arXiv subjects

Han Xu

Publications and source records attributed to Han Xu.

At least 127 records · Page 7Linked to original sources

arXiv2021

Analytical Differential Calculus with Integration

Differential lambda-calculus was first introduced by Thomas Ehrhard and Laurent Regnier in 2003. Despite more than 15 years of history, little work has been done on a differential calculus with integration. In this paper, we shall propose a differential calculus with integration from programming point of view. We show its good correspondence with mathematics, which is manifested by how we construct these reduction rules and how we preserve important mathematical theorems in our calculus. Moreover, we highlight applications of the calculus in incremental computation, automatic differentiation, and computation approximation.

cs.PL↗

arXiv2021

Generalizable control for multiparameter quantum metrology

Quantum control can be employed in quantum metrology to improve the precision limit for the estimation of unknown parameters. The optimal control, however, typically depends on the actual values of the parameters and thus needs to be designed adaptively with the updated estimations of those parameters. Traditional methods, such as gradient ascent pulse engineering (GRAPE), need to be rerun for each new set of parameters encountered, making the optimization costly, especially when many parameters are involved. Here we study the generalizability of optimal control, namely, optimal controls that can be systematically updated across a range of parameters with minimal cost. In cases where control channels can completely reverse the shift in the Hamiltonian due to a change in parameters, we provide an analytical method which efficiently generates optimal controls for any parameter starting from an initial optimal control found by either GRAPE or reinforcement learning. When the control channels are restricted, the analytical scheme is invalid, but reinforcement learning still retains a level of generalizability, albeit in a narrower range. In cases where the shift in the Hamiltonian is impossible to decompose to available control channels, no generalizability is found for either the reinforcement learning or the analytical scheme. We argue that the generalization of reinforcement learning is through a mechanism similar to the analytical scheme. Our results provide insights into when and how the optimal control in multiparameter quantum metrology can be generalized, thereby facilitating efficient implementation of optimal quantum estimation of multiple parameters, particularly for an ensemble of systems with ranges of parameters.

quant-ph↗

arXiv2021

Generalizable control for quantum parameter estimation through reinforcement learning

Measurement and estimation of parameters are essential for science and engineering, where one of the main quests is to find systematic schemes that can achieve high precision. While conventional schemes for quantum parameter estimation focus on the optimization of the probe states and measurements, it has been recently realized that control during the evolution can significantly improve the precision. The identification of optimal controls, however, is often computationally demanding, as typically the optimal controls depend on the value of the parameter which then needs to be re-calculated after the update of the estimation in each iteration. Here we show that reinforcement learning provides an efficient way to identify the controls that can be employed to improve the precision. We also demonstrate that reinforcement learning is highly generalizable, namely the neural network trained under one particular value of the parameter can work for different values within a broad range. These desired features make reinforcement learning an efficient alternative to conventional optimal quantum control methods.

quant-ph↗

arXiv2021

Strain engineering of epitaxial oxide heterostructures beyond substrate limitations

The limitation of commercially available single-crystal substrates and the lack of continuous strain tunability preclude the ability to take full advantage of strain engineering for further exploring novel properties and exhaustively studying fundamental physics in complex oxides. Here we report an approach for imposing continuously tunable, large epitaxial strain in oxide heterostructures beyond substrate limitations by inserting an interface layer through tailoring its gradual strain relaxation. Taking BiFeO3 as a model system, we demonstrate that the introduction of an ultrathin interface layer allows the creation of a desired strain that can induce phase transition and stabilize a new metastable super-tetragonal phase as well as morphotropic phase boundaries overcoming substrate limitations. Furthermore, continuously tunable strain from tension to compression can be generated by precisely adjusting the thickness of the interface layer, leading to the first achievement of continuous O-R-T phase transition in BiFeO3 on a single substrate. This proposed route could be extended to other oxide heterostructures, providing a platform for creating exotic phases and emergent phenomena.

cond-mat.mtrl-sci↗

arXiv2020

Adversarial Attacks and Defenses on Graphs: A Review, A Tool and Empirical Studies

