Interacciones entre Procalus (Coleoptera, Chrysomelidae) y Lithraea caustica (Sapindales, Anacardiaceae). Un caso de monofagia en el matorral de Chile central

Experiments and observations have been perfomed in order to show the effective dependence of Procalus (Chrysomelidae) beetles on Lithraea caustica (Anacardiaceae) shrubs in a preandean matorral site in Central Chile. Procalus insects, particularely P. viridis, are only found on L. caustica, even when they are not able to recognize their host plant while flying. This fact could be explained by the obsewation that beetles abandon L. caustica shrubs more slowly than other shrub species. This behavior of adults is coherent with lamae constraints because the latter feed only on L. caustica new leaves in spite of their previous alimentary experience. The above results support the idea that Procalus beetles are monophagous of L. caustica in the study site. Finally, as the phenology of L. caustica in the study site is highly unpredictable both intra and interannually, a possible phenological coupling mechanism is proposed to explain this monophagous relationship.En este trabajo se reafizan experimentos y observaciones con el proposito de mostrar el grado de dependencia de los coleópteros del género Procalus (Chrysomelidae) sobre el arbusto Lithraea caustica (Anacardiaceae) en un sector de matorral preandino en Chile central. Los resultados indican que los insectos del género Procalus, en particular P. viridis, no son capaces de reconocer su planta hospedadora en vuelo, aunque se distribuyen solo sobre L. caustica. Esto podria tener su explicación en el hecho de que los insectos abandonan los arbustos de L. caustica más lentamente de lo que lo hacen con otras especies arbustivas. Esta conducta de los adultos es coherente con las limitaciones biológicas de las lamas, pues éstas, tengan o no experiencia alimentaria previa, sólo se alimentan de hojas nuevas de L. caustica. Se muestra asila monofagia de Procalus por L. caustica en el lugar de estudio. Sin embargo, como la fenologia de L. caustica en el mismo lugar es altamente impredecible de año en año e incluso dentro del atio, se propone un posible mecanismo de acopiamiento fenológico entre los coleópteros del género Procalus y L. caustica

Sequential Transfer in Reinforcement Learning with a Generative Model

We are interested in how to design reinforcement learning agents that provably reduce the sample complexity for learning new tasks by transferring knowledge from previously-solved ones. The availability of solutions to related problems poses a fundamental trade-off: whether to seek policies that are expected to achieve high (yet sub-optimal) performance in the new task immediately or whether to seek information to quickly identify an optimal solution, potentially at the cost of poor initial behavior. In this work, we focus on the second objective when the agent has access to a generative model of state-action pairs. First, given a set of solved tasks containing an approximation of the target one, we design an algorithm that quickly identifies an accurate solution by seeking the state-action pairs that are most informative for this purpose. We derive PAC bounds on its sample complexity which clearly demonstrate the benefits of using this kind of prior knowledge. Then, we show how to learn these approximate tasks sequentially by reducing our transfer setting to a hidden Markov model and employing spectral methods to recover its parameters. Finally, we empirically verify our theoretical findings in simple simulated domains.Comment: ICML 202

Pure Exploration under Mediators' Feedback

Stochastic multi-armed bandits are a sequential-decision-making framework, where, at each interaction step, the learner selects an arm and observes a stochastic reward. Within the context of best-arm identification (BAI) problems, the goal of the agent lies in finding the optimal arm, i.e., the one with highest expected reward, as accurately and efficiently as possible. Nevertheless, the sequential interaction protocol of classical BAI problems, where the agent has complete control over the arm being pulled at each round, does not effectively model several decision-making problems of interest (e.g., off-policy learning, partially controllable environments, and human feedback). For this reason, in this work, we propose a novel strict generalization of the classical BAI problem that we refer to as best-arm identification under mediators' feedback (BAI-MF). More specifically, we consider the scenario in which the learner has access to a set of mediators, each of which selects the arms on the agent's behalf according to a stochastic and possibly unknown policy. The mediator, then, communicates back to the agent the pulled arm together with the observed reward. In this setting, the agent's goal lies in sequentially choosing which mediator to query to identify with high probability the optimal arm while minimizing the identification time, i.e., the sample complexity. To this end, we first derive and analyze a statistical lower bound on the sample complexity specific to our general mediator feedback scenario. Then, we propose a sequential decision-making strategy for discovering the best arm under the assumption that the mediators' policies are known to the learner. As our theory verifies, this algorithm matches the lower bound both almost surely and in expectation. Finally, we extend these results to cases where the mediators' policies are unknown to the learner obtaining comparable results

