2,403 research outputs found
Collective dynamics of belief evolution under cognitive coherence and social conformity
Human history has been marked by social instability and conflict, often
driven by the irreconcilability of opposing sets of beliefs, ideologies, and
religious dogmas. The dynamics of belief systems has been studied mainly from
two distinct perspectives, namely how cognitive biases lead to individual
belief rigidity and how social influence leads to social conformity. Here we
propose a unifying framework that connects cognitive and social forces together
in order to study the dynamics of societal belief evolution. Each individual is
endowed with a network of interacting beliefs that evolves through interaction
with other individuals in a social network. The adoption of beliefs is affected
by both internal coherence and social conformity. Our framework explains how
social instabilities can arise in otherwise homogeneous populations, how small
numbers of zealots with highly coherent beliefs can overturn societal
consensus, and how belief rigidity protects fringe groups and cults against
invasion from mainstream beliefs, allowing them to persist and even thrive in
larger societies. Our results suggest that strong consensus may be insufficient
to guarantee social stability, that the cognitive coherence of belief-systems
is vital in determining their ability to spread, and that coherent
belief-systems may pose a serious problem for resolving social polarization,
due to their ability to prevent consensus even under high levels of social
exposure. We therefore argue that the inclusion of cognitive factors into a
social model is crucial in providing a more complete picture of collective
human dynamics
Optimal modularity and memory capacity of neural reservoirs
The neural network is a powerful computing framework that has been exploited
by biological evolution and by humans for solving diverse problems. Although
the computational capabilities of neural networks are determined by their
structure, the current understanding of the relationships between a neural
network's architecture and function is still primitive. Here we reveal that
neural network's modular architecture plays a vital role in determining the
neural dynamics and memory performance of the network of threshold neurons. In
particular, we demonstrate that there exists an optimal modularity for memory
performance, where a balance between local cohesion and global connectivity is
established, allowing optimally modular networks to remember longer. Our
results suggest that insights from dynamical analysis of neural networks and
information spreading processes can be leveraged to better design neural
networks and may shed light on the brain's modular organization
Quantum-Enhanced Transmittance Sensing
We consider the problem of estimating unknown transmittance of a
target bathed in thermal background light. As quantum estimation theory yields
the fundamental limits, we employ the lossy thermal-noise bosonic channel
model, which describes sensor-target interaction quantum mechanically in many
practical active-illumination systems (e.g., using emissions at optical,
microwave, or radio frequencies). We prove that quantum illumination using
two-mode squeezed vacuum (TMSV) states asymptotically achieves minimal quantum
Cram\'{e}r-Rao bound (CRB) over all quantum states (not necessarily Gaussian)
in the limit of low transmitted power. We characterize the optimal receiver
structure for TMSV input, and show its advantage over other receivers using
both analysis and Monte Carlo simulation.Comment: Minor revision, 17 pages, 11 figures. in IEEE J. Sel. Top. Signal
Proces
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
Complete genome sequences of 15 chikungunya virus isolates from Puerto Rico
Here, we report the complete genome sequences of 15 chikungunya virus strains isolated from human plasma from infected patients in Puerto Rico. The results show that currently circulating chikungunya strains in Puerto Rico are closely related
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