We report on the numerically exact simulation of the dissipative dynamics governed by quantum master equations that feature fractional quantum Hall states as unique steady states. In particular, for the paradigmatic Hofstadter model, we show how Laug
hlin states can be to good approximation prepared in a dissipative fashion from arbitrary initial states by simply pumping strongly interacting bosons into the lowest Chern band of the corresponding single-particle spectrum. While pure (up to topological degeneracy) steady states are only reached in the low-flux limit or for extended hopping range, we observe a certain robustness regarding the overlap of the steady state with fractional quantum Hall states for experimentally well-controlled flux densities. This may be seen as an encouraging step towards addressing the long-standing challenge of preparing strongly correlated topological phases in quantum simulators.
We investigate the disorder-driven phase transitions in bosonic fractional quantum Hall liquids at filling factors $f=1/2$ and $f=1$ in the lowest Landau level. We use the evolution of ground-state entanglement entropy, fidelity susceptibility, and H
all conductance with the increasing of disorder strength to identify the underlying phase transitions. The critical disorder strengths obtained from these different quantities are consistent with each other, validating the reliability of our numerical calculations based on exact diagonalization. At $f=1/2$, we observe a clear transition from the bosonic Laughlin state to a trivial insulating phase. At $f=1$, we identify a direct phase transition from the non-Abelian bosonic Moore-Read state to a trivial insulating phase, although some signs of a disorder-induced intermediate fractional quantum Hall phase were recently reported for the $f=5/2$ fermionic Moore-Read cousin.
Recently, the intrinsic magnetic topological insulator MnBi2Te4 has attracted enormous research interest due to the great success in realizing exotic topological quantum states, such as the quantum anomalous Hall effect (QAHE), axion insulator state,
high-Chern-number and high-temperature Chern insulator states. One key issue in this field is to effectively manipulate these states and control topological phase transitions. Here, by systematic angle-dependent transport measurements, we reveal a magnetization-tuned topological quantum phase transition from Chern insulator to magnetic insulator with gapped Dirac surface states in MnBi2Te4 devices. Specifically, as the magnetic field is tilted away from the out-of-plane direction by around 40-60 degrees, the Hall resistance deviates from the quantization value and a colossal, anisotropic magnetoresistance is detected. The theoretical analyses based on modified Landauer-Buttiker formalism show that the field-tilt-driven switching from ferromagnetic state to canted antiferromagnetic state induces a topological quantum phase transition from Chern insulator to magnetic insulator with gapped Dirac surface states in MnBi2Te4 devices. Our work provides an efficient means for modulating topological quantum states and topological quantum phase transitions.
Offline reinforcement learning (RL) tries to learn the near-optimal policy with recorded offline experience without online exploration. Current offline RL research includes: 1) generative modeling, i.e., approximating a policy using fixed data; and 2
) learning the state-action value function. While most research focuses on the state-action function part through reducing the bootstrapping error in value function approximation induced by the distribution shift of training data, the effects of error propagation in generative modeling have been neglected. In this paper, we analyze the error in generative modeling. We propose AQL (action-conditioned Q-learning), a residual generative model to reduce policy approximation error for offline RL. We show that our method can learn more accurate policy approximations in different benchmark datasets. In addition, we show that the proposed offline RL method can learn more competitive AI agents in complex control tasks under the multiplayer online battle arena (MOBA) game Honor of Kings.
Binary decision-making process is ubiquitous in social life and is of vital significance in many real-world issues, ranging from public health to political campaigns. While continuous opinion evolution independent of discrete choice behavior has been
extensively studied, few works unveil how the group binary decision-making result is determined by the coupled dynamics of these two processes. To this end, we propose an agent-based model to study the collective behaviors of individual binary decision-making process through competitive opinion dynamics on social networks. Three key factors are considered: bounded confidence that describes the cognitive scope of the population, stubbornness level that characterizes the opinion updating speed, and the opinion strength that represents the asymmetry power or attractiveness of the two choices. We find that bounded confidence plays an important role in determining competing evolution results. As bounded confidence grows, population opinions experience polarization to consensus, leading to the emergence of phase transition from co-existence to winner-takes-all state under binary decisions. Of particular interest, we show how the combined effects of bounded confidence and asymmetry opinion strength may reverse the initial supportive advantage in competitive dynamics. Notably, our model qualitatively reproduces the important dynamical pattern during a brutal competition, namely, cascading collapse, as observed by real data. Finally and intriguingly, we find that individual cognitive heterogeneity can bring about randomness and unpredictability in binary decision-making process, leading to the emergence of indeterministic oscillation. Our results reveal how the diverse behavioral patterns of binary decision-making can be interpreted by the complicated interactions of the proposed elements, which provides important insights toward competitive dynamics
Existing blind image quality assessment (BIQA) methods are mostly designed in a disposable way and cannot evolve with unseen distortions adaptively, which greatly limits the deployment and application of BIQA models in real-world scenarios. To addres
s this problem, we propose a novel Lifelong blind Image Quality Assessment (LIQA) approach, targeting to achieve the lifelong learning of BIQA. Without accessing to previous training data, our proposed LIQA can not only learn new distortions, but also mitigate the catastrophic forgetting of seen distortions. Specifically, we adopt the Split-and-Merge distillation strategy to train a single-head network that makes task-agnostic predictions. In the split stage, we first employ a distortion-specific generator to obtain the pseudo features of each seen distortion. Then, we use an auxiliary multi-head regression network to generate the predicted quality of each seen distortion. In the merge stage, we replay the pseudo features paired with pseudo labels to distill the knowledge of multiple heads, which can build the final regressed single head. Experimental results demonstrate that the proposed LIQA method can handle the continuous shifts of different distortion types and even datasets. More importantly, our LIQA model can achieve stable performance even if the task sequence is long.
