arXiv subjects

Jian Liu

Publications and source records attributed to Jian Liu.

At least 415 records · Page 23Linked to original sources

arXiv2019

Spin-Orientation Dependent Topological States in Two-Dimensional Antiferromagnetic NiTl$_2$S$_4$ Monolayers

The topological states of matters arising from the nontrivial magnetic configuration provide a better understanding of physical properties and functionalities of solid materials. Such studies benefit from the active control of spin orientation in any solid, which is yet known to rarely take place in the two-dimensional (2D) limit. Here we demonstrate by the first-principles calculations that spin-orientation dependent topological states can appear in the geometrically frustrated monolayer antiferromagnet. Different topological states including quantum anomalous Hall (QAH) effect and time-reversal-symmetry (TRS) broken quantum spin Hall (QSH) effect can be obtained by changing spin orientation in the NiTl2S4 monolayer. Remarkably, the dilated nc-AFM NiTl2S4 monolayer gives birth to the QAH effect with hitherto reported largest number of quantized conducting channels (Chern number C = -4) in 2D materials. Interestingly, under tunable chemical potential, the nc-AFM NiTl2S4 monolayer hosts a novel state supporting the coexistence of QAH and TRS broken QSH effects with a Chern number C = 3 and spin Chern number C_s = 1. This work manifests a promising concept and material realization toward topological spintronics in 2D antiferromagnets by manipulating its spin degree of freedom.

cond-mat.mes-hall↗

arXiv2019

iQIYI-VID: A Large Dataset for Multi-modal Person Identification

Person identification in the wild is very challenging due to great variation in poses, face quality, clothes, makeup and so on. Traditional research, such as face recognition, person re-identification, and speaker recognition, often focuses on a single modal of information, which is inadequate to handle all the situations in practice. Multi-modal person identification is a more promising way that we can jointly utilize face, head, body, audio features, and so on. In this paper, we introduce iQIYI-VID, the largest video dataset for multi-modal person identification. It is composed of 600K video clips of 5,000 celebrities. These video clips are extracted from 400K hours of online videos of various types, ranging from movies, variety shows, TV series, to news broadcasting. All video clips pass through a careful human annotation process, and the error rate of labels is lower than 0.2\%. We evaluated the state-of-art models of face recognition, person re-identification, and speaker recognition on the iQIYI-VID dataset. Experimental results show that these models are still far from being perfect for the task of person identification in the wild. We proposed a Multi-modal Attention module to fuse multi-modal features that can improve person identification considerably. We have released the dataset online to promote multi-modal person identification research.

cs.CV↗

arXiv2019

Biophysics at the coffee shop: lessons learned working with George Oster

Over the past 50 years, the use of mathematical models, derived from physical reasoning, to describe molecular and cellular systems has evolved from an art of the few to a cornerstone of biological inquiry. George Oster stood out as a pioneer of this paradigm shift from descriptive to quantitative biology not only through his numerous research accomplishments, but also through the many students and postdocs he mentored over his long career. Those of us fortunate enough to have worked with George agree that his sharp intellect, physical intuition and passion for scientific inquiry not only inspired us as scientists but also greatly influenced the way we conduct research. We would like to share a few important lessons we learned from George in honor of his memory and with the hope that they may inspire future generations of scientists.

q-bio.OT↗

arXiv2019

Field-portable quantitative lensless microscopy based on translated speckle illumination and sub-sampled ptychographic phase retrieval

We report a compact, cost-effective and field-portable lensless imaging platform for quantitative microscopy. In this platform, the object is placed on top of an image sensor chip without using any lens. We use a low-cost galvo scanner to rapidly scan an unknown laser speckle pattern on the object. To address the positioning repeatability and accuracy issues, we directly recover the positional shifts of the speckle pattern based on the phase correlation of the captured images. To bypass the resolution limit set by the imager pixel size, we employ a sub-sampled ptychographic phase retrieval process to recover the complex object. We validate our approach using a resolution target, a phase target, and a biological sample. Our results show that accurate, high-quality complex images can be obtained from a lensless dataset with as few as ~10 images. We also demonstrate the reported approach to achieve a 6.4 mm by 4.6 mm field of view and a half pitch resolution of 1 miron. The reported approach may provide a quantitative lensless imaging strategy for addressing point-of-care, global-health, and telemedicine related challenges.

