arXiv subjects

Shuai Li

Publications and source records attributed to Shuai Li.

At least 361 records · Page 20Linked to original sources

arXiv2020

Modelling large scale airgun-bubble dynamics with highly non-spherical features

A thorough understanding of the dynamics of meter-sized airgun-bubbles is very crucial to seabed geophysical exploration. In this study, we use the boundary integral method to investigate the highly non-spherical airgun-bubble dynamics and its corresponding pressure wave emission. Moreover, a model is proposed to also consider the process of air release from the airgun port, which is found to be the most crucial factor to estimate the initial peak of the pressure wave. The numerical simulations show good agreement with experiments, in terms of non-spherical bubble shapes and pressure waves. Thereafter, the effects of the port opening time $T\rm_{open}$, airgun firing depth, heat transfer, and gravity are numerically investigated. We find that a smaller $T\rm_{open}$ leads to a more violent air release that consequently causes stronger high-frequency pressure wave emissions; however, the low-frequency pressure waves are little affected. Additionally, the non-spherical bubble dynamics is highly dependent on the Froude number $Fr$. Starting from $Fr=2$, as $Fr$ increases, the jet contains lower kinetic energy, resulting in a stronger energy focusing of the bubble collapse itself and thus a larger pressure peak during the bubble collapse phase. For $Fr \ge 7$, the spherical bubble theory becomes an appropriate description of the airgun-bubble. The new findings of this study may provide a reference for practical operations and designing environmentally friendly airguns in the near future.

physics.flu-dyn↗

arXiv2020

Electric field and current induced electroforming modes in NbOx

Electroforming is used to initiate the memristive response in metal/oxide/metal devices by creating a filamentary conduction path in the oxide film. Here we use a simple photoresist-based detection technique to map the spatial distribution of conductive filaments formed in Nb/NbOx/Pt devices, and correlate these with current-voltage characteristics and in-situ thermoreflectance measurements to identify distinct modes of electroforming in low and high conductivity NbOx films. In low conductivity films the filaments are randomly distributed within the oxide film, consistent with a field-induced weakest-link mechanism, while in high conductivity films they are concentrated in the center of the film. In the latter case the current-voltage characteristics and in-situ thermoreflectance imaging show that electroforming is associated with current bifurcation into regions of low and high current density. This is supported by finite element modelling of the current distribution and shown to be consistent with predictions of a simple core-shell model of the current distribution. These results clearly demonstrate two distinct modes of electroforming in the same materials system and show that the dominant mode depends on the conductivity of the film, with field-induced electroforming dominant in low conductivity films and current-bifurcation induced electroforming dominant in high conductivity films. Finally, we demonstrate S-type and snap-back negative differential resistance in the high conductivity films and explain this behavior in terms of two-zone model.

cond-mat.mtrl-sci↗

arXiv2019

Metal-oxide interface reactions and their effect on integrated resistive/threshold switching in NbOx

Reactive metal electrodes (Nb, Ti, Cr, Ta, and Hf) are shown to play an important role in controlling the volatile switching characteristics of metal/Nb2O5/Pt devices. In particular, devices are shown to exhibit stable threshold switching under negative bias but to have a response under positive bias that depends on the choice of metal. Three distinct responses are highlighted: Devices with Nb and Ti top electrodes are shown to exhibit stable threshold switching with symmetric characteristics for both positive and negative polarities; devices with Cr top electrodes are shown to exhibit stable threshold switching but with asymmetric hysteresis windows under positive and negative polarities; and devices with Ta and Hf electrodes are shown to exhibit an integrated threshold-memory (1S1M) response. Based on thermodynamic data and lumped element modelling these effects are attributed to the formation of a metal-oxide interlayer and its response to field-induced oxygen exchange. These results provide important insight into the physical origin of the switching response and pathways for engineering devices with reliable switching characteristics.

physics.app-ph↗

arXiv2019

Schottky barrier induced asymmetry in the negative differential resistance response of Nb/NbOx/Pt cross-point devices

The negative differential resistance (NDR) response of Nb/NbOx/Pt cross-point devices is shown to have a polarity dependence due to the effect of the metal/oxide Schottky barriers on the contact resistance. Three distinct responses are observed under opposite polarity testing: bipolar S-type NDR, bipolar snap-back NDR, and combined S-type and snap-back NDR, depending on the stoichiometry of the oxide film and device area. In-situ thermoreflectance imaging is used to show that these NDR responses are associated with strong current localisation, thereby justifying the use of a previously developed two-zone, core shell thermal model of the device. The observed polarity dependent NDR responses, and their dependence on stoichiometry and area are then explained by extending this model to include the effect of the polarity dependent contact resistance. This study provides an improved understanding of the NDR response of metal/oxide/metal structures and informs the engineering of devices for neuromorphic computing and non-volatile memory applications.

