arXiv subjects

Jun Zhao

Publications and source records attributed to Jun Zhao.

At least 523 records · Page 29Linked to original sources

arXiv2018

Intertwined Spin and Orbital Density Waves in MnP Uncovered by Resonant Soft X-ray Scattering

Unconventional superconductors are often characterized by numerous competing and even intertwined orders in their phase diagrams. In particular, the electronic nematic phases, which spontaneously break rotational symmetry and often simultaneously involve spin, charge and/or orbital orders, appear conspicuously in both the cuprate and iron-based superconductors. The fluctuations associated with these phases may provide the exotic pairing glue that underlies their high-temperature superconductivity. Helimagnet MnP, the first Mn-based superconductor under pressure, lacks high rotational symmetry. However our resonant soft X-ray scattering (RSXS) experiment discovers novel helical orbital density wave (ODW) orders in this three-dimensional, low-symmetry system, and reveals intertwined ordering phenomena in unprecedented detail. In particular, a ODW forms with half the period of the spin order and fully develops slightly above the spin ordering temperature, their domains develop simultaneously, yet the spin order domains are larger than those of the ODW, and they cooperatively produce another ODW with 1/3 the period of the spin order. These observations provide a comprehensive picture of the intricate interplay between spin and orbital orders in correlated materials, and they suggest that nematic-like physics ubiquitously exists beyond two-dimensional and high-symmetry systems, and the superconducting mechanism of MnP is likely analogous to those of cuprate and iron-based superconductors.

cond-mat.supr-con↗

arXiv2018

Coexistence of ferromagnetic and stripe-type antiferromagnetic spin fluctuations in YFe$_2$Ge$_2$

We report neutron scattering measurements of single-crystalline YFe$_2$Ge$_2$ in the normal state, which has the same crystal structure to the 122 family of iron pnictide superconductors. YFe$_2$Ge$_2$ does not exhibit long range magnetic order, but exhibits strong spin fluctuations. Like the iron pnictides, YFe$_2$Ge$_2$ displays anisotropic stripe-type antiferromagnetic spin fluctuations at ($π$, $0$, $π$). More interesting, however, is the observation of strong spin fluctuations at the in-plane ferromagnetic wavevector ($0$, $0$, $π$). These ferromagnetic spin fluctuations are isotropic in the ($H$, $K$) plane, whose intensity exceeds that of stripe spin fluctuations. Both the ferromagnetic and stripe spin fluctuations remain gapless down to the lowest measured energies. Our results naturally explain the absence of magnetic order in YFe$_2$Ge$_2$ and also imply that the ferromagnetic correlations may be a key ingredient for iron-based materials.

cond-mat.supr-con↗

arXiv2018

Conditional Tail-Related Risk Estimation Using Composite Asymmetric Least Squares and Empirical Likelihood

In this article, by using composite asymmetric least squares (CALS) and empirical likelihood, we propose a two-step procedure to estimate the conditional value at risk (VaR) and conditional expected shortfall (ES) for the GARCH series. First, we perform asymmetric least square regressions at several significance levels to model the volatility structure and separate it from the innovation process in the GARCH model. Note that expectile can serve as a bond to make up the gap from VaR estimation to ES estimation because there exists a bijective mapping from expectiles to specific quantile, and ES can be induced by expectile through a simple formula. Then, we introduce the empirical likelihood method to determine the relation above; this method is data-driven and distribution-free. Theoretical studies guarantee the asymptotic properties, such as consistency and the asymptotic normal distribution of the estimator obtained by our proposed method. A Monte Carlo experiment and an empirical application are conducted to evaluate the performance of the proposed method. The results indicate that our proposed estimation method is competitive with some alternative existing tail-related risk estimation methods.

math.ST↗

arXiv2018

End-to-End Neural Ranking for eCommerce Product Search: an application of task models and textual embeddings

We consider the problem of retrieving and ranking items in an eCommerce catalog, often called SKUs, in order of relevance to a user-issued query. The input data for the ranking are the texts of the queries and textual fields of the SKUs indexed in the catalog. We review the ways in which this problem both resembles and differs from the problems of IR in the context of web search. The differences between the product-search problem and the IR problem of web search necessitate a different approach in terms of both models and datasets. We first review the recent state-of-the-art models for web search IR, distinguishing between two distinct types of model which we call the distributed type and the local-interaction type. The different types of relevance models developed for IR have complementary advantages and disadvantages when applied to eCommerce product search. Further, we explain why the conventional methods for dataset construction employed in the IR literature fail to produce data which suffices for training or evaluation of models for eCommerce product search. We explain how our own approach, applying task modeling techniques to the click-through logs of an eCommerce site, enables the construction of a large-scale dataset for training and robust benchmarking of relevance models. Our experiments consist of applying several of the models from the IR literature to our own dataset. Empirically, we have established that, when applied to our dataset, certain models of local-interaction type reduce ranking errors by one-third compared to the baseline tf-idf. Applied to our dataset, the distributed models fail to outperform the baseline. As a basis for a deployed system, the distributed models have several advantages, computationally, over the local-interaction models. This motivates an ongoing program of work, which we outline at the conclusion of the paper.

