The phenomenon of stochastic resonance was introduced by Benzi et al.1 to explain the periodicity of the earth’s ice ages. The primary signature of the phenomenon is that the addition of random noise can improve the SNR of a periodically modulated system, relative to that observed with no externally injected noise. We report the first observation of stochastic resonance in an optical device, the bistable ring laser. The experiment exploits a new technique to modulate periodically the asymmetry between the two counterrotating lasing modes. Large enhancements of the SNR of up to 11 dB have been obtained. A theoretical model of a particle in a double well potential is used to obtain a theoretical fit to the experimental data.
Introduction. In the context of escalating international tensions, strengthening traditional values as a factor in ensuring the country's national security is becoming increasingly important. Given the growing need for Russian science and practice to develop effective solutions to the problem of introducing students to traditional Russian values, international experience is of interest, particularly that of Russia's closest neighbors, with whom Russia shares much in common in culture, value orientations, and folk traditions. These countries include the Republic of Belarus, whose experience in developing scientific problems of introducing students to national values is of particular interest to Russian pedagogical science and practice today.Materials and methods. The study utilized methods of theoretical analysis, synthesis, search, selection, systematization of sources, comparison of research results, and generalization of the obtained data. Pedagogical periodicals from the Republic of Belarus were reviewed over five years (2021-2025): scientific articles were selected and analyzed that examine various aspects of pedagogical axiology, approaches to developing students' value orientations, and preparing teachers to address this pressing issue. Results. A review of research trends in pedagogical axiology in the Republic of Belarus was conducted using materials from periodicals published over the past five years. Development trends in this research area, relevant aspects, and theoretical and practical experience in enhancing the value potential of education and developing teachers' readiness to transmit traditional national values to students were identified.Discussion and conclusions. The analysis demonstrated a strengthening of the axiological approach to the development of all levels of education and to the training of teaching staff; an expansion of the scope of pedagogical axiology in terms of updating and in-depth development of approaches to developing specific groups of values in students (patriotic, spiritual and moral, environmental, aesthetic, family, etc.); and a development of understanding of the essence and independent significance of axiological competence, teachers' professional values, and the conditions for their development.
Large language models (LLMs) based on the Transformer have demonstrated strong performance across diverse tasks. However, current models still exhibit substantial limitations in out-of-distribution (OOD) generalization compared with humans. We investigate this gap through periodicity, one of the basic OOD scenarios. Periodicity captures invariance amid variation. Periodicity generalization represents a model's ability to extract periodic patterns from training data and generalize to OOD scenarios. We introduce a unified interpretation of periodicity from the perspective of abstract algebra and reasoning, including both single and composite periodicity, to explain why Transformers struggle to generalize periodicity. Then we construct Coper about composite periodicity, a controllable generative benchmark with two OOD settings, Hollow and Extrapolation. Experiments reveal that periodicity generalization in Transformers is limited, where models can memorize periodic data during training, but cannot generalize to unseen composite periodicity. We release the source code to support future research.
Introduction. Modern socially responsible systems (SRS) operate in conditions of low uncertainty and rapid changes in the external environment, which predetermines the need for prompt and high-quality management decision-making. These systems are focused on the opinions of a wide range of international parties, stakeholders – employees, consumers, partners and society. It seems appropriate to conduct a multi-criteria and comprehensive study of the application of artificial intelligence (AI) technologies with the projection of accelerating management processes, increasing their validity and efficiency.Goal. Algorithmization of approaches to managing socially responsible systems and installation of methods for their interpretation using AI technologies.Materials and methods. The work used materials from periodicals, analyzed scientific publications on the problems of managing socially responsible systems using artificial intelligence, Internet resources, analytical reviews. During the study, methods of graphical and tabular presentation of results, the method of comparative analysis, collection and systematization of data on the topic of the study were used.Results and discussions. During the study, the author analyzed the role of artificial intelligence in overcoming the challenges of socially responsible management systems. The analysis showed the obstacles in the transparency of reporting of modern companies. Quantitative analysis made it possible to accumulate the best global practices of integrating artificial intelligence in the direction of intensifying management decision-making and acquiring significant advantages in managing socially responsible systems.Conclusion. Conclusions are made based on the conducted research and measures are proposed to reduce the level of risks and dangers from the use of AI in management decisions.
