7,484 research outputs found
Testing parity symmetry of gravity with gravitational waves
The examination of parity symmetry in gravitational interactions has drawn
increasing attention. Although Einstein's General Relativity is
parity-conserved, numerous theories of parity-violating (PV) gravity in
different frameworks have recently been proposed for different motivations. In
this review, we briefly summarize the recent progress of these theories, and
focus on the observable effects of PV terms in the gravitational waves (GWs),
which are mainly reflected in the difference between the left-hand and
right-hand polarization modes. We are primarily concerned with the implications
of these theories for GWs generated by the compact binary coalescences and the
primordial GWs generated in the early Universe. The deviation of GW waveforms
and/or primordial power spectrum can always be quantified by the energy scale
of parity violation of the theory. Applying the current and future GW
observation from laser interferometers and cosmic microwave background
radiation, the current and potential constraints on the PV energy scales are
presented, which indicates that the parity symmetry of gravity can be tested in
high energy scale in this new era of gravitational waves.Comment: 22 pages, no figure
The convergence investigation of meshless finite block method and finite element method
The finite element method is one of the most widely used numerical method in engineering analysis, however, the bad convergence and the complexity of meshing reduce the reliability of the simulated results. Therefore, in this work, a meshless finite block method was applied on heat transfer analysis and elastic deformation analysis. It combines the ideas of finite element and boundary element. A better convergence of meshless finite block method than finite element method was proved
Constraints on the ghost-free parity-violating gravity from Laser-ranged Satellites
This paper explores the evolutionary behavior of the Earth-satellite binary
system within the framework of the ghost-free parity-violating gravity and the
corresponding discussion on the parity-violating effect from the laser-ranged
satellites. For this purpose, we start our study with the Parameterized
Post-Newtonian (PPN) metric of this gravity theory to study the orbital
evolution of the satellites in which the spatial-time sector of the spacetime
is modified due to the parity violation. With this modified PPN metric, we
calculate the effects of the parity-violating sector of metrics on the time
evolution of the orbital elements for an Earth-satellite binary system. We find
that among the five orbital elements, the parity violation has no effect on the
semi-latus rectum, inclination and ascending node, which are the same as the
results of general relativity and consistent with the observations of the
current experiment. In particular, parity violation produces non-zero
corrections to the eccentricity and pericenter, which will accumulate with the
evolution of time, indicating that the parity violation of gravity produces
observable effects. The observational constraint on the parity-violating effect
is derived by confronting the theoretical prediction with the observation by
the LAGEOS II pericenter advance, giving a constraint on the parity-violating
parameter space from the satellite experiments.Comment: 13 pages, no figur
EulerNet: Adaptive Feature Interaction Learning via Euler's Formula for CTR Prediction
Learning effective high-order feature interactions is very crucial in the CTR
prediction task. However, it is very time-consuming to calculate high-order
feature interactions with massive features in online e-commerce platforms. Most
existing methods manually design a maximal order and further filter out the
useless interactions from them. Although they reduce the high computational
costs caused by the exponential growth of high-order feature combinations, they
still suffer from the degradation of model capability due to the suboptimal
learning of the restricted feature orders. The solution to maintain the model
capability and meanwhile keep it efficient is a technical challenge, which has
not been adequately addressed. To address this issue, we propose an adaptive
feature interaction learning model, named as EulerNet, in which the feature
interactions are learned in a complex vector space by conducting space mapping
according to Euler's formula. EulerNet converts the exponential powers of
feature interactions into simple linear combinations of the modulus and phase
of the complex features, making it possible to adaptively learn the high-order
feature interactions in an efficient way. Furthermore, EulerNet incorporates
the implicit and explicit feature interactions into a unified architecture,
which achieves the mutual enhancement and largely boosts the model
capabilities. Such a network can be fully learned from data, with no need of
pre-designed form or order for feature interactions. Extensive experiments
conducted on three public datasets have demonstrated the effectiveness and
efficiency of our approach. Our code is available at:
https://github.com/RUCAIBox/EulerNet.Comment: 10 pages, 7 figures, accepted for publication in SIGIR'2
UFIN: Universal Feature Interaction Network for Multi-Domain Click-Through Rate Prediction
Click-Through Rate (CTR) prediction, which aims to estimate the probability
of a user clicking on an item, is a key task in online advertising. Numerous
existing CTR models concentrate on modeling the feature interactions within a
solitary domain, thereby rendering them inadequate for fulfilling the
requisites of multi-domain recommendations in real industrial scenarios. Some
recent approaches propose intricate architectures to enhance knowledge sharing
and augment model training across multiple domains. However, these approaches
encounter difficulties when being transferred to new recommendation domains,
owing to their reliance on the modeling of ID features (e.g., item id). To
address the above issue, we propose the Universal Feature Interaction Network
(UFIN) approach for CTR prediction. UFIN exploits textual data to learn
universal feature interactions that can be effectively transferred across
diverse domains. For learning universal feature representations, we regard the
text and feature as two different modalities and propose an encoder-decoder
network founded on a Large Language Model (LLM) to enforce the transfer of data
from the text modality to the feature modality. Building upon the above
foundation, we further develop a mixtureof-experts (MoE) enhanced adaptive
feature interaction model to learn transferable collaborative patterns across
multiple domains. Furthermore, we propose a multi-domain knowledge distillation
framework to enhance feature interaction learning. Based on the above methods,
UFIN can effectively bridge the semantic gap to learn common knowledge across
various domains, surpassing the constraints of ID-based models. Extensive
experiments conducted on eight datasets show the effectiveness of UFIN, in both
multidomain and cross-platform settings. Our code is available at
https://github.com/RUCAIBox/UFIN
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