747 research outputs found
The Dynamics of Public Opinion in Complex Networks
This paper studies the problem of public opinion formation and concentrates on the interplays among three factors: individual attributes, environmental influences and information flow. We present a simple model to analyze the dynamics of four types of networks. Our simulations suggest that regular communities establish not only local consensus, but also global diversity in public opinions. However, when small world networks, random networks, or scale-free networks model social relationships, the results are sensitive to the elasticity coefficient of environmental influences and the average connectivity of the type of network. For example, a community with a higher average connectivity has a higher probability of consensus. Yet, it is misleading to predict results merely based on the characteristic path length of networks. In the process of changing environmental influences and average connectivity, sensitive areas are discovered in the system. By sensitive areas we mean that interior randomness emerges and we cannot predict unequivocally how many opinions will remain upon reaching equilibrium. We also investigate the role of authoritative individuals in information control. While enhancing average connectivity facilitates the diffusion of the authoritative opinion, it makes individuals subject to disturbance from non-authorities as well. Thus, a moderate average connectivity may be preferable because then the public will most likely form an opinion that is parallel with the authoritative one. In a community with a scale-free structure, the influence of authoritative individuals keeps constant with the change of the average connectivity. Provided that the influence of individuals is proportional to the number of their acquaintances, the smallest percentage of authorities is required for a controlled consensus in a scale free network. This study shows that the dynamics of public opinion varies from community to community due to the different degree of impressionability of people and the distinct social network structure of the community.Public Opinion, Complex Network, Consensus, Agent-Based Model
Context-aware Adversarial Attack on Named Entity Recognition
In recent years, large pre-trained language models (PLMs) have achieved
remarkable performance on many natural language processing benchmarks. Despite
their success, prior studies have shown that PLMs are vulnerable to attacks
from adversarial examples. In this work, we focus on the named entity
recognition task and study context-aware adversarial attack methods to examine
the model's robustness. Specifically, we propose perturbing the most
informative words for recognizing entities to create adversarial examples and
investigate different candidate replacement methods to generate natural and
plausible adversarial examples. Experiments and analyses show that our methods
are more effective in deceiving the model into making wrong predictions than
strong baselines
A Simple Approach to Jointly Rank Passages and Select Relevant Sentences in the OBQA Context
In the open book question answering (OBQA) task, selecting the relevant
passages and sentences from distracting information is crucial to reason the
answer to a question. HotpotQA dataset is designed to teach and evaluate
systems to do both passage ranking and sentence selection. Many existing
frameworks use separate models to select relevant passages and sentences
respectively. Such systems not only have high complexity in terms of the
parameters of models but also fail to take the advantage of training these two
tasks together since one task can be beneficial for the other one. In this
work, we present a simple yet effective framework to address these limitations
by jointly ranking passages and selecting sentences. Furthermore, we propose
consistency and similarity constraints to promote the correlation and
interaction between passage ranking and sentence selection.The experiments
demonstrate that our framework can achieve competitive results with previous
systems and outperform the baseline by 28\% in terms of exact matching of
relevant sentences on the HotpotQA dataset.Comment: Accepted to NAACL SWR 202
Energy-Efficient Wireless Communications with Distributed Reconfigurable Intelligent Surfaces
This paper investigates the problem of resource allocation for a wireless
communication network with distributed reconfigurable intelligent surfaces
(RISs). In this network, multiple RISs are spatially distributed to serve
wireless users and the energy efficiency of the network is maximized by
dynamically controlling the on-off status of each RIS as well as optimizing the
reflection coefficients matrix of the RISs. This problem is posed as a joint
optimization problem of transmit beamforming and RIS control, whose goal is to
maximize the energy efficiency under minimum rate constraints of the users. To
solve this problem, two iterative algorithms are proposed for the single-user
case and multi-user case. For the single-user case, the phase optimization
problem is solved by using a successive convex approximation method, which
