arXiv subjects

Qiang Li

Publications and source records attributed to Qiang Li.

At least 397 records · Page 22Linked to original sources

arXiv2021

Estimation of Playable Piano Fingering by Pitch-difference Fingering Matching Model

The existing piano fingering labeling statistical models usually consider the constraints among the fingers and the correlation between fingering and notes, and rarely include the relationship among the notes directly. The limited learned finger-transfer rules often cause that some parts of the fingering cannot be playable in fact. And traditional models often adopt the original notes, which cannot help to explore the mapping nature between the pitches and fingering. Inspired from manual-ly annotation which acquire the fingering knowledge directly from pitch-difference, we proposed a pitch-difference sequence and fingering (PdF) matching model. And to get playable fingering, be-sides learned finger-transfer rules, prior finger-transfer knowledge is especially combined into the model. In order to characterize the playable performance of the model, we also presented a new evaluation index named incapable-performing fingering rate (IFR). Comprehensive experimental re-sults show that compared with the existing state-of-the-art third-order hidden Markov labeling model, the general and the highest matching rate of our model increases by 3% and 1.6% respective-ly, and the fingering for all scores can be playable.

cs.SD↗

arXiv2021

A Parallel Distributed Algorithm for the Power SVD Method

In this work, we study how to implement a distributed algorithm for the power method in a parallel manner. As the existing distributed power method is usually sequentially updating the eigenvectors, it exhibits two obvious disadvantages: 1) when it calculates the $h$th eigenvector, it needs to wait for the results of previous $(h-1)$ eigenvectors, which causes a delay in acquiring all the eigenvalues; 2) when calculating each eigenvector, it needs a certain cost of information exchange within the neighboring nodes for every power iteration, which could be unbearable when the number of eigenvectors or the number of nodes is large. This motivates us to propose a parallel distributed power method, which simultaneously calculates all the eigenvectors at each power iteration to ensure that more information could be exchanged in one shaking-hand of communication. We are particularly interested in the distributed power method for both an eigenvalue decomposition (EVD) and a singular value decomposition (SVD), wherein the distributed process is proceed based on a gossip algorithm. It can be shown that, under the same condition, the communication cost of the gossip-based parallel method is only $1/H$ times of that for the sequential counterpart, where $H$ is the number of eigenvectors we want to compute, while the convergence time and error performance of the proposed parallel method are both comparable to those of its sequential counterpart.

cs.IT↗

arXiv2021

Self-Supervised Regional and Temporal Auxiliary Tasks for Facial Action Unit Recognition

Automatic facial action unit (AU) recognition is a challenging task due to the scarcity of manual annotations. To alleviate this problem, a large amount of efforts has been dedicated to exploiting various methods which leverage numerous unlabeled data. However, many aspects with regard to some unique properties of AUs, such as the regional and relational characteristics, are not sufficiently explored in previous works. Motivated by this, we take the AU properties into consideration and propose two auxiliary AU related tasks to bridge the gap between limited annotations and the model performance in a self-supervised manner via the unlabeled data. Specifically, to enhance the discrimination of regional features with AU relation embedding, we design a task of RoI inpainting to recover the randomly cropped AU patches. Meanwhile, a single image based optical flow estimation task is proposed to leverage the dynamic change of facial muscles and encode the motion information into the global feature representation. Based on these two self-supervised auxiliary tasks, local features, mutual relation and motion cues of AUs are better captured in the backbone network with the proposed regional and temporal based auxiliary task learning (RTATL) framework. Extensive experiments on BP4D and DISFA demonstrate the superiority of our method and new state-of-the-art performances are achieved.

