arXiv subjects

Yi Liu

Publications and source records attributed to Yi Liu.

At least 739 records · Page 41Linked to original sources

arXiv2019

Kondo Impurities in Two Dimensional MoS2 for Achieving Ultrahigh Thermoelectric Powerfactor

Local magnetic impurities arising from atomic vacancies in two-dimensional (2D) nanosheets are predicted to have a profound effect on charge transport due to resonant scattering, and provide a handle for enhancing thermoelectric properties through the Kondo effect. However, the effects of these impurities are often masked by external fluctuations and turbostratic interfaces, therefore, it is highly challenging to probe the correlation between magnetic impurities and thermoelectric parameters experimentally. In this work, we demonstrate that by placing Molybdenum Disulfide on a hexagonal Boron Nitride substrate, a colossal spin splitting of the conduction sub-band up to ~50.0 meV is observed at the sulfur vacancies, suggesting that these are local magnetic states. Transport measurements reveal a large anomalous positive Seebeck coefficient in highly conducting n type MoS2, originating from quasiparticle resonance near the Fermi level described by the Kondo effect. Furthermore, by tuning the chemical potential, a record power factor of 50mW/mK2 in low-dimensional materials was achieved. Our work shows that defect engineering of 2D materials affords a strategy for controlling Kondo impurities and tuning thermoelectric transport.

cond-mat.mes-hall↗

arXiv2018

Estimation of Inter-Sentiment Correlations Employing Deep Neural Network Models

This paper focuses on sentiment mining and sentiment correlation analysis of web events. Although neural network models have contributed a lot to mining text information, little attention is paid to analysis of the inter-sentiment correlations. This paper fills the gap between sentiment calculation and inter-sentiment correlations. In this paper, the social emotion is divided into six categories: love, joy, anger, sadness, fear, and surprise. Two deep neural network models are presented for sentiment calculation. Three datasets - the titles, the bodies, the comments of news articles - are collected, covering both objective and subjective texts in varying lengths (long and short). From each dataset, three kinds of features are extracted: explicit expression, implicit expression, and alphabet characters. The performance of the two models are analyzed, with respect to each of the three kinds of the features. There is controversial phenomenon on the interpretation of anger (fn) and love (gd). In subjective text, other emotions are easily to be considered as anger. By contrast, in objective news bodies and titles, it is easy to regard text as caused love (gd). It means, journalist may want to arouse emotion love by writing news, but cause anger after the news is published. This result reflects the sentiment complexity and unpredictability.

cs.CL↗

arXiv2018

Supercondutivity in SnSb with natural superlattice structure

We report the results of electrical resistivity, magnetic and thermodynamic measurements on polycrystalline SnSb, whose structure consists of stacks of Sb bilayers and Sn4Sb3 septuple layers along the c-axis. The material is found to be a weakly coupled, fully gapped, type-II superconductor with a bulk Tc of 1.50 K, while showing a zero resistivity transition at a significantly higher temperature of 2.48 K. The Sommerfeld coefficient and upper critical field, obtained from specific heat measurements, are 2.29 mJ/mol K and 520 Oe, respectively. Compositional inhomogeneity and strain effect at the grain boundaries are proposed as possible origins for the difference in resistive and bulk superconducting transitions.In addition, a comparison with the rock-salt structure SnAs superconductor is presented. Our results provide the first clear evidence of bulk superconductivity in a natural superlattice derived from a topological semimetal.