eess.IV↗

arXiv2019

Temperature dependence of normalized sensitivity of Love wave sensor with unidirectional carbon fiber epoxy composite/Mn-doped 0.24PIN-0.46PMN-0.30PT ternary single crystal configuration

We have derived a general formula for sensitivity optimization of gravimetric sensors and use it to design a high precision and high sensitivity gravimetric sensor using unidirectional carbon fiber epoxy composite (CFEC) guiding layer on single crystal Mn-doped yPb(In1/2Nb1/2)O3-(1-x-y)Pb(Mg1/3Nb2/3)O3-xPbTiO3 (Mn: PIN-PMN-PT) piezoelectric substrate. The normalized maximum sensitivity exhibits a decreasing tendency with temperature up to 55 degrees Celsius. For the CFEC-on-Mn: PIN-PMN-PT sensor configuration with wavelength 24 {mu}m at 25 degrees Celsius, the maximum sensitivity can reach as high as 760.88 cm2/g, which is nearly twice that of traditional SiO2/ST quartz configuration gravimetric sensor.

physics.app-ph↗

arXiv2019

Effects of empathy on the evolution of fairness in group-structured populations

The ultimatum game has been a prominent paradigm in studying the evolution of fairness. It predicts that responders should accept any nonzero offer and proposers should offer the smallest possible amount according to orthodox game theory. However, the prediction strongly contradicts with experimental findings where responders usually reject low offers below $20\%$ and proposers usually make higher offers than expected. To explain the evolution of such fair behaviors, we here introduce empathy in group-structured populations by allowing a proportion $α$ of the population to play empathetic strategies. Interestingly, we find that for high mutation probabilities, the mean offer decreases with $α$ and the mean demand increases, implying empathy inhibits the evolution of fairness. For low mutation probabilities, the mean offer and demand approach to the fair ones with increasing $α$, implying empathy promotes the evolution of fairness. Furthermore, under both weak and strong intensities of natural selection, we analytically calculate the mean offer and demand for different levels of $α$. Counterintuitively, we demonstrate that although a higher mutation probability leads to a higher level of fairness under weak selection, an intermediate mutation probability corresponds to the lowest level of fairness under strong selection. Our study provides systematic insights into the evolutionary origin of fairness in group-structured populations with empathetic strategies.

cs.GT↗

arXiv2019

Near-field Fourier ptychography: super-resolution phase retrieval via speckle illumination

Achieving high spatial resolution is the goal of many imaging systems. Designing a high-resolution lens with diffraction-limited performance over a large field of view remains a difficult task in imaging system design. On the other hand, creating a complex speckle pattern with wavelength-limited spatial features is effortless and can be implemented via a simple random diffuser. With this observation and inspired by the concept of near-field ptychography, we report a new imaging modality, termed near-field Fourier ptychography, for tackling high-resolution imaging challenges in both microscopic and macroscopic imaging settings. The meaning of 'near-field' is referred to placing the object at a short defocus distance with a large Fresnel number. In our implementations, we project a speckle pattern with fine spatial features on the object instead of directly resolving the spatial features via a high-resolution lens. We then translate the object (or speckle) to different positions and acquire the corresponding images using a low-resolution lens. A ptychographic phase retrieval process is used to recover the complex object, the unknown speckle pattern, and the coherent transfer function at the same time. In a microscopic imaging setup, we use a 0.12 numerical aperture (NA) lens to achieve a NA of 0.85 in the reconstruction process. In a macroscale photographic imaging setup, we achieve ~7-fold resolution gain using a photographic lens. The final achievable resolution is not determined by the collection optics. Instead, it is determined by the feature size of the speckle pattern. The reported imaging modality can be employed in light, coherent X-ray, and transmission electron imaging systems to increase resolution and provide quantitative absorption and phase contrast of the object.