Deep neural networks (DNNs) have achieved significant performance in various tasks. However, recent studies have shown that DNNs can be easily fooled by small perturbation on the input, called adversarial attacks. As the extensions of DNNs to graphs, Graph Neural Networks (GNNs) have been demonstrated to inherit this vulnerability. Adversary can mislead GNNs to give wrong predictions by modifying the graph structure such as manipulating a few edges. This vulnerability has arisen tremendous concerns for adapting GNNs in safety-critical applications and has attracted increasing research attention in recent years. Thus, it is necessary and timely to provide a comprehensive overview of existing graph adversarial attacks and the countermeasures. In this survey, we categorize existing attacks and defenses, and review the corresponding state-of-the-art methods. Furthermore, we have developed a repository with representative algorithms (https://github.com/DSE-MSU/DeepRobust/tree/master/deeprobust/graph). The repository enables us to conduct empirical studies to deepen our understandings on attacks and defenses on graphs.

cs.LG↗

arXiv2020

Collaborative Attention Network for Person Re-identification

Jointly utilizing global and local features to improve model accuracy is becoming a popular approach for the person re-identification (ReID) problem, because previous works using global features alone have very limited capacity at extracting discriminative local patterns in the obtained feature representation. Existing works that attempt to collect local patterns either explicitly slice the global feature into several local pieces in a handcrafted way, or apply the attention mechanism to implicitly infer the importance of different local regions. In this paper, we show that by explicitly learning the importance of small local parts and part combinations, we can further improve the final feature representation for Re-ID. Specifically, we first separate the global feature into multiple local slices at different scale with a proposed multi-branch structure. Then we introduce the Collaborative Attention Network (CAN) to automatically learn the combination of features from adjacent slices. In this way, the combination keeps the intrinsic relation between adjacent features across local regions and scales, without losing information by partitioning the global features. Experiment results on several widely-used public datasets including Market-1501, DukeMTMC-ReID and CUHK03 prove that the proposed method outperforms many existing state-of-the-art methods.

cs.CV↗

arXiv2020

Yet Meta Learning Can Adapt Fast, It Can Also Break Easily

Meta learning algorithms have been widely applied in many tasks for efficient learning, such as few-shot image classification and fast reinforcement learning. During meta training, the meta learner develops a common learning strategy, or experience, from a variety of learning tasks. Therefore, during meta test, the meta learner can use the learned strategy to quickly adapt to new tasks even with a few training samples. However, there is still a dark side about meta learning in terms of reliability and robustness. In particular, is meta learning vulnerable to adversarial attacks? In other words, would a well-trained meta learner utilize its learned experience to build wrong or likely useless knowledge, if an adversary unnoticeably manipulates the given training set? Without the understanding of this problem, it is extremely risky to apply meta learning in safety-critical applications. Thus, in this paper, we perform the initial study about adversarial attacks on meta learning under the few-shot classification problem. In particular, we formally define key elements of adversarial attacks unique to meta learning and propose the first attacking algorithm against meta learning under various settings. We evaluate the effectiveness of the proposed attacking strategy as well as the robustness of several representative meta learning algorithms. Experimental results demonstrate that the proposed attacking strategy can easily break the meta learner and meta learning is vulnerable to adversarial attacks. The implementation of the proposed framework will be released upon the acceptance of this paper.

cs.LG↗

arXiv2020

Butterfly-like anisotropic magnetoresistance and angle-dependent Berry phase in Type-II Weyl semimetal WP2

Weyl semimetal emerges as a new topologically nontrivial phase of matter, hosting low-energy excitations of massless Weyl fermions. Here, we present a comprehensive study of the type-II Weyl semimetal WP2. Transport studies show a butterfly-like magnetoresistance at low temperature, reflecting the anisotropy of the electron Fermi surfaces. The four-lobed feature gradually evolves into a two-lobed one upon increasing temperature, mainly due to the reduced relative contribution of electron Fermi surfaces compared to hole Fermi surfaces for the magnetoresistance. Moreover, angle-dependent Berry phase is further discovered from the quantum oscillations, which is ascribed to the effective manipulation of the extremal Fermi orbits by the magnetic field to feel the nearby topological singularities in the momentum space. The revealed topological characters and anisotropic Fermi surfaces of WP2 substantially enrich the physical properties of Weyl semimetals and hold great promises in topological electronic and Fermitronic device applications.