Recent theoretical advancement of information density in natural language has brought the following question on desk: To what degree does natural language exhibit periodicity pattern in its encoded information? We address this question by introducing a new method called AutoPeriod of Surprisal (APS). APS adopts a canonical periodicity detection algorithm and is able to identify any significant periods that exist in the surprisal sequence of a single document. By applying the algorithm to a set of corpora, we have obtained the following interesting results: Firstly, a considerable proportion of human language demonstrates a strong pattern of periodicity in information; Secondly, new periods that are outside the distributions of typical structural units in text (e.g., sentence boundaries, elementary discourse units, etc.) are found and further confirmed via harmonic regression modeling. We conclude that the periodicity of information in language is a joint outcome from both structured factors and other driving factors that take effect at longer distances. The advantages of our periodicity detection method and its potentials in LLM-generation detection are further discussed.
Audio-tutorial science lessons were provided to 191 first and second grade children (instructed), and interviews were conducted periodically to assess changes in science concept under standing from grades one through twelve. A similar sample (n = 48) not receiving audio-tutorial lessons in grades one and two (uninstructed) was also interviewed periodically from grades one through twelve. Instructed students showed substantially more valid concept understandings and fewer invalid concepts (misconceptions) than uninstructed students in grades two, seven, ten, and twelve. Concept maps prepared from interview transcripts showed wide variation in knowledge for both groups, and concept maps scored using a scoring algorithm also showed significant differences favoring instructed students. The data show the lasting impact of early instruction in science and the value of concept maps as a representational tool for cognitive developmental changes.
O objetivo deste estudo foi mapear e investigar o desenvolvimento e a estrutura das redes sociais da produção científica do tema Balanced Scorecard publicada nos periódicos científicos nacionais brasileiros indexados na biblioteca eletrônica Scientific Periodicals Electronic Library (SPELL). Metodologicamente, esta pesquisa apropriou-se das técnicas de Análise de Redes Sociais sob as perspectivas da análise de redes sociais one-mode e two-mode. Os principais resultados foram: Sérgio Murilo Petri foi o autor mais profícuo e com o maior degree; a Universidade Federal de Santa Catarina foi a mais produtiva, e, obteve destaque na centralidade de grau; a Revista Eletrônica de Estratégia & Negócios foi o periódico científico mais central; as palavras-chave: planejamento estratégico, estratégia, indicadores de desempenho, avaliação de desempenho e gestão estratégica, foram as que tiveram mais relevo no degree; e os temas mais abordados pelos autores foram: gestão estratégica, indicadores de desempenho, gestão pública, planejamento estratégico e avaliação de desempenho.
