arXiv subjects

Pei Wang

Publications and source records attributed to Pei Wang.

At least 217 records · Page 12Linked to original sources

arXiv2020

A single pulse study of PSR J1022+1001

Using the Five-hundred-meter Aperture Spherical radio Telescope (FAST), we have recorded 10^5 single pulses from PSR J1022+1001. We studied the polarization properties, their energy distribution and their times of arrival. This is only possible with the high sensitivity available using FAST. There is no indication that PSR~J1022+1001 exhibits giant pulse, nulling or traditional mode changing phenomena. The energy in the leading and trailing components of the integrated profile is shown to be correlated. The degree of both linear and circular polarization increases with the pulse flux density for individual pulses. Our data indicates that pulse jitter leads to an excess noise in the timing residuals of 67 ns when scaled to one hour, which is consistent with Liu et al. (2015). We have unsuccessfully trialled various methods to improve timing precision through the selection of specific single pulses. Our work demonstrates that FAST can detect individual pulses from pulsars that are observed in order to detect and study gravitational waves. This capability enables detailed studies, and parameterisation, of the noise processes that affect the sensitivity of a pulsar timing array.

astro-ph.HE↗

arXiv2020

Density matrix of chaotic quantum systems

The nonequilibrium dynamics in chaotic quantum systems denies a fully understanding up to now, even if thermalization in the long-time asymptotic state has been explained by the eigenstate thermalization hypothesis which assumes a universal form of the observable matrix elements in the eigenbasis of Hamiltonian. It was recently proposed that the density matrix elements have also a universal form, which can be used to understand the nonequilibrium dynamics at the whole time scale, from the transient regime to the long-time steady limit. In this paper, we numerically test these assumptions for density matrix in the models of spins.

cond-mat.stat-mech↗

arXiv2020

Derive the Born's rule from environment-induced stochastic dynamics of wave-functions in an open system

The lack of superposition of different position states or the emergence of classicality in macroscopic systems have been a puzzle for decades. Classicality exists in every measuring apparatus, and is the key for understanding what can be measured in quantum theory. Different theories have been proposed, including decoherence, einselection and the spontaneous wave-function collapse, with no consensus reached up to now. In this paper, we propose a stochastic dynamics for the wave-function in an open system (e.g. the measuring apparatus) that interacts with its environment. The trajectory of wave-function is random with a well-defined probability distribution. We show that the stochastic evolution results in the wave-function collapse and the Born's rule for specific system-environment interactions. While it reproduces the unitary evolution governed by the Schrödinger equation when the interaction vanishes. Our results suggest that it is the way of system interacting with environment that determines whether quantum superposition dominates or classicality emerges.

quant-ph↗

arXiv2020

Automatic sampling and training method for wood-leaf classification based on tree terrestrial point cloud

Terrestrial laser scanning technology provides an efficient and accuracy solution for acquiring three-dimensional information of plants. The leaf-wood classification of plant point cloud data is a fundamental step for some forestry and biological research. An automatic sampling and training method for classification was proposed based on tree point cloud data. The plane fitting method was used for selecting leaf sample points and wood sample points automatically, then two local features were calculated for training and classification by using support vector machine (SVM) algorithm. The point cloud data of ten trees were tested by using the proposed method and a manual selection method. The average correct classification rate and kappa coefficient are 0.9305 and 0.7904, respectively. The results show that the proposed method had better efficiency and accuracy comparing to the manual selection method.

cs.CV↗

arXiv2020

Learning a manifold from a teacher's demonstrations

We consider the problem of learning a manifold from a teacher's demonstration. Extending existing approaches of learning from randomly sampled data points, we consider contexts where data may be chosen by a teacher. We analyze learning from teachers who can provide structured data such as individual examples (isolated data points) and demonstrations (sequences of points). Our analysis shows that for the purpose of teaching the topology of a manifold, demonstrations can yield remarkable decreases in the amount of data points required in comparison to teaching with randomly sampled points. We also discuss the implications of our analysis for learning in humans and machines.

