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A particle that is immersed in a uniform temperature bath performs Brownian diffusion, as discussed by Einstein. But Sinai has realized that in a ‘random environment’ the diffusion is suppressed. Follow-up works have pointed out that in the presence of bias f there are delocalization and sliding transitions, with threshold value f c that depends on the disorder strength. We discuss in a critical way the emergence of Sinai physics for both passive and active Brownian particles. Tight-binding and Fokker–Planck versions of the model are addressed on equal footing. We assume that the transition rates between sites are enhanced either due to a driving mechanism or due to self-propulsion mechanism that are induced by an irradiation source. Consequently, counter intuitively, the dynamics becomes sub-diffusive and the relaxation modes become over-damped. For a finite system, spontaneous delocalization may arise, due to residual bias that is induced by the irradiation.

Many social challenges stem from individuals’ tendency to prefer immediately rewarding but suboptimal behaviors (“Give-Up” options) over more costly endeavors that yield much better outcomes in the long run (“Try” options). For example, many people forgo the long-term benefits of formal education, healthy diets, learning new technologies, and even finding true love. This paper examines various incentivization programs that combine external rewards and punishments to discourage such counterproductive behaviors, which often result in only temporary behavioral change. Our findings suggest that some interventions’ limited impact may be due to their focus on only shifting behaviors from “Give-Up” (e.g., dropping out of college, avoiding the gym) to “Try” (e.g., attending college, exercising regularly), without promoting sufficient exploration of these “Try” options. Yet exploration of the long-term benefits of “Trying” may be crucial to increase the chances of long-term learning and commitment. Using a simplified abstraction of this dilemma, our results show a high tendency to choose “Give-Up” options prior to intervention. Examination of four different incentivization strategies suggests that only rewarding exploration of new “Try” options is a straightforward strategy to increase exploration and optimal choice. Punishing both the selection of “Give-Up” options and the choice to exploit suboptimal “Try” options produced similar results. Other common guidance strategies were less effective, as these strategies simply tended to replace one suboptimal behavior with another. Surprisingly, punishments seemed to be a relatively more successful incentive than rewards. We discuss how these insights can help guide policy aiming to improve long-term outcomes through incentivization.

Quasi-static protocols for systems that feature a mixed phase-space with both chaos and quasi-regular regions are beyond the standard paradigm of adiabatic processes. We focus on many-body system of atoms that are described by the Bose–Hubbard Hamiltonian, specifically a circuit that consists of bosonic sites. We consider a sweep process: slow variation of the rotation frequency of the device (time dependent Sagnac phase). The parametric variation of phase-space topology implies that the quasi-static limit is not compatible with linear response theory. Detailed analysis is essential in order to determine the outcome of such transfer protocol, and its efficiency.

This paper reconceptualizes the role of the teacher in the university foreign language classroom in an age of generative AI chatbots and automatic translation tools. We call for a reconceptualization of this role based on two factors: the unique social interactivity of the university language classroom and the need for effective instruction on how to use Intelligent Computer-Assisted Language Learning (ICALL) tools outside of the classroom. We argue that the teacher must master and integrate these two different modes of teaching and learning. Interpersonal exchanges in class respond to the need for real-time human interaction and relatedness in language learning and so cannot, and should not, be wholly replaced by chatbots. Rather, these sorts of exchanges must form a cornerstone of on-campus foreign language pedagogy. In contrast, teachers must also be able to leverage the benefits of learner-facing AI tools, especially for use outside of the classroom, given the learning gains associated with them. We provide detailed examples of how this dual approach can be realized and propose a five-step approach for incorporating AI into university language pedagogy.

Environmental perturbations and noise are source of mode mixing and interferences between the propagating modes of a complex multi-mode fiber (MMF). Typically, they are characterized by their correlation (paraxial) length, and their spectral content which describes the degree of coupling between various modes. We show that an appropriate control of these quantities allows to engineer Levy-type relaxation processes of an initial mode excitation. Our theory, based on random matrix theory modeling, is tested against realistic simulations with MMFs.