cs.IR↗

arXiv2018

TeV SUSY dark matter confronted with the current direct and indirect detection data

In the minimal supersymmetric standard model (MSSM) the lightest superparticle (LSP) can be a TeV neutralino (mainly dominated by higgsino or wino) which serves as a dark matter candidate with correct thermal relic density. In this work we confront the 1-2 TeV neutralino dark matter with the latest direct and indirect detections from PandaX and AMS-02/DAMPE. Considering various scenarios with decoupled sfermions, with \textit{A}-mediated annihilation, with squark or stop coannihilation, we find that the parameter space is stringently constrained by the direct detection limits. In the allowed parameter space, the TeV neutralino dark matter annihilation contribution to the anti-proton flux is found to agree with the AMS-02 data while its contribution to eletron/positron flux is too small to cause any visible excess. The current survived parameter space can be mostly covered by the future direct detection experiment LZ7.2T.

hep-ph↗

arXiv2018

Loss Rank Mining: A General Hard Example Mining Method for Real-time Detectors

Modern object detectors usually suffer from low accuracy issues, as foregrounds always drown in tons of backgrounds and become hard examples during training. Compared with those proposal-based ones, real-time detectors are in far more serious trouble since they renounce the use of region-proposing stage which is used to filter a majority of backgrounds for achieving real-time rates. Though foregrounds as hard examples are in urgent need of being mined from tons of backgrounds, a considerable number of state-of-the-art real-time detectors, like YOLO series, have yet to profit from existing hard example mining methods, as using these methods need detectors fit series of prerequisites. In this paper, we propose a general hard example mining method named Loss Rank Mining (LRM) to fill the gap. LRM is a general method for real-time detectors, as it utilizes the final feature map which exists in all real-time detectors to mine hard examples. By using LRM, some elements representing easy examples in final feature map are filtered and detectors are forced to concentrate on hard examples during training. Extensive experiments validate the effectiveness of our method. With our method, the improvements of YOLOv2 detector on auto-driving related dataset KITTI and more general dataset PASCAL VOC are over 5% and 2% mAP, respectively. In addition, LRM is the first hard example mining strategy which could fit YOLOv2 perfectly and make it better applied in series of real scenarios where both real-time rates and accurate detection are strongly demanded.

cs.CV↗

arXiv2018

Deep Reinforcement Learning for Sponsored Search Real-time Bidding

Bidding optimization is one of the most critical problems in online advertising. Sponsored search (SS) auction, due to the randomness of user query behavior and platform nature, usually adopts keyword-level bidding strategies. In contrast, the display advertising (DA), as a relatively simpler scenario for auction, has taken advantage of real-time bidding (RTB) to boost the performance for advertisers. In this paper, we consider the RTB problem in sponsored search auction, named SS-RTB. SS-RTB has a much more complex dynamic environment, due to stochastic user query behavior and more complex bidding policies based on multiple keywords of an ad. Most previous methods for DA cannot be applied. We propose a reinforcement learning (RL) solution for handling the complex dynamic environment. Although some RL methods have been proposed for online advertising, they all fail to address the "environment changing" problem: the state transition probabilities vary between two days. Motivated by the observation that auction sequences of two days share similar transition patterns at a proper aggregation level, we formulate a robust MDP model at hour-aggregation level of the auction data and propose a control-by-model framework for SS-RTB. Rather than generating bid prices directly, we decide a bidding model for impressions of each hour and perform real-time bidding accordingly. We also extend the method to handle the multi-agent problem. We deployed the SS-RTB system in the e-commerce search auction platform of Alibaba. Empirical experiments of offline evaluation and online A/B test demonstrate the effectiveness of our method.

cs.AI↗

arXiv2018

Measuring third party tracker power across web and mobile

Third-party networks collect vast amounts of data about users via web sites and mobile applications. Consolidations among tracker companies can significantly increase their individual tracking capabilities, prompting scrutiny by competition regulators. Traditional measures of market share, based on revenue or sales, fail to represent the tracking capability of a tracker, especially if it spans both web and mobile. This paper proposes a new approach to measure the concentration of tracking capability, based on the reach of a tracker on popular websites and apps. Our results reveal that tracker prominence and parent-subsidiary relationships have significant impact on accurately measuring concentration.