cs.LG↗

arXiv2020

A mathematical theory of cooperative communication

Cooperative communication plays a central role in theories of human cognition, language, development, culture, and human-robot interaction. Prior models of cooperative communication are algorithmic in nature and do not shed light on why cooperation may yield effective belief transmission and what limitations may arise due to differences between beliefs of agents. Through a connection to the theory of optimal transport, we establishing a mathematical framework for cooperative communication. We derive prior models as special cases, statistical interpretations of belief transfer plans, and proofs of robustness and instability. Computational simulations support and elaborate our theoretical results, and demonstrate fit to human behavior. The results show that cooperative communication provably enables effective, robust belief transmission which is required to explain feats of human learning and improve human-machine interaction.

cs.LG↗

arXiv2020

Poincaré crystal on the one-dimensional lattice

In this paper, we develop the quantum theory of particles that has discrete Poincaré symmetry on the one-dimensional Bravais lattice. We review the recently discovered discrete Lorentz symmetry, which is the unique Lorentz symmetry that coexists with the discrete space translational symmetry on a Bravais lattice. The discrete Lorentz transformations and spacetime translations form the discrete Poincaré group, which are represented by unitary operators in a quantum theory. We find the conditions for the existence of representation, which are expressed as the congruence relation between quasi-momentum and quasi-energy. We then build the Lorentz-invariant many-body theory of indistinguishable particles by expressing both the unitary operators and Floquet Hamiltonians in terms of the field operators. Some typical Hamiltonians include the long-range hopping which fluctuates as the distance between sites increases. We calculate the Green's functions of the lattice theory. The spacetime points where the Green's function is nonzero display a lattice structure. During the propagation, the particles stay localized on a single or a few sites to preserve the Lorentz symmetry.

cond-mat.quant-gas↗

arXiv2020

Dissipative phase transitions in the fully-connected Ising model with $p$-spin interaction

In this paper, we study the driven-dissipative p-spin models for $p\geq 2$. In thermodynamics limit, the equation of motion is derived by using a semiclassical approach. The long-time asymptotic states are obtained analytically, which exhibit multi-stability in some regions of the parameter space. The steady state is unique as the number of spins is finite. But the thermodynamic limit of the steady-state magnetization displays nonanalytic behavior somewhere inside the semiclassical multi-stable region. We find both the first-order and continuous dissipative phase transitions. As the number of spins increases, both the Liouvillian gap and magnetization variance vanish according to a power law at the continuous transition. At the first-order transition, the gap vanishes exponentially accompanied by a jump of magnetization in thermodynamic limit. The properties of transitions depend on the symmetry and semiclassical multistability, being qualitatively different among $p=2$, odd $p$ ($p\geq 3$) and even $p$ ($p\geq 4$).

cond-mat.quant-gas↗

arXiv2020

Solving Long-tailed Recognition with Deep Realistic Taxonomic Classifier

Long-tail recognition tackles the natural non-uniformly distributed data in real-world scenarios. While modern classifiers perform well on populated classes, its performance degrades significantly on tail classes. Humans, however, are less affected by this since, when confronted with uncertain examples, they simply opt to provide coarser predictions. Motivated by this, a deep realistic taxonomic classifier (Deep-RTC) is proposed as a new solution to the long-tail problem, combining realism with hierarchical predictions. The model has the option to reject classifying samples at different levels of the taxonomy, once it cannot guarantee the desired performance. Deep-RTC is implemented with a stochastic tree sampling during training to simulate all possible classification conditions at finer or coarser levels and a rejection mechanism at inference time. Experiments on the long-tailed version of four datasets, CIFAR100, AWA2, Imagenet, and iNaturalist, demonstrate that the proposed approach preserves more information on all classes with different popularity levels. Deep-RTC also outperforms the state-of-the-art methods in longtailed recognition, hierarchical classification, and learning with rejection literature using the proposed correctly predicted bits (CPB) metric.

cs.CV↗

arXiv2020

Sequential Cooperative Bayesian Inference

Cooperation is often implicitly assumed when learning from other agents. Cooperation implies that the agent selecting the data, and the agent learning from the data, have the same goal, that the learner infer the intended hypothesis. Recent models in human and machine learning have demonstrated the possibility of cooperation. We seek foundational theoretical results for cooperative inference by Bayesian agents through sequential data. We develop novel approaches analyzing consistency, rate of convergence and stability of Sequential Cooperative Bayesian Inference (SCBI). Our analysis of the effectiveness, sample efficiency and robustness show that cooperation is not only possible in specific instances but theoretically well-founded in general. We discuss implications for human-human and human-machine cooperation.