cs.CV↗

arXiv2021

Firmware Re-hosting Through Static Binary-level Porting

The rapid growth of the Industrial Internet of Things (IIoT) has brought embedded systems into focus as major targets for both security analysts and malicious adversaries. Due to the non-standard hardware and diverse software, embedded devices present unique challenges to security analysts for the accurate analysis of firmware binaries. The diversity in hardware components and tight coupling between firmware and hardware makes it hard to perform dynamic analysis, which must have the ability to execute firmware code in virtualized environments. However, emulating the large expanse of hardware peripherals makes analysts have to frequently modify the emulator for executing various firmware code in different virtualized environments, greatly limiting the ability of security analysis. In this work, we explore the problem of firmware re-hosting related to the real-time operating system (RTOS). Specifically, developers create a Board Support Package (BSP) and develop device drivers to make that RTOS run on their platform. By providing high-level replacements for BSP routines and device drivers, we can make the minimal modification of the firmware that is to be migrated from its original hardware environment into a virtualized one. We show that an approach capable of offering the ability to execute firmware at scale through patching firmware in an automated manner without modifying the existing emulators. Our approach, called static binary-level porting, first identifies the BSP and device drivers in target firmware, then patches the firmware with pre-built BSP routines and drivers that can be adapted to the existing emulators. Finally, we demonstrate the practicality of the proposed method on multiple hardware platforms and firmware samples for security analysis. The result shows that the approach is flexible enough to emulate firmware for vulnerability assessment and exploits development.

cs.CR↗

arXiv2021

Longitudinally polarized ZZ scattering at the Muon Collider

Measuring longitudinally polarized vector boson scattering in, e.g., the ZZ channel is a promising way to investigate the unitarization scheme from the Higgs and possible new physics beyond the Standard Model. However, at the LHC, it demands the end of the HL-LHC lifetime luminosity, 3000/fb, and advanced data analysis technique to reach the discovery threshold due to its small production rates. Instead, there could be great potential for future colliders. In this paper, we perform a Monte Carlo study and examine the projected sensitivity of longitudinally polarized ZZ scattering at a TeV scale muon collider. We conduct studies at 14 TeV and 6 TeV muon colliders respectively and find that a 5 standard deviation discovery can be achieved at a 14 TeV muon collider, with 3000/fb of data collected. While a 6 TeV muon collider can already surpass HL-LHC, reaching 2 standard deviations with around 4000/fb of data. The effect from lepton isolation and detector granularity is also discussed, which may be more obvious at higher energy muon colliders, as the leptons from longitudinally polarized Z decays tend to be closer.

hep-ph↗

arXiv2021

Mass spectra and wave functions of $T_{QQ\bar Q\bar Q}$ tetraquarks

The compact tetraquark states with fully heavy quark contents $QQ\bar Q\bar Q$ are studied as the bound states of the diquark-antidiquark within the Bethe-Salpeter framework. The (anti)diquark masses and form factors used are the same as we calculated the doubly heavy baryons in a previous work. Under the instantaneous approximation, the three-dimensional (Bethe-)Salpeter equation of the tetraquarks is derived and solved numerically to obtain the corresponding mass spectra and wave functions of the tetraquarks with $J^{PC}=0^{++}$, $1^{+-}$, and $2^{++}$. Our results show that the three ground states of $cc\bar c\bar c$ locate in the mass range of $6.4\sim6.5$GeV, and the $bb\bar b\bar b$ states in mass range of $19.2\sim19.3$GeV. The obtained relativistic wave functions naturally include the mixing effects from the possible $D$\,(or $G$) partial waves, and then can be further used to do precise calculations of the tetraquark decays. Based on the obtained results, the LHCb's observation $X(6900)$ is less likely to be the ground states of compact $cc\bar c\bar c$ tetraquarks but might be the first or second excited states. In addition, a widely used propagator-like form factor is also investigated and discussed.

hep-ph↗

arXiv2021

Learning compliant grasping and manipulation by teleoperation with adaptive force control

In this work, we focus on improving the robot's dexterous capability by exploiting visual sensing and adaptive force control. TeachNet, a vision-based teleoperation learning framework, is exploited to map human hand postures to a multi-fingered robot hand. We augment TeachNet, which is originally based on an imprecise kinematic mapping and position-only servoing, with a biomimetic learning-based compliance control algorithm for dexterous manipulation tasks. This compliance controller takes the mapped robotic joint angles from TeachNet as the desired goal, computes the desired joint torques. It is derived from a computational model of the biomimetic control strategy in human motor learning, which allows adapting the control variables (impedance and feedforward force) online during the execution of the reference joint angle trajectories. The simultaneous adaptation of the impedance and feedforward profiles enables the robot to interact with the environment in a compliant manner. Our approach has been verified in multiple tasks in physics simulation, i.e., grasping, opening-a-door, turning-a-cap, and touching-a-mouse, and has shown more reliable performances than the existing position control and the fixed-gain-based force control approaches.

