103 research outputs found
Temporal similarity metrics for latent network reconstruction: The role of time-lag decay
When investigating the spreading of a piece of information or the diffusion
of an innovation, we often lack information on the underlying propagation
network. Reconstructing the hidden propagation paths based on the observed
diffusion process is a challenging problem which has recently attracted
attention from diverse research fields. To address this reconstruction problem,
based on static similarity metrics commonly used in the link prediction
literature, we introduce new node-node temporal similarity metrics. The new
metrics take as input the time-series of multiple independent spreading
processes, based on the hypothesis that two nodes are more likely to be
connected if they were often infected at similar points in time. This
hypothesis is implemented by introducing a time-lag function which penalizes
distant infection times. We find that the choice of this time-lag strongly
affects the metrics' reconstruction accuracy, depending on the network's
clustering coefficient and we provide an extensive comparative analysis of
static and temporal similarity metrics for network reconstruction. Our findings
shed new light on the notion of similarity between pairs of nodes in complex
networks
Explainable Recommendation with Personalized Review Retrieval and Aspect Learning
Explainable recommendation is a technique that combines prediction and
generation tasks to produce more persuasive results. Among these tasks, textual
generation demands large amounts of data to achieve satisfactory accuracy.
However, historical user reviews of items are often insufficient, making it
challenging to ensure the precision of generated explanation text. To address
this issue, we propose a novel model, ERRA (Explainable Recommendation by
personalized Review retrieval and Aspect learning). With retrieval enhancement,
ERRA can obtain additional information from the training sets. With this
additional information, we can generate more accurate and informative
explanations. Furthermore, to better capture users' preferences, we incorporate
an aspect enhancement component into our model. By selecting the top-n aspects
that users are most concerned about for different items, we can model user
representation with more relevant details, making the explanation more
persuasive. To verify the effectiveness of our model, extensive experiments on
three datasets show that our model outperforms state-of-the-art baselines (for
example, 3.4% improvement in prediction and 15.8% improvement in explanation
for TripAdvisor)
Interaction-Driven Active 3D Reconstruction with Object Interiors
We introduce an active 3D reconstruction method which integrates visual
perception, robot-object interaction, and 3D scanning to recover both the
exterior and interior, i.e., unexposed, geometries of a target 3D object.
Unlike other works in active vision which focus on optimizing camera viewpoints
to better investigate the environment, the primary feature of our
reconstruction is an analysis of the interactability of various parts of the
target object and the ensuing part manipulation by a robot to enable scanning
of occluded regions. As a result, an understanding of part articulations of the
target object is obtained on top of complete geometry acquisition. Our method
operates fully automatically by a Fetch robot with built-in RGBD sensors. It
iterates between interaction analysis and interaction-driven reconstruction,
scanning and reconstructing detected moveable parts one at a time, where both
the articulated part detection and mesh reconstruction are carried out by
neural networks. In the final step, all the remaining, non-articulated parts,
including all the interior structures that had been exposed by prior part
manipulations and subsequently scanned, are reconstructed to complete the
acquisition. We demonstrate the performance of our method via qualitative and
quantitative evaluation, ablation studies, comparisons to alternatives, as well
as experiments in a real environment.Comment: Accepted to SIGGRAPH Asia 2023, project page at
https://vcc.tech/research/2023/InterReco
Popularity Ratio Maximization: Surpassing Competitors through Influence Propagation
In this paper, we present an algorithmic study on how to surpass competitors
in popularity by strategic promotions in social networks. We first propose a
novel model, in which we integrate the Preferential Attachment (PA) model for
popularity growth with the Independent Cascade (IC) model for influence
propagation in social networks called PA-IC model. In PA-IC, a popular item and
a novice item grab shares of popularity from the natural popularity growth via
the PA model, while the novice item tries to gain extra popularity via
influence cascade in a social network. The {\em popularity ratio} is defined as
the ratio of the popularity measure between the novice item and the popular
item. We formulate {\em Popularity Ratio Maximization (PRM)} as the problem of
selecting seeds in multiple rounds to maximize the popularity ratio in the end.
We analyze the popularity ratio and show that it is monotone but not
submodular. To provide an effective solution, we devise a surrogate objective
function and show that empirically it is very close to the original objective
function while theoretically, it is monotone and submodular. We design two
efficient algorithms, one for the overlapping influence and non-overlapping
seeds (across rounds) setting and the other for the non-overlapping influence
and overlapping seed setting, and further discuss how to deal with other models
and problem variants. Our empirical evaluation further demonstrates that the
proposed PRM-IMM method consistently achieves the best popularity promotion
compared to other methods. Our theoretical and empirical analyses shed light on
the interplay between influence maximization and preferential attachment in
social networks.Comment: 22 pages, 8 figures, to be appear SIGMOD 202
Influence of EOM sideband modulation noise on space-borne gravitational wave detection
Clock noise is one of the dominant noises in the space-borne gravitational
wave (GW) detection. To suppress this noise, the clock noise-calibrated
time-delay-interferometry (TDI) technique is proposed. In this technique, an
inter-spacecraft clock tone transfer chain is necessary to obtain the
comparison information of the clock noises in two spacecraft, during which an
electro-optic-modulator (EOM) is critical and used to modulate the clock noise
to the laser phase. Since the EOM sideband modulation process introduces
modulation noise, it is significant to put forward the corresponding
requirements and assess whether the commercial EOM meets. In this work, based
on the typical Michelson TDI algorithm and the fundamental noise requirement of
GW detectors, the analytic expression of the modulation noise requirement is
strictly derived, which relax the component indicator need compared to the
existing commonly used rough assessments. Furthermore, a commercial EOM
(iXblue-NIR-10 GHz) is tested, and the experimental results show that it can
meet the requirement of the typical GW detection mission LISA in whole
scientific bandwidth by taking the optimal combination of the data stream. Even
when the displacement measurement accuracy of LISA is improved to 1 pm/
in the future, it still meets the demand
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