cs.LG↗

arXiv2020

Towards Memory Safe Python Enclave for Security Sensitive Computation

Intel SGX Guard eXtensions (SGX), a hardware-supported trusted execution environment (TEE), is designed to protect security-sensitive applications. However, since enclave applications are developed with memory unsafe languages such as C/C++, traditional memory corruption is not eliminated in SGX. Rust-SGX is the first toolkit providing enclave developers with a memory-language. However, Rust is considered a Systems language and has become the right choice for concurrent applications and web browsers. Many application domains such as Big Data, Machine Learning, Robotics, Computer Vision are more commonly developed in the python programming language. Therefore, Python application developers cannot benefit from secure enclaves like Intel SGX and rust-SGX. To fill this gap, we propose Python-SGX, which is a memory-safe SGX SDK providing enclave developers a memory-safe Python development environment. The key idea is to enable memory-safe Python language in SGX by solving the following key challenges: (1) defining a memory-safe Python interpreter (2)replacing unsafe elements of Python interpreter with safe ones,(3) achieving comparable performance to non-enclave Python applications, and (4) not introducing any unsafe new code or libraries into SGX. We propose to build Python-SGX with PyPy, a Python interpreter written by RPython, which is a subset of Python, and tame unsafe parts in PyPy by formal verification, security hardening, and memory safe language. We have implemented python-SGX and tested it with a series of benchmarks programs. Our evaluation results show that Python-SGX does not cause significant overhead.

cs.CR↗

arXiv2020

Building and Maintaining a Third-Party Library Supply Chain for Productive and Secure SGX Enclave Development

The big data industry is facing new challenges as concerns about privacy leakage soar. One of the remedies to privacy breach incidents is to encapsulate computations over sensitive data within hardware-assisted Trusted Execution Environments (TEE). Such TEE-powered software is called secure enclaves. Secure enclaves hold various advantages against competing for privacy-preserving computation solutions. However, enclaves are much more challenging to build compared with ordinary software. The reason is that the development of TEE software must follow a restrictive programming model to make effective use of strong memory encryption and segregation enforced by hardware. These constraints transitively apply to all third-party dependencies of the software. If these dependencies do not officially support TEE hardware, TEE developers have to spend additional engineering effort in porting them. High development and maintenance cost is one of the major obstacles against adopting TEE-based privacy protection solutions in production. In this paper, we present our experience and achievements with regard to constructing and continuously maintaining a third-party library supply chain for TEE developers. In particular, we port a large collection of Rust third-party libraries into Intel SGX, one of the most mature trusted computing platforms. Our supply chain accepts upstream patches in a timely manner with SGX-specific security auditing. We have been able to maintain the SGX ports of 159 open-source Rust libraries with reasonable operational costs. Our work can effectively reduce the engineering cost of developing SGX enclaves for privacy-preserving data processing and exchange.

cs.SE↗

arXiv2020

A Fast Radio Burst discovered in FAST drift scan survey

We report the discovery of a highly dispersed fast radio burst, FRB~181123, from an analysis of $\sim$1500~hr of drift-scan survey data taken using the Five-hundred-meter Aperture Spherical radio Telescope (FAST). The pulse has three distinct emission components, which vary with frequency across our 1.0--1.5~GHz observing band. We measure the peak flux density to be $>0.065$~Jy and the corresponding fluence $>0.2$~Jy~ms. Based on the observed dispersion measure of 1812~cm$^{-3}$~pc, we infer a redshift of $\sim 1.9$. From this, we estimate the peak luminosity and isotropic energy to be $\lesssim 2\times10^{43}$~erg~s$^{-1}$ and $\lesssim 2\times10^{40}$~erg, respectively. With only one FRB from the survey detected so far, our constraints on the event rate are limited. We derive a 95\% confidence lower limit for the event rate of 900 FRBs per day for FRBs with fluences $>0.025$~Jy~ms. We performed follow-up observations of the source with FAST for four hours and have not found a repeated burst. We discuss the implications of this discovery for our understanding of the physical mechanisms of FRBs.

