arXiv subjects

Qi Zhang

Publications and source records attributed to Qi Zhang.

At least 1,027 records · Page 57Linked to original sources

arXiv2020

Spin-orbit quantum impurity and quantization in a topological magnet

Quantum states induced by single-atomic impurities are at the frontier of physics and material science. While such states have been reported in high-temperature superconductors and dilute magnetic semiconductors, they are unexplored in topological magnets which can feature spin-orbit tunability. Here we use spin-polarized scanning tunneling microscopy/spectroscopy (STM/S) to study the engineered quantum impurity in a topological magnet Co3Sn2S2. We find that each substituted In impurity introduces a striking localized bound state. Our systematic magnetization-polarized probe reveals that this bound state is spin-down polarized, in lock with a negative orbital magnetization. Moreover, the magnetic bound states of neighboring impurities interact to form quantized orbitals, exhibiting an intriguing spin-orbit splitting, analogous to the splitting of the topological fermion line. Our work collectively demonstrates the strong spin-orbit effect of the single-atomic impurity at the quantum level, suggesting that a nonmagnetic impurity can introduce spin-orbit coupled magnetic resonance in topological magnets.

cond-mat.mtrl-sci↗

arXiv2020

On quotients of Boolean control networks

In this paper, we focus on the study of quotients of Boolean control networks (BCNs) with the motivation that they might serve as smaller models that still carry enough information about the original network. Given a BCN and an equivalence relation on the state set, we consider a labeled transition system that is generated by the BCN. The resulting quotient transition system then naturally captures the quotient dynamics of the BCN concerned. We therefore develop a method for constructing a Boolean system that behaves equivalently to the resulting quotient transition system. The use of the obtained quotient system for control design is discussed and we show that for BCNs, controller synthesis can be done by first designing a controller for a quotient and subsequently lifting it to the original model. We finally demonstrate the applicability of the proposed techniques on a biological example.

math.OC↗

arXiv2020

Non-Hermitian effects of the intrinsic signs in topologically ordered wavefunctions

Negative signs in many-body wavefunctions play an important role in quantum mechanics. The ground-state wavefunction of double semion model on a two-dimensional hexagonal lattice contains an intrinsic sign which cannot be removed by any local transformation. Here we proposed a generic double semion wavefunction in tensor network representation, and the wavefunction norm is mapped to the partition function of a triangular lattice Ashkin-Teller model with imaginary magnetic fields and imaginary three-spin triangular face interactions. To solve this non-Hermitian model with parity-time (PT) symmetry, numerical tensor-network methods are employed, and a global phase diagram is determined. Adjacent to the double semion phase, we find a gapless dense loop phase described by non-unitary conformal field theory and a PT-symmetry breaking phase with zeros of the partition function. So a connection has established between the intrinsic signs in the topologically ordered wavefunction and the PT-symmetric non-Hermitian statistical model.

cond-mat.str-el↗

arXiv2020

ReCO: A Large Scale Chinese Reading Comprehension Dataset on Opinion

This paper presents the ReCO, a human-curated ChineseReading Comprehension dataset on Opinion. The questions in ReCO are opinion based queries issued to the commercial search engine. The passages are provided by the crowdworkers who extract the support snippet from the retrieved documents. Finally, an abstractive yes/no/uncertain answer was given by the crowdworkers. The release of ReCO consists of 300k questions that to our knowledge is the largest in Chinese reading comprehension. A prominent characteristic of ReCO is that in addition to the original context paragraph, we also provided the support evidence that could be directly used to answer the question. Quality analysis demonstrates the challenge of ReCO that requires various types of reasoning skills, such as causal inference, logical reasoning, etc. Current QA models that perform very well on many question answering problems, such as BERT, only achieve 77% accuracy on this dataset, a large margin behind humans nearly 92% performance, indicating ReCO presents a good challenge for machine reading comprehension. The codes, datasets are freely available at https://github.com/benywon/ReCO.

cs.CL↗

arXiv2020

Pentaquarks with the $qqs\bar{Q}Q$ configuration in the Chiral Quark Model

We study the five-quark system composed of $qqs\bar{Q}Q$ configuration ($q = u$ or $d$, $Q=b$ or $c$), in the framework of the chiral quark model. In consequence, a series of bound states with heavy flavors are predicted by precise five-body dynamical calculations. We found that taking color-octet structure into consideration always provides more bounding energy than color-singlet structure, and the more heavier quark prevents, the easier to form the bound states. We suggest $qqs\bar{b}b$ configuration is a compact $\bar{b}b$-pair surrounded by three other quarks, while $qqs\bar{b}c$, $qqs\bar{c}b$ and $qqs\bar{c}c$ configurations are molecular states.