physics.app-ph↗

arXiv2019

Asymptotic properties of the plane shear thickening fluids with bounded energy integral

In this note we investigate the asymptotic behavior of plane shear thickening fluids around a bounded obstacle. Different from the Navier-Stokes case considered by Gilbarg-Weinberger in \cite{GW1978}, where the good structure of the vorticity can be exploited and weighted energy estimates can be applied, we have to overcome the nonlinear term of high order. The decay estimates of the velocity was obtained by combining Point-wise Behavior Theorem in \cite{Galdi} and Brezis-Gallouet inequality in \cite{BG1980} together, which is independent of interest.

math.AP↗

arXiv2019

Local magnetic moment oscillation around an Anderson impurity on graphene

We theoretically investigate the spin resolved Friedel oscillation (FO) and quasiparticle interference (QPI) in graphene induced by an Anderson impurity. Once the impurity becomes magnetic, the resulted FO becomes spin dependent, which gives rise to a local magnetic moment oscillation with an envelop decaying as $r^{-2}$ in real space in the doping cases. Meanwhile, at half filling, the charge density and local magnetic moment will not oscillate but decay as $r^{-3}$. Such spin resolved FO has both sublattice and spin asymmetry. Interestingly, the local magnetic moment decay at half filling only occurs at one sublattice of graphene, which is quite like the phenomenon observed in a recent STM experiment [H. Gonzalez-Herrero et al., Science 352, 437 (2016)]. We further give an analytic formula about such spin dependent FO based on the stationary phase approximation. Finally, we study the interference of quasiparticles around the magnetic impurity by calculating the spin-dependent Fourier-transformed local density of states (FT-LDOS). Our work gives a comprehensive understanding about the local magnetic moment oscillation around an Anderson impurity on graphene.

cond-mat.mes-hall↗

arXiv2019

Stochastic Online Learning with Probabilistic Graph Feedback

We consider a problem of stochastic online learning with general probabilistic graph feedback, where each directed edge in the feedback graph has probability $p_{ij}$. Two cases are covered. (a) The one-step case, where after playing arm $i$ the learner observes a sample reward feedback of arm $j$ with independent probability $p_{ij}$. (b) The cascade case where after playing arm $i$ the learner observes feedback of all arms $j$ in a probabilistic cascade starting from $i$ -- for each $(i,j)$ with probability $p_{ij}$, if arm $i$ is played or observed, then a reward sample of arm $j$ would be observed with independent probability $p_{ij}$. Previous works mainly focus on deterministic graphs which corresponds to one-step case with $p_{ij} \in \{0,1\}$, an adversarial sequence of graphs with certain topology guarantees, or a specific type of random graphs. We analyze the asymptotic lower bounds and design algorithms in both cases. The regret upper bounds of the algorithms match the lower bounds with high probability.

cs.LG↗

arXiv2019

Hydrogen-induced magnetism in graphene: a simple effective model description

Hydrogen adatoms induced magnetic moment on graphene has been observed in atomic scale in a recent experiment [Gonzalez-Herrero et al., Science 352, 437 (2016)]. Here, we demonstrate that all the experimental phenomena can be simply and well described by an equivalent Anderson impurity model, where the electronic correlations on both carbon and hydrogen atoms are represented by an effective on-site Coulomb interaction on hydrogen adatom $U_H$. This simple equivalent model works so well is because that the main effect of Coulomb interaction on both carbon and hydrogen atoms is just to split the impurity resonance states with different spin. This effective Anderson impurity model can also well reproduce all the behaviours of hydrogen dimer on graphene observed in experiment. We thus argue that it should be a general model to describe the hydrogen adatoms on graphene with various adsorption configurations, especially for their magnetic properties.

