arXiv subjects

Yuan Liu

Publications and source records attributed to Yuan Liu.

At least 433 records · Page 24Linked to original sources

arXiv2020

Addressing the Real-world Class Imbalance Problem in Dermatology

Class imbalance is a common problem in medical diagnosis, causing a standard classifier to be biased towards the common classes and perform poorly on the rare classes. This is especially true for dermatology, a specialty with thousands of skin conditions but many of which have low prevalence in the real world. Motivated by recent advances, we explore few-shot learning methods as well as conventional class imbalance techniques for the skin condition recognition problem and propose an evaluation setup to fairly assess the real-world utility of such approaches. We find the performance of few-show learning methods does not reach that of conventional class imbalance techniques, but combining the two approaches using a novel ensemble improves model performance, especially for rare classes. We conclude that ensembling can be useful to address the class imbalance problem, yet progress can further be accelerated by real-world evaluation setups for benchmarking new methods.

cs.CV↗

arXiv2020

Finite Temperature Auxiliary Field Quantum Monte Carlo in the Canonical Ensemble

Finite temperature auxiliary field-based Quantum Monte Carlo methods, including Determinant Quantum Monte Carlo (DQMC) and Auxiliary Field Quantum Monte Carlo (AFQMC), have historically assumed pivotal roles in the investigation of the finite temperature phase diagrams of a wide variety of multidimensional lattice models and materials. Despite their utility, however, these techniques are typically formulated in the grand canonical ensemble, which makes them difficult to apply to condensates like superfluids and difficult to benchmark against alternative methods that are formulated in the canonical ensemble. Working in the grand canonical ensemble is furthermore accompanied by the increased overhead associated with having to determine the chemical potentials that produce desired fillings. Given this backdrop, in this work, we present a new recursive approach for performing AFQMC simulations in the canonical ensemble that does not require knowledge of chemical potentials. To derive this approach, we exploit the convenient fact that AFQMC solves the many-body problem by decoupling many-body propagators into integrals over one-body problems to which non-interacting theories can be applied. We benchmark the accuracy of our technique on illustrative Bose and Fermi Hubbard models and demonstrate that it can converge more quickly to the ground state than grand canonical AFQMC simulations. We believe that our novel use of HS-transformed operators to implement algorithms originally derived for non-interacting systems will motivate the development of a variety of other methods and anticipate that our technique will enable direct performance comparisons against other many-body approaches formulated in the canonical ensemble.

quant-ph↗

arXiv2020

Object Tracking Using Spatio-Temporal Future Prediction

Occlusion is a long-standing problem that causes many modern tracking methods to be erroneous. In this paper, we address the occlusion problem by exploiting the current and future possible locations of the target object from its past trajectory. To achieve this, we introduce a learning-based tracking method that takes into account background motion modeling and trajectory prediction. Our trajectory prediction module predicts the target object's locations in the current and future frames based on the object's past trajectory. Since, in the input video, the target object's trajectory is not only affected by the object motion but also the camera motion, our background motion module estimates the camera motion. So that the object's trajectory can be made independent from it. To dynamically switch between the appearance-based tracker and the trajectory prediction, we employ a network that can assess how good a tracking prediction is, and we use the assessment scores to choose between the appearance-based tracker's prediction and the trajectory-based prediction. Comprehensive evaluations show that the proposed method sets a new state-of-the-art performance on commonly used tracking benchmarks.

cs.CV↗

arXiv2020

Gated Res2Net for Multivariate Time Series Analysis

Multivariate time series analysis is an important problem in data mining because of its widespread applications. With the increase of time series data available for training, implementing deep neural networks in the field of time series analysis is becoming common. Res2Net, a recently proposed backbone, can further improve the state-of-the-art networks as it improves the multi-scale representation ability through connecting different groups of filters. However, Res2Net ignores the correlations of the feature maps and lacks the control on the information interaction process. To address that problem, in this paper, we propose a backbone convolutional neural network based on the thought of gated mechanism and Res2Net, namely Gated Res2Net (GRes2Net), for multivariate time series analysis. The hierarchical residual-like connections are influenced by gates whose values are calculated based on the original feature maps, the previous output feature maps and the next input feature maps thus considering the correlations between the feature maps more effectively. Through the utilization of gated mechanism, the network can control the process of information sending hence can better capture and utilize the both the temporal information and the correlations between the feature maps. We evaluate the GRes2Net on four multivariate time series datasets including two classification datasets and two forecasting datasets. The results demonstrate that GRes2Net have better performances over the state-of-the-art methods thus indicating the superiority

cs.LG↗

arXiv2020

Electric field control of disorder-tunable superconductivity and the emergence of quantum metal at an oxide interface

