The effects of a therapy dog vs mindfulness vs a student advisor on student anxiety and well-being

There are growing concerns about the psychological well-being of university students; both in the UK and globally. In light of emerging research on the benefits of therapy dogs for student well-being, this study aimed to compare the use of therapy dogs to more conventional methods for improving students’ well-being. 94 university students were randomly assigned to one of three 30-minute treatment sessions: dog therapy, mindfulness, or the control group who were given the university’s standard treatment – a session with a student well-being adviser. All participants completed an anxiety scale and a mood scale, both immediately before and immediately after their allocated session. The results showed that whilst all three groups showed a significant decrease in anxiety after their allocated treatment, only the dog therapy and mindfulness groups’ anxiety levels dropped to at or below normal levels. Both the dog therapy and mindfulness groups reported post-treatment anxiety levels which were significantly lower than those of the controls. The dog therapy and mindfulness groups’ mood also showed a significant improvement after treatment whereas the control group’s did not. The findings of this study therefore suggest that the use of therapy dogs is as effective as mindfulness in reducing students’ anxiety and improving their well-being. However, more research investigating the use of multiple treatment sessions and comparing the more long-term effects of the two treatments are recommended

Appearance-based localization for mobile robots using digital zoom and visual compass

This paper describes a localization system for mobile robots moving in dynamic indoor environments, which uses probabilistic integration of visual appearance and odometry information. The approach is based on a novel image matching algorithm for appearance-based place recognition that integrates digital zooming, to extend the area of application, and a visual compass. Ambiguous information used for recognizing places is resolved with multiple hypothesis tracking and a selection procedure inspired by Markov localization. This enables the system to deal with perceptual aliasing or absence of reliable sensor data. It has been implemented on a robot operating in an office scenario and the robustness of the approach demonstrated experimentally

Strain control of superlattice implies weak charge-lattice coupling in La0.5_{0.5}Ca0.5_{0.5}MnO3_3

We have recently argued that manganites do not possess stripes of charge order, implying that the electron-lattice coupling is weak [Phys Rev Lett \textbf{94} (2005) 097202]. Here we independently argue the same conclusion based on transmission electron microscopy measurements of a nanopatterned epitaxial film of La$_{0.5}$Ca$_{0.5}$MnO$_3$. In strain relaxed regions, the superlattice period is modified by 2-3% with respect to the parent lattice, suggesting that the two are not strongly tied.Comment: 4 pages, 4 figures It is now explained why the work provides evidence to support weak-coupling, and rule out charge orde

MACOC: a medoid-based ACO clustering algorithm

The application of ACO-based algorithms in data mining is growing over the last few years and several supervised and unsupervised learning algorithms have been developed using this bio-inspired approach. Most recent works concerning unsupervised learning have been focused on clustering, showing great potential of ACO-based techniques. This work presents an ACO-based clustering algorithm inspired by the ACO Clustering (ACOC) algorithm. The proposed approach restructures ACOC from a centroid-based technique to a medoid-based technique, where the properties of the search space are not necessarily known. Instead, it only relies on the information about the distances amongst data. The new algorithm, called MACOC, has been compared against well-known algorithms (K-means and Partition Around Medoids) and with ACOC. The experiments measure the accuracy of the algorithm for both synthetic datasets and real-world datasets extracted from the UCI Machine Learning Repository

Data Mining and Machine Learning in Astronomy

We review the current state of data mining and machine learning in astronomy. 'Data Mining' can have a somewhat mixed connotation from the point of view of a researcher in this field. If used correctly, it can be a powerful approach, holding the potential to fully exploit the exponentially increasing amount of available data, promising great scientific advance. However, if misused, it can be little more than the black-box application of complex computing algorithms that may give little physical insight, and provide questionable results. Here, we give an overview of the entire data mining process, from data collection through to the interpretation of results. We cover common machine learning algorithms, such as artificial neural networks and support vector machines, applications from a broad range of astronomy, emphasizing those where data mining techniques directly resulted in improved science, and important current and future directions, including probability density functions, parallel algorithms, petascale computing, and the time domain. We conclude that, so long as one carefully selects an appropriate algorithm, and is guided by the astronomical problem at hand, data mining can be very much the powerful tool, and not the questionable black box.Comment: Published in IJMPD. 61 pages, uses ws-ijmpd.cls. Several extra figures, some minor additions to the tex

