Patient Satisfaction with Psychological Treatment on a Voluntary Inpatient Psychiatric Unit.

Despite the empirical literature suggesting the benefits of providing patient support and psychotherapy, research examining patient satisfaction with psychological services integrated within inpatient psychiatric treatment settings remains scarce. A sample of 122 adults within a voluntary inpatient psychiatric unit, who were receiving psychological services completed a satisfaction questionnaire. Overall, participants reported high levels of satisfaction with psychological services and perceived them as helpful to their overall care.

On cheap entropy-sparsified regression learning.

Regression learning is one of the long-standing problems in statistics, machine learning, and deep learning (DL). We show that writing this problem as a probabilistic expectation over (unknown) feature probabilities - thus increasing the number of unknown parameters and seemingly making the problem more complex-actually leads to its simplification, and allows incorporating the physical principle of entropy maximization. It helps decompose a very general setting of this learning problem (including discretization, feature selection, and learning multiple piece-wise linear regressions) into an iterative sequence of simple substeps, which are either analytically solvable or cheaply computable through an efficient second-order numerical solver with a sublinear cost scaling.

eSPA+: Scalable Entropy-Optimal Machine Learning Classification for Small Data Problems.

Classification problems in the small data regime (with small data statistic T and relatively large feature space dimension D) impose challenges for the common machine learning (ML) and deep learning (DL) tools. The standard learning methods from these areas tend to show a lack of robustness when applied to data sets with significantly fewer data points than dimensions and quickly reach the overfitting bound, thus leading to poor performance beyond the training set. To tackle this issue, we propose eSPA+, a significant extension of the recently formulated entropy-optimal scalable probabilistic approximation algorithm (eSPA).

The examination of sleep hygiene, quality of life, and schizotypy in young adults.

The co-occurrence of sleep disruption and schizophrenia-spectrum symptomology is common, with current research supporting the use of interventions, such as cognitive behavioral therapy for insomnia (CBTi), which include sleep hygiene education. Sleep hygiene refers to patterns of pre-sleep behaviors that can promote or impair sleep. These behaviors are easily identified and modifiable, potentially holding promise as targets of research and clinical practice.

Systematic calculation of finite-time mixed singular vectors and characterization of error growth for persistent coherent atmospheric disturbances over Eurasia.

Singular vectors (SVs) have long been employed in the initialization of ensemble numerical weather prediction (NWP) in order to capture the structural organization and growth rates of those perturbations or "errors" associated with initial condition errors and instability processes of the large scale flow. Due to their (super) exponential growth rates and spatial scales, initial SVs are typically combined empirically with evolved SVs in order to generate forecast perturbations whose structures and growth rates are tuned for specified lead-times. Here, we present a systematic approach to generating finite time or "mixed" SVs (MSVs) based on a method for the calculation of covariant Lyapunov vectors and appropriate choices of the matrix cocycle.