cond-mat.mtrl-sci↗

arXiv2020

DeepRobust: A PyTorch Library for Adversarial Attacks and Defenses

DeepRobust is a PyTorch adversarial learning library which aims to build a comprehensive and easy-to-use platform to foster this research field. It currently contains more than 10 attack algorithms and 8 defense algorithms in image domain and 9 attack algorithms and 4 defense algorithms in graph domain, under a variety of deep learning architectures. In this manual, we introduce the main contents of DeepRobust with detailed instructions. The library is kept updated and can be found at https://github.com/DSE-MSU/DeepRobust.

cs.LG↗

arXiv2020

Observation of Dynamic Stark Resonances in Strong-Field Excitation

We investigate AC Stark-shifted resonances in argon with ultrashort near-infrared pulses. Using 30 fs pulses we observe periodic enhancements of the excitation yield in the intensity regions corresponding to the absorption of 13 and 14 photons. By reducing the pulse duration to 6 fs with only a few optical cycles, we also demonstrate that the enhancements are significantly reduced beyond what is measurable in the experiment. Comparing these to numerical predictions, which are in quantitative agreement with experimental results, we find that even though the quantum-state distribution can be broad, the enhancements are largely due to efficient population of a select few AC Stark-shifted resonant states rather than the closing of an ionization channel. Because these resonances are dependent on the frequency and intensity of the laser field, the broad bandwidth of the 6 fs pulses means that the resonance condition is fulfilled across a large range of intensities. This is further exaggerated by volume-averaging effects, resulting in excitation of the $5g$ state at almost all intensities and reducing the apparent magnitude of the enhancements. For 30 fs pulses, volume averaging also broadens the quantum state distribution but the enhancements are still large enough to survive. In this case, selectivity of excitation to a single state is reduced below 25% of the relative population. However, an analysis of TDSE simulations indicates that excitation of up to 60% into a single state is possible if volume averaging can be eliminated and the intensity can be precisely controlled.

physics.atom-ph↗

arXiv2019

Adversarial Attacks and Defenses in Images, Graphs and Text: A Review

Deep neural networks (DNN) have achieved unprecedented success in numerous machine learning tasks in various domains. However, the existence of adversarial examples has raised concerns about applying deep learning to safety-critical applications. As a result, we have witnessed increasing interests in studying attack and defense mechanisms for DNN models on different data types, such as images, graphs and text. Thus, it is necessary to provide a systematic and comprehensive overview of the main threats of attacks and the success of corresponding countermeasures. In this survey, we review the state of the art algorithms for generating adversarial examples and the countermeasures against adversarial examples, for the three popular data types, i.e., images, graphs and text.

cs.LG↗

arXiv2019

Nanosecond Optically Induced Phase Transformation in Compressively Strained BiFeO3 on LaAlO3

Above-bandgap optical illumination induces a transformation from tilted tetragonal-like (T-like) and rhombohedral-like (R-like) phases to an untilted T-like phase in compressively strained BiFeO3. Optical excitation leads to an out-of-plane lattice expansion in the T-like phase. The transformation proceeds in regions with boundaries between the T-like and tilted R-like and tilted T-like phases, consistent with the motion of a boundary. The optically induced transformation indicates that there are new optically driven routes towards nanosecond-scale control of phase transformations in ferroelectrics and multiferroics.

cond-mat.mes-hall↗

arXiv2019

Moiré Engineering of Electronic Phenomena in Correlated Oxides

Moiré engineering has recently emerged as a capable approach to control quantum phenomena in condensed matter systems. In van der Waals heterostructures, moiré patterns can be formed by lattice misorientation between adjacent atomic layers, creating long range electronic order. To date, moiré engineering has been executed solely in stacked van der Waals multilayers. Herein, we describe our discovery of electronic moiré patterns in films of a prototypical magnetoresistive oxide La0.67Sr0.33MnO3 (LSMO) epitaxially grown on LaAlO3 (LAO) substrates. Using scanning probe nano-imaging, we observe microscopic moiré profiles attributed to the coexistence and interaction of two distinct incommensurate patterns of strain modulation within these films. The net effect is that both electronic conductivity and ferromagnetism of LSMO are modulated by periodic moiré textures extending over mesoscopic scales. Our work provides an entirely new route with potential to achieve spatially patterned electronic textures on demand in strained epitaxial materials.

cond-mat.mes-hall↗

arXiv2019

Relativistic non-dipole effects in strong-field atomic ionization at moderate intensities