hep-ph↗

arXiv2020

The Non-Hermitian quantum mechanics and its canonical structure

The non-Hermitian Schrödinger equation is re-expressed generally in the form of Hamilton's canonical equation without any approximation. Its quantization called non-Hermitian quantum field theory is discussed. By virtue of the canonical equation, the theory of non-Hermitian quantum mechanics is totally reformulated, including the probability amplitudes of states, the expectations of operators, as well as the expressions of interaction terms. The conventional difficulties in non-Hermitian quantum mechanics are totally overcome by the reformulation. Specifically, the imaginary parts the non-Hermitian eigenenergy and adiabatic geometric phase are actually unphysical, although they are mathematically perfect.

quant-ph↗

arXiv2020

Quantum Reflections of Nonlocal Optical Solitons in a Cold Rydberg Atomic Gas

Quantum reflection refers to a non-vanishing reflection probability in the absence of a classically turning point. Much attention has been paid to such reflections due to their fundamental, intriguing physics and potential practical applications. Here we propose a scheme to realize a quantum reflection of nonlocal nonlinear optical beams in a cold Rydberg atomic gas via electromagnetically induced transparency working in a dispersion regime. Based on the long-range interaction between Rydberg atoms, we found that the system supports low-power nonlocal optical solitons. Such nonlocal solitons can display a sharp transition between reflection, trapping, and transmission when scattered by a linear attractive potential, created by gate photons stored in another Rydberg state. Different from conventional physical systems explored up to now, the quantum reflection of the nonlocal optical solitons in the Rydberg atomic gas exhibits interesting anomalous behaviors, which can be actively manipulated by tuning the incident velocity and intensity of the probe field, as well as the nonlocality of the Kerr nonlinearity inherent in the Rydberg atomic gas. The results reported here are not only useful for developing Rydberg nonlinear optics but also helpful for characterizing the physical property of the Rydberg gas and for designing novel nonlinear optical devices.

physics.optics↗

arXiv2020

Observation of sixfold degenerate fermions in PdSb$_2$

Three types of fermions have been extensively studied in topological quantum materials: Dirac, Weyl, and Majorana fermions. Beyond the fundamental fermions in high energy physics, exotic fermions are allowed in condensed matter systems residing in three-, six- or eightfold degenerate band crossings. Here, we use angle-resolved photoemission spectroscopy to directly visualize three-doubly-degenerate bands in PdSb$_2$. The ultrahigh energy resolution we are able to achieve allows for the confirmation of all the sixfold degenerate bands at the R point, in remarkable consistency with first-principles calculations. Moreover, we find that this sixfold degenerate crossing has quadratic dispersion as predicted by theory. Finally, we compare sixfold degenerate fermions with previously confirmed fermions to demonstrate the importance of this work: our study indicates a topological fermion beyond the constraints of high energy physics.

cond-mat.mes-hall↗

arXiv2020

A framework for the analysis of supervised discrete event systems under attack

This paper focuses on the problem of cyber attacks for discrete event systems under supervisory control. In more detail, the goal of the supervisor, who has a partial observation of the system evolution, is that of preventing the system from reaching a set of unsafe states. An attacker may act in two different ways: he can corrupt the observation of the supervisor editing the sensor readings, and can enable events that are disabled by the supervisor. This is done with the aim of leading the plant to an unsafe state, and keeping the supervisor unaware of that before the unsafe state is reached. A special automaton, called attack structure is constructed as the parallel composition of two special structures. Such an automaton can be used by the attacker to select appropriate actions (if any) to reach the above goal, or equivalently by the supervisor, to validate its robustness with respect to such attacks.

eess.SY↗

arXiv2020

Terahertz Emission From an Exchange-Coupled Synthetic Antiferromagnet

We report on terahertz emission from FeMnPt/Ru/FeMnPt and Pt/CoFeB/Ru/CoFeB/Pt synthetic antiferromagnet (SAF) structures upon irradiation by a femtosecond laser; the former is via the anomalous Hall effect, whereas the latter is through the inverse spin Hall effect. The antiparallel alignment of the two ferromagnetic layers leads to a terahertz emission peak amplitude that is almost double that for a corresponding single-layer or bilayer emitter with the same equivalent thickness. In addition, we demonstrate by both simulation and experiment that terahertz emission provides a powerful tool to probe the magnetization reversal processes of individual ferromagnetic layers in a SAF structure, as the terahertz signal is proportional to the vector difference of the magnetizations of the two ferromagnetic layers.