cond-mat.supr-con↗

arXiv2018

Giant anisotropy in superconducting single crystals of CsCa$_2$Fe$_4$As$_4$F$_2$

CsCa$_2$Fe$_4$As$_4$F$_2$ is a newly discovered iron-based superconductor with $T_\mathrm{c}\sim$ 30 K containing double Fe$_2$As$_2$ layers that are separated by insulating Ca$_2$F$_2$ spacer layers. Here we report the transport and magnetization measurements on CsCa$_2$Fe$_4$As$_4$F$_2$ single crystals grown for the first time using the self flux of CsAs. We observed a huge resistivity anisotropy $ρ_c(T)/ρ_{ab}(T)$, which increases with decreasing temperature, from 750 at 300 K to 3150 at 32 K. The $ρ_c(T)$ data exhibit a non-metallic behavior above $\sim$140 K, suggesting an incoherent electronic state at high temperatures due to the dimension crossover. The superconducting onset transition temperature in $ρ_{ab}$ is 0.7 K higher than that in $ρ_c$, suggesting two-dimensional (2D) superconducting fluctuations. The lower and upper critical fields also show an exceptional anisotropy among iron-based superconductors. The $H_{c1}^\bot(T)$ data are well fitted using the model with two $s$-wave-like superconducting gaps, $Δ_1(0)=6.75$ meV and $Δ_2(0)=2.32$ meV. The inter-plane coherence length $ξ_c(0)$ is $3.6$ Å, remarkably smaller than the distance between conducting layers (8.6 Å), consolidating the 2D nature in the title material.

cond-mat.supr-con↗

arXiv2018

DSNet: Deep and Shallow Feature Learning for Efficient Visual Tracking

In recent years, Discriminative Correlation Filter (DCF) based tracking methods have achieved great success in visual tracking. However, the multi-resolution convolutional feature maps trained from other tasks like image classification, cannot be naturally used in the conventional DCF formulation. Furthermore, these high-dimensional feature maps significantly increase the tracking complexity and thus limit the tracking speed. In this paper, we present a deep and shallow feature learning network, namely DSNet, to learn the multi-level same-resolution compressed (MSC) features for efficient online tracking, in an end-to-end offline manner. Specifically, the proposed DSNet compresses multi-level convolutional features to uniform spatial resolution features. The learned MSC features effectively encode both appearance and semantic information of objects in the same-resolution feature maps, thus enabling an elegant combination of the MSC features with any DCF-based methods. Additionally, a channel reliability measurement (CRM) method is presented to further refine the learned MSC features. We demonstrate the effectiveness of the MSC features learned from the proposed DSNet on two DCF tracking frameworks: the basic DCF framework and the continuous convolution operator framework. Extensive experiments show that the learned MSC features have the appealing advantage of allowing the equipped DCF-based tracking methods to perform favorably against the state-of-the-art methods while running at high frame rates.

cs.CV↗

arXiv2018

An Efficient Bandit Algorithm for Realtime Multivariate Optimization

Optimization is commonly employed to determine the content of web pages, such as to maximize conversions on landing pages or click-through rates on search engine result pages. Often the layout of these pages can be decoupled into several separate decisions. For example, the composition of a landing page may involve deciding which image to show, which wording to use, what color background to display, etc. Such optimization is a combinatorial problem over an exponentially large decision space. Randomized experiments do not scale well to this setting, and therefore, in practice, one is typically limited to optimizing a single aspect of a web page at a time. This represents a missed opportunity in both the speed of experimentation and the exploitation of possible interactions between layout decisions. Here we focus on multivariate optimization of interactive web pages. We formulate an approach where the possible interactions between different components of the page are modeled explicitly. We apply bandit methodology to explore the layout space efficiently and use hill-climbing to select optimal content in realtime. Our algorithm also extends to contextualization and personalization of layout selection. Simulation results show the suitability of our approach to large decision spaces with strong interactions between content. We further apply our algorithm to optimize a message that promotes adoption of an Amazon service. After only a single week of online optimization, we saw a 21% conversion increase compared to the median layout. Our technique is currently being deployed to optimize content across several locations at Amazon.com.