cs.RO↗

arXiv2021

Topological contextuality and anyonic statistics of photonic-encoded parafermions

Quasiparticle poisoning, expected to arise during the measurement of Majorana zero mode state, poses a fundamental problem towards the realization of Majorana-based quantum computation. Parafermions, a natural generalization of Majorana fermions, can encode topological qudits immune to quasiparticle poisoning. While parafermions are expected to emerge in superconducting fractional quantum Hall systems, they are not yet attainable with current technology. To bypass this problem, we employ a photonic quantum simulator to experimentally demonstrate the key components of parafermion-based universal quantum computation. Our contributions in this article are twofold. First, by manipulating the photonic states, we realize Clifford operator Berry phases that correspond to braiding statistics of parafermions. Second, we investigate the quantum contextuality in a topological system for the first time by demonstrating the contextuality of parafermion encoded qudit states. Importantly, we find that the topologically-encoded contextuality opens the way to magic state distillation, while both the contextuality and the braiding-induced Clifford gates are resilient against local noise. By introducing contextuality, our photonic quantum simulation provides the first step towards a physically robust methodology for realizing topological quantum computation.

quant-ph↗

arXiv2021

Room temperature coherent manipulation of single-spin qubits in silicon carbide with a high readout contrast

Spin defects in silicon carbide (SiC) with mature wafer-scale fabrication and micro/nano-processing technologies have recently drawn considerable attention. Although room temperature single-spin manipulation of colour centres in SiC has been demonstrated, the typically detected contrast is less than 2%, and the photon count rate is also low. Here, we present the coherent manipulation of single divacancy spins in 4H-SiC with a high readout contrast (-30%) and a high photon count rate (150 kilo counts per second) under ambient conditions, which are competitive with the nitrogen-vacancy (NV) centres in diamond. Coupling between a single defect spin and a nearby nuclear spin is also observed. We further provide a theoretical explanation for the high readout contrast by analysing the defect levels and decay paths. Since the high readout contrast is of utmost importance in many applications of quantum technologies, this work might open a new territory for SiC-based quantum devices with many advanced properties of the host material.

cond-mat.mes-hall↗

arXiv2021

Reinvestigation of crystal symmetry and fluctuations in La$_2$CuO$_4$

New surprises continue to be revealed about La$_2$CuO$_4$, the parent compound of the original cuprate superconductor. Here we present neutron scattering evidence that the structural symmetry is lower than commonly assumed. The static distortion results in anisotropic Cu-O bonds within the CuO$_2$ planes; such anisotropy is relevant to pinning charge stripes in hole-doped samples. Associated with the extra structural modulation is a soft phonon mode. If this phonon were to soften completely, the resulting change in CuO$_6$ octahedral tilts would lead to weak ferromagnetism. Hence, we suggest that this mode may be the "chiral" phonon inferred from recent studies of the thermal Hall effect. We also note the absence of interaction between the antiferromagnetic spin waves and low-energy optical phonons, in contrast to what is observed in hole-doped samples.

cond-mat.supr-con↗

arXiv2021

Hidden Dark Matter from Starobinsky Inflation

The Starobinsky inflation model is one of the simplest inflation models that is consistent with the cosmic microwave background observations. In order to explain dark matter of the universe, we consider a minimal extension of the Starobinsky inflation model with introducing the dark sector which communicates with the visible sector only via the gravitational interaction. In Starobinsky inflation model, a sizable amount of dark-sector particle may be produced by the inflaton decay. Thus, a scalar, a fermion or a vector boson in the dark sector may become dark matter. We pay particular attention to the case with dark non-Abelian gauge interaction to make a dark glueball a dark matter candidate. In the minimal setup, we show that it is difficult to explain the observed dark matter abundance without conflicting observational constraints on the coldness and the self-interaction of dark matter. We propose scenarios in which the dark glueball, as well as other dark-sector particles, from the inflaton decay become viable dark matter candidates. We also discuss possibilities to test such scenarios.

