arXiv subjects

Yong Jiang

Publications and source records attributed to Yong Jiang.

At least 217 records · Page 12Linked to original sources

arXiv2020

An ACE/CRIS-observation-based Galactic Cosmic Rays heavy nuclei spectra model II

An observation-based Galactic Cosmic Ray (GCR) spectral model for heavy nuclei is developed. Zhao and Qin (J. Geophys. Res. Space Phys.118, 1837(2013)) proposed an empirical elemental GCR spectra model for nuclear charge 5-28 over the energy range from 30 to 500 MeV/nuc, which is proved to be successful in predicting yearly averaged GCR heavy nuclei spectra.Based on the latest highly statistically precise measurements from ACE/CRIS,a further elemental GCR model with monthly averaged spectra is presented. The model can reproduce the past and predict the futureGCR intensity monthly by correlating model parameters with thecontinuous sunspot number (SSN) record. The effects of solar activity on GCR modulation are considered separately for odd and even solar cycles. Compared with other comprehensive GCR models, our modeling results are satisfyingly consistent with the GCR spectral measurements from ACE/SIS and IMP-8, and have comparable prediction accuracy as the Badhwar & O'Neill 2014 model.A detailed error analysis is also provided.Finally, the GCR carbon and iron nuclei fluxes for the subsequent two solar cycles (SC 25 and 26) are predicted and they show a potential trend in reduced flux amplitude, which is suspected to be relevant to possible weak solar cycles.

astro-ph.IM↗

arXiv2020

Detecting Suspected Epidemic Cases Using Trajectory Big Data

Emerging infectious diseases are existential threats to human health and global stability. The recent outbreaks of the novel coronavirus COVID-19 have rapidly formed a global pandemic, causing hundreds of thousands of infections and huge economic loss. The WHO declares that more precise measures to track, detect and isolate infected people are among the most effective means to quickly contain the outbreak. Based on trajectory provided by the big data and the mean field theory, we establish an aggregated risk mean field that contains information of all risk-spreading particles by proposing a spatio-temporal model named HiRES risk map. It has dynamic fine spatial resolution and high computation efficiency enabling fast update. We then propose an objective individual epidemic risk scoring model named HiRES-p based on HiRES risk maps, and use it to develop statistical inference and machine learning methods for detecting suspected epidemic-infected individuals. We conduct numerical experiments by applying the proposed methods to study the early outbreak of COVID-19 in China. Results show that the HiRES risk map has strong ability in capturing global trend and local variability of the epidemic risk, thus can be applied to monitor epidemic risk at country, province, city and community levels, as well as at specific high-risk locations such as hospital and station. HiRES-p score seems to be an effective measurement of personal epidemic risk. The accuracy of both detecting methods are above 90\% when the population infection rate is under 20\%, which indicates great application potential in epidemic risk prevention and control practice.

stat.AP↗

arXiv2020

Breaking the $O(1/ε)$ Optimal Rate for a Class of Minimax Problems

It is known that for convex optimization $\min_{\mathbf{w}\in\mathcal{W}}f(\mathbf{w})$, the best possible rate of first order accelerated methods is $O(1/\sqrtε)$. However, for the bilinear minimax problem: $\min_{\mathbf{w}\in\mathcal{W}}\max_{\mathbf{v}\in\mathcal{V}}$ $f(\mathbf{w})$ $+\langle\mathbf{w}, \boldsymbol{A}\mathbf{v}\rangle$ $-h(\mathbf{v})$ where both $f(\mathbf{w})$ and $h(\mathbf{v})$ are convex, the best known rate of first order methods slows down to $O(1/ε)$. It is not known whether one can achieve the accelerated rate $O(1/\sqrtε)$ for the bilinear minimax problem without assuming $f(\mathbf{w})$ and $h(\mathbf{v})$ being strongly convex. In this paper, we fill this theoretical gap by proposing a bilinear accelerated extragradient (BAXG) method. We show that when $\mathcal{W}=\mathbb{R}^d$, $f(\mathbf{w})$ and $h(\mathbf{v})$ are convex and smooth, and $\boldsymbol{A}$ has full column rank, then the BAXG method achieves an accelerated rate $O(1/\sqrtε\log \frac{1}ε)$, within a logarithmic factor to the likely optimal rate $O(1/\sqrtε)$. As result, a large class of bilinear convex concave minimax problems, including a few problems of practical importance, can be solved much faster than previously known methods.