cs.LG↗

arXiv2018

Anomalous quantum Griffiths singularity in ultrathin crystalline lead films

We study the magnetic field induced superconductor-metal transition (SMT) in ultrathin crystalline Pb films. By performing ultralow temperature transport measurement, the divergent critical exponent as an indication of quantum Griffiths singularity (QGS) is observed when approaching zero temperature quantum critical point. Distinctively, the phase boundary of SMT exhibits an anomalous behavior in low temperature regime that the onset critical field decreases with decreasing temperatures, which distinguishes our observation from earlier reports of QGS in various two-dimensional superconductors. We demonstrate that this observed anomalous phase boundary has its origin from the superconducting fluctuations in ultrathin Pb films. Our findings reveal a novel aspect of the QGS of SMT in two-dimentional superconducting systems with anomalous phase boundary.

cond-mat.supr-con↗

arXiv2018

Investigation of Frame Alignments for GMM-based Digit-prompted Speaker Verification

Frame alignments can be computed by different methods in GMM-based speaker verification. By incorporating a phonetic Gaussian mixture model (PGMM), we are able to compare the performance using alignments extracted from the deep neural networks (DNN) and the conventional hidden Markov model (HMM) in digit-prompted speaker verification. Based on the different characteristics of these two alignments, we present a novel content verification method to improve the system security without much computational overhead. Our experiments on the RSR2015 Part-3 digit-prompted task show that, the DNN based alignment performs on par with the HMM alignment. The results also demonstrate the effectiveness of the proposed Kullback-Leibler (KL) divergence based scoring to reject speech with incorrect pass-phrases.

cs.SD↗

arXiv2018

Probing physical origin of anisotropic thermal transport in black phosphorus nanoribbons

Black phosphorus (BP) has emerged as a promising candidate for next generation electronics and optoelectronics among the 2D family materials due to its extraordinary electrical/optical/optoelectronic properties. Interestingly, BP shows strong anisotropic transport behaviour because of its puckered honeycomb structure. Previous studies have demonstrated the thermal transport anisotropy of BP and theoretically attribute this to the anisotropy in both phonon dispersion relation and phonon relaxation time. However, the exact origin of such strong anisotropy lacks clarity and has yet to be proven experimentally. In this work, we probe the thermal transport anisotropy of BP nanoribbons (NRs) by an electron beam technique. We provide direct evidence that the origin of this anisotropy is dominated by the anisotropic phonon group velocity for the first time, verified by Young modulus measurements along different directions. It turns out that the ratio of thermal conductivity between zigzag (ZZ) and armchair (AC) ribbons is almost same as that of the corresponding Young modulus values. The results from first-principles calculation are consistent with this experimental observation, where anisotropic phonon group velocity between ZZ and AC is shown. Our results provide fundamental insight into the anisotropic thermal transport in low symmetric crystals.

cond-mat.mtrl-sci↗

arXiv2018

Multi-Modal Coreference Resolution with the Correlation between Space Structures

Multi-modal data is becoming more common in big data background. Finding the semantically similar objects from different modality is one of the heart problems of multi-modal learning. Most of the current methods try to learn the inter-modal correlation with extrinsic supervised information, while intrinsic structural information of each modality is neglected. The performance of these methods heavily depends on the richness of training samples. However, obtaining the multi-modal training samples is still a labor and cost intensive work. In this paper, we bring a extrinsic correlation between the space structures of each modalities in coreference resolution. With this correlation, a semi-supervised learning model for multi-modal coreference resolution is proposed. We firstly extract high-level features of images and text, then compute the distances of each object from some reference points to build the space structure of each modality. With a shared reference point set, the space structures of each modality are correlated. We employ the correlation to build a commonly shared space that the semantic distance between multi-modal objects can be computed directly. The experiments on two multi-modal datasets show that our model performs better than the existing methods with insufficient training data.

