Advances in Virtual Reality (VR) technologies allow the investigation of simulated moral actions in visually immersive environments. Using a robotic manipulandum and an interactive sculpture, we now ...also incorporate realistic haptic feedback into virtual moral simulations. In two experiments, we found that participants responded with greater utilitarian actions in virtual and haptic environments when compared to traditional questionnaire assessments of moral judgments. In experiment one, when incorporating a robotic manipulandum, we found that the physical power of simulated utilitarian responses (calculated as the product of force and speed) was predicted by individual levels of psychopathy. In experiment two, which integrated an interactive and life-like sculpture of a human into a VR simulation, greater utilitarian actions continued to be observed. Together, these results support a disparity between simulated moral action and moral judgment. Overall this research combines state-of-the-art virtual reality, robotic movement simulations, and realistic human sculptures, to enhance moral paradigms that are often contextually impoverished. As such, this combination provides a better assessment of simulated moral action, and illustrates the embodied nature of morally-relevant actions.
ROOT is an object-oriented C++ framework conceived in the high-energy physics (HEP) community, designed for storing and analyzing petabytes of data in an efficient way. Any instance of a C++ class ...can be stored into a ROOT file in a machine-independent compressed binary format. In ROOT the TTree object container is optimized for statistical data analysis over very large data sets by using vertical data storage techniques. These containers can span a large number of files on local disks, the web, or a number of different shared file systems. In order to analyze this data, the user can chose out of a wide set of mathematical and statistical functions, including linear algebra classes, numerical algorithms such as integration and minimization, and various methods for performing regression analysis (fitting). In particular, the RooFit package allows the user to perform complex data modeling and fitting while the RooStats library provides abstractions and implementations for advanced statistical tools. Multivariate classification methods based on machine learning techniques are available via the TMVA package. A central piece in these analysis tools are the histogram classes which provide binning of one- and multi-dimensional data. Results can be saved in high-quality graphical formats like Postscript and PDF or in bitmap formats like JPG or GIF. The result can also be stored into ROOT macros that allow a full recreation and rework of the graphics. Users typically create their analysis macros step by step, making use of the interactive C++ interpreter CINT, while running over small data samples. Once the development is finished, they can run these macros at full compiled speed over large data sets, using on-the-fly compilation, or by creating a stand-alone batch program. Finally, if processing farms are available, the user can reduce the execution time of intrinsically parallel tasks — e.g. data mining in HEP — by using PROOF, which will take care of optimally distributing the work over the available resources in a transparent way.
Program title: ROOT
Catalogue identifier: AEFA_v1_0
Program summary URL:
http://cpc.cs.qub.ac.uk/summaries/AEFA_v1_0.html
Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland
Licensing provisions: LGPL
No. of lines in distributed program, including test data, etc.: 3 044 581
No. of bytes in distributed program, including test data, etc.: 36 325 133
Distribution format: tar.gz
Programming language: C++
Computer: Intel i386, Intel x86-64, Motorola PPC, Sun Sparc, HP PA-RISC
Operating system: GNU/Linux, Windows XP/Vista, Mac OS X, FreeBSD, OpenBSD, Solaris, HP-UX, AIX
Has the code been vectorized or parallelized?: Yes
RAM:
>
55
Mbytes
Classification: 4, 9, 11.9, 14
Nature of problem: Storage, analysis and visualization of scientific data
Solution method: Object store, wide range of analysis algorithms and visualization methods
Additional comments: For an up-to-date author list see:
http://root.cern.ch/drupal/content/root-development-team and
http://root.cern.ch/drupal/content/former-root-developers
Running time: Depending on the data size and complexity of analysis algorithms
References:
1
http://root.cern.ch.
Containerization technology is becoming more and more popular because it provides an efficient way to improve deployment flexibility by packaging up code into software micro-environments. Yet, ...containerization has limitations and one of the main ones is the fact that entire container images need to be transferred before they can be used. Container images can be seen as software stacks and High Energy Physics has long solved the distribution problem for large software stacks with CernVM-FS. CernVM-FS provides a global, shared software area, where clients only load the small subset of binaries that are accessed for any given compute job. In this paper, we propose a solution to the problem of efficient image distribution using CernVM-FS for storage and transport of container images. We chose to implement our solution for the Docker platform, due to its popularity and widespread use. To minimize the impact on existing workflows our implementation comes as a Docker plugin, meaning that users will continue to pull, run, modify, and store Docker images using standard Docker tools. We introduce the concept of a thin image, whose contents are served on demand from CernVM-FS repositories. Such behavior closely reassembles the lazy evaluation strategy in programming language theory. Our measurements confirm that the time before a task starts executing depends only on the size of the files actually used, minimizing the cold start-up time in all cases.
