Multi-role pedagogical agents can create engaging and immersive learning experiences, helping learners better understand knowledge in history learning. However, existing pedagogical agents often struggle with multi-role interactions due to complex controls, limited feedback forms, and difficulty dynamically adapting to user inputs. In this study, we developed a VR prototype with LLM-powered adaptive role-switching and action-switching pedagogical agents to help users learn about the history of the Pavilion of Prince Teng. A 2 x 2 between-subjects study was conducted with 84 participants to assess how adaptive role-switching and action-switching affect participants' learning outcomes and experiences. The results suggest that adaptive role-switching enhances participants' perception of the pedagogical agent's trustworthiness and expertise but may lead to inconsistent learning experiences. Adaptive action-switching increases participants' perceived social presence, expertise, and humanness. The study did not uncover any effects of role-switching and action-switching on usability, learning motivation, and cognitive load. Based on the findings, we proposed five design implications for incorporating adaptive role-switching and action-switching into future VR history education tools.
Latin America's education systems are fragmented and segregated, with substantial differences by school type. The concept of school efficiency (the ability of school to produce the maximum level of outputs given available resources) is policy relevant due to scarcity of resources in the region. Knowing whether private and public schools are making an efficient use of resources --and which are the leading drivers of efficiency-- is critical, even more so after the learning crisis brought by the COVID-19 pandemic. In this paper, relying on data of 2,034 schools and nine Latin American countries from PISA 2022, I offer new evidence on school efficiency (both on cognitive and non-cognitive dimensions) using Data Envelopment Analysis (DEA) by school type and, then, I estimate efficiency leading determinants through interpretable machine learning methods (IML). This hybrid DEA-IML approach allows to accommodate the issue of big data (jointly assessing several determinants of school efficiency). I find a cognitive efficiency gap of nearly 0.10 favouring private schools and of 0.045 for non-cognitive outcomes, and with a lower heterogeneity in private than public schools. For cognitive efficiency, leading determinants for the chance of a private school of being highly efficient are higher stock of books and PCs at home, lack of engagement in paid work and school's high autonomy; whereas low-efficient public schools are shaped by poor school climate, large rates of repetition, truancy and intensity of paid work, few books at home and increasing barriers for homework during the pandemic.
Time-delay embedding is a technique that uses snapshots of state history over time to build a linear state space model of a nonlinear smooth system. We demonstrate that periodic non-smooth or hybrid system can also be modeled as a linear state space system using this approach as long as its behavior is consistent in modes and timings. We extend time-delay embeddings to generate a linear model of two periodic hybrid systems: the bouncing pendulum and the simplest walker with control inputs. This leads to a state history augmented linear quadratic regulator (LQR) which uses current and past state history for feedback control. Example code can be found at https://github.com/Chun-MingYang/koopman-timeDelay-lqr.git
As artificial intelligence (AI) enters the agentic era, large language models (LLMs) are increasingly deployed as autonomous agents that interact with one another rather than operate in isolation. This shift raises a fundamental question: how do machine agents behave in interdependent environments where outcomes depend not only on their own choices but also on the coordinated expectations of peers? To address this question, we study LLM agents in a canonical network-effect game, where economic theory predicts convergence to a fulfilled expectation equilibrium (FEE). We design an experimental framework in which 50 heterogeneous GPT-5-based agents repeatedly interact under systematically varied network-effect strengths, price trajectories, and decision-history lengths. The results reveal that LLM agents systematically diverge from FEE: they underestimate participation at low prices, overestimate at high prices, and sustain persistent dispersion. Crucially, the way history is structured emerges as a design lever. Simple monotonic histories-where past outcomes follow a steady upward or downward trend-help stabilize coordination, whereas nonmonotonic histories amplify divergence and path dependence. Regression analyses at the individual level further show that price is the dominant driver of deviation, history moderates this effect, and network effects amplify contextual distortions. Together, these findings advance machine behavior research by providing the first systematic evidence on multi-agent AI systems under network effects and offer guidance for configuring such systems in practice.