cs.CY↗

arXiv2018

'It's Reducing a Human Being to a Percentage'; Perceptions of Justice in Algorithmic Decisions

Data-driven decision-making consequential to individuals raises important questions of accountability and justice. Indeed, European law provides individuals limited rights to 'meaningful information about the logic' behind significant, autonomous decisions such as loan approvals, insurance quotes, and CV filtering. We undertake three experimental studies examining people's perceptions of justice in algorithmic decision-making under different scenarios and explanation styles. Dimensions of justice previously observed in response to human decision-making appear similarly engaged in response to algorithmic decisions. Qualitative analysis identified several concerns and heuristics involved in justice perceptions including arbitrariness, generalisation, and (in)dignity. Quantitative analysis indicates that explanation styles primarily matter to justice perceptions only when subjects are exposed to multiple different styles---under repeated exposure of one style, scenario effects obscure any explanation effects. Our results suggests there may be no 'best' approach to explaining algorithmic decisions, and that reflection on their automated nature both implicates and mitigates justice dimensions.

cs.HC↗

arXiv2018

Probing GeV-scale MSSM neutralino dark matter in collider and direct detection experiments

Given the recent constraints from the dark matter (DM) direct detections, we examine a light GeV-scale (2-30 GeV) neutralino DM in the alignment limit of the Minimal Supersymmetric Standard Model (MSSM). In this limit without decoupling, the heavy CP-even scalar $H$ plays the role of the Standard Model (SM) Higgs boson while the other scalar $h$ can be rather light so that the DM can annihilate through the $h$ resonance or into a pair of $h$ to achieve the observed relic density. With the current collider and cosmological constraints, we find that such a light neutralino DM above 6 GeV can be excluded by the XENON-1T (2017) limits while the survivied parameter space below 6 GeV can be fully covered by the future germanium-based light dark matter detections (such as CDEX), by the Higgs coupling precison measurements or by the production process $e^+e^- \to hA$ at an electron-positron collider (Higgs factory).

hep-ph↗

arXiv2018

Removal of Batch Effects using Distribution-Matching Residual Networks

Sources of variability in experimentally derived data include measurement error in addition to the physical phenomena of interest. This measurement error is a combination of systematic components, originating from the measuring instrument, and random measurement errors. Several novel biological technologies, such as mass cytometry and single-cell RNA-seq, are plagued with systematic errors that may severely affect statistical analysis if the data is not properly calibrated. We propose a novel deep learning approach for removing systematic batch effects. Our method is based on a residual network, trained to minimize the Maximum Mean Discrepancy (MMD) between the multivariate distributions of two replicates, measured in different batches. We apply our method to mass cytometry and single-cell RNA-seq datasets, and demonstrate that it effectively attenuates batch effects.

stat.ML↗

arXiv2017

The Higgs properties in the MSSM after the LHC Run-2

We scrutinize the parameter space of the SM-like Higgs boson in the minimal supersymmetric standard model (MSSM) under current experimental constraints. The constraints are from (i) the precison electroweak data and various flavor observables; (ii) the direct 22 separate ATLAS searches in Run-1; (iii) the latest LHC Run-2 Higgs data and tri-lepton search of electroweakinos. We perform a scan over the parameter space and find that the Run-2 data can further exclude a part of parameter space. For the property of the SM-like Higgs boson, its gauge couplings further approach to the SM values with a deviation below 0.1\%, while its Yukawa couplings $hb\bar{b}$ and $hτ\barτ$ can still sizably differ from the SM predictions by several tens percent.

hep-ph↗

arXiv2017

Transitional Behavior of q-Composite Random Key Graphs with Applications to Networked Control

Random key graphs have received considerable attention and been used in various applications including secure sensor networks, social networks, the study of epidemics, cryptanalysis, and recommender systems. In this paper, we investigate a $q$-composite random key graph, whose construction on $n$ nodes is as follows: each node independently selects a set of $K_n$ different keys uniformly at random from the same pool of $P_n$ distinct keys, and two nodes establish an undirected edge in between if and only if they share at least $q$ key(s). Such graph denoted by $G_q(n,K_n,P_n)$ models a secure sensor network employing the well-known $q$-composite key predistribution. For $G_q(n,K_n,P_n)$, we analyze the probabilities of $G_q(n,K_n,P_n)$ having $k$-connectivity, $k$-robustness, a Hamilton cycle and a perfect matching, respectively. Our studies of these four properties are motivated by a detailed discussion of their applications to networked control. Our results reveal that $G_q(n,K_n,P_n)$ exhibits a sharp transition for each property: as $K_n$ increases, the probability that $G_q(n,K_n,P_n)$ has the property sharply increases from $0$ to $1$. These results provide fundamental guidelines to design secure sensor networks for different control-related applications: distributed in-network parameter estimation, fault-tolerant consensus, and resilient data backup.

