arXiv subjects

Yanan Li

Publications and source records attributed to Yanan Li.

At least 73 records · Page 4Linked to original sources

arXiv2020

Tunable discrete scale invariance in transition-metal pentatelluride flakes

Log-periodic quantum oscillations discovered in transition-metal pentatelluride give a clear demonstration of discrete scale invariance (DSI) in solid-state materials. The peculiar phenomenon is convincingly interpreted as the presence of two-body quasi-bound states in a Coulomb potential. However, the modifications of the Coulomb interactions in many-body systems having a Dirac-like spectrum are not fully understood. Here, we report the observation of tunable log-periodic oscillations and DSI in ZrTe5 and HfTe5 flakes. By reducing the flakes thickness, the characteristic scale factor is tuned to a much smaller value due to the reduction of the vacuum polarization effect. The decreasing of the scale factor demonstrates the many-body effect on the DSI, which has rarely been discussed hitherto. Furthermore, the cut-offs of oscillations are quantitatively explained by considering the Thomas-Fermi screening effect. Our work clarifies the many-body effect on DSI and paves a way to tune the DSI in quantum materials.

cond-mat.mtrl-sci↗

arXiv2020

Capping layer influence and isotropic in-plane upper critical field of the superconductivity at the FeSe/SrTiO3 interface

Understanding the superconductivity at the interface of FeSe/SrTiO3 is a problem of great contemporary interest due to the significant increase in critical temperature (Tc) compared to that of bulk FeSe, as well as the possibility of an unconventional pairing mechanism and topological superconductivity. We report a study of the influence of a capping layer on superconductivity in thin films of FeSe grown on SrTiO3 using molecular beam epitaxy. We used in vacuo four-probe electrical resistance measurements and ex situ magneto-transport measurements to examine the effect of three capping layers that provide distinctly different charge transfer into FeSe: compound FeTe, non-metallic Te, and metallic Zr. Our results show that FeTe provides an optimal cap that barely influences the inherent Tc found in pristine FeSe/SrTiO3, while the transfer of holes from a non-metallic Te cap completely suppresses superconductivity and leads to insulating behavior. Finally, we used ex situ magnetoresistance measurements in FeTe-capped FeSe films to extract the angular dependence of the in-plane upper critical magnetic field. Our observations reveal an almost isotropic in-plane upper critical field, providing insight into the symmetry and pairing mechanism of high temperature superconductivity in FeSe.

cond-mat.supr-con↗

arXiv2020

Unconventional Hall Effect induced by Berry Curvature

Berry phase and Berry curvature play a key role in the development of topology in physics and do contribute to the transport properties in solid state systems. In this paper, we report the finding of novel nonzero Hall effect in topological material ZrTe5 flakes when in-plane magnetic field is parallel and perpendicular to the current. Surprisingly, both symmetric and antisymmetric components with respect to magnetic field are detected in the in-plane Hall resistivity. Further theoretical analysis suggests that the magnetotransport properties originate from the anomalous velocity induced by Berry curvature in a tilted Weyl semimetal. Our work not only enriches the Hall family but also provides new insights into the Berry phase effect in topological materials.

cond-mat.mes-hall↗

arXiv2020

Possible Lattice and Charge Order in CuxBi2Te2Se

Metal intercalation into layered topological insulator materials such as the binary chalcogenide Bi2X3 (X=Te or Se) has yielded novel two-dimensional electron-gas physics, phase transitions to superconductivity, as well as interesting magnetic ground states. Of recent interest is the intercalation-driven interplay between lattice distortions, density wave ordering, and the emergence of new phenomena in the vicinity of instabilities induced by intercalation. Here, we examine the effects of Cu-intercalation on the ternary chalcogenide Bi2Te2Se. We report the discovery, in Cu0.3Bi2Te2Se, of a periodic lattice distortion at room temperature, together with a charge density wave transition around Td = 220K. We also report, for the first time, a complete study of the CuxBi2Te2Se system, and the effect of Cu-intercalation on crystal structure, phonon structure, and electronic properties for 0.0 $\le$ x $\le$ 0.5. Our electron diffraction studies reveal strong Bragg spots at reciprocal lattice positions forbidden by ABC stacking, possibly resulting from stacking faults, or a superlattice. The c-axis lattice parameter varies monotonically with x for 0 $\lt$ x $\lt$ 0.2, but drops precipitously for higher x. Similarly, Raman phonon modes $A^2_{1g}$ and $E_g$ soften monotonically for 0 $\lt$ x $\lt$ 0.2 but harden sharply for x $\gt$ 0.2. This indicates that Cu likely intercalates up to x $\sim$0.2, followed by partial site-substitutions at higher values. The resulting strain makes the 0.2 $\lt$ x $\lt$ 0.3 region susceptible to instabilities and distortions. Our results point toward the presence of an incommensurate CDW above Td = 220 K. This work strengthens prevalent thought that intercalation contributes significantly to instabilities in the lattice and charge degrees of freedom in layered chalcogenides.

