WaveTrain: A Python package for numerical quantum mechanics of chain-like systems based on tensor trains.

WaveTrain is an open-source software for numerical simulations of chain-like quantum systems with nearest-neighbor (NN) interactions only. The Python package is centered around tensor train (TT, or matrix product) format representations of Hamiltonian operators and (stationary or time-evolving) state vectors. It builds on the Python tensor train toolbox Scikit_tt, which provides efficient construction methods and storage schemes for the TT format. Its solvers for eigenvalue problems and linear differential equations are used in WaveTrain for the time-independent and time-dependent Schrödinger equations, respectively. Employing efficient decompositions to construct low-rank representations, the tensor-train ranks of state vectors are often found to depend only marginally on the chain length N. This results in the computational effort growing only slightly more than linearly with N, thus mitigating the curse of dimensionality. As a complement to the classes for full quantum mechanics, WaveTrain also contains classes for fully classical and mixed quantum-classical (Ehrenfest or mean field) dynamics of bipartite systems. The graphical capabilities allow visualization of quantum dynamics "on the fly," with a choice of several different representations based on reduced density matrices. Even though developed for treating quasi-one-dimensional excitonic energy transport in molecular solids or conjugated organic polymers, including coupling to phonons, WaveTrain can be used for any kind of chain-like quantum systems, with or without periodic boundary conditions and with NN interactions only. The present work describes version 1.0 of our WaveTrain software, based on version 1.2 of scikit_tt, both of which are freely available from the GitHub platform where they will also be further developed. Moreover, WaveTrain is mirrored at SourceForge, within the framework of the WavePacket project for numerical quantum dynamics. Worked-out demonstration examples with complete input and output, including animated graphics, are available.

Simulation of a Particle Domain in a Continuum, Fluctuating Hydrodynamics Reservoir.

In molecular simulation and fluid mechanics, the coupling of a particle domain with a continuum representation of its embedding environment is an ongoing challenge. In this Letter, we show a novel approach where the latest version of the adaptive resolution scheme (AdResS), with noninteracting tracers as particles' reservoir, is combined with a fluctuating hydrodynamics (FHD) solver. The resulting algorithm, supported by a solid mathematical model, allows for a physically consistent exchange of matter and energy between the particle domain and its fluctuating continuum reservoir. Numerical tests are performed to show the validity of the algorithm. Differently from previous algorithms of the same kind, the current approach allows for simulations where, in addition to density fluctuations, also thermal fluctuations can be accounted for, thus large complex molecular systems, as, for example, hydrated biological membranes in a thermal field, can now be efficiently treated.

Solving the time-independent Schrödinger equation for chains of coupled excitons and phonons using tensor trains.

We demonstrate how to apply the tensor-train format to solve the time-independent Schrödinger equation for quasi-one-dimensional excitonic chain systems with and without periodic boundary conditions. The coupled excitons and phonons are modeled by Fröhlich-Holstein type Hamiltonians with on-site and nearest-neighbor interactions only. We reduce the memory consumption as well as the computational costs significantly by employing efficient decompositions to construct low-rank tensor-train representations, thus mitigating the curse of dimensionality. In order to compute also higher quantum states, we introduce an approach that directly incorporates the Wielandt deflation technique into the alternating linear scheme for the solution of eigenproblems. Besides systems with coupled excitons and phonons, we also investigate uncoupled problems for which (semi-)analytical results exist. There, we find that in the case of homogeneous systems, the tensor-train ranks of state vectors only marginally depend on the chain length, which results in a linear growth of the storage consumption. However, the central processing unit time increases slightly faster with the chain length than the storage consumption because the alternating linear scheme adopted in our work requires more iterations to achieve convergence for longer chains and a given rank. Finally, we demonstrate that the tensor-train approach to the quantum treatment of coupled excitons and phonons makes it possible to directly tackle the phenomenon of mutual self-trapping. We are able to confirm the main results of the Davydov theory, i.e., the dependence of the wave packet width and the corresponding stabilization energy on the exciton-phonon coupling strength, although only for a certain range of that parameter. In future work, our approach will allow calculations also beyond the validity regime of that theory and/or beyond the restrictions of the Fröhlich-Holstein type Hamiltonians.