cs.IT↗

arXiv2017

Evidence for a spinon Fermi surface in a triangular lattice quantum spin liquid candidate

A quantum spin liquid is an exotic quantum state of matter in which spins are highly entangled and remain disordered down to zero temperature. Such a state of matter is potentially relevant to high-temperature superconductivity and quantum-information applications, and experimental identification of a quantum spin liquid state is of fundamental importance for our understanding of quantum matter. Theoretical studies have proposed various quantum-spin-liquid ground states, most of which are characterized by exotic spin excitations with fractional quantum numbers (termed `spinon'). Here, we report neutron scattering measurements that reveal broad spin excitations covering a wide region of the Brillouin zone in a triangular antiferromagnet YbMgGaO4. The observed diffusive spin excitation persists at the lowest measured energy and shows a clear upper excitation edge, which is consistent with the particle-hole excitation of a spinon Fermi surface. Our results therefore point to a QSL state with a spinon Fermi surface in YbMgGaO4 that has a perfect spin-1/2 triangular lattice as in the original proposal of quantum spin liquids.

cond-mat.str-el↗

arXiv2017

Structure of spin excitations in heavily electron-doped Li0.8Fe0.2ODFeSe superconductors

Heavily electron-doped iron-selenide (HEDIS) high-transition-temperature (high-$T_{\rm{c}}$) superconductors, which have no hole Fermi pockets, but have a notably high $T_{\rm{c}}$, have challenged the prevailing $s$$_\pm$ pairing scenario originally proposed for iron pnictides containing both electron and hole pockets. The microscopic mechanism underlying the enhanced superconductivity in HEDIS remains unclear. Here, we used neutron scattering to study the spin excitations of the HEDIS material Li$_{0.8}$Fe$_{0.2}$ODFeSe ($T_{\rm{c}}$ = 41 K). Our data revealed nearly ring-shaped magnetic resonant excitations surrounding ($π$, $π$) at $\sim$ 21 meV. As the energy increased, the spin excitations assumed a diamond shape, and they dispersed outward until the energy reached $\sim$ 60 meV and then inward at higher energies. The observed energy-dependent momentum structure and twisted dispersion of spin excitations near ($π$, $π$) are analogous to those of hole-doped cuprates in several aspects, thus implying that such spin excitations are essential for the remarkably high $T_{\rm{c}}$ in these materials.

cond-mat.supr-con↗

arXiv2017

Deformations and generalized derivations of Hom-Lie conformal algebras

The purpose of this paper is to extend the cohomology and conformal derivation theories of the classical Lie conformal algebras to Hom-Lie conformal algebras. In this paper, we develop cohomology theory of Hom-Lie conformal algebras and discuss some applications to the study of deformations of regular Hom-Lie conformal algebras. Also, we introduce $α^k$-derivations of multiplicative Hom-Lie conformal algebras and study their properties.

math.RA↗

arXiv2017

Self-Taught Convolutional Neural Networks for Short Text Clustering

Short text clustering is a challenging problem due to its sparseness of text representation. Here we propose a flexible Self-Taught Convolutional neural network framework for Short Text Clustering (dubbed STC^2), which can flexibly and successfully incorporate more useful semantic features and learn non-biased deep text representation in an unsupervised manner. In our framework, the original raw text features are firstly embedded into compact binary codes by using one existing unsupervised dimensionality reduction methods. Then, word embeddings are explored and fed into convolutional neural networks to learn deep feature representations, meanwhile the output units are used to fit the pre-trained binary codes in the training process. Finally, we get the optimal clusters by employing K-means to cluster the learned representations. Extensive experimental results demonstrate that the proposed framework is effective, flexible and outperform several popular clustering methods when tested on three public short text datasets.

cs.IR↗

arXiv2016

Deformations and generalized derivations of Lie conformal superalgebras

The purpose of this paper is to extend the cohomology and conformal derivation theories of the classical Lie conformal algebras to Lie conformal superalgebras. Firstly, we construct the semidirect product of a Lie conformal superalgebra and its conformal module, and study derivations of this semidirect product. Secondly, we develop cohomology theory of Lie conformal superalgebras and discuss some applications to the study of deformations of Lie conformal superalgebras. Finally, we introduce generalized derivations of Lie conformal superalgebras and study their properties.

math.RA↗