956 research outputs found
Echo chamber effects based on a novel three-dimensional Deffuant-Weisbuch model
In order to solve the problem of opinion polarization and distortion caused
by echo chamber effect in the evolution process of online public opinion,a
three-dimensional Deffuant-Weisbuch model is proposed to study the formation
and elimination of echo chamber effect in this paper. Firstly, the original
pairwise interaction model is generalized to three-point interaction model.
Secondly, we consider individual psychological mechanism and introduce
individual emotional factor into the trust threshold of original model.
Finally, the natural evolution coefficient of opinion is introduced to modify
the model. The improved model is used to conduct simulation experiments on
social networks with different structures, and opinion leaders and active
agents are introduced into the network, so as to study the corresponding
generation and breaking mechanism of echo chamber. The experimental results
show that the change of network structure cannot eliminate the echo chamber
effect, and the increase of network stability and connectivity can only slow
down the echo chamber effect. Opinion leaders can aggregate opinions within
their scope of influence and have a guiding effect on opinions. Therefore, if
opinion leaders can change their opinions over time, they can well guide
opinions to converge to neutral opinions, thus achieving the purpose of
breaking the echo chamber. Active agents can lead the opinions in the network
to converge to the neutral, and active agents with high stubbornness can lead
the free views to converge to the neutral, thus achieving the purpose of
breaking the echo chamber effect.Comment: 34pages 57figure
Proving Expected Sensitivity of Probabilistic Programs with Randomized Variable-Dependent Termination Time
The notion of program sensitivity (aka Lipschitz continuity) specifies that
changes in the program input result in proportional changes to the program
output. For probabilistic programs the notion is naturally extended to expected
sensitivity. A previous approach develops a relational program logic framework
for proving expected sensitivity of probabilistic while loops, where the number
of iterations is fixed and bounded. In this work, we consider probabilistic
while loops where the number of iterations is not fixed, but randomized and
depends on the initial input values. We present a sound approach for proving
expected sensitivity of such programs. Our sound approach is martingale-based
and can be automated through existing martingale-synthesis algorithms.
Furthermore, our approach is compositional for sequential composition of while
loops under a mild side condition. We demonstrate the effectiveness of our
approach on several classical examples from Gambler's Ruin, stochastic hybrid
systems and stochastic gradient descent. We also present experimental results
showing that our automated approach can handle various probabilistic programs
in the literature
Some new decay estimates for -dimensional degenerate oscillatory integral operators
In this paper, we consider the dimensional oscillatory integral
operators with cubic homogeneous polynomial phases, which are degenerate in the
sense of \cite{Tan06}. We improve the previously known decay rate
to and also establish a sharp decay estimate based on
fractional integration method.Comment: This new version adds some missed argument and correct some typo
The Application of Two-level Attention Models in Deep Convolutional Neural Network for Fine-grained Image Classification
Fine-grained classification is challenging because categories can only be
discriminated by subtle and local differences. Variances in the pose, scale or
rotation usually make the problem more difficult. Most fine-grained
classification systems follow the pipeline of finding foreground object or
object parts (where) to extract discriminative features (what).
In this paper, we propose to apply visual attention to fine-grained
classification task using deep neural network. Our pipeline integrates three
types of attention: the bottom-up attention that propose candidate patches, the
object-level top-down attention that selects relevant patches to a certain
object, and the part-level top-down attention that localizes discriminative
parts. We combine these attentions to train domain-specific deep nets, then use
it to improve both the what and where aspects. Importantly, we avoid using
expensive annotations like bounding box or part information from end-to-end.
The weak supervision constraint makes our work easier to generalize.
We have verified the effectiveness of the method on the subsets of ILSVRC2012
dataset and CUB200_2011 dataset. Our pipeline delivered significant
improvements and achieved the best accuracy under the weakest supervision
condition. The performance is competitive against other methods that rely on
additional annotations
Federated Neural Architecture Search
To preserve user privacy while enabling mobile intelligence, techniques have
been proposed to train deep neural networks on decentralized data. However,
training over decentralized data makes the design of neural architecture quite
difficult as it already was. Such difficulty is further amplified when
designing and deploying different neural architectures for heterogeneous mobile
platforms. In this work, we propose an automatic neural architecture search
into the decentralized training, as a new DNN training paradigm called
Federated Neural Architecture Search, namely federated NAS. To deal with the
primary challenge of limited on-client computational and communication
resources, we present FedNAS, a highly optimized framework for efficient
federated NAS. FedNAS fully exploits the key opportunity of insufficient model
candidate re-training during the architecture search process, and incorporates
three key optimizations: parallel candidates training on partial clients, early
dropping candidates with inferior performance, and dynamic round numbers.
Tested on large-scale datasets and typical CNN architectures, FedNAS achieves
comparable model accuracy as state-of-the-art NAS algorithm that trains models
with centralized data, and also reduces the client cost by up to two orders of
magnitude compared to a straightforward design of federated NAS
Local Reasoning about Probabilistic Behaviour for Classical-Quantum Programs
Verifying the functional correctness of programs with both classical and
quantum constructs is a challenging task. The presence of probabilistic
behaviour entailed by quantum measurements and unbounded while loops complicate
the verification task greatly. We propose a new quantum Hoare logic for local
reasoning about probabilistic behaviour by introducing distribution formulas to
specify probabilistic properties. We show that the proof rules in the logic are
sound with respect to a denotational semantics. To demonstrate the
effectiveness of the logic, we formally verify the correctness of non-trivial
quantum algorithms including the HHL and Shor's algorithms.Comment: 27 pages. arXiv admin note: text overlap with arXiv:2107.0080
Probabilistic spectrum Gaussian noise estimate for random bandwidth traffic
A probabilistic spectrum Gaussian noise (PSGN) model is proposed to predict the nonlinear noise for random bandwidth traffic in long-haul elastic optical networks. The model reduces the noise estimate 9.1% on average compared to the standard Gaussian noise model applied to the maximum bandwidth
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