We present a detailed experimental and theoretical study on the relativistic non-dipole effects in strong-field atomic ionisation by near-infrared linearly-polarised few-cycle laser pulses in the intensity range 1014 -1015 W/cm2. We record high-resolution photoelectron momentum distributions of argon using a reaction microscope and compare our measurements with a truly ab-initio fully relativistic 3D model based on the time-dependent Dirac equation. We observe counter-intuitive peak shifts of the transverse electron momentum distribution in the direction opposite to that of laser propagation as a function of laser intensity and demonstrate an excellent agreement between experimental results and theoretical predictions.

physics.atom-ph↗

arXiv2018

Covariance-Insured Screening

Modern bio-technologies have produced a vast amount of high-throughput data with the number of predictors far greater than the sample size. In order to identify more novel biomarkers and understand biological mechanisms, it is vital to detect signals weakly associated with outcomes among ultrahigh-dimensional predictors. However, existing screening methods, which typically ignore correlation information, are likely to miss these weak signals. By incorporating the inter-feature dependence, we propose a covariance-insured screening methodology to identify predictors that are jointly informative but only marginally weakly associated with outcomes. The validity of the method is examined via extensive simulations and real data studies for selecting potential genetic factors related to the onset of cancer.

stat.ML↗

arXiv2018

Attosecond angular streaking and tunnelling time in atomic hydrogen

Tunnelling, one of the key features of quantum mechanics, ignited an ongoing debate about the value, meaning and interpretation of 'tunnelling time'. Until recently the debate was purely theoretical, with the process considered to be instantaneous for all practical purposes. This changed with the development of ultrafast lasers and in particular, the 'attoclock' technique that is used to probe the attosecond dynamics of electrons. Although the initial attoclock measurements hinted at instantaneous tunnelling, later experiments contradicted those findings, claiming to have measured finite tunnelling times. In each case these measurements were performed with multi-electron atoms. Atomic hydrogen (H), the simplest atomic system with a single electron, can be 'exactly' (subject only to numerical limitations) modelled using numerical solutions of the 3D-TDSE with measured experimental parameters and acts as a convenient benchmark for both accurate experimental measurements and calculations. Here we report the first attoclock experiment performed on H and find that our experimentally determined offset angles are in excellent agreement with accurate 3D-TDSE simulations performed using our experimental pulse parameters. The same simulations with a short-range Yukawa potential result in zero offset angles for all intensities. We conclude that the offset angle measured in the attoclock experiments originates entirely from electron scattering by the long-range Coulomb potential with no contribution from tunnelling time delay. That conclusion is supported by empirical observation that the electron offset angles follow closely the simple formula for the deflection angle of electrons undergoing classical Rutherford scattering by the Coulomb potential. Thus we confirm that, in H, tunnelling is instantaneous (with an upperbound of 1.8 as) within our experimental and numerical uncertainty.

physics.atom-ph↗

arXiv2016

Linking particle properties to paste extrusion flow characteristics using discrete element simulations

Extrusion is a widely used process for forming pastes into designed shapes, and is central to the manufacture of many industrial products. The extrusion through a square-entry die of a model paste of non-Brownian spheres suspended in a Newtonian fluid is investigated using discrete element simulations, capturing individual particle contacts and hydrodynamic interactions. The simulations reveal inhomogeneous velocity and stress distributions, originating in the inherent microstructure formed by the constituent particles. Such features are shown to be relevant to generic paste extrusion behaviour, such as die swell. The pressure drop across the extruder is correlated with the extrudate velocity using the Benbow-Bridgwater equation, with the empirical parameters being linked directly to particle properties such as surface friction, and processing conditions such as extruder wall roughness. Our model and results bring recent advances in suspension rheology into an industrial setting, laying foundations for future model development, paste formulation and extrusion design.

cond-mat.soft↗

arXiv2016

The interaction of excited atoms and few-cycle laser pulses

This work describes the first observations of the ionisation of neon in a metastable atomic state utilising a strong-field, few-cycle light pulse. We compare the observations to theoretical predictions based on the Ammosov-Delone-Krainov (ADK) theory and a solution to the time-dependent Schrodinger equation (TDSE). The TDSE provides better agreement with the experimental data than the ADK theory. We optically pump the target atomic species and demonstrate that the ionisation rate depends on the spin state of the target atoms and provide physically transparent interpretation of such a spin dependence in the frameworks of the spin-polarised Hartree-Fock and random-phase approximations.

physics.atom-ph↗