Accurate short-term passenger flow prediction of subway stations plays a vital role in enabling subway station personnel to proactively address changes in passenger volume. Despite existing literature in this field, there is a lack of research on effectively integrating features from different periods, particularly intra-period and inter-period features, for subway station passenger flow prediction. In this paper, we propose a novel model called \textbf{M}uti \textbf{P}eriod \textbf{S}patial \textbf{T}emporal \textbf{N}etwork \textbf{MPSTN}) that leverages features from different periods by transforming one-dimensional time series data into two-dimensional matrices based on periods. The folded matrices exhibit structural characteristics similar to images, enabling the utilization of image processing techniques, specifically convolutional neural networks (CNNs), to integrate features from different periods. Therefore, our MPSTN model incorporates a CNN module to extract temporal information from different periods and a graph neural network (GNN) module to integrate spatial information from different stations. We compared our approach with various state-of-the-art methods for spatiotemporal data prediction using a publicly available dataset and achieved minimal prediction errors. The code for our model is publicly available in the following repository: https://github.com/xiannanhuang/MPSTN
This paper is a follow-up to the paper [Matrix periods and competition periods of Boolean Toeplitz matrices, {\it Linear Algebra Appl.} 672:228--250, (2023)]. Given subsets $S$ and $T$ of $\{1,\ldots,n-1\}$, an $n\times n$ Toeplitz matrix $A=T_n\langle S ; T \rangle$ is defined to have $1$ as the $(i,j)$-entry if and only if $j-i \in S$ or $i-j \in T$. In the previous paper, we have shown that the matrix period and the competition period of Toeplitz matrices $A=T_n\langle S; T \rangle$ satisfying the condition ($\star$) $\max S+\min T \le n$ and $\min S+\max T \le n$ are $d^+/d$ and $1$, respectively, where $d^+= \gcd (s+t \mid s \in S, t \in T)$ and $d = \gcd(d, \min S)$. In this paper, we claim that even if ($\star$) is relaxed to the existence of elements $s \in S$ and $t \in T$ satisfying $s+t \le n$ and $\gcd(s,t)=1$, the same result holds. There are infinitely many Toeplitz matrices that do not satisfy ($\star$) but the relaxed condition. For example, for any positive integers $k, n$ with $2k+1 \le n$, it is easy to see that $T_n\langle k, n-k;k+1, n-k-1 \rangle$ does not satisfies ($\star$) but satisfies the relaxed condition. Furthermore, we show that the limit of the matrix sequence $\{A^m(A^T)^m\}_{m=1}^\infty$ is $T_n\langle d^+,2d^+, \ldots, \lfloor n/d^+\rfloor d^+\rangle$.
Period-luminosity relations of long period variables (LPVs) are a powerful tool to map the distances of stars in our galaxy, and are typically calibrated using stars in the Large Magellanic Cloud (LMC). Recent results demonstrated that these relations show a strong dependence on the amplitude of the variability, which can be used to greatly improve distance estimates. However, one of the only highly sampled catalogs of such variables in the LMC is based on OGLE photometry, which does not provide all-sky coverage. Here, we provide the first measurement of the period-luminosity relation of long-period variables in the LMC using photometry from the Asteroid Terrestrial-impact Last Alert System (ATLAS). We derive conversions between ugriz, Gaia, and ATLAS c and o passbands with a precision of approximately 0.02 mag, which enable the measurement of reliable amplitudes with ATLAS for crowded fields. We successfully reproduce the known PL sequences A through E, and show evidence for sequence F using the ratios of amplitudes observed in both ATLAS pass-bands. Our work demonstrates that the ATLAS survey can recover variability in evolved red giants and lays the foundation for an all-sky distance map of the Milky Way using long-period variables.
A dc current can be pumped through a quantum dot by periodically varying two independent parameters ${X}_{1}$ and ${X}_{2},$ like a gate voltage or magnetic field. We present a formula that relates the pumped current to the parametric derivatives of the scattering matrix ${S(X}_{1}{,X}_{2})$ of the system. As an application we compute the statistical distribution of the pumped current in the case of a chaotic quantum dot.
We grow epitaxial graphene monolayers on Ru(0001) that cover uniformly the substrate over lateral distances larger than several microns. The weakly coupled graphene monolayer is periodically rippled and it shows charge inhomogeneities in the charge distribution. Real space measurements by scanning tunneling spectroscopy reveal the existence of electron pockets at the higher parts of the ripples, as predicted by a simple theoretical model. We also visualize the geometric and electronic structure of edges of graphene nanoislands.
We prove the existence of an effective universal upper bound for the order of any integral periodic orbit of any integral algebraic dynamical system in a fixed ambient space. Using this, we demonstrate the decidability of periodicity in arbitrary finitely generated algebraic dynamical systems over fields of characteristic zero.