Virtual Forest Bathing Programming as Experienced by Disabled Adults with Mobility Impairments and/or Low Energy: A Qualitative Study

Background: Although access to nature is demonstrated to benefit health and wellbeing, adults with mobility impairments and/or low energy often face barriers in accessing nature environments and nature-based programs. This study aimed to examine the experiences and impacts of virtual forest bathing by capturing the perspectives of disabled adults with mobility impairments and/or low energy. Methods: A total of 26 adults with mobility impairments provided written and spoken qualitative feedback during and following virtual forest bathing programs and 23 participants provided feedback at a one month follow-up. Virtual programs were presented online, using an accessible format, 2D videos, and images of nature accompanied by guidance led by a certified forest bathing guide and mindfulness teacher. The programs involved disabled facilitators and participants, which created a social environment of peer support. Results: Qualitative thematic analysis revealed 10 themes comprising intervention themes (virtual delivery and soothing facilitation); process themes (nature connection, relaxation, embodiment, and memories with complex emotions); and outcome themes (happiness, agency, metaphor making, and belonging). Conclusions: Virtual forest bathing may offer an effective adjunct to improve wellbeing and provide peer support for disabled adults with mobility impairments and/or low energy

Ensemble learning of linear perceptron; Online learning theory

Within the framework of on-line learning, we study the generalization error of an ensemble learning machine learning from a linear teacher perceptron. The generalization error achieved by an ensemble of linear perceptrons having homogeneous or inhomogeneous initial weight vectors is precisely calculated at the thermodynamic limit of a large number of input elements and shows rich behavior. Our main findings are as follows. For learning with homogeneous initial weight vectors, the generalization error using an infinite number of linear student perceptrons is equal to only half that of a single linear perceptron, and converges with that of the infinite case with O(1/K) for a finite number of K linear perceptrons. For learning with inhomogeneous initial weight vectors, it is advantageous to use an approach of weighted averaging over the output of the linear perceptrons, and we show the conditions under which the optimal weights are constant during the learning process. The optimal weights depend on only correlation of the initial weight vectors.Comment: 14 pages, 3 figures, submitted to Physical Review

Statistical Mechanics of Nonlinear On-line Learning for Ensemble Teachers

We analyze the generalization performance of a student in a model composed of nonlinear perceptrons: a true teacher, ensemble teachers, and the student. We calculate the generalization error of the student analytically or numerically using statistical mechanics in the framework of on-line learning. We treat two well-known learning rules: Hebbian learning and perceptron learning. As a result, it is proven that the nonlinear model shows qualitatively different behaviors from the linear model. Moreover, it is clarified that Hebbian learning and perceptron learning show qualitatively different behaviors from each other. In Hebbian learning, we can analytically obtain the solutions. In this case, the generalization error monotonically decreases. The steady value of the generalization error is independent of the learning rate. The larger the number of teachers is and the more variety the ensemble teachers have, the smaller the generalization error is. In perceptron learning, we have to numerically obtain the solutions. In this case, the dynamical behaviors of the generalization error are non-monotonic. The smaller the learning rate is, the larger the number of teachers is; and the more variety the ensemble teachers have, the smaller the minimum value of the generalization error is.Comment: 13 pages, 9 figure

Optimization of the Asymptotic Property of Mutual Learning Involving an Integration Mechanism of Ensemble Learning

We propose an optimization method of mutual learning which converges into the identical state of optimum ensemble learning within the framework of on-line learning, and have analyzed its asymptotic property through the statistical mechanics method.The proposed model consists of two learning steps: two students independently learn from a teacher, and then the students learn from each other through the mutual learning. In mutual learning, students learn from each other and the generalization error is improved even if the teacher has not taken part in the mutual learning. However, in the case of different initial overlaps(direction cosine) between teacher and students, a student with a larger initial overlap tends to have a larger generalization error than that of before the mutual learning. To overcome this problem, our proposed optimization method of mutual learning optimizes the step sizes of two students to minimize the asymptotic property of the generalization error. Consequently, the optimized mutual learning converges to a generalization error identical to that of the optimal ensemble learning. In addition, we show the relationship between the optimum step size of the mutual learning and the integration mechanism of the ensemble learning.Comment: 13 pages, 3 figures, submitted to Journal of Physical Society of Japa

Statistical Mechanics of Time Domain Ensemble Learning

Conventional ensemble learning combines students in the space domain. On the other hand, in this paper we combine students in the time domain and call it time domain ensemble learning. In this paper, we analyze the generalization performance of time domain ensemble learning in the framework of online learning using a statistical mechanical method. We treat a model in which both the teacher and the student are linear perceptrons with noises. Time domain ensemble learning is twice as effective as conventional space domain ensemble learning.Comment: 10 pages, 10 figure

