1,543 research outputs found
Individualism-collectivism and interpersonal memory guidance of attention
Recently it has been shown that the allocation of attention by a participant in a visual search task can be affected by memory items that have to be maintained by a co-actor, when similar tasks are jointly engaged by dyads (He, Lever, & Humphreys, 2011). In the present study we examined the contribution of individualism-collectivism to this āinterpersonal memory guidanceā effect. Actors performed visual search while a preview image was either held by the critical participant, held by a co-actor or was irrelevant to either participant. Attention during search was attracted to stimuli that matched the contents of the co-actorās memory. This interpersonal effect correlated with the collectivism scores, and was enhanced by priming with a collectivistic scenario. The dimensions of individualism, however, did not contribute to performance. These data suggest that collectivism, but not individualism, modulates interpersonal influences on memory and attention in joint action
Predicting Urban Dispersal Events: A Two-Stage Framework through Deep Survival Analysis on Mobility Data
Urban dispersal events are processes where an unusually large number of
people leave the same area in a short period. Early prediction of dispersal
events is important in mitigating congestion and safety risks and making better
dispatching decisions for taxi and ride-sharing fleets. Existing work mostly
focuses on predicting taxi demand in the near future by learning patterns from
historical data. However, they fail in case of abnormality because dispersal
events with abnormally high demand are non-repetitive and violate common
assumptions such as smoothness in demand change over time. Instead, in this
paper we argue that dispersal events follow a complex pattern of trips and
other related features in the past, which can be used to predict such events.
Therefore, we formulate the dispersal event prediction problem as a survival
analysis problem. We propose a two-stage framework (DILSA), where a deep
learning model combined with survival analysis is developed to predict the
probability of a dispersal event and its demand volume. We conduct extensive
case studies and experiments on the NYC Yellow taxi dataset from 2014-2016.
Results show that DILSA can predict events in the next 5 hours with F1-score of
0.7 and with average time error of 18 minutes. It is orders of magnitude better
than the state-ofthe-art deep learning approaches for taxi demand prediction.Comment: To appear in AAAI-19 proceedings. The reason for the replacement was
the misspelled author name in the meta-data field. Author name was corrected
from "Ynahua Li" to "Yanhua Li". The author list in the paper was correct and
remained unchange
Quantum dynamics of an Ising spin-chain in a random transverse field
We consider an Ising spin-chain in a random transverse magnetic field and
compute the zero temperature wave vector and frequency dependent dynamic
structure factor numerically by using Jordan-Wigner transformation. Two types
of distributions of magnetic fields are introduced. For a rectangular
distribution, a dispersing branch is observed, and disorder tends to broaden
the dispersion peak and close the excitation gap. For a binary distribution, a
non-dispersing branch at almost zero energy is recovered. We discuss the
relationship of our work to the neutron scattering measurement in
.Comment: 4 pages and 6 eps figures; minor clarifications were made; the text
was shortened to add an additional figur
Internal Josephson-Like Tunneling in Two-Component Bose-Einstein Condensates Affected by Sign of the Atomic Interaction and External Trapping Potential
We study the Josephson-like tunneling in two-component Bose-Einstein
condensates coupled with microwave field in respond to various attractive and
repulsive atomic interaction under the various aspect ratio of trapping
potential and the gravitational field. It is very interesting to find that the
dynamic of Josephson-like tunneling can be controlled from fast damped
oscillations and asymmetric occupation to nondamped oscillation and symmetric
occupation.Comment: 4 pages, 5 figure
Learning Rich Geographical Representations: Predicting Colorectal Cancer Survival in the State of Iowa
Neural networks are capable of learning rich, nonlinear feature
representations shown to be beneficial in many predictive tasks. In this work,
we use these models to explore the use of geographical features in predicting
colorectal cancer survival curves for patients in the state of Iowa, spanning
the years 1989 to 2012. Specifically, we compare model performance using a
newly defined metric -- area between the curves (ABC) -- to assess (a) whether
survival curves can be reasonably predicted for colorectal cancer patients in
the state of Iowa, (b) whether geographical features improve predictive
performance, and (c) whether a simple binary representation or richer, spectral
clustering-based representation perform better. Our findings suggest that
survival curves can be reasonably estimated on average, with predictive
performance deviating at the five-year survival mark. We also find that
geographical features improve predictive performance, and that the best
performance is obtained using richer, spectral analysis-elicited features.Comment: 8 page
Quantum Hall plateau transition in the lowest Landau level of disordered graphene
We investigate, analytically and numerically, the effects of disorder on the
density of states and on the localization properties of the relativistic two
dimensional fermions in the lowest Landau level. Employing a supersymmetric
technique, we calculate the exact density of states for the Cauchy (Lorentzian)
distribution for various types of disorders. We use a numerical technique to
establish the localization-delocalization (LD) transition in the lowest Landau
level. For some types of disorder the LD transition is shown to belong to a
different universality class, as compared to the corresponding nonrelativistic
problem. The results are relevant to the integer quantum Hall plateau
transitions observed in graphene.Comment: 18 pages and 11 figure
New interpretation of matter-antimatter asymmetry based on branes and possible observational consequences
Motivated by the AMS project, we assume that after the Big Bang or inflation
epoch, antimatter was repelled onto one brane which is separated from our brane
where all the observational matter resides. It is suggested that CP may be
spontaneously broken, the two branes would correspond to ground states for
matter and antimatter respectively. Generally a complex scalar field which is
responsible for the spontaneous CP violation, exists in the space between the
branes and causes a repulsive force against the gravitation. A possible
potential barrier prevents the mater(antimatter) particles to enter the space
between two branes. However, by the quantum tunnelling, a sizable anti-matter
flux may come to our brane. In this work by considering two possible models,
i.e. the naive flat space-time and Randall-Sundrum models and using the
observational data on the visible matter in our universe as inputs, we derive
the antimatter flux which would be observed by the AMS detector.Comment: 10 pages, 4 figures and 2 tables. Replaced by new versio
Empirical risk minimization for metric learning using privileged information
Traditional metric learning methods usually make decisions based on a fixed threshold, which may result in a suboptimal metric when the inter-class and inner-class variations are complex. To address this issue, in this paper we propose an effective metric learning method by exploiting privileged information to relax the fixed threshold under the empirical risk minimization framework. Privileged information describes useful high-level semantic information that is only available during training. Our goal is to improve the performance by incorporating privileged information to design a locally adaptive decision function. We jointly learn two distance metrics by minimizing the empirical loss penalizing the difference between the distance in the original space and that in the privileged space. The distance in the privileged space functions as a locally adaptive decision threshold, which can guide the decision making like a teacher. We optimize the objective function using the Accelerated Proximal Gradient approach to obtain a global optimum solution. Experiment results show that by leveraging privileged information, our proposed method can achieve satisfactory performance
Antibunching photons in a cavity coupled to an optomechanical system
We study the photon statistics of a cavity linearly coupled to an
optomechanical system via second order correlation functions. Our calculations
show that the cavity can exhibit strong photon antibunching even when
optomechanical interaction in the optomechanical system is weak. The
cooperation between the weak optomechanical interaction and the destructive
interference between different paths for two-photon excitation leads to the
efficient antibunching effect. Compared with the standard optomechanical
system, the coupling between a cavity and an optomechanical system provides a
method to relax the constraints to obtain single photon by optomechanical
interaction.Comment: 7 papes, 5 figure
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