Cloud is critical for planetary climate and habitability, but it is also one of the most challenging parts of studying planets in and beyond the solar system. Previous simulations using global general circulation models (GCMs) found that for 1:1 tida
lly locked (i.e., synchronously rotating) terrestrial planets with oceans, strong convergence and convection produce optically thick clouds over the substellar area. One obvious weakness of these studies is that clouds are parameterized based on the knowledge on Earth, and whether it is applicable to exoplanetary environment is unknown. Here we use a cloud-resolving model (CRM) with high resolution (2 km) in a two-dimensional (2D) configuration to simulate the clouds and circulation on tidally locked aqua-planets. We confirm that the substellar area is covered by deep convective clouds, the nightside is dominated by low-level stratus clouds, and these two are linked by a global-scale overturning circulation. We further find that a uniform warming of the surface causes the width of convection and clouds to decrease, but a decrease of day-night surface temperature contrast or an increase of longwave radiative cooling rate causes the width of convection and clouds to increase. These relationships can be roughly interpreted based on some simple thermodynamic theories. Comparing the results between CRM and GCM, we find that the results are broadly similar although there are many significant differences. Future work is required to use 3D CRM(s) with realistic radiative transfer and with the Coriolis force to examine the clouds and climate of tidally locked planets.
Localizing individuals in crowds is more in accordance with the practical demands of subsequent high-level crowd analysis tasks than simply counting. However, existing localization based methods relying on intermediate representations (textit{i.e.},
density maps or pseudo boxes) serving as learning targets are counter-intuitive and error-prone. In this paper, we propose a purely point-based framework for joint crowd counting and individual localization. For this framework, instead of merely reporting the absolute counting error at image level, we propose a new metric, called density Normalized Average Precision (nAP), to provide more comprehensive and more precise performance evaluation. Moreover, we design an intuitive solution under this framework, which is called Point to Point Network (P2PNet). P2PNet discards superfluous steps and directly predicts a set of point proposals to represent heads in an image, being consistent with the human annotation results. By thorough analysis, we reveal the key step towards implementing such a novel idea is to assign optimal learning targets for these proposals. Therefore, we propose to conduct this crucial association in an one-to-one matching manner using the Hungarian algorithm. The P2PNet not only significantly surpasses state-of-the-art methods on popular counting benchmarks, but also achieves promising localization accuracy. The codes will be available at: https://github.com/TencentYoutuResearch/CrowdCounting-P2PNet.
Recently, the problem of inaccurate learning targets in crowd counting draws increasing attention. Inspired by a few pioneering work, we solve this problem by trying to predict the indices of pre-defined interval bins of counts instead of the count v
alues themselves. However, an inappropriate interval setting might make the count error contributions from different intervals extremely imbalanced, leading to inferior counting performance. Therefore, we propose a novel count interval partition criterion called Uniform Error Partition (UEP), which always keeps the expected counting error contributions equal for all intervals to minimize the prediction risk. Then to mitigate the inevitably introduced discretization errors in the count quantization process, we propose another criterion called Mean Count Proxies (MCP). The MCP criterion selects the best count proxy for each interval to represent its count value during inference, making the overall expected discretization error of an image nearly negligible. As far as we are aware, this work is the first to delve into such a classification task and ends up with a promising solution for count interval partition. Following the above two theoretically demonstrated criterions, we propose a simple yet effective model termed Uniform Error Partition Network (UEPNet), which achieves state-of-the-art performance on several challenging datasets. The codes will be available at: https://github.com/TencentYoutuResearch/CrowdCounting-UEPNet.
A central question in a large class of strongly correlated electron systems, including heavy fermion compounds and iron pnictides, is the identification of different phases and their origins. It has been shown that the antiferromagnetic (AFM) phase i
n some heavy fermion compounds is induced by Ruderman-Kittel-Kasuya-Yosida (RKKY) interaction between localized moments, and that the competition between this interaction and Kondo effect is responsible for quantum criticality. However, conclusive experimental evidence of the RKKY interaction in pnictides is lacking. Here, using high resolution $^{23}$Na NMR measurements on lightly Cu-doped metallic single crystals of NaFeCuAs ~($x approx 0.01$) and numerical simulation, we show direct evidence of the RKKY interaction in this pnictide system. Aided by computer simulation, we identify the $^{23}$Na NMR satellite resonances with the RKKY oscillations of spin polarization at Fe sites. Our NMR results indicate coexistence of local and itinerant magnetism in lightly Cu-doped NaFeCuAs.
In this paper, we focus on the fairness issues regarding unsupervised outlier detection. Traditional algorithms, without a specific design for algorithmic fairness, could implicitly encode and propagate statistical bias in data and raise societal con
cerns. To correct such unfairness and deliver a fair set of potential outlier candidates, we propose Deep Clustering based Fair Outlier Detection (DCFOD) that learns a good representation for utility maximization while enforcing the learnable representation to be subgroup-invariant on the sensitive attribute. Considering the coupled and reciprocal nature between clustering and outlier detection, we leverage deep clustering to discover the intrinsic cluster structure and out-of-structure instances. Meanwhile, an adversarial training erases the sensitive pattern for instances for fairness adaptation. Technically, we propose an instance-level weighted representation learning strategy to enhance the joint deep clustering and outlier detection, where the dynamic weight module re-emphasizes contributions of likely-inliers while mitigating the negative impact from outliers. Demonstrated by experiments on eight datasets comparing to 17 outlier detection algorithms, our DCFOD method consistently achieves superior performance on both the outlier detection validity and two types of fairness notions in outlier detection.