Yadir Garnica, América Morales, Carlos A. Vaquera-Araujo
We propose a $U(1)_{B-L}$ gauge extension to the SM, in which the dark sector is stabilized through a matter parity symmetry preserved after spontaneous symmetry breaking. The fermion spectrum includes three neutral right-handed fields with $B-L$ charges $(-4,-4, 5)$, that make the model free of gauge anomalies. Two of these neutral fermion fields serve as mediators in a scotogenic mechanism for light-active Majorana neutrino masses. The corresponding neutrino mass matrix has rank 2, predicting a massless state and a lower bound for neutrinoless double beta decay. Regions in the parameter space consistent with dark matter relic abundance are accomplished by the lightest neutral mediator.
HNCO and SiO are well known shock tracers and have been observed in nearby galaxies, including the nearby (D=3.5 Mpc) starburst galaxy NGC 253. The simultaneous detection of these two species in regions where the star formation rate is high may be used to study the shock history of the gas. We perform a multi-line molecular study using these two shock tracers (SiO and HNCO) with the aim of characterizing the gas properties. We also explore the possibility of reconstructing the shock history in NGC 253's Central Molecular Zone (CMZ). Six SiO transitions and eleven HNCO transitions were imaged at high resolution $1''.6$ (28 pc) with the Atacama Large Millimeter/submillimeter Array (ALMA) as part of the ALCHEMI Large Programme. Both non-LTE radiative transfer analysis and chemical modelling were performed in order to characterize the gas properties, and to investigate the chemical origin of the emission. The non-LTE radiative transfer analysis coupled with Bayesian inference shows clear evidence that the gas traced by SiO has different densities and temperatures than that traced by HNCO, with an indication that shocks are needed to produce both species. Chemical modelling further confirms such a scenario and suggests that fast and slow shocks are responsible for SiO and HNCO production, respectively, in most GMCs. We are also able to infer the physical characteristics of the shocks traced by SiO and HNCO for each GMC. Radiative transfer and chemical analysis of the SiO and HNCO in the CMZ of NGC 253 reveal a complex picture whereby most of the GMCs are subjected to shocks. We speculate on the possible shock scenarios responsible for the observed emission and provide potential history and timescales for each shock scenario. Higher spatial resolution observations of these two species are required in order to quantitatively differentiate between scenarios.
The use of the Monte Carlo technique in a reliable and inexpensive way without the need for a standard radioactive source in determining the detector efficiency is becoming widespread every passing day. It is important to model the detector with the real dimensions for an accurate and precise results for the method. Another parameter as important as detector modeling is the number of histories in the simulation code examined in this study. In this study, the effect of the number of histories on the efficiency was examined in detail using different simulation codes. The results obtained in this work, at least 107 particle numbers should be used in all three programs where the uncertainty is below 1%. If the existing facilities are sufficient, it can be increased to 108s in case of having a more equipped and fast computer. However, going higher than this value does not make any sense as seen from the study.
Pre-training has been adopted in a few of recent works for Vision-and-Language Navigation (VLN). However, previous pre-training methods for VLN either lack the ability to predict future actions or ignore the trajectory contexts, which are essential for a greedy navigation process. In this work, to promote the learning of spatio-temporal visual-textual correspondence as well as the agent's capability of decision making, we propose a novel history-and-order aware pre-training paradigm (HOP) with VLN-specific objectives that exploit the past observations and support future action prediction. Specifically, in addition to the commonly used Masked Language Modeling (MLM) and Trajectory-Instruction Matching (TIM), we design two proxy tasks to model temporal order information: Trajectory Order Modeling (TOM) and Group Order Modeling (GOM). Moreover, our navigation action prediction is also enhanced by introducing the task of Action Prediction with History (APH), which takes into account the history visual perceptions. Extensive experimental results on four downstream VLN tasks (R2R, REVERIE, NDH, RxR) demonstrate the effectiveness of our proposed method compared against several state-of-the-art agents.
Robot navigation in dynamic environments shared with humans is an important but challenging task, which suffers from performance deterioration as the crowd grows. In this paper, multi-subgoal robot navigation approach based on deep reinforcement learning is proposed, which can reason about more comprehensive relationships among all agents (robot and humans). Specifically, the next position point is planned for the robot by introducing history information and interactions in our work. Firstly, based on subgraph network, the history information of all agents is aggregated before encoding interactions through a graph neural network, so as to improve the ability of the robot to anticipate the future scenarios implicitly. Further consideration, in order to reduce the probability of unreliable next position points, the selection module is designed after policy network in the reinforcement learning framework. In addition, the next position point generated from the selection module satisfied the task requirements better than that obtained directly from the policy network. The experiments demonstrate that our approach outperforms state-of-the-art approaches in terms of both success rate and collision rate, especially in crowded human environments.
