arXiv subjects

Pengfei Wang

Publications and source records attributed to Pengfei Wang.

At least 217 records · Page 12Linked to original sources

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↗

arXiv2019

Two-dimensional layered materials for memristive and neuromorphic applications

With many fantastic properties, memristive devices have been proposed as top candidate for next-generation memory and neuromorphic computing chips. Significant research progresses have been made in improving performance of individual memristive devices and in demonstrating functional applications based on small-scale memristive crossbar arrays. However, practical deployment of large-scale traditional metal oxides based memristive crossbar array has been challenging due to several issues, such as high-power consumption, poor device reliability, low integration density and so on. To solve these issues, new materials that possess superior properties are required. Two-dimensional (2D) layered materials exhibit many unique physical properties and show great promise in solving these challenges, further providing new opportunities to implement practical applications in neuromorphic computing. Here, recent research progress on 2D layered materials based memristive device applications is reviewed. We provide an overview of the progresses and challenges on how 2D layered materials are used to solve the issues of conventional memristive devices and to realize more complex functionalities in neuromorphic computing. Besides, we also provide an outlook on exploitation of unique properties of 2D layered materials and van der Waals heterostructures for developing new types of memristive devices and artificial neural mircrocircuits.

physics.app-ph↗

arXiv2019

Realization of two-dimensional crystal of ions in a monolithic Paul trap

We present a simple Paul trap that stably accommodates up to a couple of dozens of \ensuremath{^{171}\mathrm{Yb}^+~} ions in a stationary two-dimensional lattice. The trap is constructed on a single plate of gold-plated laser-machined alumina and can produce a pancake-like pseudo-potential that makes ions form a self-assembly two-dimensional crystal which locates on the plane composed of axial and one of the transverse axes with around 5 $μ$m spacing. We use Raman laser beams to coherently manipulate these ion-qubits where the net propagation direction is perpendicular to the plane of the crystal and micromotion. We perform the coherent operations and study the spectrum of vibrational modes through globally addressed Raman laser-beams on a dozen of ions in the two-dimensional crystal. We measure the amplitude of micro-motion by comparing the strengths of carrier and micro-motion sideband transitions with three ions, where the micro-motion amplitude is similar to that of a single ion. The spacings of ions are small enough for large coupling strengths, which is a favorable condition for two-dimensional quantum simulation.

quant-ph↗

arXiv2019

An Accurate Edge-based FEM for Electromagnetic Analysis with Its Applications to Multiscale Structures

This paper introduces an accurate edge-based smoothed finite element method (ES-FEM) for electromagnetic analysis for both two dimensional cylindrical and three dimensional cartesian systems, which shows much better performance in terms of accuracy and numerical stability for mesh distortion compared with the traditional FEM. Unlike the traditional FEM, the computational domain in ES-FEM is divided into nonoverlapping smoothing domains associated with each edge of elements, triangles in two dimensional domain and tetrahedrons in three dimensional domain. Then, the gradient smoothing technique (GST) is used to smooth the gradient components in the stiff matrix of the FEM. Several numerical experiments are carried out to validate its accuracy and numerical stability. Numerical results show that the ES-FEM can obtain much more accurate results and is almost independent of mesh distortion.

cs.CE↗

arXiv2019

A Single-Shot Arbitrarily-Shaped Text Detector based on Context Attended Multi-Task Learning

Detecting scene text of arbitrary shapes has been a challenging task over the past years. In this paper, we propose a novel segmentation-based text detector, namely SAST, which employs a context attended multi-task learning framework based on a Fully Convolutional Network (FCN) to learn various geometric properties for the reconstruction of polygonal representation of text regions. Taking sequential characteristics of text into consideration, a Context Attention Block is introduced to capture long-range dependencies of pixel information to obtain a more reliable segmentation. In post-processing, a Point-to-Quad assignment method is proposed to cluster pixels into text instances by integrating both high-level object knowledge and low-level pixel information in a single shot. Moreover, the polygonal representation of arbitrarily-shaped text can be extracted with the proposed geometric properties much more effectively. Experiments on several benchmarks, including ICDAR2015, ICDAR2017-MLT, SCUT-CTW1500, and Total-Text, demonstrate that SAST achieves better or comparable performance in terms of accuracy. Furthermore, the proposed algorithm runs at 27.63 FPS on SCUT-CTW1500 with a Hmean of 81.0% on a single NVIDIA Titan Xp graphics card, surpassing most of the existing segmentation-based methods.

