Convergent Perturbation Theory for a q-deformed Anharmonic Oscillator

A $q$--deformed anharmonic oscillator is defined within the framework of $q$--deformed quantum mechanics. It is shown that the Rayleigh--Schr\"odinger perturbation series for the bounded spectrum converges to exact eigenstates and eigenvalues, for $q$ close to 1. The radius of convergence becomes zero in the undeformed limit.Comment: 14 pages, 2 figure using eps

An Investigation into the Consequences of Performing Emotional Labour in Mental Health Care

Performing emotional labour in health care has been widely studied. However, there is a gap in the literature regarding mental health care. Therefore, the aim of this study was to identify (1) the method of emotional labour (i.e. hiding, faking, deep acting) adopted by mental health workers when interacting with patients; (2) the consequences associated with performing emotional labour- burnout, job satisfaction, and stress; and (3) which of these variables mentioned above predict the health and well-being of mental health workers. Findings revealed greater use of hiding emotions, than deep acting or faking emotions with patients. Several consequences, both positive and negative were identified. Among the negative consequences found, performing emotional labour through hiding and faking emotions was associated with burnout, job dissatisfaction, and stress. Conversely, through deep acting, increased personal accomplishment and job satisfaction was confirmed. No association between emotional labour and psychological distress, and physical symptoms were found

Sparse grid approximation of stochastic parabolic PDEs: The Landau--Lifshitz--Gilbert equation

We show convergence rates for a sparse grid approximation of the distribution of solutions of the stochastic Landau-Lifshitz-Gilbert equation. Beyond being a frequently studied equation in engineering and physics, the stochastic Landau-Lifshitz-Gilbert equation poses many interesting challenges that do not appear simultaneously in previous works on uncertainty quantification: The equation is strongly non-linear, time-dependent, and has a non-convex side constraint. Moreover, the parametrization of the stochastic noise features countably many unbounded parameters and low regularity compared to other elliptic and parabolic problems studied in uncertainty quantification. We use a novel technique to establish uniform holomorphic regularity of the parameter-to-solution map based on a Gronwall-type estimate and the implicit function theorem. This method is very general and based on a set of abstract assumptions. Thus, it can be applied beyond the Landau-Lifshitz-Gilbert equation as well. We demonstrate numerically the feasibility of approximating with sparse grid and show a clear advantage of a multi-level sparse grid scheme.Comment: 36 pages, 4 figure

Context Meta-Reinforcement Learning via Neuromodulation

Meta-reinforcement learning (meta-RL) algorithms enable agents to adapt quickly to tasks from few samples in dynamic environments. Such a feat is achieved through dynamic representations in an agent's policy network (obtained via reasoning about task context, model parameter updates, or both). However, obtaining rich dynamic representations for fast adaptation beyond simple benchmark problems is challenging due to the burden placed on the policy network to accommodate different policies. This paper addresses the challenge by introducing neuromodulation as a modular component to augment a standard policy network that regulates neuronal activities in order to produce efficient dynamic representations for task adaptation. The proposed extension to the policy network is evaluated across multiple discrete and continuous control environments of increasing complexity. To prove the generality and benefits of the extension in meta-RL, the neuromodulated network was applied to two state-of-the-art meta-RL algorithms (CAVIA and PEARL). The result demonstrates that meta-RL augmented with neuromodulation produces significantly better result and richer dynamic representations in comparison to the baselines

Municipal Development Policy in Germany: Current Trends, Challenges and Recommendations for Further Promotion

How has municipal development policy in Germany continued to unfold over the last few years, and where do things stand today? What has been achieved, and what are the challenges for municipal development engagement? Moreover, how can German municipalities be further supported in maximising their contribution to globally sustainable development up to 2030 and beyond? These questions are addressed in two complementary investigations: a study by the German Institute of Development and Sustainability (IDOS), and an evaluation by the German Institute for Development Evaluation (DEval). This policy brief presents the key findings and recommendations of both investigations