Truncating Trajectories in Monte Carlo Reinforcement Learning

In Reinforcement Learning (RL), an agent acts in an unknown environment to maximize the expected cumulative discounted sum of an external reward signal, i.e., the expected return. In practice, in many tasks of interest, such as policy optimization, the agent usually spends its interaction budget by collecting episodes of fixed length within a simulator (i.e., Monte Carlo simulation). However, given the discounted nature of the RL objective, this data collection strategy might not be the best option. Indeed, the rewards taken in early simulation steps weigh exponentially more than future rewards. Taking a cue from this intuition, in this paper, we design an a-priori budget allocation strategy that leads to the collection of trajectories of different lengths, i.e., truncated. The proposed approach provably minimizes the width of the confidence intervals around the empirical estimates of the expected return of a policy. After discussing the theoretical properties of our method, we make use of our trajectory truncation mechanism to extend Policy Optimization via Importance Sampling (POIS, Metelli et al., 2018) algorithm. Finally, we conduct a numerical comparison between our algorithm and POIS: the results are consistent with our theory and show that an appropriate truncation of the trajectories can succeed in improving performance

Sistemas de Spins Interagentes: uma abordagem pedagógica

Teaching Physics is inherently challenging. However, with the growing popularity of quantum mechanics, the topic is sometimes associated with non-scientific contexts, which urges the dissemination of basic knowledge about physics of the quantum realm in elementary education. Therefore, this study aims to promote an accessible and uncomplicated introduction to interacting spin systems, using the Heisenberg model to analyze spin chains. To this end, we built a theoretical foundation that supports the diagonalization of matrices and the study of quantum phenomena, including addressing the Kondo Effect. Furthermore, from the creation and use of computer programs, we obtained exact numerical results that allowed us to interpret relevant physical quantities, which were fundamental for the contextualization of interacting systems. All of this is contextualized for basic education students using tangible and colorful fitting pieces to illustrate concepts from the quantum world. This approach fits into the gamification methodology, which makes learning more engaging and accessible to students. Students become protagonists in the construction of knowledge, taking advantage of the classic pastime of assembling parts and working towards inclusion. Finally, we realized that gamification proved to be an effective strategy for teachers to make learning more engaging and accessible, enabling students to explore more complex concepts in the field of quantum mechanics, which are elucidated in the theoretical construction of this work.CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível SuperiorCNPq - Conselho Nacional de Desenvolvimento Científico e TecnológicoFAPEMIG - Fundação de Amparo a Pesquisa do Estado de Minas GeraisTrabalho de Conclusão de Curso (Graduação)O ensino de física é, por si só, desafiador. Entretanto, com a crescente popularidade da mecânica quântica, a temática é por vezes associada a contextos não científicos, o que torna urgente a necessidade de difundir conhecimentos básicos sobre física de sistemas quânticos ainda na educação básica. Em razão disso, este estudo visa promover uma introdução acessível e descomplicada aos sistemas de spins interagentes, utilizando o modelo de Heisenberg para analisar cadeias de spins. Para isso, construímos uma fundamentação teórica que ampara a diagonalização de matrizes e o estudo de fenômenos quânticos, inclusive abordando o Efeito Kondo. Ademais, a partir da criação e uso de programas computacionais, obtivemos resultados numéricos exatos que nos permitiram interpretar quantidades físicas relevantes, que foram fundamentais para a contextualização dos sistemas interagentes. Tudo isso é contextualizado para os alunos da educação básica utilizando peças de encaixe palpáveis e coloridas para ilustrar conceitos do mundo quântico. Essa abordagem se enquadra à metodologia de gamificação, que torna o aprendizado mais envolvente e acessível aos alunos. Os discentes se tornam protagonistas da construção do saber aproveitando o passatempo clássico de montagem de peças e trabalhando a inclusão. Por fim, percebemos que a gamificação se mostrou uma estratégia eficaz para o docente tornar o aprendizado mais envolvente e acessível, habilitando os alunos a explorarem conceitos mais complexos no campo da mecânica quântica, que são elucidados na construção teórica desse trabalho