Previous surveys of public attitudes toward automated vehicle (AV) and transit integration primarily took place in large urban areas. AV-transit integration also has a great potential in small urban areas. A survey of public attitudes towards AV-tran
sit integration was carried out in two small urban areas in Wisconsin, United States. A total of 266 finished responses were analyzed using text mining, factor analysis, and regression analysis. Results showed that respondents knew about AVs and driving assistance technologies. Respondents welcome AV-transit integration but were unsure about its potential impacts. Technology-savvy respondents were more positive but had more concerns about AV-transit integration than others. Respondents who enjoyed driving were not necessarily against transit, as they were more positive about AV-transit integration and were more willing to use automated buses than those who did not enjoy driving as much. Transit users were more positive toward AV-transit integration than non-transit users.
Tunable terahertz plasmons are essential for reconfigurable photonics, which have been demonstrated in graphene through gating, though with relatively weak responses. Here, we demonstrate strong terahertz plasmons in graphite thin films via infrared
spectroscopy, with dramatic tunability by even a moderate temperature change or an in-situ bias voltage. Meanwhile, through magneto-plasmon studies, we reveal that massive electrons and massless Dirac holes make comparable contributions to the plasmon response. Our study not only sets up a platform for further exploration of two-component plasmas, but also opens an avenue for terahertz modulation through electrical bias or all-optical means.
The transverse Rashba effect is proposed and investigated by the first-principle calculations based on density functional theory in a quasi-one-dimensional antiferromagnet with a strong perpendicular magnetocrystalline anisotropy, which is materializ
ed by the Gd-adsorbed graphene nanoribbon with a centric symmetry. The Rashba effect in this system is associated with the local dipole field transverse to and in the plane of the nanoribbon. That dipole field is induced by the off-center adsorption of the Gd adatom above the hex-carbon ring near the nanoribbon edges. The transverse Rashba effect at the two Gd adatoms enhances each other in the antiferromagnetic (AFM) ground state and cancels each other in the ferromagnetic (FM) meta-stable state, because of the centrosymmetric atomic structure. The transverse Rashba parameter is 1.51 eV A. This system shows a strong perpendicular magnetocrystalline anisotropy (MCA), which is 1.4 meV per Gd atom in the AFM state or 2.2 meV per Gd atom in the FM state. The origin of the perpendicular MCA is analyzed in k-space by filtering out the contribution of the transverse Rashba effect from the band structures perturbed by the spin-orbit coupling interactions. The first-order perturbation of the orbit and spin angular momentum coupling is the major source of the MCA, which is associated with the one-dimensionality of the system. The transverse Rashba effect and the strong perpendicular magnetization hosted simultaneously by the proposed AFM Gd-adsorbed graphene nanoribbon lock the up- (or down-) spin quantization direction to the forward (or backward) movement. This finding offers a magnetic approach to a high coherency spin propagation in one-dimensionality, and open a new door to manipulating spin transportation in graphene-based spintronics.
In the real world, networks often contain multiple relationships among nodes, manifested as the heterogeneity of the edges in the networks. We convert the heterogeneous networks into multiple views by using each view to describe a specific type of re
lationship between nodes, so that we can leverage the collaboration of multiple views to learn the representation of networks with heterogeneous edges. Given this, we propose a emph{regularized graph auto-encoders} (RGAE) model, committed to utilizing abundant information in multiple views to learn robust network representations. More specifically, RGAE designs shared and private graph auto-encoders as main components to capture high-order nonlinear structure information of the networks. Besides, two loss functions serve as regularization to extract consistent and unique information, respectively. Concrete experimental results on realistic datasets indicate that our model outperforms state-of-the-art baselines in practical applications.
With safety being one of the primary motivations for developing automated vehicles (AVs), extensive field and simulation tests are being carried out to ensure AVs can operate safely on roadways. Since 2014, the California DMV has been collecting AV c
ollision and disengagement reports, which are valuable data sources for studying AV crash patterns. In this study, crash sequence data extracted from California AV collision reports were used to investigate patterns and how they may be used to develop AV test scenarios. Employing sequence analysis, this study evaluated 168 AV crashes (with AV in automatic driving mode before disengagement or collision) from 2015 to 2019. Analysis of subsequences showed that the most representative pattern in AV crashes was (collision following AV stop) type. Analysis of event transition showed that disengagement, as an event in 24 percent of all studied AV crash sequences, had a transition probability of 68 percent to an immediate collision. Cluster analysis characterized AV crash sequences into seven groups with distinctive crash dynamic features. Cross-tabulation analysis showed that sequence groups were significantly associated with variables measuring crash outcomes and describing environmental conditions. Crash sequences are useful for developing AV test scenarios. Based on the findings, a scenario-based AV safety testing framework was proposed with sequence of events embedded as a core component.