cond-mat.str-el↗

arXiv2020

Improving Tracking through Human-Robot Sensory Augmentation

This paper introduces human-robot sensory augmentation and illustrates it on a tracking task, where performance can be improved by the exchange of sensory information between the robot and its human user. It was recently found that during interaction between humans, the partners use each other's sensory information to improve their own sensing, thus also their performance and learning. In this paper, we develop a computational model of this unique human ability, and use it to build a novel control framework for human-robot interaction. The human partner's control is formulated as a feedback control with unknown control gains and desired trajectory. A Kalman filter is used to estimate first the control gains and then the desired trajectory. The estimated human partner's desired trajectory is used as augmented sensory information about the system and combined with the robot's measurement to estimate an uncertain target trajectory. Simulations and an implementation of the presented framework on a robotic interface validate the proposed observer-predictor pair for a tracking task. The results obtained using this robot demonstrate how the human user's control can be identified, and exhibit similar benefits of this sensory augmentation as was observed between interacting humans.

cs.RO↗

arXiv2019

Asynchronous Federated Learning with Differential Privacy for Edge Intelligence

Federated learning has been showing as a promising approach in paving the last mile of artificial intelligence, due to its great potential of solving the data isolation problem in large scale machine learning. Particularly, with consideration of the heterogeneity in practical edge computing systems, asynchronous edge-cloud collaboration based federated learning can further improve the learning efficiency by significantly reducing the straggler effect. Despite no raw data sharing, the open architecture and extensive collaborations of asynchronous federated learning (AFL) still give some malicious participants great opportunities to infer other parties' training data, thus leading to serious concerns of privacy. To achieve a rigorous privacy guarantee with high utility, we investigate to secure asynchronous edge-cloud collaborative federated learning with differential privacy, focusing on the impacts of differential privacy on model convergence of AFL. Formally, we give the first analysis on the model convergence of AFL under DP and propose a multi-stage adjustable private algorithm (MAPA) to improve the trade-off between model utility and privacy by dynamically adjusting both the noise scale and the learning rate. Through extensive simulations and real-world experiments with an edge-could testbed, we demonstrate that MAPA significantly improves both the model accuracy and convergence speed with sufficient privacy guarantee.

cs.LG↗

arXiv2019

A non-autonomous bifurcation problem for a non-local scalar one-dimensional parabolic equation

In this paper we study the asymptotic behavior of solutions for a non-local non-autonomous scalar quasilinear parabolic problem in one space dimension. Our aim is to give a fairly complete description of the the forwards asymptotic behavior of solutions for models with Kirchoff type diffusion. In the autonomous we use the gradient structure of the model, some symmetry properties of solutions and develop comparison results to obtain a sequence of bifurcations of equilibria analogous to that seen in the model with local diffusivity. We give conditions so that the autonomous problem admits at most one positive equilibrium and analyse the existence of sign changing equilibria. Also using symmetry and our comparison results we construct what is called non-autonomous equilibria to describe part of the asymptotics of the associated non-autonomous non-local parabolic problem.

math.AP↗

arXiv2019

Uniform attractors of non-autonomous Kirchhoff wave models

The paper investigates the existence and upper semicontinuity of uniform attractors of the perturbed non-autonomous Kirchhoff wave equations with strong damping and supercritical nonlinearity: $u_{tt}-Δu_{t}-(1+ε\|\nabla u\|^{2})Δu+f(u)=g(x,t)$, where $ε\in [0,1]$ is a perturbed parameter. It shows that when the nonlinearity $f(u)$ is of supercritical growth $p: \frac{N+2}{N-2}=p^*<p<p^{**}=\frac{N+4}{(N-4)^+}$: (i) the related evolution process has a compact uniform attractor $\mathcal{A}_\ls^\e $ for each $ε\in [0,1]$; (ii) the family of uniform attractor $\mathcal{A}_\ls^\e $ is upper semicontinuous on the perturbed parameter $ε$ in the sense of partially strong topology.

math.AP↗

arXiv2019

Impact of Prior Knowledge and Data Correlation on Privacy Leakage: A Unified Analysis