cond-mat.mtrl-sci↗

arXiv2020

Person Re-identification in Aerial Imagery

Nowadays, with the rapid development of consumer Unmanned Aerial Vehicles (UAVs), visual surveillance by utilizing the UAV platform has been very attractive. Most of the research works for UAV captured visual data are mainly focused on the tasks of object detection and tracking. However, limited attention has been paid to the task of person Re-identification (ReID) which has been widely studied in ordinary surveillance cameras with fixed emplacements. In this paper, to facilitate the research of person ReID in aerial imagery, we collect a large scale airborne person ReID dataset named as Person ReID for Aerial Imagery (PRAI-1581), which consists of 39,461 images of 1581 person identities. The images of the dataset are shot by two DJI consumer UAVs flying at an altitude ranging from 20 to 60 meters above the ground, which covers most of the real UAV surveillance scenarios. In addition, we propose to utilize subspace pooling of convolution feature maps to represent the input person images. Our method can learn a discriminative and compact feature representation for ReID in aerial imagery and can be trained in an end-to-end fashion efficiently. We conduct extensive experiments on the proposed dataset and the experimental results demonstrate that re-identify persons in aerial imagery is a challenging problem, where our method performs favorably against state of the arts. Our dataset can be accessed via \url{https://github.com/stormyoung/PRAI-1581}.

cs.CV↗

arXiv2020

Nanoscale electrometry based on a magnetic-field-resistant spin sensor

The nitrogen-vacancy (NV) center is a potential atomic-scale spin sensor for electric field sensing. However, its natural susceptibility to the magnetic field hinders effective detection of the electric field. Here we propose a robust electrometric method utilizing continuous dynamic decoupling (CDD) technique. During the CDD period, the NV center evolves in a dressed-state space, where the sensor is resistant to magnetic fields but remains sensitive to electric fields. As an example, we use this method to isolate the electric noise from a complex electro-magnetical environment near diamond surface via measuring the dephasing rate between dressed states. By reducing the surface electric noise with different covered liquids, we observe an unambiguous relation between the dephasing rate and the dielectric permittivity of the liquid, which enables a quantitative investigation of electric noise model near diamond surface.

quant-ph↗

arXiv2020

An under-approximation for the robust uncertain two-level cooperative set covering problem

This paper investigates the robust uncertain two-level cooperative set covering problem (RUTLCSCP). Given two types of facilities, which are called y-facility and z-facility. The problem is to decide which facilities of both types to be selected, in order to cover the demand nodes cooperatively with minimal cost. It combines the concepts of robust, probabilistic, and cooperative covering by introducting "$Γ$-robust two-level-cooperative $α$-cover" constraints. Additionally, the constraint relaxed verison of the RUTLCSCP, which is also a linear approximation robust counterpart version of RUTLCSCP (RUTLCSCP-LA-RC), is developed by linear approximation of the constraints, and can be stated as a compact mixed-integer linear programming problem. We show that the solution for RUTLCSCP-LA-RC, $\varepsilon$-under-approximate solution, can also be the solution for RUTLCSCP on some conditions. Computational experiments show that the solutions in 333 instances (10125 instances in total) with 12 types which tinily violate the constraints of RUTLCSCP, can be an efficient under-approximate solutions, while the feasible solutions in other instances are proven to be optimal.

math.OC↗

arXiv2020

Spin-orbit quantum impurity in a topological kagome magnet

Quantum states induced by single-atomic-impurities are the current frontier of material and information science. Recently the spin-orbit coupled correlated kagome magnets are emerging as a new class of topological quantum materials, although the effect of single-atomic impurities remains unexplored. Here we use state-of-the-art scanning tunneling microscopy/spectroscopy (STM/S) to study the atomic indium impurity in a topological kagome magnet Co3Sn2S2, which is designed to support the spin-orbit quantum state. We find each impurity features a strongly localized bound state. Our systematic magnetization-polarized tunneling probe reveals its spin-down polarized nature with an unusual moment of -5uB, indicative of additional orbital magnetization. As the separation between two impurities progressively shrinks, their respective bound states interact and form quantized molecular orbital states. The molecular orbital of three neighboring impurities further exhibits an intriguing splitting owing to the combination of geometry, magnetism, and spin-orbit coupling, analogous to the splitting of the topological Weyl fermion line12,19. Our work demonstrates the quantum-level interplay between magnetism and spin-orbit coupling at an individual atomic impurity, which provides insights into the emergent impurity behavior in a topological kagome magnet and the potential of spin-orbit quantum impurities for information science.