astro-ph.HE↗

arXiv2020

SCOUT: Self-aware Discriminant Counterfactual Explanations

The problem of counterfactual visual explanations is considered. A new family of discriminant explanations is introduced. These produce heatmaps that attribute high scores to image regions informative of a classifier prediction but not of a counter class. They connect attributive explanations, which are based on a single heat map, to counterfactual explanations, which account for both predicted class and counter class. The latter are shown to be computable by combination of two discriminant explanations, with reversed class pairs. It is argued that self-awareness, namely the ability to produce classification confidence scores, is important for the computation of discriminant explanations, which seek to identify regions where it is easy to discriminate between prediction and counter class. This suggests the computation of discriminant explanations by the combination of three attribution maps. The resulting counterfactual explanations are optimization free and thus much faster than previous methods. To address the difficulty of their evaluation, a proxy task and set of quantitative metrics are also proposed. Experiments under this protocol show that the proposed counterfactual explanations outperform the state of the art while achieving much higher speeds, for popular networks. In a human-learning machine teaching experiment, they are also shown to improve mean student accuracy from chance level to 95\%.

cs.CV↗

arXiv2020

Discrete Lorentz symmetry and discrete spacetime translational symmetry in two- and three-dimensional crystals

As is well known, crystals have discrete space translational symmetry. It was recently noticed that one-dimensional crystals possibly have discrete Poincaré symmetry, which contains discrete Lorentz and discrete time translational symmetry as well. In this paper, we classify the discrete Poincaré groups on two- and three-dimensional Bravais lattices. They are the candidate symmetry groups of two- or three-dimensional crystals, respectively. The group is determined by an integer generator $g$, and it reduces to the space group of crystals at $g=2$.

cond-mat.mtrl-sci↗

arXiv2020

Coherent transport through a resonant level coupled to random-matrix leads

We study the transport through a resonant level coupled to two leads with the latter being described by Wigner's random matrices. By taking appropriate thermodynamic limit before taking the long time limit, we obtain the stationary current as a function of voltage bias. The I-V curve is similar to that of single impurity Anderson model. On the other hand, the current matrix and initial density matrix in our model look like random matrices in the eigenbasis of Hamiltonian. They satisfy the description of eigenstate thermalization hypothesis (ETH) and nonequilibrium steady state hypothesis (NESSH), respectively. A statistical formula of current has been derived based on ETH and NESSH (J. Stat. Mech.: Theo. Exp., 093105 (2017)). We check this formula in our model and find it to predict the stationary current to a high precision. The shape of I-V curve is explained by the peak structure in the characteristic function of NESSH, which is reminiscent of the transmission coefficient.

cond-mat.mes-hall↗

arXiv2020

A theory of nonequilibrium steady states in quantum chaotic systems

Nonequilibrium steady state (NESS) is a quasistationary state, in which exist currents that continuously produce entropy, but the local observables are stationary everywhere. We propose a theory of NESS under the framework of quantum chaos. In an isolated quantum system, there exist some initial states for which the thermodynamic limit and the long-time limit are noncommutative. The density matrix $\hat ρ$ of these states displays a universal structure. Suppose that $α$ and $β$ are different eigenstates of the Hamiltonian with energies $E_α$ and $E_β$, respectively. $<α|\hat ρ|β>$ behaves as a random number which approximately follows the Laplace distribution with zero mean. In thermodynamic limit, the variance of $<α|\hat ρ|β>$ is a smooth function of $\left|E_α-E_β\right|$, scaling as $1/(E_α-E_β)^2$ in the limit $\left|E_α-E_β\right|\to 0$. If and only if this scaling law is obeyed, the initial state evolves into NESS in the long time limit. We present numerical evidence of our hypothesis in a few chaotic models. Furthermore, we find that our hypothesis implies the eigenstate thermalization hypothesis (ETH) in a bipartite system.

cond-mat.stat-mech↗

arXiv2020

Connecting dynamical quantum phase transitions and topological steady-state transitions by tuning the energy gap

Considerable theoretical and experimental efforts have been devoted to the quench dynamics, in particular, the dynamical quantum phase transition (DQPT) and the steady-state transition. These developments have motivated us to study the quench dynamics of the topological systems, from which we find the connection between these two transitions, that is, the DQPT, accompanied by a nonanalytic behavior as a function of time, always merges into a steady-state transition signaled by the nonanalyticity of observables in the steady limit. As the characteristic time of the DQPT diverges, it exhibits universal scaling behavior, which is related to the scaling behavior at the corresponding steady-state transition.

cond-mat.quant-gas↗