cs.CV↗

arXiv2019

Breaking the quantum adiabatic speed limit by jumping along geodesics

Quantum adiabatic evolutions find a broad range of applications in quantum physics and quantum technologies. The traditional form of the quantum adiabatic theorem limits the speed of adiabatic evolution by the minimum energy gaps of the system Hamiltonian. Here, we experimentally show using a nitrogen-vacancy center in diamond that, even in the presence of vanishing energy gaps, quantum adiabatic evolution is possible. This verifies a recently derived necessary and sufficient quantum adiabatic theorem and offers paths to overcome the conventionally assumed constraints on adiabatic methods. By fast modulation of dynamic phases, we demonstrate near-unit-fidelity quantum adiabatic processes in finite times. These results challenge traditional views and provide deeper understanding on quantum adiabatic processes, as well as promising strategies for the control of quantum systems.

quant-ph↗

arXiv2019

Structural analysis of nuclear spin clusters via two-dimensional nanoscale nuclear magnetic resonance spectroscopy

Two-dimensional Nuclear Magnetic Resonance (NMR) is essential in molecular structure determination. The Nitrogen-Vacancy (NV) center in diamond has been proposed and developed as an outstanding quantum sensor to realize NMR in nanoscale. In this work, we develop a scheme for two-dimensional nanoscale NMR spectroscopy based on quantum controls on an NV center. We carry out a proof of principle experiment on a target of two coupled $^{13}$C nuclear spins in diamond. A COSY-like sequences is used to acquire the data on time domain, which is then converted to frequency domain with the fast Fourier transform (FFT). With the two-dimensional NMR spectrum, the structure and location of the set of nuclear spin are resolved. This work marks a fundamental step towards resolving the structure of a single molecule.

quant-ph↗

arXiv2019

Direct evidence for charge compensation induced large magnetoresistance in thin WTe2

Since the discovery of extremely large non-saturating magnetoresistance (MR) in WTe2, much effort has been devoted to understanding the underlying mechanism, which is still under debate. Here, we explicitly identify the dominant physical origin of the large non-saturating MR through in-situ tuning of the magneto-transport properties in thin WTe2 film. With an electrostatic doping approach, we observed a non-monotonic gate dependence of the MR. The MR reaches a maximum (10600%) in thin WTe2 film at certain gate voltage where electron and hole concentrations are balanced, indicating that the charge compensation is the dominant mechanism of the observed large MR. Besides, we show that the temperature dependent magnetoresistance exhibits similar tendency with the carrier mobility when the charge compensation is retained, revealing that distinct scattering mechanisms may be at play for the temperature dependence of magneto-transport properties. Our work would be helpful for understanding mechanism of the large MR in other nonmagnetic materials and offers an avenue for achieving large MR in the non-magnetic materials with electron-hole pockets.

cond-mat.mes-hall↗

arXiv2019

Experimental observation of dynamical bulk-surface correspondence for topological phases

We experimentally demonstrate a dynamical classification approach for investigation of topological quantum phases using a solid-state spin system through nitrogen-vacancy (NV) center in diamond. Similar to the bulkboundary correspondence in real space at equilibrium, we observe a dynamical bulk-surface correspondence in the momentum space from a dynamical quench process. An emergent dynamical topological invariant is precisely measured in experiment by imaging the dynamical spin-textures on the recently defined band-inversion surfaces, with high topological numbers being implemented. Importantly, the dynamical classification approach is shown to be independent of quench ways and robust to the decoherence effects, offering a novel and practical strategy for dynamical topology characterization, especially for high dimensional gapped topological phases.