WavePacket: A Matlab package for numerical quantum dynamics. III. Quantum-classical simulations and surface hopping trajectories.

WavePacket is an open-source program package for numerical simulations in quantum dynamics. Building on the previous Part I (Schmidt and Lorenz, Comput. Phys. Commun. 2017, 213, 223] and Part II (Schmidt and Hartmann, Comput. Phys. Commun. 2018, 228, 229] which dealt with quantum dynamics of closed and open systems, respectively, the present Part III adds fully classical and mixed quantum-classical propagation techniques to WavePacket. There classical phase-space densities are sampled by trajectories which follow (diabatic or adiabatic) potential energy surfaces. In the vicinity of (genuine or avoided) intersections of those surfaces, trajectories may switch between them. To model these transitions, two classes of stochastic algorithms have been implemented: (1) Tully's fewest switches surface hopping and (2) Landau-Zener-based single switch surface hopping. The latter one offers the advantage of being based on adiabatic energy gaps only, thus not requiring nonadiabatic coupling information any more. The present work describes the MATLAB version of WavePacket 6.1.0, which is essentially an object-oriented rewrite of previous versions, allowing to perform fully classical, quantum-classical and quantum-mechanical simulations on an equal footing, that is, for the same physical system described by the same WavePacket input. The software package is hosted and further developed at the Sourceforge platform, where also extensive Wiki-documentation as well as numerous worked-out demonstration examples with animated graphics are available. © 2019 Wiley Periodicals, Inc.

Competitiveness facets and sensation seeking as predictors of problem gambling among a sample of university student gamblers.

Understanding the factors that contribute to problem gambling (PG) is imperative. Individual differences in sensation seeking (SS), as measured by the Sensation Seeking Scale Form (SSS-V), have been found to be predictive of PG among university student samples. However, what is less clear, is if the four SSS-V subscales capture unique facets of SS that are particularly predictive of PG. Much less studied than SS, competitiveness has also been found to be predictive of PG. The Competitiveness Orientation Measure (COM) is a newly developed measure of competitiveness, comprising of four facets. The main purpose of the current study was to examine if these four facets of competitiveness predicted variance in PG over and above the variance predicted by the four SSS-V subscales. Participants included 158 university student gamblers. Sequential regression analysis showed that after accounting for gender, age, and the four SSS-V subscales the only facet of the COM found to be a significant predictor of PG severity was Dominant Competitiveness. Dominant Competitiveness predicted an additional 11% of PG severity. These results provide support for the Dominant Competitiveness subscale of the COM as having utility in predicting PG over and above the predictive utility of the SSS-V subscales. Practical implications for the current findings are discussed.

Mindfulness-based cognitive therapy reduces symptoms of depression in people with a traumatic brain injury: results from a randomized controlled trial.

OBJECTIVE: We sought to determine if we could reduce symptoms of depression in individuals with a traumatic brain injury using mindfulness-based cognitive therapy. SETTING: The study was conducted in a community setting. PARTICIPANTS: We enrolled adults with symptoms of depression after a traumatic brain injury. DESIGN: We conducted a randomized controlled trial; participants were randomized to the 10-week mindfulness-based cognitive therapy intervention arm or to the wait-list control arm. MAIN MEASURES: The primary outcome measure was symptoms of depression using the Beck Depression Inventory-II. RESULTS: The parallel group analysis revealed a greater reduction in Beck Depression Inventory-II scores for the intervention group (6.63, n = 38,) than the control group (2.13, n = 38, P = .029). A medium effect size was observed (Cohen d = 0.56). The improvement in Beck Depression Inventory-II scores was maintained at the 3-month follow-up. CONCLUSION: These results are consistent with those of other researchers that use mindfulness-based cognitive therapy to reduce symptoms of depression and suggest that further work to replicate these findings and improve upon the efficacy of the intervention is warranted.

