We introduce a class of quantum non-Markovian processes—dubbed process trees—that exhibit polynomially decaying temporal correlations and memory distributed across timescales. This class of processes is described by a tensor network with treelike geometry whose component tensors are (1) causality-preserving maps (superprocesses) and (2) locality-preserving temporal change-of-scale transformations. We show that the long-range correlations in this class of processes tends to originate almost entirely from memory effects and can accommodate genuinely quantum power-law correlations in time. Importantly, this class allows efficient computation of multitime correlation functions. To showcase the potential utility of this model-agnostic class for numerical simulation of physical models, we show how it can efficiently approximate the strong memory dynamics of the paradigmatic spin-boson model, in terms of arbitrary multitime features. In contrast to an equivalently costly matrix-product-operator representation, the ansatz produces a fiducial characterization of the relevant physics. Finally, leveraging 2D tensor-network renormalization-group methods, we detail an algorithm for deriving a process tree from an underlying Hamiltonian via the Feynmann-Vernon influence functional. Our work lays the foundation for the development of more efficient numerical techniques in the field of strongly interacting open quantum systems, as well as the theoretical development of a temporal renormalization-group scheme.

Este trabalho discute a implementação e aprimoramento de uma solução de Learning Analytics destinada à identificação de padrões de comportamento de estudantes em Ambientes Virtuais de Aprendizagem (AVA) e à criação de modelos preditivos para evasão e reprovação. A estratégia proposta engloba a instalação de um plugin no servidor Moodle da instituição, responsável pela coleta de informações relativas às interações dos estudantes no AVA. Estes dados são posteriormente processados em uma plataforma na nuvem e apresentados aos usuários através de um dashboard interativo. O principal objetivo desta solução é fornecer insights valiosos a educadores e administradores, possibilitando a detecção precoce de tendências de evasão ou reprovação e a tomada de medidas proativas para aprimorar os desempenhos acadêmicos.

The search for improved teaching methods and a more personalized education has been a constant challenge in the educational field. Learning Analytics (LA), the measurement, collection, analysis, and reporting of data about students and their contexts, has emerged as a promising approach to understand and optimize learning environments. This paper focuses on identifying the expectations of high school teachers in the southern region of Santa Catarina, Brazil, regarding the use of LA. Through a questionnaire distributed to high school teachers, the study investigated their perceptions and opinions about the application of LA in their pedagogical practices. The results indicate that teachers have positive expectations regarding the potential impact of LA on improving teaching and learning. They understand the concept of LA and believe that the feedback provided by the system should be presented in a clear and accessible format. Furthermore, the study reveals that teachers expect support and guidance from educational institutions in accessing and interpreting analytical results. They emphasize the importance of proper training for all stakeholders involved in the implementation of LA. Overall, the study highlights the teachers' positive attitudes towards utilizing LA as a powerful tool to enhance education and promote students' academic and professional development in the southern region of Santa Catarina.

Big Data and Artificial Intelligence (AI) confer substantial advancements across diverse sectors of society, education included. However, it is imperative to extend the discourse surrounding these interventions to align with ethical research principles, data usage, and prevailing legislation. Consequently, this study endeavors to scrutinize scholarly literature spanning the interval between 2011 and 2022, concentrating on the ethical facets recommended for inquiries that meld big data and artificial intelligence within educational contexts. Employing a bibliographic research approach, the inquiry was conducted on the CAPES Periodicals Portal, utilizing descriptors such as “ethics,” “big data,” “artificial intelligence,” and “education.“ Out of a corpus of 84 articles, nine were incorporated into this research subsequent to the application of inclusion and exclusion criteria. These works encompass both empirical and theoretical contributions, interlinking big data and AI with educational settings. Researchers employ documentary sources, questionnaires, and their individual pedagogical experiences for data collection. Methodologically, these studies lean towards techniques such as descriptive content analysis, descriptive statistical analysis, confirmatory factor analysis, and textual data mining for data analysis. With regard to ethical principles spotlighted in the studies, salient themes include responsibility, transparency, reliability, and privacy. The outcomes suggest that crucial lacunae still exist, particularly concerning aspects like informed consent and other pivotal ethical protocols.

The photoluminescence intermittency (blinking) of quantum dots is interesting because it is an easily measured quantum process whose transition statistics cannot be explained by Fermi's golden rule. Commonly, the transition statistics are power-law distributed, implying that quantum dots possess at least trivial memories. By investigating the temporal correlations in the blinking data, we demonstrate with high statistical confidence that there is nontrivial memory between the on and off brightness duration data of blinking quantum dots. We define nontrivial memory to be statistical complexity greater than one. We show that this memory cannot be discovered using the transition distribution. We show by simulation that this memory does not arise from standard data manipulations. Finally, we conclude that at least three physical mechanisms can explain the measured nontrivial memory: (1) storage of state information in the chemical structure of a quantum dot; (2) the existence of more than two intensity levels in a quantum dot; and (3) the overlap in the intensity distributions of the quantum dot states, which arises from fundamental photon statistics.

We apply techniques from the field of computational mechanics to evaluate the statistical complexity of neural recording data from fruit flies. First, we connect statistical complexity to the flies' level of conscious arousal, which is manipulated by general anesthesia (isoflurane). We show that the complexity of even single channel time series data decreases under anesthesia. The observed difference in complexity between the two states of conscious arousal increases as higher orders of temporal correlations are taken into account. We then go on to show that, in addition to reducing complexity, anesthesia also modulates the informational structure between the forward- and reverse-time neural signals. Specifically, using three distinct notions of temporal asymmetry we show that anesthesia reduces temporal asymmetry on information-theoretic and information-geometric grounds. In contrast to prior work, our results show that: (1) Complexity differences can emerge at very short timescales and across broad regions of the fly brain, thus heralding the macroscopic state of anesthesia in a previously unforeseen manner, and (2) that general anesthesia also modulates the temporal asymmetry of neural signals. Together, our results demonstrate that anesthetized brains become both less structured and more reversible.