arXiv subjects

Xiaofan Yu

Publications and source records attributed to Xiaofan Yu.

23 records · Page 2Linked to original sources

arXiv2023

Quantum Tunneling in the Surface Diffusion of Single Hydrogen Atoms on Cu(001)

The adsorption and diffusion of hydrogen atoms on Cu(001) are studied using first-principles calculations. By taking into account the contribution of zero-point energy (ZPE), the originally identical barriers are shown to be different for H and D, which are respectively calculated to be ~ 158 meV and ~ 139 meV in height. Using the transfer matrix method (TMM), we are able to calculate the accurate probability of transmission across the barriers. The crucial role of quantum tunneling is clearly demonstrated at low-temperature region. By introducing a temperature-dependent attempting frequency prefactor, the rate constants and diffusion coefficients are calculated. The results are in agreement with the experimental measurements at temperatures from ~ 50 K to 80 K.

cond-mat.mes-hall↗

arXiv2023

Activated Dissociation of H2 on Cu(001): The Role of Quantum Tunneling

The activation and dissociation of H2 molecules on Cu(001) surface is studied theoretically. The activation barrier for the dissociation of H2 on Cu(001) is determined by first-principles calculations to be ~ 0.59 eV in height. Electron transfer from the substrate Cu to H2 plays a key role in the activation, breaking of the H-H bond and the formation of the Cu-H bonds. At around the critical height of bond breaking, two stationary states are identified, which correspond respectively to the molecular and dissociative state. Using the transfer matrix method, we are able to study the role of quantum tunneling in the dissociation process along the minimum energy pathway (MEP), which is found to be significant at room temperature and below. At given temperatures, the tunneling contributions from the translational and vibrational motions of H2 are quantified for the dissociation process. Within a wide range of temperatures, the effects of quantum tunneling on the effective barriers of dissociation and the rate constants are revealed. The deduced energetic parameters associated with thermal equilibrium and non-equilibrium (molecular beam) conditions are comparable with experimental data. In the low-temperature region, crossover from classical to quantum regime is identified.

cond-mat.mes-hall↗

arXiv2023

SCALE: Online Self-Supervised Lifelong Learning without Prior Knowledge

Unsupervised lifelong learning refers to the ability to learn over time while memorizing previous patterns without supervision. Although great progress has been made in this direction, existing work often assumes strong prior knowledge about the incoming data (e.g., knowing the class boundaries), which can be impossible to obtain in complex and unpredictable environments. In this paper, motivated by real-world scenarios, we propose a more practical problem setting called online self-supervised lifelong learning without prior knowledge. The proposed setting is challenging due to the non-iid and single-pass data, the absence of external supervision, and no prior knowledge. To address the challenges, we propose Self-Supervised ContrAstive Lifelong LEarning without Prior Knowledge (SCALE) which can extract and memorize representations on the fly purely from the data continuum. SCALE is designed around three major components: a pseudo-supervised contrastive loss, a self-supervised forgetting loss, and an online memory update for uniform subset selection. All three components are designed to work collaboratively to maximize learning performance. We perform comprehensive experiments of SCALE under iid and four non-iid data streams. The results show that SCALE outperforms the state-of-the-art algorithm in all settings with improvements up to 3.83%, 2.77% and 5.86% in terms of kNN accuracy on CIFAR-10, CIFAR-100, and TinyImageNet datasets.

cs.LG↗

arXiv2023

Async-HFL: Efficient and Robust Asynchronous Federated Learning in Hierarchical IoT Networks

Federated Learning (FL) has gained increasing interest in recent years as a distributed on-device learning paradigm. However, multiple challenges remain to be addressed for deploying FL in real-world Internet-of-Things (IoT) networks with hierarchies. Although existing works have proposed various approaches to account data heterogeneity, system heterogeneity, unexpected stragglers and scalibility, none of them provides a systematic solution to address all of the challenges in a hierarchical and unreliable IoT network. In this paper, we propose an asynchronous and hierarchical framework (Async-HFL) for performing FL in a common three-tier IoT network architecture. In response to the largely varied delays, Async-HFL employs asynchronous aggregations at both the gateway and the cloud levels thus avoids long waiting time. To fully unleash the potential of Async-HFL in converging speed under system heterogeneities and stragglers, we design device selection at the gateway level and device-gateway association at the cloud level. Device selection chooses edge devices to trigger local training in real-time while device-gateway association determines the network topology periodically after several cloud epochs, both satisfying bandwidth limitation. We evaluate Async-HFL's convergence speedup using large-scale simulations based on ns-3 and a network topology from NYCMesh. Our results show that Async-HFL converges 1.08-1.31x faster in wall-clock time and saves up to 21.6% total communication cost compared to state-of-the-art asynchronous FL algorithms (with client selection). We further validate Async-HFL on a physical deployment and observe robust convergence under unexpected stragglers.

cs.LG↗

arXiv2018

A Robotic Auto-Focus System based on Deep Reinforcement Learning

Considering its advantages in dealing with high-dimensional visual input and learning control policies in discrete domain, Deep Q Network (DQN) could be an alternative method of traditional auto-focus means in the future. In this paper, based on Deep Reinforcement Learning, we propose an end-to-end approach that can learn auto-focus policies from visual input and finish at a clear spot automatically. We demonstrate that our method - discretizing the action space with coarse to fine steps and applying DQN is not only a solution to auto-focus but also a general approach towards vision-based control problems. Separate phases of training in virtual and real environments are applied to obtain an effective model. Virtual experiments, which are carried out after the virtual training phase, indicates that our method could achieve 100% accuracy on a certain view with different focus range. Further training on real robots could eliminate the deviation between the simulator and real scenario, leading to reliable performances in real applications.

cs.CV↗