It has been widely understood that differential privacy (DP) can guarantee rigorous privacy against adversaries with arbitrary prior knowledge. However, recent studies demonstrate that this may not be true for correlated data, and indicate that three factors could influence privacy leakage: the data correlation pattern, prior knowledge of adversaries, and sensitivity of the query function. This poses a fundamental problem: what is the mathematical relationship between the three factors and privacy leakage? In this paper, we present a unified analysis of this problem. A new privacy definition, named \textit{prior differential privacy (PDP)}, is proposed to evaluate privacy leakage considering the exact prior knowledge possessed by the adversary. We use two models, the weighted hierarchical graph (WHG) and the multivariate Gaussian model to analyze discrete and continuous data, respectively. We demonstrate that positive, negative, and hybrid correlations have distinct impacts on privacy leakage. Considering general correlations, a closed-form expression of privacy leakage is derived for continuous data, and a chain rule is presented for discrete data. Our results are valid for general linear queries, including count, sum, mean, and histogram. Numerical experiments are presented to verify our theoretical analysis.

cs.LG↗

arXiv2019

Printing surface charge as a new paradigm to program droplet transport

Directed, long-range and self-propelled transport of droplets on solid surfaces, especially on water repellent surfaces, is crucial for many applications from water harvesting to bio-analytical devices. One appealing strategy to achieve the preferential transport is to passively control the surface wetting gradients, topological or chemical, to break the asymmetric contact line and overcome the resistance force. Despite extensive progress, the directional droplet transport is limited to small transport velocity and short transport distance due to the fundamental trade-off: rapid transport of droplet demands a large wetting gradient, whereas long-range transport necessitates a relatively small wetting gradient. Here, we report a radically new strategy that resolves the bottleneck through the creation of an unexplored gradient in surface charge density (SCD). By leveraging on a facile droplet printing on superamphiphobic surfaces as well as the fundamental understanding of the mechanisms underpinning the creation of the preferential SCD, we demonstrate the self-propulsion of droplets with a record-high velocity over an ultra-long distance without the need for additional energy input. Such a Leidenfrost-like droplet transport, manifested at ambient condition, is also genetic, which can occur on a variety of substrates such as flexible and vertically placed surfaces. Moreover, distinct from conventional physical and chemical gradients, the new dimension of gradient in SCD can be programmed in a rewritable fashion. We envision that our work enriches and extends our capability in the manipulation of droplet transport and would find numerous potential applications otherwise impossible.

cond-mat.soft↗

arXiv2018

Discovery of Log-Periodic Oscillations in Ultra-Quantum Topological Materials

Quantum oscillations are usually the manifestation of the underlying physical nature in condensed matter systems. Here we report a new type of log-periodic quantum oscillations in ultra-quantum three-dimensional topological materials. Beyond the quantum limit (QL), the log-periodic oscillations involving up to five oscillating cycles (5 peaks and 5 dips) are observed on the magnetoresistance (MR) of high quality single-crystal ZrTe5, virtually showing the clearest feature of discrete scale invariance (DSI). Further theoretical analyses show that the two-body quasi-bound states can be responsible for the DSI feature. Our work provides a new perspective on the ground state of topological materials beyond the QL.

cond-mat.mtrl-sci↗

arXiv2018

Semi-Supervised Classification for oil reservoir

This paper addresses the general problem of accurate identification of oil reservoirs. Recent improvements in well or borehole logging technology have resulted in an explosive amount of data available for processing. The traditional methods of analysis of the logs characteristics by experts require significant amount of time and money and is no longer practicable. In this paper, we use the semi-supervised learning to solve the problem of ever-increasing amount of unlabelled data available for interpretation. The experts are needed to label only a small amount of the log data. The neural network classifier is first trained with the initial labelled data. Next, batches of unlabelled data are being classified and the samples with the very high class probabilities are being used in the next training session, bootstrapping the classifier. The process of training, classifying, enhancing the labelled data is repeated iteratively until the stopping criteria are met, that is, no more high probability samples are found. We make an empirical study on the well data from Jianghan oil field and test the performance of the neural network semi-supervised classifier. We compare this method with other classifiers. The comparison results show that our neural network semi-supervised classifier is superior to other classification methods.

cs.LG↗

arXiv2017

Zero-Shot Learning with Generative Latent Prototype Model

Zero-shot learning, which studies the problem of object classification for categories for which we have no training examples, is gaining increasing attention from community. Most existing ZSL methods exploit deterministic transfer learning via an in-between semantic embedding space. In this paper, we try to attack this problem from a generative probabilistic modelling perspective. We assume for any category, the observed representation, e.g. images or texts, is developed from a unique prototype in a latent space, in which the semantic relationship among prototypes is encoded via linear reconstruction. Taking advantage of this assumption, virtual instances of unseen classes can be generated from the corresponding prototype, giving rise to a novel ZSL model which can alleviate the domain shift problem existing in the way of direct transfer learning. Extensive experiments on three benchmark datasets show our proposed model can achieve state-of-the-art results.