cond-mat.mes-hall↗

arXiv2019

Secure Logging with Security against Adaptive Crash Attack

Logging systems are an essential component of security systems and their security has been widely studied. Recently (2017) it was shown that existing secure logging protocols are vulnerable to crash attack in which the adversary modifies the log file and then crashes the system to make it indistinguishable from a normal system crash. The attacker was assumed to be non-adaptive and not be able to see the file content before modifying and crashing it (which will be immediately after modifying the file). The authors also proposed a system called SLiC that protects against this attacker. In this paper, we consider an (insider) adaptive adversary who can see the file content as new log operations are performed. This is a powerful adversary who can attempt to rewind the system to a past state. We formalize security against this adversary and introduce a scheme with provable security. We show that security against this attacker requires some (small) protected memory that can become accessible to the attacker after the system compromise. We show that existing secure logging schemes are insecure in this setting, even if the system provides some protected memory as above. We propose a novel mechanism that, in its basic form, uses a pair of keys that evolve at different rates, and employ this mechanism in an existing logging scheme that has forward integrity to obtain a system with provable security against adaptive (and hence non-adaptive) crash attack. We implemented our scheme on a desktop computer and a Raspberry Pi, and showed in addition to higher security, a significant efficiency gain over SLiC.

cs.CR↗

arXiv2019

Orthogonal Deep Neural Networks

In this paper, we introduce the algorithms of Orthogonal Deep Neural Networks (OrthDNNs) to connect with recent interest of spectrally regularized deep learning methods. OrthDNNs are theoretically motivated by generalization analysis of modern DNNs, with the aim to find solution properties of network weights that guarantee better generalization. To this end, we first prove that DNNs are of local isometry on data distributions of practical interest; by using a new covering of the sample space and introducing the local isometry property of DNNs into generalization analysis, we establish a new generalization error bound that is both scale- and range-sensitive to singular value spectrum of each of networks' weight matrices. We prove that the optimal bound w.r.t. the degree of isometry is attained when each weight matrix has a spectrum of equal singular values, among which orthogonal weight matrix or a non-square one with orthonormal rows or columns is the most straightforward choice, suggesting the algorithms of OrthDNNs. We present both algorithms of strict and approximate OrthDNNs, and for the later ones we propose a simple yet effective algorithm called Singular Value Bounding (SVB), which performs as well as strict OrthDNNs, but at a much lower computational cost. We also propose Bounded Batch Normalization (BBN) to make compatible use of batch normalization with OrthDNNs. We conduct extensive comparative studies by using modern architectures on benchmark image classification. Experiments show the efficacy of OrthDNNs.

cs.LG↗

arXiv2019

Current localisation and redistribution as the basis of discontinuous current controlled negative differential resistance in NbOx

In-situ thermo-reflectance imaging is used to show that the discontinuous, snap-back mode of current-controlled negative differential resistance (CC-NDR) in NbOx-based devices is a direct consequence of current localization and redistribution. Current localisation is shown to result from the creation of a conductive filament either during electroforming or from current bifurcation due to the super-linear temperature dependence of the film conductivity. The snap-back response then arises from current redistribution between regions of low and high current-density due to the rapid increase in conductivity created within the high current density region. This redistribution is further shown to depend on the relative resistance of the low current-density region with the characteristics of NbOx cross-point devices transitioning between continuous and discontinuous snap-back modes at critical values of film conductivity, area, thickness and temperature, as predicted. These results clearly demonstrate that snap-back is a generic response that arises from current localization and redistribution within the oxide film rather than a material-specific phase transition, thus resolving a long-standing controversy.

physics.app-ph↗

arXiv2019

Anharmonicity Induced Supersolidity In Spin-Orbit Coupled Bose-Einstein Condensates

Supersolid, a fascinating quantum state of matter, features novel phenomena such as the non-classical rotational inertia and transport anomalies. It is a long standing issue of the coexistence of superfluidity and broken translational symmetry in condensed matter physics. By recent experimental advances to create tunable synthetic spin-orbit coupling in ultracold gases, such highly controllable atomic systems would provide new possibilities to access supersolidity with no counterpart in solids. Here we report that the combination of anharmonicity of trapping potential and spin-orbit coupling will provide a new paradigm to achieve supersolids. By means of imaginary time evolution of the Gross-Pitaevskii equation, we demonstrate that a supersolid state can be found when considering a trapped Rashba-type spin-orbit coupled bosonic atoms loaded in a one-dimensional optical lattice. Furthermore, a skyrmion-anti-skyrmion lattice is associated with the appearance of such supersoildity, indicating the topological nontrivial properties of our proposed supersolids.

cond-mat.quant-gas↗

arXiv2019

Formation control for multiple agents with local measurements: continuous-time and sampled-data-based cases