cond-mat.mes-hall↗

arXiv2019

Randomness expansion secured by quantum contextuality

The output randomness from a random number generator can be certified by observing the violation of quantum contextuality inequalities based on the Kochen-Specker theorem. Contextuality can be tested in a single quantum system, which significantly simplifies the experimental requirements to observe the violation comparing to the ones based on nonlocality tests. However, it is not yet resolved how to ensure compatibilities for sequential measurements that is required in contextuality tests. Here, we employ a modified Klyachko-Can-Binicioğlu-Shumovsky contextuality inequality, which can ease the strict compatibility requirement on measurements. On a trapped single \Ba ion system, we experimentally demonstrate violation of the contextuality inequality and realize self-testing quantum random number expansion by closing detection loopholes. We perform $1.29 \times 10^8$ trials of experiments and extract the randomness of $8.06 \times 10^5$ bits with a speed of 270 bits s$^{-1}$. Our demonstration paves the way for the practical high-speed spot-checking quantum random number expansion and other secure information processing applications.

quant-ph↗

arXiv2019

Floquet dynamical quantum phase transitions

Dynamical quantum phase transitions (DQPTs) are manifested by time-domain nonanalytic behaviors of many-body systems.Introducing a quench is so far understood as a typical scenario to induce DQPTs.In this work, we discover a novel type of DQPTs, termed "Floquet DQPTs", as intrinsic features of systems with periodic time modulation.Floquet DQPTs occur within each period of continuous driving, without the need for any quenches.In particular, in a harmonically driven spin chain model, we find analytically the existence of Floquet DQPTs in and only in a parameter regime hosting a certain nontrivial Floquet topological phase. The Floquet DQPTs are further characterized by a dynamical topological invariant defined as the winding number of the Pancharatnam geometric phase versus quasimomentum.These findings are experimentally demonstrated with a single spin in diamond.This work thus opens a door for future studies of DQPTs in connection with topological matter.

quant-ph↗

arXiv2019

HetMEC: Latency-optimal Task Assignment and Resource Allocation for Heterogeneous Mobile Edge Computing

Driven by great demands on low-latency services of the edge devices (EDs), mobile edge computing (MEC) has been proposed to enable the computing capacities at the edge of the radio access network. However, conventional MEC servers suffer disadvantages such as limited computing capacity, preventing the computation-intensive tasks to be processed in time. To relief this issue, we propose the heterogeneous MEC (HetMEC) where the data that cannot be timely processed at the edge are allowed be offloaded to the upper-layer MEC servers, and finally to the cloud center (CC) with more powerful computing capacity. We design the latency minimization algorithm by jointly coordinating the task assignment, computing and transmission resources among the EDs, multi-layer MEC servers, and the CC. Simulation results indicate that our proposed algorithm can achieve a lower latency and higher processing rate than the conventional MEC scheme.

eess.SP↗

arXiv2018

Dynamic Feature Generation Network for Answer Selection

Extracting appropriate features to represent a corpus is an important task for textual mining. Previous attention based work usually enhance feature at the lexical level, which lacks the exploration of feature augmentation at the sentence level. In this paper, we exploit a Dynamic Feature Generation Network (DFGN) to solve this problem. Specifically, DFGN generates features based on a variety of attention mechanisms and attaches features to sentence representation. Then a thresholder is designed to filter the mined features automatically. DFGN extracts the most significant characteristics from datasets to keep its practicability and robustness. Experimental results on multiple well-known answer selection datasets show that our proposed approach significantly outperforms state-of-the-art baselines. We give a detailed analysis of the experiments to illustrate why DFGN provides excellent retrieval and interpretative ability.

cs.CL↗

arXiv2018

A Detection and Segmentation Architecture for Skin Lesion Segmentation on Dermoscopy Images

This report summarises our method and validation results for the ISIC Challenge 2018 - Skin Lesion Analysis Towards Melanoma Detection - Task 1: Lesion Segmentation. We present a two-stage method for lesion segmentation with optimised training method and ensemble post-process. Our method achieves state-of-the-art performance on lesion segmentation and we win the first place in ISIC 2018 task1.

cs.CV↗

arXiv2018

Frequency stabilization of a 650 nm laser to I$_{2}$ spectrum for trapped $^{138}$Ba$^{+}$ ions