cs.CV↗

arXiv2017

Learning Robust Features with Incremental Auto-Encoders

Automatically learning features, especially robust features, has attracted much attention in the machine learning community. In this paper, we propose a new method to learn non-linear robust features by taking advantage of the data manifold structure. We first follow the commonly used trick of the trade, that is learning robust features with artificially corrupted data, which are training samples with manually injected noise. Following the idea of the auto-encoder, we first assume features should contain much information to well reconstruct the input from its corrupted copies. However, merely reconstructing clean input from its noisy copies could make data manifold in the feature space noisy. To address this problem, we propose a new method, called Incremental Auto-Encoders, to iteratively denoise the extracted features. We assume the noisy manifold structure is caused by a diffusion process. Consequently, we reverse this specific diffusion process to further contract this noisy manifold, which results in an incremental optimization of model parameters . Furthermore, we show these learned non-linear features can be stacked into a hierarchy of features. Experimental results on real-world datasets demonstrate the proposed method can achieve better classification performances.

cs.LG↗

arXiv2017

Indirect Shared Control of Highly Automated Vehicles for Cooperative Driving between Driver and Automation

It is widely acknowledged that drivers should remain in the control loop of automated vehicles before they completely meet real-world operational conditions. This paper introduces an `indirect shared control' scheme for steer-by-wire vehicles, which allows the vehicle control authority to be continuously shared between the driver and automation through unphysical cooperation. This paper first balances the control objectives of the driver and automation in a weighted summation, and then models the driver's adaptive control behavior using a predictive control approach. The driver adaptation modeling enables off-line evaluations of indirect shared control systems and thus facilitates the design of the assistant controller. Unlike any conventional driver model for manual driving, this model assumes that the driver can learn and incorporate the controller strategy into his internal model for more accurate path following. To satisfy the driving demands in different scenarios, a sliding-window detector is designed to continuously monitor the driver intention and automatically switch the authority weights between the driver and automation. The simulation results illustrate the advantages of considering the driver adaptation in path-following and obstacle-avoidance tasks, and show the effectiveness of indirect shared control for cooperative driving.

cs.RO↗

arXiv2017

Zero-Shot Recognition using Dual Visual-Semantic Mapping Paths

Zero-shot recognition aims to accurately recognize objects of unseen classes by using a shared visual-semantic mapping between the image feature space and the semantic embedding space. This mapping is learned on training data of seen classes and is expected to have transfer ability to unseen classes. In this paper, we tackle this problem by exploiting the intrinsic relationship between the semantic space manifold and the transfer ability of visual-semantic mapping. We formalize their connection and cast zero-shot recognition as a joint optimization problem. Motivated by this, we propose a novel framework for zero-shot recognition, which contains dual visual-semantic mapping paths. Our analysis shows this framework can not only apply prior semantic knowledge to infer underlying semantic manifold in the image feature space, but also generate optimized semantic embedding space, which can enhance the transfer ability of the visual-semantic mapping to unseen classes. The proposed method is evaluated for zero-shot recognition on four benchmark datasets, achieving outstanding results.

cs.CV↗

arXiv2016

Dynamic analysis of simultaneous adaptation of force, impedance and trajectory

When carrying out tasks in contact with the environment, humans are found to concurrently adapt force, impedance and trajectory. Here we develop a robotic model of this mechanism in humans and analyse the underlying dynamics. We derive a general adaptive controller for the interaction of a robot with an environment solely characterised by its stiffness and damping, using Lyapunov theory.

cs.RO↗

arXiv2015

The Chiral Anomaly and Ultrahigh Mobility in Crystalline HfTe5

HfTe5 is predicted to be a promising platform for studying topological phases. Here through an electrical transport study, we present the first observation of chiral anomaly and ultrahigh mobility in HfTe5 crystals. Negative magneto-resistivity in HfTe5 is observed when the external magnetic and electrical fields are parallel (B//E) and quickly disappears once B deviates from the direction of E. Quantitative fitting further confirms the chiral anomaly as the underlying physics. Moreover, by analyzing the conductivity tensors of longitudinal and Hall traces, ultrahigh mobility and ultralow carrier density are revealed in HfTe5, which paves the way for potential electronic applications.

cond-mat.mtrl-sci↗