We report on an extraordinary field effect of the superconducting LaAlO3/KTaO3(111) interface with Tc ~2 K. By applying a gate voltage (VG) across KTaO3, the interface can be continuously tuned from superconducting into insulating states, yielding a dome-shaped Tc-VG dependence. The electric gating has only a minor effect on carrier density as evidenced in the Hall-effect measurement, while it changes spatial profile of the carriers in the interface, hence the carrier's disorder level significantly. As temperature is decreased, the resistance saturates at lowest temperature in both superconducting and insulating sides, despite an initial dramatic dropping or increasing, which suggests an emergence of quantum metallic state associated with failed superconductor and/or fragile insulator. A VG-modulation of the magnetic-field-driven superconductor to insulator quantum phase transition reveals a non-universal criticality.

cond-mat.supr-con↗

arXiv2020

The TianQin project: current progress on science and technology

TianQin is a planned space-based gravitational wave (GW) observatory consisting of three earth orbiting satellites with an orbital radius of about $10^5~{\rm km}$. The satellites will form a equilateral triangle constellation the plane of which is nearly perpendicular to the ecliptic plane. TianQin aims to detect GWs between $10^{-4}~{\rm Hz}$ and $1~{\rm Hz}$ that can be generated by a wide variety of important astrophysical and cosmological sources, including the inspiral of Galactic ultra-compact binaries, the inspiral of stellar-mass black hole binaries, extreme mass ratio inspirals, the merger of massive black hole binaries, and possibly the energetic processes in the very early universe or exotic sources such as cosmic strings. In order to start science operations around 2035, a roadmap called the 0123 plan is being used to bring the key technologies of TianQin to maturity, supported by the construction of a series of research facilities on the ground. Two major projects of the 0123 plan are being carried out. In this process, the team has created a new generation $17~{\rm cm}$ single-body hollow corner-cube retro-reflector which has been launched with the QueQiao satellite on 21 May 2018; a new laser ranging station equipped with a $1.2~{\rm m}$ telescope has been constructed and the station has successfully ranged to all the five retro-reflectors on the Moon; and the TianQin-1 experimental satellite has been launched on 20 December 2019 and the first round result shows that the satellite has exceeded all of its mission requirements.

gr-qc↗

arXiv2020

The First Round Result from the TianQin-1 Satellite

The TianQin-1 satellite (TQ-1), which is the first technology demonstration satellite for the TianQin project, was launched on 20 December 2019. The first round of experiment had been carried out from 21 December 2019 until 1 April 2020. The residual acceleration of the satellite is found to be about $1\times10^{-10}~{\rm m}/{\rm s}^{2}/{\rm Hz}^{1/2}$ at $0.1~{\rm Hz}\,$ and about $5\times10^{-11}~{\rm m}/{\rm s}^{2}/{\rm Hz}^{1/2}$ at $0.05~{\rm Hz}\,$, measured by an inertial sensor with a sensitivity of $5\times10^{-12}~{\rm m}/{\rm s}^{2}/{\rm Hz}^{1/2}$ at $0.1~{\rm Hz}\,$. The micro-Newton thrusters has demonstrated a thrust resolution of $0.1~μ{\rm N}$ and a thrust noise of $0.3~μ{\rm N}/{\rm Hz}^{1/2}$ at $0.1~{\rm Hz}$. The residual noise of the satellite with drag-free control is $3\times10^{-9}~{\rm m}/{\rm s}^{2}/{\rm Hz}^{1/2}$ at $0.1~{\rm Hz}\,$. The noise level of the optical readout system is about $30~{\rm pm}/{\rm Hz}^{1/2}$ at $0.1~{\rm Hz}\,$. The temperature stability at temperature monitoring position is controlled to be about $\pm3~{\rm mK}$ per orbit, and the mismatch between the center-of-mass of the satellite and that of the test mass is measured with a precision of better than $0.1~{\rm mm}$.