physics.optics↗

arXiv2019

Flat AgTe Honeycomb Monolayer with Topologically Nontrivial States

The intriguing properties, especially Dirac physics in graphene, have inspired the pursuit of two-dimensional materials in honeycomb structure. Here we achieved a monolayer transition metal monochalcogenide AgTe on Ag(111) by tellurization of the substrate. High-resolution scanning tunneling microscopy, combined with low-energy electron diffraction, angle-resolved photoemission spectroscopy, and density functional theory calculations, demonstrates the planar honeycomb structure of AgTe. The first principle calculations further reveal that, protected by the in-plane mirror reflection symmetry, two Dirac node-line Fermions exist in the electronic structures of free-standing AgTe when spin-orbit coupling (SOC) is ignored. While in fact the SOC leads to the gap opening, and resulting in the emergence of the topologically nontrivial quantum spin Hall edge state. Importantly, our experiments evidence the chemical stability of the monolayer AgTe in ambient conditions. It is possible to study AgTe by more ex-situ measurements and even to apply it in novel electronic devices.

cond-mat.mtrl-sci↗

arXiv2018

Angular instability in high optical power suspended cavities

Advanced gravitational wave detectors use suspended test masses to form optical resonant cavities for enhancing the detector sensitivity. These cavities store hundreds of kilowatts of coherent light and even higher optical power for future detectors. With such high optical power, the radiation pressure effect inside the cavity creates sufficiently strong coupling between test masses whose dynamics are significantly altered. The dynamics of two independent nearly free masses become a coupled mechanical resonator system. The transfer function of the local control system used for controlling the test masses is modified by the radiation pressure effect. The changes in the transfer function of the local control systems can result in a new type of angular instability which occurs at only 1.3 \% of the Sidles-Sigg instability threshold power. We report experimental results on a 74~m suspended cavity with a few kilowatts of circulating power, for which the power to mass ratio is comparable to the current Advanced LIGO. The radiation pressure effect on the test masses behaves like an additional optical feedback with respect to the local angular control, potentially making the mirror control system unstable. When the local angular control system is optimized for maximum stability margin, the instability threshold power increases from 4~kW to 29~kW. The system behavior is consistent with our simulation and the power dependent evolution of both the cavity soft and hard mode is observed. We show that this phenomenon is likely to significantly affect proposed gravitational wave detectors that require very high optical power.

astro-ph.IM↗

arXiv2018

Stochastic simulation of collisions using high-order weak convergence algorithms

Electron collisions, described by stochastic differential equations (SDEs), were simulated using a second-order weak convergence algorithm. Using stochastic analysis, we constructed an SDE for energetic electrons in Lorentz plasma to incorporate the collisions with heavy static ions as well as background thermal electrons. Instead of focusing on the errors of each trajectory, we initiated from the weak convergence condition to directly improve the numerical accuracy of the distribution and its moments. A second-order weak convergence algorithm was developed, estimated the order of the algorithm in a collision simulation case, and compared it with the results obtained using the Euler-Maruyama method and the Cadjan-Ivanov method. For a fixed error level, the second order method largely reduce the computation time compared to the Euler-Maruyama method and the Cadjan-Ivnaov method. Finally, the backward runaway phenomenon was numerically studied and the probability of this runaway was calculated using the new method.