math.OC↗

arXiv2020

AC-frequency switchable correlated transports in rare-earth perovskite nickelates

Whilst electron correlations were previously recognized to trigger beyond conventional direct current (DC) electronic transportations (e.g. metal-to-insulator transitions, bad metal, thermistors), their respective influences to the alternation current (AC) transport are largely overlooked. Herein, we demonstrate active regulations in the electronic functionalities of d-band correlated rare-earth nickelate (ReNiO3) thin films, by simply utilizing their electronic responses to AC-frequencies (fAC). Assisted by temperature dependent near edge X-ray absorption fine structure analysis, we discovered positive temperature dependences in Coulomb viscosity of ReNiO3 that moderates their AC impedance. Distinguished crosslinking among R(Real)-fAC measured in nearby temperatures is observed that differs to conventional oxides. It enables active adjustability in correlated transports of ReNiO3, among NTCR-, TDelta- and PTCR- thermistors, via fAC from the electronic perspective without varying materials or device structures. The TDelta-fAC relationship can be further widely adjusted via Re composition and interfacial strains. The AC-frequency sensitivity discovered in ReNiO3 brings in a new freedom to regulating and switching the device working states beyond the present semiconductor technologies. It opens a new paradigm for enriching novel electronic applications catering automatic transmission or artificial intelligence in sensing temperatures and frequencies.

cond-mat.mtrl-sci↗

arXiv2020

Perpendicularly magnetized YIG films with small Gilbert damping constant and anomalous spin transport properties

The Y3Fe5O12 (YIG) films with perpendicular magnetic anisotropy (PMA) have recently attracted a great deal of attention for spintronics applications. Here, we report the induced PMA in the ultrathin YIG films grown on (Gd2.6Ca0.4)(Ga4.1Mg0.25Zr0.65)O12 (SGGG) substrates by epitaxial strain without preprocessing. Reciprocal space mapping shows that the films are lattice-matched to the substrates without strain relaxation. Through ferromagnetic resonance and polarized neutron reflectometry measurements, we find that these YIG films have ultra-low Gilbert damping constant with a magnetic dead layer as thin as about 0.3 nm at the YIG/SGGG interfaces. Moreover, the transport behavior of the Pt/YIG/SGGG films reveals an enhancement of spin mixing conductance and a large non-monotonic magnetic field dependence of anomalous Hall effect as compared with the Pt/YIG/Gd3Ga5O12 (GGG) films. The non-monotonic anomalous Hall signal is extracted in the temperature range from 150 to 350 K, which has been ascribed to the possible non-collinear magnetic order at the Pt/YIG interface induced by uniaxial strain.

cond-mat.mtrl-sci↗

arXiv2020

Does the price of strategic commodities respond to U.S. Partisan Conflict?

A noteworthy feature of U.S. politics in recent years is serious partisan conflict, which has led to intensifying polarization and exacerbating high policy uncertainty. The US is a significant player in oil and gold markets. Oil and gold also form the basis of important strategic reserves in the US. We investigate whether U.S. partisan conflict affects the returns and price volatility of oil and gold using a parametric test of Granger causality in quantiles. The empirical results suggest that U.S. partisan conflict has an effect on the returns of oil and gold, and the effects are concentrated at the tail of the conditional distribution of returns. More specifically, the partisan conflict mainly affects oil returns when the crude oil market is in a bearish state (lower quantiles). By contrast, partisan conflict matters for gold returns only when the gold market is in a bullish scenario (higher quantiles). In addition, for the volatility of oil and gold, the predictability of partisan conflict index virtually covers the entire distribution of volatility.

econ.GN↗

arXiv2019

Deep Flow Collaborative Network for Online Visual Tracking

The deep learning-based visual tracking algorithms such as MDNet achieve high performance leveraging to the feature extraction ability of a deep neural network. However, the tracking efficiency of these trackers is not very high due to the slow feature extraction for each frame in a video. In this paper, we propose an effective tracking algorithm to alleviate the time-consuming problem. Specifically, we design a deep flow collaborative network, which executes the expensive feature network only on sparse keyframes and transfers the feature maps to other frames via optical flow. Moreover, we raise an effective adaptive keyframe scheduling mechanism to select the most appropriate keyframe. We evaluate the proposed approach on large-scale datasets: OTB2013 and OTB2015. The experiment results show that our algorithm achieves considerable speedup and high precision as well.

