Construction of equilibrium networks with an energy function

We construct equilibrium networks by introducing an energy function depending on the degree of each node as well as the product of neighboring degrees. With this topological energy function, networks constitute a canonical ensemble, which follows the Boltzmann distribution for given temperature. It is observed that the system undergoes a topological phase transition from a random network to a star or a fully-connected network as the temperature is lowered. Both mean-field analysis and numerical simulations reveal strong first-order phase transitions at temperatures which decrease logarithmically with the system size. Quantitative discrepancies of the simulation results from the mean-field prediction are discussed in view of the strong first-order nature.Comment: To appear in J. Phys.

Slow relaxation in the Ising model on a small-world network with strong long-range interactions

We consider the Ising model on a small-world network, where the long-range interaction strength $J_2$ is in general different from the local interaction strength $J_1$, and examine its relaxation behaviors as well as phase transitions. As $J_2/J_1$ is raised from zero, the critical temperature also increases, manifesting contributions of long-range interactions to ordering. However, it becomes saturated eventually at large values of $J_2/J_1$ and the system is found to display very slow relaxation, revealing that ordering dynamics is inhibited rather than facilitated by strong long-range interactions. To circumvent this problem, we propose a modified updating algorithm in Monte Carlo simulations, assisting the system to reach equilibrium quickly.Comment: 5 pages, 5 figure

Proxy Anchor-based Unsupervised Learning for Continuous Generalized Category Discovery

Recent advances in deep learning have significantly improved the performance of various computer vision applications. However, discovering novel categories in an incremental learning scenario remains a challenging problem due to the lack of prior knowledge about the number and nature of new categories. Existing methods for novel category discovery are limited by their reliance on labeled datasets and prior knowledge about the number of novel categories and the proportion of novel samples in the batch. To address the limitations and more accurately reflect real-world scenarios, in this paper, we propose a novel unsupervised class incremental learning approach for discovering novel categories on unlabeled sets without prior knowledge. The proposed method fine-tunes the feature extractor and proxy anchors on labeled sets, then splits samples into old and novel categories and clusters on the unlabeled dataset. Furthermore, the proxy anchors-based exemplar generates representative category vectors to mitigate catastrophic forgetting. Experimental results demonstrate that our proposed approach outperforms the state-of-the-art methods on fine-grained datasets under real-world scenarios.Comment: Accepted to ICCV 202

AI-KD: Adversarial learning and Implicit regularization for self-Knowledge Distillation

We present a novel adversarial penalized self-knowledge distillation method, named adversarial learning and implicit regularization for self-knowledge distillation (AI-KD), which regularizes the training procedure by adversarial learning and implicit distillations. Our model not only distills the deterministic and progressive knowledge which are from the pre-trained and previous epoch predictive probabilities but also transfers the knowledge of the deterministic predictive distributions using adversarial learning. The motivation is that the self-knowledge distillation methods regularize the predictive probabilities with soft targets, but the exact distributions may be hard to predict. Our method deploys a discriminator to distinguish the distributions between the pre-trained and student models while the student model is trained to fool the discriminator in the trained procedure. Thus, the student model not only can learn the pre-trained model's predictive probabilities but also align the distributions between the pre-trained and student models. We demonstrate the effectiveness of the proposed method with network architectures on multiple datasets and show the proposed method achieves better performance than state-of-the-art methods.Comment: 12 pages, 7 figure

1/f spectrum and memory function analysis of solvation dynamics in a room-temperature ionic liquid

To understand the non-exponential relaxation associated with solvation dynamics in the ionic liquid 1-ethyl-3-methylimidazolium hexafluorophosphate, we study power spectra of the fluctuating Franck-Condon energy gap of a diatomic probe solute via molecular dynamics simulations. Results show 1/f dependence in a wide frequency range over 2 to 3 decades, indicating distributed relaxation times. We analyze the memory function and solvation time in the framework of the generalized Langevin equation using a simple model description for the power spectrum. It is found that the crossover frequency toward the white noise plateau is directly related to the time scale for the memory function and thus the solvation time. Specifically, the low crossover frequency observed in the ionic liquid leads to a slowly-decaying tail in its memory function and long solvation time. By contrast, acetonitrile characterized by a high crossover frequency and (near) absence of 1/f behavior in its power spectra shows fast relaxation of the memory function and single-exponential decay of solvation dynamics in the long-time regime.Comment: 10 pages, 4 figure

Multiscale Simulation of Photoluminescence Quenching in Phosphorescent OLED Materials

Bimolecular exciton-quenching processes such as triplet–triplet annihilation (TTA) and triplet–polaron quenching play a central role in phosphorescent organic light-emitting diode (PhOLED) device performance and are, therefore, an essential component in computational models. However, the experiments necessary to determine microscopic parameters underlying such processes are complex and the interpretation of their results is not straightforward. Here, a multiscale simulation protocol to treat TTA is presented, in which microscopic parameters are computed with ab initio electronic structure methods. With this protocol, virtual photoluminescence experiments are performed on a prototypical PhOLED emission material consisting of 93 wt% of 4,4ʹ,4ʺ-tris(N-carbazolyl)triphenylamine and 7 wt% of the green phosphorescent dye fac-tris(2-phenylpyridine)iridium. A phenomenological TTA quenching rate of 8.5 × 10$^{-12}$ cm$^{3}$ s$^{-1}$, independent of illumination intensity, is obtained. This value is comparable to experimental results in the low-intensity limit but differs from experimental rates at higher intensities. This discrepancy is attributed to the difficulties in accounting for fast bimolecular quenching during exciton generation in the interpretation of experimental data. This protocol may aid in the experimental determination of TTA rates, as well as provide an order-of-magnitude estimate for device models containing materials for which no experimental data are available

Photo-induced charge carrier dynamics in a semiconductor-based ion trap investigated via motion-sensitive qubit transitions

Ion trap systems built upon microfabricated chips have emerged as a promising platform for quantum computing to achieve reproducible and scalable structures. However, photo-induced charging of materials in such chips can generate undesired stray electric fields that disrupt the quantum state of the ion, limiting high-fidelity quantum control essential for practical quantum computing. While crude understanding of the phenomena has been gained heuristically over the past years, explanations for the microscopic mechanism of photo-generated charge carrier dynamics remains largely elusive. Here, we present a photo-induced charging model for semiconductors, whose verification is enabled by a systematic interaction between trapped ions and photo-induced stray fields from exposed silicon surfaces in our chip. We use motion-sensitive qubit transitions to directly characterize the stray field and analyze its effect on the quantum dynamics of the trapped ion. In contrast to incoherent errors arising from the thermal motion of the ion, coherent errors are induced by the stray field, whose effect is significantly imprinted during the quantum control of the ion. These errors are investigated in depth and methods to mitigate them are discussed. Finally, we extend the implications of our study to other photo-induced charging mechanisms prevalent in ion traps.Comment: 27 pages, 11 figure