Adolescent exposure to cannabis marketing following recreational cannabis legalization in Canada: A pilot study using ecological momentary assessment.

OBJECTIVE: The goal of this pilot study was to assess the feasibility of a 9-day, smartphone-based ecological momentary assessment (EMA) protocol for tracking the frequency of Canadian adolescents' exposures to cannabis marketing, their reactions to such exposures, and the context in which exposures occur in the real-world and in real-time. METHOD: Participants were n = 18 adolescents between the ages of 14 and 18 years of age. They used an EMA application to capture and describe cannabis marketing exposures through photographs and brief questionnaires assessing marketing channel and context. Participants also rated their reactions to each exposure in real-time. RESULTS: Results showed that participants were generally compliant with the protocol. Participants recorded 40 total exposures to cannabis marketing, representing an average of 2.2 (SD 2.3) exposures per participant during the 9-day study. Exposures tended to occur in the afternoon (45.0%) or evening (37.5%), and while participants were at home (70%) and alone (52.5%). Most exposures occurred through promotion by public figures (27.5%) or explicitly marked internet ads (27.5%). CONCLUSION: This is the first study to demonstrate the feasibility and utility of EMA to capture adolescent exposures to cannabis marketing as it occurs in participants' natural environments. Our research offers an early look at the predictable wave of cannabis advertising targeting youth and a promising approach for studying its impacts in a post-legalization context, as well as a strategy for assessing policies, such as advertising restrictions, intending to mitigate the harms of early cannabis use among youth.

Age and engine displacement are associated with unsafe motorcycle rider actions.

BACKGROUND: Our goal was to examine the relationship between age and engine displacement in cubic centimeters (CCs) and crash responsibility. METHODS: Male motorcyclists, aged 16-94, involved in a fatal crash in the United States (1987-2015) who tested negative for both drugs and alcohol were included. Employing a case control design, cases had committed one or more Unsafe Motorcyclist Actions (UMAs), the proxy measure of responsibility; controls had no UMAs recorded. Odds ratios were computed via multinomial regression examining the effect of motorcyclists' age and motorcycle displacement (up to 1500 CCs, in 250 CC increments) on crash responsibility by any UMA and top three individual UMAs committed. RESULTS: A total of 19,166 motorcyclists met our inclusion criteria. Increased displacement was observed in older motorcyclists and in more recent crashes. Fifty-six percent of motorcyclists committed one or more UMAs (n = 10,743). The top three individual UMAs were: Speeding (35%, n = 6,728), Weaving (24%, n = 3,269), and Erratic Operation (6%, n = 1,162). Odds ratios for committing any UMA were the greatest for riders on 750 CC motorcycles, followed closely by 500 and 1000 CC motorcycles. By 1250 CCs the effect of displacement on rider crash responsibility (any UMA) was no longer statistically significant. Typically, younger ages (e.g., 20-30) on motorcycles with 500-1000 CCs were associated with the highest odds of either speeding, weaving, or erratic riding compared to similar aged riders on 250 CC motorcycles. Exceptions were observed, for example riders at 70 years of age on 1500 CCs having higher odds of speeding than younger riders on equivalent CC motorcycles. CONCLUSION: Education and legislative measures should be considered. Educationally, the development of training interventions focusing on control, stability, and breaking differences with more powerful motorcycles (750 to 1250 CCs) is needed. Legislatively, licensing tiers could be employed based on displacement and educational requirements. Education and legislative measures could help to curb the trend seen between high-powered motorcycles and crash responsibility.

A method for computing the fraction of attributable risk related to climate damages.