Kathryn Garside, Aida Gjoka, Robin Henderson
et al.
Persistent homology is used to track the appearance and disappearance of features as we move through a nested sequence of topological spaces. Equating the nested sequence to a filtration and the appearance and disappearance of features to events, we show that simple event history methods can be used for the analysis of topological data. We propose a version of the well known Nelson-Aalen cumulative hazard estimator for the comparison of topological features of random fields and for testing parametric assumptions. We suggest a Cox proportional hazards approach for the analysis of embedded metric trees. The Nelson-Aalen method is illustrated on globally distributed climate data and on neutral hydrogen distribution in the Milky Way. The Cox method is use to compare vascular patterns in fundus images of the eyes of healthy and diabetic retinopathy patients.
In 1614 Johann Georg Locher, a student of the Jesuit astronomer Christoph Scheiner, proposed a physical mechanism to explain how the Earth could orbit the sun. An orbit, Locher said, is a perpetual fall. He proposed this despite the fact that he rejected the Copernican system, citing problems with falling bodies and the sizes of stars under that system. In 1651 and again in 1680, Jesuit writers Giovanni Battista Riccioli and Athanasius Kircher, respectively, considered and rejected outright Locher's idea of an orbit as a perpetual fall. Thus this important concept of an orbit was proposed, considered, and rejected well before Isaac Newton would use an entirely different physics to make the idea that an orbit is a perpetual fall the common way of envisioning and explaining orbits.
Source localization in ocean acoustics is posed as a machine learning problem in which data-driven methods learn source ranges directly from observed acoustic data. The pressure received by a vertical linear array is preprocessed by constructing a normalized sample covariance matrix (SCM) and used as the input. Three machine learning methods (feed-forward neural networks (FNN), support vector machines (SVM) and random forests (RF)) are investigated in this paper, with focus on the FNN. The range estimation problem is solved both as a classification problem and as a regression problem by these three machine learning algorithms. The results of range estimation for the Noise09 experiment are compared for FNN, SVM, RF and conventional matched-field processing and demonstrate the potential of machine learning for underwater source localization..
A continuous transition from early Friedmann-like radiation era through to late time cosmic acceleration passing through a long Friedmann-like matter dominated era followed by a second phase of radiation era has been realized in modified theory of gravity containing a combination of curvature squared term, a linear term, a three-half term and an ideal fluid. Thus the history of cosmic evolution is explained by modified theory of gravity singlehandedly. The second phase of radiation-like era might provide an explanation to the hydrogen and helium reionization at low redshift.
The application of conventional phylogenetic techniques for inferring cultural history is problematic due to differences in the nature of information transmission in biological and cultural realms. In culture, units of transmission are not just measurable attributes, but communicable concepts. Therefore, relatedness amongst cultural elements often resides at the conceptual level not captured by traditional phylogenetic methods. This paper takes a cognitively inspired approach to analyzing material cultural history. We show that combining data for physical attributes of cultural artifacts with conceptual information can uncover cultural influences among different ethnolinguistic groups, and reveal new patterns of cultural ancestry. Using the Baltic psaltery, a musical instrument with a well-documented ethnographic and archaeological record, we recovered a previously unacknowledged pattern of historical relationship that is more congruent with geographical distribution and temporal data than is obtained with other approaches.
One way to understand the role history plays on evolutionary trajectories is by giving ancient life a second opportunity to evolve. Our ability to empirically perform such an experiment, however, is limited by current experimental designs. Combining ancestral sequence reconstruction with synthetic biology allows us to resurrect the past within a modern context and has expanded our understanding of protein functionality within a historical context. Experimental evolution, on the other hand, provides us with the ability to study evolution in action, under controlled conditions in the laboratory. Here we describe a novel experimental setup that integrates two disparate fields - ancestral sequence reconstruction and experimental evolution. This allows us to rewind and replay the evolutionary history of ancient biomolecules in the laboratory. We anticipate that our combination will provide a deeper understanding of the underlying roles that contingency and determinism play in shaping evolutionary processes.
