Zeeman effect in centrosymmetric antiferromagnets controlled by an electric field

Centrosymmetric antiferromagnetic semiconductors, although abundant in nature, seem less promising than ferromagnets and ferroelectrics for practical applications in semiconductor spintronics. As a matter of fact, the lack of spontaneous polarization and magnetization hinders the efficient utilization of electronic spin in these materials. Here, we propose a paradigm to harness electronic spin in centrosymmetric antiferromagnets via Zeeman spin splittings of electronic energy levels -- termed as spin Zeeman effect -- which is controlled by electric field.By symmetry analysis, we identify twenty-one centrosymmetric antiferromagnetic point groups that accommodate such a spin Zeeman effect. We further predict by first-principles that two antiferromagnetic semiconductors, Fe$_2$TeO$_6$ and SrFe$_2$S$_2$O, are excellent candidates showcasing Zeeman splittings as large as $\sim$55 and $\sim$30 meV, respectively, induced by an electric field of 6 MV/cm. Moreover, the electronic spin magnetization associated to the splitting energy levels can be switched by reversing the electric field. Our work thus sheds light on the electric-field control of electronic spin in antiferromagnets, which broadens the scope of application of centrosymmetric antiferromagnetic semiconductors

Modeling Heterogeneous Relations across Multiple Modes for Potential Crowd Flow Prediction

Potential crowd flow prediction for new planned transportation sites is a fundamental task for urban planners and administrators. Intuitively, the potential crowd flow of the new coming site can be implied by exploring the nearby sites. However, the transportation modes of nearby sites (e.g. bus stations, bicycle stations) might be different from the target site (e.g. subway station), which results in severe data scarcity issues. To this end, we propose a data driven approach, named MOHER, to predict the potential crowd flow in a certain mode for a new planned site. Specifically, we first identify the neighbor regions of the target site by examining the geographical proximity as well as the urban function similarity. Then, to aggregate these heterogeneous relations, we devise a cross-mode relational GCN, a novel relation-specific transformation model, which can learn not only the correlations but also the differences between different transportation modes. Afterward, we design an aggregator for inductive potential flow representation. Finally, an LTSM module is used for sequential flow prediction. Extensive experiments on real-world data sets demonstrate the superiority of the MOHER framework compared with the state-of-the-art algorithms.Comment: Accepted by the 35th AAAI Conference on Artificial Intelligence (AAAI 2021

Few-femtosecond Electron Beam with THz-frequency Wakefield-driven Compression

We propose and demonstrate a novel method to produce few-femtosecond electron beam with relatively low timing jitter. In this method a relativistic electron beam is compressed from about 150 fs (rms) to about 7 fs (rms, upper limit) with the wakefield at THz frequency produced by a leading drive beam in a dielectric tube. By imprinting the energy chirp in a passive way, we demonstrate through laser-driven THz streaking technique that no additional timing jitter with respect to an external laser is introduced in this bunch compression process, a prominent advantage over the conventional method using radio-frequency bunchers. We expect that this passive bunching technique may enable new opportunities in many ultrashort-beam based advanced applications such as ultrafast electron diffraction and plasma wakefield acceleration.Comment: 5 pages, 4 figure

Engineering ferroelectricity in monoclinic hafnia

Ferroelectricity in the complementary metal-oxide semiconductor (CMOS)-compatible hafnia (HfO$_2$) is crucial for the fabrication of high-integration nonvolatile memory devices. However, the capture of ferroelectricity in HfO$_2$ requires the stabilization of thermodynamically-metastable orthorhombic or rhombohedral phases, which entails the introduction of defects (e.g., dopants and vacancies) and pays the price of crystal imperfections, causing unpleasant wake-up and fatigue effects. Here, we report a theoretical strategy on the realization of robust ferroelectricity in HfO$_2$-based ferroelectrics by designing a series of epitaxial (HfO$_2$)$_1$/(CeO$_2$)$_1$ superlattices. The advantages of the designated ferroelectric superlattices are defects free, and most importantly, on the base of the thermodynamically stable monoclinic phase of HfO$_2$. Consequently, this allows the creation of superior ferroelectric properties with an electric polarization $>$25 $\mu$C/cm$^2$ and an ultralow polarization-switching energy barrier at $\sim$2.5 meV/atom. Our work may open an entirely new route towards the fabrication of high-performance HfO$_2$ based ferroelectric devices

How does urbanization affect public health? New evidence from 175 countries worldwide

Urbanization is an essential indicator of contemporary society and a necessary historic stage in the industrialization of all countries. Thus, we explore the impact of urbanization on public health using the OLS estimation and a two-way fixed effect model based on annual panel data from 175 countries from 2000 to 2018. This paper also addresses potential endogeneity issues and identifies causal relationships using the coefficient stability tests, system GMM, and instrumental variable method. The results demonstrate that urbanization positively affects public health. Furthermore, we find that the impact of urbanization on public health can be mediated through living standards, and nations with higher living standards reduce the effect of urbanization on public health. An increase in the urbanization rate can promote public health by improving residents' living standards. Our results have significant real-world implications for the research of urbanization and the formulation of public health policy

TACO: Temporal Latent Action-Driven Contrastive Loss for Visual Reinforcement Learning

Despite recent progress in reinforcement learning (RL) from raw pixel data, sample inefficiency continues to present a substantial obstacle. Prior works have attempted to address this challenge by creating self-supervised auxiliary tasks, aiming to enrich the agent's learned representations with control-relevant information for future state prediction. However, these objectives are often insufficient to learn representations that can represent the optimal policy or value function, and they often consider tasks with small, abstract discrete action spaces and thus overlook the importance of action representation learning in continuous control. In this paper, we introduce TACO: Temporal Action-driven Contrastive Learning, a simple yet powerful temporal contrastive learning approach that facilitates the concurrent acquisition of latent state and action representations for agents. TACO simultaneously learns a state and an action representation by optimizing the mutual information between representations of current states paired with action sequences and representations of the corresponding future states. Theoretically, TACO can be shown to learn state and action representations that encompass sufficient information for control, thereby improving sample efficiency. For online RL, TACO achieves 40% performance boost after one million environment interaction steps on average across nine challenging visual continuous control tasks from Deepmind Control Suite. In addition, we show that TACO can also serve as a plug-and-play module adding to existing offline visual RL methods to establish the new state-of-the-art performance for offline visual RL across offline datasets with varying quality