Deception is a complex cognitive activity, and different types of lies could arise from different neural systems. We investigated this possibility by first classifying lies according to two ...dimensions, whether they fit into a coherent story and whether they were previously memorized. fMRI revealed that well-rehearsed lies that fit into a coherent story elicit more activation in right anterior frontal cortices than spontaneous lies that do not fit into a story, whereas the opposite pattern occurs in the anterior cingulate and in posterior visual cortex. Furthermore, both types of lies elicited more activation than telling the truth in anterior prefrontal cortices (bilaterally), the parahippocampal gyrus (bilaterally), the right precuneus, and the left cerebellum. At least in part, distinct neural networks support different types of deception.
The Glasford structure in Illinois (USA) was recognized as a buried impact crater in the early 1960s but has never been reassessed in light of recent advances in planetary science. Here, we document ...shatter cones and previously unknown quartz microdeformation features that support an impact origin for the Glasford structure. We identify the 4 km wide structure as a complex buried impact crater and describe syn‐ and postimpact deposits from its annular trough. We have informally designated these deposits as the Kingston Mines unit (KM). The fossils and sedimentology of the KM indicate a marine depositional setting. The various intervals within the KM constitute a succession of breccia, carbonate, sandstone, and shale similar to marine sedimentary successions preserved in other craters. Graptolite specimens retrieved from the KM place the time of deposition at approximately 455 ± 2 Ma (Late Ordovician, Sandbian). This age determination suggests a possible link between the Glasford impact and the Ordovician meteorite shower, an increase in the rate of terrestrial meteorite impacts attributed to the breakup of the L‐chondrite parent body in the main asteroid belt.
A new stable version (“production version”) v5.28.00 of ROOT 1 has been published 2. It features several major improvements in many areas, most noteworthy data storage performance as well as ...statistics and graphics features. Some of these improvements have already been predicted in the original publication Antcheva et al. (2009) 3. This version will be maintained for at least 6 months; new minor revisions (“patch releases”) will be published 4 to solve problems reported with this version.
Program title: ROOT
Catalogue identifier: AEFA_v2_0
Program summary URL:
http://cpc.cs.qub.ac.uk/summaries/AEFA_v2_0.html
Program obtainable from: CPC Program Library, Queenʼs University, Belfast, N. Ireland
Licensing provisions: GNU Lesser Public License v.2.1
No. of lines in distributed program, including test data, etc.: 2 934 693
No. of bytes in distributed program, including test data, etc.: 1009
Distribution format: tar.gz
Programming language: C++
Computer: Intel i386, Intel x86-64, Motorola PPC, Sun Sparc, HP PA-RISC
Operating system: GNU/Linux, Windows XP/Vista/7, Mac OS X, FreeBSD, OpenBSD, Solaris, HP-UX, AIX
Has the code been vectorized or parallelized?: Yes
RAM: > 55 Mbytes
Classification: 4, 9, 11.9, 14
Catalogue identifier of previous version: AEFA_v1_0
Journal reference of previous version: Comput. Phys. Commun. 180 (2009) 2499
Does the new version supersede the previous version?: Yes
Nature of problem: Storage, analysis and visualization of scientific data
Solution method: Object store, wide range of analysis algorithms and visualization methods
Reasons for new version: Added features and corrections of deficiencies
Summary of revisions: The release notes at
http://root.cern.ch/root/v528/Version528.news.html give a module-oriented overview of the changes in v5.28.00. Highlights include
•
File format Reading of TTrees has been improved dramatically with respect to CPU time (30%) and notably with respect to disk space.
•
Histograms A new TEfficiency class has been provided to handle the calculation of efficiencies and their uncertainties, TH2Poly for polygon-shaped bins (e.g. maps), TKDE for kernel density estimation, and TSVDUnfold for singular value decomposition.
•
Graphics Kerning is now supported in TLatex, PostScript and PDF; a table of contents can be added to PDF files. A new font provides italic symbols. A TPad containing GL can be stored in a binary (i.e. non-vector) image file; add support for full-scene anti-aliasing. Usability enhancements to EVE.
•
Math New interfaces for generating random number according to a given distribution, goodness of fit tests of unbinned data, binning multidimensional data, and several advanced statistical functions were added.
•
RooFit Introduction of HistFactory; major additions to RooStats.
•
TMVA Updated to version 4.1.0, adding e.g. the support for simultaneous classification of multiple output classes for several multivariate methods.
•
PROOF Many new features, adding to PROOFʼs usability, plus improvements and fixes.
•
PyROOT Support of Python 3 has been added.
•
Tutorials Several new tutorials were provided for above new features (notably RooStats).
A detailed list of all the changes is available at
http://root.cern.ch/root/htmldoc/examples/V5.
Additional comments: For an up-to-date author list see:
http://root.cern.ch/drupal/content/root-development-team and
http://root.cern.ch/drupal/content/former-root-developers.
The distribution file for this program is over 30 Mbytes and therefore is not delivered directly when download or E-mail is requested. Instead a html file giving details of how the program can be obtained is sent.