physics.plasm-ph↗

arXiv2018

Scalable Byzantine Consensus via Hardware-assisted Secret Sharing

The surging interest in blockchain technology has revitalized the search for effective Byzantine consensus schemes. In particular, the blockchain community has been looking for ways to effectively integrate traditional Byzantine fault-tolerant (BFT) protocols into a blockchain consensus layer allowing various financial institutions to securely agree on the order of transactions. However, existing BFT protocols can only scale to tens of nodes due to their $O(n^2)$ message complexity. In this paper, we propose FastBFT, a fast and scalable BFT protocol. At the heart of FastBFT is a novel message aggregation technique that combines hardware-based trusted execution environments (TEEs) with lightweight secret sharing primitives. Combining this technique with several other optimizations (i.e., optimistic execution, tree topology and failure detection), FastBFT achieves low latency and high throughput even for large scale networks. Via systematic analysis and experiments, we demonstrate that FastBFT has better scalability and performance than previous BFT protocols.

cs.CR↗

arXiv2018

Unexpectedly high salt accumulation inside carbon nanotubes soaked in very dilute salt solutions

We experimentally demonstrate the formation of salt aggregations with unexpectedly high concentration inside multi-walled carbon nanotubes (CNTs) soaked only in dilute salt solutions and even in solutions containing only traces of salts. This finding suggests the blocking of fluid across CNTs by the salt aggregations when CNTs are soaked in a dilute salt solution with the concentration of seawater or even lower, which may open new avenues for the development of novel CNT-based desalination techniques. The high salt accumulation of CNTs also provides a new CNT-based strategy for the collection/extraction of noble metal salts in solutions containing traces of noble metal salts. Theoretical analyses reveal that this high salt accumulation inside CNTs can be mainly attributed to the strong hydrated cation-pi interactions of hydrated cations and pi electrons in the aromatic rings of CNTs.

cond-mat.mtrl-sci↗

arXiv2018

Decoupling carrier concentration and electron-phonon coupling in oxide heterostructures observed with resonant inelastic x-ray scattering

We report the observation of multiple phonon satellite features in ultra thin superlattices of form $n$SrIrO$_3$/$m$SrTiO$_3$ using resonant inelastic x-ray scattering. As the values of $n$ and $m$ vary the energy loss spectra show a systematic evolution in the relative intensity of the phonon satellites. Using a closed-form solution for the cross section, we extract the variation in the electron-phonon coupling strength as a function of $n$ and $m$. Combined with the negligible carrier doping into the SrTiO$_3$ layers, these results indicate that tuning of the electron-phonon coupling can be effectively decoupled from doping. This work showcases both a feasible method to extract the electron-phonon coupling in superlattices and unveils a potential route for tuning this coupling which is often associated with superconductivity in SrTiO$_3$-based systems.

cond-mat.str-el↗

arXiv2018

Skepxels: Spatio-temporal Image Representation of Human Skeleton Joints for Action Recognition

Human skeleton joints are popular for action analysis since they can be easily extracted from videos to discard background noises. However, current skeleton representations do not fully benefit from machine learning with CNNs. We propose "Skepxels" a spatio-temporal representation for skeleton sequences to fully exploit the "local" correlations between joints using the 2D convolution kernels of CNN. We transform skeleton videos into images of flexible dimensions using Skepxels and develop a CNN-based framework for effective human action recognition using the resulting images. Skepxels encode rich spatio-temporal information about the skeleton joints in the frames by maximizing a unique distance metric, defined collaboratively over the distinct joint arrangements used in the skeletal image. Moreover, they are flexible in encoding compound semantic notions such as location and speed of the joints. The proposed action recognition exploits the representation in a hierarchical manner by first capturing the micro-temporal relations between the skeleton joints with the Skepxels and then exploiting their macro-temporal relations by computing the Fourier Temporal Pyramids over the CNN features of the skeletal images. We extend the Inception-ResNet CNN architecture with the proposed method and improve the state-of-the-art accuracy by 4.4% on the large scale NTU human activity dataset. On the medium-sized N-UCLA and UTH-MHAD datasets, our method outperforms the existing results by 5.7% and 9.3% respectively.