cs.CV↗

arXiv2019

Overlooked transportation anisotropies in d-band correlated rare-earth perovskite nickelates

Anisotropies in electronic transportations conventionally originate from the nature of low symmetries in crystal structures, and were not anticipated for perovskite oxides, the crystal asymmetricity of which is far below, e.g. van der Waals or topological crystal. Beyond conventional expectations, herein we demonstrate pronounced anisotropies in the inter-band coulomb repulsion dominated electronic transportation behaviors under low-dimensional confinement for the perovskite family of rare-earth nickelates (ReNiO3). From one aspect, imparting bi-axial interfacial strains upon various lattice planes results in extrinsic anisotropies in the abrupt orbital transitions of ReNiO3, and their metal to insulator transition behaviors that elevates the transition temperature beyond the existing merit. From the other aspect, the in-plane orbital entropy associated to the in-plane symmetry of the NiO6 octahedron within ReNiO3 causes intrinsic anisotropies for the gradually orbital transition with temperature to further improve their thermistor transportation properties. The present work unveils the overlooked role of the electronic orbital directionality within low dimensional correlated perovskites that can trigger anisotropic transportation behaviors, in spite of their relatively symmetric crystal structures. Establishing anisotropic transportations integrating the electron correlation and quantum confinement effects will bring in a new freedom for achieving further improvement in transportation properties of multi-functional perovskite oxides.

cond-mat.mtrl-sci↗

arXiv2019

Observation of Spin Hall Effect in Weyl Semimetal WTe2 at Room Temperature

Discovery of topological Weyl semimetals has revealed the opportunities to realize several extraordinary physical phenomena in condensed matter physics. Specifically, these semimetals with strong spin-orbit coupling, broken inversion symmetry and novel spin texture are predicted to exhibit a large spin Hall effect that can efficiently convert the charge current to a spin current. Here we report the direct experimental observation of a large spin Hall and inverse spin Hall effects in Weyl semimetal WTe2 at room temperature obeying Onsager reciprocity relation. We demonstrate the detection of the pure spin current generated by spin Hall phenomenon in WTe2 by making van der Waals heterostructures with graphene, taking advantage of its long spin coherence length and spin transmission at the heterostructure interface. These experimental findings well supported by ab initio calculations show a large charge-spin conversion efficiency in WTe2; which can pave the way for utilization of spin-orbit induced phenomena in spintronic memory and logic circuit architectures.

cond-mat.mes-hall↗

arXiv2019

Unified Acceleration of High-Order Algorithms under Hölder Continuity and Uniform Convexity

In this paper, through a very intuitive vanilla proximal method perspective, we derive accelerated high-order optimization algorithms for minimizing a convex function that has Hölder continuous derivatives. In this general convex setting, we propose a concise unified acceleration framework (UAF), which reconciles the two different high-order acceleration approaches, one by Nesterov and Baes [29, 3, 33] and one by Monteiro and Svaiter [25]. As result, the UAF unifies the high-order acceleration instances [29, 3, 33, 15, 16, 25, 19, 6, 14] of the two approaches by only two problem-related parameters and two additional parameters for framework design. Furthermore, the UAF (and its analysis) is the first approach to make high-order methods applicable for high-order smoothness conditions with respect to non-Euclidean norms. If the function is further uniformly convex, we propose a general restart scheme for the UAF. The iteration complexities of instances of both the UAF and the restarted UAF match existing lower bounds in most important cases [2, 16]. For practical implementation, we introduce a new and effective heuristic that significantly simplifies the binary search procedure required by the framework. We use experiments to verify the effectiveness of the heuristic and demonstrate clear and consistent advantages of high-order acceleration methods over first-order ones, in terms of run-time complexity. Finally, the UAF is proposed directly in the general composite convex setting, thus show that the existing high-order algorithms [29, 3, 33, 16, 6, 14] can be naturally extended to the general composite convex setting.

math.OC↗

arXiv2019

Interface-driven unusual anomalous Hall effect in MnxGa/Pt bilayers: No correlation with chiral spin structures

