95,286 research outputs found
Consensus and diversity in multi-state noisy voter models
We study a variant of the voter model with multiple opinions; individuals can
imitate each other and also change their opinion randomly in mutation events.
We focus on the case of a population with all-to-all interaction. A
noise-driven transition between regimes with multi-modal and unimodal
stationary distributions is observed. In the former, the population is mostly
in consensus states; in the latter opinions are mixed. We derive an effective
death-birth process, describing the dynamics from the perspective of one of the
opinions, and use it to analytically compute marginals of the stationary
distribution. These calculations are exact for models with homogeneous
imitation and mutation rates, and an approximation if rates are heterogeneous.
Our approach can be used to characterize the noise-driven transition and to
obtain mean switching times between consensus states.Comment: 14 pages, 8 figure
Visual world studies of conversational perspective taking: similar findings, diverging interpretations
Visual-world eyetracking greatly expanded the potential for insight into how listeners access and use common ground during situated language comprehension. Past reviews of visual world studies on perspective taking have largely taken the diverging findings of the various studies at face value, and attributed these apparently different findings to differences in the extent to which the paradigms used by different labs afford collaborative interaction. Researchers are asking questions about perspective taking of an increasingly nuanced and sophisticated nature, a clear indicator of progress. But this research has the potential not only to improve our understanding of conversational perspective taking. Grappling with problems of data interpretation in such a complex domain has the unique potential to drive visual world researchers to a deeper understanding of how to best map visual world data onto psycholinguistic theory. I will argue against this interactional affordances explanation, on two counts. First, it implies that interactivity affects the overall ability to form common ground, and thus provides no straightforward explanation of why, within a single noninteractive study, common ground can have very large effects on some aspects of processing (referential anticipation) while having negligible effects on others (lexical processing). Second, and more importantly, the explanation accepts the divergence in published findings at face value. However, a closer look at several key studies shows that the divergences are more likely to reflect inconsistent practices of analysis and interpretation that have been applied to an underlying body of data that is, in fact, surprisingly consistent. The diverging interpretations, I will argue, are the result of differences in the handling of anticipatory baseline effects (ABEs) in the analysis of visual world data. ABEs arise in perspective-taking studies because listeners have earlier access to constraining information about who knows what than they have to referential speech, and thus can already show biases in visual attention even before the processing of any referential speech has begun. To be sure, these ABEs clearly indicate early access to common ground; however, access does not imply integration, since it is possible that this information is not used later to modulate the processing of incoming speech. Failing to account for these biases using statistical or experimental controls leads to over-optimistic assessments of listeners’ ability to integrate this information with incoming speech. I will show that several key studies with varying degrees of interactional affordances all show similar temporal profiles of common ground use during the interpretive process: early anticipatory effects, followed by bottom-up effects of lexical processing that are not modulated by common ground, followed (optionally) by further late effects that are likely to be post-lexical. Furthermore, this temporal profile for common ground radically differs from the profile of contextual effects related to verb semantics. Together, these findings are consistent with the proposal that lexical processes are encapsulated from common ground, but cannot be straightforwardly accounted for by probabilistic constraint-based approaches
Learning and Matching Multi-View Descriptors for Registration of Point Clouds
Critical to the registration of point clouds is the establishment of a set of
accurate correspondences between points in 3D space. The correspondence problem
is generally addressed by the design of discriminative 3D local descriptors on
the one hand, and the development of robust matching strategies on the other
hand. In this work, we first propose a multi-view local descriptor, which is
learned from the images of multiple views, for the description of 3D keypoints.
Then, we develop a robust matching approach, aiming at rejecting outlier
matches based on the efficient inference via belief propagation on the defined
graphical model. We have demonstrated the boost of our approaches to
registration on the public scanning and multi-view stereo datasets. The
superior performance has been verified by the intensive comparisons against a
variety of descriptors and matching methods
ARES: Adaptive, Reconfigurable, Erasure coded, atomic Storage
Atomicity or strong consistency is one of the fundamental, most intuitive,
and hardest to provide primitives in distributed shared memory emulations. To
ensure survivability, scalability, and availability of a storage service in the
presence of failures, traditional approaches for atomic memory emulation, in
message passing environments, replicate the objects across multiple servers.
Compared to replication based algorithms, erasure code-based atomic memory
algorithms has much lower storage and communication costs, but usually, they
are harder to design. The difficulty of designing atomic memory algorithms
further grows, when the set of servers may be changed to ensure survivability
of the service over software and hardware upgrades, while avoiding service
interruptions. Atomic memory algorithms for performing server reconfiguration,
in the replicated systems, are very few, complex, and are still part of an
active area of research; reconfigurations of erasure-code based algorithms are
non-existent.
In this work, we present ARES, an algorithmic framework that allows
reconfiguration of the underlying servers, and is particularly suitable for
erasure-code based algorithms emulating atomic objects. ARES introduces new
configurations while keeping the service available. To use with ARES we also
propose a new, and to our knowledge, the first two-round erasure code based
algorithm TREAS, for emulating multi-writer, multi-reader (MWMR) atomic objects
in asynchronous, message-passing environments, with near-optimal communication
and storage costs. Our algorithms can tolerate crash failures of any client and
some fraction of servers, and yet, guarantee safety and liveness property.
Moreover, by bringing together the advantages of ARES and TREAS, we propose an
optimized algorithm where new configurations can be installed without the
objects values passing through the reconfiguration clients
Causal Consistency: Beyond Memory
In distributed systems where strong consistency is costly when not
impossible, causal consistency provides a valuable abstraction to represent
program executions as partial orders. In addition to the sequential program
order of each computing entity, causal order also contains the semantic links
between the events that affect the shared objects -- messages emission and
reception in a communication channel , reads and writes on a shared register.
Usual approaches based on semantic links are very difficult to adapt to other
data types such as queues or counters because they require a specific analysis
of causal dependencies for each data type. This paper presents a new approach
to define causal consistency for any abstract data type based on sequential
specifications. It explores, formalizes and studies the differences between
three variations of causal consistency and highlights them in the light of
PRAM, eventual consistency and sequential consistency: weak causal consistency,
that captures the notion of causality preservation when focusing on convergence
; causal convergence that mixes weak causal consistency and convergence; and
causal consistency, that coincides with causal memory when applied to shared
memory.Comment: 21st ACM SIGPLAN Symposium on Principles and Practice of Parallel
Programming, Mar 2016, Barcelone, Spai
Opinions on Tax Deductions and the Consensus Effect in a Survey-Experiment
We present the results of a survey-experiment using a representative sample of the Dutch population in which we relate respondents' opinion about the tax deductibility of mortgages to their estimates about other people's opinion.The experiment employs three treatment variables: monetary incentives, the provision of arguments pro and contra, and ambiguity of the question posed.We find that respondents are characterized by a significant consensus effect.Respondents estimates of others opinions are strongly related to their own opinion.The size of the effect, however, is not affected by ambiguity of the question posed. Information by means of the provision of arguments pro and contra the tax provision does reduce the consensus effect significantly, though.Monetary incentives appear to have only a weak effect.We also find a strong effect of house ownership.Not only are house owners more in favor of the tax provision, they are also characterized by a significantly stronger consensus effect.These results suggest that both cognitive factors and motivational factors are responsible for the consensus effect.consensus effect;experiment;survey;taxation
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