cond-mat.mtrl-sci↗

arXiv2020

Single DNA Electron Spin Resonance Spectroscopy in Aqueous Solutions

Magnetic resonance spectroscopy of single biomolecules under near-physiological conditions may substantially advance understanding of biological function, yet remains very challenging. Here we use nitrogen-vacancy centers in diamonds to detect electron spin resonance spectra of individual, tethered DNA duplexes labeled with a nitroxide spin label in aqueous buffer solutions at ambient temperatures. This paves the way for magnetic resonance studies on single biomolecules and their inter-molecular interactions in a native-like environment.

physics.bio-ph↗

arXiv2020

Computation Resource Allocation for Heterogeneous Time-Critical IoT Services in MEC

Mobile edge computing (MEC) is one of the promising solutions to process computational-intensive tasks within short latency for emerging Internet-of-Things (IoT) use cases, e.g., virtual reality (VR), augmented reality (AR), autonomous vehicle. Due to the coexistence of heterogeneous services in MEC system, the task arrival interval and required execution time can vary depending on services. It is challenging to schedule computation resource for the services with stochastic arrivals and runtime at an edge server (ES). In this paper, we propose a flexible computation offloading framework among users and ESs. Based on the framework, we propose a Lyapunov-based algorithm to dynamically allocate computation resource for heterogeneous time-critical services at the ES. The proposed algorithm minimizes the average timeout probability without any prior knowledge on task arrival process and required runtime. The numerical results show that, compared with the standard queuing models used at ES, the proposed algorithm achieves at least 35% reduction of the timeout probability, and approximated utilization efficiency of computation resource to non-cause queuing model under various scenarios.

cs.NI↗

arXiv2020

Adaptive Task Partitioning at Local Device or Remote Edge Server for Offloading in MEC

Mobile edge computing (MEC) is one of the promising solutions to process computational-intensive tasks for the emerging time-critical Internet-of-Things (IoT) use cases, e.g., virtual reality (VR), augmented reality (AR), autonomous vehicle. The latency can be reduced further, when a task is partitioned and computed by multiple edge servers' (ESs) collaboration. However, the state-of-the-art work studies the MEC-enabled offloading based on a static framework, which partitions tasks at either the local user equipment (UE) or the primary ES. The dynamic selection between the two offloading schemes has not been well studied yet. In this paper, we investigate a dynamic offloading framework in a multi-user scenario. Each UE can decide who partitions a task according to the network status, e.g., channel quality and allocated computation resource. Based on the framework, we model the latency to complete a task, and formulate an optimization problem to minimize the average latency among UEs. The problem is solved by jointly optimizing task partitioning and the allocation of the communication and computation resources. The numerical results show that, compared with the static offloading schemes, the proposed algorithm achieves the lower latency in all tested scenarios. Moreover, both mathematical derivation and simulation illustrate that the wireless channel quality difference between a UE and different ESs can be used as an important criterion to determine the right scheme.

cs.NI↗

arXiv2020

Deep-AIR: A Hybrid CNN-LSTM Framework forFine-Grained Air Pollution Forecast

Poor air quality has become an increasingly critical challenge for many metropolitan cities, which carries many catastrophicphysical and mental consequences on human health and quality of life. However, accurately monitoring and forecasting air qualityremains a highly challenging endeavour. Limited by geographically sparse data, traditional statistical models and newly emergingdata-driven methods of air quality forecasting mainly focused on the temporal correlation between the historical temporal datasets of airpollutants. However, in reality, both distribution and dispersion of air pollutants are highly location-dependant. In this paper, we proposea novel hybrid deep learning model that combines Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM)together to forecast air quality at high-resolution. Our model can utilize the spatial correlation characteristic of our air pollutant datasetsto achieve higher forecasting accuracy than existing deep learning models of air pollution forecast.

eess.SP↗