The recent decision of the U.S. Supreme Court on the regulation of CO2 emissions from new motor vehicles shows the need for a robust methodology to evaluate the fraction of attributable risk from such emissions. The methodology must enable decisionmakers to reach practically relevant conclusions on the basis of expert assessments the decisionmakers see as an expression of research in progress, rather than as knowledge consolidated beyond any reasonable doubt. This article presents such a methodology and demonstrates its use for the Alpine heat wave of 2003. In a Bayesian setting, different expert assessments on temperature trends and volatility can be formalized as probability distributions, with initial weights (priors) attached to them. By Bayesian learning, these weights can be adjusted in the light of data. The fraction of heat wave risk attributable to anthropogenic climate change can then be computed from the posterior distribution. We show that very different priors consistently lead to the result that anthropogenic climate change has contributed more than 90% to the probability of the Alpine summer heat wave in 2003. The present method can be extended to a wide range of applications where conclusions must be drawn from divergent assessments under uncertainty.

Adaptive molecular resolution approach in Hamiltonian form: An asymptotic analysis.

Adaptive molecular resolution approaches in molecular dynamics are becoming relevant tools for the analysis of molecular liquids characterized by the interplay of different physical scales. The essential difference among these methods is in the way the change of molecular resolution is made in a buffer (transition) region. In particular a central question concerns the possibility of the existence of a global Hamiltonian which, by describing the change of resolution, is at the same time physically consistent, mathematically well defined, and numerically accurate. In this paper we present an asymptotic analysis of the adaptive process complemented by numerical results and show that under certain mathematical conditions a Hamiltonian, which is physically consistent and numerically accurate, may exist. Such conditions show that molecular simulations in the current computational implementation require systems of large size, and thus a Hamiltonian approach such as the one proposed, at this stage, would not be practical from the numerical point of view. However, the Hamiltonian proposed provides the basis for a simplification and generalization of the numerical implementation of adaptive resolution algorithms to other molecular dynamics codes.

Renal impairment and medication use among psychogeriatric inpatients.

OBJECTIVES: To determine the prevalence of renal impairment and the dosage appropriateness of several medications for which dosing guidelines in the advent of renal impairment are available. METHOD: The authors assessed the renal function and medication use of 37 psychogeriatric patients (mean age of 75 years) living in a psychiatric hospital. Renal function was determined by serum creatinine (SCr) levels and creatinine clearance (CCr) data using the Cockcroft and Gault equation. RESULTS: Thirteen of 37 patients (35%) were found to have renal impairment according to CCr data but only two patients (6%) were considered impaired according to SCr levels (k=0.19). Of 44 medications for which dosing guidelines were available, only two (5%) were prescribed inappropriately. CONCLUSION: These data suggest that CCr data should be used over SCr levels to determine renal impairment and to support the monitoring of psychogeriatric patients for declines in renal function and required medication adjustments.

Pilot evaluation of a mindfulness-based intervention to improve quality of life among individuals who sustained traumatic brain injuries.

PRIMARY OBJECTIVE: To examine the potential efficacy of a mindfulness-based stress reduction approach to improve quality of life in individuals who have suffered traumatic brain injuries. RESEARCH DESIGN: Pre-post design with drop-outs as controls. METHODS AND PROCEDURES: We recruited individuals with mild to moderate brain injuries, at least 1 year post-injury. We measured their quality of life, psychological status, and function. Results of 10 participants who completed the programme were compared to three drop-outs with complete data. EXPERIMENTAL INTERVENTION: The intervention was delivered in 12-weekly group sessions. The intervention relied on insight meditation, breathing exercises, guided visualization, and group discussion. We aimed to encourage a new way of thinking about disability and life to bring a sense of acceptance, allowing participants to move beyond limiting beliefs. MAIN OUTCOMES AND RESULTS: The treatment group mean quality of life (SF-36) improved by 15.40 (SD = 9.08) compared to - 1.67 (SD = 16.65; p = 0.036) for controls. Improvements on the cognitive-affective domain of the Beck Depression Inventory II (BDI-II) were reported (p = 0.029), while changes in the overall BDI-II (p = 0.059) and the Positive Symptom Distress Inventory of the SCL-90R (p = 0.054) approached statistical significance. CONCLUSIONS: The intervention was simple, and improved quality of life after other treatment avenues for these participants were exhausted.