cs.CV↗

arXiv2018

Caging Loops in Shape Embedding Space: Theory and Computation

We propose to synthesize feasible caging grasps for a target object through computing Caging Loops, a closed curve defined in the shape embedding space of the object. Different from the traditional methods, our approach decouples caging loops from the surface geometry of target objects through working in the embedding space. This enables us to synthesize caging loops encompassing multiple topological holes, instead of always tied with one specific handle which could be too small to be graspable by the robot gripper. Our method extracts caging loops through a topological analysis of the distance field defined for the target surface in the embedding space, based on a rigorous theoretical study on the relation between caging loops and the field topology. Due to the decoupling, our method can tolerate incomplete and noisy surface geometry of an unknown target object captured on-the-fly. We implemented our method with a robotic gripper and demonstrate through extensive experiments that our method can synthesize reliable grasps for objects with complex surface geometry and topology and in various scales.

cs.RO↗

arXiv2018

The first super-Earth Detection from the High Cadence and High Radial Velocity Precision Dharma Planet Survey

The Dharma Planet Survey (DPS) aims to monitor about 150 nearby very bright FGKM dwarfs (within 50 pc) during 2016$-$2020 for low-mass planet detection and characterization using the TOU very high resolution optical spectrograph (R$\approx$100,000, 380-900nm). TOU was initially mounted to the 2-m Automatic Spectroscopic Telescope at Fairborn Observatory in 2013-2015 to conduct a pilot survey, then moved to the dedicated 50-inch automatic telescope on Mt. Lemmon in 2016 to launch the survey. Here we report the first planet detection from DPS, a super-Earth candidate orbiting a bright K dwarf star, HD 26965. It is the second brightest star ($V=4.4$ mag) on the sky with a super-Earth candidate. The planet candidate has a mass of 8.47$\pm0.47M_{\rm Earth}$, period of $42.38\pm0.01$ d, and eccentricity of $0.04^{+0.05}_{-0.03}$. This RV signal was independently detected by Diaz et al. (2018), but they could not confirm if the signal is from a planet or from stellar activity. The orbital period of the planet is close to the rotation period of the star (39$-$44.5 d) measured from stellar activity indicators. Our high precision photometric campaign and line bisector analysis of this star do not find any significant variations at the orbital period. Stellar RV jitters modeled from star spots and convection inhibition are also not strong enough to explain the RV signal detected. After further comparing RV data from the star's active magnetic phase and quiet magnetic phase, we conclude that the RV signal is due to planetary-reflex motion and not stellar activity.

astro-ph.EP↗

arXiv2018

A micro dalton resolution mass sensor using optically cooled microdisks via short-time measurement

We propose a mass sensor using optically trapped and cooled dielectric microdisks with "measuring after cooling" scheme. The center-of-mass motion of a trapped particle in vacuum can experience extremely low dissipation resulting in robust decoupling from the heat bath. Thus the measurement can be accomplished in a short time before a jump in the phonon number after the optomechanical cooling is finished. This method can lead to 10^(-6) dalton mass sensitivity which is a thousand times smaller than the mass of one electron.

physics.app-ph↗

arXiv2018

Hierarchical Attention-Based Recurrent Highway Networks for Time Series Prediction

Time series prediction has been studied in a variety of domains. However, it is still challenging to predict future series given historical observations and past exogenous data. Existing methods either fail to consider the interactions among different components of exogenous variables which may affect the prediction accuracy, or cannot model the correlations between exogenous data and target data. Besides, the inherent temporal dynamics of exogenous data are also related to the target series prediction, and thus should be considered as well. To address these issues, we propose an end-to-end deep learning model, i.e., Hierarchical attention-based Recurrent Highway Network (HRHN), which incorporates spatio-temporal feature extraction of exogenous variables and temporal dynamics modeling of target variables into a single framework. Moreover, by introducing the hierarchical attention mechanism, HRHN can adaptively select the relevant exogenous features in different semantic levels. We carry out comprehensive empirical evaluations with various methods over several datasets, and show that HRHN outperforms the state of the arts in time series prediction, especially in capturing sudden changes and sudden oscillations of time series.

cs.LG↗