We study the formation control problem for a group of mobile agents in a plane, in which each agent is modeled as a kinematic point and can only use the local measurements in its local frame. The agents are required to maintain a geometric pattern while keeping a desired distance to a static/moving target. The prescribed formation is a general one which can be any geometric pattern, and the neighboring relationship of the N-agent system only has the requirement of containing a directed spanning tree. To solve the formation control problem, a distributed controller is proposed based on the idea of decoupled design. One merit of the controller is that it only uses each agent's local measurements in its local frame, so that a practical issue that the lack of a global coordinate frame or a common reference direction for real multi-robot systems is successfully solved. Considering another practical issue of real robotic applications that sampled data is desirable instead of continuous-time signals, the sampled-data based controller is developed. Theoretical analysis of the convergence to the desired formation is provided for the multi-agent system under both the continuous-time controller with a static/moving target and the sampled-data based one with a static target. Numerical simulations are given to show the effectiveness and performance of the controllers.

math.OC↗

arXiv2019

Learning to Synthesize: Robust Phase Retrieval at Low Photon counts

The quality of inverse problem solutions obtained through deep learning [Barbastathis et al, 2019] is limited by the nature of the priors learned from examples presented during the training phase. In the case of quantitative phase retrieval [Sinha et al, 2017, Goy et al, 2019], in particular, spatial frequencies that are underrepresented in the training database, most often at the high band, tend to be suppressed in the reconstruction. Ad hoc solutions have been proposed, such as pre-amplifying the high spatial frequencies in the examples [Li et al, 2018]; however, while that strategy improves resolution, it also leads to high-frequency artifacts as well as low-frequency distortions in the reconstructions. Here, we present a new approach that learns separately how to handle the two frequency bands, low and high; and also learns how to synthesize these two bands into the full-band reconstructions. We show that this "learning to synthesize" (LS) method yields phase reconstructions of high spatial resolution and artifact-free; and it is also resilient to high-noise conditions, e.g. in the case of very low photon flux. In addition to the problem of quantitative phase retrieval, the LS method is applicable, in principle, to any inverse problem where the forward operator treats different frequency bands unevenly, i.e. is ill-posed.

eess.IV↗

arXiv2019

Improved Algorithm on Online Clustering of Bandits

We generalize the setting of online clustering of bandits by allowing non-uniform distribution over user frequencies. A more efficient algorithm is proposed with simple set structures to represent clusters. We prove a regret bound for the new algorithm which is free of the minimal frequency over users. The experiments on both synthetic and real datasets consistently show the advantage of the new algorithm over existing methods.

cs.LG↗

arXiv2019

Origin of current-controlled negative differential resistance modes and the emergence of composite characteristics with high complexity

Current-controlled negative differential resistance has significant potential as a fundamental building block in brain-inspired neuromorphic computing. However, achieving desired negative differential resistance characteristics, which is crucial for practical implementation, remains challenging due to little consensus on the underlying mechanism and unclear design criteria. Here, we report a material-independent model of current-controlled negative differential resistance to explain a broad range of characteristics, including the origin of the discontinuous snap-back response observed in many transition metal oxides. This is achieved by explicitly accounting for a non-uniform current distribution in the oxide film and its impact on the effective circuit of the device, rather than a material-specific phase transition. The predictions of the model are then compared with experimental observations to show that the continuous S-type and discontinuous snap-back characteristics serve as fundamental building blocks for composite behaviour with higher complexity. Finally, we demonstrate the potential of our approach for predicting and engineering unconventional compound behaviour with novel functionality for emerging electronic and neuromorphic computing applications.

physics.app-ph↗

arXiv2019

Low Photon Budget Phase Retrieval with Perceptual Loss Trained Deep Neural Networks

Deep neural networks (DNNs) are efficient solvers for ill-posed problems and have been shown to outperform classical optimization techniques in several computational imaging problems. DNNs are trained by solving an optimization problem implies the choice of an appropriate loss function, i.e., the function to optimize. In a recent paper [A. Goy \textit{et al.}, Phys. Rev. Lett. 121(24), 243902 (2018)], we showed that DNNs trained with the negative Pearson correlation coefficient as the loss function are particularly fit for photon-starved phase retrieval problems, which are fundamentally affected by strong Poison noise. In this paper we demonstrate that the use of a perceptual loss function significantly improves the reconstructions.

eess.IV↗

arXiv2019

Measuring Information Leakage in Website Fingerprinting Attacks and Defenses

Tor provides low-latency anonymous and uncensored network access against a local or network adversary. Due to the design choice to minimize traffic overhead (and increase the pool of potential users) Tor allows some information about the client's connections to leak. Attacks using (features extracted from) this information to infer the website a user visits are called Website Fingerprinting (WF) attacks. We develop a methodology and tools to measure the amount of leaked information about a website. We apply this tool to a comprehensive set of features extracted from a large set of websites and WF defense mechanisms, allowing us to make more fine-grained observations about WF attacks and defenses.

cs.CR↗