arXiv subjects

Yongjun Zhang

Publications and source records attributed to Yongjun Zhang.

78 records · Page 5Linked to original sources

arXiv2021

Group-Aware Graph Neural Network for Nationwide City Air Quality Forecasting

The problem of air pollution threatens public health. Air quality forecasting can provide the air quality index hours or even days later, which can help the public to prevent air pollution in advance. Previous works focus on citywide air quality forecasting and cannot solve nationwide city forecasting problem, whose difficulties lie in capturing the latent dependencies between geographically distant but highly correlated cities. In this paper, we propose the group-aware graph neural network (GAGNN), a hierarchical model for nationwide city air quality forecasting. The model constructs a city graph and a city group graph to model the spatial and latent dependencies between cities, respectively. GAGNN introduces differentiable grouping network to discover the latent dependencies among cities and generate city groups. Based on the generated city groups, a group correlation encoding module is introduced to learn the correlations between them, which can effectively capture the dependencies between city groups. After the graph construction, GAGNN implements message passing mechanism to model the dependencies between cities and city groups. The evaluation experiments on Chinese city air quality dataset indicate that our GAGNN outperforms existing forecasting models.

cs.LG↗

arXiv2021

Anisotropic c-f hybridization in the ferromagnetic quantum critical metal CeRh$_6$Ge$_4$

Heavy fermion compounds exhibiting a ferromagnetic quantum critical point have attracted considerable interest. Common to two known cases, i.e., CeRh$_6$Ge$_4$ and YbNi$_4$P$_2$, is that the 4f moments reside along chains with a large inter-chain distance, exhibiting strong magnetic anisotropy that was proposed to be vital for the ferromagnetic quantum criticality. Here we report an angle-resolved photoemission study on CeRh6Ge4, where we observe sharp momentum-dependent 4f bands and clear bending of the conduction bands near the Fermi level, indicating considerable hybridization between conduction and 4f electrons. The extracted hybridization strength is anisotropic in momentum space and is obviously stronger along the Ce chain direction. The hybridized 4f bands persist up to high temperatures, and the evolution of their intensity shows clear band dependence. Our results provide spectroscopic evidence for anisotropic hybridization between conduction and 4f electrons in CeRh$_6$Ge$_4$, which could be important for understanding the electronic origin of the ferromagnetic quantum criticality.

cond-mat.str-el↗

arXiv2021

Localized 4f-electrons in the quantum critical heavy fermion ferromagnet CeRh$_6$Ge$_4$

Ferromagnetic quantum critical points were predicted to be prohibited in clean itinerant ferromagnetic systems, yet such a phenomenon was recently revealed in CeRh$_6$Ge$_4$, where the Curie temperature can be continuously suppressed to zero under a moderate hydrostatic pressure. Here we report the observation of quantum oscillations in CeRh$_6$Ge$_4$ from measurements using the cantilever and tunnel-diode oscillator methods in fields up to 45 T, clearly demonstrating that the ferromagnetic quantum criticality occurs in a clean system. In order to map the Fermi surface of CeRh$_6$Ge$_4$, we performed angle-dependent measurements of quantum oscillations at ambient pressure, and compared the results to density functional theory calculations. The results are consistent with the Ce 4f electrons remaining localized, and not contributing to the Fermi surface, suggesting that localized ferromagnetism is a key factor for the occurrence of a ferromagnetic quantum critical point in CeRh$_6$Ge$_4$.

cond-mat.str-el↗

arXiv2020

Collaboratively boosting data-driven deep learning and knowledge-guided ontological reasoning for semantic segmentation of remote sensing imagery

As one kind of architecture from the deep learning family, deep semantic segmentation network (DSSN) achieves a certain degree of success on the semantic segmentation task and obviously outperforms the traditional methods based on hand-crafted features. As a classic data-driven technique, DSSN can be trained by an end-to-end mechanism and competent for employing the low-level and mid-level cues (i.e., the discriminative image structure) to understand images, but lacks the high-level inference ability. By contrast, human beings have an excellent inference capacity and can be able to reliably interpret the RS imagery only when human beings master the basic RS domain knowledge. In literature, ontological modeling and reasoning is an ideal way to imitate and employ the domain knowledge of human beings, but is still rarely explored and adopted in the RS domain. To remedy the aforementioned critical limitation of DSSN, this paper proposes a collaboratively boosting framework (CBF) to combine data-driven deep learning module and knowledge-guided ontological reasoning module in an iterative way.

cs.CV↗

arXiv2019

Strange metal behavior in a pure ferromagnetic Kondo lattice

The strange metal phases found to develop in a wide range of materials near a quantum critical point (QCP), have posed a long-standing mystery. The frequent association of strange metals with unconventional superconductivity and antiferromagnetic QCPs has led to a belief that they are highly entangled quantum states. Ferromagnets, by contrast are regarded as an unlikely setting for strange metals, for they are weakly entangled and their QCPs are often interrupted by competing phases or first order phase transitions. Here, we provide compelling evidence that the stoichiometric heavy fermion ferromagnet CeRh$_6$Ge$_4$ becomes a strange metal at a pressure-induced QCP: specific heat and resistivity measurements demonstrate that the FM transition is continuously suppressed to zero temperature revealing a strange metal phase. We argue that strong magnetic anisotropy plays a key role in this process,injecting entanglement, in the form of triplet resonating valence bonds (tRVBs) into the ordered ferromagnet. We show that the singular transformation from tRVBs into Kondo singlets that occurs at the QCP causes a jump in the Fermi surface volume: a key driver of strange metallic behavior. Our results open up a new direction for research into FM quantum criticality, while also establishing an important new setting for the strange metal problem. Most importantly, strange metallic behavior at a FM quantum critical point suggests that it is quantum entanglement rather than the destruction of antiferromagnetism that is the common driver of the many varied examples of strange metallic behavior.

cond-mat.str-el↗

arXiv2019

FA-Harris: A Fast and Asynchronous Corner Detector for Event Cameras

Recently, the emerging bio-inspired event cameras have demonstrated potentials for a wide range of robotic applications in dynamic environments. In this paper, we propose a novel fast and asynchronous event-based corner detection method which is called FA-Harris. FA-Harris consists of several components, including an event filter, a Global Surface of Active Events (G-SAE) maintaining unit, a corner candidate selecting unit, and a corner candidate refining unit. The proposed G-SAE maintenance algorithm and corner candidate selection algorithm greatly enhance the real-time performance for corner detection, while the corner candidate refinement algorithm maintains the accuracy of performance by using an improved event-based Harris detector. Additionally, FA-Harris does not require artificially synthesized event-frames and can operate on asynchronous events directly. We implement the proposed method in C++ and evaluate it on public Event Camera Datasets. The results show that our method achieves approximately 8x speed-up when compared with previously reported event-based Harris detector, and with no compromise on the accuracy of performance.

cs.CV↗