. To simulate the hybrid multi-layer structure of bones, the studied composite comprises a Polyurethane core sandwiched between Glass-Fiber Reinforced Polymer (GFRP) layers—the former resembling the cancellous part of the bone and the latter cortical layers. This study also identifies an empirical relationship between thrust force, temperature, drilling feed and speed, and composite composition. Empirical models are developed using multivariate polynomial regression (MPR) and artificial neural network (ANN) to predict the force and temperature during drilling. The models have a correlation coefficient of above 0.9 between predicted and measured results and can be used to improve orthopedic drilling design and control. [ABSTRACT FROM AUTHOR]

Since the publication of Meltzoff and Moore's seminal paper, neonatal imitation has been discussed, debated, and scrutinised at considerable length. Despite this, the temporal structure within which the interaction sits, has received limited attention. We hypothesise that underlying successful examples of neonatal imitation exists a narrative temporal structure, expressed and perceived not only through vocalisations but also (if not primarily) through movement. We contextualise neonatal imitation through a communicative lens, viewing the phenomenon as an early dialogue between adult and infant, underpinned by the same narrative structure as other "proto-conversations" in infancy. From this perspective, several of the leading and traditional theories that have been proposed to explain neonatal imitation are considered. Ultimately, we argue neonatal imitation is an innately dialogical phenomenon that forms one of the first examples of primary intersubjectivity, exemplifying the importance of the neonatal period in human psychological and social development. On this basis we propose further study is required into the temporal structure underlying neonatal imitation. [ABSTRACT FROM AUTHOR]

This paper takes the China's A-share listed companies in the heavy pollution industry from 2010 to 2021 as the research sample and constructs a corresponding model to explore the existence, heterogeneity, and economic consequences of peer effect of green innovation. The results show that the enterprises' green innovation not only has strategic behaviors of direct imitation, but also receives the environmental information disclosed by peer enterprises to correct their own decision-making; that is, there are two types of peer effects: imitation and reference. At the same time, the enterprises tend to imitate and learn from large-scale and high-quality enterprises with high governance level while making decisions on green innovation. External situational characteristics can moderate the peer effects of green innovation. The strengthening of intellectual property protection and the improvement of market competition will promote mutual learning and reference among enterprises, and the improvement of the institutional environment will inhibit the interaction of enterprises. Further analysis shows that the peer effect of imitation amplifies the promoting effect of green innovation on business performance, while the peer effect of reference does not show obvious spillover characteristics. The above analysis is helpful to reveal the more real decision-making motivation and potential driving mechanism for enterprises to take green innovation actions and provides new ideas and experience for green innovation to serve the development of entity enterprises., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

In this paper, we model dynamics of pediatric vaccination as an imitation game, in which the rate of switching of vaccination strategies is proportional to perceived payoff gain that consists of the difference between perceived risk of infection and perceived risk of vaccine side effects. To account for the fact that vaccine side effects may affect people's perceptions of vaccine safety for some period of time, we use a delay distribution to represent how memory of past side effects influences current perception of risk. We find disease-free, pure vaccinator, and endemic equilibria and obtain conditions for their stability in terms of system parameters and characteristics of a delay distribution. Numerical bifurcation analysis illustrates how stability of the endemic steady state varies with the imitation rate and the mean time delay, and this shows that it is not just the mean duration of memory of past side effects, but also the actual distribution that determines whether disease will be maintained in the population at some steady level, or if sustained periodic oscillations around this steady state will be observed. Numerical simulations illustrate a comparison of the dynamics for different mean delays and different distributions, and they show that even when periodic solutions are observed, there are differences in their amplitude and period for different distributions. We also investigate the effect of constant public health information campaigns on vaccination dynamics. The analysis suggests that the introduction of such campaigns acts as a stabilizing factor for endemic equilibrium, allowing it to remain stable for larger values of mean time delays.