We study the atomtronic quantum interference device employing a semiclassical perspective. We consider an M site ring that is described by the Bose-Hubbard Hamiltonian. Coherent Rabi oscillations in the flow of the current are feasible, with an enhanced frequency due to chaos-assisted tunneling. We highlight the consequences of introducing a weak-link into the circuit. In the latter context we clarify the phase-space considerations that are involved in setting up an effective 'systems plus bath' description in terms of Josephson-Caldeira-Leggett Hamiltonian.

Experience is the best teacher. Yet, in the context of repeated decisions, experience was found to trigger deviations from maximization in the direction of underweighting of rare events. Evaluations of alternative explanations for this bias led to contradicting conclusions. Studies that focused on the aggregate choice rates, including a series of choice prediction competitions, favored the assumption that this bias reflects reliance on small samples. In contrast, studies that focused on individual decisions suggest that the bias reflects a strong myopic tendency by a significant minority of participants. The current analysis clarifies the apparent inconsistency by reanalyzing a data set that previously led to contradicting conclusions. Our analysis suggests that the apparent inconsistency reflects the differing focus of the cognitive models. Specifically, sequential adjustment models (that assume sensitivity to the payoffs’ weighted averages) tend to find support for the hypothesis that the deviations from maximization are a product of strong positive recency (a form of myopia). Conversely, models assuming random sampling of past experiences tend to find support to the hypothesis that the deviations reflect reliance on small samples. We propose that the debate should be resolved by using the assumptions that provide better predictions. Applying this solution to the data set we analyzed shows that the random sampling assumption outperforms the weighted average assumption both when predicting the aggregate choice rates and when predicting the individual decisions.

New technology can be used to enhance safety by imposing costs, or taxes, on certain reckless behaviors. The current paper presents two pre-registered experiments that clarify the impact of taxation of this type on decisions from experience between three alternatives. Experiment 1 focuses on an environment in which safe choices maximize expected returns and examines the impact of taxing the more attractive of two risky options. The results reveal a U-shaped effect of taxation: some taxation improves safety, but too much taxation impairs safety. Experiment 2 shows a clear negative effect of high taxation even when the taxation eliminates the expected benefit from risk-taking. Comparison of alternative models suggests that taxing reckless behaviors backfires when it significantly increases the proportion of experiences in which a more dangerous behavior leads to better outcomes than the taxed behavior. Qualitative hypotheses derived from naïve sampling models assuming small samples were only partially supported by the data.

We highlight a dynamical anomaly in which the rate of relaxation towards thermal equilibrium in a bi-partite quantum system violates the standard linear-response (Kubo) formulation, even when the underlying dynamics is highly chaotic. This anomaly originates from an -dependent sparsity of the underlying quantum network of transitions. Using a minimal bi-partite Bose-Hubbard model as an example, we find that the relaxation rate acquires an anomalous dependence that reflects percolation-like dynamics in energy space.

In Narcissus tazetta, a monocotyledonous bulbous geophyte, floral initiation and differentiation occur within the bulb during the quiescent period in summer, when ambient temperatures are relatively high and the bulb is located underground with no foliage or roots. In many plant species, FLOWERING LOCUS T (FT) and its homologues are considered powerful promoters of flowering. The Narcissus FT gene homologue (NtFT) was isolated, and organ-specific expression patterns of NtFT during the annual cycle and reproductive development under different temperature regimes were analysed using quantitative reverse transcription–PCR (qRT–PCR) and RNA in situ hybridization. During floral induction, NtFT was not expressed in bulb scales, roots, or foliage leaves, but it was detected inside the bulb in the apical meristem and leaf primordia. The expression of another key flowering gene, NLF, the LEAFY homologue in N. tazetta, was also observed only in meristem and leaf primordia within the bulbs; however, its expression did not coincide with that of NtFT during meristem transition to reproductive stage. Under high temperatures (25–30 °C) in the dark, NtFT expression occurred simultaneously with floral induction timing, indicating that floral induction is affected by high temperatures but not by photoperiod or vernalization. Monitoring the apical meristem of Narcissus in February–August of two growing seasons under ambient and controlled storage conditions showed that transition to flowering is temperature dependent and varies between years. Lack of NtFT and NLF expression in foliage leaves suggests that flower initiation control in Narcissus differs from that in common model plants.