cs.AI↗

arXiv2018

Magnetoresistance from Fermi Surface Topology

Extremely large non-saturating magnetoresistance has recently been reported for a large number of both topologically trivial and non-trivial materials. Different mechanisms have been proposed to explain the observed magnetotransport properties, yet without arriving to definitive conclusions or portraying a global picture. In this work, we investigate the transverse magnetoresistance of materials by combining the Fermi surfaces calculated from first principles with the Boltzmann transport theory approach relying on the semiclassical model and the relaxation time approximation. We first consider a series of simple model Fermi surfaces to provide a didactic introduction into the charge-carrier compensation and open-orbit mechanisms leading to non-saturating magnetoresistance. We then address in detail magnetotransport in three representative materials: (i) copper, a prototypical nearly free-electron metal characterized by the open Fermi surface that results in an intricate angular magnetoresistance, (ii) bismuth, a topologically trivial semimetal in which very large magnetoresistance is known to result from charge-carrier compensation, and (iii) tungsten diphosphide WP2, a recently discovered type-II Weyl semimetal that holds the record of magnetoresistance in compounds. In all three cases our calculations show excellent agreement with both the field dependence of magnetoresistance and its anisotropy measured at low temperatures. Furthermore, the calculations allow for a full interpretation of the observed features in terms of the Fermi surface topology. These results will help addressing a number of outstanding questions, such as the role of the topological phase in the pronounced large non-saturating magnetoresistance observed in topological materials.

cond-mat.mtrl-sci↗

arXiv2018

Exploring a Unified Attention-Based Pooling Framework for Speaker Verification

The pooling layer is an essential component in the neural network based speaker verification. Most of the current networks in speaker verification use average pooling to derive the utterance-level speaker representations. Average pooling takes every frame as equally important, which is suboptimal since the speaker-discriminant power is different between speech segments. In this paper, we present a unified attention-based pooling framework and combine it with the multi-head attention. Experiments on the Fisher and NIST SRE 2010 dataset show that involving outputs from lower layers to compute the attention weights can outperform average pooling and achieve better results than vanilla attention method. The multi-head attention further improves the performance.

cs.SD↗

arXiv2018

Magnetism and superconductivity in Eu(Fe$_{1-x}$Ni$_{x}$)As$_2$ ($x$ = 0, 0.04)

We report Eu-local-spin magnetism and Ni-doping-induced superconductivity (SC) in a 112-type ferroarsenide system Eu(Fe$_{1-x}$Ni$_{x}$)As$_2$. The non-doped EuFeAs$_2$ exhibits two primary magnetic transitions at $\sim$100 and $\sim$ 40 K, probably associated with a spin-density-wave (SDW) transition and an antiferromagnetic ordering in the Fe and Eu sublattices, respectively. Two additional successive transitions possibly related to Eu-spin modulations appear at 15.5 and 6.5 K. For the Ni-doped sample with $x$ = 0.04, the SDW transition disappears, and SC emerges at $T_\mathrm{c}$ = 17.5 K. The Eu-spin ordering remains at around 40 K, followed by the possible reentrant magnetic modulations with enhanced spin canting. Consequently, SC coexists with a weak spontaneous magnetization below 6.2 K in Eu(Fe$_{0.96}$Ni$_{0.04}$)As$_2$, which provides a complementary playground for the study of the interplay between SC and magnetism.

cond-mat.supr-con↗

arXiv2018

Social media and mobility landscape: uncovering spatial patterns of urban human mobility with multi source data

In this paper, we present a three-step methodological framework, including location identification, bias modification, and out-of-sample validation, so as to promote human mobility analysis with social media data. More specifically, we propose ways of identifying personal activity-specific places and commuting patterns in Beijing, China, based on Weibo (China's Twitter) check-in records, as well as modifying sample bias of check-in data with population synthesis technique. An independent citywide travel logistic survey is used as the benchmark for validating the results. Obvious differences are discerned from Weibo users' and survey respondents' activity-mobility patterns, while there is a large variation of population representativeness between data from the two sources. After bias modification, the similarity coefficient between commuting distance distributions of Weibo data and survey observations increases substantially from 23% to 63%. Synthetic data proves to be a satisfactory cost-effective alternative source of mobility information. The proposed framework can inform many applications related to human mobility, ranging from transportation, through urban planning to transport emission modelling.