hep-ph↗

arXiv2021

Federated Meta Learning Enhanced Acoustic Radio Cooperative Framework for Ocean of Things Underwater Acoustic Communications

Sixth-generation wireless communication (6G) will be an integrated architecture of "space, air, ground and sea". One of the most difficult part of this architecture is the underwater information acquisition which need to transmitt information cross the interface between water and air.In this senario, ocean of things (OoT) will play an important role, because it can serve as a hub connecting Internet of things (IoT) and Internet of underwater things (IoUT). OoT device not only can collect data through underwater methods, but also can utilize radio frequence over the air. For underwater communications, underwater acoustic communications (UWA COMMs) is the most effective way for OoT devices to exchange information, but it is always tormented by doppler shift and synchronization errors. In this paper, in order to overcome UWA tough conditions, a deep neural networks based receiver for underwater acoustic chirp communication, called C-DNN, is proposed. Moreover, to improve the performance of DL-model and solve the problem of model generalization, we also proposed a novel federated meta learning (FML) enhanced acoustic radio cooperative (ARC) framework, dubbed ARC/FML, to do transfer. Particularly, tractable expressions are derived for the convergence rate of FML in a wireless setting, accounting for effects from both scheduling ratio, local epoch and the data amount on a single node.From our analysis and simulation results, it is shown that, the proposed C-DNN can provide a better BER performance and lower complexity than classical matched filter (MF) in underwater acoustic communications scenario. The ARC/FML framework has good convergence under a variety of channels than federated learning (FL). In summary, the proposed ARC/FML for OoT is a promising scheme for information exchange across water and air.

eess.SP↗

arXiv2021

Explainable Hierarchical Imitation Learning for Robotic Drink Pouring

To accurately pour drinks into various containers is an essential skill for service robots. However, drink pouring is a dynamic process and difficult to model. Traditional deep imitation learning techniques for implementing autonomous robotic pouring have an inherent black-box effect and require a large amount of demonstration data for model training. To address these issues, an Explainable Hierarchical Imitation Learning (EHIL) method is proposed in this paper such that a robot can learn high-level general knowledge and execute low-level actions across multiple drink pouring scenarios. Moreover, with EHIL, a logical graph can be constructed for task execution, through which the decision-making process for action generation can be made explainable to users and the causes of failure can be traced out. Based on the logical graph, the framework is manipulable to achieve different targets while the adaptability to unseen scenarios can be achieved in an explainable manner. A series of experiments have been conducted to verify the effectiveness of the proposed method. Results indicate that EHIL outperforms the traditional behavior cloning method in terms of success rate, adaptability, manipulability and explainability.

cs.RO↗

arXiv2021

Practical Two-party Privacy-preserving Neural Network Based on Secret Sharing

Neural networks, with the capability to provide efficient predictive models, have been widely used in medical, financial, and other fields, bringing great convenience to our lives. However, the high accuracy of the model requires a large amount of data from multiple parties, raising public concerns about privacy. Privacy-preserving neural network based on multi-party computation is one of the current methods used to provide model training and inference under the premise of solving data privacy. In this study, we propose a new two-party privacy-preserving neural network training and inference framework in which privacy data is distributed to two non-colluding servers. We construct a preprocessing protocol for mask generation, support and realize secret sharing comparison on 2PC, and propose a new method to further reduce the communication rounds. Based on the comparison protocol, we construct building blocks such as division and exponential, and realize the process of training and inference that no longer needs to convert between different types of secret sharings and is entirely based on arithmetic secret sharing. Compared with the previous works, our work obtains higher accuracy, which is very close to that of plaintext training. While the accuracy has been improved, the runtime is reduced, considering the online phase, our work is 5x faster than SecureML, 4.32-5.75x faster than SecureNN, and is very close to the current optimal 3PC implementation, FALCON. For secure inference, as far as known knowledge is concerned, we should be the current optimal 2PC implementation, which is 4-358x faster than other works.