physics.ins-det↗

arXiv2020

Proof of Learning (PoLe): Empowering Machine Learning with Consensus Building on Blockchains

The progress of deep learning (DL), especially the recent development of automatic design of networks, has brought unprecedented performance gains at heavy computational cost. On the other hand, blockchain systems routinely perform a huge amount of computation that does not achieve practical purposes in order to build Proof-of-Work (PoW) consensus from decentralized participants. In this paper, we propose a new consensus mechanism, Proof of Learning (PoLe), which directs the computation spent for consensus toward optimization of neural networks (NN). In our mechanism, the training/testing data are released to the entire blockchain network (BCN) and the consensus nodes train NN models on the data, which serves as the proof of learning. When the consensus on the BCN considers a NN model to be valid, a new block is appended to the blockchain. We experimentally compare the PoLe protocol with Proof of Work (PoW) and show that PoLe can achieve a more stable block generation rate, which leads to more efficient transaction processing. We also introduce a novel cheating prevention mechanism, Secure Mapping Layer (SML), which can be straightforwardly implemented as a linear NN layer. Empirical evaluation shows that SML can detect cheating nodes at small cost to the predictive performance.

cs.CR↗

arXiv2020

Unveiling the Finite Temperature Physics of Hydrogen Chains via Auxiliary Field Quantum Monte Carlo

The ability to accurately predict the finite temperature properties of realistic quantum solids is central to uncovering new phases and engineering materials with novel properties. Nonetheless, there remain comparatively few many-body techniques capable of elucidating the finite temperature physics of solids from first principles. In this work, we take a significant step towards developing such a technique by generalizing our previous, fully ab initio finite temperature Auxiliary Field Quantum Monte Carlo (FT-AFQMC) method to model periodic solids and employing it to uncover the finite temperature physics of periodic hydrogen chains. Based upon our calculations of these chains' many-body thermodynamic quantities and correlation functions, we outline their metal-insulator and magnetic ordering as a function of both H-H bond distance and temperature. At low temperatures approaching the ground state, we observe both metal-insulator and ferromagnetic-antiferromagnetic crossovers at bond lengths between 0.5 and 0.75 Å. We then demonstrate how this low-temperature ordering evolves into a metallic phase with decreasing magnetic order at higher temperatures. By comparing the features we observe to those previously seen in one-dimensional, half-filled Hubbard models at finite temperature and in ground state hydrogen chains, interestingly, we identify signatures of the Pomeranchuk effect in hydrogen chains for the first time and show that spin and charge excitations that typically arise at distinct temperatures in the Hubbard model are indistinguishably coupled in these systems. Beyond qualitatively revealing the many-body phase behavior of hydrogen chains, our efforts shed light on the further theoretical developments that will be required to construct the phase diagrams of the more complex transition metal, lanthanide, and actinide solids of longstanding interest to physicists.

cond-mat.str-el↗

arXiv2020

An adaptive multiresolution ultra-weak discontinuous Galerkin method for nonlinear Schrodinger equations

This paper develops a high order adaptive scheme for solving nonlinear Schrodinger equations. The solutions to such equations often exhibit solitary wave and local structures, which makes adaptivity essential in improving the simulation efficiency. Our scheme uses the ultra-weak discontinuous Galerkin (DG) formulation and belongs to the framework of adaptive multiresolution schemes. Various numerical experiments are presented to demonstrate the excellent capability of capturing the soliton waves and the blow-up phenomenon.