The optical manipulation of Ba$^{+}$ ions is mainly performed by a 493 nm laser for the S$_{1/2}$-P$_{1/2}$ transition and a 650 nm laser for the P$_{1/2}$-D$_{3/2}$ transition. Since the branching ratio between the 493 nm and 650 nm transitions of a single Ba$^{+}$ ion is comparable, stabilization systems of both lasers are equally important for Doppler cooling, sub-Doppler cooling, optical pumping and state detection. The stabilization system of a 493 nm laser to an absolute Te$_2$ reference has been well established. However, the stabilization of a 650 nm laser has not been presented before. Here we report twenty spectral lines of I$_{2}$ in the range of 0.9 GHz above the resonance of the P$_{1/2}$-D$_{3/2}$ transition. We stabilize the 650 nm laser through the optical cavity to the lowest one among these lines, which is about 350 MHz apart, as the absolute frequency reference. Furthermore, we measure the frequency differences between these iodine lines and the Ba$^+$ resonance through fluorescence excitation spectrum with well-resolved dark states, which is in agreement with the theoretical expectation. The presented stabilization scheme enables us to perform precise experiments with Ba$^{+}$ ions.

physics.atom-ph↗

arXiv2018

Mesoscopic magnetic resonance spectroscopy with a remote spin sensor

Quantum sensing based on nitrogen-vacancy (NV) centers in diamond has been developed as a powerful tool for microscopic magnetic resonance. However, the reported sensor-to-sample distance is limited within tens of nanometers because the signal of spin fluctuation decreases cubically with the increasing distance. Here we extend the sensing distance to tens of micrometers by detecting spin polarization rather than spin fluctuation. We detected the mesoscopic magnetic resonance spectra of polarized electrons of a pentacene-doped crystal, measured its two typical decay times and observed the optically enhanced spin polarization. This work paves the way for the NV-based mesoscopic magnetic resonance spectroscopy and imaging at ambient conditions.

quant-ph↗

arXiv2018

Left-Right Comparative Recurrent Model for Stereo Matching

Leveraging the disparity information from both left and right views is crucial for stereo disparity estimation. Left-right consistency check is an effective way to enhance the disparity estimation by referring to the information from the opposite view. However, the conventional left-right consistency check is an isolated post-processing step and heavily hand-crafted. This paper proposes a novel left-right comparative recurrent model to perform left-right consistency checking jointly with disparity estimation. At each recurrent step, the model produces disparity results for both views, and then performs online left-right comparison to identify the mismatched regions which may probably contain erroneously labeled pixels. A soft attention mechanism is introduced, which employs the learned error maps for better guiding the model to selectively focus on refining the unreliable regions at the next recurrent step. In this way, the generated disparity maps are progressively improved by the proposed recurrent model. Extensive evaluations on KITTI 2015, Scene Flow and Middlebury benchmarks validate the effectiveness of our model, demonstrating that state-of-the-art stereo disparity estimation results can be achieved by this new model.

cs.CV↗

arXiv2018

Learning over Knowledge-Base Embeddings for Recommendation

State-of-the-art recommendation algorithms -- especially the collaborative filtering (CF) based approaches with shallow or deep models -- usually work with various unstructured information sources for recommendation, such as textual reviews, visual images, and various implicit or explicit feedbacks. Though structured knowledge bases were considered in content-based approaches, they have been largely neglected recently due to the availability of vast amount of data, and the learning power of many complex models. However, structured knowledge bases exhibit unique advantages in personalized recommendation systems. When the explicit knowledge about users and items is considered for recommendation, the system could provide highly customized recommendations based on users' historical behaviors. A great challenge for using knowledge bases for recommendation is how to integrated large-scale structured and unstructured data, while taking advantage of collaborative filtering for highly accurate performance. Recent achievements on knowledge base embedding sheds light on this problem, which makes it possible to learn user and item representations while preserving the structure of their relationship with external knowledge. In this work, we propose to reason over knowledge base embeddings for personalized recommendation. Specifically, we propose a knowledge base representation learning approach to embed heterogeneous entities for recommendation. Experimental results on real-world dataset verified the superior performance of our approach compared with state-of-the-art baselines.

cs.IR↗