cs.CR↗

arXiv2021

Photonic implementation of quantum information masking

Masking of quantum information spreads it over nonlocal correlations and hides it from the subsystems. It is known that no operation can simultaneously mask all pure states [Phys. Rev. Lett. 120, 230501 (2018)], so in what sense is quantum information masking useful? Here, we extend the definition of quantum information masking to general mixed states, and show that the resource of maskable quantum states are far more abundant than the no-go theorem seemingly suggests. Geometrically, the simultaneously maskable states lays on hyperdisks in the state hypersphere, and strictly contain the broadcastable states. We devise a photonic quantum information masking machine using time-correlated photons to experimentally investigate the properties of qubit masking, and demonstrate the transfer of quantum information into bipartite correlations and its faithful retrieval. The versatile masking machine has decent extensibility, and may be applicable to quantum secret sharing and fault-tolerant quantum communication. Our results provide some insights on the comprehension and potential application of quantum information masking.

quant-ph↗

arXiv2021

Forced ignition of laminar premixed spherical flames in evaporating fuel droplet mists

This study aims to build a theoretical model to describe the ignition process of spherical spray flame laden with fuel droplets under overall fuel-lean condition. Two characteristic fronts are introduced to reveal where the evaporation starts and ends in the ignition process. The results demonstrate that the flame has been strengthened with extra fuel droplet adding while the minimum ignition energy (MIE) is decreased. The evolution of the two evaporation fronts in the flame kernel and propagating branches has also be revealed coupled with the evaporation zone length.

physics.flu-dyn↗

arXiv2021

On the evolution of fuel droplet evaporation zone and its interaction with the flame front in ignition of spray flames

Evolution of fuel droplet evaporation zone and its interaction with the propagating flame front are studied in this work. A general theory is developed to describe the evolutions of flame propagation speed, flame temperature, droplet evaporation onset and completion locations in ignition and propagation of spherical flames. The influences of liquid droplet mass loading, heat exchange coefficient (or evaporation rate) and Lewis number on spherical spray flame ignition are studied. Two flame regimes are considered, i.e., heterogeneous and homogeneous flames, based on the mixture condition near the flame front. The results indicate that the spray flame trajectories are considerably affected by the ignition energy addition. The critical condition for successful ignition for the fuel-rich mixture is coincidence of inner and outer flame balls from igniting kernel and propagating flame. The flame balls always exist in homogeneous mixtures, indicating that ignition failure and critical successful events occur only in purely gaseous mixture. The fuel droplets have limited effects on minimum ignition energy, which however increases monotonically with the Lewis number. Moreover, flame kernel originates from heterogeneous mixtures due to the initially dispersed droplets near the spark. The evaporative heat loss in the burned and unburned zones of homogeneous and heterogeneous spray flames is also evaluated, and the results show that for the failed flame kernels, evaporative heat loss behind and before the flame front first increases and then decreases. The evaporative heat loss before the flame front generally increases, although non-monotonicity exists, when the flame is successfully ignited and propagate outwardly. For heterogeneous flames, the ratio of the heat loss from the burned zone to the total one decreases as the flame expands.

physics.flu-dyn↗

arXiv2021

Electron-electron interactions and weak anti-localization in few-layer ZrTe5 devices

Much effort has been devoted to the electronic properties of relatively thick ZrTe5 crystals, focusing on their three-dimensional topological effects. Thin ZrTe5 crystals, on the other hand, were much less explored experimentally. Here we present detailed magnetotransport studies of few-layer ZrTe5 devices, in which electron-electron interactions and weak anti-localization are observed. The coexistence of the two effects manifests themselves in corroborating evidence presented in the temperature and magnetic field dependence of the resistance. Notably, the temperature-dependent phase coherence length extracted from weak anti-localization agrees with strong electron-electron scattering in the sample. Meanwhile, universal conductance fluctuations have temperature and gate voltage dependence that is similar to that of the phase coherence length. Lastly, all the transport properties in thin ZrTe5 crystals show strong two-dimensional characteristics. Our results provide new insight into the highly intricate properties of topological material ZrTe5.

cond-mat.mtrl-sci↗