931 research outputs found
The radiation problem from a vertical short dipole antenna above flat and lossy ground. Novel formulation in the spectral domain with closed form analytical solution in the high frequency regime
In this paper we consider the problem of radiation from a vertical short
Hertzian dipole above flat lossy ground, which represents the well known in the
literature Sommerfeld radiation problem. The problem is formulated in a novel
spectral domain approach, and by inverse three dimensional Fourier
transformation the expressions for the received electric and magnetic field in
the physical space are derived as one dimensional integrals over the radial
component of wavevector, in cylindrical coordinates. Subsequent use of the
Stationary Phase Method in the high frequency regime yields closed form
analytical solutions for the received EM field vectors, which coincide with the
corresponding reflected EM field originating from the image point. In this way,
we conclude that the so called in the literature space wave, i.e. line of sight
plus reflected EM field, represents the total solution of the Sommerfeld
problem in the high frequency regime, in which case the surface wave can be
ignored. Finally, numerical results in the high frequency regime are presented
in this paper, in comparison with corresponding numerical results based on
Norton solution of the problem, i.e. space and surface waves
Spreading in Social Systems: Reflections
In this final chapter, we consider the state-of-the-art for spreading in
social systems and discuss the future of the field. As part of this reflection,
we identify a set of key challenges ahead. The challenges include the following
questions: how can we improve the quality, quantity, extent, and accessibility
of datasets? How can we extract more information from limited datasets? How can
we take individual cognition and decision making processes into account? How
can we incorporate other complexity of the real contagion processes? Finally,
how can we translate research into positive real-world impact? In the
following, we provide more context for each of these open questions.Comment: 7 pages, chapter to appear in "Spreading Dynamics in Social Systems";
Eds. Sune Lehmann and Yong-Yeol Ahn, Springer Natur
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Mindfulness Meditation Activates Altruism.
Clinical evidence suggests that mindfulness meditation reduces anxiety, depression, and stress, and improves emotion regulation due to modulation of activity in neural substrates linked to the regulation of emotions and social preferences. However, less was known about whether mindfulness meditation might alter pro-social behavior. Here we examined whether mindfulness meditation activates human altruism, a component of social cooperation. Using a simple donation game, which is a real-world version of the Dictator's Game, we randomly assigned 326 subjects to a mindfulness meditation online session or control and measured their willingness to donate a portion of their payment for participation as a charitable donation. Subjects who underwent the meditation treatment donated at a 2.61 times higher rate than the control (p = 0.005), after controlling for socio-demographics. We also found a larger treatment effect of meditation among those who did not go to college (p < 0.001) and those who were under 25 years of age (p < 0.001), with both subject groups contributing virtually nothing in the control condition. Our results imply high context modularity of human altruism and the development of intervention approaches including mindfulness meditation to increase social cooperation, especially among subjects with low baseline willingness to contribute
A Study of Concurrency Bugs and Advanced Development Support for Actor-based Programs
The actor model is an attractive foundation for developing concurrent
applications because actors are isolated concurrent entities that communicate
through asynchronous messages and do not share state. Thereby, they avoid
concurrency bugs such as data races, but are not immune to concurrency bugs in
general. This study taxonomizes concurrency bugs in actor-based programs
reported in literature. Furthermore, it analyzes the bugs to identify the
patterns causing them as well as their observable behavior. Based on this
taxonomy, we further analyze the literature and find that current approaches to
static analysis and testing focus on communication deadlocks and message
protocol violations. However, they do not provide solutions to identify
livelocks and behavioral deadlocks. The insights obtained in this study can be
used to improve debugging support for actor-based programs with new debugging
techniques to identify the root cause of complex concurrency bugs.Comment: - Submitted for review - Removed section 6 "Research Roadmap for
Debuggers", its content was summarized in the Future Work section - Added
references for section 1, section 3, section 4.3 and section 5.1 - Updated
citation
Guiding Dynamic Symbolic Execution Toward Unverified Program Executions
Most techniques to detect program errors, such as testing, code reviews, and static program analysis, do not fully verify all possible executions of a program. They leave executions unverified when they do not check certain properties, fail to verify properties, or check properties under certain unsound assumptions such as the absence of arithmetic overflow.