Search and Product Differentiation at an Internet Shopbot

Price dispersion among commodity goods is typically attributed to consumer search costs. We explore the magnitude of consumer search costs using a data set obtained from a major Internet shopbot. For the median consumer, the benefits to searching lower screens are $2.24 while the cost of an exhaustive search of the offers is a maximum of$2.03. Interestingly, in our setting, consumers who search more intensively are less price sensitive than other consumers, reflecting their increased weight on retailer differentiation in delivery time and reliability. Our results demonstrate that even in this nearly-perfect market, substantial price dispersion can exist in equilibrium from consumers preferences over both price and non-price attribute

Modeling treatment effect modification in multidrug-resistant tuberculosis in an individual patient data meta-analysis

Effect modification occurs while the effect of the treatment is not homogeneous across the different strata of patient characteristics. When the effect of treatment may vary from individual to individual, precision medicine can be improved by identifying patient covariates to estimate the size and direction of the effect at the individual level. However, this task is statistically challenging and typically requires large amounts of data. Investigators may be interested in using the individual patient data (IPD) from multiple studies to estimate these treatment effect models. Our data arise from a systematic review of observational studies contrasting different treatments for multidrug-resistant tuberculosis (MDR-TB), where multiple antimicrobial agents are taken concurrently to cure the infection. We propose a marginal structural model (MSM) for effect modification by different patient characteristics and co-medications in a meta-analysis of observational IPD. We develop, evaluate, and apply a targeted maximum likelihood estimator (TMLE) for the doubly robust estimation of the parameters of the proposed MSM in this context. In particular, we allow for differential availability of treatments across studies, measured confounding within and across studies, and random effects by study

Trajectories of tuberculosis-specific interferon-gamma release assay responses among medical and nursing students in rural India

AbstractBackgroundInterferon gamma release assays (IGRAs) have been shown to be highly dynamic tests when used in serial testing for TB infection. However, there is little information demonstrating a clear association between TB exposure and IGRA responses over time, particularly in high TB incidence settings.ObjectivesTo assess whether QuantiFERON-TB Gold In-Tube (QFT) responses are associated with occupational TB exposures in a cohort of young health care trainees in India.MethodsAll medical and nursing students at Mahatma Gandhi Institute of Medical Sciences were approached. Participants were followed up for 18months; QFT was performed 4 times, once every 6months. Various modeling approaches were used to define IFN-gamma trajectories and correlations with TB exposure.ResultsAmong 270 medical and nursing trainees, high rates of conversions (6.3–20.9%) and reversions (20.0–26.2%) were found depending on the definitions used. Stable converters were more likely to have had TB exposure in hospital pre-study. Recent occupational exposures were not consistently associated with QFT responses over time.ConclusionIFN-gamma responses and rates of change could not be explained by occupational exposure investigated. High conversion and subsequent reversion rates suggest many health care workers (HCWs) would revert in the absence of treatment, either by clearing the infection naturally or due to fluctuations in the underlying immunological response and/or poor assay reproducibility. QFT may not be an ideal diagnostic test for repeated screening of HCWs in a high TB incidence setting

Deep Reinforcement Learning with Modulated Hebbian plus Q Network Architecture

This paper presents a new neural architecture that combines a modulated Hebbian network (MOHN) with DQN, which we call modulated Hebbian plus Q network architecture (MOHQA). The hypothesis is that such a combination allows MOHQA to solve difficult partially observable Markov decision process (POMDP) problems which impair temporal difference (TD)-based RL algorithms such as DQN, as the TD error cannot be easily derived from observations. The key idea is to use a Hebbian network with bio-inspired neural traces in order to bridge temporal delays between actions and rewards when confounding observations and sparse rewards result in inaccurate TD errors. In MOHQA, DQN learns low level features and control, while the MOHN contributes to the high-level decisions by associating rewards with past states and actions. Thus the proposed architecture combines two modules with significantly different learning algorithms, a Hebbian associative network and a classical DQN pipeline, exploiting the advantages of both. Simulations on a set of POMDPs and on the MALMO environment show that the proposed algorithm improved DQN's results and even outperformed control tests with A2C, QRDQN+LSTM and REINFORCE algorithms on some POMDPs with confounding stimuli and sparse rewards