Home & Heart Rescue: An Animal Shelter Designed for Animal-Assisted Therapy

Millions of companion animals are placed in shelters annually, while a greater number of people struggle with some sort of disability. Designing an animal shelter that caters to these disabilities will allow those to be more comfortable in their environment. This creates an opportunity to foster stronger bonds with animals, increasing adoption rates

Exploring the Efficacy of Transfer Learning in Mining Image-Based Software Artifacts

Transfer learning allows us to train deep architectures requiring a large number of learned parameters, even if the amount of available data is limited, by leveraging existing models previously trained for another task. Here we explore the applicability of transfer learning utilizing models pre-trained on non-software engineering data applied to the problem of classifying software UML diagrams. Our experimental results show training reacts positively to transfer learning as related to sample size, even though the pre-trained model was not exposed to training instances from the software domain. We contrast the transferred network with other networks to show its advantage on different sized training sets, which indicates that transfer learning is equally effective to custom deep architectures when large amounts of training data is not available

Supplement for "Me vs. Super(wo)man: Effects of Customization and Identification in a VR Exergame"

This supplement describes an approach that can be used to create an “enhanced“ avatar based on a) a realistic, current avatar (R) and b) an idealised, desired future avatar (I) of a user. The aim of the approach is to create avatars that reflect “enhancements” of the realistic avatar along a realistic trajectory. The realistic avatar is used as a starting point, and the idealised avatar as a “goal”.The participants are asked to create a realistic avatar (R) and an Idealistic avatar (I) using an off-the-shelf software tool for video game character design, Autodesk Character Generator. The tool provides an interface for the design of avatars based on blend shapes, i.e. pre-defined body shapes that can be combined to adjust face and body design. Participants are given guidance on how to customise a base character to make an avatar in their own likeness by choosing the character sex (male or female), face shape (by blending two faces together), facial features including eye, mouth and nose shape, body shape (by blending two body shapes together). Customising body shape and eye colour. Skin tone, hairstyle, hair colour and eye colour. Finally, participants were are given a choice of garments for the top half and lower half, plus a choice of shoes. Once the realistic and idealistic avatars have been produced the aim of the approach is to create avatars that reflect “enhancements” of the realistic avatar along a realistic trajectory. The realistic avatar is used as a starting point, and the idealised avatar as a “goal”Autodesk Character Generator was used to create the Avatars this can be found at https://charactergenerator.autodesk.com

Me vs. Super(wo)man: Effects of Customization and Identification in a VR Exergame

Customised avatars are a powerful tool to increase identification, engagement and intrinsic motivation in digital games. We investigated the effects of customisation in a self-competitive VR exergame by modelling players and their previous performance in the game with customised avatars. In a first study we found that, similar to non-exertion games, customisation significantly increased identification and intrinsic motivation, as well as physical performance in the exergame. In a second study we identified a more complex relationship with the customisation style: idealised avatars increased wishful identification but decreased exergame performance compared to realistic avatars. In a third study, we found that 'enhancing' realistic avatars with idealised characteristics increased wishful identification, but did not have any adverse effects. We discuss the findings based on feedforward and self-determination theory, proposing notions of intrinsic identification (fostering a sense of self) and extrinsic identification (drawing away from the self) to explain the results

A Fortran-Keras Deep Learning Bridge for Scientific Computing

Implementing artificial neural networks is commonly achieved via high-level programming languages like Python and easy-to-use deep learning libraries like Keras. These software libraries come pre-loaded with a variety of network architectures, provide autodifferentiation, and support GPUs for fast and efficient computation. As a result, a deep learning practitioner will favor training a neural network model in Python, where these tools are readily available. However, many large-scale scientific computation projects are written in Fortran, making it difficult to integrate with modern deep learning methods. To alleviate this problem, we introduce a software library, the Fortran-Keras Bridge (FKB). This two-way bridge connects environments where deep learning resources are plentiful, with those where they are scarce. The paper describes several unique features offered by FKB, such as customizable layers, loss functions, and network ensembles. The paper concludes with a case study that applies FKB to address open questions about the robustness of an experimental approach to global climate simulation, in which subgrid physics are outsourced to deep neural network emulators. In this context, FKB enables a hyperparameter search of one hundred plus candidate models of subgrid cloud and radiation physics, initially implemented in Keras, to be transferred and used in Fortran. Such a process allows the model's emergent behavior to be assessed, i.e. when fit imperfections are coupled to explicit planetary-scale fluid dynamics. The results reveal a previously unrecognized strong relationship between offline validation error and online performance, in which the choice of optimizer proves unexpectedly critical. This reveals many neural network architectures that produce considerable improvements in stability including some with reduced error, for an especially challenging training dataset