Beekeeping practice: effects of Apis mellifera virgin queen management on ovary development

International audienceAbstractNewly emerged virgin queens are frequently imprisoned in cages either inside or outside of colonies before delivery to a new hive. Ovary integrity and proper functioning is the primary factor in a queen’s success as the colony mother. In this work, histological studies on ovaries are used to evaluate the effect of virgin queen imprisonment both out and inside the colony. The results show that the ovarian follicles of virgin queens maintained out of the colony advance only until the beginning of differentiation of oocytes and nurse cells, the vitellarium does not differentiate, and cell death is observed. For virgin queens caged inside a colony, oogenesis progresses until nurse cell and oocyte differentiation is completed, and the vitellarium shows initial differentiation. The results suggest that the best method for a head start of a queen’s fertility is to maintain her inside the colony until introduction into a new hive

Optimizing Empty Container Repositioning and Fleet Deployment via Configurable Semi-POMDPs

With the continuous growth of the global economy and markets, resource imbalance has risen to be one of the central issues in real logistic scenarios. In marine transportation, this trade imbalance leads to Empty Container Repositioning (ECR) problems. Once the freight has been delivered from an exporting country to an importing one, the laden will turn into empty containers that need to be repositioned to satisfy new goods requests in exporting countries. In such problems, the performance that any cooperative repositioning policy can achieve strictly depends on the routes that vessels will follow (i.e., fleet deployment). Historically, Operation Research (OR) approaches were proposed to jointly optimize the repositioning policy along with the fleet of vessels. However, the stochasticity of future supply and demand of containers, together with black-box and non-linear constraints that are present within the environment, make these approaches unsuitable for these scenarios. In this paper, we introduce a novel framework, Configurable Semi-POMDPs, to model this type of problems. Furthermore, we provide a two-stage learning algorithm“, Configure & Conquer” (CC), that first configures the environment by finding an approximation of the optimal fleet deployment strategy, and then "conquers" it by learning an ECR policy in this tuned environmental setting. We validate our approach in large and real-world instances of the problem. Our experiments highlight that CC avoids the pitfalls of OR methods and that it is successful at optimizing both the ECR policy and the fleet of vessels, leading to superior performance in world trade environments

Modal parameters identification with environmental tests and advanced numerical analyses for masonry bell towers: a meaningful case study

Abstract In the first part, a dynamic monitoring for non-destructive evaluation of heritage structures is discussed with reference to a case study, namely the Pomposa Abbey belfry, located in the Ferrara Province (Italy). The main dynamic parameters constitute an important reference to define an advanced numerical model, discussed in the second part, based on Non-Smooth Contact Dynamics (NSCD) method. Schematised as a system of rigid blocks undergoing frictional sliding and plastic impacts, the tower has exhibited complex dynamics, because of both geometrical nonlinearity and the non-smooth nature of the contact laws. First, harmonic oscillations have been applied to the basement of the tower and a systematic parametric study has been conducted, aimed at correlating the system vulnerability to the values of amplitude and frequency of the assigned excitation corroborated by the dynamic identification results. In addition, numerical analyses have been done to highlight the effects of the friction coefficient and of the blocks geometries on the dynamics, in particular on the collapse modes. Finally, a study of the tower stability against seismic excitations has been addressed and 3D simulations have been performed with a real earthquake