The effects of spin-orbit coupling and symmetry breaking at the interface between a ferromagnet and heavy metal are particularly important for spin-based information storage and computation. Recent discoveries suggest they can create chiral spin structures (e.g. skyrmions), which have often been identified through the appearance of the bump/dip features of Hall signals, the so-called topological Hall effect (THE). In this work, however, we have present an unusual anomalous Hall effect (UAHE) in MnxGa/Pt bilayers and demonstrated that the features extremely similar to THE can be generated without involving any chiral spin structures. The low temperature magnetic force microscopy has been used to explore the magnetic field-dependent behavior of spin structures, and the UAHE as a function of magnetic field does not peak near the maximal density of magnetic bubbles. The results unambiguously evidence that the UAHE in MnxGa/Pt bilayers shows no correlation with chiral spin structures but is driven by the modified interfacial properties. The bump/dip features of Hall signals cannot be taken as an unambiguous signature for the emergence of chiral spin structures, and a wealth of underlying and interesting physics need explored.

cond-mat.mtrl-sci↗

arXiv2019

DAL: Dual Adversarial Learning for Dialogue Generation

In open-domain dialogue systems, generative approaches have attracted much attention for response generation. However, existing methods are heavily plagued by generating safe responses and unnatural responses. To alleviate these two problems, we propose a novel framework named Dual Adversarial Learning (DAL) for high-quality response generation. DAL is the first work to innovatively utilizes the duality between query generation and response generation to avoid safe responses and increase the diversity of the generated responses. Additionally, DAL uses adversarial learning to mimic human judges and guides the system to generate natural responses. Experimental results demonstrate that DAL effectively improves both diversity and overall quality of the generated responses. DAL outperforms the state-of-the-art methods regarding automatic metrics and human evaluations.

cs.CL↗

arXiv2019

Effect of Star Rotation Rates on the Characteristics of Energetic Particle Events

Recent detection of superflares on solar-type stars by Kepler mission raised a possibility that they can be associated with energetic coronal mass ejections (CMEs) and energetic particle events (SEPs). These space weather events can impact habitability of exoplanets around these stars. Here we use the improved Particle Acceleration and Transport in the Heliosphere (iPATH) model, to model the time intensity profile and spectrum of SEPs accelerated at CME-driven shocks from stars of different ages traced by their rotation rates. We consider a solar-like (G-type) star with 6 different rotation rates varying from 0.5 to 3.0 times rotation speed of the sun. In all 6 cases, a fast CME is launched with the same speed of 1500 km/sec and the resulting time intensity profiles at 3 locations and and energy spectra at 5 locations at 1 AU are obtained. The maximum particle energy at the shock front as a function of r is also shown. Our results suggest that within 0.8 AU the maximum particle energy at the shock front increases with the rotation rate of the star. However, event integrated spectra for the five selected locations along the CME path show complicated patterns. This is because the Parker magnetic field for rapidly rotating stars is more tightly winded. Our results can be used in estimating the radiation environments of terrestrial-type exoplanets around solar-type stars.

astro-ph.SR↗

arXiv2019

Entropy driven reverse-metal-to-insulator transition and delta-temperatural transports in metastable perovskites of correlated rare-earth nickelate

The metal to insulator transition (MIT) in Mott-Hubbard systems is one of the most important discoveries in condensed matter physics, and results in abrupt orbital transitions from the insulating to metallic phases by elevating temperature across a critical point (TMIT). Although the MIT was previously expected to be mainly driven by the orbital Coulomb repulsion energy, the entropy contribution to the orbital free energy that also determines the relative stability of the metallic and insulating phases was largely overlooked. Herein, we demonstrate an orbital-entropy dominated reversible electronic phase transition in the metastable perovskite family of correlated rare-earth nicklates (ReNiO3), in addition to their previously known MIT driven by orbital Coulomb energies. In reverse to MIT, the resistivity of ReNiO3 abruptly increases by 2-3 orders by elevating T across another critical point (TR-MIT) below TMIT, and such transition is named as reverse-metal to insulator transition (R-MIT). Combining the afterwards exponentially decreasing resistivity in the insulating phase of ReNiO3 at further temperature elevation, a distinguished delta-temperatural transport character is established, which is potentially applicable for locking the working temperatures range for electric devices. The TR-MIT is shown to be enhanced via reducing the compositional complexity and size of Re or imparting bi-axial compressive strains, and meanwhile the transition sharpness of delta-temperatural transport is reduced. Our discovery indicates that temperature range for a thermodynamically stable insulating phase of ReNiO3 is in between of TR-MIT and TMIT, while a new conductive phase with high orbital entropy is formed by further descending temperature below TR-MIT.