Running time: Depending on the data size and complexity of analysis algorithms.
References:
1
http://root.cern.ch.
2
http://root.cern.ch/drupal/content/production-version-528.
3
I. Antcheva, M. Ballintijn, B. Bellenot, M. Biskup, R. Brun, N. Buncic, Ph. Canal, D. Casadei, O. Couet, V. Fine, L. Franco, G. Ganis, A. Gheata, D. Gonzalez Maline, M. Goto, J. Iwaszkiewicz, A. Kreshuk, D. Marcos Segura, R. Maunder, L. Moneta, A. Naumann, E. Offermann, V. Onuchin, S. Panacek, F. Rademakers, P. Russo, M. Tadel, ROOT — A C++ framework for petabyte data storage, statistical analysis and visualization, Comput. Phys. Commun. 180 (2009) 2499.
4
http://root.cern.ch/drupal/content/root-version-v5-28-00-patch-release-notes.
Neuroimaging studies have shown that motor structures are activated not only during overt motor behavior but also during tasks that require no overt motor behavior, such as motor imagery and mental ...rotation. We tested the hypothesis that activation of the primary motor cortex is needed for mental rotation by using single- pulse transcranial magnetic stimulation (TMS). Single-pulse TMS was delivered to the representation of the hand in left primary motor cortex while participants performed mental rotation of pictures of hands and feet. Relative to a peripheral magnetic stimulation control condition, response times (RTs) were slower when TMS was delivered at 650 ms but not at 400 ms after stimulus onset. The magnetic stimulation effect at 650 ms was larger for hands than for feet. These findings demonstrate that (i) activation of the left primary motor cortex has a causal role in the mental rotation of pictures of hands; (ii) this role is stimulus-specific because disruption of neural activity in the hand area slowed RTs for pictures of hands more than feet; and (iii) left primary motor cortex is involved relatively late in the mental rotation process.
Visual imagery is used in a wide range of mental activities, ranging from memory to reasoning, and also plays a role in perception proper. The contribution of early visual cortex, specifically Area ...17, to visual mental imagery was examined by the use of two convergent techniques. In one, subjects closed their eyes during positron emission tomography (PET) while they visualized and compared properties (for example, relative length) of sets of stripes. The results showed that when people perform this task, Area 17 is activated. In the other, repetitive transcranial magnetic stimulation (rTMS) was applied to medial occipital cortex before presentation of the same task. Performance was impaired after rTMS compared with a sham control condition; similar results were obtained when the subjects performed the task by actually looking at the stimuli. In sum, the PET results showed that when patterns of stripes are visualized, Area 17 is activated, and the rTMS results showed that such activation underlies information processing.
The CernVM File System (CernVM-FS) is a snapshotting read-only file system designed to deliver software to grid worker nodes over HTTP in a fast, scalable and reliable way. In recent years it became ...the de-facto standard method to distribute HEP experiment software in the WLCG and starts to be adopted by other grid computing communities outside HEP. This paper focusses on the recent developments of the CernVM-FS Server, the central publishing point of new file system snapshots. Using a union file system, the CernVM-FS Server allows for direct manipulation of a (normally read-only) CernVM-FS volume with copy-on-write semantics. Eventually the collected changeset is transformed into a new CernVM-FS snapshot, constituting a transactional feedback loop. The generated repository data is pushed into a content addressable storage requiring only a RESTful interface and gets distributed through a hierarchy of caches to individual grid worker nodes. Additonally we describe recent features, such as file chunking, repository garbage collection and file system history that enable CernVM- FS for a wider range of use cases.
The bright future of particle physics at the Energy and Intensity frontiers poses exciting challenges to the scientific software community. The traditional strategies for processing and analysing ...data are evolving in order to (i) offer higher-level programming model approaches and (ii) exploit parallelism to cope with the ever increasing complexity and size of the datasets. This contribution describes how the ROOT framework, a cornerstone of software stacks dedicated to particle physics, is preparing to provide adequate solutions for the analysis of large amount of scientific data on parallel architectures. The functional approach to parallel data analysis provided with the ROOT TDataFrame interface is then characterised. The design choices behind this new interface are described also comparing with other widely adopted tools such as Pandas and Apache Spark. The programming model is illustrated highlighting the reduction of boilerplate code, composability of the actions and data transformations as well as the capabilities of dealing with different data sources such as ROOT, JSON, CSV or databases. Details are given about how the functional approach allows transparent implicit parallelisation of the chain of operations specified by the user. The progress done in the field of distributed analysis is examined. In particular, the power of the integration of ROOT with Apache Spark via the PyROOT interface is shown. In addition, the building blocks for the expression of parallelism in ROOT are briefly characterised together with the structural changes applied in the building and testing infrastructure which were necessary to put them in production.
PDF