math.NA↗

arXiv2020

The first photometric investigation of the contact binary OQ Cam

The first charge-coupled device photometric light curves in B, V, R${_c}$, and I${_c}$ bands of the short-period contact binary star OQ Cam are presented. Through analyzing the light curves with the Wilson-Devinney code, it is found that OQ Cam is a W-type shallow contact binary star with a contact degree of f =20.2%. Based on the O-C analyzing, the orbit period displays a long term increasing with a rate of $dP/dt=4.40\times10{^{-7}} day \cdot year{^{-1}}$. The increasing in orbit period can be interpreted by mass transfer from the less massive component to the more massive one. As the orbit period increasing, OQ Cam would evolve from the present shallow contact configuration to a none contact stage. So it may be a potential candidate to confirm the thermal relaxation oscillation theory.

astro-ph.SR↗

arXiv2020

Data-Importance Aware Radio Resource Allocation: Wireless Communication Helps Machine Learning

The rich mobile data and edge computing enabled wireless networks motivate to deploy artificial intelligence (AI) at network edge, known as \emph{edge AI}, which integrates wireless communication and machine learning. In communication, data bits are equally important, while in machine learning some data bits are more important. Therefore we can allocate more radio resources to the more important data and allocate less radio resources to the less important data, so as to efficiently utilize the limited radio resources. To this end, how to define "more or less important" of data is the key problem. In this article, we propose two importance criteria to differentiate data's importance based on their effects on machine learning, one for centralized edge machine learning and the other for distributed edge machine learning. Then, the corresponding radio resource allocation schemes are proposed to improve performance of machine learning. Extensive experiments are conducted for verifying the effectiveness of the proposed data-importance aware radio resource allocation schemes.

cs.IT↗

arXiv2020

MagnifierNet: Towards Semantic Adversary and Fusion for Person Re-identification

Although person re-identification (ReID) has achieved significant improvement recently by enforcing part alignment, it is still a challenging task when it comes to distinguishing visually similar identities or identifying the occluded person. In these scenarios, magnifying details in each part features and selectively fusing them together may provide a feasible solution. In this work, we propose MagnifierNet, a triple-branch network which accurately mines details from whole to parts. Firstly, the holistic salient features are encoded by a global branch. Secondly, to enhance detailed representation for each semantic region, the "Semantic Adversarial Branch" is designed to learn from dynamically generated semantic-occluded samples during training. Meanwhile, we introduce "Semantic Fusion Branch" to filter out irrelevant noises by selectively fusing semantic region information sequentially. To further improve feature diversity, we introduce a novel loss function "Semantic Diversity Loss" to remove redundant overlaps across learned semantic representations. State-of-the-art performance has been achieved on three benchmarks by large margins. Specifically, the mAP score is improved by 6% and 5% on the most challenging CUHK03-L and CUHK03-D benchmarks.

cs.CV↗

arXiv2020

An adaptive multiresolution interior penalty discontinuous Galerkin method for wave equations in second order form

In this paper, we propose a class of adaptive multiresolution (also called adaptive sparse grid) discontinuous Galerkin (DG) methods for simulating scalar wave equations in second order form in space. The two key ingredients of the schemes include an interior penalty DG formulation in the adaptive function space and two classes of multiwavelets for achieving multiresolution. In particular, the orthonormal Alpert's multiwavelets are used to express the DG solution in terms of a hierarchical structure, and the interpolatory multiwavelets are further introduced to enhance computational efficiency in the presence of variable wave speed or nonlinear source. Some theoretical results on stability and accuracy of the proposed method are presented. Benchmark numerical tests in 2D and 3D are provided to validate the performance of the method.

math.NA↗

arXiv2020

Discovery of two-dimensional anisotropic superconductivity at KTaO$_3$ (111) interfaces

The unique electronic structure found at interfaces between materials can allow unconventional quantum states to emerge. Here we observe superconductivity in electron gases formed at interfaces between (111) oriented KTaO$_3$ and insulating overlayers of either EuO or LaAlO$_3$. The superconducting transition temperature, approaching 2.2 K, is about one order of magnitude higher than that of the LaAlO$_3$/SrTiO$_3$ system. Strikingly, similar electron gases at (001) KTaO$_3$ interfaces remain normal down to 25 mK. The critical field and current-voltage measurements indicate that the superconductivity is two dimensional. Higher mobility EuO/KTaO$_3$ (111) samples show a large in-plane anisotropy in transport properties at low temperatures prior to onset of superconductivity, suggesting the emergence of a stripe like phase where the superconductivity is nearly homogeneous in one direction, but strongly modulated in the other.