In this paper, we explore aspects of the production and perception of certain consonant clusters (in particular s + C clusters) in second language learners. We administered perception tasks (ABX and non-word transcription) and production tasks (reading, picture-based discussion, and elicited imitation) to native speakers of Persian and Arabic, and compare their results to those in previously published studies of other L1s. We will arrive at two broad conclusions. The first is that many subjects who demonstrate non-targetlike production of consonantal sequences by producing epenthetic vowels between the consonants are not hearing an illusory vowel in perception tasks. Thus, non-nativelike production is not always reflective of non-nativelike perception; non-nativelike production is not always caused by non-nativelike perception. Our second conclusion is that the locus of explanation for the accurate perception in subjects whose L1s lack s + C clusters is the presence or absence in the L1 of right-edge syllabic appendices. L1s which do not license appendices (Japanese, Brazilian Portuguese) will have difficulty perceiving L2 English s + C sequences, while L1s which do license appendices (Persian, Arabic) will not have difficulty perceiving L2 English s + C strings. [ABSTRACT FROM AUTHOR]

This study presents a brief analysis of the forgery of official instruments offence, through the lens of new regulations. The analysis included some resembling provisions, as well as differing provisions between the regulations in force and the previous one. Both the objective, as well as the subjective aspect of the offence have been taken into consideration, as well as some opinions about forms, modalities and legal precedents. Novelty elements concern both the analysis itself in relation to recent doctrine, as well as recent and relevant case law. The paper may be useful both for the academic environment, as well as for practitioners, and it will be published in the volume of a university course. [ABSTRACT FROM AUTHOR]

Mass panics can arise in response to a variety of situations, such as the spread of pathogens, bank failures, or insecurities about economic supplies. Such panics can produce contagious behaviors such as fleeing social contacts, bank withdrawals, and panic buying. In such situations, the processing of external stimuli, mediated by the perceptions and biases of the individual, can reach a threshold point at which panic behaviors are triggered. Due to the human propensity to imitate the behavior of others in uncertain crisis situations, one individual's panic behavior can spread contagiously. This paper looks at the similarities among case studies of mass panic around the world and from these cases synthesizes a conceptual model to aid our understanding. Decision-makers can use this model to strengthen national resilience against panic-generated behaviors and ensure an orderly and successful public response to future biological, financial, or economic crises. [ABSTRACT FROM AUTHOR]

Generating good and human-like locomotion or other legged motions for bipedal robots has always been challenging. One of the emerging solutions to this challenge is to use imitation learning. The sources for imitation are mostly state-only demonstrations, so using state-of-the-art Generative Adversarial Imitation Learning (GAIL) with Imitation from Observation (IfO) ability will be an ideal framework to use in solving this problem. However, it is often difficult to allow new or complicated movements as the common sources for these frameworks are either expensive to set up or hard to produce satisfactory results without computationally expensive preprocessing, due to accuracy problems. Inspired by how people learn advanced knowledge after acquiring a basic understanding of specific subjects, this paper proposes a Motion capture-aided Video Imitation (MoVI) learning framework based on Adversarial Motion Priors (AMP) by combining motion capture data of primary actions like walking with video clips of target motion like running, aiming to create smooth and natural imitation results of the target motion. This framework is able to produce various human-like locomotion by taking the most common and abundant motion capture data with any video clips of motion without the need for expensive datasets or sophisticated preprocessing. [ABSTRACT FROM AUTHOR]

Modeling and predicting player behavior is of the utmost importance in game development and matchmaking. A variety of methods have been proposed to build artificial intelligence (AI), human-like players. However, these human-like players have a limited ability to imitate the behavior of individual players. In this paper, we propose a player behavior imitation method using imitation learning under the framework of meta-learning. A generic behavior model of game players was learned from historical records using adversarial imitation learning. Then, we personalized the policy by imitating the behavior of each individual player. Convolutional neural networks were used to construct the feature extractor of game board states. The experiments were conducted using the Reversi game, and 18,000 game records of different players were used to train the generic behavior model. The behavior of each new player was learned using only hundreds of records. The results demonstrate that our method can be utilized to imitate individual behavior in terms of action similarity well. [ABSTRACT FROM AUTHOR]

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Transfer learning is an effective approach for adapting an autonomous agent to a new target task by transferring knowledge learned from the previously learned source task. The major problem with traditional transfer learning is that it only focuses on optimizing learning performance on the target task. Thus, the performance on the target task may be improved in exchange for the deterioration of the source task's performance, resulting in an agent that is not able to revisit the earlier task. Therefore, transfer learning methods are still far from being comparable with the learning capability of humans, as humans can perform well on both source and new target tasks. In order to address this limitation, a task adaptation method for imitation learning is proposed in this paper. Being inspired by the idea of repetition learning in neuroscience, the proposed adaptation method enables the agent to repeatedly review the learned knowledge of the source task, while learning the new knowledge of the target task. This ensures that the learning performance on the target task is high, while the deterioration of the learning performance on the source task is small. A comprehensive evaluation over several simulated tasks with varying difficulty levels shows that the proposed method can provide high and consistent performance on both source and target tasks, outperforming existing transfer learning methods.