Mindfulness-based cognitive therapy: benefits in reducing depression following a traumatic brain injury.

CONTEXT: Current therapies for traumatic brain injury (TBI) include pharmacotherapy, psychotherapy, and cognitive rehabilitation. Unfortunately, psychological and emotional issues regularly go untreated in individuals with TBI even after they receive treatment for physical, behavioral, and cognitive issues. Mindfulness-based cognitive therapy (MBCT) may offer new rehabilitation opportunities for individuals with TBI. OBJECTIVE: To demonstrate the efficacy of MBCT in the treatment of clinically diagnosed depression in a TBI population. DESIGN: The research team measured depression, pain frequency and intensity, energy levels, health status, and function preintervention and postintervention. SETTING: The research team conducted the study at the Ottawa Hospital Rehabilitation Centre, Ontario, Canada. PARTICIPANTS: The research team recruited 23 participants from two sources: (1) the brain injury program at the hospital and (2) the local head-injury association. Twenty participants completed the study. INTERVENTION: The intervention was 8 weeks in length, with a 90-minute MBCT session once a week. The research team based the specific content of the study's intervention on a combination of Kabat-Zinn's manualized mindfulness-based stress reduction program and Segal and colleague's manual for MBCT. OUTCOME MEASURES: The research team determined statistical significance using paired t-tests for continuous outcomes and the McNemar chi-square test for dichotomous categorical outcomes. They also calculated effect sizes for all depression measures. RESULTS: Postintervention, the study found that MBCT significantly reduced (P < .050) depression symptoms on all scales compared to baseline. The study demonstrated medium to large effect sizes for each depression measure. Participants indicated reduced pain intensity (P = .033) and increased energy levels (P = .004). No significant changes occurred in anxiety symptoms, pain frequency, and level of functioning postintervention. CONCLUSION: MBCT was efficacious in reducing depression in the TBI population, providing ample rationale for further research with more robust designs. This study marks an important step toward the development and provision of MBCT on a wider scale to support the rehabilitation efforts of people who have depression symptoms following TBI.

The Toronto and Philadelphia mindfulness scales: associations with satisfaction with life and health-related symptoms

The treatment efficacy of mindfulness for improved quality of life and health-related symptoms has reliably been found in the literature. Questionnaires have been developed to assess both state mindfulness (Toronto Mindfulness Scale, TMS) and trait mindfulness (Philadelphia Mindfulness Scale, PHLMS). The objective of this study was to directly compare state and trait mindfulness measures to self-reported satisfaction with life and health outcomes. Healthy adults (n= 28) completed self-report questionnaires assessing mindfulness, a Satisfaction with Life Scale and a health outcome measure (Symptom Checklist 90-revised) prior to and after undergoing a 10-week mindfulness meditation intervention program. Correlational analyses between the mindfulness measures and outcome measures clearly demonstrated the association between the PHLMS Acceptance subscale and reductions in symptom severity r(26)= -.46, p= .015. These results suggest that a trait mindfulness measure (i.e., PHLMS) can detect change in mindfulness that is associated with health outcome measures whereas the state-like mindfulness (i.e., TMS) did not (AU)

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Predicting attention and avoidance: when do avoiders attend?

Three avoidance measures, the Miller Behavioural Style Scale (MBSS), Index of Self-Regulation of Emotion (ISE) and Mainz Coping Inventory (MCI), were compared in their ability to predict attention and avoidance of threats in the emotional Stroop task. It was also examined if the avoidance mechanism of individuals who would normally avoid threat-indicating words becomes disrupted under conditions of dopamine reduction. Results show that only the ISE predicted attention/avoidance of threat-indicating words. In addition, the avoidance mechanism, as measured by the ISE and MCI, was not activated when regular smokers abstained from smoking.