In this paper, we present a technique to complement partial verification results by automatic test case generation. In contrast to existing work, our technique supports the common case that the verification results are based on unsound assumptions. We annotate programs to reflect which executions have been verified, and under which assumptions. These annotations are then used to guide dynamic symbolic execution toward unverified program executions. Our main technical contribution is a code instrumentation that causes dynamic symbolic execution to abort tests that lead to verified executions, to prune parts of the search space, and to prioritize tests that cover more properties that are not fully verified. We have implemented our technique for the .NET static analyzer Clousot and the dynamic symbolic execution tool Pex. It produces smaller test suites (by up to 19.2%), covers more unverified executions (by up to 7.1%), and reduces testing time (by up to 52.4%) compared to combining Clousot and Pex without our technique
Testing for Network and Spatial Autocorrelation
Testing for dependence has been a well-established component of spatial
statistical analyses for decades. In particular, several popular test
statistics have desirable properties for testing for the presence of spatial
autocorrelation in continuous variables. In this paper we propose two
contributions to the literature on tests for autocorrelation. First, we propose
a new test for autocorrelation in categorical variables. While some methods
currently exist for assessing spatial autocorrelation in categorical variables,
the most popular method is unwieldy, somewhat ad hoc, and fails to provide
grounds for a single omnibus test. Second, we discuss the importance of testing
for autocorrelation in data sampled from the nodes of a network, motivated by
social network applications. We demonstrate that our proposed statistic for
categorical variables can both be used in the spatial and network setting
Your Proof Fails? Testing Helps to Find the Reason
Applying deductive verification to formally prove that a program respects its
formal specification is a very complex and time-consuming task due in
particular to the lack of feedback in case of proof failures. Along with a
non-compliance between the code and its specification (due to an error in at
least one of them), possible reasons of a proof failure include a missing or
too weak specification for a called function or a loop, and lack of time or
simply incapacity of the prover to finish a particular proof. This work
proposes a new methodology where test generation helps to identify the reason
of a proof failure and to exhibit a counter-example clearly illustrating the
issue. We describe how to transform an annotated C program into C code suitable
for testing and illustrate the benefits of the method on comprehensive
examples. The method has been implemented in STADY, a plugin of the software
analysis platform FRAMA-C. Initial experiments show that detecting
non-compliances and contract weaknesses allows to precisely diagnose most proof
failures.Comment: 11 pages, 10 figure
IC-Cut: A Compositional Search Strategy for Dynamic Test Generation
Abstract. We present IC-Cut, short for “Interface-Complexity-based Cut”, a new compositional search strategy for systematically testing large programs. IC-Cut dynamically detects function interfaces that are simple enough to be cost-effective for summarization. IC-Cut then hierarchically decomposes the program into units defined by such functions and their sub-functions in the call graph. These units are tested independently, their test results are recorded as low-complexity function summaries, and the summaries are reused when testing higher-level functions in the call graph, thus limiting overall path explosion. When the decomposed units are tested exhaustively, they constitute verified components of the program. IC-Cut is run dynamically and on-the-fly during the search, typically refining cuts as the search advances. We have implemented this algorithm as a new search strategy in the whitebox fuzzer SAGE, and present detailed experimental results ob-tained when fuzzing the ANI Windows image parser. Our results show that IC-Cut alleviates path explosion while preserving or even increasing code coverage and bug finding, compared to the current generational-search strategy used in SAGE.
Eigenvector localization as a tool to study small communities in online social networks
We present and discuss a mathematical procedure for identification of small
"communities" or segments within large bipartite networks. The procedure is
based on spectral analysis of the matrix encoding network structure. The
principal tool here is localization of eigenvectors of the matrix, by means of
which the relevant network segments become visible. We exemplified our approach
by analyzing the data related to product reviewing on Amazon.com. We found
several segments, a kind of hybrid communities of densely interlinked reviewers
and products, which we were able to meaningfully interpret in terms of the type
and thematic categorization of reviewed items. The method provides a
complementary approach to other ways of community detection, typically aiming
at identification of large network modules
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