320,911 research outputs found
Lower bounds for Arrangement-based Range-Free Localization in Sensor Networks
Colander are location aware entities that collaborate to determine
approximate location of mobile or static objects when beacons from an object
are received by all colanders that are within its distance . This model,
referred to as arrangement-based localization, does not require distance
estimation between entities, which has been shown to be highly erroneous in
practice. Colander are applicable in localization in sensor networks and
tracking of mobile objects.
A set is an -colander if by placing
receivers at the points of , a wireless device with transmission radius
can be localized to within a circle of radius . We present tight
upper and lower bounds on the size of -colanders. We measure the
expected size of colanders that will form -colanders if they
distributed uniformly over the plane
Validation of Twitter opinion trends with national polling aggregates: Hillary Clinton vs Donald Trump
Measuring and forecasting opinion trends from real-time social media is a
long-standing goal of big-data analytics. Despite its importance, there has
been no conclusive scientific evidence so far that social media activity can
capture the opinion of the general population. Here we develop a method to
infer the opinion of Twitter users regarding the candidates of the 2016 US
Presidential Election by using a combination of statistical physics of complex
networks and machine learning based on hashtags co-occurrence to develop an
in-domain training set approaching 1 million tweets. We investigate the social
networks formed by the interactions among millions of Twitter users and infer
the support of each user to the presidential candidates. The resulting Twitter
trends follow the New York Times National Polling Average, which represents an
aggregate of hundreds of independent traditional polls, with remarkable
accuracy. Moreover, the Twitter opinion trend precedes the aggregated NYT polls
by 10 days, showing that Twitter can be an early signal of global opinion
trends. Our analytics unleash the power of Twitter to uncover social trends
from elections, brands to political movements, and at a fraction of the cost of
national polls
Testing two cognitive theories of insight
Insight in problem solving occurs when the problem solver fails to see how to solve a problem and then-"aha!"-there is a sudden realization how to solve it. Two contemporary theories have been proposed to explain insight. The representational change theory (e.g., G. Knoblich, S. Ohlsson, & G. E. Rainey, 2001) proposes that insight occurs through relaxing self-imposed constraints on a problem and by decomposing chunked items in the problem. The progress monitoring theory (e.g., J. N. MacGregor, T. C. Ormerod, & E. P. Chronicle, 2001) proposes that insight is only sought once it becomes apparent that the distance to the goal is unachievable in the moves remaining. These 2 theories are tested in an unlimited move problem, to which neither theory has previously been applied. The results lend support to both, but experimental manipulations to the problem suggest that the representational change theory is the better indicator of performance. The findings suggest that testable opposing predictions can be made to examine theories of insight and that the use of eye movement data is a fruitful method of both examining insight and testing theories of insight
Robust Temporally Coherent Laplacian Protrusion Segmentation of 3D Articulated Bodies
In motion analysis and understanding it is important to be able to fit a
suitable model or structure to the temporal series of observed data, in order
to describe motion patterns in a compact way, and to discriminate between them.
In an unsupervised context, i.e., no prior model of the moving object(s) is
available, such a structure has to be learned from the data in a bottom-up
fashion. In recent times, volumetric approaches in which the motion is captured
from a number of cameras and a voxel-set representation of the body is built
from the camera views, have gained ground due to attractive features such as
inherent view-invariance and robustness to occlusions. Automatic, unsupervised
segmentation of moving bodies along entire sequences, in a temporally-coherent
and robust way, has the potential to provide a means of constructing a
bottom-up model of the moving body, and track motion cues that may be later
exploited for motion classification. Spectral methods such as locally linear
embedding (LLE) can be useful in this context, as they preserve "protrusions",
i.e., high-curvature regions of the 3D volume, of articulated shapes, while
improving their separation in a lower dimensional space, making them in this
way easier to cluster. In this paper we therefore propose a spectral approach
to unsupervised and temporally-coherent body-protrusion segmentation along time
sequences. Volumetric shapes are clustered in an embedding space, clusters are
propagated in time to ensure coherence, and merged or split to accommodate
changes in the body's topology. Experiments on both synthetic and real
sequences of dense voxel-set data are shown. This supports the ability of the
proposed method to cluster body-parts consistently over time in a totally
unsupervised fashion, its robustness to sampling density and shape quality, and
its potential for bottom-up model constructionComment: 31 pages, 26 figure
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Mental Imagery and Chunks: Empirical and Computational Findings
To investigate experts’ imagery in chess, players were required to recall briefly-presented positions in which the pieces were placed on the intersections between squares (intersection positions). Position types ranged from game positions to positions where both the piece distribution and location were randomized. Simulations were run with the CHREST model (Gobet & Simon, 2000). The simulations assumed that pieces had to be centered back one by one to the middle of the squares in the mind’s eye before chunks could be recognized. Consistent with CHREST’s predictions, chess players (N = 36), ranging from weak amateurs to grandmasters, exhibited much poorer recall on intersection positions than on standard positions (pieces placed on centers of squares). On the intersection positions, the skill difference in recall was larger on game positions than on the randomized positions. Participants recalled bishops better than knights, suggesting that Stroop-like interference impairs recall of the latter. The data supported both the time parameter in CHREST for shifting pieces in the mind’s eye (125 ms per piece) and the seriality assumption. In general, the study reinforces the plausibility of CHREST as a model of cognition
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