There is considerable interest in the pH-dependent, switchable, biocatalytic properties of cerium oxide (CeO2) nanoparticles (CeNPs) in biomedicine, where these materials exhibit beneficial antioxidant activity against reactive oxygen species (ROS) a
t basic physiological pH but cytotoxic prooxidant activity in acidic cancer cell pH microenvironment. While the general characteristics of the role of oxygen vacancies are known, the mechanism of their action at the atomic scale under different pH conditions has yet to be elucidated. The present work applies density functional theory (DFT) calculations to interpret, at the atomic scale, the pH-induced behavior of the stable {111} surface of CeO2 containing oxygen vacancies. Analysis of the surface-adsorbed media species reveals the critical role of pH on the interaction between ROS and the defective CeO2 {111} surface. Under basic conditions, the superoxide dismutase (SOD) and catalase (CAT) biomimetic reactions can be performed cyclically, scavenging and decomposing ROS to harmless products, making CeO2 an excellent antioxidant. However, under acidic conditions, the CAT biomimetic reaction is hindered owing to the limited reversibility of Ce3+ and Ce4+ and formation and annihilation of oxygen vacancies. A Fenton biomimetic reaction is predicted to occur simultaneously with the SOD and CAT biomimetic reactions, resulting in the formation of hydroxyl radicals, making CeO2 a cytotoxic prooxidant.
There is considerable interest in the pH-dependent switchable biocatalytic properties of cerium oxide nanoparticles (CeNPs) in biomedicine, where these materials exhibit beneficial antioxidant activity against reactive oxygen species at neutral and b
asic physiological pH but cytotoxic prooxidant activity at acidic pathological pH. Oxygen vacancies play a key role in such biocatalytic activities. While the general characteristics of the role of oxygen vacancies are known, the mechanism of their action at the atomic scale under different pH conditions has yet to be elucidated. The present work applies density functional theory (DFT) calculations to interpret the pH-induced behavior of the stable {111} surface of CeO2 at the atomic scale. Analysis of the surface-adsorbed media species reveals the critical role of pH on the reversibility of the Ce3+ and Ce4+ redox equilibria and the formation and annihilation of the oxygen vacancies. Under acidic conditions, this reversible switching is hindered owing to incomplete volumetric filling of the oxygen vacancies by the oxygen in the water molecules, hence effective retention of the oxygen vacancies, and consequent inhibition of redox biomimetic reactions. Under neutral and basic conditions, the capacity for this reversible switching is preserved due to complete filling of the oxygen vacancies by the OH ions owing to their ready size accommodation, thereby retaining the capacity for performing redox biomimetic reactions cyclically.
Simulating and imitating the neuronal network of humans or mammals is a popular topic that has been explored for many years in the fields of pattern recognition and computer vision. Inspired by neuronal conduction characteristics in the primary visua
l cortex of cats, pulse-coupled neural networks (PCNNs) can exhibit synchronous oscillation behavior, which can process digital images without training. However, according to the study of single cells in the cat primary visual cortex, when a neuron is stimulated by an external periodic signal, the interspike-interval (ISI) distributions represent a multimodal distribution. This phenomenon cannot be explained by all PCNN models. By analyzing the working mechanism of the PCNN, we present a novel neuron model of the primary visual cortex consisting of a continuous-coupled neural network (CCNN). Our model inherited the threshold exponential decay and synchronous pulse oscillation property of the original PCNN model, and it can exhibit chaotic behavior consistent with the testing results of cat primary visual cortex neurons. Therefore, our CCNN model is closer to real visual neural networks. For image segmentation tasks, the algorithm based on CCNN model has better performance than the state-of-art of visual cortex neural network model. The strength of our approach is that it helps neurophysiologists further understand how the primary visual cortex works and can be used to quantitatively predict the temporal-spatial behavior of real neural networks. CCNN may also inspire engineers to create brain-inspired deep learning networks for artificial intelligence purposes.
Researches on anomalous Hall effect (AHE) have been lasting for a century to make clear the underlying physical mechanism. Generally, the AHE appears in magnetic materials, in which extrinsic process related to scattering effects and intrinsic contri
bution connected with Berry curvature are crucial. Recently, AHE has been counterintuitively observed in non-magnetic topological materials and attributed to the existence of Weyl points. However, the Weyl point scenario would lead to unsaturated AHE even in large magnetic fields and contradicts the saturation of AHE in several tesla (T) in experiments. In this work, we investigate the Hall effect of ZrTe5 and HfTe5 thin flakes in static ultrahigh magnetic fields up to 33 T. We find the AHE saturates to 55 (70) Ohm^-1*cm^-1 for ZrTe5 (HfTe5) thin flakes above ~ 10 T. Combining detailed magnetotransport experiments and Berry curvature calculations, we clarify that the splitting of massive Dirac bands without Weyl points can be responsible for AHE in non-magnetic topological materials ZrTe5 and HfTe5 thin flakes. This model can identify our thin flake samples to be weak topological insulators and serve as a new tool to probe the band structure topology in topological materials.