admits a closed-form solution at each step. Moreover, the optimal RIS on-off
status is obtained by using the dual method. For the multi-user case, a
low-complexity greedy searching method is proposed to solve the RIS on-off
optimization problem. Simulation results show that the proposed scheme achieves
up to 33\% and 68\% gains in terms of the energy efficiency in both single-user
and multi-user cases compared to the conventional RIS scheme and
amplify-and-forward relay scheme, respectively
Convergence Time Optimization for Federated Learning over Wireless Networks
In this paper, the convergence time of federated learning (FL), when deployed
over a realistic wireless network, is studied. In particular, a wireless
network is considered in which wireless users transmit their local FL models
(trained using their locally collected data) to a base station (BS). The BS,
acting as a central controller, generates a global FL model using the received
local FL models and broadcasts it back to all users. Due to the limited number
of resource blocks (RBs) in a wireless network, only a subset of users can be
selected to transmit their local FL model parameters to the BS at each learning
step. Moreover, since each user has unique training data samples, the BS
prefers to include all local user FL models to generate a converged global FL
model. Hence, the FL performance and convergence time will be significantly
affected by the user selection scheme. Therefore, it is necessary to design an
appropriate user selection scheme that enables users of higher importance to be
selected more frequently. This joint learning, wireless resource allocation,
and user selection problem is formulated as an optimization problem whose goal
is to minimize the FL convergence time while optimizing the FL performance. To
solve this problem, a probabilistic user selection scheme is proposed such that
the BS is connected to the users whose local FL models have significant effects
on its global FL model with high probabilities. Given the user selection
policy, the uplink RB allocation can be determined. To further reduce the FL
convergence time, artificial neural networks (ANNs) are used to estimate the
local FL models of the users that are not allocated any RBs for local FL model
transmission at each given learning step, which enables the BS to enhance its
global FL model and improve the FL convergence speed and performance.Comment: This paper has been accepted in the IEEE Transactions on Wireless
Communication
Soft Actor-Critic Learning-Based Joint Computing, Pushing, and Caching Framework in MEC Networks
To support future 6G mobile applications, the mobile edge computing (MEC)
network needs to be jointly optimized for computing, pushing, and caching to
reduce transmission load and computation cost. To achieve this, we propose a
framework based on deep reinforcement learning that enables the dynamic
orchestration of these three activities for the MEC network. The framework can
implicitly predict user future requests using deep networks and push or cache
the appropriate content to enhance performance. To address the curse of
dimensionality resulting from considering three activities collectively, we
adopt the soft actor-critic reinforcement learning in continuous space and
design the action quantization and correction specifically to fit the discrete
optimization problem. We conduct simulations in a single-user single-server MEC
network setting and demonstrate that the proposed framework effectively
decreases both transmission load and computing cost under various
configurations of cache size and tolerable service delay
Recommended from our members
Fully solvable lower dimensional dynamics of Cartesian product of Kuramoto models
Implementing a positive correlation between the natural frequencies of nodes and their connectivity on a single star graph leads to a pronounced explosive transition to synchronization, additionally presenting hysteresis behavior. From the viewpoint of network connectivity, a star has been considered as a building motif to generate a big graph by graph operations. On the other hand, we propose to construct complex synchronization dynamics by applying the Cartesian product of two Kuramoto models on two star networks. On the product model, the lower dimensional equations describing the ensemble dynamics in terms of collective order parameters are fully solved by the Watanabe-Strogatz method. Different graph parameter choices lead to three different interacting scenarios of the hysteresis areas of two individual factor graphs, which further change the basins of attraction of multiple fixed points. Furthermore, we obtain coupling regimes where cluster synchronization states are often present on the product graph and the number of clusters is fully controlled. More specifically, oscillators on one star graph are synchronized while those on the other star are not synchronized, which induces clustered state on the product model. The numerical results agree perfectly with the theoretic predictions. © 2019 The Author(s). Published by IOP Publishing Ltd on behalf of the Institute of Physics and Deutsche Physikalische Gesellschaft
- …