arXiv subjects

Yi Liu

Publications and source records attributed to Yi Liu.

At least 721 records · Page 40Linked to original sources

arXiv2019

Large Margin Softmax Loss for Speaker Verification

In neural network based speaker verification, speaker embedding is expected to be discriminative between speakers while the intra-speaker distance should remain small. A variety of loss functions have been proposed to achieve this goal. In this paper, we investigate the large margin softmax loss with different configurations in speaker verification. Ring loss and minimum hyperspherical energy criterion are introduced to further improve the performance. Results on VoxCeleb show that our best system outperforms the baseline approach by 15\% in EER, and by 13\%, 33\% in minDCF08 and minDCF10, respectively.

cs.SD↗

arXiv2019

Gene-based Association Analysis for Bivariate Time-to-event Data through Functional Regression with Copula Models

Several gene-based association tests for time-to-event traits have been proposed recently, to detect whether a gene region (containing multiple variants), as a set, is associated with the survival outcome. However, for bivariate survival outcomes, to the best of our knowledge, there is no statistical method that can be directly applied for gene-based association analysis. Motivated by a genetic study to discover gene regions associated with the progression of a bilateral eye disease, Age-related Macular Degeneration (AMD), we implement a novel functional regression method under the copula framework. Specifically, the effects of variants within a gene region are modeled through a functional linear model, which then contributes to the marginal survival functions within the copula. Generalized score test and likelihood ratio test statistics are derived to test for the association between bivariate survival traits and the genetic region. Extensive simulation studies are conducted to evaluate the type-I error control and power performance of the proposed approach, with comparisons to several existing methods for a single survival trait, as well as the marginal Cox functional regression model using the robust sandwich estimator for bivariate survival traits. Finally, we apply our method to a large AMD study, the Age-related Eye Disease Study (AREDS), to identify gene regions that are associated with AMD progression.

stat.AP↗

arXiv2019

A State-Failure--Network Method to Identify Critical Components in Power Systems

In order to mitigate cascading failure blackout risks in power systems, the critical components whose failures lead to high blackout risks should be identified. In this paper, such critical components are identified by the state-failure network (SF-network) formed by cascading failure chain and loss data, which can be gathered from either utilities or simulations. The failures along the chains are recombined in the SF-network, where each failure is allocated a value that can reveal the blackout risks after their occurrences. Thus, critical failures can be identified in the SF-network where the failures raise up blackout risks, and thus the critical components can be found based on their critical failure risks. The simulation results validate the effectiveness of the proposed method.

physics.soc-ph↗

arXiv2019

Tunable Electronic Structure and Surface States in Rare Earth Mono-Bismuthides with Partially Filled f Shell

Here we report the evolution of bulk band structure and surface states in rare earth mono-bismuthides with partially filled f shell. Utilizing synchrotron-based photoemission spectroscopy, we determined the three-dimensional bulk band structure and identified the bulk band inversions near the X points, which, according to the topological theory, could give rise to nontrivial band topology with odd number of gapless topological surface states. Near the surface Gamma bar point, no clear evidence for predicted gapless topological surface state is observed due to its strong hybridization with the bulk bands. Near the M bar point, the two surface states, due to projections from two inequivalent bulk band inversions, interact and give rise to two peculiar sets of gapped surface states. The bulk band inversions and corresponding surface states can be tuned substantially by varying rare earth elements, in good agreement with density-functional theory calculations assuming local f electrons. Our study therefore establishes rare earth mono-bismuthides as an interesting class of materials possessing tunable electronic properties and magnetism, providing a promising platform to search for novel properties in potentially correlated topological materials.

cond-mat.mes-hall↗

arXiv2019

Probing the Origin of Extreme Magnetoresistance in Pr/Sm Mono-Antimonides/Bismuthides

Combining angle-resolved photoemission spectroscopy and magneto-transport measurements, we systematically investigated the possible origin of the extreme magnetoresistance in Pr/Sm mono-antimonides/bismuthides (PrSb, SmSb, PrBi, SmBi). Our photoemission measurements reveal that the bulk band inversion and surface states are absent (present) in Pr/Sm antimonides (bismuthides), implying that topological surface states are unlikely to play an important role for the observed extreme magnetoresistance. We found that the electron-hole compensation is well satisfied in all these compounds and the bulk band structure exhibits no obvious temperature dependence from 10 K up to 150 K. Simultaneous fittings of the magnetoresistance and Hall coefficient reveal that the carrier mobility is dramatically enhanced at low temperature, which naturally explains the suppression of extreme magnetoresistance at high temperatures. Our results therefore show that the extreme magnetoresistance in these compounds can be well accounted for by the two-band model with good electron-hole compensation. Finally, we found that both PrSb and SmSb exhibit highly linear bulk bands near the X point and lie close to the transition point between a topologically trivial and nontrivial phase, which might be relevant for the observed anomalous quantum oscillations.

cond-mat.mes-hall↗

arXiv2019

Two-dimensional honeycomb borophene oxide: Strong anisotropy and nodal loop transformation

The search for topological semimetals is mainly focused on heavy-element compounds as following the footsteps of previous research on topological insulators, with less attention on light-element materials. However, the negligible spin orbit coupling with light elements may turn out to be beneficial for realizing topological band features.Here, using first-principles calculations, we propose a new two-dimensional light-element material-the honeycomb borophene oxide (h-B2O), which has nontrivial topological properties.The proposed structure is based on the recently synthesized honeycomb borophene on Al (111) substrate [Sci. Bull. 63, 282 (2018)]. The h-B2O monolayer is completely flat, unlike the oxides of graphene or silicene. We systematically investigate the structural properties of h-B2O, and find that it has very good stability and exhibits significant mechanical anisotropy. Interestingly, the electronic band structure of h-B2O hosts a nodal loop centered around the Y point in the Brillouin zone, protected by the mirror symmetry. Furthermore, under moderate lattice strain, the single nodal loop can be transformed into two loops, each penetrating through the Brillouin zone. The loops before and after the transition are characterized by different Z*Z topological indices. Our work not only predicts a new two-dimensional material with interesting physical properties, but also offers an alternative approach to search for new topological phases in 2D light-element systems.

cond-mat.mtrl-sci↗

arXiv2019

Quantum coherence across bosonic superconductor-anomalous metal-insulator transitions

After decades of explorations, suffering from low critical temperature and subtle nature, whether a metallic ground state exists in a two-dimensional system beyond Anderson localization is still a mystery. Supremely, phase coherence could be the key that unlocks its intriguing nature. This work reveals how quantum phase coherence evolves across bosonic superconductor-metal-insulator transitions via magneto-conductance quantum oscillations in high-Tc superconducting films. A robust intervening anomalous metallic state characterized by both resistance and oscillation amplitude saturations in the low temperature regime is detected. By contrast, with decreasing temperature the oscillation amplitude monotonically grows on the superconducting side, but decreases at low temperatures on the insulating side. It suggests that the saturation of phase coherence plays a prominent role in the formation of this anomalous metallic state.

cond-mat.supr-con↗

arXiv2019

Big Data Platform Architecture Under The Background of Financial Technology

With the rise of the concept of financial technology, financial and technology gradually in-depth integration, scientific and technological means to become financial product innovation, improve financial efficiency and reduce financial transaction costs an important driving force. In this context, the new technology platform is from the business philosophy, business model, technical means, sales, internal management, and other dimensions to re-shape the financial industry. In this paper, the existing big data platform architecture technology innovation, adding space-time data elements, combined with the insurance industry for practical analysis, put forward a meaningful product circle and customer circle.

cs.CY↗

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↗