This paper proposes a new hybrid algorithm for grey wolf optimization (GWO) integrated with a robust learning mechanism to solve the large-scale economic load dispatch (ELD) problem. The robust learning grey wolf optimization (RLGWO) algorithm imitates the hunting behavior and social hierarchy of grey wolves in nature and is reinforced by robust tolerance-based adjust searching direction and opposite-based learning. This technique could effectively prevent search agents from being trapped in local optima and also generate potential candidates to obtain a feasible solution. Several constraints of power generators, such as generation limits, local demand, valve-point loading effect, and transmission losses, are considered in practical operation. Five test systems are used to evaluate the effectiveness and robustness of the proposed algorithm in solving the ELD problem. The simulation results clearly reveal the superiority and feasibility of RLGWO to find better solutions in terms of fuel cost and computational efficiency when compared with the previous literature. [ABSTRACT FROM AUTHOR]

This paper develops a North–South trade model with heterogeneous labour and horizontally differentiated products, and compares the implications of two policies: Southern intellectual property rights (IPR) and Northern immigration policy, with the latter aiming to attract Southern talent as a means of pre‐empting imitation. Individuals self‐select into becoming entrepreneurs and innovate (imitate) in the North (South). The likelihood of imitation depends on product quality, imitator's talent and IPR strength. Several interrelated channels of competition are identified. Allowing high‐talent migration when IPR protection in the South is weak shifts imitation to low‐quality products and innovation to high‐quality products. The outcome is in stark contrast to the policy of strengthening Southern IPR, which limits low‐talent imitation in the South and encourages low‐quality innovation in the North. Migration also increases the income of low‐talent entrepreneurs, as well as the average quality of products imitated by high‐talent entrepreneurs in the South. Global income rises with migration, but is not guaranteed to rise with stronger Southern IPR. [ABSTRACT FROM AUTHOR]

The impact of industrial peers' donations on firms' charitable practices has been tested and verified in existing literatures. This paper further studies the motivations and scenarios of non‐listed companies to imitate their listed counterparts in the same industry to formulate charitable policies. Deeply rooted in institutional isomorphism theory, uncertainty and professional networks are employed as philanthropic motives for unlisted companies to mimic their listed peers. Managerial decision mechanisms and network status perceptions enhance imitation by reinforcing decision uncertainty and network effect. Institutional environment heterogeneity can amplify or reduce the imitative stimulus of managers' decision mechanisms and status perceptions. Our empirical tests are based on a unique dataset of 14,873 unlisted firms and their corresponding listed counterparts in China from 2006–2016, and use Tobit regression methods. The results show a significantly positive association between listed peers' giving and non‐listed firms' donation. The peer effects are intensified when non‐listed firms' decentralized decision‐making and higher managers' status identification are present. Furthermore, when the institutional environment changes from weak to strong, the impetus of decentralized decision‐making and status identification will be weakened. These findings contribute to the corporate philanthropy literature and facilitate the development of effective corporate charity policies and government promotion of philanthropic responsibility. [ABSTRACT FROM AUTHOR]

This paper discusses three extraordinary imitations from India the obverses of which are based on Roman issues while the reverses imitate Sasanian coins. These specimens are exceptional and puzzling for two reasons: the unique combination of obverse and reverse designs and the absence of genuine issues which could have been used as their reverse prototypes among finds from the territories they were made and used in. Employing the object biography paradigm for investigating the imitations provides a dynamic perspective on objects actively involved in social relationships. The role of those imitations in understanding the use of Sasanian coins in the Indian Ocean trade will be discussed as well. [ABSTRACT FROM AUTHOR]

This paper explores metalinguistic representations of Moroccan Arabic, through speakers' discourses – taken from various spontaneous recordings and interviews with Moroccan Arabic speakers, of various ages, social backgrounds, and, for the most part, living in Meknes – and humoristic images and memes mocking local accents to be found on social media. By comparing language practices and metalinguistic representations with internet memes related to language stereotypes, our objective is to highlight indexicality, iconization and rhematization, in the circulation of metalinguistic representations, especially their reproduction through copy and imitation, which is the main attribute of memes that interests us. Markers, values, meanings associated with variation constantly change and shift through speakers' practices and discourses, interacting with language ideologies, representations and stereotypes. With that in mind, we examine metalinguistic discourses and sociolinguistic variables in parallel to each other in order to study old and new representations along with social categories such as young/old, virile/effeminate, as well as regional stereotypes. [ABSTRACT FROM AUTHOR]