163 research outputs found
The effect of the gravitational constant variation on the propagation of gravitational waves
Since the first detection of gravitational waves, they have been used to
investigate various fundamental problems, including the variation of physical
constants. Regarding the gravitational constant, previous works focused on the
effect of the gravitational constant variation on the gravitational wave
generation. In this paper, we investigate the effect of the gravitational
constant variation on the gravitational wave propagation. The Maxwell-like
equation that describes the propagation of gravitational waves is extended in
this paper to account for situations where the gravitational constant varies.
Based on this equation, we find that the amplitude of gravitational waves will
be corrected. Consequently the estimated distance to the gravitational wave
source without considering such a correction may be biased. Applying our
correction result to the well known binary neutron star coalescence event
GW170817, we get a constraint on the variation of the gravitational constant.
Relating our result to the Yukawa deviation of gravity, we for the first time
get the constraint of the Yukawa parameters in 10Mpc scale. This scale
corresponds to a graviton mass eV
Improved OOD Generalization via Conditional Invariant Regularizer
Recently, generalization on out-of-distribution (OOD) data with correlation
shift has attracted great attention. The correlation shift is caused by the
spurious attributes that correlate to the class label, as the correlation
between them may vary in training and test data. For such a problem, we show
that given the class label, the conditionally independent models of spurious
attributes are OOD generalizable. Based on this, a metric Conditional Spurious
Variation (CSV) which controls OOD generalization error, is proposed to measure
such conditional independence. To improve the OOD generalization, we regularize
the training process with the proposed CSV. Under mild assumptions, our
training objective can be formulated as a nonconvex-concave mini-max problem.
An algorithm with provable convergence rate is proposed to solve the problem.
Extensive empirical results verify our algorithm's efficacy in improving OOD
generalization
Capacity Constrained Influence Maximization in Social Networks
Influence maximization (IM) aims to identify a small number of influential
individuals to maximize the information spread and finds applications in
various fields. It was first introduced in the context of viral marketing,
where a company pays a few influencers to promote the product. However, apart
from the cost factor, the capacity of individuals to consume content poses
challenges for implementing IM in real-world scenarios. For example, players on
online gaming platforms can only interact with a limited number of friends. In
addition, we observe that in these scenarios, (i) the initial adopters of
promotion are likely to be the friends of influencers rather than the
influencers themselves, and (ii) existing IM solutions produce sub-par results
with high computational demands. Motivated by these observations, we propose a
new IM variant called capacity constrained influence maximization (CIM), which
aims to select a limited number of influential friends for each initial adopter
such that the promotion can reach more users. To solve CIM effectively, we
design two greedy algorithms, MG-Greedy and RR-Greedy, ensuring the
-approximation ratio. To improve the efficiency, we devise the scalable
implementation named RR-OPIM+ with -approximation and
near-linear running time. We extensively evaluate the performance of 9
approaches on 6 real-world networks, and our solutions outperform all
competitors in terms of result quality and running time. Additionally, we
deploy RR-OPIM+ to online game scenarios, which improves the baseline
considerably.Comment: The technical report of the paper entitled 'Capacity Constrained
Influence Maximization in Social Networks' in SIGKDD'2
CALICO: Self-Supervised Camera-LiDAR Contrastive Pre-training for BEV Perception
Perception is crucial in the realm of autonomous driving systems, where
bird's eye view (BEV)-based architectures have recently reached
state-of-the-art performance. The desirability of self-supervised
representation learning stems from the expensive and laborious process of
annotating 2D and 3D data. Although previous research has investigated
pretraining methods for both LiDAR and camera-based 3D object detection, a
unified pretraining framework for multimodal BEV perception is missing. In this
study, we introduce CALICO, a novel framework that applies contrastive
objectives to both LiDAR and camera backbones. Specifically, CALICO
incorporates two stages: point-region contrast (PRC) and region-aware
distillation (RAD). PRC better balances the region- and scene-level
representation learning on the LiDAR modality and offers significant
performance improvement compared to existing methods. RAD effectively achieves
contrastive distillation on our self-trained teacher model. CALICO's efficacy
is substantiated by extensive evaluations on 3D object detection and BEV map
segmentation tasks, where it delivers significant performance improvements.
Notably, CALICO outperforms the baseline method by 10.5% and 8.6% on NDS and
mAP. Moreover, CALICO boosts the robustness of multimodal 3D object detection
against adversarial attacks and corruption. Additionally, our framework can be
tailored to different backbones and heads, positioning it as a promising
approach for multimodal BEV perception
Truck model recognition for an automatic overload detection system based on the improved MMAL-Net
Efficient and reliable transportation of goods through trucks is crucial for road logistics. However, the overloading of trucks poses serious challenges to road infrastructure and traffic safety. Detecting and preventing truck overloading is of utmost importance for maintaining road conditions and ensuring the safety of both road users and goods transported. This paper introduces a novel method for detecting truck overloading. The method utilizes the improved MMAL-Net for truck model recognition. Vehicle identification involves using frontal and side truck images, while APPM is applied for local segmentation of the side image to recognize individual parts. The proposed method analyzes the captured images to precisely identify the models of trucks passing through automatic weighing stations on the highway. The improved MMAL-Net achieved an accuracy of 95.03% on the competitive benchmark dataset, Stanford Cars, demonstrating its superiority over other established methods. Furthermore, our method also demonstrated outstanding performance on a small-scale dataset. In our experimental evaluation, our method achieved a recognition accuracy of 85% when the training set consisted of 20 sets of photos, and it reached 100% as the training set gradually increased to 50 sets of samples. Through the integration of this recognition system with weight data obtained from weighing stations and license plates information, the method enables real-time assessment of truck overloading. The implementation of the proposed method is of vital importance for multiple aspects related to road traffic safety
Evaluation of Chinese Quad-polarization Gaofen-3 SAR Wave Mode Data for Significant Wave Height Retrieval
Our work describes the accuracy of Chinese quad-polarization Gaofen-3 (GF-3) synthetic aperture radar (SAR) wave mode data for wave retrieval and provides guidance for the operational applications of GF-3 SAR. In this study, we evaluated the accuracy of the SAR-derived significant wave height (SWH) from 10,514 GF-3 SAR images with visible wave streaks acquired in wave mode by using the existing wave retrieval algorithms, e.g., the theoretical-based algorithm parameterized first-guess spectrum method (PFSM), the empirical algorithm CSAR_WAVE2 for VV-polarization, and the algorithm for quad-polarization (Q-P). The retrieved SWHs were compared with the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis field with 0.125° grids. The root mean square error (RMSE) of the SWH is 0.57 m, found using CSAR_WAVE2, and this RMSE value was less than the RMSE values for the analysis results achieved with the PFSM and Q-P algorithms. The statistical analysis also indicated that wind speed had little impact on the bias with increasing wind speed. However, the retrieval tended to overestimate when the SWH was smaller than 2.5 m and underestimate with an increasing SWH. This behavior provides a perspective of the improvement needed for the SWH retrieval algorithm using the GF-3 SAR acquired in wave mode
Extreme risk induced by communities in interdependent networks
10.1038/s42005-019-0144-6Communications Physics214
- …