Inferential models: A framework for prior-free posterior probabilistic inference

Posterior probabilistic statistical inference without priors is an important but so far elusive goal. Fisher's fiducial inference, Dempster-Shafer theory of belief functions, and Bayesian inference with default priors are attempts to achieve this goal but, to date, none has given a completely satisfactory picture. This paper presents a new framework for probabilistic inference, based on inferential models (IMs), which not only provides data-dependent probabilistic measures of uncertainty about the unknown parameter, but does so with an automatic long-run frequency calibration property. The key to this new approach is the identification of an unobservable auxiliary variable associated with observable data and unknown parameter, and the prediction of this auxiliary variable with a random set before conditioning on data. Here we present a three-step IM construction, and prove a frequency-calibration property of the IM's belief function under mild conditions. A corresponding optimality theory is developed, which helps to resolve the non-uniqueness issue. Several examples are presented to illustrate this new approach.Comment: 29 pages with 3 figures. Main text is the same as the published version. Appendix B is an addition, not in the published version, that contains some corrections and extensions of two of the main theorem

Salmonid carrying capacity of streams in the Connemara region, a resource appraisal

Standing crops of salmonids in the Connemara region are described from 80 site fishings made between March 1982 and May of the following year. Trout were more widely distributed than salmon, being able to exploit isolated water bodies as resident populations. High salmonid densities were associated with salmon which during the dry summer months were caught in large numbers on riffles. The smallest streams in the region supported only trout presumably because there was insufficient depth of water to permit the entry of salmon. Trout biomass and density within the region were distributed within the lower range reported from a number of countries in which brown trout are endemic and naturalised. Low saimonid densities at 16% of sites were in some cases associated with the rooting of angiosperms, and possibly oligotrophic conditions resulting from geological structure. Length at age of salmon and trout was similar to measurements recorded in Britain. The streams were important only for the first year of the trout life cycle. Because trout move downstream as they grow, occupying lakes during the later parr phase, and the entire streambed area in Connemara is one fortieth of the lake area, space is unlikely to be a critical constraint on the later parr phase. The condition of the stream substratum may be a factor in the production of sea trout; where loose gravels do not occur in shallow nursery streams, the catchments tend towards producing "brown" or "resident" rather than sea trout.Funder: Marine Institut

Analysis of Fourier transform valuation formulas and applications

The aim of this article is to provide a systematic analysis of the conditions such that Fourier transform valuation formulas are valid in a general framework; i.e. when the option has an arbitrary payoff function and depends on the path of the asset price process. An interplay between the conditions on the payoff function and the process arises naturally. We also extend these results to the multi-dimensional case, and discuss the calculation of Greeks by Fourier transform methods. As an application, we price options on the minimum of two assets in L\'evy and stochastic volatility models.Comment: 26 pages, 3 figures, to appear in Appl. Math. Financ

Inducing Probabilistic Grammars by Bayesian Model Merging

We describe a framework for inducing probabilistic grammars from corpora of positive samples. First, samples are {\em incorporated} by adding ad-hoc rules to a working grammar; subsequently, elements of the model (such as states or nonterminals) are {\em merged} to achieve generalization and a more compact representation. The choice of what to merge and when to stop is governed by the Bayesian posterior probability of the grammar given the data, which formalizes a trade-off between a close fit to the data and a default preference for simpler models (`Occam's Razor'). The general scheme is illustrated using three types of probabilistic grammars: Hidden Markov models, class-based $n$-grams, and stochastic context-free grammars.Comment: To appear in Grammatical Inference and Applications, Second International Colloquium on Grammatical Inference; Springer Verlag, 1994. 13 page