A Fortran-Keras Deep Learning Bridge for Scientific Computing

Implementing artificial neural networks is commonly achieved via high-level programming languages such as Python and easy-to-use deep learning libraries such as Keras. These software libraries come preloaded with a variety of network architectures, provide autodifferentiation, and support GPUs for fast and efficient computation. As a result, a deep learning practitioner will favor training a neural network model in Python, where these tools are readily available. However, many large-scale scientific computation projects are written in Fortran, making it difficult to integrate with modern deep learning methods. To alleviate this problem, we introduce a software library, the Fortran-Keras Bridge (FKB). This two-way bridge connects environments where deep learning resources are plentiful with those where they are scarce. The paper describes several unique features offered by FKB, such as customizable layers, loss functions, and network ensembles. The paper concludes with a case study that applies FKB to address open questions about the robustness of an experimental approach to global climate simulation, in which subgrid physics are outsourced to deep neural network emulators. In this context, FKB enables a hyperparameter search of one hundred plus candidate models of subgrid cloud and radiation physics, initially implemented in Keras, to be transferred and used in Fortran. Such a process allows the model’s emergent behavior to be assessed, i.e., when fit imperfections are coupled to explicit planetary-scale fluid dynamics. The results reveal a previously unrecognized strong relationship between offline validation error and online performance, in which the choice of the optimizer proves unexpectedly critical. This in turn reveals many new neural network architectures that produce considerable improvements in climate model stability including some with reduced error, for an especially challenging training dataset

Infrared Emission from the Nearby Cool Core Cluster Abell 2597

We observed the brightest central galaxy (BCG) in the nearby (z=0.0821) cool core galaxy cluster Abell 2597 with the IRAC and MIPS instruments on board the Spitzer Space Telescope. The BCG was clearly detected in all Spitzer bandpasses, including the 70 and 160 micron wavebands. We report aperture photometry of the BCG. The spectral energy distribution exhibits a clear excess in the FIR over a Rayleigh-Jeans stellar tail, indicating a star formation rate of ~4-5 solar masses per year, consistent with the estimates from the UV and its H-alpha luminosity. This large FIR luminosity is consistent with that of a starburst or a Luminous Infrared Galaxy (LIRG), but together with a very massive and old population of stars that dominate the energy output of the galaxy. If the dust is at one temperature, the ratio of 70 to 160 micron fluxes indicate that the dust emitting mid-IR in this source is somewhat hotter than the dust emitting mid-IR in two BCGs at higher-redshift (z~0.2-0.3) and higher FIR luminosities observed earlier by Spitzer, in clusters Abell 1835 and Zwicky 3146.Comment: Accepted at Ap

Grain-size controls on the morphology and internal geometry of river-dominated deltas

Predictions of a delta's morphology, facies, and stratigraphy are typically derived from its relative wave, tide, and river energies, with sediment type playing a lesser role. Here we test the hypothesis that, all other factors being equal, the topset of a relatively noncohesive, sandy delta will have more active distributaries, a less rugose shoreline morphology, less topographic variation in its topset, and less variability in foreset dip directions than a highly cohesive, muddy delta. As a consequence its stratigraphy will have greater clinoform dip magnitudes and clinoform concavity, a greater percentage of channel facies, and less rugose sand bodies than a highly cohesive, muddy delta. Nine self-formed deltas having different sediment grain sizes and critical shear stresses required for re-entrainment of mud are simulated using Deflt3D, a 2D flow and sediment-transport model. Model results indicate that sand-dominated deltas are more fan-shaped while mud-dominated deltas are more birdsfoot in planform, because the sand-dominated deltas have more active distributaries and a smaller variance of topset elevations, and thereby experience a more equitable distribution of sediment to their perimeters. This results in a larger proportion of channel facies in sand-dominated deltas, and more uniformly distributed clinoform dip directions, steeper dips, and greater clinoform concavity. These conclusions are consistent with data collected from the Goose River Delta, a coarse-grained fan delta prograding into Goose Bay, Labrador, Canada. A reinterpretation of the Kf-1 parasequence set of the Cretaceous Last Chance Delta, a unit of the Ferron Sandstone near Emery, Utah, USA uses Ferron grain-size data, clinoform-dip data, clinoform concavity, and variance of dip directions to hindcast the delta's planform. The Kf-1 Last Chance Delta is predicted to have been more like a fan delta in planform than a birdsfoot delta

Visualizing the Influence of Social Networks on Recovery:A Mixed-Methods Social Identity Mapping Study with Recovering Adolescents

BackgroundSocial recovery capital (SRC) refers to resources and supports gained through relationships and is vital to adolescent addiction recovery. Much is known about how substance use relates to social networks, but little is known about how other dimensions of social networks influence recovery (e.g., network size/exposure, degree of conflict). MethodsThis mixed-methods study sampled 28 adolescents who received treatment for alcohol and other drug (AOD) use disorder (14-19 yrs.: 71% male; M=17.32 yrs., SD=1.33; White 82%): 20 were recovery high school (RHS) students. Adolescents completed a social identity map for addiction recovery (SIM-AR), survey, and interview. Qualitative data were content analyzed and the data from the SIM-AR were quantified. ResultsOn average, participants reported belonging to five having 5 distinct groups (Range, 2-9; SD=1.63; M=27.89 people, SD = 20.09) in their network. Of their social network connections, on average, 51% drank alcohol and 46% used other substances. Larger networks involved more conflict (r=0.57). Participants were more likely to spend more time with groups that had greater proportions of non-substance usinge members; these relationships were stronger for RHS than for non-RHS students. Qualitative analyses revealed that youth reported their recovery-oriented groups as supportive, yet some felt their substance-using friends also supported their recovery.DiscussionSIM-AR was a useful measurement tool, and, through qualitative interviews, we identified unique aspects of youth’s social networks important for further examination. Research with recovering youth should examine SRC-related elements within their networks including relationship quality, belonging, and conflict, in addition to the substance use behaviors of network members. <br/