physics.app-ph↗

arXiv2019

Inexact Proximal Cubic Regularized Newton Methods for Convex Optimization

In this paper, we use Proximal Cubic regularized Newton Methods (PCNM) to optimize the sum of a smooth convex function and a non-smooth convex function, where we use inexact gradient and Hessian, and an inexact subsolver for the cubic regularized second-order subproblem. We propose inexact variants of PCNM and accelerated PCNM respectively, and show that both variants can achieve the same convergence rate as in the exact case, provided that the errors in the inexact gradient, Hessian and subsolver decrease at appropriate rates. Meanwhile, in the online stochastic setting where data comes endlessly, we give the overall complexity of the proposed algorithms and show that they are as competitive as the stochastic gradient descent. Moreover, we give the overall complexity of the proposed algorithms in the finite-sum setting and show that it is as competitive as the state of the art variance reduced algorithms. Finally, we propose an efficient algorithm for the cubic regularized second-order subproblem, which can converge to an enough small neighborhood of the optimal solution in a superlinear rate.

math.OC↗

arXiv2019

Fully Implicit Online Learning

Regularized online learning is widely used in machine learning applications. In online learning, performing exact minimization ($i.e.,$ implicit update) is known to be beneficial to the numerical stability and structure of solution. In this paper we study a class of regularized online algorithms without linearizing the loss function or the regularizer, which we call \emph{fully implicit online learning} (FIOL). We show that for arbitrary Bregman divergence, FIOL has the $O(\sqrt{T})$ regret for general convex setting and $O(\log T)$ regret for strongly convex setting, and the regret has an one-step improvement effect because it avoids the approximation error of linearization. Then we propose efficient algorithms to solve the subproblem of FIOL. We show that even if the solution of the subproblem has no closed form, it can be solved with complexity comparable to the linearized online algoritms. Experiments validate the proposed approaches.

cs.LG↗

arXiv2018

Interface-Based Side Channel Attack Against Intel SGX

Intel has introduced a trusted computing technology, Intel Software Guard Extension (SGX), which provides an isolated and secure execution environment called enclave for a user program without trusting any privilege software (e.g., an operating system or a hypervisor) or firmware. Nevertheless, SGX is vulnerable to several side channel attacks (e.g. page-fault-based attack and cache-based attack). In this paper, we explore a new, yet critical side channel attack in SGX, interface-based side channel attack, which can infer the information of the enclave input data. The root cause of the interface-based side channel attack is the input dependent interface invocation information (e.g., interface information and invocation patterns) which can be observed by the untrusted privilege software can reveal the control flow in the enclave. We study the methodology which can be used to conduct the interface-based side channel attack. To illustrate the effectiveness of the interface-based side-channel attacks, we use our methodology to infer whether tracked web pages have been processed by the SGX-assisted NFV platforms and achieve the accuracy of 87.6% and recall of 76.6%. We also identify the packets which belong to the tracked web pages, with the accuracy of 67.9%and recall of 71.1%. We finally propose some countermeasures to defense the interface-based side channel attack in SGX-assisted applications.

cs.CR↗

arXiv2018

Generative Stock Question Answering

We study the problem of stock related question answering (StockQA): automatically generating answers to stock related questions, just like professional stock analysts providing action recommendations to stocks upon user's requests. StockQA is quite different from previous QA tasks since (1) the answers in StockQA are natural language sentences (rather than entities or values) and due to the dynamic nature of StockQA, it is scarcely possible to get reasonable answers in an extractive way from the training data; and (2) StockQA requires properly analyzing the relationship between keywords in QA pair and the numerical features of a stock. We propose to address the problem with a memory-augmented encoder-decoder architecture, and integrate different mechanisms of number understanding and generation, which is a critical component of StockQA. We build a large-scale dataset containing over 180K StockQA instances, based on which various technique combinations are extensively studied and compared. Experimental results show that a hybrid word-character model with separate character components for number processing, achieves the best performance. By analyzing the results, we found that 44.8% of answers generated by our best model still suffer from the generic answer problem, which can be alleviated by a straightforward hybrid retrieval-generation model.

cs.CL↗