Statistical Mechanics of Linear and Nonlinear Time-Domain Ensemble Learning

Conventional ensemble learning combines students in the space domain. In this paper, however, we combine students in the time domain and call it time-domain ensemble learning. We analyze, compare, and discuss the generalization performances regarding time-domain ensemble learning of both a linear model and a nonlinear model. Analyzing in the framework of online learning using a statistical mechanical method, we show the qualitatively different behaviors between the two models. In a linear model, the dynamical behaviors of the generalization error are monotonic. We analytically show that time-domain ensemble learning is twice as effective as conventional ensemble learning. Furthermore, the generalization error of a nonlinear model features nonmonotonic dynamical behaviors when the learning rate is small. We numerically show that the generalization performance can be improved remarkably by using this phenomenon and the divergence of students in the time domain.Comment: 11 pages, 7 figure

Gastrointestinal motility during sleep assessed by tracking of telemetric capsules combined with polysomnography - a pilot study.

Studies of gastrointestinal function during sleep are hampered by lack of applicable techniques. Recent development of a novel ambulatory telemetric capsule system, which can be used in conjunction with polysomnography, offers a solution to this problem. The 3D-Transit system consists of ingestible electromagnetic capsules traceable through a portable extracorporeal receiver while traversing the gut. During sleep monitored by polysomnography, gastrointestinal motility was concurrently investigated using 3D-Transit in nine healthy subjects. Overall, the amplitude of gastric contractions decreased with depth of sleep (light sleep, N2 versus deep sleep, N3; P<0.05). Progression through the small intestine did not change with depth of sleep (Kruskal-Wallis probability =0.1), and there was no association between nocturnal awakenings or arousals and the occurrence of colonic or small intestinal propagating movements. Basal colonic activity was suppressed during both deep sleep (P<0.05) and light sleep (P<0.05) when compared with nocturnal wake periods. In conclusion, the novel ambulatory 3D-Transit system combined with polysomnography allows minimally invasive and completely ambulatory investigation of associations between sleep patterns and gastrointestinal motility

String Junctions and Bound States of Intersecting Branes

We study four-dimensional black hole configurations which result from wrapping M5-branes on a Calabi-Yau manifold, as well as U-dual realizations. Our aim is to understand the microscopic degrees of freedom responsible for the existence of bound states of multiple branes. The details depend on the chosen U-frame; in some cases, they are massless string junctions. We also identify a perturbative description in which these states correspond to twisted strings of intersecting D3-branes at an orbifold singularity. In each case, these are the preponderant states of the spacetime infrared conformal field theory and account for the entropy of the blackhole.Comment: 14 pages; 2 figures; uses latex with epsf and hyperref package

Epigenome-wide association study reveals decreased average methylation levels years before breast cancer diagnosis

Interest in the potential of DNA methylation in peripheral blood as a biomarker of cancer risk is increasing. We aimed to assess whether epigenome-wide DNA methylation measured in peripheral blood samples obtained before onset of the disease is associated with increased risk of breast cancer. We report on three independent prospective nested case-control studies from the European Prospective Investigation into Cancer and Nutrition (EPIC-Italy; n = 162 matched case-control pairs), the Norwegian Women and Cancer study (NOWAC; n = 168 matched pairs), and the Breakthrough Generations Study (BGS; n = 548 matched pairs). We used the Illumina 450k array to measure methylation in the EPIC and NOWAC cohorts. Whole-genome bisulphite sequencing (WGBS) was performed on the BGS cohort using pooled DNA samples, combined to reach 50× coverage across ~16 million CpG sites in the genome including 450k array CpG sites. Mean β values over all probes were calculated as a measurement for epigenome-wide methylation

Dyons in N=4 Supersymmetric Theories and Three-Pronged Strings

We construct and explore BPS states that preserve 1/4 of supersymmetry in N=4 Yang-Mills theories. Such states are also realized as three-pronged strings ending on D3-branes. We correct the electric part of the BPS equation and relate its solutions to the unbroken abelian gauge group generators. Generic 1/4-BPS solitons are not spherically symmetric, but consist of two or more dyonic components held apart by a delicate balance between static electromagnetic force and scalar Higgs force. The instability previously found in three-pronged string configurations is due to excessive repulsion by one of these static forces. We also present an alternate construction of these 1/4-BPS states from quantum excitations around a magnetic monopole, and build up the supermultiplet for arbitrary (quantized) electric charge. The degeneracy and the highest spin of the supermultiplet increase linearly with a relative electric charge. We conclude with comments.Comment: 33 pages, two figures, LaTex, a footnote added, the figure caption of Fig.2 expanded, one more referenc