cs.SI↗

arXiv2018

Coherent control of superradiance from nitrogen ions pumped with femtosecond pulses

Singly ionized nitrogen molecules in ambient air pumped by near-infrared femtosecond laser give rise to superradiant emission. Here we demonstrate coherent control of this superradiance by injecting a pair of resonant seeding pulses inside the nitrogen gas plasma. Strong modulation of the 391.4 nm superradiance with a period of 1.3 fs is observed when the delay between the two seeding pulses are finely tuned, pinpointing the essential role of macroscopic coherence in this lasing process. Based on this time-resolved method, the complex temporal evolution of the macroscopic coherence between two involved energy levels has been experimentally revealed, which is found to last for around 10 picoseconds in the low gas pressure range. These observations provide a new level of control on the "air lasing" based on nitrogen ions, which can find potential applications in optical remote sensing.

physics.optics↗

arXiv2018

Superconductivity in a misfit layered compound (SnSe)$_{1.16}$(NbSe$_2$)

The large size single crystals of (SnSe)$_{1.16}$(NbSe$_2$) misfit layered compound were grown and superconductivity with $T_c$ of 3.4 K was first discovered in this system. Powder X-ray diffraction (XRD) and high resolution transmission electron microscopy (HRTEM) clearly display the misfit feature between SnSe and NbSe$_2$ subsystems. The Sommerfeld coefficient $γ$ inferred from specific-heat measurements is 16.73 mJ mol$^{-1}$ K$^{-2}$, slightly larger than the usual misfit compounds. The normalized specific heat jump $Δ$$C_e$/$γ$$T_{\rm c}$ is about 0.98, and the electron-phonon coupling constant $λ$$_{e-ph}$ is estimated to be 0.80. The estimated value of the in-plane upper critical magnetic field, $H_{c2}^{ab}$(0), is about 7.82 T, exceeding the Pauli paramagnetic limit slightly. Both the specific-heat and $H_{c2}$ data suggest that (SnSe)$_{1.16}$(NbSe$_2$) is a multi-band superconductor.

cond-mat.supr-con↗

arXiv2018

Detection of bosonic mode as a signature of magnetic excitation in one unit cell FeSe on SrTiO3

We report an in situ scanning tunneling spectroscopy study of one-unit-cell (1-UC) FeSe film on SrTiO3(001) (STO) substrate. In quasiparticle density of states, bosonic excitation mode characterized by the "dip-hump" structure is detected outside the larger superconducting gap with energy comparable with phonon and spin resonance modes in heavily electron-doped iron selenides. Statistically, the excitation mode, which is intimately correlated with superconductivity, shows an anticorrelation with pairing strength and yields an energy scale upper-bounded by twice the superconducting gap coinciding with the characteristics of magnetic resonance in cuprates and iron-based superconductors. The local response of tunneling spectra to magnetically different Se defects all exhibits the induced in-gap quasiparticle bound states, indicating an unconventional sign-reversing pairing. These results support the magnetic nature of the excitation mode and possibly reveal a signature of electron-magnetic-excitation coupling in high-temperature superconductivity of 1-UC FeSe/STO.

cond-mat.supr-con↗

arXiv2018

A Multi-sentiment-resource Enhanced Attention Network for Sentiment Classification

Deep learning approaches for sentiment classification do not fully exploit sentiment linguistic knowledge. In this paper, we propose a Multi-sentiment-resource Enhanced Attention Network (MEAN) to alleviate the problem by integrating three kinds of sentiment linguistic knowledge (e.g., sentiment lexicon, negation words, intensity words) into the deep neural network via attention mechanisms. By using various types of sentiment resources, MEAN utilizes sentiment-relevant information from different representation subspaces, which makes it more effective to capture the overall semantics of the sentiment, negation and intensity words for sentiment prediction. The experimental results demonstrate that MEAN has robust superiority over strong competitors.

cs.CL↗