We describe a new methodology to analyze the reionization process in numerical simulations: the chronology and the geometry of reionization is investigated by means of merger histories of individual HII regions. From the merger tree of ionized patches, one can track the individual evolution of the regions properties such as e.g. their size, or the intensity of the percolation process by looking at the formation rate, the frequency of mergers and the number of individual HII regions involved in the mergers. We apply the merger tree technique to simulations of reionization with three different kinds of ionizing source models and two resolutions. Two of them use star particles as ionizing sources. In this case we confront two emissivity evolutions for the sources in order to reach the reionization at z ~ 6. As an alternative we built a semi-analytical model where the dark matter halos extracted from the density fields are assumed as ionizing sources. We then show how this methodology is a good candidate to quantify the impact of the adopted star formation on the history of the observed reionization. The semi-analytical model shows a homogeneous reionization history with 'local' hierarchical growth steps for individual HII regions. On the other hand auto-consistent models for star formation tend to present fewer regions with a dominant region in size which governs the fusion process early in the reionization at the expense of the 'local' reionizations. The differences are attenuated when the resolution of the simulation is increased.
Until is a notoriously difficult temporal operator as it is both existential and universal at the same time: A until B holds at the current time instant w iff either B holds at w or there exists a time instant w' in the future at which B holds and such that A holds in all the time instants between the current one and w'. This "ambivalent" nature poses a significant challenge when attempting to give deduction rules for until. In this paper, in contrast, we make explicit this duality of until to provide well-behaved natural deduction rules for linear-time logics by introducing a new temporal operator that allows us to formalize the "history" of until, i.e., the "internal" universal quantification over the time instants between the current one and w'. This approach provides the basis for formalizing deduction systems for temporal logics endowed with the until operator. For concreteness, we give here a labeled natural deduction system for a linear-time logic endowed with the new operator and show that, via a proper translation, such a system is also sound and complete with respect to the linear temporal logic LTL with until.
Kazuhide Ichikawa, Rennan Barkana, Ilian T. Iliev
et al.
The 21-cm PDF (i.e., distribution of pixel brightness temperatures) is expected to be highly non-Gaussian during reionization and to provide important information on the distribution of density and ionization. We measure the 21-cm PDF as a function of redshift in a large simulation of cosmic reionization and propose a simple empirical fit. Guided by the simulated PDF, we then carry out a maximum likelihood analysis of the ability of upcoming experiments to measure the shape of the 21-cm PDF and derive from it the cosmic reionization history. Under the strongest assumptions, we find that upcoming experiments can measure the reionization history in the mid to late stages of reionization to 1-10% accuracy. Under a more flexible approach that allows for four free parameters at each redshift, a similar accuracy requires the lower noise levels of second-generation 21-cm experiments.
Quantum field theory in curved spacetime is a theory wherein matter is treated fully in accord with the principles of quantum field theory, but gravity is treated classically in accord with general relativity. It is not expected to be an exact theory of nature, but it should provide a good approximate description when the quantum effects of gravity itself do not play a dominant role. A major impetus to the theory was provided by Hawking's calculation of particle creation by black holes, showing that black holes radiate as perfect black bodies. During the past 30 years, considerable progress has been made in giving a mathematically rigorous formulation of quantum field theory in curved spacetime. Major issues of principle with regard to the formulation of the theory arise from the lack of Poincare symmetry and the absence of a preferred vacuum state or preferred notion of ``particles''. By the mid-1980's, it was understood how all of these difficulties could be overcome for free (i.e., non-self-interacting) quantum fields by formulating the theory via the algebraic approach and focusing attention on the local field observables rather than a notion of ``particles''. However, these ideas, by themselves, were not adequate for the formulation of interacting quantum field theory, even at a perturbative level, since standard renormalization prescriptions in Minkowski spacetime rely heavily on Poincare invariance and the existence of a Poincare invariant vacuum state. However, during the past decade, great progress has been made, mainly due to the importation into the theory of the methods of ``microlocal analysis''. This article will describe the historical development of the subject and describe some of the recent progress.