cond-mat.supr-con↗

arXiv2020

Laser-assisted high-energy proton pulse extraction for feasibility study of co-located muon source at the SNS

We have experimentally demonstrated the first non-intrusive 1-GeV proton beam extraction for the generation of muons with a temporal structure optimized for Muon Spin Relaxation/Rotation/Resonance (MuSR) applications. The proton pulses are extracted based on the laser neutralization of 1 GeV hydrogen ion (H-) beam in the high energy beam transport of the Spallation Neutron Source (SNS) accelerator. The maximum flux of the extracted proton beam accounts for only 0.2% of the total proton beam used for neutron production, a marked difference from the 20% reduction at other co-located muon and neutron facilities, and thus the proposed method will result in negligible impact on the SNS operation. This paper describes the development of a fiber/solid-state hybrid laser system that has high flexibility of pulse structure and output power, initial experiments on laser neutralization of H- beam and separation of H0 beam from the existing SNS accelerator beam line, conversion of H0 to proton at the SNS linac dump, and measurement results of 30 ns/50 kHz proton pulses. This system conclusively demonstrates the feasibility of laser-based proton beam extraction to power a world-leading MuSR facility at the SNS.

physics.acc-ph↗

arXiv2020

Methodology and Performance of the Two-Year Galactic Plane Scanning Survey of Insight-HXMT

The Galactic plane scanning survey is one of the main scientific objectives of the Hard X-ray Modulation Telescope (known as Insight-HXMT). During the two-year operation of Insight-HXMT, more than 1000 scanning observations have been performed and the whole Galactic plane ($\rm 0^{\circ}<l<360^{\circ}$, $\rm -10^{\circ}<b<10^{\circ}$) has been covered completely. We summarize the Galactic plane scanning survey of Insight-HXMT for two years, including the characteristics of the scanning data, the data analysis process and the preliminary results of the Low-Energy telescope, the Medium-Energy telescope and the High-Energy telescope. With the light curve PSF fitting method, the fluxes of the known sources in the scanned area as well as the flux errors are obtained for each scanning observation. From the relationships of SNRs and fluxes, the $5σ$ sensitivities of three telescopes of Insight-HXMT are estimated as $\rm \sim7.6\times10^{-11}~erg cm^{-2}~s^{-1}$ ($\rm 3 mCrab,~1-6 keV$), $\rm \sim4.0\times10^{-10}~erg~cm^{-2}~s^{-1}$ ($\rm 20~mCrab,~7-40 keV$) and $\rm \sim2.6\times10^{-10}~erg cm^{-2}~s^{-1}$ ($\rm 18 mCrab,~25-100 keV$) for an individual scanning observation of $2-3$ hours, respectively. Up to September 2019, more than 800 X-ray sources with various types are monitored by the three telescopes and their long-term light curves with three energy bands are obtained to make further scientific analyses.

astro-ph.IM↗

arXiv2020

The electron affinity of astatine

One of the most important properties influencing the chemical behavior of an element is the energy released with the addition of an extra electron to the neutral atom, referred to as the electron affinity (EA). Among the remaining elements with unknown EA is astatine, the purely radioactive element 85. Astatine is the heaviest naturally occurring halogen and its isotope $^{211}$At is remarkably well suited for targeted radionuclide therapy of cancer. With the At$^-$ anion being involved in many aspects of current astatine labelling protocols, the knowledge of the electron affinity of this element is of prime importance. In addition, the EA can be used to deduce other concepts such as the electronegativity, thereby further improving the understanding of astatine's chemistry. Here, we report the first measurement of the EA for astatine to be 2.41578(7)eV. This result is compared to state-of-the-art relativistic quantum mechanical calculations, which require incorporation of the electron-electron correlation effects on the highest possible level. The developed technique of laser-photodetachment spectroscopy of radioisotopes opens the path for future EA measurements of other radioelements such as polonium, and eventually super-heavy elements, which are produced at a one-atom-at-a-time rate.

physics.atom-ph↗